**Meet the editor**

Dr Sivakumar Joghi Thatha Gowder got his academic training and carried out his research in institutions of high academic ranking in India and US. He earned his PhD from the University of Madras, India and continued his research at AIIMS, India. He then moved to US to continue his research at the UT Southwestern Medical Center, LSU Medical Center, and University of Pitts-

burgh School of Medicine. Currently, he is working as an Associate Professor of Pharmacology and Biochemistry at the Qassim University, KSA. Sivakumar received several prizes / awards during his academic career. He developed his own methods / techniques relevant to his research projects. Currently, he serves as an author / editor for books; an editorial member; and a reviewer for international journals and is a fellow of various international organizations. Sivakumar has also served as an invited speaker and a chairperson for international conferences.

Contents

**Preface IX** 

Chapter 1 **Cholera and Spatial Epidemiology 3** 

Frank B Osei, Alfred A Duker and Alfred Stein

Toru Watanabe, Rattanaphone Phethsouvanh,

Chapter 4 *Vibrio cholerae* **Flagellar Synthesis and Virulence 59** 

**and Antibiotic Resistance Determinants** 

**Controlling** *Vibrio Cholerae* **Behavior 91** 

Choo Yee Yu, Geik Yong Ang and Chan Yean Yean

Olivera Noelia, Maia Cédola and Ricardo M Gómez

Satoshi Nakamura, Yutaka Midorikawa, Masami Nakatsu,

Phengta Vongphrachanh, Kongsap Akkhavong and Paul Brey

**to the Atypical El Tor Biotype from Kelantan, Malaysia 75** 

Chapter 2 **Evaluating Spatial and Space-Time Clustering of Cholera in Ashanti-Region-Ghana 19**  Frank B Osei, Alfred A Duker and Alfred Stein

Chapter 3 **Cholera in Lao P. D. R.: Past and Present 33** 

Anastasia R. Rugel and Karl E. Klose

**in** *Vibrio cholerae* **O1 Belonging** 

Chapter 6 **Integration of Global Regulatory Mechanisms** 

Jorge A. Benitez and Anisia J. Silva

**as a Biotechnological Tool 129** 

**Part 3 Cholera Toxin and Antagonists 127** 

Chapter 7 **The Cholera Toxin** 

**Part 2 Biology of** *Vibrio Cholera* **57** 

Chapter 5 **Genetic Analysis of CTX Prophage** 

**Part 1 Epidemiology 1** 

## Contents

### **Preface XI**

#### **Part 1 Epidemiology 1**


#### **Part 2 Biology of** *Vibrio Cholera* **57**

	- **Part 3 Cholera Toxin and Antagonists 127**

#### **Part 4 Treatment 201**

Chapter 10 **Evidence Based Treatment of Cholera: A review of Existing Literature 203**  Marzia Lazzerini

## Preface

On 21 October, 2010 Haitian public health authorities conrmed an outbreak of cholera. Ten months later the toll of this outbreak tallied 386,429 cases, including 5,885 deaths, with the outbreak spreading to the neighboring Dominican Republic and Florida, United States. Cholera is a world problem. One of the most basic lessons, which was so elegantly restated in an editorial in the New England Journal of Medicine, is that no one should lack access to clean water and sanitation. If we are to control and ultimately eradicate the deadly threat of cholera, the approach must include healthcare workers, scientists, and general public. The book Cholera focuses on various aspects of this disease with information significant for all people, from scientist and educators to general public.

This book is comprised of four parts: Epidemiology, Biology of Vibrio cholerae, Cholera Toxin, and Antagonists and Treatment. First two parts describe the history of cholera, its geographical distribution, mode of transmission, and structural and functional activities of V. cholerae. The third part deals with cholera toxin in a study of antagonist drugs used to treat cholera. The author's detailed discussion of the structural and functional aspects of cholera toxin paves the way for future drug discovery to both prevent and cure cholera. In addition to W.H.O. and other regulatory treatment regimens, the fourth part adds to an overall understanding of current methods and potential areas for enhancement of outcomes for the welfare of individuals and society. Some key points of interest in Cholera include: the emergence of an epidemiologically dominant new strain of V. cholerae, the importance of the bacterial flagellum, biotechnological utilities of cholera toxin and methods to design cholera drugs, and the spatial epidemiologic tools applied in cholera studies. This book is a significant resource not only for cholera researchers but also for scientists, physicians, healthcare professionals, faculty and students, local administrators, and general public and it is my privilege to present this book.

I extend my gratitude to my mother, my late father and my brothers for introducing me to higher education. My thanks to higher authorities, and colleagues of Qassim University for their motivation to carry out this project. I am indebted to my wife Anitha for her encouragement and technical support for this project. I also acknowledge the interest and commitment from the Publishing Process Manager at InTech, Ms. Irena Voric, whose patience and focus were an immense support in this

#### X Preface

project. Finally, I express deep and sincere gratitude to all the authors for their valuable contributions and scholarly cooperation for timely completion of this book.

## **Dr. Sivakumar Joghi Thatha Gowder**

Department of Pharmacology & Biochemistry, Al-Qassim University, College of Pharmacy, Buraidah, Saudi Arabia

X Preface

project. Finally, I express deep and sincere gratitude to all the authors for their valuable contributions and scholarly cooperation for timely completion of this book.

**Dr. Sivakumar Joghi Thatha Gowder** 

College of Pharmacy, Buraidah,

Al-Qassim University,

Saudi Arabia

Department of Pharmacology & Biochemistry,

**Part 1** 

**Epidemiology** 

**Part 1** 

**Epidemiology** 

**1** 

1, 2*Ghana 3Netherlands* 

**Cholera and Spatial Epidemiology** 

*Kwame Nkrumah University of Science and Technology, Kumasi,* 

*3Faculty of Geo-Information Science and Earth Observation (ITC), Twente University,* 

Cholera is an acute intestinal infection caused by the water borne bacteria *Vibrio cholerae* O1 or O139 (*V. cholerae*). Infection is mainly through ingestion of contaminated water or food (Kelly, 2001). Approximately 102-103 cells are required to cause severe diarrhea and dehydration (Sack et al., 1998; Hornich et al., 1971). Ingested cholera *vibrios* from contaminated water or food must pass through the acid stomach before they are able to colonize the upper part of the small intestine. After penetrating the mucus layer, *V. cholerae* colonizes the epithelial lining of the gut, secreting cholera toxin which affects the small

Clinically, the majority of cholera episodes are characterized by a sudden onset of massive diarrhea and vomiting. This is accompanied by the loss of profuse amounts of protein-free fluid along with electrolytes, bicarbonates and ions. The resulting dehydration produces tachycardia, hypotension, and vascular collapse, which can lead to sudden death. The diagnosis of cholera is commonly established by isolating the causative organism from the stools of infected individuals. The main mode of treatment is the replacement of electrolyte loss through the intake of a rehydration fluid, i.e. Oral Rehydration Salts (ORS) (Sack et al., 2004). Without prompt treatment, fatality rate can be as high as 50% (WHO, 1993; Sack et al., 2004). With adequate treatment, i.e. intravenous and oral rehydration therapy, supplemented with appropriate antibiotics, the fatality rate can drop to approximately 1.0%

In its extreme manifestation, cholera is one of the most rapidly fatal infectious illnesses known. Within 3–4 hours of onset of symptoms, a previously healthy person may become severely dehydrated and if not treated may die within 24 hours (WHO, 2010). The disease is one of the most researched in the world today; nevertheless, it is still an important public health problem despite more than a century of study, especially in developing tropical countries. Cholera is currently listed as one of three internationally quarantinable diseases by the World Health Organization (WHO), along with plague and yellow fever (WHO, 2000a). The growing number and frequency of major cholera outbreaks, especially in

**1. Introduction** 

intestine.

(Carpenter et al., 1966; Mahalanabis et al., 1992).

Frank B Osei1, Alfred A Duker2 and Alfred Stein3

*1Faculty of Public Health and Allied Sciences, Catholic University College of Ghana, Sunyani*

<sup>2</sup>*Department of Geomatic Engineering,* 

## **Cholera and Spatial Epidemiology**

Frank B Osei1, Alfred A Duker2 and Alfred Stein3

*1Faculty of Public Health and Allied Sciences, Catholic University College of Ghana, Sunyani* <sup>2</sup>*Department of Geomatic Engineering, Kwame Nkrumah University of Science and Technology, Kumasi, 3Faculty of Geo-Information Science and Earth Observation (ITC), Twente University,*  1, 2*Ghana 3Netherlands* 

## **1. Introduction**

Cholera is an acute intestinal infection caused by the water borne bacteria *Vibrio cholerae* O1 or O139 (*V. cholerae*). Infection is mainly through ingestion of contaminated water or food (Kelly, 2001). Approximately 102-103 cells are required to cause severe diarrhea and dehydration (Sack et al., 1998; Hornich et al., 1971). Ingested cholera *vibrios* from contaminated water or food must pass through the acid stomach before they are able to colonize the upper part of the small intestine. After penetrating the mucus layer, *V. cholerae* colonizes the epithelial lining of the gut, secreting cholera toxin which affects the small intestine.

Clinically, the majority of cholera episodes are characterized by a sudden onset of massive diarrhea and vomiting. This is accompanied by the loss of profuse amounts of protein-free fluid along with electrolytes, bicarbonates and ions. The resulting dehydration produces tachycardia, hypotension, and vascular collapse, which can lead to sudden death. The diagnosis of cholera is commonly established by isolating the causative organism from the stools of infected individuals. The main mode of treatment is the replacement of electrolyte loss through the intake of a rehydration fluid, i.e. Oral Rehydration Salts (ORS) (Sack et al., 2004). Without prompt treatment, fatality rate can be as high as 50% (WHO, 1993; Sack et al., 2004). With adequate treatment, i.e. intravenous and oral rehydration therapy, supplemented with appropriate antibiotics, the fatality rate can drop to approximately 1.0% (Carpenter et al., 1966; Mahalanabis et al., 1992).

In its extreme manifestation, cholera is one of the most rapidly fatal infectious illnesses known. Within 3–4 hours of onset of symptoms, a previously healthy person may become severely dehydrated and if not treated may die within 24 hours (WHO, 2010). The disease is one of the most researched in the world today; nevertheless, it is still an important public health problem despite more than a century of study, especially in developing tropical countries. Cholera is currently listed as one of three internationally quarantinable diseases by the World Health Organization (WHO), along with plague and yellow fever (WHO, 2000a). The growing number and frequency of major cholera outbreaks, especially in

Cholera and Spatial Epidemiology 5

known to be a water-borne bacterium that is natural inhabitant of brackish aquatic environments, which survives and multiplies in association with zooplankton and phytoplankton, quite independently of infected human beings (Colwell and Spira, 1993; Colwell and Huq, 1994; Islam et al; 1994; Nair et al., 1988; Huq et al., 1983; Islam et al., 1990). Colwell et al. (1977) first proposed that *V. cholerae* is ecologically autochthonous in estuarine and coastal waters. Colwell et al. (1977, 1980) isolated *V. cholerae* from plankton samples from Bangladesh waters and Chesapeake Bay (United States) and suggested that an association between *V. cholerae* and chitinous plankton may exist. Survival of *V. cholerae* in the aquatic environment, abundance and expression of virulence factors including cholera toxin (CT), and colonization factors such as the toxin-coregulated pilus (TCP), are strongly influenced by both biotic and abiotic factors. Abiotic factors such as sunlight, pH, temperature, salinity and nutrients enhance the growth and multiplication of aquatic lives such as phytoplankton and zooplanktons. Sequestration of CO2 during photosynthesis of phytoplankton alter the dissolved O2 and CO2 contents of the surrounding which in turn

The Ganges Delta region (India) is believed to be the traditional home of cholera from the time of recorded history (Harmer and Cash, 1999). From this region, cholera has spread throughout the world, causing six major pandemics between 1817 and 1961 (Faruque et al., 1998). It is believed that the European invasions of India and India's fostering of trade with the Dutch Indies spread the disease to other parts of the world. The seventh pandemic, which began in 1961 in Sulawesi, Indonesia, has now involved almost the whole world and is still continuing. The pandemic (i.e. the seventh) reached India in 1964, Africa in 1970 (Barua, 1972; Cvjetanovic and Barua, 1972; Goodgame and Greenough, 1975; Küstner et al., 1981, Glass et al., 1991), southern Europe in 1970 (Editorial, 1971), and South America in 1991 (Swerdlow et al., 1992; Weil and Berche, 1992). The seventh pandemic was confined in Asia for nearly 10 years which later reached the west coast of Africa, the south coast of Europe, and the western Pacific islands in 1970. The seventh pandemic reached the Americas in 1991, starting from the Peruvian coast (Blake, 1994). The fifth and the sixth pandemics epidemiologically incriminated the classical biotype as the causative agent. The earlier pandemics are also believed to have been caused by the classical biotype as well, although there is no hard evidence. The seventh pandemic this time caused by the El Tor biotype has subsequently spread worldwide and largely replaced the classical biotype.

The burden of cholera is characterized by both endemic disease and epidemics. Globally, cholera cases and deaths have increased steadily since the beginning of the 21st century. From 2004 to 2008, a total of 838,315 cases were notified to WHO, compared with 676,651 cases between 2000 and 2004, representing a 24% increase in the number of cases (WHO, 2009). The burden of the disease is currently enormous on developing countries and catastrophically on the African continent. The seventh pandemic is the first to have established persistent residence on the African continent. Africa alone has recorded over 2.4 million cases and 120,000 deaths from 1970 to 2005. This accounts for over 90% of both worldwide cases and deaths (WHO, 2000b, 2001, 2002, 20003, 2004, 2005, 2006). The burden

leads to elevated pH in the estuarine.

**3. Epidemiology** 

**3.1 Global distribution** 

countries on the African continent, have heightened concerns of focusing epidemiological research on the underlying risk factors and the identification of high risk areas.

Using simple geographical mapping, John Snow (1855) first associated cholera with contaminated drinking water in the 1850s even before any bacterium was known to exist. After Snow's seminal work, most epidemiological studies of cholera have focused on the pathogenesis and biological characteristics of *V. cholerae* (Yamai et al*.*, 1977; Faruque et al*.*, 1998; Ramamurthy et al*.*, 1993; Felsenfeld, 1966; Singleton et al., 1982a, 1982b; Colwell et al., 1977; Barua and Paguio, 1977; Glass et al., 1985). However useful these studies are, they usually cannot establish accurate individual exposure levels for the critical risk factors of the disease (Haining, 1998). Spatial epidemiological tools applied in cholera studies can facilitate the identification of high risk areas and the formulation of hypotheses about the causal factors responsible for such variations, as well as the optimal allocation of health facilities to improve health care provision. The objective of this study is to present from published literature the general epidemiology of cholera, its spatial epidemiology as well as important spatial epidemiologic tools utilized in cholera studies.

## **2. Biology and ecology of** *V. cholerae*

The biology and ecology of *V. cholerae* has been described by many authors (Yamai et al., 1977; Faruque et al., 1998; Ramamurthy et al., 1993; Felsenfeld, 1966; Singleton et al., 1982a, 1982b; Colwell et al., 1977; Barua and Paguio, 1977; Glass et al., 1985). *V. cholerae* is an aerobic, motile, Gram-negative rod that is shaped like a comma (Hamer and Cash, 1999). When ingested in the body, *V. cholerae* produces an exotoxin that either stimulates the mucosal cells to secrete large quantities of isotonic fluid, or increases the permeability of the vascular endothelium, thus allowing isotonic fluid to pass through in abnormal amount, resulting in watery diarrhea.

*V. cholerae* is differentiated serologically by the O antigen of its lipopolysaccharide. Over 200 serogroups of *V. cholerae* have been documented (Yamai et al., 1997). The toxigenic *V. cholerae* serogroups, which cause epidemic cholera, are the O1 and O139 (Faruque et al., 1998). Until 1992 when a newly serogroup designated O139 was identified after unusual outbreaks in India and Bangladesh (Ramamurthy et al., 1993), only the O1 serogroup was known to cause epidemic. The two major biotypes of the *V. cholerae* O1 serogroup are the classical and the El Tor (named after the El Tor quarantine camp on the Sinai peninsula where it was first isolated in 1905 from the intestines of pilgrims returning from Mecca) (Hamer and Cash, 1999). Admirably, *V. cholerae* O1 infection induces adaptive immune responses that are protective against subsequent infection. Volunteer studies in nonendemic regions have demonstrated that infection with classical biotype of *V. cholerae* O1 provides 100% protection for 3 years from subsequent challenge with a classical biotype strain, while infection with the El Tor biotype of *V. cholerae* O1 provides 90% protection for 3 years from subsequent challenge with an El Tor strain (Levine et al., 1981). In an endemic region, an initial episode of El Tor cholera reduces the risk of a second clinically apparent infection by 90% over the next several years (Glass et al., 1982).

The general assumption by most workers, until the mid 1960's, was that *V. cholerae* was an organism whose normal habitat was the human gut and/or intestine, and incapable of surviving for more than a few days outside the gut (Falsenfeld, 1966). *V. cholerae* is now

countries on the African continent, have heightened concerns of focusing epidemiological

Using simple geographical mapping, John Snow (1855) first associated cholera with contaminated drinking water in the 1850s even before any bacterium was known to exist. After Snow's seminal work, most epidemiological studies of cholera have focused on the pathogenesis and biological characteristics of *V. cholerae* (Yamai et al*.*, 1977; Faruque et al*.*, 1998; Ramamurthy et al*.*, 1993; Felsenfeld, 1966; Singleton et al., 1982a, 1982b; Colwell et al., 1977; Barua and Paguio, 1977; Glass et al., 1985). However useful these studies are, they usually cannot establish accurate individual exposure levels for the critical risk factors of the disease (Haining, 1998). Spatial epidemiological tools applied in cholera studies can facilitate the identification of high risk areas and the formulation of hypotheses about the causal factors responsible for such variations, as well as the optimal allocation of health facilities to improve health care provision. The objective of this study is to present from published literature the general epidemiology of cholera, its spatial epidemiology as well as

The biology and ecology of *V. cholerae* has been described by many authors (Yamai et al., 1977; Faruque et al., 1998; Ramamurthy et al., 1993; Felsenfeld, 1966; Singleton et al., 1982a, 1982b; Colwell et al., 1977; Barua and Paguio, 1977; Glass et al., 1985). *V. cholerae* is an aerobic, motile, Gram-negative rod that is shaped like a comma (Hamer and Cash, 1999). When ingested in the body, *V. cholerae* produces an exotoxin that either stimulates the mucosal cells to secrete large quantities of isotonic fluid, or increases the permeability of the vascular endothelium, thus allowing isotonic fluid to pass through in abnormal amount,

*V. cholerae* is differentiated serologically by the O antigen of its lipopolysaccharide. Over 200 serogroups of *V. cholerae* have been documented (Yamai et al., 1997). The toxigenic *V. cholerae* serogroups, which cause epidemic cholera, are the O1 and O139 (Faruque et al., 1998). Until 1992 when a newly serogroup designated O139 was identified after unusual outbreaks in India and Bangladesh (Ramamurthy et al., 1993), only the O1 serogroup was known to cause epidemic. The two major biotypes of the *V. cholerae* O1 serogroup are the classical and the El Tor (named after the El Tor quarantine camp on the Sinai peninsula where it was first isolated in 1905 from the intestines of pilgrims returning from Mecca) (Hamer and Cash, 1999). Admirably, *V. cholerae* O1 infection induces adaptive immune responses that are protective against subsequent infection. Volunteer studies in nonendemic regions have demonstrated that infection with classical biotype of *V. cholerae* O1 provides 100% protection for 3 years from subsequent challenge with a classical biotype strain, while infection with the El Tor biotype of *V. cholerae* O1 provides 90% protection for 3 years from subsequent challenge with an El Tor strain (Levine et al., 1981). In an endemic region, an initial episode of El Tor cholera reduces the risk of a second clinically apparent

The general assumption by most workers, until the mid 1960's, was that *V. cholerae* was an organism whose normal habitat was the human gut and/or intestine, and incapable of surviving for more than a few days outside the gut (Falsenfeld, 1966). *V. cholerae* is now

research on the underlying risk factors and the identification of high risk areas.

important spatial epidemiologic tools utilized in cholera studies.

infection by 90% over the next several years (Glass et al., 1982).

**2. Biology and ecology of** *V. cholerae*

resulting in watery diarrhea.

known to be a water-borne bacterium that is natural inhabitant of brackish aquatic environments, which survives and multiplies in association with zooplankton and phytoplankton, quite independently of infected human beings (Colwell and Spira, 1993; Colwell and Huq, 1994; Islam et al; 1994; Nair et al., 1988; Huq et al., 1983; Islam et al., 1990). Colwell et al. (1977) first proposed that *V. cholerae* is ecologically autochthonous in estuarine and coastal waters. Colwell et al. (1977, 1980) isolated *V. cholerae* from plankton samples from Bangladesh waters and Chesapeake Bay (United States) and suggested that an association between *V. cholerae* and chitinous plankton may exist. Survival of *V. cholerae* in the aquatic environment, abundance and expression of virulence factors including cholera toxin (CT), and colonization factors such as the toxin-coregulated pilus (TCP), are strongly influenced by both biotic and abiotic factors. Abiotic factors such as sunlight, pH, temperature, salinity and nutrients enhance the growth and multiplication of aquatic lives such as phytoplankton and zooplanktons. Sequestration of CO2 during photosynthesis of phytoplankton alter the dissolved O2 and CO2 contents of the surrounding which in turn leads to elevated pH in the estuarine.

#### **3. Epidemiology**

#### **3.1 Global distribution**

The Ganges Delta region (India) is believed to be the traditional home of cholera from the time of recorded history (Harmer and Cash, 1999). From this region, cholera has spread throughout the world, causing six major pandemics between 1817 and 1961 (Faruque et al., 1998). It is believed that the European invasions of India and India's fostering of trade with the Dutch Indies spread the disease to other parts of the world. The seventh pandemic, which began in 1961 in Sulawesi, Indonesia, has now involved almost the whole world and is still continuing. The pandemic (i.e. the seventh) reached India in 1964, Africa in 1970 (Barua, 1972; Cvjetanovic and Barua, 1972; Goodgame and Greenough, 1975; Küstner et al., 1981, Glass et al., 1991), southern Europe in 1970 (Editorial, 1971), and South America in 1991 (Swerdlow et al., 1992; Weil and Berche, 1992). The seventh pandemic was confined in Asia for nearly 10 years which later reached the west coast of Africa, the south coast of Europe, and the western Pacific islands in 1970. The seventh pandemic reached the Americas in 1991, starting from the Peruvian coast (Blake, 1994). The fifth and the sixth pandemics epidemiologically incriminated the classical biotype as the causative agent. The earlier pandemics are also believed to have been caused by the classical biotype as well, although there is no hard evidence. The seventh pandemic this time caused by the El Tor biotype has subsequently spread worldwide and largely replaced the classical biotype.

The burden of cholera is characterized by both endemic disease and epidemics. Globally, cholera cases and deaths have increased steadily since the beginning of the 21st century. From 2004 to 2008, a total of 838,315 cases were notified to WHO, compared with 676,651 cases between 2000 and 2004, representing a 24% increase in the number of cases (WHO, 2009). The burden of the disease is currently enormous on developing countries and catastrophically on the African continent. The seventh pandemic is the first to have established persistent residence on the African continent. Africa alone has recorded over 2.4 million cases and 120,000 deaths from 1970 to 2005. This accounts for over 90% of both worldwide cases and deaths (WHO, 2000b, 2001, 2002, 20003, 2004, 2005, 2006). The burden

Cholera and Spatial Epidemiology 7

al., 2002). The synergy of poverty, high population density, poor sanitation, poor housing, and lack of good water supplies enhance exposure to pathogenic cholera *vibros*. In epidemic prone regions like Africa, cholera outbreaks have been linked to multiple environmental and socio-economic sources (Acosta et al., 2001; Shapiro et al., 1999). Cholera diffuses rapidly in environments that lack basic infrastructure with regard to access to safe water and proper sanitation. The cholera vibrios can survive and multiply outside the human body and can spread rapidly in environments where living conditions are overcrowded and where there is no safe disposal of solid waste, liquid waste, and human feces (Ali et al., 2002a, 2002b). Root (1997) and Siddique et al (1992) have reported that increase in population density can strain sanitation systems, thus putting people at increased risk of contracting cholera. Ali et al (2002a, 2002b) have identified high population density and low educational status as important risk factors of cholera in an endemic area of Bangladesh.

Many researchers have hypothesized the temporal variation of cholera as due to environmental and climatic factors that affect the seasonal patterns of infection (Alam et al., 2006; Lipp et al., 2002; Sack et al., 2003; Colwell and Huq, 2001; Pascual and Dobson, 2005; Huq and Colwell, 1996; Huq et al., 2005; Islam, 1990; Islam et al., 1990, 1993, 1999, 2004). The temporal variation of endemic and epidemic cholera has been associated with both regional and local environmental forces such as rainfall patterns, sea surface temperature and the El Nino Southern Oscillation (Epstein, 1993; Patz et al., 1996; Colwell, 1996; Bouma and Pascual, 2001; Colwell and Huq, 2001; Pascual et al., 2002; Koelle et al., 2005, Huq et al., 2001). Outbreaks in Peru and Bangladesh have been linked to periodic climatic cycles of the El Nino Southern Oscillation (Salazar-Lindo et al., 1997; Pascual et al., 2002; Rodo et al., 2002). In Bangladesh cholera epidemics occur twice a year in the spring and fall, before and after the monsoons (Merson et al., 1980; Islam et al., 1993; Emch and Ali, 2001; Longini et al., 2002). Several studies have also described a regular seasonal cycle of outbreaks in Bangladesh, including specific studies on the different strains: classical (Samadi et al., 1984), El Tor (Khan et al., 1984) and O139 (Alam et al., 2006). Temporal variation of cholera has also been related to variations in physical and nutritional aquatic parameters, including conditions in both coastal and estuarine environments (Faruque et al., 2005). Studies in Bangladesh have also shown environmental associations with *V. cholerae*, including water temperature and depth, rainfall, and copepod counts (Huq et al., 2005). These factors may contribute to the seasonality and secular trends seen in cholera outbreaks. In Dhaka Lobitz et al (2000) were the first to observe that both sea surface temperature and sea surface height are correlated with temporal fluctuations of cholera. In Ghana, de Magny et al (2007) observed a coherence between cholera outbreak resurgences and climatic/environmental parameters

such as rainfall, Southern Oscillation Index and Land Surface Temperature.

The analysis of the spatial distribution of disease incidence and its relationship to potential risk factors (referred to in general in this paper as *spatial epidemiology*) has an important role to play in various kinds of public health and epidemiological studies. Recent advancements in technology and the increasingly powerful and versatile spatial statistical tools developed

**4. Spatial epidemiology and cholera** 

**3.4 Temporal variations** 

of the disease on the African continent, however, is possibly worse than officially reported owing to underreporting, limitations in the surveillance and reporting system, as well as fear of unjustified restrictions on travel and trade (WHO, 2000a).

## **3.2 Transmission hypothesis**

Two routes of cholera transmission have been described, primary and secondary transmission. Primary transmission occurs through exposure to an environmental reservoir of *V. cholerae* (Hartley et al., 2006) or contaminated water sources regardless of previously infected persons, and thus responsible for the beginning of initial outbreaks. Primary transmission is enabled by both micro-and macro-level environmental and climatic factors that affect the seasonal patterns of infection (Islam et al., 1994; Alam et al., 2006; Lipp et al., 2002; Sack et al., 2003; Colwell, 1996; Huq and Colwell, 1996; Islam et al, 1989, 1990a, 1990b, 1999). In locations like Africa and South America where one yearly peak of cholera is often observed, the beginning of the epidemics has been associated with environmental conditions that favor the growth and survival of the bacterium (Codeço, 2001; Glass et al., 1991; Swerdlow et al., 1992). Primary transmission appears to play a limited role in the epidemiological process since it does not fully explain the exponential growth of incidences during epidemics.

Secondary transmission or fecal-oral transmission occurs via the fecal-oral route through exposure to contaminated water sources. Fecal-oral transmission provides a mechanism for exhibiting a strong feedback between present and past levels of infection. The importance of fecal-oral transmission in cholera epidemics is also supported by recent time series models fitted to the endemic dynamics of cholera in Bangladesh (Koelle and Pascual, 2004; Koelle et al., 2005). In an epidemic situation, the initial reproduction rate of fecal-oral transmissions is positively affected by the degree of contamination of water supply as well as the frequency of contacts with such contaminated water supply (Codeço, 2001), which in turn is influenced by human dimensions such as local environmental factors, socioeconomic, demographic as well as sanitation conditions. Fecal-oral transmissions reflect a complicated transmission pattern since multiple factors may play a role in the spread of the disease. Although cholera control measures that target primary transmission is clearly important (from the perspective of disease persistence (Colwell et al, 2003)), the dominant role of fecal-oral transmission as observed in several studies (Ali et al., 2002a, 2002b; Mugoya et al., 2008; Borroto and Martinez-Piedra, 2000; Ackers et al., 1998; Sasaki et al., 2008; Sur et al., 2005), suggest that the containment of fecal-oral infections may be a viable and useful strategy to control epidemics.

### **3.3 Socioeconomic and demographic variations**

Socioeconomic and demographic factors have been reported to significantly enhance the vulnerability of a population to infection and contribute to epidemic spread (Ali et al., 2002a, 2002b; Borroto and Martinez-Piedra, 2000; Ackers et al., 1998; Sasaki et al., 2008; Sur et al., 2005). Such factors also mandate the extent to which the disease will reach epidemic proportions (Miller, 1985; Emch et al., 2008) and also modulate the size of the epidemic (Pascual et al., 2002, 2006; Koelle and Pascual, 2004; Hartley et al., 2005). Known populationlevel (local-level) risk factors of cholera include poverty, lack of development, high population density, low education, and lack of previous exposure (Ackers et al., 1998; Ali et

of the disease on the African continent, however, is possibly worse than officially reported owing to underreporting, limitations in the surveillance and reporting system, as well as

Two routes of cholera transmission have been described, primary and secondary transmission. Primary transmission occurs through exposure to an environmental reservoir of *V. cholerae* (Hartley et al., 2006) or contaminated water sources regardless of previously infected persons, and thus responsible for the beginning of initial outbreaks. Primary transmission is enabled by both micro-and macro-level environmental and climatic factors that affect the seasonal patterns of infection (Islam et al., 1994; Alam et al., 2006; Lipp et al., 2002; Sack et al., 2003; Colwell, 1996; Huq and Colwell, 1996; Islam et al, 1989, 1990a, 1990b, 1999). In locations like Africa and South America where one yearly peak of cholera is often observed, the beginning of the epidemics has been associated with environmental conditions that favor the growth and survival of the bacterium (Codeço, 2001; Glass et al., 1991; Swerdlow et al., 1992). Primary transmission appears to play a limited role in the epidemiological process since it does not fully explain the exponential growth of incidences

Secondary transmission or fecal-oral transmission occurs via the fecal-oral route through exposure to contaminated water sources. Fecal-oral transmission provides a mechanism for exhibiting a strong feedback between present and past levels of infection. The importance of fecal-oral transmission in cholera epidemics is also supported by recent time series models fitted to the endemic dynamics of cholera in Bangladesh (Koelle and Pascual, 2004; Koelle et al., 2005). In an epidemic situation, the initial reproduction rate of fecal-oral transmissions is positively affected by the degree of contamination of water supply as well as the frequency of contacts with such contaminated water supply (Codeço, 2001), which in turn is influenced by human dimensions such as local environmental factors, socioeconomic, demographic as well as sanitation conditions. Fecal-oral transmissions reflect a complicated transmission pattern since multiple factors may play a role in the spread of the disease. Although cholera control measures that target primary transmission is clearly important (from the perspective of disease persistence (Colwell et al, 2003)), the dominant role of fecal-oral transmission as observed in several studies (Ali et al., 2002a, 2002b; Mugoya et al., 2008; Borroto and Martinez-Piedra, 2000; Ackers et al., 1998; Sasaki et al., 2008; Sur et al., 2005), suggest that the containment of fecal-oral

Socioeconomic and demographic factors have been reported to significantly enhance the vulnerability of a population to infection and contribute to epidemic spread (Ali et al., 2002a, 2002b; Borroto and Martinez-Piedra, 2000; Ackers et al., 1998; Sasaki et al., 2008; Sur et al., 2005). Such factors also mandate the extent to which the disease will reach epidemic proportions (Miller, 1985; Emch et al., 2008) and also modulate the size of the epidemic (Pascual et al., 2002, 2006; Koelle and Pascual, 2004; Hartley et al., 2005). Known populationlevel (local-level) risk factors of cholera include poverty, lack of development, high population density, low education, and lack of previous exposure (Ackers et al., 1998; Ali et

fear of unjustified restrictions on travel and trade (WHO, 2000a).

infections may be a viable and useful strategy to control epidemics.

**3.3 Socioeconomic and demographic variations** 

**3.2 Transmission hypothesis** 

during epidemics.

al., 2002). The synergy of poverty, high population density, poor sanitation, poor housing, and lack of good water supplies enhance exposure to pathogenic cholera *vibros*. In epidemic prone regions like Africa, cholera outbreaks have been linked to multiple environmental and socio-economic sources (Acosta et al., 2001; Shapiro et al., 1999). Cholera diffuses rapidly in environments that lack basic infrastructure with regard to access to safe water and proper sanitation. The cholera vibrios can survive and multiply outside the human body and can spread rapidly in environments where living conditions are overcrowded and where there is no safe disposal of solid waste, liquid waste, and human feces (Ali et al., 2002a, 2002b). Root (1997) and Siddique et al (1992) have reported that increase in population density can strain sanitation systems, thus putting people at increased risk of contracting cholera. Ali et al (2002a, 2002b) have identified high population density and low educational status as important risk factors of cholera in an endemic area of Bangladesh.

## **3.4 Temporal variations**

Many researchers have hypothesized the temporal variation of cholera as due to environmental and climatic factors that affect the seasonal patterns of infection (Alam et al., 2006; Lipp et al., 2002; Sack et al., 2003; Colwell and Huq, 2001; Pascual and Dobson, 2005; Huq and Colwell, 1996; Huq et al., 2005; Islam, 1990; Islam et al., 1990, 1993, 1999, 2004). The temporal variation of endemic and epidemic cholera has been associated with both regional and local environmental forces such as rainfall patterns, sea surface temperature and the El Nino Southern Oscillation (Epstein, 1993; Patz et al., 1996; Colwell, 1996; Bouma and Pascual, 2001; Colwell and Huq, 2001; Pascual et al., 2002; Koelle et al., 2005, Huq et al., 2001). Outbreaks in Peru and Bangladesh have been linked to periodic climatic cycles of the El Nino Southern Oscillation (Salazar-Lindo et al., 1997; Pascual et al., 2002; Rodo et al., 2002). In Bangladesh cholera epidemics occur twice a year in the spring and fall, before and after the monsoons (Merson et al., 1980; Islam et al., 1993; Emch and Ali, 2001; Longini et al., 2002). Several studies have also described a regular seasonal cycle of outbreaks in Bangladesh, including specific studies on the different strains: classical (Samadi et al., 1984), El Tor (Khan et al., 1984) and O139 (Alam et al., 2006). Temporal variation of cholera has also been related to variations in physical and nutritional aquatic parameters, including conditions in both coastal and estuarine environments (Faruque et al., 2005). Studies in Bangladesh have also shown environmental associations with *V. cholerae*, including water temperature and depth, rainfall, and copepod counts (Huq et al., 2005). These factors may contribute to the seasonality and secular trends seen in cholera outbreaks. In Dhaka Lobitz et al (2000) were the first to observe that both sea surface temperature and sea surface height are correlated with temporal fluctuations of cholera. In Ghana, de Magny et al (2007) observed a coherence between cholera outbreak resurgences and climatic/environmental parameters such as rainfall, Southern Oscillation Index and Land Surface Temperature.

## **4. Spatial epidemiology and cholera**

The analysis of the spatial distribution of disease incidence and its relationship to potential risk factors (referred to in general in this paper as *spatial epidemiology*) has an important role to play in various kinds of public health and epidemiological studies. Recent advancements in technology and the increasingly powerful and versatile spatial statistical tools developed

Cholera and Spatial Epidemiology 9

Fundamental to the spatial epidemiologist is the investigation of possible disease clusters. Cluster analysis provides opportunities for the epidemiologist to understand possible associations between demographic and environmental exposures and the spatial distribution of diseases (Besag and Newell, 1991; Kulldorff and Nagarwalla, 1995; Kulldorff et al., 1997). There are numerous methods for testing global clustering, including those methods proposed by Alt and Vach (1991), Besag and Newell (1991), Cuzick and Edwards (1990), Diggle and Chetwynd (1991), Grimson (1991), Moran (1950), Tango (1995, 1999, 2000), Walter (1992a, 1992b, 1993) and Whittemore et al (1987). Siddiqui et al (2006) applied Cuzick-Edward's k-Nearest Neighbors test (Cuzick and Edwards, 1990) to evaluate clustering of cholera cases in Pakistan. Using the Moran's Index, Borroto and Martinez-Piedra (2000) have described the spatial distribution of cholera in Mexican states as clustered. This clustering reflects a north-south gradient and spatial clustering of southern states with higher incidence and spatial clustering of northern states with low incidence. Likewise, the Moran's Index has been used to evaluate the clustering of cholera in the Lusaka area of Zambia (Sasaki et al., 2008) and in Madras (India) (Ruiz-Moreno et al., 2007). Osei et al (2008) have also used the Moran's Index to evaluate global clustering of cholera in the Ashanti Region of Ghana. However, global cluster analysis ran the risk of obscuring local effects since the assumption of stationarity is rarely met. Locating and/or defining the characteristics of disease clusters, i.e. local cluster analysis, can inform hypothesis of population or environmental drivers of ill-health, as well as direct the prevention or treatment efforts of health care workers. Using the popular spatial statistics approach, i.e. Ripley's K index, Ruiz-Moreno et al (2007) observed that clustering of cholera in Bangladesh occur at different spatial scales. Local clustering methods such as the Circular Scan Statistic (Kulldorff, 1997) and the Flexible Scan Statistic (Tango and Takahashi, 2005) have been used to detect and map the clustering of cholera in the city of Kumasi-Ghana (Osei et al., 2010; 2011). They emphasize that the Circular Scan Statistic can underestimate the relative risk of cholera clusters compared with the Flexible Scan Statistic. Emch and Ali (2003) have also

**4.2 Disease clustering and cholera** 

used the spatial scan statistic to evaluate clustering of cholera.

A significant interest in spatial epidemiology also lies in identifying associated risk factors which enhance the risk of infection, the so called *ecological analysis* (Lawson et al., 1999a, 1999b; Lawson, 2001) or *geographic correlations studies* (Elliott et al., 2000). Understanding the spatial relationship between cholera and ecological risk factors has always been a challenge. Most authors ignore the geographical structure (spatial autocorrelation) of the data in the statistical analysis. For instance, Ali et al (2001, 2002a, 2002b) have utilized logistic regression, simple and multiple regression models to study the spatial epidemiology of cholera in an endemic area of Bangladesh. In their study, spatial filtering methods (Talbot et al., 2000), typically spatial moving average (Kafadar, 1996), and traditional geostatistics were only used to remove noise and transform cholera and environmental data into a spatially continuous form. This notwithstanding, the effect of spatial proximity or geographical structure of the data was not incorporated in the statistical model. Sasaki et al (2008) investigated risk factors of cholera with a GIS and matched case-case control in a peri-urban area of Luzaka, Zambia. Although a spatial autocorrelation analysis using Moran's Index

**4.3 Ecological analysis and cholera** 

in this application area are capable of addressing more complex health issues than was hitherto the case. The field of spatial statistics involves the statistical analysis of observations with associated geographical location. Often these observations are not Gaussian distribution and are not independent (two main-stays in the development of statistical methods). Fortunately, a wide variety of statistical techniques for spatial epidemiologic inference have developed in recent years, coalescing into a collection of approaches which address specific questions. Consequently, the field of spatial epidemiology has been a subject of several lengthy texts (Elliott et al., 2000, Lawson, 2001, Waller and Gotway, 2004). Yet, few authors have addressed the spatial epidemiology of cholera (Ali et al, 2002a, 2002b; Ali et al., 2006; Borroto and Martinez-Piedra, 2000). Following Elliot et al (2000) and Lawson (2001), spatial epidemiology generically comprises at least three types of study focus: These are (1) *disease mapping*, (2) *disease clustering* and (3) *ecological analysis* (geographical correlation analysis). In this regard, we discuss methodological significance of *disease mapping*, *disease clustering* and *ecological analysis* with special emphasis on their applications in cholera studies.

#### **4.1 Disease mapping and cholera**

Disease maps have played a key descriptive role in spatial epidemiology. Disease maps are useful in suggesting hypotheses for further investigation or as part of general health surveillance and the monitoring of health problems. A famous historical example is the classical epidemiological work of John Snow. Mapping the locations of cholera victims, Snow was able to trace the cause of the disease to a contaminated water source. Surprisingly, this was done 20 years before Koch and Pasteur established the beginnings of microbiology (Koch, 1884). Disease mapping has long been in the form of plotting the observed disease cases or prevalence. Borroto and Martinez-Piedra (2000) used Geographic Information System (GIS) to map cumulative incidence rates of cholera in 32 Mexican states. Chevallier et al (2004) used cartographic representation of cholera incidence rates to study the spatial distribution of cholera in Ecuador. Raw disease rates yield less precise estimates for small populations and vice versa; hence, mapping the raw estimates of disease occurrence can lead to spurious spatial features. Thus, maps of raw disease incidences are not suitable for appropriate epidemiologic inferences. Bithel (2000), Diggle (2000), Lawson (2001), and Lawson and Clark (2002) provide recent reviews of current appropriate disease mapping methods. Several statistical smoothing techniques have been proposed to filter out the noise (rate variations) caused by population variability (e.g. *median-based head-banging* (Hansen, 1991), *spatial filtering* (Bithel, 1990; Rushton and Lolonis, 1996), *empirical Bayes smoothing* (Clayton and Kaldor, 1987), *full Bayesian smoothing* (Besag et al., 1991, 1995), and *geostatisticsal methods* (Oliver et al., 1998; Webster et al., 1994; Carrat and Valleron, 1992; Goovaerts, 2005; Goovaerts and Jacquez, 2004; Berke, 2004)). However, few have been applied in cholera studies. Kuo and Fukui (2007) have used the inverse distance weighted (IDW) interpolation technique to map the temporal features of cholera in the Fukushima prefecture Japan. Ali et al (2002) used kriging to interpolate and map the spatial risk of cholera in Bangladesh at regularly space interval. Ali et al (2006) presented the first application of Poisson kriging to the spatial interpolation of local cholera rates, resulting in continuous maps of cholera rate estimates and associated prediction variance.

in this application area are capable of addressing more complex health issues than was hitherto the case. The field of spatial statistics involves the statistical analysis of observations with associated geographical location. Often these observations are not Gaussian distribution and are not independent (two main-stays in the development of statistical methods). Fortunately, a wide variety of statistical techniques for spatial epidemiologic inference have developed in recent years, coalescing into a collection of approaches which address specific questions. Consequently, the field of spatial epidemiology has been a subject of several lengthy texts (Elliott et al., 2000, Lawson, 2001, Waller and Gotway, 2004). Yet, few authors have addressed the spatial epidemiology of cholera (Ali et al, 2002a, 2002b; Ali et al., 2006; Borroto and Martinez-Piedra, 2000). Following Elliot et al (2000) and Lawson (2001), spatial epidemiology generically comprises at least three types of study focus: These are (1) *disease mapping*, (2) *disease clustering* and (3) *ecological analysis* (geographical correlation analysis). In this regard, we discuss methodological significance of *disease mapping*, *disease clustering* and *ecological analysis* with special emphasis on their applications

Disease maps have played a key descriptive role in spatial epidemiology. Disease maps are useful in suggesting hypotheses for further investigation or as part of general health surveillance and the monitoring of health problems. A famous historical example is the classical epidemiological work of John Snow. Mapping the locations of cholera victims, Snow was able to trace the cause of the disease to a contaminated water source. Surprisingly, this was done 20 years before Koch and Pasteur established the beginnings of microbiology (Koch, 1884). Disease mapping has long been in the form of plotting the observed disease cases or prevalence. Borroto and Martinez-Piedra (2000) used Geographic Information System (GIS) to map cumulative incidence rates of cholera in 32 Mexican states. Chevallier et al (2004) used cartographic representation of cholera incidence rates to study the spatial distribution of cholera in Ecuador. Raw disease rates yield less precise estimates for small populations and vice versa; hence, mapping the raw estimates of disease occurrence can lead to spurious spatial features. Thus, maps of raw disease incidences are not suitable for appropriate epidemiologic inferences. Bithel (2000), Diggle (2000), Lawson (2001), and Lawson and Clark (2002) provide recent reviews of current appropriate disease mapping methods. Several statistical smoothing techniques have been proposed to filter out the noise (rate variations) caused by population variability (e.g. *median-based head-banging* (Hansen, 1991), *spatial filtering* (Bithel, 1990; Rushton and Lolonis, 1996), *empirical Bayes smoothing* (Clayton and Kaldor, 1987), *full Bayesian smoothing* (Besag et al., 1991, 1995), and *geostatisticsal methods* (Oliver et al., 1998; Webster et al., 1994; Carrat and Valleron, 1992; Goovaerts, 2005; Goovaerts and Jacquez, 2004; Berke, 2004)). However, few have been applied in cholera studies. Kuo and Fukui (2007) have used the inverse distance weighted (IDW) interpolation technique to map the temporal features of cholera in the Fukushima prefecture Japan. Ali et al (2002) used kriging to interpolate and map the spatial risk of cholera in Bangladesh at regularly space interval. Ali et al (2006) presented the first application of Poisson kriging to the spatial interpolation of local cholera rates, resulting in

continuous maps of cholera rate estimates and associated prediction variance.

in cholera studies.

**4.1 Disease mapping and cholera** 

#### **4.2 Disease clustering and cholera**

Fundamental to the spatial epidemiologist is the investigation of possible disease clusters. Cluster analysis provides opportunities for the epidemiologist to understand possible associations between demographic and environmental exposures and the spatial distribution of diseases (Besag and Newell, 1991; Kulldorff and Nagarwalla, 1995; Kulldorff et al., 1997). There are numerous methods for testing global clustering, including those methods proposed by Alt and Vach (1991), Besag and Newell (1991), Cuzick and Edwards (1990), Diggle and Chetwynd (1991), Grimson (1991), Moran (1950), Tango (1995, 1999, 2000), Walter (1992a, 1992b, 1993) and Whittemore et al (1987). Siddiqui et al (2006) applied Cuzick-Edward's k-Nearest Neighbors test (Cuzick and Edwards, 1990) to evaluate clustering of cholera cases in Pakistan. Using the Moran's Index, Borroto and Martinez-Piedra (2000) have described the spatial distribution of cholera in Mexican states as clustered. This clustering reflects a north-south gradient and spatial clustering of southern states with higher incidence and spatial clustering of northern states with low incidence. Likewise, the Moran's Index has been used to evaluate the clustering of cholera in the Lusaka area of Zambia (Sasaki et al., 2008) and in Madras (India) (Ruiz-Moreno et al., 2007). Osei et al (2008) have also used the Moran's Index to evaluate global clustering of cholera in the Ashanti Region of Ghana. However, global cluster analysis ran the risk of obscuring local effects since the assumption of stationarity is rarely met. Locating and/or defining the characteristics of disease clusters, i.e. local cluster analysis, can inform hypothesis of population or environmental drivers of ill-health, as well as direct the prevention or treatment efforts of health care workers. Using the popular spatial statistics approach, i.e. Ripley's K index, Ruiz-Moreno et al (2007) observed that clustering of cholera in Bangladesh occur at different spatial scales. Local clustering methods such as the Circular Scan Statistic (Kulldorff, 1997) and the Flexible Scan Statistic (Tango and Takahashi, 2005) have been used to detect and map the clustering of cholera in the city of Kumasi-Ghana (Osei et al., 2010; 2011). They emphasize that the Circular Scan Statistic can underestimate the relative risk of cholera clusters compared with the Flexible Scan Statistic. Emch and Ali (2003) have also used the spatial scan statistic to evaluate clustering of cholera.

#### **4.3 Ecological analysis and cholera**

A significant interest in spatial epidemiology also lies in identifying associated risk factors which enhance the risk of infection, the so called *ecological analysis* (Lawson et al., 1999a, 1999b; Lawson, 2001) or *geographic correlations studies* (Elliott et al., 2000). Understanding the spatial relationship between cholera and ecological risk factors has always been a challenge. Most authors ignore the geographical structure (spatial autocorrelation) of the data in the statistical analysis. For instance, Ali et al (2001, 2002a, 2002b) have utilized logistic regression, simple and multiple regression models to study the spatial epidemiology of cholera in an endemic area of Bangladesh. In their study, spatial filtering methods (Talbot et al., 2000), typically spatial moving average (Kafadar, 1996), and traditional geostatistics were only used to remove noise and transform cholera and environmental data into a spatially continuous form. This notwithstanding, the effect of spatial proximity or geographical structure of the data was not incorporated in the statistical model. Sasaki et al (2008) investigated risk factors of cholera with a GIS and matched case-case control in a peri-urban area of Luzaka, Zambia. Although a spatial autocorrelation analysis using Moran's Index

Cholera and Spatial Epidemiology 11

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was found to be statistically significant, this was never incorporated in the logistic and multiple regression models. Other authors have also used classical statistical methods to analyze the risk factors of cholera. Sasaki et al (2008) applied logistic and multiple regression models to examine risk factors of cholera in a peri-urban area of Luzaka, Zambia. Mugoya et al (2005) used logistic regression analysis to investigate the spread of cholera in Kenya. Ackers et al (1998) used Pearson correlation coefficient to determine the correlation between cholera incidence rates and socioeconomic and environmental risk factors in Latin America. Kuo and Fukui (2007) used a logarithmic regression to model the diffusion of cholera in Japan. De Magny et al (2008) used a Poisson regression to model environmental variables associated with cholera in Bangladesh.

Geographical data are correlated in space; therefore, data in close geographical proximity is more likely to be influenced by similar ecological factors and therefore affected in a similar way, i.e. spatial autocorrelation. Consequently, when these standard statistical methods are used to analyze geographically correlated data, the standard error of the covariate parameters is underestimated and thus the statistical significance is overestimated (Cressie, 1993). Yet, few studies have incorporated the effect of geographical proximity in cholera studies (Ali et al., 2002a, 2002b, 2006; Borroto and Martinez-Piedra, 2000). Spatial statistical methods, such as spatial regression models, incorporate spatial autocorrelation according to the way geographical neighbors are defined. Osei and Duker (2008) have used spatial regression models (both spatial lag and spatial error models) to explore the spatial dependency of cholera prevalence on an important local environmental factor (open-space refuse dumps) in Kumasi, Ghana. Inhabitants with high density of refuse dumps were observed to have higher cholera prevalence than those with lower density of refuse dumps (Osei and Duker, 2008). Moreover, inhabitants close to refuse dumps were observed to have higher cholera prevalence than those farther. Similarly, Osei et al (2010) have used spatial regression models to explore the spatial dependency of cholera on potential contaminated water bodies.

## **5. Conclusion**

Cholera has been a public health burden for ages. Unlike the biological characteristics, relatively little effort has been made to understand the spatial epidemiology. Understanding the spatial patterns is useful for effective health planning and resource allocation. This review emphasized on the generic and spatial epidemiology of cholera. Important spatial epidemiologic tools applied in cholera studies have also been discussed in this review. However, not all the knowledge of cholera epidemiology has been captured in this review. Further studies are required to fully explain the spatial epidemiology of cholera.

#### **6. References**


was found to be statistically significant, this was never incorporated in the logistic and multiple regression models. Other authors have also used classical statistical methods to analyze the risk factors of cholera. Sasaki et al (2008) applied logistic and multiple regression models to examine risk factors of cholera in a peri-urban area of Luzaka, Zambia. Mugoya et al (2005) used logistic regression analysis to investigate the spread of cholera in Kenya. Ackers et al (1998) used Pearson correlation coefficient to determine the correlation between cholera incidence rates and socioeconomic and environmental risk factors in Latin America. Kuo and Fukui (2007) used a logarithmic regression to model the diffusion of cholera in Japan. De Magny et al (2008) used a Poisson regression to model environmental variables

Geographical data are correlated in space; therefore, data in close geographical proximity is more likely to be influenced by similar ecological factors and therefore affected in a similar way, i.e. spatial autocorrelation. Consequently, when these standard statistical methods are used to analyze geographically correlated data, the standard error of the covariate parameters is underestimated and thus the statistical significance is overestimated (Cressie, 1993). Yet, few studies have incorporated the effect of geographical proximity in cholera studies (Ali et al., 2002a, 2002b, 2006; Borroto and Martinez-Piedra, 2000). Spatial statistical methods, such as spatial regression models, incorporate spatial autocorrelation according to the way geographical neighbors are defined. Osei and Duker (2008) have used spatial regression models (both spatial lag and spatial error models) to explore the spatial dependency of cholera prevalence on an important local environmental factor (open-space refuse dumps) in Kumasi, Ghana. Inhabitants with high density of refuse dumps were observed to have higher cholera prevalence than those with lower density of refuse dumps (Osei and Duker, 2008). Moreover, inhabitants close to refuse dumps were observed to have higher cholera prevalence than those farther. Similarly, Osei et al (2010) have used spatial regression models to explore the spatial dependency of cholera on potential contaminated

Cholera has been a public health burden for ages. Unlike the biological characteristics, relatively little effort has been made to understand the spatial epidemiology. Understanding the spatial patterns is useful for effective health planning and resource allocation. This review emphasized on the generic and spatial epidemiology of cholera. Important spatial epidemiologic tools applied in cholera studies have also been discussed in this review. However, not all the knowledge of cholera epidemiology has been captured in this review.

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Further studies are required to fully explain the spatial epidemiology of cholera.

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**5. Conclusion** 

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**2** 

*1,2Ghana 3Netherlands* 

**Evaluating Spatial and Space-Time Clustering** 

*3Faculty of Geo-Information Science and Earth Observation (ITC), Twente University* 

Basic problems in geographical surveillance for a spatially distributed disease data are the identification of areas of exceptionally high prevalence or clusters, test of their statistical significance, and identification of the reasons behind the elevated prevalence of the disease. Knowledge of the location of high risk areas of diseases and factors leading to such elevated risk is essential to better understand human interaction with its environment, especially when the disease transmission is enhanced by environmental or demographic factors. Cluster analysis provides opportunities for environmental epidemiologist to study associations between demographic and environmental exposures and the spatial distribution of diseases (Myaux et al*.*, 1997; Kulldorff and Nagarwalla, 1995; Besag and

Cholera is caused by specific strains of the water borne bacterial *Vibrio cholerae* O1 or O139 (*V. cholerae* here after), following ingestion of infective dose through contaminated water or food (Kelly, 2001). The disease has remained as an important cause of mortality and morbidity in the world, especially in developing tropical countries. African countries report approximately 90% of the world wide cholera cases and deaths (WHO, 2001-2006). In most African countries, the synergy of poverty, high population density, poor sanitation, poor housing, and lack of good water supplies enhance exposure to *V. cholerae*. Despite the prevalence and/or fatality and demographic overlap, little has been studied about the spatial and temporal patterns of cholera in Africa. In Ghana, the disease has been a public health problem since its introduction in the 1970s (Pobee and Grant, 1970). Cholera infection is primarily driven by environmental factors (Ali et al., 2002a, 2002b; Huq et al., 2005), and since environmental processes are spatially continuous in nature (Webster et al*.*, 1994), high incidence rates of the disease are expected to cluster together. A previous study carried out in Ashanti Region used Moran's Index for spatial autocorrelation to explore the existence of clusters of cholera. Also in the above study, empirical Bayesians smoothed rates of cholera

**1. Introduction** 

Newell, 1991; Kulldorff, 2001; Kulldorff et al., 1998).

 **of Cholera in Ashanti-Region-Ghana** 

Frank B Osei1, Alfred A Duker2 and Alfred Stein3

 *Kwame Nkrumah University of Science and Technology, Kumasi* 

*1Faculty of Public Health and Allied Sciences, Catholic University College of Ghana, Sunyani* 

*2Department of Geomatic Engineering,* 


## **Evaluating Spatial and Space-Time Clustering of Cholera in Ashanti-Region-Ghana**

Frank B Osei1, Alfred A Duker2 and Alfred Stein3 *1Faculty of Public Health and Allied Sciences, Catholic University College of Ghana, Sunyani 2Department of Geomatic Engineering, Kwame Nkrumah University of Science and Technology, Kumasi 3Faculty of Geo-Information Science and Earth Observation (ITC), Twente University 1,2Ghana 3Netherlands* 

## **1. Introduction**

18 Cholera

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disease. *Biometrika* 74(3):631-635

Health Organization, Geneva.

Basic problems in geographical surveillance for a spatially distributed disease data are the identification of areas of exceptionally high prevalence or clusters, test of their statistical significance, and identification of the reasons behind the elevated prevalence of the disease. Knowledge of the location of high risk areas of diseases and factors leading to such elevated risk is essential to better understand human interaction with its environment, especially when the disease transmission is enhanced by environmental or demographic factors. Cluster analysis provides opportunities for environmental epidemiologist to study associations between demographic and environmental exposures and the spatial distribution of diseases (Myaux et al*.*, 1997; Kulldorff and Nagarwalla, 1995; Besag and Newell, 1991; Kulldorff, 2001; Kulldorff et al., 1998).

Cholera is caused by specific strains of the water borne bacterial *Vibrio cholerae* O1 or O139 (*V. cholerae* here after), following ingestion of infective dose through contaminated water or food (Kelly, 2001). The disease has remained as an important cause of mortality and morbidity in the world, especially in developing tropical countries. African countries report approximately 90% of the world wide cholera cases and deaths (WHO, 2001-2006). In most African countries, the synergy of poverty, high population density, poor sanitation, poor housing, and lack of good water supplies enhance exposure to *V. cholerae*. Despite the prevalence and/or fatality and demographic overlap, little has been studied about the spatial and temporal patterns of cholera in Africa. In Ghana, the disease has been a public health problem since its introduction in the 1970s (Pobee and Grant, 1970). Cholera infection is primarily driven by environmental factors (Ali et al., 2002a, 2002b; Huq et al., 2005), and since environmental processes are spatially continuous in nature (Webster et al*.*, 1994), high incidence rates of the disease are expected to cluster together. A previous study carried out in Ashanti Region used Moran's Index for spatial autocorrelation to explore the existence of clusters of cholera. Also in the above study, empirical Bayesians smoothed rates of cholera

Evaluating Spatial and Space-Time Clustering of Cholera in Ashanti-Region-Ghana 21

the Kumasi Metropolis. The proportion of the population with access to potable (pipeborne) water is relatively low in the districts, including the Kumasi Metropolis. A number of factors, particularly high fertility and internal migration, have accounted for the rapid population growth in the region. About two-thirds of the population in the region was born where they were enumerated; the remaining one third are in-migrants to the region. In 6 of the 18 districts, at least seven out of every ten persons were enumerated in the localities in which they were born, indicating that these districts have less in-migrant than other districts

Fig. 1. A map of Ghana showing Ashanti region (in gray color). The figure also shows the

The Ashanti region has a Disease Control Unit (DCU) to which all District Health Directorates (DHD) report suspected outbreaks of various infectious diseases at the end of each year. In this study, all cholera cases used were based on hospital data which were reported to the various DHD. For the detection of statistically significant clusters of cholera, the spatial scan statistic software, SaTScan, developed by Kulldorff, was used. This software

Case file contains information about cholera cases for specified districts and times. Reported cases of cholera from 1997 to 2001 for each district within the region were retrieved from the DCU. Case definition of cholera was based on the WHO (1993) guidance on formulation of national policy on the control of cholera. According to this guidance, in an area where the disease is not known to be present a case of cholera should be suspected, when a patient, 5 years of age or older develops severe dehydration or dies from acute watery diarrhea, or where an epidemic is occurring, a patient, 5 years of age or older develops acute watery

spatial distribution of the various districts in Ashanti region

in the region (PHC, 2000).

**2.2 Data sources** 

**2.2.1 Case file** 

requires three main data files to run:

diarrhea, with or without vomiting.

(i.e. visual inspection) revealed possible spatial and temporal clustering of cholera for the 5 year period, i.e. from 1997 to 2001 (Osei and Duker, 2008). However, the exact locations of these cluster, as well as the correlations with some demographic and socioeconomic factors were not systematically investigated. The purpose of this study, however, is to investigate spatial and space-time clusters of cholera in Kumasi. Correlation analysis of cholera rates with demographic factors, i.e. sanitation, drinking water and internal migration are also explored to assess the extent to which these factors might explain high rate clusters of cholera.

This study utilizes the *spatial scan statistic* (Kulldorff, 1997) to detect spatial and space-time clusters of cholera. The spatial scan statistic offers several advantages over other clustering methods: (1) it corrects for multiple comparisons, (2) adjusts for the heterogeneous population densities among the different areas in the study, (3) detects and identifies the location of the clusters without prior specification of their suspected location or size thereby overcoming pre-selection bias, (4) and the method allows for adjustment for covariates. Also Kulldorff's spatial scan statistic is both deterministic (i.e., it identifies the locations of clustering) and inferential (i.e., it allows for hypothesis testing and evaluation of significance). The spatial scan statistic has been used to detect and evaluate various disease clusters including cancer (Michelozzi et al*.*, 2002; Viel et al*.*, 2000; Sheehan and DeChelo, 2005; Hjalmars et al*.*, 1996; Turnbull, 1990, Kulldorff et al*.*, 1998), giardiasis (Odoi et al*.*, 2004) tuberculosis (Tiwari et al*.*, 2006), diabetes (Green et al*.*, 2003), Creutzfeldt-Jacob disease (Cousens, 2001), granulocytic elrlichiosis (Chput et al*.*, 2002), and sclerosis (Sabel et al*.*, 2003). The spatial scan statistic*,* as implemented in SaTScan software (Kulldorff, 2005; Kulldorff, 2006) has the capabilities of detecting purely spatial clusters, temporal clusters, and space-time clusters.

### **2. Methods**

#### **2.1 Study area**

This study was conducted in Ashanti Region, one of the ten regions in Ghana. The region lies between longitudes 0° 9'W and 2° 15'W, and latitudes 5° 30'N and 7° 27'N. The Ashanti Region is dominated by Ashantis, who constitute 14.8% of all Ghanaians by birth. The Ashantis have a great history of culture of which the influence of the Ashanti Kingdom stretches beyond the borders of Ghana. The region occupies a total land area of 24,389 square kilometers representing 10.2% of the total land area of Ghana. The region has a population density of 148.1 persons per square kilometer, which is about two times higher than the overall population density in Ghana. There are 18 administrative districts in the Ashanti region including Kumasi Metropolis of which the capital is Kumasi, and is the only district which has gained a metropolitan status. The Kumasi Metropolis is the most populous district in the region. The 2000 census recorded the region's population as 3,612,950, representing 19.1 per cent of the country's population. The urban population (51.3%) in the region exceeds that of the rural population (48.7%). In-house pit latrines and public toilets, which may be pit, Kumasi ventilated improved pit (KVIP) or bucket latrines, are the main toilet facilities used in the districts. Water closet (WC) is used by small proportions of households, ranging from 0.5 per cent in Ahafo Ano South to 27.8 per cent in the Kumasi Metropolis. The proportion of the population with access to potable (pipeborne) water is relatively low in the districts, including the Kumasi Metropolis. A number of factors, particularly high fertility and internal migration, have accounted for the rapid population growth in the region. About two-thirds of the population in the region was born where they were enumerated; the remaining one third are in-migrants to the region. In 6 of the 18 districts, at least seven out of every ten persons were enumerated in the localities in which they were born, indicating that these districts have less in-migrant than other districts in the region (PHC, 2000).

Fig. 1. A map of Ghana showing Ashanti region (in gray color). The figure also shows the spatial distribution of the various districts in Ashanti region

#### **2.2 Data sources**

20 Cholera

(i.e. visual inspection) revealed possible spatial and temporal clustering of cholera for the 5 year period, i.e. from 1997 to 2001 (Osei and Duker, 2008). However, the exact locations of these cluster, as well as the correlations with some demographic and socioeconomic factors were not systematically investigated. The purpose of this study, however, is to investigate spatial and space-time clusters of cholera in Kumasi. Correlation analysis of cholera rates with demographic factors, i.e. sanitation, drinking water and internal migration are also explored to assess the extent to which these factors might explain high rate clusters of

This study utilizes the *spatial scan statistic* (Kulldorff, 1997) to detect spatial and space-time clusters of cholera. The spatial scan statistic offers several advantages over other clustering methods: (1) it corrects for multiple comparisons, (2) adjusts for the heterogeneous population densities among the different areas in the study, (3) detects and identifies the location of the clusters without prior specification of their suspected location or size thereby overcoming pre-selection bias, (4) and the method allows for adjustment for covariates. Also Kulldorff's spatial scan statistic is both deterministic (i.e., it identifies the locations of clustering) and inferential (i.e., it allows for hypothesis testing and evaluation of significance). The spatial scan statistic has been used to detect and evaluate various disease clusters including cancer (Michelozzi et al*.*, 2002; Viel et al*.*, 2000; Sheehan and DeChelo, 2005; Hjalmars et al*.*, 1996; Turnbull, 1990, Kulldorff et al*.*, 1998), giardiasis (Odoi et al*.*, 2004) tuberculosis (Tiwari et al*.*, 2006), diabetes (Green et al*.*, 2003), Creutzfeldt-Jacob disease (Cousens, 2001), granulocytic elrlichiosis (Chput et al*.*, 2002), and sclerosis (Sabel et al*.*, 2003). The spatial scan statistic*,* as implemented in SaTScan software (Kulldorff, 2005; Kulldorff, 2006) has the capabilities of detecting purely spatial clusters, temporal clusters,

This study was conducted in Ashanti Region, one of the ten regions in Ghana. The region lies between longitudes 0° 9'W and 2° 15'W, and latitudes 5° 30'N and 7° 27'N. The Ashanti Region is dominated by Ashantis, who constitute 14.8% of all Ghanaians by birth. The Ashantis have a great history of culture of which the influence of the Ashanti Kingdom stretches beyond the borders of Ghana. The region occupies a total land area of 24,389 square kilometers representing 10.2% of the total land area of Ghana. The region has a population density of 148.1 persons per square kilometer, which is about two times higher than the overall population density in Ghana. There are 18 administrative districts in the Ashanti region including Kumasi Metropolis of which the capital is Kumasi, and is the only district which has gained a metropolitan status. The Kumasi Metropolis is the most populous district in the region. The 2000 census recorded the region's population as 3,612,950, representing 19.1 per cent of the country's population. The urban population (51.3%) in the region exceeds that of the rural population (48.7%). In-house pit latrines and public toilets, which may be pit, Kumasi ventilated improved pit (KVIP) or bucket latrines, are the main toilet facilities used in the districts. Water closet (WC) is used by small proportions of households, ranging from 0.5 per cent in Ahafo Ano South to 27.8 per cent in

cholera.

and space-time clusters.

**2. Methods 2.1 Study area** 

> The Ashanti region has a Disease Control Unit (DCU) to which all District Health Directorates (DHD) report suspected outbreaks of various infectious diseases at the end of each year. In this study, all cholera cases used were based on hospital data which were reported to the various DHD. For the detection of statistically significant clusters of cholera, the spatial scan statistic software, SaTScan, developed by Kulldorff, was used. This software requires three main data files to run:

#### **2.2.1 Case file**

Case file contains information about cholera cases for specified districts and times. Reported cases of cholera from 1997 to 2001 for each district within the region were retrieved from the DCU. Case definition of cholera was based on the WHO (1993) guidance on formulation of national policy on the control of cholera. According to this guidance, in an area where the disease is not known to be present a case of cholera should be suspected, when a patient, 5 years of age or older develops severe dehydration or dies from acute watery diarrhea, or where an epidemic is occurring, a patient, 5 years of age or older develops acute watery diarrhea, with or without vomiting.

Evaluating Spatial and Space-Time Clustering of Cholera in Ashanti-Region-Ghana 23

*I* depends on the comparison between *Chol* E(C) and*Chol* <sup>C</sup> . *I* is 1 when *Chol Chol* C E(C) , otherwise 0. The test of significance level of clusters is through the Monte Carlo hypothesis testing (Dwass, 1957). In this study, the maximum window size was set as 50% of the total population. The null hypothesis of no cluster was rejected when the simulated *p*-*value* was less than or equal to 0.05 for most likely clusters and 0.1 for secondary

A smaller window size (defined as ≤ 25% of the total population) was also used to investigate the possibility of smaller clusters. This varied from ≤ 25% to ≤ 50% with successive increments of 5%. This was meant to check the sensitivity of spatial scan statistic to smaller window sizes

For the detection of space-time clusters, SaTScan imposes a cylindrical window with a circular geographic base and with height corresponding to the time of occurrences. In this way, the base of the cylinder is centered around one of several possible centroids located throughout the study region with the radius varying continuously in size, whereas the height of the cylinder reflects any possible time interval of less than or equal to half the total study period, as well as the whole study period. The window is then moved in space and time so that for each possible geographic location and size, it also visits each possible time interval (Kulldorf et al*.*, 1998). The likelihood ratio test statistic is constructed in the same way as for the purely spatial scan statistic. However, the computational algorithm for calculating the likelihood for each window is in three rather than two dimensions (Kulldorff, 2001). Here, we used a spatial window that could include up to 50% of population at risk and a maximum temporal window of 50%, without including purely spatial clusters. Moreover, most likely clusters for different time lengths (i.e. 1, 2, 3, or 4 year length) were scanned using a temporal cluster size of 90% of the study period and also included purely spatial clusters with temporal size of 100%. The maximum spatial cluster size was set at 50% of population at risk and included purely temporal clusters (spatial

Three main risk factors, i.e. sanitation, source of drinking water, and internal migration, were used to explore the extent at which these variables affect cholera prevalence within the study area. These were obtained from the 2000 Population and Housing census of Ghana (PHC, 2000). Four different types of sanitation facilities are used in the study area; WC*,* Pit latrine*,* KVIP*,* bucket or pan. A number of households in the districts have no access to toilet facilities. When a substantial number of households do not have toilet facilities, it is to be expected that inhabitants will defecate in the bush, drains, etc. Bucket or pan is the most unsafe sanitation method because the bucket is open and can attract filth breeding flies. Moreover, faeces have to be transferred to a different bucket when it is full; thus faeces can spread to nearby areas in the course of transfer. In this study, sanitation condition for a district is described as the percentage of the district's share of the region's population who do not have access to toilet facilities, and who use bucket or pan sanitation method. For this, larger values reflect poor or bad sanitation condition, while smaller values reflect good

clusters since the latter have conservative *p-values* (Kulldorff, 2006).

when there are larger spatial units and small number of spatial units.

cluster size = 100%) as well.

sanitation condition.

**2.4 Correlation between cholera and risk factors** 

#### **2.2.2 Population file**

The population file provides information about the background population at risk for each spatial district. The population database was obtained from the 2000 Population and Housing Census of Ghana conducted by the National Statistical Service (PHC, 2000).

#### **2.2.3 Coordinate file**

The coordinate file provides information about the spatial location of each district. In this study, the spatial scale of analysis was at the district level. The centroids of the districts were used as the coordinates of the districts.

#### **2.3 Cluster analysis**

The spatial scan statistic was used to detect the presence spatial and space-time clusters of cholera. The spatial scan statistic was developed by Kulldorff (1997, 2006) and it is been implemented in the SaTScan software. Spatial scan statistic has a disadvantage of being difficult to incorporate prior knowledge about the size and shape of an outbreak as well as its impact on disease rate (Neill et al*.*, 2005). However, we used this as an advantage to get rid of pre-selection biases of clusters and their locations. Spatial scan statistic method is based on the principle that the number of cholera cases in a geographic area is Poissondistributed according to a known underlying population at risk (Kulldorff, 2006). For the detection of purely spatial clusters, SaTScan imposes a circular window on the study region which is moved over the region and centered on the centroid of each district. The size of the circular window, which is also the cluster size, is expressed as a percentage of the total population at risk. This varies from 0 to a maximum (not exceeding 100), as specified by the user. The maximum window size should not exceed 50% of the total population because clusters of larger sizes would indicate areas of exceptionally low rates outside the circle rather than an area of exceptionally high rate within the circle. Possible clusters are tested within the window whenever it is centered on the centroid of each district. Whenever the window finds a new case, the software calculates a likelihood function to test for elevated risk within the window in comparison with those outside the window. The likelihood function for any given window *W* is proportional to:

$$L(\mathbf{W}) = \sup\_{\mathbf{W} \in \mathbf{W}} \left( \frac{\operatorname{Collol}\_{\{\mathbf{C}\}}(\mathbf{W})}{\operatorname{Collol}\_{\{\operatorname{E(C)}\}}(\mathbf{W})} \right)^{\operatorname{Chol}\_{\{\mathbf{C}\}}(\mathbf{W})} \left( \frac{\operatorname{Collol}\_{\{\mathbf{C}\}}(\mathbf{\hat{V}})}{\operatorname{Collol}\_{\{\operatorname{E(C)}\}}(\mathbf{\hat{V}})} \right)^{\operatorname{Chol}\_{\{\mathbf{C}\}}(\mathbf{\hat{V}})} \tag{1}$$
 
$$\times I \left( \frac{\operatorname{Collol}\_{\{\mathbf{C}\}}(\mathbf{V})}{\operatorname{Collol}\_{\{\operatorname{E(C)}\}}(\mathbf{V})} \times \frac{\operatorname{Collol}\_{\{\mathbf{C}\}}(\mathbf{\hat{V}})}{\operatorname{Collol}\_{\{\operatorname{E(C)}\}}(\mathbf{\hat{V}})} \right)$$

where *W*<sup>ˆ</sup> indicates all the regions outside the window *<sup>W</sup>* , and *Chol* <sup>C</sup> and *Chol* E(C) denote the observed and expected number of cases within the specified window, respectively. The window *W* to be scanned by the spatial scan statistic is included in the set: **W** *W im kK ik* 1 ,1 *<sup>i</sup>* , where *Wik* , 1,..., *<sup>i</sup> k K* , denote the window composed by the (*k −*1) nearest neighbors to region *i*. The window *W* that attains the maximum likelihood is defined as the *most likely cluster* (MLC). The indicator function

The population file provides information about the background population at risk for each spatial district. The population database was obtained from the 2000 Population and

The coordinate file provides information about the spatial location of each district. In this study, the spatial scale of analysis was at the district level. The centroids of the districts were

The spatial scan statistic was used to detect the presence spatial and space-time clusters of cholera. The spatial scan statistic was developed by Kulldorff (1997, 2006) and it is been implemented in the SaTScan software. Spatial scan statistic has a disadvantage of being difficult to incorporate prior knowledge about the size and shape of an outbreak as well as its impact on disease rate (Neill et al*.*, 2005). However, we used this as an advantage to get rid of pre-selection biases of clusters and their locations. Spatial scan statistic method is based on the principle that the number of cholera cases in a geographic area is Poissondistributed according to a known underlying population at risk (Kulldorff, 2006). For the detection of purely spatial clusters, SaTScan imposes a circular window on the study region which is moved over the region and centered on the centroid of each district. The size of the circular window, which is also the cluster size, is expressed as a percentage of the total population at risk. This varies from 0 to a maximum (not exceeding 100), as specified by the user. The maximum window size should not exceed 50% of the total population because clusters of larger sizes would indicate areas of exceptionally low rates outside the circle rather than an area of exceptionally high rate within the circle. Possible clusters are tested within the window whenever it is centered on the centroid of each district. Whenever the window finds a new case, the software calculates a likelihood function to test for elevated risk within the window in comparison with those outside the window. The likelihood

Housing Census of Ghana conducted by the National Statistical Service (PHC, 2000).

**2.2.2 Population file** 

**2.2.3 Coordinate file** 

**2.3 Cluster analysis** 

used as the coordinates of the districts.

function for any given window *W* is proportional to:

*L*

**W**

*I*

<sup>ˆ</sup> sup <sup>W</sup> <sup>ˆ</sup>

C C E(C) E(C)

 

*Chol W Chol W*

where *W*<sup>ˆ</sup> indicates all the regions outside the window *<sup>W</sup>* , and *Chol* <sup>C</sup> and *Chol* E(C) denote the observed and expected number of cases within the specified window, respectively. The window *W* to be scanned by the spatial scan statistic is included in the set: **W** *W im kK ik* 1 ,1 *<sup>i</sup>* , where *Wik* , 1,..., *<sup>i</sup> k K* , denote the window composed by the (*k −*1) nearest neighbors to region *i*. The window *W* that attains the maximum likelihood is defined as the *most likely cluster* (MLC). The indicator function

*Chol W Chol W*

*W Chol W Chol W*

  C C E(C) E(C)

*Chol W Chol W*

 

ˆ ˆ

 

<sup>C</sup> <sup>C</sup> <sup>ˆ</sup>

*Chol W Chol W*

(1)

*I* depends on the comparison between *Chol* E(C) and*Chol* <sup>C</sup> . *I* is 1 when *Chol Chol* C E(C) , otherwise 0. The test of significance level of clusters is through the Monte Carlo hypothesis testing (Dwass, 1957). In this study, the maximum window size was set as 50% of the total population. The null hypothesis of no cluster was rejected when the simulated *p*-*value* was less than or equal to 0.05 for most likely clusters and 0.1 for secondary clusters since the latter have conservative *p-values* (Kulldorff, 2006).

A smaller window size (defined as ≤ 25% of the total population) was also used to investigate the possibility of smaller clusters. This varied from ≤ 25% to ≤ 50% with successive increments of 5%. This was meant to check the sensitivity of spatial scan statistic to smaller window sizes when there are larger spatial units and small number of spatial units.

For the detection of space-time clusters, SaTScan imposes a cylindrical window with a circular geographic base and with height corresponding to the time of occurrences. In this way, the base of the cylinder is centered around one of several possible centroids located throughout the study region with the radius varying continuously in size, whereas the height of the cylinder reflects any possible time interval of less than or equal to half the total study period, as well as the whole study period. The window is then moved in space and time so that for each possible geographic location and size, it also visits each possible time interval (Kulldorf et al*.*, 1998). The likelihood ratio test statistic is constructed in the same way as for the purely spatial scan statistic. However, the computational algorithm for calculating the likelihood for each window is in three rather than two dimensions (Kulldorff, 2001). Here, we used a spatial window that could include up to 50% of population at risk and a maximum temporal window of 50%, without including purely spatial clusters. Moreover, most likely clusters for different time lengths (i.e. 1, 2, 3, or 4 year length) were scanned using a temporal cluster size of 90% of the study period and also included purely spatial clusters with temporal size of 100%. The maximum spatial cluster size was set at 50% of population at risk and included purely temporal clusters (spatial cluster size = 100%) as well.

#### **2.4 Correlation between cholera and risk factors**

Three main risk factors, i.e. sanitation, source of drinking water, and internal migration, were used to explore the extent at which these variables affect cholera prevalence within the study area. These were obtained from the 2000 Population and Housing census of Ghana (PHC, 2000). Four different types of sanitation facilities are used in the study area; WC*,* Pit latrine*,* KVIP*,* bucket or pan. A number of households in the districts have no access to toilet facilities. When a substantial number of households do not have toilet facilities, it is to be expected that inhabitants will defecate in the bush, drains, etc. Bucket or pan is the most unsafe sanitation method because the bucket is open and can attract filth breeding flies. Moreover, faeces have to be transferred to a different bucket when it is full; thus faeces can spread to nearby areas in the course of transfer. In this study, sanitation condition for a district is described as the percentage of the district's share of the region's population who do not have access to toilet facilities, and who use bucket or pan sanitation method. For this, larger values reflect poor or bad sanitation condition, while smaller values reflect good sanitation condition.

Evaluating Spatial and Space-Time Clustering of Cholera in Ashanti-Region-Ghana 25

733 328.62 12.253 434.73 0.001

956 383.32 15.597 673.86 0.001

**Cluster Area** *Chol*(C) *Chol*(E(C)) *Chol*(RR) LLR *p*-*value*

Kumasi 1033 421.23 7.42 592.29 0.001

Kwabre 2727 1161.47 9.699 1618.26 0.001

Ashanti region, Ghana, during 1998-2001: LLR (Log Likelihood Ratio)

Table 1. Most likely purely spatial clusters of cholera in Ashanti region, Ghana, detected by retrospective spatial analysis. This Table shows the results of the purely spatial cluster analysis using a spatial window that could include up to 50% of the population at risk in

Fig. 2. Locations of the detected clusters of cholera and spatial distribution of the relative

risks for 1998(2a), 1999(2b), 2001 (2c), and 1998-2001 (2c)

**Year: 1998**  Kumasi Kwabre Bosumtwe AK **Year: 1999** 

**Year: 2001**  Kumasi Kwabre

Kumasi

**Year: 1997-2001** 

Since the natural reservoir of cholera is the aquatic environment, inhabitants who drink from wells, streams, rivers, ponds, dugouts and dams are assumed to be at a higher risk of cholera than those who drink from pipe borne water. Therefore, inhabitants who drink from wells, streams, rivers, ponds, dugouts and dams are classified as inhabitants who do not have access to potable water. The indicator for drinking water for each district was computed as the percentage of the district's share of the region's population who drink from wells, streams, rivers, ponds, dugouts and dams.

Internal migration is one of the important demographic characteristics that accounts for rapid population growth in a place. This variable was computed as a percentage of the district's share of the region's population in the year 2000 that were born outside the district during the time of enumeration.

Global Pearson's correlation coefficient was used to determine the correlation between cholera cumulative incidence rates from 1997 to 2001 and sanitation, drinking water, and internal migration. *P-values* were calculated to serve as a guide to access the significance of all correlation coefficients. Most health planning strategies in Ghana are based on the level of urbanization of a district. In other words, groups of districts with similar urbanization levels are planned together. With this in mind, all districts in the study region were stratified according to the level of urbanization; i.e. *low, medium and high*. Pearson's correlation analyses were repeated for each stratum of districts in order to assess the effects of the risk factors on cholera within each urbanization stratum.

## **3. Results and analyses**

#### **3.1 Purely spatial clusters**

No cluster was detected for the years 1997 and 2000 since very few cases were reported for these years. Only most likely significant clusters were detected for the years 1998, 1999, 2001 (Table 1 and Figure 2). These clusters encompassed Kumasi, Bosumtwe AK and Kwabre in 1998 (relative risk RR *Chol* 12.25 , <sup>C</sup> *Chol* 733 , E(C) *Chol* 328.62 ), Kumasi in 1999 ( RR *Chol* 7.42 , <sup>C</sup> *Chol* 1033 , E(C) *Chol* 421.33 ), Kumasi and Kwabre in 2001( RR *Chol* 15.60 , <sup>C</sup> *Chol* 956 , E(C) *Chol* 383.32 ), and Kumasi and Kwabre from 1998 to 2001( RR *Chol* 9.70 , <sup>C</sup> *Chol* 2727 , E(C) *Chol* 1161.47 ). No differences were observed between the results of the varying window sizes and the window size of ≤ 50% of the total population. Hence tables for these results are not shown.

#### **3.2 Space-time clusters**

While testing whether the purely spatial clusters were long term or temporary i.e. spacetime analysis, a statistically significant (*p* = 0.001) most likely cluster was identified at Kumasi metropolis for the year 1998-1999. This cluster has RR *Chol* 5.86 with <sup>C</sup> *Chol* 1668 as against E(C) *Chol* 508.75 (See Table 2). One statistically significant (*P* = 0.001) secondary cluster encompassing 3 districts (Ahafo Ano North, Ahafo Ano South, and Atwima) was detected for 1999. For this cluster RR *Chol* 1.91 and <sup>C</sup> *Chol* 179 as against E(C) *Chol* 96.34 .


Since the natural reservoir of cholera is the aquatic environment, inhabitants who drink from wells, streams, rivers, ponds, dugouts and dams are assumed to be at a higher risk of cholera than those who drink from pipe borne water. Therefore, inhabitants who drink from wells, streams, rivers, ponds, dugouts and dams are classified as inhabitants who do not have access to potable water. The indicator for drinking water for each district was computed as the percentage of the district's share of the region's population who drink from

Internal migration is one of the important demographic characteristics that accounts for rapid population growth in a place. This variable was computed as a percentage of the district's share of the region's population in the year 2000 that were born outside the district

Global Pearson's correlation coefficient was used to determine the correlation between cholera cumulative incidence rates from 1997 to 2001 and sanitation, drinking water, and internal migration. *P-values* were calculated to serve as a guide to access the significance of all correlation coefficients. Most health planning strategies in Ghana are based on the level of urbanization of a district. In other words, groups of districts with similar urbanization levels are planned together. With this in mind, all districts in the study region were stratified according to the level of urbanization; i.e. *low, medium and high*. Pearson's correlation analyses were repeated for each stratum of districts in order to assess the effects

No cluster was detected for the years 1997 and 2000 since very few cases were reported for these years. Only most likely significant clusters were detected for the years 1998, 1999, 2001 (Table 1 and Figure 2). These clusters encompassed Kumasi, Bosumtwe AK and Kwabre in 1998 (relative risk RR *Chol* 12.25 , <sup>C</sup> *Chol* 733 , E(C) *Chol* 328.62 ), Kumasi in 1999 ( RR *Chol* 7.42 , <sup>C</sup> *Chol* 1033 , E(C) *Chol* 421.33 ), Kumasi and Kwabre in 2001( RR *Chol* 15.60 , <sup>C</sup> *Chol* 956 , E(C) *Chol* 383.32 ), and Kumasi and Kwabre from 1998 to 2001( RR *Chol* 9.70 , <sup>C</sup> *Chol* 2727 , E(C) *Chol* 1161.47 ). No differences were observed between the results of the varying window sizes and the window size of ≤ 50% of the total

While testing whether the purely spatial clusters were long term or temporary i.e. spacetime analysis, a statistically significant (*p* = 0.001) most likely cluster was identified at Kumasi metropolis for the year 1998-1999. This cluster has RR *Chol* 5.86 with <sup>C</sup> *Chol* 1668 as against E(C) *Chol* 508.75 (See Table 2). One statistically significant (*P* = 0.001) secondary cluster encompassing 3 districts (Ahafo Ano North, Ahafo Ano South, and Atwima) was detected for 1999. For this cluster RR *Chol* 1.91 and <sup>C</sup> *Chol* 179 as against

wells, streams, rivers, ponds, dugouts and dams.

of the risk factors on cholera within each urbanization stratum.

population. Hence tables for these results are not shown.

during the time of enumeration.

**3. Results and analyses 3.1 Purely spatial clusters** 

**3.2 Space-time clusters** 

E(C) *Chol* 96.34 .


Table 1. Most likely purely spatial clusters of cholera in Ashanti region, Ghana, detected by retrospective spatial analysis. This Table shows the results of the purely spatial cluster analysis using a spatial window that could include up to 50% of the population at risk in Ashanti region, Ghana, during 1998-2001: LLR (Log Likelihood Ratio)

Fig. 2. Locations of the detected clusters of cholera and spatial distribution of the relative risks for 1998(2a), 1999(2b), 2001 (2c), and 1998-2001 (2c)

Evaluating Spatial and Space-Time Clustering of Cholera in Ashanti-Region-Ghana 27

strata (See Table 2). For instance there was a high, but non-significant correlation between cholera and drinking water within the *medium-urban* strata (*R2 = 0.62, p = 0.12*), and no significant correlation between cholera and drinking water within the *low-urban* strata (*R2 = 0.001, p = 0.96*). However, there was a high and significant correlation between cholera and

> Correlation and (*p-value*) Sanitation Drinking

Global a0.55 (0.001) a0.39 (0.001) a0.73 (0.001) Low urban c0.21 (0.36) c0.04(0.66) c0.001 (0.96)

urban c0.48 (0.13) c0.62 (0.12) c 0.62 (0.11) High urban b0.86 (0.007) b0.79 (0.018) b0.89 (0.005)

demographic factors. This Table depicts both the Global Pearson's correlation analyses, and Pearson's correlation analyses for each urbanization strata of districts. The associated *p– values* are shown in brackets. *asignificant correlations at 0.1% significance level; bsignificant* 

In this study, the purely spatial and space-time scan statistic methods implemented in SaTScan software have been used to analyze cholera cases from 1998 to 2001 in Kumasi, Ghana. These methods identifies whether unusual concentration of disease cases can be explained by chance or statistically significant. The findings of this study reveal several notable points. First, there is the existence of both purely spatial and space-time clusters, not explainable by chance (See Tables 1,2, and 3). Also, the results of both the purely spatial and space-time analysis are somewhat similar. In particular, the purely spatial analysis reported an excess incidence of cholera in Kumasi during the years 1998, 1999, and 2001 (See Table 3.1 and Figure 3.2), and the space-time analysis also reported an excess incidence of cholera

Second, the excess incidence of cholera mainly existed at Kumasi Metropolis throughout the period under study. Specifically, the purely spatial analysis reported excess incidence of cholera at Kumasi in 1998, 1999, and 2001. While testing whether the purely spatial clusters were long term or temporary, the space-time analysis also reported excess incidence of cholera at Kumasi Metropolis from the year 1999 to 2001. When the space-time analysis was modified to detect 1, 2, 3, 4, or 5 year length clusters, the space-time most likely cluster at Kumasi Metropolis became a purely spatial cluster (i.e. existed for 1997 to 2001, see Table 3). This indicates a sustained transmission of cholera at Kumasi Metropolis from 1997 through to 2001. Two main reasons may explain these patterns. (1) *Demographic status*: Kumasi is the most urbanized and highly commercialized district in Ashanti region, and therefore there is always a high daily influx of traders and civil workers from neighboring districts to Kumasi Metropolis. Such a high daily influx strain existing sanitation systems, thereby putting people at increased risk of cholera transmission. The rural poor also often migrate to city centers with the hope of a better life. However, due to the high cost of housing, such

Table 4. Pearson's correlation coefficients for the relationship between cholera and

water Migration

drinking water within the *high-urban* strata (*R2 = 0.86, p = 0.007*).

Moderate

from 1999 to 2001 at the same area.

**4. Discussion** 

*correlations at 5% significance level. cnot significant*.


Table 2. Significant high rate spatial clusters of cholera in Ashanti region, Ghana, detected by retrospective space-time analysis. This Table shows the results of the space-time cluster analysis using a spatial window that could include up to 50% of the population at risk and a maximum temporal window of 50% without including purely spatial clusters, in Ashanti region, Ghana, during 1998-2001: LLR (Log Likelihood Ratio).

The results of the space-time analysis when modified, i.e. when using a maximum temporal window of 90% (which included purely spatial clusters as well) and a spatial window that could include up to 50% of the population at risk (which included purely temporal clusters also) are shown in Table 3. Most likely statically significant (*p* = 0.001) cluster of high rates of cholera was again found to exist at the Kumasi Metropolis and Kwabre district for the year 1998-2001. This indicates that Kumasi Metropolis and Kwabre remained statistically significant throughout the year 1998-2001. One statistically significant (*p* = 0.001) secondary cluster encompassing Ahafo Ano South, Ahafo Ano North and Atwima for the year 1999 was also detected.


Table 3. Significant high rate spatial clusters of cholera in Ashanti region, Ghana, detected by retrospective space-time analysis. This Table shows the results of the space-time cluster analysis when modified to find 1, 2, 3 or 4-year length clusters using a maximum temporal window of 90%, which included purely spatial clusters as well, and a spatial window of ≤50% of the population at risk, which included purely temporal clusters also, in Ashanti region, Ghana, during 1997-2001: LLR (Log Likelihood Ratio)

#### **3.3 Correlation between cholera and risk factors**

Pearson's correlation coefficients and their associated *p*-values were computed to determine the relationship between cholera cumulative incidence rate and the demographic risk factors (see Table 4). For the whole region, statistically significant relationship was observed for sanitation (*R2 = 0.55, p = 0.001*), drinking water (*R2 = 0.39, p = 0.001*), and internal migration (*R2 = 0.73, p = 0.001*). However, when the analyses were repeated for each strata of urbanization, statistically significant correlations were observed for only the *high* urban strata (See Table 2). For instance there was a high, but non-significant correlation between cholera and drinking water within the *medium-urban* strata (*R2 = 0.62, p = 0.12*), and no significant correlation between cholera and drinking water within the *low-urban* strata (*R2 = 0.001, p = 0.96*). However, there was a high and significant correlation between cholera and drinking water within the *high-urban* strata (*R2 = 0.86, p = 0.007*).


Table 4. Pearson's correlation coefficients for the relationship between cholera and demographic factors. This Table depicts both the Global Pearson's correlation analyses, and Pearson's correlation analyses for each urbanization strata of districts. The associated *p– values* are shown in brackets. *asignificant correlations at 0.1% significance level; bsignificant correlations at 5% significance level. cnot significant*.

## **4. Discussion**

26 Cholera

**Cluster Area** Year *Chol*(C) *Chol*(E(C)) *Chol*(RR) LLR *p*-*value*

1. Kumasi Metro 1998-1999 1688 508.75 5.86 1149.02 0.001

Atwima 1999 179 96.34 1.908 29.34 0.001 Table 2. Significant high rate spatial clusters of cholera in Ashanti region, Ghana, detected by retrospective space-time analysis. This Table shows the results of the space-time cluster analysis using a spatial window that could include up to 50% of the population at risk and a maximum temporal window of 50% without including purely spatial clusters, in Ashanti

The results of the space-time analysis when modified, i.e. when using a maximum temporal window of 90% (which included purely spatial clusters as well) and a spatial window that could include up to 50% of the population at risk (which included purely temporal clusters also) are shown in Table 3. Most likely statically significant (*p* = 0.001) cluster of high rates of cholera was again found to exist at the Kumasi Metropolis and Kwabre district for the year 1998-2001. This indicates that Kumasi Metropolis and Kwabre remained statistically significant throughout the year 1998-2001. One statistically significant (*p* = 0.001) secondary cluster encompassing Ahafo Ano South, Ahafo Ano North and Atwima for the year 1999

**Cluster Area** Year *Chol*(C) *Chol*(E(C)) *Chol*(RR) LLR *p-value* 

Kwabre 1998-2001 2727 1161.47 9.699 1618.26 0.001

Table 3. Significant high rate spatial clusters of cholera in Ashanti region, Ghana, detected by retrospective space-time analysis. This Table shows the results of the space-time cluster analysis when modified to find 1, 2, 3 or 4-year length clusters using a maximum temporal window of 90%, which included purely spatial clusters as well, and a spatial window of ≤50% of the population at risk, which included purely temporal clusters also, in Ashanti

Pearson's correlation coefficients and their associated *p*-values were computed to determine the relationship between cholera cumulative incidence rate and the demographic risk factors (see Table 4). For the whole region, statistically significant relationship was observed for sanitation (*R2 = 0.55, p = 0.001*), drinking water (*R2 = 0.39, p = 0.001*), and internal migration (*R2 = 0.73, p = 0.001*). However, when the analyses were repeated for each strata of urbanization, statistically significant correlations were observed for only the *high* urban

1999 179 96.34 1.91 29.33 0.001

region, Ghana, during 1998-2001: LLR (Log Likelihood Ratio).

region, Ghana, during 1997-2001: LLR (Log Likelihood Ratio)

**3.3 Correlation between cholera and risk factors** 

**Most Likely Cluster** 

**Secondary Cluster**  2. Ahafo Ano North Ahafo Ano South

was also detected.

Most Likely Cluster 1. Kumasi Metro

Secondary Cluster 2. Ahafo Ano North Ahafo Ano South

Atwima

In this study, the purely spatial and space-time scan statistic methods implemented in SaTScan software have been used to analyze cholera cases from 1998 to 2001 in Kumasi, Ghana. These methods identifies whether unusual concentration of disease cases can be explained by chance or statistically significant. The findings of this study reveal several notable points. First, there is the existence of both purely spatial and space-time clusters, not explainable by chance (See Tables 1,2, and 3). Also, the results of both the purely spatial and space-time analysis are somewhat similar. In particular, the purely spatial analysis reported an excess incidence of cholera in Kumasi during the years 1998, 1999, and 2001 (See Table 3.1 and Figure 3.2), and the space-time analysis also reported an excess incidence of cholera from 1999 to 2001 at the same area.

Second, the excess incidence of cholera mainly existed at Kumasi Metropolis throughout the period under study. Specifically, the purely spatial analysis reported excess incidence of cholera at Kumasi in 1998, 1999, and 2001. While testing whether the purely spatial clusters were long term or temporary, the space-time analysis also reported excess incidence of cholera at Kumasi Metropolis from the year 1999 to 2001. When the space-time analysis was modified to detect 1, 2, 3, 4, or 5 year length clusters, the space-time most likely cluster at Kumasi Metropolis became a purely spatial cluster (i.e. existed for 1997 to 2001, see Table 3). This indicates a sustained transmission of cholera at Kumasi Metropolis from 1997 through to 2001. Two main reasons may explain these patterns. (1) *Demographic status*: Kumasi is the most urbanized and highly commercialized district in Ashanti region, and therefore there is always a high daily influx of traders and civil workers from neighboring districts to Kumasi Metropolis. Such a high daily influx strain existing sanitation systems, thereby putting people at increased risk of cholera transmission. The rural poor also often migrate to city centers with the hope of a better life. However, due to the high cost of housing, such

Evaluating Spatial and Space-Time Clustering of Cholera in Ashanti-Region-Ghana 29

internal migration affect only *high-urban* communities in the study area. This is consistent with the findings of the cluster analysis. Both the purely spatial and space-time analysis identified Kumasi Metropolis and Kwabre district as significant high rate clusters of cholera, which are

Cholera primarily attack individuals with insufficient knowledge of and inappropriate attitudes towards hygienic practices, and who live in dwellings that lack access to safe drinking water supply and to adequate facilities for sanitation, sewerage disposal and treatment (Glass and Black, 1992). Majority of the region's population who do not have access to good sanitation systems, and drink from rivers, streams and ponds are people living in most urbanized and densely populated districts. For instance, Kumasi metropolis's share of the region's population who do not have access to potable water is close to 13%, a

Fecal contamination of rivers is a major water quality issue in many fast growing cities like the Kumasi Metropolis where population growth far exceeds the rate of development of wastewater collection and treatment. The water bodies near densely populated areas may have high fecal concentrations due to defecation and sanitation practices of the people. Ali et al. (2002a) has asserted that fecal contamination of surface water in densely populated area is higher than a sparsely populated area. Although Kumasi Metropolis and other urbanized districts are served with potable water, this water does not flow throughout the year. At certain times no water flow for a period of a week or two. Residents are therefore compelled to exploit nearby streams, rivers and ponds. If such water bodies are contaminated and is

After several decades of research into cholera, the risk factors which contribute to its transmission have not changed. The spatial and temporal patterns that the disease displays, however, are not the same from one outbreak area to another. Although several of the findings of this research are more confirmatory, it draws the attention of health officials and policymakers about the area where there has been sustained transmission of the disease over the years. The study also provides very useful information to health officials and policymaker about the spatial and temporal patterns of cholera in Ghana. For example, this study clearly shows that there has been a sustained transmission of cholera in Kumasi during the period under study. The findings of this study will also have important implications for public health officials since control strategies would vary depending on the most important risk factors in most important districts. With the important high rate cluster locations and risk factors identified, optimal efforts can be taken at appropriate districts to prevent and control cholera. There is no doubt that the fecal oral route of cholera transmission should be of primary concern because of its importance in the development of secondary cases and in subsequent spread of the disease. It should therefore be the concern of health officials and policymakers to provide better sanitation systems to prevent fecal contamination of water bodies within *highurban* districts. Moreover, potable (pipe-borne) water supply in urban and densely populated

This study has shown the presence of both spatial and space-time hotspots of cholera in Ashanti region, suggesting that there has been sustained transmission of cholera within

also amongst the most urbanized and overcrowded areas in Ashanti Region.

value 2.3 times higher than the mean percentage.

used for drinking or cooking, there is the likelihood of infection.

districts should be expanded and improved to prevent cholera outbreaks.

**5. Conclusion** 

migrants settle at slummy and/or squatter areas where environmental sanitation is poor. This largely explains the high *northern population* (inhabitants from the northern sector of Ghana; which is the most deprived sector) within Kumasi Metropolis. (2) *Geographic location*: Kumasi Metropolis is the central nodal district of Ghana, and therefore, all road networks linking the northern sector and the southern sector of Ghana pass through Kumasi. There is the high probability of stoppage and transit by travelers, resulting in a high daily population increase and overcrowding at city centers.

Third, the findings of the space-time analysis clearly depict the statistical power of the scan statistic for detecting recently emerging clusters. The space-time analysis detected an important cluster during the year 1999 that would otherwise not be detected by a purely spatial analysis. This cluster encompassed areas surrounding Ahafo Ano North, Ahafo Ano South, and Atwima districts (See Tables 2 and 3).

Fourth, both the purely spatial and space-time cluster analysis detected no cluster during the years 1997 and 2000. This is somewhat consistent with both the overall global and national cholera trends. Although officially notified cases do not reflect the overall burden of the diseases, cholera cases reported to WHO in 1996 was 4.4 times higher than cases in 1997 (a decrease of 77% from 1996 to 1997 ), and cases in 1998 was 9 times higher than cases in 1997(an increase of 80.3% from 1997 to 1998 ). Compared to 1999, the year 2000 saw 46% global reduction in the total number of cases, and about 65% reduction in the total number of cases reported in Ghana. After a massive outbreak in Ghana from 1998 to 1999, health officials and policy makers implemented several measures to curb the menace. Notable among these measures were effective waste collection and disposal (including solid waste, sewage, industrial and clinical waste), cleansing of public areas, food hygiene, hygiene education and related programs. Consequently, the reduced number of cholera cases in the year 2000.

When the maximum window size was varied from ≤ 25% to ≤ 50% of the total population, the same results were obtained as with the window size of ≤ 50% of the total population. This clearly shows that for large geographical scales with fewer spatial units, spatial scan statistic will likely not be sensitive to varying window size. Chen et al. (2008) clearly demonstrated the sensitivity of the spatial scan statistic to the issues of varying window sizes (SaTScan scaling issues) through a geo-visual analytic technique. Their study was partly a quest to determine an optimal setting for SaTScan scaling parameters due to the confusing and even misleading results which are possible if the parameter choices are made arbitrarily. However, their data was across larger spatial geographical area with larger number of spatial units; giving SaTScan much flexibility on the varying window sizes. Contrary to our data used, there were only 18 spatial units; a number probably too small for spatial scan statistic. Therefore the interpretation of our findings should fall within the framework of the above limitation.

The findings of the correlations analysis suggest that cholera is high when majority of the people do not have access to good sanitation facilities; do not have access to potable water; and when internal migration is high. When the correlation analyses were repeated for each strata of urbanization, statistically significant correlations were observed for only the *high*-*urban* strata. Considering drinking water for instance, there was no significant correlation within the *low-urban* strata and the *medium-urban* strata, but a high significant correlation was observed within the *high-urban* strata (See Table 4). This implies that drinking water, sanitation and internal migration affect only *high-urban* communities in the study area. This is consistent with the findings of the cluster analysis. Both the purely spatial and space-time analysis identified Kumasi Metropolis and Kwabre district as significant high rate clusters of cholera, which are also amongst the most urbanized and overcrowded areas in Ashanti Region.

Cholera primarily attack individuals with insufficient knowledge of and inappropriate attitudes towards hygienic practices, and who live in dwellings that lack access to safe drinking water supply and to adequate facilities for sanitation, sewerage disposal and treatment (Glass and Black, 1992). Majority of the region's population who do not have access to good sanitation systems, and drink from rivers, streams and ponds are people living in most urbanized and densely populated districts. For instance, Kumasi metropolis's share of the region's population who do not have access to potable water is close to 13%, a value 2.3 times higher than the mean percentage.

Fecal contamination of rivers is a major water quality issue in many fast growing cities like the Kumasi Metropolis where population growth far exceeds the rate of development of wastewater collection and treatment. The water bodies near densely populated areas may have high fecal concentrations due to defecation and sanitation practices of the people. Ali et al. (2002a) has asserted that fecal contamination of surface water in densely populated area is higher than a sparsely populated area. Although Kumasi Metropolis and other urbanized districts are served with potable water, this water does not flow throughout the year. At certain times no water flow for a period of a week or two. Residents are therefore compelled to exploit nearby streams, rivers and ponds. If such water bodies are contaminated and is used for drinking or cooking, there is the likelihood of infection.

After several decades of research into cholera, the risk factors which contribute to its transmission have not changed. The spatial and temporal patterns that the disease displays, however, are not the same from one outbreak area to another. Although several of the findings of this research are more confirmatory, it draws the attention of health officials and policymakers about the area where there has been sustained transmission of the disease over the years. The study also provides very useful information to health officials and policymaker about the spatial and temporal patterns of cholera in Ghana. For example, this study clearly shows that there has been a sustained transmission of cholera in Kumasi during the period under study. The findings of this study will also have important implications for public health officials since control strategies would vary depending on the most important risk factors in most important districts. With the important high rate cluster locations and risk factors identified, optimal efforts can be taken at appropriate districts to prevent and control cholera. There is no doubt that the fecal oral route of cholera transmission should be of primary concern because of its importance in the development of secondary cases and in subsequent spread of the disease. It should therefore be the concern of health officials and policymakers to provide better sanitation systems to prevent fecal contamination of water bodies within *highurban* districts. Moreover, potable (pipe-borne) water supply in urban and densely populated districts should be expanded and improved to prevent cholera outbreaks.

## **5. Conclusion**

28 Cholera

migrants settle at slummy and/or squatter areas where environmental sanitation is poor. This largely explains the high *northern population* (inhabitants from the northern sector of Ghana; which is the most deprived sector) within Kumasi Metropolis. (2) *Geographic location*: Kumasi Metropolis is the central nodal district of Ghana, and therefore, all road networks linking the northern sector and the southern sector of Ghana pass through Kumasi. There is the high probability of stoppage and transit by travelers, resulting in a high daily population

Third, the findings of the space-time analysis clearly depict the statistical power of the scan statistic for detecting recently emerging clusters. The space-time analysis detected an important cluster during the year 1999 that would otherwise not be detected by a purely spatial analysis. This cluster encompassed areas surrounding Ahafo Ano North, Ahafo Ano

Fourth, both the purely spatial and space-time cluster analysis detected no cluster during the years 1997 and 2000. This is somewhat consistent with both the overall global and national cholera trends. Although officially notified cases do not reflect the overall burden of the diseases, cholera cases reported to WHO in 1996 was 4.4 times higher than cases in 1997 (a decrease of 77% from 1996 to 1997 ), and cases in 1998 was 9 times higher than cases in 1997(an increase of 80.3% from 1997 to 1998 ). Compared to 1999, the year 2000 saw 46% global reduction in the total number of cases, and about 65% reduction in the total number of cases reported in Ghana. After a massive outbreak in Ghana from 1998 to 1999, health officials and policy makers implemented several measures to curb the menace. Notable among these measures were effective waste collection and disposal (including solid waste, sewage, industrial and clinical waste), cleansing of public areas, food hygiene, hygiene education and

related programs. Consequently, the reduced number of cholera cases in the year 2000.

When the maximum window size was varied from ≤ 25% to ≤ 50% of the total population, the same results were obtained as with the window size of ≤ 50% of the total population. This clearly shows that for large geographical scales with fewer spatial units, spatial scan statistic will likely not be sensitive to varying window size. Chen et al. (2008) clearly demonstrated the sensitivity of the spatial scan statistic to the issues of varying window sizes (SaTScan scaling issues) through a geo-visual analytic technique. Their study was partly a quest to determine an optimal setting for SaTScan scaling parameters due to the confusing and even misleading results which are possible if the parameter choices are made arbitrarily. However, their data was across larger spatial geographical area with larger number of spatial units; giving SaTScan much flexibility on the varying window sizes. Contrary to our data used, there were only 18 spatial units; a number probably too small for spatial scan statistic. Therefore the interpretation of our findings should fall within the

The findings of the correlations analysis suggest that cholera is high when majority of the people do not have access to good sanitation facilities; do not have access to potable water; and when internal migration is high. When the correlation analyses were repeated for each strata of urbanization, statistically significant correlations were observed for only the *high*-*urban* strata. Considering drinking water for instance, there was no significant correlation within the *low-urban* strata and the *medium-urban* strata, but a high significant correlation was observed within the *high-urban* strata (See Table 4). This implies that drinking water, sanitation and

increase and overcrowding at city centers.

framework of the above limitation.

South, and Atwima districts (See Tables 2 and 3).

This study has shown the presence of both spatial and space-time hotspots of cholera in Ashanti region, suggesting that there has been sustained transmission of cholera within

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#### **6. Acknowledgements**

We extend our sincere appreciation to the Disease Control Unit-Ashanti Region and the Ghana Statistical Service for providing all the necessary data and background information for this research.

#### **7. References**


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*1,2,3Japan* 

**Cholera in Lao P. D. R.: Past and Present** 

Lao People's Democratic Republic (Lao PDR) is a mountainous country in Southeast Asia, situated along the Mekong and bordering China, Vietnam, Cambodia, Thailand, and Myanmar. Cholera epidemics are known to have occurred in Laos during the time of the French colonial era and the Kingdom of Laos period prior to the establishment of Lao PDR in 1975. There are public records of cholera epidemics in 1895 to 1902 of the French era (Monnais-Rousselot, 1999) and in the 1910s, 1953 and 1969 of the Kingdom era (Nakamura and Iwasa, 2008). However, after the establishment of Lao People's Democratic Republic (Lao PDR), it was presumed that there were no epidemics of cholera and, at the level of both rural and central government, cholera was not sufficiently recognized prior to 1993. Acquisition of pandemic information on cholera was severely restricted, due to the establishment of a socialist state system in Laos and its isolationist state from 1975 to the mid-1980s. Moreover, while diarrhea and/or fever outbreaks caused by unknown pathogens were common in remote areas of mountainous districts equipped with few health resources, case detection or confirmation was quite difficult because almost all the cases had ceased by the time the reports reached the central government. Since these areas had scant populations with a scattered distribution of small size villages, the roads to which they were connected were poor; thus, confirmed diagnoses of the diseases could not be made because the areas could not be accessed easily, and prudent control was not exercised with the

\*Yutaka Midorikawa2, Masami Nakatsu1, Toru Watanabe3, Rattanaphone Phethsouvanh4, Phengta

Vongphrachanh5, Kongsap Akkhavong6 and Paul Brey7

*2Faculty of Health Sciences, Suzuka University of Medical Sciences, Mie* 

*6National Institute of Public Health, Ministry of Health, Vientiane 7Institut Pasteur du Laos, Ministry of Health, Vientiane* 

*3Graduate School of Agriculture, Yamagata University, Yamagata 4Laboratory of Bacteriology, National Mahosot Hospital, Ministry of Health, Vientiane 5National Centre of Laboratory and Epidemiology, Ministry of Health, Vientiane* 

*1Department of Tropical Medicine and Malaria, National Centre for Global Health and Medicine, Tokyo* 

*4,5,6,7Lao People's Democratic Republic*

\*Corresponding Author

Satoshi Nakamura1,7,\*,\*

*7Lao People's Democratic Republic* 

*1Japan* 

*1Department of Tropical Medicine and Malaria,* 

*National Centre for Global Health and Medicine, Tokyo 7Institut Pasteur du Laos, Ministry of Health, Vientiane* 

WHO (2005). Cholera, 2003. *Weekly epidemiological record*. 80(31):261-268. WHO (2006). Cholera, 2004. *Weekly epidemiological record*. 81(31):297-308. **3** 

## **Cholera in Lao P. D. R.: Past and Present**

## Satoshi Nakamura1,7,\*,\*

*1Department of Tropical Medicine and Malaria, National Centre for Global Health and Medicine, Tokyo 7Institut Pasteur du Laos, Ministry of Health, Vientiane 1Japan 7Lao People's Democratic Republic* 

### **1. Introduction**

32 Cholera

WHO (2005). Cholera, 2003. *Weekly epidemiological record*. 80(31):261-268.

Lao People's Democratic Republic (Lao PDR) is a mountainous country in Southeast Asia, situated along the Mekong and bordering China, Vietnam, Cambodia, Thailand, and Myanmar. Cholera epidemics are known to have occurred in Laos during the time of the French colonial era and the Kingdom of Laos period prior to the establishment of Lao PDR in 1975. There are public records of cholera epidemics in 1895 to 1902 of the French era (Monnais-Rousselot, 1999) and in the 1910s, 1953 and 1969 of the Kingdom era (Nakamura and Iwasa, 2008). However, after the establishment of Lao People's Democratic Republic (Lao PDR), it was presumed that there were no epidemics of cholera and, at the level of both rural and central government, cholera was not sufficiently recognized prior to 1993. Acquisition of pandemic information on cholera was severely restricted, due to the establishment of a socialist state system in Laos and its isolationist state from 1975 to the mid-1980s. Moreover, while diarrhea and/or fever outbreaks caused by unknown pathogens were common in remote areas of mountainous districts equipped with few health resources, case detection or confirmation was quite difficult because almost all the cases had ceased by the time the reports reached the central government. Since these areas had scant populations with a scattered distribution of small size villages, the roads to which they were connected were poor; thus, confirmed diagnoses of the diseases could not be made because the areas could not be accessed easily, and prudent control was not exercised with the

*1Department of Tropical Medicine and Malaria,* 

<sup>\*</sup>Yutaka Midorikawa2, Masami Nakatsu1, Toru Watanabe3, Rattanaphone Phethsouvanh4, Phengta Vongphrachanh5, Kongsap Akkhavong6 and Paul Brey7

*National Centre for Global Health and Medicine, Tokyo* 

*<sup>2</sup>Faculty of Health Sciences, Suzuka University of Medical Sciences, Mie* 

*<sup>3</sup>Graduate School of Agriculture, Yamagata University, Yamagata 4Laboratory of Bacteriology, National Mahosot Hospital, Ministry of Health, Vientiane* 

*<sup>5</sup>National Centre of Laboratory and Epidemiology, Ministry of Health, Vientiane* 

*<sup>6</sup>National Institute of Public Health, Ministry of Health, Vientiane 7Institut Pasteur du Laos, Ministry of Health, Vientiane* 

*<sup>1,2,3</sup>Japan* 

*<sup>4,5,6,7</sup>Lao People's Democratic Republic*

<sup>\*</sup>Corresponding Author

Cholera in Lao P. D. R.: Past and Present 35

2000), and the epidemic had spread to the whole of the country by 1996 (Midorikawa *et al*, 1996). Table 1 shows a summary of the epidemics in Lao PDR during the above period.

Table 1. Cholera epidemics in Lao PDR during 1993-1996.

The government of Lao PDR (GOL) responded swiftly in requesting cholera control assistance from foreign countries including NGOs through the UN Department of Humanitarian Affairs, Geneva, and received donations of 570,000 US dollars for control activities (Anonymous 1995). In addition, GOL cooperated with the WHO after 1994, a joint National Cholera Epidemic Control Committee was established in the CDD (Diarrheal Disease Control) program at the Department of Preventive Health within the Ministry of Health, GOL, and formalized control measures against cholera were advanced. Committee meetings, and training for control in the major cities took place, and more than ten examples

exception of the itinerary of EPI sentinels. As a result, most of the outbreaks occurred unquestioned and health measurement in these remote areas remained limited.

We will present details of the cholera situation in Lao PDR from 1993-1996, a period during which overt epidemics were reported. Hitherto, these epidemics had been little known to other countries with the exception of some documents or reports (Anonymous, 1993; Global Task Force on Cholera Control, 2008). Hence, we would like to give details of case studies and especially to clarify some characteristics of non-O1 non-139 *Vibrio cholerae* strains which have not yet been studied as a potential causative of diarrhea in the country. These results will necessarily be limited; however, they might contribute to further epidemiological study of cholera in the Southeast Asia region.

In this chapter, we refer to non-O1 non-139 *Vibrio cholerae* simply as NAG (non-agglutinable) *V. cholerae*, a former common abbreviation.

#### **2. Cholera epidemics in Lao PDR from 1993 to the present**

In Lao PDR, a cholera outbreak began in the Napo area of Hinboun district in Khammouane Province on April 7th of 1993 and spread to Boulapa (at the end of April and in June), Gnyomalath (in July), Mahaxay (in July), Takehk (in July), other parts of Hinboun (in July), Xebanphai and Nonbok districts (in August). In the same year it spread within the entire region including Nakai district, and became an epidemic of 5276 cases (including 250 deaths; a case fatality rate of 4.8%). An outbreak even occurred in the Pin district of the neighboring province of Savannakhet on May 1st of the same year, and this spread within the four districts of Xephon (in June), Nong (in June), Vilaburi (in September), and Atsaphan (in December) with a total of 1614 cases (86; 5.3%). In 1994, cases of severe diarrhea first occurred at Ta Oy village in the Toum Laan district of Saravane Province in the south, before the epidemic spread to two further districts, Lakhopheng and Vaphi, with 1111 cases (88; 11.3%) by March. A further 53 (18; 43%) cases of cholera occurred again in two areas of Nong district in Savannakhet in April of the same year. By June, the outbreak had expanded to the nine districts of Champon, Tumphon, Khamthabuli, Phin, Thapanthong, Songkhon, Xaybuli, Sonbuli, and Atsaphan with a total of 1209 cases (126; 10.4%).The number of inpatients at the provincial hospital between May 8th and June 18th was 360; this rapidly increased to 1554 (126; 8.1%) in the following three months. In the north of the country, nine cases of severe diarrhea were reported by the provincial health office of Bokeo Province in April 1994. Cases also spread to the three provinces of Oudomxay, Xaignaburi, and Luangnamtha within the same month, and two cases of cholera were reported in Luangphabang Province in May. Severe diarrhea cases spread to Xienkhouane province in the north and to Attapeu province in the south by June but cholera bacterium was not detected in patients from Attapeu Province. The official statistic report on cases of severe diarrhea including cholera in 1994 was not very precise; however, more than 5200 cases were observed during the two months from April to May.

In the year 1995, 192 cases (36; 18.9%) of serious diarrhea occurred in Attapeu between early January and the end of February. As in the previous year, the causative bacterium was not confirmed. In June and beyond, 260 cases (25; 9.6%) of cholera broke out in the four provinces of Sekon, Xaignaburi, Luangphabang, and Khammouane. From December 11- 19th, a cholera outbreak with 141 cases occurred at Ban Phailom in Xaithani district, the first of its kind in Vientiane Capital (Vientiane Municipality at the time) (Nakamura, Marui,

exception of the itinerary of EPI sentinels. As a result, most of the outbreaks occurred

We will present details of the cholera situation in Lao PDR from 1993-1996, a period during which overt epidemics were reported. Hitherto, these epidemics had been little known to other countries with the exception of some documents or reports (Anonymous, 1993; Global Task Force on Cholera Control, 2008). Hence, we would like to give details of case studies and especially to clarify some characteristics of non-O1 non-139 *Vibrio cholerae* strains which have not yet been studied as a potential causative of diarrhea in the country. These results will necessarily be limited; however, they might contribute to further epidemiological study

In this chapter, we refer to non-O1 non-139 *Vibrio cholerae* simply as NAG (non-agglutinable)

In Lao PDR, a cholera outbreak began in the Napo area of Hinboun district in Khammouane Province on April 7th of 1993 and spread to Boulapa (at the end of April and in June), Gnyomalath (in July), Mahaxay (in July), Takehk (in July), other parts of Hinboun (in July), Xebanphai and Nonbok districts (in August). In the same year it spread within the entire region including Nakai district, and became an epidemic of 5276 cases (including 250 deaths; a case fatality rate of 4.8%). An outbreak even occurred in the Pin district of the neighboring province of Savannakhet on May 1st of the same year, and this spread within the four districts of Xephon (in June), Nong (in June), Vilaburi (in September), and Atsaphan (in December) with a total of 1614 cases (86; 5.3%). In 1994, cases of severe diarrhea first occurred at Ta Oy village in the Toum Laan district of Saravane Province in the south, before the epidemic spread to two further districts, Lakhopheng and Vaphi, with 1111 cases (88; 11.3%) by March. A further 53 (18; 43%) cases of cholera occurred again in two areas of Nong district in Savannakhet in April of the same year. By June, the outbreak had expanded to the nine districts of Champon, Tumphon, Khamthabuli, Phin, Thapanthong, Songkhon, Xaybuli, Sonbuli, and Atsaphan with a total of 1209 cases (126; 10.4%).The number of inpatients at the provincial hospital between May 8th and June 18th was 360; this rapidly increased to 1554 (126; 8.1%) in the following three months. In the north of the country, nine cases of severe diarrhea were reported by the provincial health office of Bokeo Province in April 1994. Cases also spread to the three provinces of Oudomxay, Xaignaburi, and Luangnamtha within the same month, and two cases of cholera were reported in Luangphabang Province in May. Severe diarrhea cases spread to Xienkhouane province in the north and to Attapeu province in the south by June but cholera bacterium was not detected in patients from Attapeu Province. The official statistic report on cases of severe diarrhea including cholera in 1994 was not very precise; however, more than 5200 cases

In the year 1995, 192 cases (36; 18.9%) of serious diarrhea occurred in Attapeu between early January and the end of February. As in the previous year, the causative bacterium was not confirmed. In June and beyond, 260 cases (25; 9.6%) of cholera broke out in the four provinces of Sekon, Xaignaburi, Luangphabang, and Khammouane. From December 11- 19th, a cholera outbreak with 141 cases occurred at Ban Phailom in Xaithani district, the first of its kind in Vientiane Capital (Vientiane Municipality at the time) (Nakamura, Marui,

unquestioned and health measurement in these remote areas remained limited.

**2. Cholera epidemics in Lao PDR from 1993 to the present** 

were observed during the two months from April to May.

of cholera in the Southeast Asia region.

*V. cholerae*, a former common abbreviation.


2000), and the epidemic had spread to the whole of the country by 1996 (Midorikawa *et al*, 1996). Table 1 shows a summary of the epidemics in Lao PDR during the above period.

Table 1. Cholera epidemics in Lao PDR during 1993-1996.

The government of Lao PDR (GOL) responded swiftly in requesting cholera control assistance from foreign countries including NGOs through the UN Department of Humanitarian Affairs, Geneva, and received donations of 570,000 US dollars for control activities (Anonymous 1995). In addition, GOL cooperated with the WHO after 1994, a joint National Cholera Epidemic Control Committee was established in the CDD (Diarrheal Disease Control) program at the Department of Preventive Health within the Ministry of Health, GOL, and formalized control measures against cholera were advanced. Committee meetings, and training for control in the major cities took place, and more than ten examples

Cholera in Lao P. D. R.: Past and Present 37

Although the patients' average case fatality rate exceeded 11% during the epidemic from 1993 to 1996, it henceforth fell to about 4.5% in 1998. Moreover, the rate in 2000 fell even

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Fig. 3. Average monthly severe diarrheal cases pattern including cholera in Lao PDR from

In terms of epidemics in 2000-2003 and beyond, a distinct outbreak occurred in the two districts of Thateng and Lamam in Sekong Province in December 2007- January 2008

The causative vibrios collected by the active surveillance in 1993-1995 were all identified as *Vibrio cholerae* O1 serotype Ogawa and biotype El Tor at the National Institute of Hygiene and Epidemiology (NIHE) (Nakamura *et al*. 1998), and the strains contributed for further publications (Toma *et al*., 1997). Amongst these strains, antibiotic resistance was limited to Ampicillins. However, the characteristics of the cholera bacteria changed in the strains collected in 1996 and afterwards as reported in India, Vietnam and Thailand (Bag *et al*., 1998; Dalsgaard *et al*., 1999; Dalsgaard *et al*., 2000). The genomic analyses revealed that these strains were introduced with a SXT constin gene that regulates for multidrug resistancy (Iwanaga *et al*., 2004). We also speculate that the change was mediated by the presence of NAG vibrios in the country. Although a *V. cholerae* O139 epidemic occurred in Thailand from 1993 to1994 (Chongsa-nguan M. *et al*., 1993; Bodhidatta L., *et al*., 1995), we have no

Even though epidemics have ceased and severe diarrheal cases have been limited to sporadic cases since 2008, it is possible that another epidemic could easily reoccur in Lao PDR because people's natural immunity to the cholera vibrios is limited to O1 serotype Ogawa, biotype El Tor and, as such, this immunity will not be very long lasting (Kabir, 2005). Moreover, in some local areas, people were indifferent about the cholera disease itself (Midorikawa *et al*., 2010). Local people also suffer from poor food hygiene related to the common custom of eating raw materials (mixed dishes known as *laap*; fish, meat, *Tao etc.*: Nakamura *et al*., 2008), as well as poor knowledge of water-food sanitation, which was

The following case studies focused on the characteristics of water-borne and food-borne

further than previously, to 4.2%.

January 1994 to August 2000

(Lenglet *et al*., 2010).

evidence that the strain was recovered from the country.

confirmed by our experiences including active surveillance.

aspects related to disease control in the northern part of Laos in 1994-1995.

of active surveillance were conducted up until 1996. For example, surveillance including carrier surveys was conducted in 12 districts in 4 provinces and resulted in the identification of 306 diarrheal cases and 25 confirmed death cases from May to August, 1994. The existence of NAG *V. cholerae* in the country was demonstrated for the first time prior to 1995 through this active surveillance. In the same year, the first cholera vaccination was performed, using oral killed vaccine donated by the Vietnamese government, at several villages in Xekong and Attapeu provinces (Nakamura, 2003). Moreover, the first National Conference on Diarrhea was held in Vientiane on 14-15th December, 1994.

The cholera epidemic temporarily ceased around the end of 1996. Although cholera in the country was categorized as severe diarrhea in the diseases statistic reports of GOL in 1997 and beyond, it continued sporadically. In 1998, cases of severe diarrhea rapidly increased to 6,000, and a cholera epidemic reemerged in 2000 with more than 12,000 recognized cases, although this decreased dramatically in 2002 (Figure.1). The monthly cases of severe diarrhea including cholera from 1994 to 2000 are shown in Figure 2. According to the average of the cumulated data, diarrheal cases were most frequent between April and June with a peak in May (Figure 3), results which will require further epidemiological analysis in relation to weather conditions.

Fig. 1. Case and death case of severe diarrhea from 1993 to in Lao PDR.

Fig. 2. Severe diarrheal cases reported by Ministry of Health, Lao PDR, Jan 1994 - Aug 2000

of active surveillance were conducted up until 1996. For example, surveillance including carrier surveys was conducted in 12 districts in 4 provinces and resulted in the identification of 306 diarrheal cases and 25 confirmed death cases from May to August, 1994. The existence of NAG *V. cholerae* in the country was demonstrated for the first time prior to 1995 through this active surveillance. In the same year, the first cholera vaccination was performed, using oral killed vaccine donated by the Vietnamese government, at several villages in Xekong and Attapeu provinces (Nakamura, 2003). Moreover, the first National

The cholera epidemic temporarily ceased around the end of 1996. Although cholera in the country was categorized as severe diarrhea in the diseases statistic reports of GOL in 1997 and beyond, it continued sporadically. In 1998, cases of severe diarrhea rapidly increased to 6,000, and a cholera epidemic reemerged in 2000 with more than 12,000 recognized cases, although this decreased dramatically in 2002 (Figure.1). The monthly cases of severe diarrhea including cholera from 1994 to 2000 are shown in Figure 2. According to the average of the cumulated data, diarrheal cases were most frequent between April and June with a peak in May (Figure 3), results which will require further epidemiological analysis in

> Case Death case

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Fig. 1. Case and death case of severe diarrhea from 1993 to in Lao PDR.

Conference on Diarrhea was held in Vientiane on 14-15th December, 1994.

relation to weather conditions.

Jan

Feb

Mar

Apr May Jun

Jul

Aug Sep Oct

Nov

Dec

Jan

Feb

Mar

Apr May Jun Jul

Aug Sep Oct Nov

Dec

Jan

Feb

Mar

Apr May Jun

Jul

Aug Sep Oct

Nov

Dec

Jan

Feb

Fig. 2. Severe diarrheal cases reported by Ministry of Health, Lao PDR, Jan 1994 - Aug 2000

Mar

Apr May Jun

1994 1995 1996 1997 1998 1999 2000

Jul

Aug Sep Oct

Nov

Dec

Jan

Feb

Mar

Apr May Jun

Jul

Aug Sep Oct Nov

Dec

Jan

Feb

Mar

Apr May Jun

Jul

Aug Sep Oct

Nov

Dec

Jan

Feb

Mar

Apr May Jun Jul

Aug

Although the patients' average case fatality rate exceeded 11% during the epidemic from 1993 to 1996, it henceforth fell to about 4.5% in 1998. Moreover, the rate in 2000 fell even further than previously, to 4.2%.

Fig. 3. Average monthly severe diarrheal cases pattern including cholera in Lao PDR from January 1994 to August 2000

In terms of epidemics in 2000-2003 and beyond, a distinct outbreak occurred in the two districts of Thateng and Lamam in Sekong Province in December 2007- January 2008 (Lenglet *et al*., 2010).

The causative vibrios collected by the active surveillance in 1993-1995 were all identified as *Vibrio cholerae* O1 serotype Ogawa and biotype El Tor at the National Institute of Hygiene and Epidemiology (NIHE) (Nakamura *et al*. 1998), and the strains contributed for further publications (Toma *et al*., 1997). Amongst these strains, antibiotic resistance was limited to Ampicillins. However, the characteristics of the cholera bacteria changed in the strains collected in 1996 and afterwards as reported in India, Vietnam and Thailand (Bag *et al*., 1998; Dalsgaard *et al*., 1999; Dalsgaard *et al*., 2000). The genomic analyses revealed that these strains were introduced with a SXT constin gene that regulates for multidrug resistancy (Iwanaga *et al*., 2004). We also speculate that the change was mediated by the presence of NAG vibrios in the country. Although a *V. cholerae* O139 epidemic occurred in Thailand from 1993 to1994 (Chongsa-nguan M. *et al*., 1993; Bodhidatta L., *et al*., 1995), we have no evidence that the strain was recovered from the country.

Even though epidemics have ceased and severe diarrheal cases have been limited to sporadic cases since 2008, it is possible that another epidemic could easily reoccur in Lao PDR because people's natural immunity to the cholera vibrios is limited to O1 serotype Ogawa, biotype El Tor and, as such, this immunity will not be very long lasting (Kabir, 2005). Moreover, in some local areas, people were indifferent about the cholera disease itself (Midorikawa *et al*., 2010). Local people also suffer from poor food hygiene related to the common custom of eating raw materials (mixed dishes known as *laap*; fish, meat, *Tao etc.*: Nakamura *et al*., 2008), as well as poor knowledge of water-food sanitation, which was confirmed by our experiences including active surveillance.

The following case studies focused on the characteristics of water-borne and food-borne aspects related to disease control in the northern part of Laos in 1994-1995.

Cholera in Lao P. D. R.: Past and Present 39

of Nale. The major ethnic group living in the area were *Lao Teun* (mid-mountain people). It was confirmed that 98 severe diarrhea cases including 8 death cases amongst 4 villages occurred in the district (Table 2). Also 108 cases of diarrhea inpatients were confirmed at the district hospital. Almost all the cases were of people over the age of five, as cholera

Figure 4 shows that the epidemic route was from upstream to downstream along the Namtha River. The first unreported case was of a 50-year-old male who developed severe diarrhea at Moklao (or Vienglao) village on 12th April, 1994, before *Phimai Lao* (Lao New Year). The first reported case was of a 30-year-old at Hatte village, located on the opposite side of the Namtha River from Moklao, who visited relatives from Moklao and suffered from severe diarrhea for 5-6 days in the same period. Subsequently, a second case occurred in the village of a male aged over 30 who also suffered and died from diarrhea. Sixty five cases of severe diarrhea in sufferers over 10 years of age were then reported to the district health office on 19th April, 1994. Two to three days after this outbreak, an incidence of diarrhea occurred at Vath village and spread to 34 cases on 29-30th April, reported on May 1st , 1994. This village is located on the riverbank, about 3 km downstream from Hatte. A villager had returned to Vath after attending the funeral of a relative at Hatte, and subsequently contracted severe diarrhea. At the funeral, the local people had buried the body; however, the victim's clothes were washed in the Namtha River. Dwellers in the area

Fig. 4. Cholera transmission route in Nale district along the Nale River in Luangnamtha

Province in 1994

epidemics are defined. The case fatality rate was confirmed as 8.2%.

had no toilet at the time other than excreting near the river.

## **3. Case studies of cholera in northern Lao PDR in the epidemic year of 1994**

#### **3.1 Cases in Luangnamtha Province**

In early April of 1994, outbreaks of cholera occurred successively in northern Bokeo and Oudomxay provinces, and in Luangnamtha province.


Table 2. The diarrhea case detection at four onset villages in Nale district on 16th May and age distribution of recorded cases treated at Nale district hospital during April to May 1994

Based on a request by the local government of Luangnamtha province, a GOL surveillance team visited Nale district as part of a national active case finding mission on May 12th-25th, 1994. The district is 86 km in distance from the capital of Luangnamtha. There was no carroad access but the Namtha River was accessible by boat at the time. The district had a population of 20,108 consisting of 113 villages in 12 communes. A wooden-made district hospital with a director and 13 medical aids and 11 nurses was located in the capital village

**3. Case studies of cholera in northern Lao PDR in the epidemic year of 1994** 

In early April of 1994, outbreaks of cholera occurred successively in northern Bokeo and

Table 2. The diarrhea case detection at four onset villages in Nale district on 16th May and age distribution of recorded cases treated at Nale district hospital during April to May 1994

Based on a request by the local government of Luangnamtha province, a GOL surveillance team visited Nale district as part of a national active case finding mission on May 12th-25th, 1994. The district is 86 km in distance from the capital of Luangnamtha. There was no carroad access but the Namtha River was accessible by boat at the time. The district had a population of 20,108 consisting of 113 villages in 12 communes. A wooden-made district hospital with a director and 13 medical aids and 11 nurses was located in the capital village

**3.1 Cases in Luangnamtha Province** 

Oudomxay provinces, and in Luangnamtha province.

of Nale. The major ethnic group living in the area were *Lao Teun* (mid-mountain people). It was confirmed that 98 severe diarrhea cases including 8 death cases amongst 4 villages occurred in the district (Table 2). Also 108 cases of diarrhea inpatients were confirmed at the district hospital. Almost all the cases were of people over the age of five, as cholera epidemics are defined. The case fatality rate was confirmed as 8.2%.

Figure 4 shows that the epidemic route was from upstream to downstream along the Namtha River. The first unreported case was of a 50-year-old male who developed severe diarrhea at Moklao (or Vienglao) village on 12th April, 1994, before *Phimai Lao* (Lao New Year). The first reported case was of a 30-year-old at Hatte village, located on the opposite side of the Namtha River from Moklao, who visited relatives from Moklao and suffered from severe diarrhea for 5-6 days in the same period. Subsequently, a second case occurred in the village of a male aged over 30 who also suffered and died from diarrhea. Sixty five cases of severe diarrhea in sufferers over 10 years of age were then reported to the district health office on 19th April, 1994. Two to three days after this outbreak, an incidence of diarrhea occurred at Vath village and spread to 34 cases on 29-30th April, reported on May 1st , 1994. This village is located on the riverbank, about 3 km downstream from Hatte. A villager had returned to Vath after attending the funeral of a relative at Hatte, and subsequently contracted severe diarrhea. At the funeral, the local people had buried the body; however, the victim's clothes were washed in the Namtha River. Dwellers in the area had no toilet at the time other than excreting near the river.

Fig. 4. Cholera transmission route in Nale district along the Nale River in Luangnamtha Province in 1994

Cholera in Lao P. D. R.: Past and Present 41

(Halpern, 1963). Throughout this survey, we witnessed many charms in front of the gates of villages and the entrances of each house of villages in the district (Picture 2). People told us that the epidemic of diseases that year was caused by a female *Phi*; therefore the target was men. It was well known that rehydration therapy, such as giving oral rehydration solution to patients, is extremely effective in diminishing the number of fatalities among cholera sufferers. However, our surveillance confirmed cases of deaths in which even water was not given to the patient for fear that it would lead to even greater possession by the *Phi*. In such situations, it was thought that a control objective should have been set to improve education regarding rehydration therapy, so that skills could be gained in order to reduce cholera deaths among the local residents, even though these beliefs still remain in the present day.

**3.2 Cholera expansion due to local customs and cooking food: Cases in Oudomxay** 

We now turn to cases in Oudomxay. We conducted active surveillance in the province on 6- 8th June, 1994 in response to a request by the provincial government. Through the surveillance, it was reconfirmed clearly that food played a big role in the spread of cholera

Table 3. The diarrhea case detection at 27 villages along the Road No. 2 in Oudomxay

province during 6-8th June 1994

because it could develop in the area as food poisoning.

**Province** 

During this mission, we also found that stools of the cholera inpatients were collected in buckets and thrown directly into the river by their family members who had stayed at the district hospital in the capital of Nale district (Picture 1). Moreover, in one case in Bokeo, the body of the deceased was also observed being thrown directly into the river by another active surveillance mission in Xaygnabouri Province in April to May 1994 (personal communication). Needless to say, cholera is a water-borne infection. It is thought that the disease was also spread through the river in a remote district of Luangnamtha Province.

Picture 1. Namtha river (left: bringing human stools to the river; right: daily life of the riverside)

Picture 2. A "spirit gate" at the entrance of a village

Interestingly, the belief that such an epidemic had been evoked as the curse of an evil spirit (*Phi* by its Lao name; man or woman) remained strong in such remote regions of Laos

During this mission, we also found that stools of the cholera inpatients were collected in buckets and thrown directly into the river by their family members who had stayed at the district hospital in the capital of Nale district (Picture 1). Moreover, in one case in Bokeo, the body of the deceased was also observed being thrown directly into the river by another active surveillance mission in Xaygnabouri Province in April to May 1994 (personal communication). Needless to say, cholera is a water-borne infection. It is thought that the disease was also spread through the river in a remote district of Luangnamtha Province.

Picture 1. Namtha river (left: bringing human stools to the river; right: daily life of the

Interestingly, the belief that such an epidemic had been evoked as the curse of an evil spirit (*Phi* by its Lao name; man or woman) remained strong in such remote regions of Laos

Picture 2. A "spirit gate" at the entrance of a village

riverside)

(Halpern, 1963). Throughout this survey, we witnessed many charms in front of the gates of villages and the entrances of each house of villages in the district (Picture 2). People told us that the epidemic of diseases that year was caused by a female *Phi*; therefore the target was men. It was well known that rehydration therapy, such as giving oral rehydration solution to patients, is extremely effective in diminishing the number of fatalities among cholera sufferers. However, our surveillance confirmed cases of deaths in which even water was not given to the patient for fear that it would lead to even greater possession by the *Phi*. In such situations, it was thought that a control objective should have been set to improve education regarding rehydration therapy, so that skills could be gained in order to reduce cholera deaths among the local residents, even though these beliefs still remain in the present day.

#### **3.2 Cholera expansion due to local customs and cooking food: Cases in Oudomxay Province**

We now turn to cases in Oudomxay. We conducted active surveillance in the province on 6- 8th June, 1994 in response to a request by the provincial government. Through the surveillance, it was reconfirmed clearly that food played a big role in the spread of cholera because it could develop in the area as food poisoning.


Table 3. The diarrhea case detection at 27 villages along the Road No. 2 in Oudomxay province during 6-8th June 1994

Cholera in Lao P. D. R.: Past and Present 43

day as its onset, and did not come into contact with the people in epidemic areas during *Phi Mai Lao*. During his funeral ceremony, many condolence callers, including relatives in Bokeo Province, visited the village. After the ceremony, his wife and his sister got severe diarrhea within a few days. After that, the diarrhea became prevalent among the villagers. We consider this kind of transmission as similar to food poisoning and it was a factor in spreading cholera from one place to another in the province. Such cases caused by cholera-contaminated foods have been reported in Africa (Lous et al., 1990). In this surveillance, we happened to observe a funeral ceremony (Picture 5, right) at Sam Kang village in Beng district on 8th June 1994. The ceremony, which involved the sacrifice of a cow, started on 6th June and participants still cooked the cow meat to eat together in front of the house of the dead patient (Picture 5, left).

Picture 5. Meal preparation (left) and the funeral of a Thai Dam villager (right).

that these methods are still useful to people in the local areas in Laos.

The people of Laos, in particular, have a custom of eating raw food materials. Our observations suggested that the transmission of cholera among people in local areas depended much on cooking meals using unsafe water and on the custom of eating them using fingers at the ceremony. It is well known that a cholera bacterium can easily be disinfected by boiling, dryness, ultraviolet rays, alcohol, acid (Mata *et al*., 1994), and other means. Thus, clean food handling, sufficient cooking, and drying by sunlight for tableware, clothing, etc., are essential precautions against cholera. In particular, careful washing of hands with soap and clean water is essential before food handling. If no soap or clean water is available, hand-washing using the local alcohol *Lao-Lao* (ca.30%) or the juice of a local lime fruit (pH 4.2) called *maknao* (*Citrus aurantifolia*) is strongly recommended for disinfection of cholera vibrios. Although this recommendation was not introduced to the people, it seems

The actual transmission route of the cholera outbreak in northern provinces such as Bokeo is still quite obscure, but according to official provincial records, the first case might have occurred on 6th March 1994, before Phi Mai Lao. In Nale district, Luangnamtha Province, the first case was reported on 12th April, 1994, suggesting that quick diffusion and the spread of cholera was transmitted along the travel routes and waterways of rivers within the northern mountainous areas among Bokeo, Oudomxay and Luangnamtha provinces within April. Interestingly, there were nine cholera cases reported in Chiang Seng in

Picture 3. Making of pit for toilet

The surveillance team of GOL visited the four districts of Xai, Beng, Huon and Pak Beng in Oudomxay Province for active surveillance of cholera on June 6-8th, 1994. There were 27 villages, with a total population of 9500. There had been 1041 cholera cases in Oudomxay Province, including 44 death cases (Oudomxay Provincial Health Service, 7th June, 1994) and the epidemic was still continuing, as shown in the summary on Table 3. The surveillance was done only along the Route 2 road. There had been no reported severe diarrhea cases in Muan Namour and in Muan Xai districts. However, a total of 22 suspected cholera cases, including an infant and eight remainder cases, were confirmed in the surveillance. Most of the villagers in the areas had no toilet for disposing of or washing out the diarrheal stool. Therefore, a pit hole was made to dispose of the patient stool at each village (Pictue 3). One village, Oudom in Muan Beng district, suffered from a lack of drinking water from their fountain. Early in the dry season, this kind of water source was highly contaminated by faecal bacteria (Picture 4).

Picture 4. Well in a drying streamlet

**The situation of cholera transmission in the province**: Traditional customs for people preparing and taking meals together during ceremonies such as funerals was considered a factor in transmission in the province. For example, the first case found at Done Keo village in Huon district was on 16th April, 1994. This male case died with severe diarrhea on the same

The surveillance team of GOL visited the four districts of Xai, Beng, Huon and Pak Beng in Oudomxay Province for active surveillance of cholera on June 6-8th, 1994. There were 27 villages, with a total population of 9500. There had been 1041 cholera cases in Oudomxay Province, including 44 death cases (Oudomxay Provincial Health Service, 7th June, 1994) and the epidemic was still continuing, as shown in the summary on Table 3. The surveillance was done only along the Route 2 road. There had been no reported severe diarrhea cases in Muan Namour and in Muan Xai districts. However, a total of 22 suspected cholera cases, including an infant and eight remainder cases, were confirmed in the surveillance. Most of the villagers in the areas had no toilet for disposing of or washing out the diarrheal stool. Therefore, a pit hole was made to dispose of the patient stool at each village (Pictue 3). One village, Oudom in Muan Beng district, suffered from a lack of drinking water from their fountain. Early in the dry season, this kind of water source was

**The situation of cholera transmission in the province**: Traditional customs for people preparing and taking meals together during ceremonies such as funerals was considered a factor in transmission in the province. For example, the first case found at Done Keo village in Huon district was on 16th April, 1994. This male case died with severe diarrhea on the same

Picture 3. Making of pit for toilet

highly contaminated by faecal bacteria (Picture 4).

Picture 4. Well in a drying streamlet

day as its onset, and did not come into contact with the people in epidemic areas during *Phi Mai Lao*. During his funeral ceremony, many condolence callers, including relatives in Bokeo Province, visited the village. After the ceremony, his wife and his sister got severe diarrhea within a few days. After that, the diarrhea became prevalent among the villagers. We consider this kind of transmission as similar to food poisoning and it was a factor in spreading cholera from one place to another in the province. Such cases caused by cholera-contaminated foods have been reported in Africa (Lous et al., 1990). In this surveillance, we happened to observe a funeral ceremony (Picture 5, right) at Sam Kang village in Beng district on 8th June 1994. The ceremony, which involved the sacrifice of a cow, started on 6th June and participants still cooked the cow meat to eat together in front of the house of the dead patient (Picture 5, left).

Picture 5. Meal preparation (left) and the funeral of a Thai Dam villager (right).

The people of Laos, in particular, have a custom of eating raw food materials. Our observations suggested that the transmission of cholera among people in local areas depended much on cooking meals using unsafe water and on the custom of eating them using fingers at the ceremony. It is well known that a cholera bacterium can easily be disinfected by boiling, dryness, ultraviolet rays, alcohol, acid (Mata *et al*., 1994), and other means. Thus, clean food handling, sufficient cooking, and drying by sunlight for tableware, clothing, etc., are essential precautions against cholera. In particular, careful washing of hands with soap and clean water is essential before food handling. If no soap or clean water is available, hand-washing using the local alcohol *Lao-Lao* (ca.30%) or the juice of a local lime fruit (pH 4.2) called *maknao* (*Citrus aurantifolia*) is strongly recommended for disinfection of cholera vibrios. Although this recommendation was not introduced to the people, it seems that these methods are still useful to people in the local areas in Laos.

The actual transmission route of the cholera outbreak in northern provinces such as Bokeo is still quite obscure, but according to official provincial records, the first case might have occurred on 6th March 1994, before Phi Mai Lao. In Nale district, Luangnamtha Province, the first case was reported on 12th April, 1994, suggesting that quick diffusion and the spread of cholera was transmitted along the travel routes and waterways of rivers within the northern mountainous areas among Bokeo, Oudomxay and Luangnamtha provinces within April. Interestingly, there were nine cholera cases reported in Chiang Seng in

Cholera in Lao P. D. R.: Past and Present 45

A total of 16 strains were isolated from two areas in Laos (Table 4). Among them, three strains were isolated from 533 specimens obtained by the first national cholera carrier survey at four villages in Toum Laan district, Saravane province in the southern part of the country, where a cholera epidemic occurred in May, 1994 (Midorikawa *et al*., 1996). The details of these specimens are: 468 human stools, 14 domestic animal stools, 50 drinking water samples, and 1 sewage sample at Hon Laon. These specimens were bacteriologically analyzed at NIHE in Vientiane. They included two strains of O68 and one strain of O14. Of the other 13 samples, 6 strains were isolated from 410 healthy persons who were examined after two months of the cholera epidemic at Ban Phailom in Xaithani district in Vientiane Capital on 12th December 1995, and 7 strains were isolated from 18 human diarrhea patients at a small village named Ban Lack Sao-et (meaning "21 Km village" in Laotian) located approximately 2 km from Ban Phailom in the same capital area of the country. There were variable serogroups among the isolates including O16, O21, O41, O43, and O68 at Ban

In the latter village, the health department of Vientiane Capital reported that 28 sporadic diarrhea cases found on 28th December 1995 were considered to be cholera. The diarrhea must have been caused by food poisoning, namely the consumption of a rice noodle call "*Khao Poun*", during a period from 16:00 to 21:00 on that day. Eleven severe cases were immediately referred to Sethathilath hospital in Vientiane. Among them, a strain of *V. fluvialis* O11 was isolated from a patient along with V. cholerae O169, and two strains of enteropathogenic *E coli* (EPEC) O159 were also co-isolated with *V. cholerae* O169 from each patient. Excluding three cases referred to but with unknown names, the age distribution was 2 children under 5 years of age, 8 between 6 to 15 years, 7 over 16 years and 1 of unknown

Phailom, and 6 isolates of O169 and an O11 at Ban Lack Sao-et.

age, respectively. Fortunately, no fatal cases were reported in this episode.

Table 4. *Vibrio cholerae* non-O1 non-O139 strains isolated from humans in Lao P.D.R.

respectively as reference strains.

The representative patterns of *Not* I-digested PFGE of non-O1 non-139 *V. cholerae* strains isolated from Ban Lack Sao-et are shown in Figure 5. Of the O169 strains (lane no. 2 to 3, 5 to 7 and 9), their patterns were altogether identical except in one strain (lane 7). Other NAG Vibrio cholerae O11 (lane 4), and O21 (lane 10) were different from each other. The rest of the lanes 1, 8, 11 were V. cholera O37 (S'), V. fluvialis O11, and V. cholerae O1 (569B),

Thailand on 16th April, 1994 (information of the national cholera control committee meeting in 1994). The northern part of Laos is an important site of traffic with neighbouring countries of Thailand, Burma, China and Vietnam. Monitoring and comparing the molecular biological characteristics of the epidemic strains including NAG vibrios recovered from these areas will give useful information on controls beyond the border.

In the following section, the characteristics of the NAG vibrios recovered in Lao PDR is introduced.

## **4. Characteristics of NAG** *V. cholerae* **in Lao PDR**

Some strains of NAG *V. cholerae* are a pathogen responsible for sporadic diarrhea in developing countries (WHO Weekly Epidemiological Record, 1993). The serogroup of O139 is the most widely known and studied since 1993 (Ramamurthy *et al*., 1993; Albert MJ., 1993). However, another NAG *V. cholerae* has been frequently mentioned as the causative of diarrhea in the last two decades, and is now known as enteropathogenic *V. cholerae* (Sharma, C. *et al*. 1998). To date, 200 or more *V. cholerae* serogroups have been reported, and, in particular, future epidemics of CT producing O141 strains are cause for alert (Yamai S. *et al*, 1997). Taylor, D.N. *et al* mention that NAG *V. cholerae* was frequently isolated from food and drinking water among the H'mong refugees in camps in Thailand. Despite the importance of NAG *V. cholerae* as a diarrheal cause in Lao PDR, little information on the organism was available up to now.

We report here on the serogroups and tentative results of molecular patterns of the non-O1 non-O139 strains of *V. cholerae* isolated from two areas in Lao P.D.R. during two years from 1995 to 1996.

Isolation and identification: Isolation of these Vibrio strains was carried out at the NIHE, Ministry of Health in Vientiane, Lao P.D.R. These isolates were further classified serologically with monospecific sera at the Department of Bacteriology, National Institute of Infectious Disease (NIID) in Tokyo, Japan.

PFGE: For the pulsed-field gel electrophoresis (PFGE) study, 17 strains of *V. cholerae* were classified. These included reference strains of *V. cholerae* such as the O1 classical biotype (strain 569B), O37 (African strain S7: Yamamoto *et al*., 1986) and non-typed one (strain BDD), isolated from Ban Don Daen, Khon Kaen, Thailand, respectively. *Vibrio fluvialis* O11 isolated from a diarrhea patient caused by NAG vibrio in Vientiane Capital in 1995 was also analysed as an additional reference. The genomic DNAs of the various strains were prepared in agarose plugs following the technique described by Bag P.K. *et al*. (1998). For digestion of the DNAs, 40U of *Not* I was used. PFGE of the digested plug inserts was performed by the contour-clamped homogeneous electric field method on a CHEF Mapper TM system (Bio-Rad, CA., USA) in 0.5 x TBE buffer for 40 hours 24 minutes while maintaining the temperature of the buffer at 14C. Run conditions were generated by the auto-algorithm mode of the system using a size range of 20 kb to 300 kb. A bacteriophage λ ladder (Bio-Rad) was used as the DNA molecular mass standard. The gels were stained with ethidium bromide and photographed under UV light.

PCR assay and CT production assay: A PCR-based assay was used to determine whether the *ctx*, and NAG specific heat-stable toxin (ST) were present. CT production was confirmed with RPLA commercially purchased (Denka-Seiken, Japan). The test was performed at the Department of Bacteriology, NIID, Japan.

Thailand on 16th April, 1994 (information of the national cholera control committee meeting in 1994). The northern part of Laos is an important site of traffic with neighbouring countries of Thailand, Burma, China and Vietnam. Monitoring and comparing the molecular biological characteristics of the epidemic strains including NAG vibrios recovered from

In the following section, the characteristics of the NAG vibrios recovered in Lao PDR is

Some strains of NAG *V. cholerae* are a pathogen responsible for sporadic diarrhea in developing countries (WHO Weekly Epidemiological Record, 1993). The serogroup of O139 is the most widely known and studied since 1993 (Ramamurthy *et al*., 1993; Albert MJ., 1993). However, another NAG *V. cholerae* has been frequently mentioned as the causative of diarrhea in the last two decades, and is now known as enteropathogenic *V. cholerae* (Sharma, C. *et al*. 1998). To date, 200 or more *V. cholerae* serogroups have been reported, and, in particular, future epidemics of CT producing O141 strains are cause for alert (Yamai S. *et al*, 1997). Taylor, D.N. *et al* mention that NAG *V. cholerae* was frequently isolated from food and drinking water among the H'mong refugees in camps in Thailand. Despite the importance of NAG *V. cholerae* as a diarrheal cause in Lao PDR, little information on the organism was available up to now. We report here on the serogroups and tentative results of molecular patterns of the non-O1 non-O139 strains of *V. cholerae* isolated from two areas in Lao P.D.R. during two years from

Isolation and identification: Isolation of these Vibrio strains was carried out at the NIHE, Ministry of Health in Vientiane, Lao P.D.R. These isolates were further classified serologically with monospecific sera at the Department of Bacteriology, National Institute of

PFGE: For the pulsed-field gel electrophoresis (PFGE) study, 17 strains of *V. cholerae* were classified. These included reference strains of *V. cholerae* such as the O1 classical biotype (strain 569B), O37 (African strain S7: Yamamoto *et al*., 1986) and non-typed one (strain BDD), isolated from Ban Don Daen, Khon Kaen, Thailand, respectively. *Vibrio fluvialis* O11 isolated from a diarrhea patient caused by NAG vibrio in Vientiane Capital in 1995 was also analysed as an additional reference. The genomic DNAs of the various strains were prepared in agarose plugs following the technique described by Bag P.K. *et al*. (1998). For digestion of the DNAs, 40U of *Not* I was used. PFGE of the digested plug inserts was performed by the contour-clamped homogeneous electric field method on a CHEF Mapper TM system (Bio-Rad, CA., USA) in 0.5 x TBE buffer for 40 hours 24 minutes while maintaining the temperature of the buffer at 14C. Run conditions were generated by the auto-algorithm mode of the system using a size range of 20 kb to 300 kb. A bacteriophage λ ladder (Bio-Rad) was used as the DNA molecular mass standard. The gels were stained with ethidium bromide and photographed under UV light.

PCR assay and CT production assay: A PCR-based assay was used to determine whether the *ctx*, and NAG specific heat-stable toxin (ST) were present. CT production was confirmed with RPLA commercially purchased (Denka-Seiken, Japan). The test was performed at the

these areas will give useful information on controls beyond the border.

**4. Characteristics of NAG** *V. cholerae* **in Lao PDR** 

introduced.

1995 to 1996.

Infectious Disease (NIID) in Tokyo, Japan.

Department of Bacteriology, NIID, Japan.

A total of 16 strains were isolated from two areas in Laos (Table 4). Among them, three strains were isolated from 533 specimens obtained by the first national cholera carrier survey at four villages in Toum Laan district, Saravane province in the southern part of the country, where a cholera epidemic occurred in May, 1994 (Midorikawa *et al*., 1996). The details of these specimens are: 468 human stools, 14 domestic animal stools, 50 drinking water samples, and 1 sewage sample at Hon Laon. These specimens were bacteriologically analyzed at NIHE in Vientiane. They included two strains of O68 and one strain of O14. Of the other 13 samples, 6 strains were isolated from 410 healthy persons who were examined after two months of the cholera epidemic at Ban Phailom in Xaithani district in Vientiane Capital on 12th December 1995, and 7 strains were isolated from 18 human diarrhea patients at a small village named Ban Lack Sao-et (meaning "21 Km village" in Laotian) located approximately 2 km from Ban Phailom in the same capital area of the country. There were variable serogroups among the isolates including O16, O21, O41, O43, and O68 at Ban Phailom, and 6 isolates of O169 and an O11 at Ban Lack Sao-et.

In the latter village, the health department of Vientiane Capital reported that 28 sporadic diarrhea cases found on 28th December 1995 were considered to be cholera. The diarrhea must have been caused by food poisoning, namely the consumption of a rice noodle call "*Khao Poun*", during a period from 16:00 to 21:00 on that day. Eleven severe cases were immediately referred to Sethathilath hospital in Vientiane. Among them, a strain of *V. fluvialis* O11 was isolated from a patient along with V. cholerae O169, and two strains of enteropathogenic *E coli* (EPEC) O159 were also co-isolated with *V. cholerae* O169 from each patient. Excluding three cases referred to but with unknown names, the age distribution was 2 children under 5 years of age, 8 between 6 to 15 years, 7 over 16 years and 1 of unknown age, respectively. Fortunately, no fatal cases were reported in this episode.


Table 4. *Vibrio cholerae* non-O1 non-O139 strains isolated from humans in Lao P.D.R.

The representative patterns of *Not* I-digested PFGE of non-O1 non-139 *V. cholerae* strains isolated from Ban Lack Sao-et are shown in Figure 5. Of the O169 strains (lane no. 2 to 3, 5 to 7 and 9), their patterns were altogether identical except in one strain (lane 7). Other NAG Vibrio cholerae O11 (lane 4), and O21 (lane 10) were different from each other. The rest of the lanes 1, 8, 11 were V. cholera O37 (S'), V. fluvialis O11, and V. cholerae O1 (569B), respectively as reference strains.

Cholera in Lao P. D. R.: Past and Present 47

in Laos, and the fragment was also reported from tap water in the United States of America (Dross, M.C. et al.; FJ462454, GenBank, 2008). Hence, it seems that the fragment might be

It seems that there is a strong possibility that NAG of this country will serve as a pool of the SXT constin gene of O1 O139 *V. cholerae*. Although the place where NAG and O1 O139 *V. cholerae* in epidemics meet includes the possibility of biofilm on an animal or in nature, the most probable place is the human alimentary canal. It is necessary to advance analysis further with regard to pathogenicity about the possibility that strains of specific NAG and other *Vibrionaceae* bacteria are potential reservoirs of the drug resistance gene or of the

It is reported that NAG *V. cholerae* strains are frequently separated from market foods such as meat and fish, as well as environmental water in Vientiane Capital (Nakamura *et al.*, 2004, Midorikawa *et al*., 2007). Therefore, food and water source surveillance on the vibrios and their pathogen related laboratory monitoring are indispensable to health care administration

**5. Food market and environmental water monitoring for contaminant vibrios 5.1 Observation of contamination of food by** *Vibrionaceae* **bacteria in the major food** 

In Lao PDR, the prevalence of water-borne diseases, like diarrhea, is still very high (Midorikawa *et al.*, 1996; Rattanaphone *et al*, 1999). Recently, market food cross contamination by *Salmonella* and *Vibrio* species (vibrios) was reported (Nakamura *et al*.2004; Sano *et al*, 2004). Large cities such as Vientiane Capital and their surrounding areas, where people's life-style has changed rapidly, are facing the risk of environmental pollution, particularly with regard to drinking water and food. The aim of this research is to know the degree of contamination by vibrios on around 30 kinds of foods at the marketplace by monitoring food hygiene and to present an update of the possible diarrheal disease risk by using these results in the capital area. This small scale cross-sectional survey has been made by us from 1999 to the present. We presented a part of the study up to 2003 (Nakamura *et al.,*  2004); also, in 2008 the analysis between some *Aeromonas* species and *V. cholerae* was not performed, and therefore we report here mainly on the results of 5 years from 2004 to 2007

**Study site and date:** The food sampling survey was conducted at two major market places in the city area in Vientiane Capital, Lao PDR. The surveys were conducted once a year, in

**Sampling of food:** Objective foods of various kinds were categorized as follows: domestic animal meats (beef, water buffalo, pork, chicken, duck, and domestic fowl eggs), fresh water products (fresh water fish and shellfish), marine products (marine fish and shellfish), and

**Equipment for sampling:** In collection of food specimens, "Fuki-Fuki test kit" (EIKEN Kagaku, Tokyo) and "Seed-swab No.1" (EIKEN Kagaku, Tokyo) of the monitoring and transport media for food-borne bacteria were used. The surfaces of food samples were

December 2004, and in September 2005, 2006, 2007, and 2009, respectively.

others (frog and/or insect), sold at the marketplaces.

wiped with this equipment.

widely distributed over environmental water, and analysis has advanced further now.

pathogenic gene cassette as transposons.

in Lao PDR, which will be shown in the next section.

**markets in Vientiane Capital from 2004 to 2009** 

and 2009.

*V. cholera* O11:lane 4, O14:lane12, O16:lane 11, O21:lane 10, O41:lanes 13-14,O68:lanes 15-17, O169:lanes 2-3;5-7;9, *V. cholerae* BDD (Thailand):lane18, *V. cholerae* S7 (Africa):lane 1, *V.fluvialis* O11:lane 8

Fig. 5. PFGEF profiles of NAG *Vibrio cholera* strains obtained with *Not*I enzyme.

The 16 NAG *V. cholerae* strains examined had neither the cholera toxin gene nor the heat stable toxin gene. No CT producing strains were observed among them. These results may reveal that the varied strains isolated from Ban Phailom were irrelevant to the last epidemic of *V. cholera*e O1 (Nakamura S. and Marui E., 2000). However, distinct diarrhea cases were present at Ban Lack Sao-et (21 Km village) and their major isolates were *V. cholerae* O169, which had genetic homogeneity with that of the endemic strain, suggesting that the strain was a possible endemic pathogen of diarrhea in this area. Pathogen related gene (*tcp*, *zot*, *ace*, and others) and toxin assays other than CT and the ST were not yet performed. Whether some serogroups of the enteropathogenic *V. cholerae* would cause diarrhea by a mechanism quite different from that of toxin producing *V. cholerae* O1 and O139 has not been demonstrated yet (Sharma, C. *et al*. 1998). Further analyses are necessary to clarify the relationship between genomic patterns and the pathogenicity or drug susceptibility of these *V. cholerae* O169 strains as a possible enteric pathogen in this region.

Very recently, a death case caused by the serogroup of *Vibrio cholera* O21 was reported (Phethsouvanh *et al*., 2008). This sepsis case was caused by eating a snail obtained in a swamp in the suburbs of Vientiane. For this reason, when this group was analyzed, it became clear that the serogroup has unique variation in *omp*W domain, which was named 'ompW\_O21' by Nakatsu *et al*. (AB441168, GenBank, 2008). Moreover, since this strain entirely lacked the flagellum, unlike the others of O21 group included in this report, it was thought that the strain has variations in the domain of major flagellin regulation gene *fla*A (Klose KE and Mekalanos JJ, 1998). This DNA fragment related to OmpW\_21 was recovered from rivers in several places

*V. cholera* O11:lane 4, O14:lane12, O16:lane 11, O21:lane 10, O41:lanes 13-14,O68:lanes 15-17, O169:lanes

The 16 NAG *V. cholerae* strains examined had neither the cholera toxin gene nor the heat stable toxin gene. No CT producing strains were observed among them. These results may reveal that the varied strains isolated from Ban Phailom were irrelevant to the last epidemic of *V. cholera*e O1 (Nakamura S. and Marui E., 2000). However, distinct diarrhea cases were present at Ban Lack Sao-et (21 Km village) and their major isolates were *V. cholerae* O169, which had genetic homogeneity with that of the endemic strain, suggesting that the strain was a possible endemic pathogen of diarrhea in this area. Pathogen related gene (*tcp*, *zot*, *ace*, and others) and toxin assays other than CT and the ST were not yet performed. Whether some serogroups of the enteropathogenic *V. cholerae* would cause diarrhea by a mechanism quite different from that of toxin producing *V. cholerae* O1 and O139 has not been demonstrated yet (Sharma, C. *et al*. 1998). Further analyses are necessary to clarify the relationship between genomic patterns and the pathogenicity or drug susceptibility of these

Very recently, a death case caused by the serogroup of *Vibrio cholera* O21 was reported (Phethsouvanh *et al*., 2008). This sepsis case was caused by eating a snail obtained in a swamp in the suburbs of Vientiane. For this reason, when this group was analyzed, it became clear that the serogroup has unique variation in *omp*W domain, which was named 'ompW\_O21' by Nakatsu *et al*. (AB441168, GenBank, 2008). Moreover, since this strain entirely lacked the flagellum, unlike the others of O21 group included in this report, it was thought that the strain has variations in the domain of major flagellin regulation gene *fla*A (Klose KE and Mekalanos JJ, 1998). This DNA fragment related to OmpW\_21 was recovered from rivers in several places

2-3;5-7;9, *V. cholerae* BDD (Thailand):lane18, *V. cholerae* S7 (Africa):lane 1, *V.fluvialis* O11:lane 8 Fig. 5. PFGEF profiles of NAG *Vibrio cholera* strains obtained with *Not*I enzyme.

*V. cholerae* O169 strains as a possible enteric pathogen in this region.

in Laos, and the fragment was also reported from tap water in the United States of America (Dross, M.C. et al.; FJ462454, GenBank, 2008). Hence, it seems that the fragment might be widely distributed over environmental water, and analysis has advanced further now.

It seems that there is a strong possibility that NAG of this country will serve as a pool of the SXT constin gene of O1 O139 *V. cholerae*. Although the place where NAG and O1 O139 *V. cholerae* in epidemics meet includes the possibility of biofilm on an animal or in nature, the most probable place is the human alimentary canal. It is necessary to advance analysis further with regard to pathogenicity about the possibility that strains of specific NAG and other *Vibrionaceae* bacteria are potential reservoirs of the drug resistance gene or of the pathogenic gene cassette as transposons.

It is reported that NAG *V. cholerae* strains are frequently separated from market foods such as meat and fish, as well as environmental water in Vientiane Capital (Nakamura *et al.*, 2004, Midorikawa *et al*., 2007). Therefore, food and water source surveillance on the vibrios and their pathogen related laboratory monitoring are indispensable to health care administration in Lao PDR, which will be shown in the next section.

## **5. Food market and environmental water monitoring for contaminant vibrios**

#### **5.1 Observation of contamination of food by** *Vibrionaceae* **bacteria in the major food markets in Vientiane Capital from 2004 to 2009**

In Lao PDR, the prevalence of water-borne diseases, like diarrhea, is still very high (Midorikawa *et al.*, 1996; Rattanaphone *et al*, 1999). Recently, market food cross contamination by *Salmonella* and *Vibrio* species (vibrios) was reported (Nakamura *et al*.2004; Sano *et al*, 2004). Large cities such as Vientiane Capital and their surrounding areas, where people's life-style has changed rapidly, are facing the risk of environmental pollution, particularly with regard to drinking water and food. The aim of this research is to know the degree of contamination by vibrios on around 30 kinds of foods at the marketplace by monitoring food hygiene and to present an update of the possible diarrheal disease risk by using these results in the capital area. This small scale cross-sectional survey has been made by us from 1999 to the present. We presented a part of the study up to 2003 (Nakamura *et al.,*  2004); also, in 2008 the analysis between some *Aeromonas* species and *V. cholerae* was not performed, and therefore we report here mainly on the results of 5 years from 2004 to 2007 and 2009.

**Study site and date:** The food sampling survey was conducted at two major market places in the city area in Vientiane Capital, Lao PDR. The surveys were conducted once a year, in December 2004, and in September 2005, 2006, 2007, and 2009, respectively.

**Sampling of food:** Objective foods of various kinds were categorized as follows: domestic animal meats (beef, water buffalo, pork, chicken, duck, and domestic fowl eggs), fresh water products (fresh water fish and shellfish), marine products (marine fish and shellfish), and others (frog and/or insect), sold at the marketplaces.

**Equipment for sampling:** In collection of food specimens, "Fuki-Fuki test kit" (EIKEN Kagaku, Tokyo) and "Seed-swab No.1" (EIKEN Kagaku, Tokyo) of the monitoring and transport media for food-borne bacteria were used. The surfaces of food samples were wiped with this equipment.

Cholera in Lao P. D. R.: Past and Present 49

shown in Figure 6. Due to the sample size, and variability within the sampling place, a direct comparison of each set of annual data is difficult; however, the results in 2004 and afterwards are reviewed here. The detection rate of food contaminated by NAG *V. cholerae* varied widely from 0 (December 2004) to 64% (2009). However there was a tendency of decreasing rates from 57% to 19% during the years from 2005 to 2007. According to Disease Statistics of Laos in 1999-2002, many food poisoning cases occurred from April to May, and the case occurrence was lowest in September (Anonymous: statistics, 2003). Since most of our investigations were conducted in September, it is necessary to similarly investigate in April and May when food poisoning occurs frequently, and to grasp the actual conditions of *V. cholerae* contamination among the foods. In addition, a more advanced investigation should also be performed on rats, flies, and sewage which are all regarded as reservoirs in

In investigations into enteropathogenic bacteria carriers among residents in the year 2005 in the suburb of Vientiane, a carrier of *V. cholerae* (1.5%) was detected among 63 healthy volunteers. In the same investigation, Nakamura *et al*., (2005) confirmed that some of the Salmonella recovered from humans and market foods in the capital city showed common DNA restriction patterns. It must be emphasized that the tendency towards frequent detection of NAG *V. cholerae* from livestock meats in the marketplaces of Vientiane will be a

Fig. 6. Percent recovery of *Vibrionaceae* species from the foods at the two major markets in

The population density of the country is very low and it has maintained abundant water in its rivers with dense forest covering its mountainous areas. However, access to safe water is very limited among the 70% of the population living in the countryside, and risks have been pointed out about infections caused by environmental water consumption. Although the tackling of water-borne infections such as severe diarrhea among children has been an important subject for attaining MDGs of the country, there is little research in connection

the marketplaces.

potential risk of diarrheal outbreak.

Vientiane Capital, Lao PDR. from 1999 to 2009

**5.2 Environmental water monitoring on contaminant vibrios** 

**Isolation and identification of the bacteria:** The cotton part of Fuki-Fuki test kit sample and Seed-swab or Cary-Blair kit was applied to peptone water for growth culture media, and subsequently TCBS medium was employed for selective culture of vibrios at 37C for 24 hours. Colonies on the selective medium were screened with classical *InVic* system (Phetsouvanh *et al*. 1999) and the suspected bacteria were identified with commercial identification kits such as API 20e system (bioMerieux, Tokyo, Japan). Identification of the family *Vibrionaceae* was performed using the criteria of the multiply in 0% and 7% saline broths for halophilic species, string test using 0.5% solution of sodium deoxycholate in saline added (Keast & Riley, 1997), and O129 disk susceptibility for differentiate of genus *Vibrio* and *Aeromonas*. O-antigen serotype of identified *V. cholerae* strains were determined further using diagnostic anti-O1 and anti-O139 antisera (DENKA Seiken, Tokyo).

**Observation of bacterial contamination in the market foods in 2004-2009:** Excepting the results of the year 2008, a total of 166 food items were examined during six years from 2004 to 2009. Suspected food poisoning *Vibrionaceae* species contamination was widely confirmed on the surface of food of animal origin (Table 5). *Aeromonas* spp including *Ae. hydrophila*, *Ae. sobria* detected from 68 items was the most prevalent except among marine fish. NAG *V. cholerae* strains confirmed in 50 food items was the next most prevalent. Among these, 26 (52%) were confirmed in the meat of domestic animals, such as cattle and pork. Moreover, contamination by the other vibrios including *V. parahaemolyticus* was commonly detected in the same category. In particular, the recovery of *V. parahaemolyticus* and other halophile vibrios from freshwater fish is uncommon in developed countries and suggests that some cross contamination reported by us is still frequent in the markets (Nakamura *et al*., 2004).

There are still insufficient regulations within food laws regarding food handling in the markets of Laos. It is still observed that the degree of cleanliness and the state of order change greatly according to each retailer's counter. Moreover, changes in collection time and sampling place also greatly influence these kinds of investigative results.


Table 5. Number of food items found containing *Vibrionaceae* species in the major markets in Vientiane Capital during years from 2004 to 2007, and 2009

O antigen typing and analysis of diarrheagenic toxins of these NAG *V. cholerae* have not yet been performed; however, the possible risk of severe diarrheal outbreaks was evident, as mentioned in section 4 of this chapter.

The detection rate of the food items contaminated with bacteria of *Vibrionaceae* among small size sampled food items at two major markets in Vientiane Capital from 1999 to 2009 is

**Isolation and identification of the bacteria:** The cotton part of Fuki-Fuki test kit sample and Seed-swab or Cary-Blair kit was applied to peptone water for growth culture media, and subsequently TCBS medium was employed for selective culture of vibrios at 37C for 24 hours. Colonies on the selective medium were screened with classical *InVic* system (Phetsouvanh *et al*. 1999) and the suspected bacteria were identified with commercial identification kits such as API 20e system (bioMerieux, Tokyo, Japan). Identification of the family *Vibrionaceae* was performed using the criteria of the multiply in 0% and 7% saline broths for halophilic species, string test using 0.5% solution of sodium deoxycholate in saline added (Keast & Riley, 1997), and O129 disk susceptibility for differentiate of genus *Vibrio* and *Aeromonas*. O-antigen serotype of identified *V. cholerae* strains were determined

**Observation of bacterial contamination in the market foods in 2004-2009:** Excepting the results of the year 2008, a total of 166 food items were examined during six years from 2004 to 2009. Suspected food poisoning *Vibrionaceae* species contamination was widely confirmed on the surface of food of animal origin (Table 5). *Aeromonas* spp including *Ae. hydrophila*, *Ae. sobria* detected from 68 items was the most prevalent except among marine fish. NAG *V. cholerae* strains confirmed in 50 food items was the next most prevalent. Among these, 26 (52%) were confirmed in the meat of domestic animals, such as cattle and pork. Moreover, contamination by the other vibrios including *V. parahaemolyticus* was commonly detected in the same category. In particular, the recovery of *V. parahaemolyticus* and other halophile vibrios from freshwater fish is uncommon in developed countries and suggests that some cross contamination reported by us is still frequent in the markets (Nakamura *et al*., 2004). There are still insufficient regulations within food laws regarding food handling in the markets of Laos. It is still observed that the degree of cleanliness and the state of order change greatly according to each retailer's counter. Moreover, changes in collection time and

V. cholerae

Number of Number of item found of Bacteria of Vibrionaceae (%)

Total 166 50 (30.1) 8 (4.8) 38 (22.8) 68 (40.9)

Table 5. Number of food items found containing *Vibrionaceae* species in the major markets in

O antigen typing and analysis of diarrheagenic toxins of these NAG *V. cholerae* have not yet been performed; however, the possible risk of severe diarrheal outbreaks was evident, as

The detection rate of the food items contaminated with bacteria of *Vibrionaceae* among small size sampled food items at two major markets in Vientiane Capital from 1999 to 2009 is

Cattle 25 7 (28) \* 2 (8) \* 3 (12) \* 11 (44) Buffalo 7 2 (28.5) \* 0 2 (28.5) \* 1 (14.2) Pork 33 7 (21.2) \* 1 (3) \* 8 (24.2) \* 12 (36.3) Chicken 23 8 (34.7) \* 0 9 (39.1) \* 8 (34.7) \* Duck or other poultry 9 2 (22.2) 0 2 (22.2) 3 (33.3) # Fish 51 18 (35.2) # 2 (4) \* 8 (15.6) \* 26 (50.9) # Shellfish 7 4 (57.1) 1 (14.2) 1 (14.2) 4 (57.1) Marine fish 5 1 (20) # 0 1 (20) # 0 Marine shellfish 4 1 (25) # 2 (50) 2 (50) # 3 (75) Others (Insect & frog) 2 0 0 2 (100) 0

V. parahaemolyticus

Other

Vibriospp. Aeromonas

spp.

further using diagnostic anti-O1 and anti-O139 antisera (DENKA Seiken, Tokyo).

sampling place also greatly influence these kinds of investigative results.

Food Item non-O1, non-O139

Vientiane Capital during years from 2004 to 2007, and 2009

items examined

mentioned in section 4 of this chapter.

\* Common contamination # Uncommon contamination shown in Figure 6. Due to the sample size, and variability within the sampling place, a direct comparison of each set of annual data is difficult; however, the results in 2004 and afterwards are reviewed here. The detection rate of food contaminated by NAG *V. cholerae* varied widely from 0 (December 2004) to 64% (2009). However there was a tendency of decreasing rates from 57% to 19% during the years from 2005 to 2007. According to Disease Statistics of Laos in 1999-2002, many food poisoning cases occurred from April to May, and the case occurrence was lowest in September (Anonymous: statistics, 2003). Since most of our investigations were conducted in September, it is necessary to similarly investigate in April and May when food poisoning occurs frequently, and to grasp the actual conditions of *V. cholerae* contamination among the foods. In addition, a more advanced investigation should also be performed on rats, flies, and sewage which are all regarded as reservoirs in the marketplaces.

In investigations into enteropathogenic bacteria carriers among residents in the year 2005 in the suburb of Vientiane, a carrier of *V. cholerae* (1.5%) was detected among 63 healthy volunteers. In the same investigation, Nakamura *et al*., (2005) confirmed that some of the Salmonella recovered from humans and market foods in the capital city showed common DNA restriction patterns. It must be emphasized that the tendency towards frequent detection of NAG *V. cholerae* from livestock meats in the marketplaces of Vientiane will be a potential risk of diarrheal outbreak.

Fig. 6. Percent recovery of *Vibrionaceae* species from the foods at the two major markets in Vientiane Capital, Lao PDR. from 1999 to 2009

#### **5.2 Environmental water monitoring on contaminant vibrios**

The population density of the country is very low and it has maintained abundant water in its rivers with dense forest covering its mountainous areas. However, access to safe water is very limited among the 70% of the population living in the countryside, and risks have been pointed out about infections caused by environmental water consumption. Although the tackling of water-borne infections such as severe diarrhea among children has been an important subject for attaining MDGs of the country, there is little research in connection

Cholera in Lao P. D. R.: Past and Present 51

and *E. dispar* were ompW (Nandy *et al*. 2000, Nakatsu *et al.* 2008) and mitochondorial rDNA 18s regions with the primers newly designed by us, respectively. The PCR tests were validated using a laboratory strain of *V. cholerae* 569B and both cultured strains of *E. histolytica* and *E. dispar* were used as controls. Nested-PCRs were performed in a DNA thermal cycler with initial denaturation at 95C for 5min, followed by 35 cycles of denaturation at 95C for 30sec, annealing at 55C for 30sec, extension at 72C for 45sec and a final elongation at 72C for 7min. Commercially-based sequencing of PCR products was also

**Results of PCR test for** *V. cholerae***,** *E. histolytica* **and** *E. dispar***:** 25 water samples were collected from 15 sampling locations. The details are shown in Table 7. Although *V. cholerae* was detected in two locations, it was detected by the second PCR. Since these did not have a ctx gene, they were judged to be NAG vibrios. One of these strains was previously detected from three samplings of the Pa River at Xansay district, Attapeu province (Midorikawa *et al*., 2010). The DNA sequence of this ompW was homologue to *V. cholerae* O21, which we reported (GenBank: AB441168). Although this sequence differed from the common one of Nandi *et al*., (2000) it was reported also from the United States and detected even in Cambodia (Nakatsu *et al*, unpublished data). We have not detected corresponding NAG vibrios yet; however, it was thought that this DNA motif might be found widely over countries. Another was detected from the sample of the Pa Hom River in Vang Vieng, Vientiane province. This amplified DNA sequence was judged to be the usual NAG vibrios

Table 6. Frequency of PCR detection on en tamoeba and cholera vibrios in drinking water in

*E. hystolytica* which has pathogenicity in its genus was found only from the source of drinking water at Xansay district, Attapeu province. On the other hand, *E. dispar* was widely

obtained to confirm homology analysis using DNA databases.

by the control and its DNA homology.

Lao PRD

with actual risk, with the exception of a few study reports. In particular, research on the actual conditions of the pathogenic organisms in the country's water cannot be found. Hence, continuous monitoring research which targets vibrios and amoeba of enteropathogenic importance was conducted to clarify distribution of these pathogenic organisms in the country.

**Study area, monitoring point, sampling and the detection methods during years 2006, 2009-2011:** The investigation was conducted from Vientiane to Attapeu along the catchment of the Mekong (Figure 7). Collecting river water samples of the Mekong and its branches was conducted mostly at fixed locations which cross the major national road No. 16 during the year 2006 and years 2009-2010. The water sampling along the Nam Som River and from lake water was performed in Vang Vieng district, Vientiane Province and in Khammouane Province, respectively, in 2011.

Fig. 7. Locality of the water sampling.

Water sample collection was performed with a sterile plastic container. Before collecting the sample, a conventional on-site coliform test using filter-paper (Sancoli, Tokyo, Japan) was conducted. In 2006, 20L of the raw water was condensed by DEAE to adsorb the microbes and about 1/200 of the volume was analyzed by PCR. During years 2009-2010, about 50 ml of the sample was collected to detect the target organisms by PCR. Common bacterial culture using TCBS (Eiken Kagaku, Tokyo, Japan) and DHL (Eiken Kagaku, Tokyo, Japan) media was also performed to detect vibrios and related enteropathogens as described in section II, except in 2006. Identification of bacteria was performed by API 20e system (BioMerieu, France). PCR analysis of a cultured sample using 10 ml of peptone water was also performed in 2011. All the samples were kept in cool and/or freezing conditions before analysis. PCR test for detection of the target organisms was as follows: DNA extraction from the water sample was performed on both the centrifuged pellet and the supernatant using commercial extraction kits. Target regions of PCR on *V. cholerae*, and *Entamoeba histolytica*

with actual risk, with the exception of a few study reports. In particular, research on the actual conditions of the pathogenic organisms in the country's water cannot be found. Hence, continuous monitoring research which targets vibrios and amoeba of enteropathogenic importance was conducted to clarify distribution of these pathogenic

**Study area, monitoring point, sampling and the detection methods during years 2006, 2009-2011:** The investigation was conducted from Vientiane to Attapeu along the catchment of the Mekong (Figure 7). Collecting river water samples of the Mekong and its branches was conducted mostly at fixed locations which cross the major national road No. 16 during the year 2006 and years 2009-2010. The water sampling along the Nam Som River and from lake water was performed in Vang Vieng district, Vientiane Province and in Khammouane

Water sample collection was performed with a sterile plastic container. Before collecting the sample, a conventional on-site coliform test using filter-paper (Sancoli, Tokyo, Japan) was conducted. In 2006, 20L of the raw water was condensed by DEAE to adsorb the microbes and about 1/200 of the volume was analyzed by PCR. During years 2009-2010, about 50 ml of the sample was collected to detect the target organisms by PCR. Common bacterial culture using TCBS (Eiken Kagaku, Tokyo, Japan) and DHL (Eiken Kagaku, Tokyo, Japan) media was also performed to detect vibrios and related enteropathogens as described in section II, except in 2006. Identification of bacteria was performed by API 20e system (BioMerieu, France). PCR analysis of a cultured sample using 10 ml of peptone water was also performed in 2011. All the samples were kept in cool and/or freezing conditions before analysis. PCR test for detection of the target organisms was as follows: DNA extraction from the water sample was performed on both the centrifuged pellet and the supernatant using commercial extraction kits. Target regions of PCR on *V. cholerae*, and *Entamoeba histolytica*

organisms in the country.

Province, respectively, in 2011.

Fig. 7. Locality of the water sampling.

and *E. dispar* were ompW (Nandy *et al*. 2000, Nakatsu *et al.* 2008) and mitochondorial rDNA 18s regions with the primers newly designed by us, respectively. The PCR tests were validated using a laboratory strain of *V. cholerae* 569B and both cultured strains of *E. histolytica* and *E. dispar* were used as controls. Nested-PCRs were performed in a DNA thermal cycler with initial denaturation at 95C for 5min, followed by 35 cycles of denaturation at 95C for 30sec, annealing at 55C for 30sec, extension at 72C for 45sec and a final elongation at 72C for 7min. Commercially-based sequencing of PCR products was also obtained to confirm homology analysis using DNA databases.

**Results of PCR test for** *V. cholerae***,** *E. histolytica* **and** *E. dispar***:** 25 water samples were collected from 15 sampling locations. The details are shown in Table 7. Although *V. cholerae* was detected in two locations, it was detected by the second PCR. Since these did not have a ctx gene, they were judged to be NAG vibrios. One of these strains was previously detected from three samplings of the Pa River at Xansay district, Attapeu province (Midorikawa *et al*., 2010). The DNA sequence of this ompW was homologue to *V. cholerae* O21, which we reported (GenBank: AB441168). Although this sequence differed from the common one of Nandi *et al*., (2000) it was reported also from the United States and detected even in Cambodia (Nakatsu *et al*, unpublished data). We have not detected corresponding NAG vibrios yet; however, it was thought that this DNA motif might be found widely over countries. Another was detected from the sample of the Pa Hom River in Vang Vieng, Vientiane province. This amplified DNA sequence was judged to be the usual NAG vibrios by the control and its DNA homology.


Table 6. Frequency of PCR detection on en tamoeba and cholera vibrios in drinking water in Lao PRD

*E. hystolytica* which has pathogenicity in its genus was found only from the source of drinking water at Xansay district, Attapeu province. On the other hand, *E. dispar* was widely

Cholera in Lao P. D. R.: Past and Present 53

disease prevention. In order to effectively promote hygiene education, we recommend the proactive introduction and utilization of IEC computer terminals making full use of information technology (IT). In addition, the introduction of a cholera vaccine for Laos will require further improvements in relevant areas, such as establishing the logistics of EPI disease monitoring specimens, creating new regional laboratories and strengthening coordination between existing regional labs and the central lab in, for example, medical

We would like to express thanks to villagers and staff of health centres and hospitals of Nale district in Luangnamtha and of the surveyed districts in Oudomxay Province. We are thankful to Professor Boungnong Boupha, Professor Sithat Insisiengmay, Professor Michel Strobel, and Professor Masaaki Iwanaga who supported for the research in Lao PDR. We are grateful for the technical assistance provided by Mr. Lay Sisavath, and Mr. Khampheuy Mummalath, Dr. Kanpheng Choumlasack, Mr. Trykhouane Phoutavane, and Dr. Noy Khaseumsy at the Bacteriology Unit of the National Centre of Laboratory and Epidemiology, Vientiane. We also express thanks to Drs. Toshio Shimada and Eiji Arakawa of the National Institute of Infectious Diseases for identification of O antigen typing of NAG *V. cholerae* and their NAG ST assay by PCR, and to Dr. Paul Newton for his critical review of section 4. We express sincere thanks to Dr. Khamthan for help in performing the water-food sampling in the natural environment and the markets in Vientiane Capital. Special thanks are due to Drs. Lianne Kuppens, and Richard Nesbit, WHO Officers, for their help in active surveillance and for providing epidemic information, and to Prof Khammouliene Pholsena, former Director of the National Centre of Malariology, Parasitology and Entomology for providing national cholera information and the

This work was supported by a Grant-in-Aid from the Ministry of Education, Science, Culture and Sports of Japan (MEXT) in the project entitled "International Cooperation Research concerning water-borne diseases in relocated people and the development of related risk management techniques" (No. 22256003), by MEXT in the project entitled "Sustainable Co-existence of Human, Nature and the Earth" under Research Revolution 2002 (Prof. Tatsuo Omura), and by MEXT in the project entitled "A Transdisciplinary Study on the Regional Eco-History in Tropical Monsoon Asia: 1945-2005, under Research Institute

Albert MJ, Siddique AK, Islam MS, Faruque AS, Ansaruzzaman M, Faruque SM, Sack RB.

Anonymous: Summarized report of surveillance on 18 symptoms/diseases classification in

Anonymous: Epidemic diarrhoea due to Vibrio cholerae non-O1. Weekly Epidemiol Rec

Large outbreak of clinical cholera due to Vibrio cholerae non-O1 in Bangladesh.

2002. The National Centre of Laboratory and Epidemiology, Ministry of Health.,

technology training, together with improvements in information technology.

**7. Acknowledgements** 

direction of the surveillance during 1993-1996.

for Humanity and Nature (Prof. Tomoya Akimichi).

Lancet. 1993 Mar 13;341(8846):704.

1993; 68: 141-2. World Health Organization

Lao PDR, 2003

**8. References** 

distributed over the rivers in the country. In particular, in this species, frequently detected in the sewers of Vientiane, it was thought that high-level fecal contamination of this water area was demonstrated.

**Bacterial detection from sampled waters:** Coliform bacteria was positive in all the samples except for the sample of well water from Xansay district, Attapeu province, and the tap water of Khong Island, Champasak province. 60 bacteria stocks were recovered from the samples through this research. A tentative classification of the strains was as follows: Enterobacteriaceae including *E. coli*, *Vibrionaceae* excluding cholera vibrios, and others such as *Pseudomonas* spp. were 42, 14, and 4, respectively. The major strains of this *Vibrionaceae* were *Aeromonas hydrophila*. No cholera vibrio was confirmed in this study.

In this study it is demonstrated that NAG vibrios and two species of *Entamoeba* were genetically confirmed in water for the first time in Lao PDR.

## **6. Conclusion**

Details of the cholera epidemic from 1993-1996 in Laos which were previously unknown have now been brought to light through records and, in particular, cases in remote mountainous areas. As a result, we now know that the cholera strain primarily responsible for the outbreak was *Vibrio cholera* O1, serotype Ogawa, biotype El Tor.

The existence of other NAG vibrios was also confirmed through research during that period, with the exception of 0139, the cause of Bengal cholera. Furthermore, it was discovered that among these vibrio bacteria, there were some strains such as O169 and O21 with a likelihood of diarrheal pathogenisis. Also, from investigations in recent years, NAG vibrios were identified over an extended period of time as contaminant strains in food available in markets in towns and cities, as well as in the water environment at a molecular level.

The NAG strains within Laos may cause new occurrences of cholera outbreaks in the future and become vehicles for drug-resistance transposons. Continued surveillance and coordinated research among neighboring countries is required to enable further surveys and studies on human and animal hosts, sewage and leftover water and food at markets.

Needless to say, water and sewer service infrastructure is of vital importance in long-term prevention of diarrhea including cholera (Watanabe *et al*., 2006). Laos has already experienced cholera epidemics. As for the prospect of prevention in the future, improvements can be seen in the habitat and infrastructure within Laos following rapid improvements in the country's economic conditions in recent years. In particular, the mountainous areas where fatality rates were high during the epidemic period from 1993-96 have been designated as focus regions in the implementation of poverty countermeasures as part of the 7th National Socio-Economic Development Plan (NSEDP) for 2011-2015. Improvements are anticipated in the installation of clean water facilities following dam construction, as well as in transportation, communication, medical care, living conditions and educational attainment. Furthermore, by training people as qualified medical technicians and introducing them to these regions, the overall standards of PHC policy implementation will be raised and consequently disease countermeasures should be considerably improved.

However, in relation to citizens' hygiene education, differences in language culture lead to problems in communicating and understanding information and knowledge on practical

distributed over the rivers in the country. In particular, in this species, frequently detected in the sewers of Vientiane, it was thought that high-level fecal contamination of this water area

**Bacterial detection from sampled waters:** Coliform bacteria was positive in all the samples except for the sample of well water from Xansay district, Attapeu province, and the tap water of Khong Island, Champasak province. 60 bacteria stocks were recovered from the samples through this research. A tentative classification of the strains was as follows: Enterobacteriaceae including *E. coli*, *Vibrionaceae* excluding cholera vibrios, and others such as *Pseudomonas* spp. were 42, 14, and 4, respectively. The major strains of this *Vibrionaceae*

In this study it is demonstrated that NAG vibrios and two species of *Entamoeba* were

Details of the cholera epidemic from 1993-1996 in Laos which were previously unknown have now been brought to light through records and, in particular, cases in remote mountainous areas. As a result, we now know that the cholera strain primarily responsible

The existence of other NAG vibrios was also confirmed through research during that period, with the exception of 0139, the cause of Bengal cholera. Furthermore, it was discovered that among these vibrio bacteria, there were some strains such as O169 and O21 with a likelihood of diarrheal pathogenisis. Also, from investigations in recent years, NAG vibrios were identified over an extended period of time as contaminant strains in food available in

The NAG strains within Laos may cause new occurrences of cholera outbreaks in the future and become vehicles for drug-resistance transposons. Continued surveillance and coordinated research among neighboring countries is required to enable further surveys and

Needless to say, water and sewer service infrastructure is of vital importance in long-term prevention of diarrhea including cholera (Watanabe *et al*., 2006). Laos has already experienced cholera epidemics. As for the prospect of prevention in the future, improvements can be seen in the habitat and infrastructure within Laos following rapid improvements in the country's economic conditions in recent years. In particular, the mountainous areas where fatality rates were high during the epidemic period from 1993-96 have been designated as focus regions in the implementation of poverty countermeasures as part of the 7th National Socio-Economic Development Plan (NSEDP) for 2011-2015. Improvements are anticipated in the installation of clean water facilities following dam construction, as well as in transportation, communication, medical care, living conditions and educational attainment. Furthermore, by training people as qualified medical technicians and introducing them to these regions, the overall standards of PHC policy implementation will be raised and consequently disease countermeasures should

However, in relation to citizens' hygiene education, differences in language culture lead to problems in communicating and understanding information and knowledge on practical

markets in towns and cities, as well as in the water environment at a molecular level.

studies on human and animal hosts, sewage and leftover water and food at markets.

were *Aeromonas hydrophila*. No cholera vibrio was confirmed in this study.

for the outbreak was *Vibrio cholera* O1, serotype Ogawa, biotype El Tor.

genetically confirmed in water for the first time in Lao PDR.

was demonstrated.

**6. Conclusion** 

be considerably improved.

disease prevention. In order to effectively promote hygiene education, we recommend the proactive introduction and utilization of IEC computer terminals making full use of information technology (IT). In addition, the introduction of a cholera vaccine for Laos will require further improvements in relevant areas, such as establishing the logistics of EPI disease monitoring specimens, creating new regional laboratories and strengthening coordination between existing regional labs and the central lab in, for example, medical technology training, together with improvements in information technology.

## **7. Acknowledgements**

We would like to express thanks to villagers and staff of health centres and hospitals of Nale district in Luangnamtha and of the surveyed districts in Oudomxay Province. We are thankful to Professor Boungnong Boupha, Professor Sithat Insisiengmay, Professor Michel Strobel, and Professor Masaaki Iwanaga who supported for the research in Lao PDR. We are grateful for the technical assistance provided by Mr. Lay Sisavath, and Mr. Khampheuy Mummalath, Dr. Kanpheng Choumlasack, Mr. Trykhouane Phoutavane, and Dr. Noy Khaseumsy at the Bacteriology Unit of the National Centre of Laboratory and Epidemiology, Vientiane. We also express thanks to Drs. Toshio Shimada and Eiji Arakawa of the National Institute of Infectious Diseases for identification of O antigen typing of NAG *V. cholerae* and their NAG ST assay by PCR, and to Dr. Paul Newton for his critical review of section 4. We express sincere thanks to Dr. Khamthan for help in performing the water-food sampling in the natural environment and the markets in Vientiane Capital. Special thanks are due to Drs. Lianne Kuppens, and Richard Nesbit, WHO Officers, for their help in active surveillance and for providing epidemic information, and to Prof Khammouliene Pholsena, former Director of the National Centre of Malariology, Parasitology and Entomology for providing national cholera information and the direction of the surveillance during 1993-1996.

This work was supported by a Grant-in-Aid from the Ministry of Education, Science, Culture and Sports of Japan (MEXT) in the project entitled "International Cooperation Research concerning water-borne diseases in relocated people and the development of related risk management techniques" (No. 22256003), by MEXT in the project entitled "Sustainable Co-existence of Human, Nature and the Earth" under Research Revolution 2002 (Prof. Tatsuo Omura), and by MEXT in the project entitled "A Transdisciplinary Study on the Regional Eco-History in Tropical Monsoon Asia: 1945-2005, under Research Institute for Humanity and Nature (Prof. Tomoya Akimichi).

## **8. References**


Cholera in Lao P. D. R.: Past and Present 55

Midorikawa Y, Nakamura S, Iwade Y, Sugiyama A, Sisavath L, Phakhounthong R. Bacterial

Midorikawa Y, Midorikawa K, Phethsouvanh RP, Phoutavane T, Newton P, Boupha B,

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**Part 2** 

**Biology of** *Vibrio Cholera*


http://www.ann-clinmicrob.com/content/pdf/1476-0711-7-10.pdf

