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## IntechOpen Book Series Infectious Diseases

### Volume 16

### Aims and Scope of the Series

This series will provide a comprehensive overview of recent research trends in various Infectious Diseases (as per the most recent Baltimore classification). Topics will include general overviews of infections, immunopathology, diagnosis, treatment, epidemiology, etiology, and current clinical recommendations for managing infectious diseases. Ongoing issues, recent advances, and future diagnostic approaches and therapeutic strategies will also be discussed. This book series will focus on various aspects and properties of infectious diseases whose deep understanding is essential for safeguarding the human race from losing resources and economies due to pathogens.

## Meet the Series Editor

Dr. Rodriguez-Morales is an expert in tropical and emerging diseases, particularly zoonotic and vector-borne diseases (especially arboviral diseases). He is the president of the Travel Medicine Committee of the Pan-American Infectious Diseases Association (API), as well as the president of the Colombian Association of Infectious Diseases (ACIN). He is a member of the Committee on Tropical Medicine, Zoonoses, and Travel Medicine of ACIN. He

is a vice-president of the Latin American Society for Travel Medicine (SLAMVI) and a Member of the Council of the International Society for Infectious Diseases (ISID). Since 2014, he has been recognized as a Senior Researcher, at the Ministry of Science of Colombia. He is a professor at the Faculty of Medicine of the Fundacion Universitaria Autonoma de las Americas, in Pereira, Risaralda, Colombia. He is an External Professor, Master in Research on Tropical Medicine and International Health, Universitat de Barcelona, Spain. He is also a professor at the Master in Clinical Epidemiology and Biostatistics, Universidad Científica del Sur, Lima, Peru. In 2021 he has been awarded the "Raul Isturiz Award" Medal of the API. Also, in 2021, he was awarded with the "Jose Felix Patiño" Asclepius Staff Medal of the Colombian Medical College, due to his scientific contributions to COVID-19 during the pandemic. He is currently the Editor in Chief of the journal Travel Medicine and Infectious Diseases. His Scopus H index is 47 (Google Scholar H index, 68).

## Meet the Volume Editor

Samuel Okware is a public health specialist with a Ph.D. in Emerging Infections. He pioneered early studies that contributed to major declines in HIV infections in Uganda. He held several national public health appointments coordinating disease control, maternal and child health, and outbreak emergency response. He was a member of the WHO Expert Committee on Research and Development. He is the director general of the Uganda National Health

Research Organization, which coordinates health research. He has published and edited several books on HIV and emerging infections. Dr. Okware is also an Associate Professor of Public Health and Epidemiology at Busitema University, Uganda, and the Uganda Christian University. He has received several national and international awards in recognition of his contributions to public health.

### Contents



## Preface

This book reviews and discusses future opportunities and tools for emerging challenges in HIV/AIDS control. The introductory section provides background on the progress and achievements made in the prevention and control of HIV/AIDS. Over time, new behavioral and societal challenges have emerged. For instance, prevention messages on risk reduction are being undermined by the successes in effective antiretroviral treatment. Complacency among the public, especially the youth, is gradually increasing. There are also emerging challenges on stagnating uptake of testing. The introduction briefly discusses these challenges and suggests future opportunities and tools for enhanced response.

Section 2 discusses challenges in surveillance and prevention. Behavior change is key to safe sexual practices and abstinence, and fidelity and condom use are key to prevention. However, surveillance systems for both case reporting and behavioral surveillance are poor in low-resource countries. Chapter 2 presents a systematic review of literature from thirteen countries, highlighting the obstacles that impact outcomes. The discussion concludes that deterioration of physical health, HIV-associated stigma, and costs, among other shortcomings, are major reasons for reduced uptake and access to services. The challenges undermining surveillance are also discussed. For instance, the chapter analyzes the risk factors associated with non-suppression of HIV viral load. The methodology used involves two sequential cross-sectional surveys conducted in 2014 and 2015 of viral load measurements in South Africa. The analysis is based on data from the HIV Incidence Provincial Surveillance System (HIPSS), which monitors HIV-related measures of HIV prevalence and incidence. According to the results, nearly half of the women surveyed had a non-suppressed viral load. Factors associated with non-suppression among women include a lack of knowledge of their HIV status, having a moderate-to-low perception of contracting HIV, and being unable to access antiretroviral therapy. Another study in Chapter 3 assesses spatial and temporal risk factors associated with the prevalence of opportunistic infections. With antiretroviral therapy, the frequency of such infections has been declining steadily, but with variations by region. The trends of these infections by region show that the commonest comorbidity by region is tuberculosis, whereas cancers are very rare. These findings are contrasted with those from the developed world.

Deep learning is a recent approach to predicting HIV test results and supporting testing services. The model in Chapter 4 analyzes demographic and sero-survey datasets from population surveys, following which the deep learning tool identifies people with HIV and estimates the prevalence of infection in the community. The model was used to construct an HIV status prediction system and results show that it has predictive accuracy of 85.3%. Such an approach based on demographic and survey data may be used to predict and forecast the HIV status of individuals. The modeling in the study supports planning and strategy development.

Section 3 discusses some emerging challenges in laboratory testing and the need for rapid, reliable, and relevant testing. Chapter 5 examines current HIV diagnostic tests and explains and provides a rationale for the use of these tests. It also discusses the various indications and criteria for HIV testing, and detection by stages and phases. It examines the difficulties encountered during the early window period of infection and suggests appropriate detection tools. It also describes the classifications of tests from first generation to fourth generation and makes recommendations for their appropriate and rational usage. Suggestions are also made for ideal screening and confirmatory tests for each stage of the disease.

There is also growing attention on platelet functions in people living with HIV/AIDS because of the high reported incidence of cardiovascular adverse effects, including thrombosis, in these individuals. Furthermore, the effects of antiretroviral therapy on platelet functions are not well understood. Chapter 6 reviews HIV-associated thrombocytopenia and discusses the immune complexes' environment including the cytokines and inflammatory markers in cytokine elaboration. The value of tests based on platelet aggregation is discussed by region, race, and ethnicity. The concept of "platelet exhaustion" where activated platelets continued to circulate in HIV infection but with decreased aggregation is also examined.

Section 4 discusses challenges in care. It includes specific systematic reviews of challenges in the care and management of perinatal HIV/AIDS. The prevention and management of perinatal HIV infections involves the administration of antiretroviral therapy to both the pregnant person and their child after delivery, in combination with regular HIV tests. The recommendations for ART medication in pregnant persons and neonates are often modeled after data obtained from non-pregnant adults or older children thus impacting efficacy and safety. Maternal physiology also changes throughout the gestational period and the pharmacodynamic parameters of a drug may be altered as the pregnancy progresses. In neonates, physiologic considerations are important when selecting safe and effective medications. Due to the underdeveloped immune system of the infant, an antigen test is not as sensitive as virologic testing. Chapter 7 examines subsequent implications, identifies barriers, and suggests options for treatment success. The study proposes revised treatment guidelines for the perinatal period for the mother and neonate. Chapter 8 discusses oral lesions common in people infected with HIV. Periodontal disease can be categorized simply as gingivitis and periodontitis and necrotizing periodontitis. It is an infectious and inflammatory disease with multifactorial etiology. This chapter provides an update on periodontal disease, discussing risk factors for oral lesions and their mechanisms.

Section 4 also addresses mental health. Effective lifelong treatment for HIV/AIDS requires a sound mind to ensure compliance and adherence. Mental disorders are often neglected, yet they undermine treatment and prevention. Chapter 9 discusses disorders such as antisocial personality disorders and borderline personality disorders. It presents a detailed examination of the various acute psychological reactions following HIV diagnosis and makes recommendations for how to manage such problems as an integrated component of HIV/AIDS. The analysis demonstrates that mental illnesses compromise treatment outcomes and undermine HIV care and prevention. The chapter concludes by recommending the integration of mental health care into HIV prevention and prevention programs.

Section 5 discusses the implications of social stigma on the health outcomes of marginalized groups. Chapter 10 focuses on the stigma associated with HIV, mental health, and sexual orientation and gender identities. Public education to regulate sexual behavior is often associated with stigma. These forms of stigma often lead to discrimination and lowered self-esteem as well as social devaluation in society. The chapter presents a systematic review of experiences in South Africa, a country with a history of complex socially structured norms based on stereotypes. The discussion suggests that the multi-layered nature of stigma and its interconnectivity makes it difficult to implement robust interventions. The chapter discusses policy implications and makes key recommendations for promoting social inclusion and improving access to care.

Social frameworks are needed to promote social inclusion and gainful social integration. These additional social issues require action as the elimination of HIV is targeted.

Section 6 reviews the health and human rights associated with HIV/AIDS. The epidemiological, and clinical approaches to HIV/AIDS are inextricably intertwined with the protection of health and human rights. Chapter 11 examines the legal human rights and health rights aspects and discusses the extent to which HIV/AIDS litigation has advanced the prevention, control, and treatment of HIV/AIDS and related issues on health and human rights. Advancing health rights through the courts highlights the limitations of law as a human rights tool in holding government service providers more accountable. The chapter examines several successful examples in which courts not only upheld the rights of individuals but also forced governments to address the holistic management of people living with HIV. The chapter recommends that the full realization of health rights to achieve health equity requires that rights-based approaches be mainstreamed into national public and private health service strategic plans and research.

I thank the chapter authors for their contributions. I also thank Author Service Managers Josip Knapić and Marica Novakovic at IntechOpen for their invaluable support and assistance.

> **Samuel Okware, Ph.D.** Associate Professor Public Health, Busitema University, Busitema, Uganda

Section 1 Introduction

#### **Chapter 1**

## Introductory Chapter: Future Opportunities and Tools for Emerging Challenges for HIV/AIDS Control

*Samuel Okware*

#### **1. Introduction**

It is almost 40 years since the first cases of HIV/AIDS were identified. The disease was a tragedy of monumental dimensions. Millions have died leaving families helpless especially in developing countries. Experiences of unprecedented suffering and social disruption prevailed in the early part of the pandemic. Orphans became heads of households and carried the family burden as the disease killed both parents. Gradually over time, some feelings of hope emerged following the launch of the UN Global Strategy for Prevention and Control of HIV/AIDS. Steady progress was made in prevention and management of persons living with HIIV/AIDS. Anti-retroviral treatment offered the best hope for the patients. The quality of life for people living with HIV/AIDS steadily improved on with anti-retroviral treatment. Mortality has reduced and AIDS is no longer a death sentence, but a chronic disease. Longevity too for them has improved significantly since the introduction of Anti-Retroviral Therapy in 2003. Opportunistic Infections, the major causes of death too have declined. New infections and HIV-related mortally is declining worldwide [1, 2]. The UNAIDS global HIV/AIDS program [3] based on combination strategy for risk reduction targeting sexual behavior has successfully reversed trends in new infections. The current UNAIDS Global Strategy targets elimination of infection by 2030 by focusing more on reducing inequities hindering progress, enhancing people-centered services, and removing legal and social constraints that hamper human rights. The overall goal of the strategy is based on human rights, gender equity free of discrimination. The strategy prioritizes the elimination of HIV infection particularly among children. The new strategy priorities the interventions for the prevention of mother to child transmission with a target of elimination of mother-to-child transmission. Reviews on the evolution of the disease are helpful in realizing missed opportunities to help in the future outbreaks.

#### **2. Challenges in behavior change**

Behavior change for safe sexual practices is essential in mitigation of spread of infection. The key components of the these safe sexual practices include abstinence, fidelity, and condom use and have been vital to HIV prevention [4]. Messages need

to be targeted using more accurate, rational, and evidence-based interventions. However, poor surveillance systems for both case reporting and behavioral surveillance remains weak especially where HIV burden is greatest. Furthermore, stigma and societal issues persist, which are barriers that have partly promoted perceived low risk among the communities [5]. There are instances where intensified implementation of the combination interventions on key populations within the context of the highest-risk scenarios and targeting local HIV epidemiology has yielded good results. Such outstanding examples ought to be shared as we approach the last mile in our containment efforts. Targeting of appropriate packaged messages has worked in some communities, the experience of which could be benchmarked toward the elimination of infection.

Other new behavioral and societal challenges have emerged in some instances. For instance, prevention messages on risk reduction are being undermined by the successes following effective and successful anti-retroviral treatment. Complacency among the general public especially the youth is gradually growing. The youth do not see the disease as a threat encouraged by the absence of the earliest typical clinical features of extreme body wasting associated with high fever and diarrhea [6]. Special interventions including condoms are to be scaled up for key populations. The high prevalence of discordancy of infection among couples in committed relationships needs to be addressed. Instead of focusing on individuals, programs should aim at couples as an entity since the risks are similar so as to maintain the discordancy in stable relationships.

Behavior and mental health are often linked. However, this relationship is often missed when planning for prevention strategies and behavior change. Sexual compulsivity and hyperactivity, for instance, are rarely considered yet it is a mental behavioral deficit that needs attention in some communities. This trait may be associated with high risk and addressing it could have some impact in reduction of HIV transmission. Mental health should be integrated into the next programs for the elimination of infection.

#### **3. The test and treat policy**

The test and treat policy is regarded as an effective way to reduce infections because undetectable viral load translates into no transmission in most circumstances. This approach should significantly support the elimination of infection. The UNAIDS global 90–90–90 strategy for the elimination of the scourge by 2030 is being implemented worldwide, but with varying levels of success. This strategy is based on test and treat policy and the sustenance of quality undetectable viral loads. For such intervention to be effective, the tests need to be accessible. Equally important for the client is that access is user-friendly. While the classifications of tests from first generation to fourth generation is well described for appropriate usage, the limited financial environment presents challenges for optimal work and calls for rational more appropriate tools. For instance, the challenges of screening and detection during the acute and the window period post infection should be examined to enhance accuracy and appropriateness. The tests should be rapid and of high quality. Such tests are most appropriate in low resource settings where costs and convenience remain a major consideration. This challenge is most pronounced in low-income countries. While early testing and diagnosis are key to achieving zero new infections, universal access to testing and treatment remains a herculean task. In Sub-Saharan Africa, for instance, testing is

*Introductory Chapter: Future Opportunities and Tools for Emerging Challenges for HIV/AIDS… DOI: http://dx.doi.org/10.5772/intechopen.105893*

not optimal due to weak health systems and costs. Other impediments may include deterioration of clinical status or death of a partner. In local settings this can significantly impact on uptake [5]. Some countries have coped with this by the expansion of primary health care through community engagements strategy and effort.

#### **4. Challenges in perinatal diagnosis and care**

Perinatal care and management of mother and baby need clear guidelines. Perinatal diagnosis preceding management in particular remains a challenge. For instance, the guidelines for the management of perinatal transmission in neonates remain unclear. This is primarily due to the administration of Anti-retroviral Therapy to pregnant mothers and her child after delivery. Thus, the identification and management of HIV infection among neonates during the perinatal period are yet to be made clearer. Thus, there is a paucity of evidence for the rationale management of HIV-infected neonates. The discussion should be made on the implications and barriers for treatment guidelines for successful outcomes for neonates and should be examined for the better management of these cases.

Overall some laboratory functions and parameters functions are yet to be clarified. For instance, the role of platelet parameters and pathophysiology is not fully understood in the managing people living with HIV/AIDS. There should therefore be increasing attention on platelet functions among this group because of reported cardiovascular severe adverse effects and thrombosis and related conditions [7]. More studies and evidence are required to improve care and social well-being.

#### **5. Responding to challenges for the elderly**

The elderly living with HIV/AIDS will increase with improved anti-retroviral treatment. A resurgence of noncommunicable diseases is bound to grow. Diabetes and hypertension usually associated with obesity will present special challenges during this life extension. Additional social programs will be needed to provide amenities for the elderly. They will need support to promote social inclusion and gainful integration in order to participate in community and societal agendas. Frameworks for housing, jobs, and direct financial support are challenges to consider during the HIV/AIDS long-term recovery.

The book chapters in the proposed updates will examine and discuss arguments on these crucial issues, the consideration of which could be the recipe for the improvement of strategies for the elimination of HIV/AIDS by 2030 and wellness for all.

*Future Opportunities and Tools for Emerging Challenges for HIV/AIDS Control*

#### **Author details**

Samuel Okware Uganda National Health Research Organization, Uganda

\*Address all correspondence to: okwares@gmail.com

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Introductory Chapter: Future Opportunities and Tools for Emerging Challenges for HIV/AIDS… DOI: http://dx.doi.org/10.5772/intechopen.105893*

#### **References**

[1] Kambugu A, Rhein J, O'Brien M, Janoff EN, Ronald AR, et al. Outcomes of cryptococcal meningitis in Uganda before and after the availability of HAART. Clinical Infectious Diseases. 2008;**46**(11):1694-1701

[2] Morgan D, Mayanja B, Whitworth JA. Progression to symptomatic disease in people infected with HIV-1 in rural Uganda: Prospective cohort study. BMJ (Online). 2002;**324**:193-196

[3] Global AIDS Strategy 2021-2026 — End Inequalities. End AIDS. Available from: 21 March 2021

[4] Okware S et al. Fighting HIV/ AIDS: Is success possible? Bulletin of the World Health Organization. 2001;**79**(12):1113-1120

[5] Musheke M, Ntalasha H, Gari S, et al. A systematic review of qualitative findings on factors enabling and deterring uptake of HIV testing in Sub-Saharan Africa. BMC Public Health. 2013;**13**:220. DOI: 10.1186/1471-2458-13-220

[6] Okware SI. Towards a national AIDScontrol program in Uganda. The Western Journal of Medicine. 1987;**147**(6):726-729

[7] Ahonkhai AA, Gebo KA, Steiff MB, Moore RD, Segal JB. Venous thromboembolism in patients with HIV/ AIDS. A case-control study. Journal of Acquired Immune Deficiency Syndromes. 2008;**48**(3):310-314

Section 2

## Surveillance and Prevention

#### **Chapter 2**

## Spatial Variation and Factors Associated with Unsuppressed HIV Viral Load among Women in An HIV Hyperendemic Area of KwaZulu-Natal, South Africa

*Adenike O. Soogun, Ayesha B.M. Kharsany, Temesgen Zewotir and Delia North*

#### **Abstract**

New HIV infections among young women remains exceptionally high and to prevent onward transmission, UNAIDS set ambitious treatment targets. This study aimed to determine the prevalence, spatial variation and factors associated with unsuppressed HIV viral load at ≥400 copies per mL. This study analysed data from women aged 15–49 years from the HIV Incidence Provincial Surveillance System (HIPSS) enrolled in two sequential cross-sectional studies undertaken in 2014 and 2015 in rural and peri-urban KwaZulu-Natal, South Africa. Bayesian geoadditive model with spatial effect for a small enumeration area was adopted using Integrated Nested Laplace Approximation (INLA) function to analyze the findings. The overall prevalence of unsuppressed HIV viral load was 45.2% in 2014 and 38.1% in 2015. Factors associated with unsuppressed viral load were no prior knowledge of HIV status, had a moderate-to-low perception of acquiring HIV, not on antiretroviral therapy (ART), and having a low CD4 cell count. In 2014, women who ever consumed alcohol and in 2015, ever ran out of money, had two or more lifetime sexual partners, ever tested for tuberculosis, and ever diagnosed with sexually transmitted infection were at higher risk of being virally unsuppressed. The nonlinear effect showed that women aged 15 to 29 years, from smaller households and had fewer number of lifetime HIV tests, were more likely to be virally unsuppressed. High viral load risk areas were the north-east and south-west in 2014, with north and west in 2015. The findings provide guidance on identifying key populations and areas for targeted interventions.

**Keywords:** Bayesian, spatial effect, geoadditive model, integrated nested Laplace approximation, unsuppressed viral load, women, UNAIDS 95–95-95 target, South Africa

#### **1. Introduction**

In 2014, the Joint United Nations Programme on HIV/AIDS (UNAIDS) set ambitious 90–90-90 HIV testing and treatment target to achieve the 73%

composite viral suppression target by the year 2020 towards ending the epidemic by year 2030 [1]. While few countries like Australia and Botswana achieved this target [2, 3], the global public health community failed to achieve this target [4]. Therefore, in 2021 the UNAIDS Global AIDS strategy raised the targets to 95–95-95 with an overall viral suppression of 86% to be met by 2025 including prioritising sexual reproductive health and rights for women living with HIV (WLHIV), with the aim of controlling the epidemic by the year 2030 [5]. The "first 95" represents 95% of people living with HIV knowing their HIV status; the "second 95" represents 95% of people who know their HIV-positive status and are on antiretroviral therapy (ART); and the "third 95" represents 95% of HIV positive people who know their HIV status are on ART and are virally suppressed [1, 4]. At the country and global level, commitment, and resources to meet these indicators has been prioritised as the strategy was expected to prevent onward transmission of HIV and reduce HIV incidence [5–7].

In 2020, globally, 36 million adults over the age of 15 were living with HIV [4]. Out of these, 84% knew their status, 73% were accessing treatment and 66% were virally suppressed [4]. South Africa contributes approximately 22% of the global HIV burden [4, 8], and KwaZulu-Natal province is the epicentre [9, 10], where the UNAIDS targets has not been met [11, 12]. Whilst South Africa has substantially scaled-up ART provision, having the largest HIV treatment programme globally, has resulted in reducing number of HIV related death [8]. However, country level HIV prevalence of 14.0%, with an estimated 231,000 new infections remains persistently high [13], and almost a fourth of women in their reproductive ages (15–49) were HIV positive at the end of 2020 [8]. KwaZulu-Natal has the highest HIV burden with prevalence of 18.1% compared to Western Cape with a prevalence of 6.8% [14]. Heterosexual sex is the key path to HIV transmission and acquisition in this region [15], where women of reproductive age are disproportionately affected [16, 17], thus increasing the potential of mother to child transmission (MTCT) of HIV during pregnancy, childbirth, or breastfeeding [13, 18]. Thus, viral suppression is critically important among this key population for the prevention of mother-to-child transmission (PMTCT) of HIV [18, 19] and transmission to sexual partners.

Small area location-based approaches have been recommended for targeted interventions, scale up of treatment and identify spatially distributed structural and behavioural risk factors towards achieving the UNAIDS targets and to help to reduce the overall HIV burden [20]. Evidently, their exist geographic variation in the complexity of HIV epidemiological measures [21]. Therefore, spatial analysis and modelling accounting for the presence of spatial autocorrelation between observation and residual must be considered [20, 21]. Failure to account for spatial heterogeneity and possible causes could result in misleading epidemiologist, public health institutions, and policy makers. The national HIV prevalence survey among pregnant women that also examined socioeconomic factors associated with unsuppressed viral load did not account for the nonlinear effect of continuous covariates or mapped the spatial effect [22]. Therefore, the aim of this study was to determine factors associated with unsuppressed HIV viral load among women living with HIV while accounting for nonlinear effects of some continuous covariates and mapping spatial risk effect using Bayesian inference. Furthermore, the study assessed progress towards UNAIDS indicators, examined the prevalence and hotspots of unsuppressed HIV viral load among women in an HIV hyperendemic area of KwaZulu-Natal, South Africa. This study applied the Bayesian *Spatial Variation and Factors Associated with Unsuppressed HIV Viral Load among Women… DOI: http://dx.doi.org/10.5772/intechopen.105547*

hierarchical Geoadditive model technique to identify risk factors associated with unsuppressed HIV viral load and mapping the spatial areas in KwaZulu-Natal, South Africa.

#### **2. Methods**

#### **2.1 Sources of data, design, and procedures**

This analysis was based on data from HIV Incidence Provincial Surveillance System (HIPSS) that monitored HIV related measures of HIV prevalence and incidence in association with the programmatic scale of HIV prevention and treatment efforts in a "real world" non-trial setting. The study undertook two sequential cross-sectional surveys with the first survey from June 2014 to 18 June 2015 (2014 Survey) and the second survey from 8 July 2015 to 7 June 2016 (2015 Survey). All study participants provided written informed consent and or assent, completed a face-to-face questionnaire to obtain socio-demographic, behavioural, knowledge of HIV testing, sexually transmitted infections (STI) and tuberculosis (TB) history and biological information. From a total of 600 Enumeration Areas (EAs), 591 EAs with more than 50 households were systematically selected at random, of which 221 were drawn for the 2014 Survey and 203 were drawn for the 2015 Survey. Households were randomly selected using multi-stage random sampling, were geo-referenced and one individual per household, within the age range 15–49 years old was randomly selected and invited to participate in the study. In the 2014 Survey a total of 9812 participants were enrolled, of whom 6265 were women, whilst in the 2015 Survey a total of 10,236 participants were enrolled, of whom 6341 were women. All enrolled participants had HIV antibody and viral load testing undertaken. In the 2014 Survey, 2955 were HIV positive and 2946 had viral load measurement, whilst 9 participants had missing viral load measurement. In the 2015 Survey, 2947 women were HIV positive and 2946 had viral load measurements, whilst 1 participant had missing viral load measurement.

HIPSS study was conducted in accordance with the approval by the Biomedical Research Ethics Committee of the University of KwaZulu-Natal (Reference number BF269/13), the KwaZulu-Natal Provincial Department of Health (HRKM 08/14), and the Associate Director of Science of the Centre for Global Health (CGH) at the United States Centre for Disease Control and Prevention (CDC) in Atlanta, United States of America (CGH 2014–080). Details about HIPSS study design, objectives and study and data collection procedures have been described elsewhere [10, 11].

#### **2.2 Study population and geographic area**

HIPSS was conducted in a geographically defined region of rural Vulindlela and peri urban Greater Edendale areas in the Msunduzi municipality, uMgungundlovu district of KwaZulu-Natal province in South Africa. Whilst this community has basic access to water, electricity and free health facilities, the area is characterised by high rates of unemployment, poverty, and HIV. The EAs are located between 29°39' South and 30°17 East of KZN, covers a total of 33 wards in the Msunduzi and a part of uMngeni municipalities, in uMgungundlovu district.

#### **2.3 Study variables**

#### *2.3.1 Dependent variable*

The primary outcome variable was HIV viral load status among women living with HIV (WLHIV) in this community, which was categorised as binary outcome:

$$
\pi\_{\text{ij}} = \begin{cases}
\text{1 virtual load} \ge 400 \text{ copies/mL (unsupppressed)} \\
\text{0 virtual load} < 400 \text{ copies/mL (suppressed)}
\end{cases}
\tag{1}
$$

This threshold was used in accordance with the country revised ART treatment guideline [23, 24] as well as evidence from several studies on transmission potential at this cut off [25, 26]. Unsuppressed viral load calculation and definition was based on the composite viral suppression of all WLHIV irrespective of being on ART or not.

#### *2.3.2 Explanatory variables*

Initial data exploration to identify potential factors associated with unsuppressed viral load was established using multiple correspondence analysis and random forest analysis [27]. The explanatory variables considered in the study comprised of sociodemographic, behavioural, knowledge of HIV status and HIV testing, medical history, and biological variables. These included age, marital status, education level, community duration, migration history, monthly income, accessing health care, meal cut, income loss, place of residence, number of household members, had sex in the last 12 months, number of sexual partners in the last 12 months, number of total lifetime sex partners, forced first time sex, ever consumed alcohol, ever tested for HIV, number of lifetime HIV test, knowledge of HIV status, perceived risk of contracting HIV, exposed to TB last 12 months, ever diagnosed of TB, had any STI symptoms, ever diagnosed of STI, ever pregnant, currently on antiretrovirals (ARV) and current CD4 cell count. The variance inflation factors (VIF) was used to check for collinearity among continuous independent variables and all variables with VIF < 4 was assumed that multicollinearity was not significantly present. Also, non-linear effect of all continuous variables was also examined, of which only age, household size, number of lifetime HIV test and total number of children ever born displayed a significant nonlinear effect and were considered in the fitted model while the remaining independent variables were included as linear fixed effect.

#### **2.4 Statistical data analysis**

To account for the complex multilevel sampling design, weighted percentage and frequency were used to describe and summarise the study characteristics across both surveys. Progress towards each of the 95-95-95 indicators and composite viral suppression was estimated. Comparisons of weighted proportion of viral load status was estimated with associated 95% confidence intervals (CIs) and p values using Taylor series methods. Initial non-spatial bivariate survey logistic regression was used to test association between each background characteristics and the dependent variable using Rao-Scott chi-square test. Statistical analyses were performed using SAS (SAS Institute, Cary, North Carolina) version 9.4. Covariates with significant association at 5% significant level for each study year was included in the multivariate model.

*Spatial Variation and Factors Associated with Unsuppressed HIV Viral Load among Women… DOI: http://dx.doi.org/10.5772/intechopen.105547*

Suppose *γijkl* denote the viral load status of women and P *γijkl* ¼ 1 � � <sup>¼</sup> *<sup>θ</sup>ijkl* is the probability that woman *l* in household *k* within cluster *j* and district *i* is unsuppressed and P *γijkl* ¼ 0 � � <sup>¼</sup> <sup>1</sup> � *<sup>θ</sup>ijkl* is the probability that the woman is suppressed. This assumes that the response variable *γijkl* is Bernoulli distributed. Thus, the hierarchical Geoadditive model is given as:

$$\log \text{it} \left( \theta\_{\text{ijkl}} \right) = X\_{\text{ijkl}} \,\theta + d\_1 Y\_{\text{ijkl}1} + d\_2 Y\_{\text{ijkl}2} + d\_n Y\_{\text{ijkln}} + d\_{\text{spatial}} \left( \mathbf{g}\_h \right) \tag{2}$$

Eq. 2 is a semi-parametrical model, where *logit θijkl* � � is the logit link function, and *Xijkl β* þ *d*1*Yijkl*<sup>1</sup> þ *d*2*Yijkl*<sup>2</sup> þ *dnYijkln* þ *dspatial gh* � � is the Geoadditive predictor. Parameter *β* is the vector of the linear fixed effects which we modelled parametrically. The unknown smooth function of the non-linear effect is denoted as *da*ð Þ*:* , *a* ¼ 1, … *:n*, which was modelled non-parametrically. *dspatial gh* � � is the spatial effect covariate of district *gh* in which a woman resides, which symbolises the unaccounted and unobserved effect that are not included in the model [28–30]. Thus, resulting in the partitioning of this spatial effect into a spatially correlated (structured) and uncorrelated (unstructured) effect, given as:

$$d\_{\text{spatial}}(\mathbf{g}\_h) = d\_{\text{struct}}(\mathbf{g}\_h) + d\_{\text{onstruc}}(\mathbf{g}\_h) \tag{3}$$

The argument is that spatial effect is the proxy of most unobserved influence, under which spatial structure assumption must be followed. The structured spatial effect accounts for the assumption that location close in proximity are more likely to be correlated in respect of their outcome. While the unstructured spatial effect accounts for the spatial variation because of the effects of interminable district-level factors that are not related spatially [31–33].

The study utilised a fully Bayesian inference, hence all parameters and functions were considered as random variables and thus assigned with appropriate prior. Parameter *β* was assigned vague Gaussian priors N (0, 1000). The Bayesian penalised spline (P-splines, second-order random walk smoothness prior and third-degree spline) was adopted for the unknown smooth function *da*ð Þ*:* [34, 35]. Borrowing strength from neighbouring locations, the intrinsic Gaussian Markov random field (IGMRF) prior as specified by Besag et al. [34] was used for the structured spatial effect *dstruct gh* � �ð Þ*:* [36, 37]. Two regions *gh* � � *and gi* � � are referred to as neighbours if they share common boundary, thus the spatial extension of random walk model was modelled by assuming the Besag-York-Mollie Conditional Autoregressive (CAR) prior given as:

$$d\_{\text{struct}}(\mathbf{g}\_h)|d\_{\text{struct}}(\mathbf{g}\_i), h \neq \mathbf{1} \sim N\left(\frac{\mathbf{1}}{w\_{\mathbf{g}\_h}} \sum\_{\mathbf{g}\_i \in \mathbf{g}\_h} d\_{\text{struct}}(\mathbf{g}\_i), \frac{\mathbf{1}}{w\_{\mathbf{g}\_h} \mathbf{1}^2\_{\text{struct}}}\right) \tag{4}$$

Where *wgh* is the number of neighbours in district *gh*, and *gi* ∈*gh* represents that *gi* is a neighbour of district *gi* . Thus, the conditional mean of *dstruct gi* � � is the average function of *dstruct gh* � � of neighbouring districts.

Independent and identically distributed random variable (i.i.d) Gaussian priors were assigned to the unstructured spatial effect to account for the unobserved covariates that are inherent within the districts, denoted as:

$$d\_{\rm untruc}\left(\mathbf{g}\_h\right) \sim \mathbf{N}\left(\mathbf{0}, \frac{\mathbf{1}}{\tau\_{\rm struct}^2}\right) \tag{5}$$

where the variance *τ*<sup>2</sup> *struct* is the unknown parameter to be estimated. Hyperpriors defined as log-gamma *m*, *n* distribution, where *m*, *n* ¼ 1 *and n* ¼ 0*:*001 were assigned at the second stage of the hierarchy. Non-linear and spatial effect were imposed with a sum-to-zero limit in order to distinguish between the effects and intercepts.

Lastly, the posterior distributions of all the parameters *π θ*ð Þ and the likelihood function *L x*ð Þ j*θ* was estimated. The study then assumes that *θ* denotes vectors of the unknown parameters in the model and likelihood *L*ð Þ*:* is the product of individual likelihood. Thus, the posterior distribution is written as:

$$\text{Tr}(\theta|\mathbf{x}) \text{ a } \text{L}(\mathbf{y}|\beta\_1, d\_1, \dots, \beta\_n d\_n, \rho) \prod\_{h=1}^p \text{\(}\beta\_h|d^2\text{\)} \text{ d}^2\_h \tag{6}$$

This is a high dimensional model and analysis which sometimes require good knowledge of advance mathematical and statistical computation. So, Markov chain Monte Carlo (MCMC) algorithm is required to generate samples from this distribution which comes with much computational difficulties. To circumvent this problem and difficulties, the Integrated Nested Laplace Approximation (INLA) was used to obtain the estimate [38, 39]. The outmost goal is to estimate marginal posterior distribution for the latent Gaussian model which was used to compute the summary statistics of interest like posterior mean, standard deviation, and 95% credible interval.

Three models were considered for comparison namely:

*Model 1:* Generalised Additive model (GAM): All categorical and some continuous variables were modelled as linear fixed effect, and nonlinear effects of covariates age, household size, total number of children ever born and number of lifetime HIV test.

*Model 2:* Structured Additive model (SAM), extension of GAM with the inclusion of CAR prior.

*Model 3:* Unstructured Geoadditive model (UGM), Model 2 with the inclusion of the spatial effect and modelled using i.i.d.

Deviance information criterion (DIC) of each model were compared. The final Geoadditive model was selected based on smallest DIC which was considered as good predictive performance and best fit model [40, 41]. The summary results give the posterior mean estimates with associated credible interval as well as the spatial effect map. The enumeration area shapefile was created in ArcGIS using the geographic attributes. Bayesian inference was analysed using INLA package in R software [37, 42].

#### **3. Findings**

#### **3.1 Study characteristics**

**Table 1** shows the sample size and characteristics of HIV positive women in rural and peri urban areas of KwaZulu-Natal, South Africa. Almost half (45.2%) of the women had unsuppressed viral load in 2014 and about one third (38.1%) in 2015. Majority of WLHIV were aged between 20 and 44 years; 86.9% in 2014 and 85% in 2015 with median age and interquartile range (IQR) of 31(25–39) in 2014 and and 32 (26-40) years in 2015. Majority of the women were never married; 84.6% in 2014 and *Spatial Variation and Factors Associated with Unsuppressed HIV Viral Load among Women… DOI: http://dx.doi.org/10.5772/intechopen.105547*



*Spatial Variation and Factors Associated with Unsuppressed HIV Viral Load among Women… DOI: http://dx.doi.org/10.5772/intechopen.105547*


*Participants missing for: a = 7, and f = 27 in 2014; b, c and d = 2, f = 9 in 2015. No response: e: = 879(64) for 2014(2015). Missing data were excluded from percentage calculation. ZAR = South African Rand (ZAR15 US\$1).*

*TB = tuberculosis, STI = sexually transmitted infections, ARV = antiretroviral drugs, ART = antiretroviral therapy, Ever had any STI symptoms = any symptoms of abnormal vaginal discharge, burning or pain when passing urine or presence of any genital warts/ulcers.*

#### **Table 1.**

*Characteristics of HIV positive women in Vulindlela and Greater Edendale, KwaZulu-Natal, South Africa, 2014–2015.*

81% in 2015. More than half had incomplete high school education 53.5% in 2014 and 57.3% in 2015. Most women had always lived in the community; 76.5% in 2014 and 54.8% in 2015 whilst never being away from home in the last 12 months was 89.8% in 2014 and 92.7% in 2015. In 2014 75.5% and in 2015, 53.0% of women reported a monthly income of ≤R2500 More than half of women sampled (57.6%) in 2014 were from rural area whilst the majority (63.8%) in 2015 were from urban areas. Overall 77.2% in 2014 and 83.5% in 2015 had engaged in sex in the last 12 months, whilst 46.7% in 2014 and 22.1% in 2015 reported having had two or more number of sex partners in the last 12 months. Overall, the majority; 77.8% in 2014 and 84.3% in 2015 reported having had two or more lifetime sex partners. Almost all the women were not forced to have sex at their first-time sex encounter. Regarding their HIV testing knowledge and perception, 88.9% of women in 2014 and 98.9% in 2015 reported having had an HIV test with 62.7% in 2014 and 69.8% had HIV test more than twice in their lifetime. In 2014, 21.5% had a perception of not likely to contract HIV, while only 14.1% in 2015. Overall, 79.6% of women in 2014 and 88.2% in 2015 had reported having been pregnant in their lifetime. Less than half, 48.8% in 2014 reported to be on ART, though this increased to 59.8% in 2015. More than half of the women 55.8% in 2014 and 58.6% in 2015 had a current CD4 cell counts of ≥500 cells per μL and 23.1% in 2014 and 21.6% in 2015 had CD4 cell counts of <350 per μL.

#### **3.2 Progress towards UNAIDS 95-95-95 indicators**

**Figure 1** provides the status on the UNAIDS 95–95-95 indicators. Of the 2955 women in 2014 and 2948 in 2015 who tested positive for HIV, 9 and 1 participants respectively had no viral load measurement. Thus, 2946 women in 2014 and 2947 women in 2015 had viral load measurements. In 2014, to meet the "first 95", 65.5% (95% CI, 62.9–68.2) (n = 1890/2955) were aware of their HIV positive status and for the "second 95", 74.2% (95% CI, 71.6–76.8 (n = 1348/1870) had initiated ART and for the "third 95", 82.9% (95% CI, 80.4–85.4) (n = 1105/1346) had achieved viral suppression, and overall viral suppression among all HIV positive women was 54.8% (95% CI, 52.0–57.5) (n = 1574/2946). While in 2015, progress towards 95–95-95 targets were: 74.7% (95% CI, 72.7–76.6) (n = 2219/2948) were aware of their HIV status; 80.0% (95% CI, 78.1–82.0) (n = 1777/2219) of these had initiated ART and 88.2% (95% CI, 86.6–89.9) (n = 1551/1777) of those on ART had achieved HIV viral suppression, resulting in the overall viral suppression among all HIV positives to be 61.9% (95% CI, 59.7–64.1) (n = 1828/2947).

Disaggregated by age groups, **Figure 1a** shows the progress towards the "first 95" Knowledge of HIV status increased from 65.6% in 2014 to 74.7% in 2015, and across age groups, with highest achieved among 35–39 (86.5%), 40–44 (82.4%) and 45–49 (82.4%) in 2015. Highest increase in the knowledge of HIV positive status was in the age group 15–29, increasing from 25.8% in 2014 to 46.7% in 2015. **Figure 1b** shows the progress towards the "second 95". Overall proportion of women who knew their HIV positive status and were on ART increased from 74.2% in 2014 to 80.0% in 2015. The uptake of ART varied across age groups, uptake was high in the 15–19 years age group at 74.8% in 2014 and 75.9% in 2015; in ages 30–34 uptake was 77.2% in 2014 and 80.5 in 2015; in ages 35–39 years uptake was 77.8% in 2014 and 85.6% in 2015; in ages 40– 44 years uptake was 77.1% in 2014 and 84.6% in 2015 and in age 45–49 uptake was 79.1% in 2014 and 84.2% in 2015. However, ART uptake in the age group 20–24 years was lowest at 62.4% in 2014 and 62.8% in 2015. **Figure 1c** shows the progress towards the "third 95", that is the proportion of HIV positive women who knew their HIV

*Spatial Variation and Factors Associated with Unsuppressed HIV Viral Load among Women… DOI: http://dx.doi.org/10.5772/intechopen.105547*

#### **Figure 1.**

*Progress of the UNAIDS 95–95-95 indicators by age group and overall, among HIV positive women (2014– 2015). (A). First 95: Women living with HIV who knew they were HIV positive. (B). Second 95: Women who knew they were HIV positive and were taking ART. (C). Third 95: Women who knew they were HIV positive, were on ART and had achieved HIV viral suppression at HIV viral load <400 copies/ml. (D). UNAIDS composite measure towards achieving HIV viral suppression among all HIV positive women.*

positive status, were on sustained ART and who had achieved viral suppression of <400 copies per mL. Proportion varied across ages group; HIV viral suppression was lowest at 66% among 20–24 years old in 2014 and increased to 74.4% in 2015. Viral suppression of 92.9% was achieved among 45–49 years old and 91.7% among 40–44 years old and 91.8% among 35–39 years old in 2015. **Figure 1d** shows the overall UNAIDS 95–95-95 composite measure of achieving viral suppression of 86% among all HIV positive women. Overall, 54.8% of women in 2014 and 61.9% in 2015 had

achieved HIV viral suppression of <400 copies per mL. Substantial variation existed across the age groups, with 27% among 15–19 years in 2014 and increased to 46% in 2015. Highest achievement was observed with 76% among 45–49 years old.

#### **3.3 Prevalence of unsuppressed HIV viral load**

**Table 2** shows that overall prevalence of unsuppressed HIV viral load was 45.2% (95 CI, 42.5–48.0), (n/N = 1372/2946) in 2014 and 38.1% (95% CI, 35.9–40.3), (n/ N = 1119/2947) in 2015. Viral suppression increased by 7.1% over the study period. Unsuppressed viral load prevalence decreased as age increased and it was 72.9% (95% CI, 62.7–83.2), (n = 95/130) in 15–19 years age group, 68.2% (95% CI, 62.4–73.9), (n = 290/433) in the 20–24 years age group, 47.3% (95% CI, 41.9–52.7), (n = 299/577) in 25–29 years age group, 43.1% (95% CI, 37.9–48.3), (n = 248/561) in 30–34 years age group, 32.5% (95% CI, 26.6–38.4), (n = 185/513) in 35–39 years age group, 36.5% (95% CI, 30.6–42.4), (n = 153/426) in 40–44 years age group, 33.0% (95% CI, 26.6–39.3), (n = 102/306) in 45–49 years age group. In 2015, prevalence also decreased by age and it was 56.0% (95% CI, 43.8–64.1), (n = 74/133); 65.1 [59.5–70.7], (n = 210/337); 46.5 [41.4–51.5], (n = 279/606); 36.4% (95% CI, 32.1–40.8), (n = 244/674); 25.2% (95% CI, 20.9–29.4), (n = 125/509); 29.1% (95% CI, 24.0–34.2), (n = 120/431); 24.0% (95% CI, 18.3–29.8), (n = 67/257) in the 15–19, 20–24, 25–29, 30–34, 35–39, 40–44, and 45–49 years age categories (*Χ*<sup>2</sup> trend *P* < 0.001).

Whilst unsuppressed viral load prevalence was similar across most variables, decrease in the trends over the study years was observed. In 2014 unsuppressed viral load prevalence was 50.1%, (n = 152/311) and declined to 20.2%, n = 113/217) in 2015, among women that were away from home in the last 12 months (compared to those that were never away from home; 44.7%, (n = 1220/2635) in 2014 and 37.2%, n = 1006/2730) in 2015. Among those that ever-consumed alcohol 58.8%, (n = 229/ 390) in 2014 and declined to 44.4%, n = 249/536) in 2015 compared to those that never consumed alcohol and 43.9%, (n = 1143/2556) (36.6%, n = 870/2411) also among those that never had HIV test 68.1%, (n = 286/441) (63.4%, n = 53/80). Among those that ever had an HIV test 42.4%, (n = 1086/2505) in 2014 and 37.3%, n = 1066/ 2867) in 2015 had unsuppressed viral load. Similarly, among women who did not know their HIV status 72.0%, (n = 753/1081) in 2014 and 74.3%, (n = 528/729) in 2015 compared to those who knew their status 31.2%, (n = 619/1875) in 2014 and 25.8%, n = 591/2218) in 2015 had unsuppressed viral load. Women who perceived they are not likely to contact HIV 70.7%, (n = 478/682) in 2014 and 70.2% (n = 281/405) in 2015 compared to those who already perceived they had been infected 28.9%, (n = 530/ 1692) (24.6%, n = 529/2081), also women who have ever been diagnosed of STI 43.7%, (n = 98/213) (47.7%, n = 155/320), among women who had never been pregnant 59.4%, (n = 343/600) (49.3%, n = 169/352) compared to those that has ever been pregnant 42.5%, (n = 1029/2346) (36.6%, n = 947/2595), likewise among WLHIV and not on ART 72.1% (n = 1131/1600) (77.3%, n = 899/1172) in comparison with those on ART 17.1% (n = 241/1346) (11.8%, n = 220/1775). Prevalence was higher among women whose current CD4 cell count were < 350 count per μ/L, 69.3%, (n = 493/695) (68.5%, n = 430/633), and those with CD4 cell count of between 350 and 499 count per μ/L 49.1%, (n = 315/638) (42.0%, n = 247/576) compared to those 500 count per μ/L 33.2%, (n = 546/1591) (25.6%, n = 437/1729) in 2014(2015) respectively.

**Figure 2** shows the observed prevalence map of unsuppressed viral load. Highest prevalence was observed in the north and south of Vulindlela and east part of Greater *Spatial Variation and Factors Associated with Unsuppressed HIV Viral Load among Women… DOI: http://dx.doi.org/10.5772/intechopen.105547*



*Spatial Variation and Factors Associated with Unsuppressed HIV Viral Load among Women… DOI: http://dx.doi.org/10.5772/intechopen.105547*



*A total of nine women in 2014 survey and one woman in 2015 survey were missing viral load data. Participants missing for: a = 7, and e = 27 in 2014; b, c and d = 2, e = 9 in 2015.*

#### **Table 2.**

*Prevalence of unsuppressed viral load by study characteristics among women in Vulindlela and Greater Edendale, KwaZulu-Natal, South Africa, 2014–2015.*

**Figure 2.**

*Observed prevalence maps of unsuppressed viral load among women (a) 2014 and (b) 2015 in Vulindlela and Greater Edendale area in uMgungundlovu district, KwaZulu-Natal province, South Africa.*

Edendale in 2014, while in the north part of Vulindlela and the south part of Greater Edendale in 2015. The north area (Mpophomeni) showed a consistently high prevalence across both surveys.

#### **3.4 Model diagnostic measures**

**Table 3** shows values of the deviance information criterion (DIC) and effective numbers of parameters (pD) for each of the fitted model. Unstructured model has the minimum values (DIC = 2593.26 and 2087.70) for 2014 and 2015 respectively, thus attesting as the best fit model for the data sets, while GAM model offers the least fit. Besides, the unstructured model is of actual interest because it contains all the variables considered, and account for spatial autocorrelation and between clusters heterogeneity, failure to do so would have produced misleading and overfitting results. Thus, further results of this study are based on the unstructured model.

#### **3.5 Non-linear effect of continuous covariates on women**

**Figure 3** shows the non-linear effect of continuous covariates after accounting for other variables. The results shows that current age, number of household members, total number of children ever born and total number of lifetime HIV test, had a non*Spatial Variation and Factors Associated with Unsuppressed HIV Viral Load among Women… DOI: http://dx.doi.org/10.5772/intechopen.105547*


#### **Table 3.**

*Model diagnostic.*

**Figure 3.** *Nonlinear effect of continuous covariate.*

linear significant effect on women being virally unsuppressed in this study area. Furthermore, in **Figure 3a** and **e** shows a slight increase in effect among ages 15 to 20 in 2014 and sharp increase in 20 to 25 in 2015, after which the effect declined. Younger age 15 to 29 have higher risk of being virally unsuppressed compared to ages 30 above. **Figure 3b** and **f** shows that risk of unsuppressed viral load decreases with higher number of household members from 5 members. Also **Figure 3c** and **h** shows that the effect of total number of children ever born decreases the risk of being virally unsuppressed in 2014 but increases in 2015. Similarly, **Figure 3d** and **g** showed that the risk of unsuppressed viral load increased as the number of lifetime HIV tests increased in 2014, whilst in contrast unsuppressed viral load decreased as the number of lifetime HIV tests increased in 2015.

#### **3.6 Fixed effect model**

**Table 4** displays the adjusted posterior mean estimates with their 95% credible intervals of the linear fixed effect from the multivariable model. If these intervals contain the number zero (0), then the parameter (estimate of the mean beta) is not significant; otherwise, it is significant. Factors associated with unsuppressed viral load across both years were knowledge of HIV status, low perceived risk of contracting HIV, ARV treatment and current CD4 cell counts. Women with no prior knowledge


*Spatial Variation and Factors Associated with Unsuppressed HIV Viral Load among Women… DOI: http://dx.doi.org/10.5772/intechopen.105547*


#### **Table 4.**

*Adjusted posterior means, standard deviation (SD) and 95% credible intervals for the best fitted model.*

of their HIV status were more likely to be virally unsuppressed than those that knew their status. Women with either unlikely or likely perception of contracting HIV, not on ARV, and for those on ARV having multiple tablets of ARV had the highest risk of being virally unsuppressed compared to their reference categories. Additionally, in 2014 those that ever consumed alcohol were also at higher risk of having unsuppressed viral load. While in 2015, we also found that women that reported being away from home in the last 12 months, had a meal cut, being with two or more sexual partners in one's lifetime, ever tested with TB and ever diagnosed with STI had the highest risk of being virally unsuppressed compared to their counterparts.

#### **3.7 Spatial effect map**

**Figure 4** shows the chloropleth spatial effect maps based on model 3, shows both positive and negative effects with predicted high and low risk areas of unsuppressed viral load. The colours on the chloropleth maps show the log-odds scale, indicating each area contribution to the odds of unsuppressed viral load in women. Predicted high risk areas are shaded in yellow and gold brown (0.00015 to 0.00020), in 2014, two distinct locations were in the north-east and south-west, while 2015 shows a clustered area in the south-east. Predicted lower risk areas are shaded in royal to dark

**Figure 4.**

*Estimated posterior mean of the unstructured spatial effect map on the log-odds of unsuppressed viral load among women in uMgungundlovu district, KwaZulu-Natal province, South Africa (2014–2015). (a) 2014, (b) 2015.*

blue (0.00015 to 0.00025), the south-west in 2014, with both north and west in 2015. Evidently their exist spatial variation of unsuppressed viral load in this hyperendemic community.

#### **4. Discussion**

This analysis examined factors associated with unsuppressed viral load among women ages 15–49 years in peri-urban Greater Edendale and rural Vulindlela areas in the uMgungundlovu district, KwaZulu-Natal, South Africa between 2014 to 2015 while accounting for possible nonlinear effect of some continuous variables and mapping the unstructured spatial effects. We fitted hierarchical Bayesian Geoadditive multivariate model while controlling for the confounding effects of the explanatory variables. Bayesian spatial approach have numerous advantages over frequentist statistics, such as ability to account for and measure uncertainty in a model, minimise bias in complex data, ability to produce smoothed risk map, increased prediction accuracy, just to name a few [39, 43, 44]. Due to the strength of this approach many studies have emanated in investigating risk factors of anaemia in Sub Saharan Africa [33, 45], of HIV variation in Kenya [32], viral suppression [46] and other infectious disease globally [47]. Application of Bayesian spatial modelling therefore helped in identifying predictors and high-risk location of unsuppressed viral load among women in a small enumeration area. This enhancement of strategically identifying areas of key population is highly recommended as part of the global AIDS strategy to end inequality in resources allocation and provide localised HIV intervention in hyperendemic communities.

Our study found that knowledge of HIV status, perceived risk of contracting HIV, not on ART, and ART dosage were consistent significant factors associated with higher odds of being virally unsuppressed across both years. Having a CD4 cell count of >350 cells per μL was more likely to be associated with viral load <400 copies per mL Additionally, alcohol consumption was significant in 2014 while meal cut, total number of lifetime sex partner, ever tested for TB and ever diagnosed with STI were factors associated with unsuppressed viral load in 2015. These revealed the heterogeneity and need for continuous surveillance of HIV and its measures, as the predictors of this outcome are dynamic. Although fewer studies on women have been conducted in the country and other developing countries. Similar findings on the association of

#### *Spatial Variation and Factors Associated with Unsuppressed HIV Viral Load among Women… DOI: http://dx.doi.org/10.5772/intechopen.105547*

higher number of sexual partners were also reported among women in uMkhanyakude district of north KZN [48]. The use of ART and dosage of ARV was also found to be significant which is similar to past studies [49, 50]. Furthermore, similar studies have found higher CD4 cell count of >350 cells per μL to be predictive of being virally unsuppressed [51, 52]. We also found similar results on alcohol [53] from Western cape, South Africa and history of TB or STI in Kenya and Uganda [49, 54]. Alcohol use has been found to be associated with non-adherence to treatment in people living with HIV [55] and prevalence of which leads to high risk of transmission [56]. Several studies have shown relationship between TB and virological non-suppression [51, 57]. Similarly, concurrent ART and TB/STI treatment has been shown to increase the risk of virological non-suppression due to impaired treatment adherence and pharmacokinetics drug interaction [49].

The nonlinear effects of age, household size, total number of children ever born and total lifetime HIV test were considered. Across both years, risk of being virally unsuppressed decreases as age increases, with younger age 15–20 having a higher risk and being older associated with reduced risk of unsuppressed viral load. This is similar to past studies [48, 58]. Also risk of being virally unsuppressed decreases with increasing size of the family. Having 5 or lower number of family member showed a higher risk of being unsuppressed. This revealed that larger family members could bring more support to WLHIV. In contrast unsuppressed viral load decreases as number of children ever born increases in 2014 while the inverse was observed in 2015 (risk increases as number of birth increases). Recent study among pregnant women revealed significant association of currently breastfeeding with increase odd of viral load non-suppression [22, 59].

The unstructured spatial effect and observed prevalence map revealed the existence of localised positive spatial variation of unsuppressed viral load among women of reproductive age in this hyperendemic community. While higher prevalence was observed in the north area from both surveys and southern area in 2015. The predicted risk map revealed that in 2014 north-east and south-west as well as south-west in 2015 have an increase likelihood of being virally unsuppressed. This evidently shows that there are regional/district specific factors contributing to unsuppressed viral load with substantial spatial variation. Spatial variation in HIV and its measures has been reported by previous studies [16].

Among women in this community progress on 95–95-95 target was 65.5%, 74.2%, 82.9% in 2014 and 74.7%, 80.0%, 86.6% in 2015. The largest shortfall was in the first target, which is the entry point to health care system. None of the UNAIDS targets were met among this key population. Although, the country has made significant progress but has not achieved the UNAIDS 95–95-95 and 86% composite viral suppression target [14]. However, significant increase in viral suppression of 7.1% over a one year period was seen in this study, while ages 35 to 49 contributed to this increase, which could be attributed to the country commitment and effort in ART scaleup and intervention [27]. However, judging by the 90–90-90 indicator, our findings showed that the "third 90" target was met among age group 35–39, 40–44, 45–49 (91.8%, 91.7%, 92.9%) in 2015.

The key strengths of our study were the robustness of the study design, high participation rate, available of spatial variables and conducting the survey in a real time setting.

#### **5. Limitation**

This was a cross sectional population based study and not a randomised clinical trial with limited ART data available. Therefore, no causal effect could be established between unsuppressed viral load and women characteristics. The results are not generalisable to older individuals or children as study only accessed men and women aged 15–49 years.

#### **6. Conclusion**

Spatial effects in the model act as a representative of the unobserved predictors which strengthen the result. Identifying high risk areas could help policy maker, epidemiologist, and public health institutions to develop develop strategies and interventions that are suitable for women in the area, thus increasing the impact of allocated resources as well as effective monitoring to improve the health status of women in the community. Increase in progress of the 95–95-95 targets over time showed that the target is achievable in this community among this key population, with intensive HIV testing service, eradication of stigmatisation, ending inequality and increasing uptake of ART treatment. Knowledge of HIV status is a proxy and entry point to achieving the other indicators, generally women are more likely to test than men and receive optimum health care especially during pregnancy.

The likelihood of being virally unsuppressed was higher among younger age group, highlighting public health implication of sustained risk of HIV transmission. Aside clinical factors, family support cannot be underestimated as part of the factors that could help in achieving undetectable viral load among women of reproductive age. Right perception and knowledge of HIV positive status, being on ART and having a higher CD4 cell count contributed to achieving viral suppression. Thus, these remain multi-factorial and important public health priority to attain viral suppression towards the goal to end the epidemic by 2030.

*Spatial Variation and Factors Associated with Unsuppressed HIV Viral Load among Women… DOI: http://dx.doi.org/10.5772/intechopen.105547*

### **Author details**

Adenike O. Soogun1,2\*†, Ayesha B.M. Kharsany2,3†, Temesgen Zewotir<sup>1</sup> and Delia North<sup>1</sup>

1 School of Mathematics, Statistics and Computer Science, College of Agriculture Engineering and Science, University of KwaZulu-Natal, Westville Campus, Durban, South Africa

2 Centre for the AIDS Programme of Research in South Africa (CAPRISA), University of KwaZulu-Natal, South Africa

3 School of Laboratory Medicine and Medical Science, Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa

\*Address all correspondence to: nike.soogun@gmail.com; adenike.Soogun@caprisa.org

† Adenike O. Soogun and Ayesha B.M. Kharsany are Joint first authors

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

### **References**

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### **Chapter 3**

## Temporal and Spatial Distribution of Opportunistic Infections Associated with the Human Immunodeficiency Virus (HIV) in Uganda

*John Rubaihayo, Nazarius Mbona Tumwesigye and Josephine Birungi*

#### **Abstract**

The human immunodeficiency virus (HIV) remains one of the greatest challenges of the twenty-first century in the absence of an effective vaccine or cure. It is estimated globally that close to 38 million people are currently living with the HIV virus and more than 36 million have succumbed to this deadly virus from the time the first case was reported in early 1980s. The virus degrades the human body immunity and makes it more vulnerable to different kinds of opportunistic infections (OIs). However, with the introduction of highly active anti-retroviral therapy (HAART) in 2003, the pattern and frequency of OIs has been progressively changing though with variations in the different parts of the World. So this chapter discusses the temporal and spatial patterns of OIs in Uganda.

**Keywords:** HIV, opportunistic infections, temporal, spatial, distribution, Uganda

#### **1. Introduction**

Opportunistic infections (OIs) are the main cause of ill-health and mortality in persons living with HIV globally. OIs usually take advantage of a weakened immune system as found in persons infected with HIV to cause disease that may lead to death in the absence of effective treatment. Opportunistic infections can be viral, bacterial, fungal, or parasitic but their pattern of attack and frequency can vary in different individuals across the world [1–3]. Thus, while some HIVinfected individuals in developed countries rarely suffer from bacterial and protozoal infections, they are a major cause of morbidity and mortality in developing countries [2–4].

#### **2. HIV and opportunistic infections**

The human immunodeficiency virus (HIV) that causes acquired immunodeficiency syndrome (AIDS) remains one of the major global health challenges of the twenty-first century. According to the Joint United Nations Programme on HIV/AIDS (UNAIDS), 37.7 million people worldwide were estimated to be living with this deadly virus by the end of 2020 of which 25.3 million (67%) were in sub-Saharan Africa [5]. Since the outbreak of the HIV pandemic, an estimated 79.3 million cases have been recorded and 36. 3 million people worldwide have died and sub-Saharan Africa accounts for almost 70% of the total deaths [5].

HIV attacks and degrades the human body immune system rendering it defenseless against opportunistic infections normally checked by a competent immune system [6]. Opportunistic infections (OIs) remain the single main cause of ill-health and death among persons living with HIV. The natural history of HIV usually begins with an acute HIV syndromic phase, followed by an asymptomatic latency phase whose duration may vary from person to person (median duration ~10 years) to clinically apparent disease or symptomatic phase characterized by AIDS-defining opportunistic infections, and finally death from AIDS (**Figure 1**) [7].

During the asymptomatic phase, the T-cell-mediated immune system attempts to fight off the HIV infection but as the viral load increases, T-lymphocytes gets exhausted, CD4 cell count progressively falls down, and opportunistic infections start to appear in most cases when CD4 cell count has dropped below 200 cells/μl [7]. According to WHO, there are four clinical stages (WHO clinical stages) of HIV disease progression characterized by different opportunistic infections [8].

The first clinical stage is the asymptomatic phase in which the virus multiplies rapidly but is still hassling with the body immune system and no clinical signs are visible. The second clinical stage signals the end of the incubation period of the virus, and at this stage, its presumed viral load has significantly increased and CD4 cells significantly depleted allowing for the first opportunistic infections to appear, which may include herpes zoster, recurrent upper respiratory tract infections (bacterial sinusitis, tonsillitis, otitis media, and pharyngitis), fungal nail infections, recurrent oral or genital ulceration due to herpes simplex virus, extensive warts virus infection, or extensive molluscum

**Figure 1.** *The natural history of HIV infection (adopted from Pantaleo G, N Engl J Med 1993, 328:327–35).*

#### *Temporal and Spatial Distribution of Opportunistic Infections Associated with the Human… DOI: http://dx.doi.org/10.5772/intechopen.105344*

contagiosum infection The third stage is the beginning of the AIDS (acquired immunodeficiency syndrome) stage in which the immune system is severely damaged and the persons becomes vulnerable to persistent diarrhea (>1 month), oral candidiasis, mycobacterium tuberculosis, oral hairy leukoplakia, and bacterial pneumonia.

The fourth and final stage is the climax of the AIDS stage characterized by multiple life-threatening opportunistic infections including pneumocystis jirovecii pneumonia, Kaposi's sarcoma, recurrent severe bacterial pneumonia, chronic herpes simplex viral infection, esophageal candidiasis, extra-pulmonary TB, cytomegalovirus infection, toxoplasmosis infection, cryptococcosis including meningitis, chronic cryptosporidiosis, chronic isosporiasis, extra-pulmonary histoplasmosis or coccidiomycosis, recurrent non-typhoid salmonella septicemia, and some may be hit by lymphomas (cerebral or B-cell non-Hodgkin).

#### **3. The first opportunistic infection experience among HIV-positive individuals in Uganda**

We conducted a study using clinical data obtained from the AIDS support organization database to assess the first opportunistic infection experience, and temporal and spatial distribution patterns of OIs in Uganda. TASO is one of the oldest and largest non-governmental organizations (NGO) providing HIV/AIDS care and treatment in Uganda and sub-Saharan Africa [9]. TASO was founded in 1987 and has 11 regional centers spread across Uganda which have been nationally recognized as centers of excellence (CoE) in HIV/AIDS care and treatment. Each center has four departments including administration, HIV counseling and psychosocial support, and medical (HIV clinic, pharmacy, medical laboratory, etc.) and data department. Additionally, TASO has 23 mini-TASO centers affiliated to public health facilities across the country. TASO serves predominantly HIV-positive patients of low socioeconomic status. All TASO centers offer free HIV testing and counseling, and comprehensive HIV treatment and care, including provision of free antiretroviral drugs and cotrimoxazole prophylaxis, home-based care, and psychosocial support [10].

National HAART rollout in Uganda started in 2004. Being one of the largest NGOs providing care and treatment to persons living with HIV, TASO has been instrumental in HAART rollout in Uganda. Initially, HAART access was based on the Ugandan Ministry of Health and WHO 2006 guidelines, that is, WHO stage 3 or 4 illness or a CD4 cell count <200 cells/μl for adults and adolescents and WHO stage III, advanced stage II or stage I with CD4 cell percentage less than 20% for those more than 18 months of age [11, 12]. However, following a policy review in HAART access, TASO adopted new HAART access guidelines in 2010 [13, 14] that raised the threshold for adults and adolescents to a CD4 cell count≤350 or WHO clinical stage 3 or 4 irrespective of CD4 cell count. In 2014, TASO started implementing the "test and treat" policy that recommends providing lifelong ART to all individuals who test HIV-positive irrespective of CD4 or WHO clinical stage. Initially, the target were HIV-positive pregnant or breast-feeding mothers, their children, HIV-positive individuals diagnosed with TB or hepatitis B co-infections, and HIV-positive individuals in sero-discordant relationships. Later in 2016, coverage was expanded to include everybody who test HIV positive to be eligible for HAART [15]. As part of comprehensive HIV care, TASO also implements universal cotrimoxazole prophylaxis as recommended by the Ministry of Health [16, 17].

The first opportunistic infection experience and the temporal and spatial distribution of each of the 17 selected OIs and Kaposi's sarcoma were assessed. Overall,

opportunistic infections (OIs) accounted for 99% of all opportunistic events compared with 1% due to opportunistic cancers (Kaposi's sarcoma, malignant melanomas, Burkitt's lymphoma, and other lymphomas). This is also additional evidence that opportunistic infections are the primary cause of morbidity and mortality among HIV-positive individuals in sub-Saharan Africa.

We assessed data pre-HAART (2001–2003), early HAART (2004–2006), mid-HAART (2007–9), and late-HAART (2010–2013). During pre-HAART period, 84.7% (n = 6549) of the participants had cough with fever, which was later confirmed to be pulmonary TB as their first opportunistic infection and 15.3% had others (diarrhea, candida, herpes zoster, etc.) (**Figure 2**). In early HAART period, 48.4% (n = 7539) had pulmonary TB as their first opportunistic infection, 18.5% had upper respiratory tract infection, 13.5% had persistent diarrhea, 9.6% had herpes zoster, and 10.1% had others (candida, malaria, genital ulcer, etc.) as their first opportunistic infection

**Figure 2.**

*First opportunistic infection to occur during pre-HAART (2001–2003). Key: TB = tuberculosis.*

#### **Figure 3.**

*First opportunistic infection to occur during early-HAART (2004–2006). Key: Urti = upper respiratory tract infection.*

*Temporal and Spatial Distribution of Opportunistic Infections Associated with the Human… DOI: http://dx.doi.org/10.5772/intechopen.105344*

#### **Figure 4.**

*First opportunistic infection to occur during mid-HAART (2007–2009).*

#### **Figure 5.**

(**Figure 3**). In mid-HAART period, 49.6% (n = 31,032) had upper respiratory tract infection as their first opportunistic infection, 21.7% had herpes zoster, 10.4% had candida, 4.9% had pulmonary TB, 13.4% had others (diarrhea, toxoplasmosis, etc.) as their first opportunistic infection (**Figure 4**). In late-HAART period, 45.7% (n = 36,236) had recurrent upper respiratory tract infection as their first opportunistic infection, 23.2% had herpes zoster, 19.7% had candida, and 11.4% had others (pulmonary TB, diarrhea, etc.) as their first opportunistic infection (**Figure 5**).

#### **4. Temporal and spatial distribution of opportunistic infections in Uganda**

#### **4.1 Fungal opportunistic infections**

#### *4.1.1 Candidiasis*

Candidiasis caused by the fungus *Candida albicans* has been associated with HIV/AIDS from the very beginning of the HIV pandemic. Candidiasis can affect the

*First opportunistic infection to occur during late-HAART (2010–2013).*

**Figure 6.** *Temporal distribution of OIs associated with HIV in Uganda.*

skin, nails, and mucous membranes throughout the body. Most commonly associated with HIV are oral and esophageal candidiasis. Oral candidiasis is usually the first clinical manifestation of AIDS in most HIV-infected persons causing oral pain that can make eating of food very difficult resulting in malnutrition and HAART failure. Esophageal candidiasis appears later in advanced stages and can also cause a lot of pain in the chest making swallowing of food difficult and hence malnutrition and HAART failure. Studies in the developed countries show high prevalence of oral candidiasis (44.8%) and as high as 67% in sub-Saharan Africa before introduction of effective antiretroviral treatment [2, 18]. In Uganda, most studies before HAART reported high prevalence of both oral and esophageal candidiasis among HIVinfected persons [19–21].

In our recent study, prevalence of oral candida substantially reduced from 34.6% before HAART to 7.2% after HAART (**Figure 6**) [22].

Our recent study also shows that the frequency of oral candida varied by geographical area. HIV-positive patients in western Uganda had higher prevalence of oral candida compared with HIV-positive patients in other geographical areas. Geographical variation in prevalence of oral candidiasis could be an issue of genetic susceptibility and probably level of endemicity. In Netherlands, it was reported that compared with patients from Western Europe, Australia and New Zealand, patients of sub-Saharan African origin had a significantly lower risk for pneumocystis jiroveci pneumonia (PJP) and the authors suggested that differences in genetic susceptibility could be the reason for the lower PJP incidence in the African patients [23]. However, further research is required to gain more insight into the cause of this geographical variation in distribution pattern of oral candida in Uganda.

*Temporal and Spatial Distribution of Opportunistic Infections Associated with the Human… DOI: http://dx.doi.org/10.5772/intechopen.105344*

#### *4.1.2 Pneumocystis carinii pneumonia (PCP) renamed pneumocystis jiroveci pneumonia (PJP)*

PCP/PJP caused by *Pneumocystis carinii/jiroveci* used to be one of the most frequent OIs associated with advanced AIDS in the developed countries [24]. Previous studies show that over 80% of the AIDS patients develop PCP when their CD4 cell count drops below 200cell/μl [25]. Before the advent of HAART, it was the most prevalent opportunistic infection in both adults and children in the USA [2] and Western Europe [26, 27].

However following the introduction of HAART, PCP has virtually disappeared among HIV-positive patients in the developed countries [28]. Previous studies also show that PCP/PJP was very rare in sub-Saharan Africa [29–31].

In our recent study, PCP/PJP was very rare reinforcing previous evidence that this OI is not endemic in sub-Saharan Africa.

#### *4.1.3 Cryptococcal meningititis*

Cryptococcal meningitis caused by *Cryptococcus neoformans* is one of the most fatal fungal opportunistic infections associated with HIV/AIDS [6]. The disease spectrum includes pneumonia, cutaneous lesions, and meningitis [24]. Cryptococcal meningitis is the most common cause of mortality in adults with HIV [32]. It is the main single cause of death for 20–30% of persons with AIDS in sub-Saharan Africa [6, 33]. Cryptococcal meningitis is often the cause of poor prognosis on HAART [33].

Review of published literature on HIV-associated cryptococcal meningitis shows that its prevalence varies widely both within and between countries [34–37]. In the developed countries, the incidence of cryptococcal disease appears to have generally decreased during the era of HAART [34, 36, 38]. A review of studies on HIV/AIDS-related opportunistic infections in sub-Saharan Africa found 25% prevalence of cryptococcal meningitis among AIDS patients in Ethiopia [39] and 12–50% in South Africa accounting for 44% of the deaths [40]. In Uganda, a study at Mulago National referral hospital found out that the rate of cryptococcal infection among HIV-infected patients was more than double that reported in HIV patients in North America (40.4/1000 person-years vs. 17–20/1000 person-years) prior to the introduction of HAART [41]. In three separate cohort studies in Uganda, cryptococcal meningitis was the leading cause of death (20–40%) [37, 41, 42].

In our recent study findings, the frequency of cryptococcal meningitis substantially declined attributed to increased availability of highly potent systemic antifungal drugs such as fluconazole and HAART (**Figure 5**). Similar findings were also obtained in studied conducted elsewhere [43, 44]. Our recent findings also show that the prevalence was lower in western Uganda compared with the rest of the geographical areas studied probably because of variation in the endemicity of the causative agent. This is consistent with previous studies that show the prevalence of cryptococcosis varied widely both within and between countries [34, 36, 45].

#### *4.1.4 Histoplasmosis*

Histoplasmosis is a fungal infection caused by *Histoplasma capsulatum* considered diagnostic of AIDS in an HIV-infected person [24]. In about two-thirds of AIDS patients with histoplasmosis, it is the initial OI and over 90% of the cases have occurred in patients with CD4+ cell count <100 [46]. Though it is a major AIDSdefining illness in Central and South American countries [46], few studies about

it have been published in Africa. In Uganda, a recent study confirmed its absence among HIV-infected individuals [22].

#### **4.2 Viral opportunistic infections**

#### *4.2.1 Cytomegalovirus (CMV)*

This virus causes HIV-associated retinitis resulting in eventual blindness [6]. Prior to the introduction of HAART, cytomegalovirus was commonly reported among HIV-infected patients in many developed countries [2, 26, 27]. A cross-sectional review of AIDS patients' medical records in France [27] found 37% of the patients suffered from cytomegalovirus infection. Another retrospective review of medical records in Italy [26] found 25.6% of the AIDS patients had cytomegalovirus infection. Cytomegalovirus was predominantly common at very low CD4 levels, with the majority of cases of CMV retinitis occurring at CD4 counts below 50 cells/mm3 . However, with the advent of HAART, there has been tremendous decline in the incidence of CMV in the developed countries.

The few studies that have reported on cytomegalovirus in sub-Saharan Africa show that it is very rare among HIV-positive patients. One study in Burundi reported cytomegalovirus retinitis diagnosed in only 1% of the AIDS patients [47] and also in only 1% of the AIDS patients in Malawi [48]. In our recent study in Uganda, it was found to be very rare (<1%) (**Figure 6**).

#### *4.2.2 Herpes simplex virus types 1 and 2(HSV-1 and HSV-2)*

Herpes infections are the most commonly diagnosed infections among HIV-positive patients both in developing and developed countries [49]. Both viruses cause severe and progressive rapture of the body mucus membranes. HSV-1 affects mainly the membranes of the nose and mouth leading to herpes-caused pneumonia, which can result into death in AIDS patients [49]. HSV-2 also affects the membranes of the anus and genitals causing severe peri-anal and genital ulcers, which can facilitate HIV transmission and is one of the most sexually transmitted co-infections with HIV [49–51]. Previous studies in Uganda found high prevalence of HSV-2 in HIV-positive patients with genital ulcer disease [52].

In our recent study, frequency of genital ulcers declined significantly attributed to increasing availability of HAART and effective treatment (**Figure 5**). Previous studies also showed that acyclovir significantly reduces both genital ulcers and HSV-2 shedding [53]. Though prevalence reduced, genital ulcer has not been completely eliminated implying that HSV-2 could be highly endemic and its sexual transmission is still ongoing in Uganda. It was more common in central and western Uganda compared with other geographical areas of Uganda.

#### *4.2.3 Herpes zoster (Shingles)*

Herpes zoster (Shingles) caused by *Varicella zoster* virus is usually a latent infection in immuno-competent persons [6]. However, the virus is quickly reactivated when the immune system is compromised and like Herpes simplex, it has the potential to cause a rapid onset of pneumonia in AIDS patients. Untreated Herpes zoster viral pneumonia can result into death and Herpes zoster is usually an early indicator that the patient is progressing to AIDS [54].

#### *Temporal and Spatial Distribution of Opportunistic Infections Associated with the Human… DOI: http://dx.doi.org/10.5772/intechopen.105344*

Most studies in the developed countries have reported on Herpes zoster as one of the most frequent AIDS-defining illnesses among HIV-infected persons [2, 27, 55]. In a cohort study in the USA, Herpes zoster accounted for 36% of the opportunistic diseases that appeared in the first 24 weeks of HAART treatment [56]. In developing countries, Herpes zoster is one of the most common opportunistic infections associated with HIV/ AIDS [57, 58]. In a population-based cohort in south-western Uganda, the incidence of Herpes zoster was found to be 5.4 per 100 person years of observation [58].

In our recent study, frequency of herpes zoster reduced substantially attributed to increasing availability of HAART. The mean annual prevalence reduced from 1.3% in 2002 to 0.33% in 2013 [59]. These findings are consistent with findings from studies that reported significant reduction in incidence of Herpes zoster after HAART rollout [60]. The prevalence was more in central and western regions of Uganda compared with other geographical areas. The variance in prevalence of herpes zoster by geographical area could partly be due to differences in the level of natural immunity or endemicity of the infectious agent or other unknown biological factors.

#### **4.3 Bacterial opportunistic infections**

#### *4.3.1 Tuberculosis (TB)*

Tuberculosis caused by *Mycobacterium tuberculosis* affects about one-third of the world's population and is the leading cause of morbidity and mortality among HIV/ AIDS patients in the world [61]. However, it is most common in low- and middle-income countries in which it is responsible for over 75% of mortality among HIV-infected patients [62–65]. A meta-analysis of the published research in sub-Saharan Africa shows that the incidence of tuberculosis varied widely among cohorts of HIV-infected patients in different countries [3]. Studies in Cote d'Ivoire and Kenya found tuberculosis to be the primary cause of death in 32 and 47% of deaths, respectively [62, 66].

Uganda was listed among the 22 high-burden TB countries in the world [67] and studies conducted in eastern Uganda showed that over 80% of the HIV-related morbidity and 30% of the HIV related death were due to TB [68, 69].

In our recent study, the frequency of *M. tuberculosis* has substantially reduced over time consistent with studies elsewhere that reported significant decreasing trends in tuberculosis prevalence attributed to HAART [70–72]. These findings are in agreement with another study that assessed the effect of HAART on TB incidence in Uganda and showed TB reduced from 7.2% at baseline to 5.5% after 1.4 yrs. of follow-up [68]. Though TB had a significant declining trend in this study, it has not been completely eliminated even after introduction of HAART. This could probably be attributed to the fact that TB is endemic in the country and improvements in TB diagnosis with introduction of Gene-Xpert technology [73] that has improved TB detection rates in Uganda. In our recent study, it was also observed that TB was more frequent among HIV-positive patients in Northern and Eastern Uganda compared with other geographical areas probably because of the socioeconomic disparities in the regions.

#### *4.3.2* Mycobacterium avium *complex (MAC)*

*M. avium* complex are benign in immuno-competent individuals but can cause severe, life-threatening diarrhea, and septicemia in HIV-infected individuals who are severely immune-compromised [56]. Unlike TB, MAC is only environmentally acquired (food, animals, water supplies, and soil) and not transmissible from person to person. MAC accounts for 18–43% of illness in HIV-positive patients and has been implicated as the main cause of a non-specific wasting syndrome in USA [2, 74]. In sub-Saharan Africa, *M. avium* complex is very rare and was only reported in South Africa and Kenya [75, 76]. No information was available on its prevalence among HIVpositive individuals in Uganda.

#### *4.3.3 Bacterial pneumonia*

Bacterial pneumonia caused by *Streptococcus pneumonia* is one of the commonest respiratory diseases in HIV-infected patients [6]. Though preventable, the disease is quite common among HIV-positive patients in sub-Saharan Africa [77–79]. Previous studies showed that the risk of bacterial pneumonia were higher among HIV-infected individuals compared with the general population [80, 81].

Though a conjugate pneumococcal vaccine is available [82], it has not been widely accessed by HIV-positive patients. HAART and cotrimoxazole prophylaxis have also been shown to be associated with a significant reduction in the risk of bacterial pneumonia [80, 83].

In our recent study, the frequency of bacterial pneumonia substantially reduced over time. This could probably be attributed to a combination of universal cotrimoxazole prophylaxis introduced in 2003 and HAART in 2004. Prevalence of bacterial pneumonia also varied by geographical area with the highest prevalence observed in Northern and Eastern Uganda. The geographical difference could be due to the socioeconomic regional disparities with Northern and Eastern Uganda being more prone to OIs due to poorer living conditions compared with other areas [84].

#### **4.4 Protozoal opportunistic infections**

#### *4.4.1 Toxoplasmosis*

Toxoplasmosis caused by *Toxoplasma gondii* is a common opportunistic infection in advanced AIDS [6]. A study in Italy reported 15.2% prevalence of cerebral toxoplasmosis among AIDS patients [26], while another study in France reported a 37% prevalence among AIDS patients [27].

However, in sub-Saharan Africa, few studies have been published on this opportunistic infection and perhaps could be under-reported due to lack of surveillance capabilities. A study in Cote d'Ivoire showed only 4% prevalence of cerebral toxoplasmosis of which 60% died [85]. In our recent study, the prevalence of toxoplasmosis was very low (< 1%) (**Figure 6**).

#### *4.4.2 Cryptosporidiosis*

Cryptosporidiosis caused by *Cryptosporidium parvum* is usually associated with chronic diarrhea (>1 month) in HIV-positive individuals [24]. Diarrhea has for long been reported to be one of the commonest complication in HIV-positive individuals associated with high mortality rate [86]. Previous studies show up to 60% of people living with HIV experience diarrhea, which negatively affects their quality of life and adherence to HAART [87].

However, diarrhea among HIV-positive individuals may be due to multiple causes including infectious causes (bacterial, viral, protozoal, heliminthic, etc.) or

#### *Temporal and Spatial Distribution of Opportunistic Infections Associated with the Human… DOI: http://dx.doi.org/10.5772/intechopen.105344*

non-infectious causes (ARV drug effects, e.g., ritonavir-boosted protease inhibitors such as lopinavir/ritonavir or nelfinavir) [87–91]. The commonest infectious causes of diarrhea were reported to be helminthic infections (29.5%), bacterial infections (19.2%), and protozoal infections (9.2%) [92]. Enteric viruses have also been reported associated with diarrhea [86]. Prevalence was significantly higher among HIV-positive people when compared with matched controls [87]. Acute diarrhea (<1 month) in adults has been associated with bacterial infections (non-typhoid salmonella), while chronic diarrhea (>1 month) was reported to be associated with cryptosporidial or helminthic infections [93–96]. In Uganda, data on diarrhea disease burden among HIV-positive individuals in different geographical areas and trends were scarce.

Cryptosporidiosis occurs in HIV-positive individuals whose immunity is severely suppressed [6]. It is rare in developed countries probably because of the high hygienic standards [2]. It is associated with communities living in unhygienic conditions with high risk of exposure to the infectious agent [29, 97].

Previous studies in sub-Saharan Africa have reported prevalence of *Cryptosporidium* chronic diarrhea among HIV-infected patients as high as 17% in Kenya [97], 25–32% in Zambia [98], and 28% among HIV patients at Mulago in Kampala, Uganda [99]. Our recent findings show diarrhea mean annual prevalence reduced by 83% (12–2%) between 2002 and 2013 most likely because of HAART [100]. Prevalence was higher in Northern and Eastern Uganda compared with Central and Western Uganda probably because of the socioeconomic disparities between these regions with the latter being relatively more developed compared with the former [84]. However, more studies are required to give more insight on diarrhea causes among HIV-positive patients on HAART in different geographical areas.

#### *4.4.3 Malaria*

Although malaria is not diagnostic of AIDS [93], several studies show that malaria tends to occur with increased frequency and severity in HIV-infected adults compared with the general population [3, 101–106]. Previous studies show that HIV increases vulnerability to malaria infection and malaria could enhance the progression of HIV infection to clinical AIDS in the absence of effective treatment [107].

A review of studies on HIV-related opportunistic infections in sub-Saharan Africa showed a relatively higher prevalence of malaria parasitemia among HIV-infected women in Malawi on their first prenatal visit (32–54%) compared with HIV-negative women (19–42%) [3]. A study conducted in Uganda [104] found that most HIV patients seeking treatment for malaria had unexpectedly high levels of HIV infection and more than 30% of adults presenting at district health centers with uncomplicated falciparum malaria were co-infected with HIV. Another study conducted by researchers from Rome's Istituto Superiore di Sanità, University of Milan in Northern Uganda [105] examined the association between HIV and malaria and found high HIV prevalence among patients admitted for malaria at Lacor Hospital (48.8%) compared with that estimated for the general population living in the hospital's catchment's area (17.8%), suggesting an association between HIV and malaria. Other previous studies in Uganda also showed that the risk of clinically diagnosed malaria was significantly higher in HIV-infected individuals compared with HIV-negative controls [101, 106].

In our recent study findings, malaria prevalence among HIV-positive individuals reduced in the period between 2001 and 2003 (80%) and leveled off in the

subsequent years. The reduction in malaria prevalence started in the period before HAART and could partly be attributed to universal cotrimoxazole prophylaxis [108, 109]. The study findings reinforce the existing evidence that malaria prevalence has significantly reduced among HIV-positive individuals due to the combined effect of cotrimoxazole prophylaxis and HAART [110–114].

However, the decline in malaria prevalence over time may not be attributed to HAART and cotrimoxazole prophylaxis alone but could also have been caused by the other malaria interventions in Uganda including massive distribution of insecticidetreated mosquito bed nets and introduction of indoor residual spraying especially in Northern Uganda [114, 115]. Though malaria prevalence among HIV-positive patients reduced in the era of HAART, it has not been completely eliminated. In view of the fact that malaria is highly endemic in Uganda and HIV-positive patients are highly vulnerable, malaria prevention/control should therefore remain an integral part of comprehensive HIV/AIDS care in Uganda. Prevalence was highest in Central Uganda, followed by Northern and Eastern regions. Geographical variation in prevalence of malaria could be influenced by malaria endemicity in the different geographical areas.

#### **4.5 Helminthic opportunistic infections**

The most common helminthic infections of public health importance are *Ascaris lumbricodes*, *Trichuris trichura*, *Necator americanus*, and *Ancylostoma duodenale*. Globally, it is estimated that about two billion people are infected with intestinal helminthic infections mainly in developing countries [116]. In HIV-positive patients, co-infection with intestinal helminthic infections was associated with dysregulation of the immune response causing inability of the HIV-positive patient to mount an effective immune response [116]. High prevalence of geohelminths can lead to increased prevalence of anemia thereby worsening the health conditions of persons living with HIV/AIDS [117].

In HIV-positive individuals, these parasites compete for food nutrients and cause mal-absorption of certain food nutrients and some of them suck blood (Hook worms) further weakening the body and causing faster progression of HIV disease [118]. The negative effects associated with these helminthic infections have been in terms of diminished physical fitness of the affected individuals who easily succumb to other opportunistic infections in addition to responding poorly to treatment [119].

In our recent study, geohelminths were the most frequent non-AIDS defining opportunistic infections before and after HAART. The study also found out that Northern and Eastern Uganda had the highest burden of the intestinal helminthic infections compared with other regions. The geographical difference could be due to the socioeconomic regional disparities with Northern and Eastern Uganda being relatively poorer compared with other areas [84]. Previous studies showed that poor socioeconomic status was associated with higher risk of geohelminths [120–122].

The high burden of geohelminths even after HAART shows that in high endemic settings, the effect of HAART on these worms is relatively insignificant and alternative or supplementary control efforts are therefore required. A Cochrane review of published literature on testing and treating HIV-positive patients for intestinal helminthic infections showed that regular deworming with a single dose of albendazole is feasible in developing countries and would potentially improve survival and the quality of life of persons living with HIV/AIDs [116]. It is therefore recommended that regular deworming becomes an integral part of comprehensive HIV/AIDS care in Uganda.

*Temporal and Spatial Distribution of Opportunistic Infections Associated with the Human… DOI: http://dx.doi.org/10.5772/intechopen.105344*

#### **4.6 Upper respiratory tract infections (URTI)**

*Upper respiratory tract infections* (URTIs) are contagious infections that affect mainly the nasal sinuses and the throat caused by a variety of bacteria and viruses such as influenza virus, streptococcus bacteria. The most frequent respiratory infections in HIV-infected patients are upper respiratory tract infections presenting as common cold (cough, fever, runny nose), epiglottitis, laryngitis, pharyngitis (sore throat), and sinusitis [123]. In this study, URTIs were the most frequent infections pre-HAART and have remained the most frequent infections even after HAART (**Figure 6**). Previous studies also show that upper respiratory tract infections are more common in HIV-infected persons compared with the general population attributed to the reduced immunity [124].

#### **4.7 HIV-associated opportunistic cancers**

Kaposi's sarcoma (KS) is the most reported opportunistic cancer associated with HIV/AIDS and with an infectious cause [24, 125, 126]. In fact, previous studies show that KS is caused by the human herpes virus type 8 (HHV8) [127, 128], and in Uganda, HHV8 has been identified in over 85% of KS tissue specimens [129, 130]. Sero-prevalence studies in Uganda also suggest that HHV8 is endemic in the general population [131, 132]. In our recent study, Kaposi's sarcoma was found rare among study participants. These findings are consistent with other studies elsewhere, which reported lower prevalence of KS in comparison with other OIs among HIV-positive individuals [133–135]. Prevalence was higher in central compared with other regions in Uganda. However, more studies giving insight on the role of HAART on HHV8 disease burden and determinants of its geographical distribution are required.

#### **5. Conclusion**

Today, most OIs are less common in people with HIV because of increasing availability of effective antiretroviral treatment. However, there are certain OIs such as intestinal helminthic infections and upper respiratory tract infections whose prevalence has persistently remained high despite increasing access to HAART. Most OIs have not been completely eliminated mainly because some people are not aware that they have HIV and so wait until they experience an OI. Some may delay to access treatment due to late diagnosis or may be on treatment but develop resistance to available drugs.

By end of 2020, around 28million people were accessing effective antiretroviral therapy. Though the global strategy to eliminate HIV by 2020 failed, there is still hope that with sustained global HIV/AIDS eradication efforts, HIV could be eliminated by 2050.

#### **Author details**

John Rubaihayo1 \*, Nazarius Mbona Tumwesigye2 and Josephine Birungi3

1 Department of Public Health, Mountains of the Moon University, School of Health Sciences, Fort Portal, Uganda

2 Department of Epidemiology and Biostatistics, Makerere University, School of Public Health, College of Health Sciences, Kampala, Uganda

3 Research and Health System Strengthening, The AIDS Support Organization (TASO), Kampala, Uganda

\*Address all correspondence to: jrubaihayo@mmu.ac.ug; rubaihayoj@yahoo.co.uk

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Temporal and Spatial Distribution of Opportunistic Infections Associated with the Human… DOI: http://dx.doi.org/10.5772/intechopen.105344*

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#### **Chapter 4**

## A Deep Learning Approaches for Modeling and Predicting of HIV Test Results Using EDHS Dataset

*Daniel Mesafint Belete and Manjaiah D. Huchaiah*

#### **Abstract**

At present, HIV/AIDS has steadily been listed in the top position as a major cause of death. However, HIV is largely preventable and can be avoided by making strategies to increase HIV early prediction. So, there is a need for a predictive tool that can help the domain experts with early prediction of the disease and hence can recommend strategies to stop the prognosis of the diseases. Using deep learning models, we investigated whether demographic and health survey dataset might be utilized to predict HIV test status. The contribution of this work is to improve the accuracy of a model for predicting an individual's HIV test status. We employed deep learning models to predict HIV status using Ethiopian demography and health survey (EDHS) datasets. Furthermore, we discovered that predictive models based on these dataset may be used to forecast individuals' HIV test status, which might assist domain experts prioritize strategies and policies to safeguard the pandemic. The outcome of the study confirms that a DL model provides the best results with the most promising extracted features. The accuracy of the all DL models can further be enhanced by including the big dataset for predicting the prognosis of the disease.

**Keywords:** deep learning, prediction, CNNLSTM, CNNRNN, EDHS, HIV/AIDS test result

#### **1. Introduction**

HIV is the world's most critical community health and development problem. Although millions of people have died as a result of AIDS since the pandemic began in 1981, around 36 million individuals now have HIV. An estimated 19 million people living with HIV are enrolled in and getting treatment through regular care programs [1].

Local HIV/AIDS epidemics require immediate investigation and development of relevant intervention plans, as well as methodologies. Behavioral and sociodemographic characteristics are key contributors to the spread of HIV and require a study on the nature and the influence of the HIV pandemic in a specific community [2]. Although the fact that HIV testing is an efficient technique for determining the test status of people, even has challenges and limitations. As a result, strong prediction models are critical for managing and monitoring the local HIV pandemic.

Ethiopian demographic and health survey (EDHS-CSA) [3] generates a massive amount of dataset that may be analyzed to extract important evidence. The development of the deep learning (DL) model supports the processing of huge datasets and the extraction of underlying dataset patterns that support decision making.

Deep learning methods have recently achieved noteworthy success in a variety of research disciplines, including speech recognition [4], natural language processing [5], recommendation systems [6], and computer vision [7]. This approach is very useful in the health sector for disease prediction and classification. Deep learning algorithms are one of the most recent breakthroughs in HIV statistical dataset prediction tools and identification approaches, allowing for faster processing of large datasets. These algorithms can also be used to predict disease. These techniques work well and can be used to predict HIV test results.

In this paper, we use numerous deep learning application models to construct an HIV status prediction system. On the Ethiopian demographic and health survey dataset (EDHS), six DL models have been developed and are being deployed. These deep learning models were tested using well-known metrics such as accuracy, precision, recall, AUC, and F1 scores.

Our contributions are presented below concerning the core goal of predicting HIV test status:


To the best of our understanding, no research has been conducted using deep learning models to predict HIV test status using the EDHS dataset. This is the first time that a deep learning model has been used in the health sector to predict HIV test results using only 20 attributes. This work may motivate researchers to validate models using other HIV/AIDS datasets.

The major goal of this study is to propose the development of a more accurate prognostic tool for HIV/AIDS test result prediction. This research comprises six DL models that were used to conduct detailed analyses on the EDHS dataset. The algorithm comparison is presented in a logical and well-organized manner, allowing DL to produce more effective and prominent findings.

The remaining section is structured as follows. Section 2 discusses relevant research studies. The proposed techniques are presented in Section 3. Section 4 describes an experimental design and results from the analysis. Section 5 is devoted to comparative analysis, while Section 6 is focused on concluding remarks.

#### **2. Related work**

Various scholars have previously done a great amount of study on health topics. This section provides an overview of previous research in the prediction of HIV

*A Deep Learning Approaches for Modeling and Predicting of HIV Test Results Using EDHS… DOI: http://dx.doi.org/10.5772/intechopen.104224*

epidemics using advanced ML algorithms and big dataset technologies. We highlighted some of the most important and significant work done by various researchers in this field.

McSharry et al. [8] used ML approaches to successfully discover HIV predictors through screening on the PHIA dataset. The study aims to analyze the HIV disease trial at various levels of society, detect HIV predictors, and forecast the risk of the disease. For the prediction tasks, six ML models were utilized in the study. The primary finding of this study is that the XGBoost algorithm greatly outperformed the other algorithms in terms of identifying HIV-positive. Another ML methodology presented by Orel et al. [9] examined more than 3,200 parameters of the current Demographic Health Surveys from ten African nations. This study trained four ML models and chose the best one through the f1 score. The primary goal of the study is to identify PLHIV at a rate of more than 95\% and to identify the number of positive persons at a rate greater than 95%. The authors emphasized the significance of attribute extraction strategies in mining information for prediction. Using four separate datasets from UCI, Lu et al. [10] employed one-hot coding to translate the protease cleavage site dataset for prediction using two DL models, RNN and LSTM. Finally, the DL model results are compared to SVM and RF. The author Wang et al. [11] created a convenient model to explain the prevalence of HIV and forecast its occurrence in Guangxi. From 2005 to 2016, the HIV incidence statistics datasets were utilized in the study. They trained the HIV incidence using four models, including LSTM, ARIMA, ES, and GRNN. Following training, all models are assessed using the most popular prediction task evaluation criteria. According to the findings of the studies, LSTM and ARIMA outperform ES and GRNN. The LSTM model, on the other hand, proved more successful than other models. Ahlström et al. [12] provided an algorithmic prediction of HIV status. The author investigated whether a dataset from a national electronic registry might be utilized to predict HIV status using machine learning techniques. The study employed multiple techniques to train prediction models, which were then verified using a dataset from Danish households. They trained the models to simulate various clinical. Steiner et al. [13] evaluate the DL models for drug resistance prediction using the HIV-1 sequencing dataset. DL algorithms are combined with HIV genotypic and phenotypic datasets and studies by the author to study the classification performance of the fundamental evolutionary methods of HIV treatment resistance. They assessed the effectiveness of three DL models using a publicly accessible HIV sequencing dataset.

As a result, we have observed several research projects being conducted in the field of HIV/AIDS prediction. All of the available approaches have been shown to perform on various datasets and produce promising results. These concerns inspire us to investigate deep learning methods for predicting HIV test status to improve prediction performance.

#### **3. Proposed methodology**

We discuss the proposed work in this part, which encompasses several phases such as pre-processing, normalizing features, and a deep learning-based prediction technique with parameter settings. **Figure 1** depicts the architecture for the proposed deep learning models for predicting HIV test status in people using the EDHS dataset.

**Figure 1.** *The proposed deep learning approach's architecture.*

#### **3.1 Dataset**

The HIV/AIDS dataset [3] has been collected from the EDHS repository from Central Statistics Agency (CSA) and DHS program https://dhsprogram.com that has been used for both training and testing purposes it is available on https://github.com/ danielmesafint/Datasets. We collect this dataset as it is from the above sources and we create the HIV/AIDS dataset by considering the criteria to create a dataset from the secondary dataset, the techniques we were used to create our dataset are dataset acquisition, dataset cleaning, dataset labeling and more. The EDHS dataset has more features or attributes, it includes various demographic and health-related datasets. After the creation of our dataset, HIV/AIDS contains 83,100 instances and 33 attributes. The output level has two classes, where "0" represents Negative results (HIV-) and "1" represents Positive results (HIV+). The preprocessing section has explained how we process the EDHS dataset.

#### **3.2 Preprocessing**

As an initial step in the pre-processing stage, the original input dataset is analyzed, making the raw dataset ready for use in the prediction process [14]. In the years 2000, 2005, 2011, and 2016, four separate datasets were used to compile the dataset. The size of the dataset is reduced as a result of preprocessing. As a result, there is a scarcity of datasets, which has a negative impact on the prediction of HIV test results. As a result, the dataset integration technique is used to combine all separate dataset sets. The tight coupling method is used for integration. There are 83,100 dataset instances collected; 4,223 of these instances are incorrect, owing to user entry errors, storage or transmission corruption, or different dataset dictionary definitions of similar items in different

#### *A Deep Learning Approaches for Modeling and Predicting of HIV Test Results Using EDHS… DOI: http://dx.doi.org/10.5772/intechopen.104224*

stores; these datasets are unreliable, inaccurate, or irrelevant. To address this issue, we use dataset cleaning techniques to identify and remove crude and incorrect instances. The dataset cleaning technique used in this process is: to remove duplicate techniques and delete all formatting techniques. After cleaning, the dataset set is uniform. There is also an issue of incompleteness with some features or variables, such as R\_SeA, Had\_Sex, and Con\_Use, and we use the imputation technique to fill in the missing values. Some of the dataset entries in the dataset have not been completed (that is not having values present for every single variable in the dataset set). At this phase, we do two simple approaches to imputation: dropping rows with null values and dropping features with high nullity. Otherwise, the most frequent value for numerical variables and the mean for quantitative variables were used to handle missing results.

Because the nominal dataset cannot be used in a DL model, all nominal attributes, including the label class (Negative/Positive), were converted to numerical binary values with "0" and "1." The Attribute AGE is classified as 1–7. (the original value of the Age should be grouped into 1 to 7). Furthermore, depending on the form of the attribute, the unsupervised discretization filter discretized all continuous numeric attributes using different bins range accuracy. The dataset discretization technique is used to perform this transformation.

The EDHS dataset has several features, each with a unique set of numerical values, which complicates the computing procedure. As a result, a normalizing methodology is utilized to normalize dataset *D*hiv in the range of "0" to "1", as well as to reduce numerical complexity during the HIV test status prediction computational process. Normalization may be accomplished using a variety of approaches. The well-known min-max normalizing approach is employed in the proposed system [15]. Using the following equation, this approach maps to a numeric value, *D*, of the initial dataset *Dhiv* into *Dnorm* with an interval of [0, 1]

$$D\_{norm} = \frac{D^{div} - D\_{min}}{D\_{max} - D\_{min}} \text{ X } [n\_- \, max \, -n\_- \, min \,] + n\_- \, min \tag{1}$$

In this case, *Dnorm, Dhiv, Dmax*, and *Dmin* represent the normalized dataset value, the original dataset value, the minimal and maximum value in the complete dataset, respectively, while *n\_max* and *n\_min* represent the range of the transformed dataset. We use the values *n\_max* = 1 and *n\_min* = 0. Using this strategy, all of the feature values fall inside the range [0, 1].

#### **3.3 Feature selection**

The EDHS-HIV/AIDS dataset were having 33 (thirty-three) variables but from these variables, we are using 20 variables as a final feature. The feature selection technique is applied to select the features. For this study, we apply the backward feature selection (BFS) technique of wrapper-based methods [16].

BFS algorithm aims to reduce the dimensionality of the initial feature subspace from *N* to *K-features* with a minimum reduction in the model performance to improve upon computational efficiency and reduce generalization error. The primary idea is to sequentially remove features from the given features list consisting of *N* features to reach the list of *K-features*. At each stage of removal, the feature that causes the least performance loss gets removed.

We use the hit and trial method for different values of *K-features* and evaluate all subsets of features using their obtaining accuracy and making the final decision. Based on this, we select the 20 best features and the selected features are presented in **Table 1**. Moreover, the selected feature helps to reduce the over-fitting of the DL models, making the training time fast, reducing the complexity, and easier to interpret our models, and then it helps to make a better prediction power.

After preprocessing we use a total of 78,877 (83,100–4223) instances and 20 (from 33) features. **Table 1** shows the statistical descriptions of the selected features.

#### **3.4 Deep learning models**

The study's goal is to create an HIV test status prediction model by employing six deep learning models that have not been used before in HIV test result prediction. Recently, different deep learning techniques and their combinations are widely used for demographic and health dataset prediction or classification based on some obtained parameters.

In this work, we create and test prediction models for HIV status based on demographic and health survey datasets. To assess the study, we trained four DL models, including Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and two hybrid DL Models such as CNNRNN and CNNLSTM.


**Table 1.**

*The statistical descriptions of the selected features using backward feature selection methods.*

#### *A Deep Learning Approaches for Modeling and Predicting of HIV Test Results Using EDHS… DOI: http://dx.doi.org/10.5772/intechopen.104224*

ANN [17] is commonly used for prediction and modeling tasks. Because of its self-learning and self-adapting abilities, ANN is an interesting choice for estimating underlying dataset relationships. It is made up of different neurons, input, output, hidden layers, and activation functions. CNN [18] is a neural network type that is often utilized in image categorization research. It has layers like pooling, convolutional, classification, and fully-connected layer. CNN, contrasting ML, acquires characteristics on its own. The dimension of the inputs is lowered in the pooling layer. RNN [19] is a type of feed-forward NN that includes internal memory. RNN employs the same procedure for each input; however, the result of the input dataset is reliant on the previous result. RNN processes inputs using its internal memory. LSTM [19] is a variant of the RNN. It is simpler to recall the previous dataset in the LSTM. The LSTM networks address the RNN vanishing gradient problem. CNNRNN [20] is a hybrid model that uses a different convolutional layer and a single recurrent layer to process the input sequence of characters. CNNLSTM [21] is a hybrid of CNN and LSTM layers that provides the benefits of both models.

#### **3.5 Performance evaluation metrics**

Before building a prediction model, all models must be assessed using several evaluation parameters [22]. We've so far used accuracy scores to evaluate our prediction models. But sometimes accuracy score isn't all enough to evaluate a model properly as the accuracy score doesn't tell exactly which class (positive or negative) is being wrongly predicted by our models in case of a low accuracy score. To clarify this, we perform precision score; recall score, f1 score, AUC, and log-loss for both models. And then we compare our models using these calculated metrics to see exactly where one model excels over the other. We utilized 10-fold CV and an 80:20 train-test split technique to validate the utilized dataset.

$$\text{Acc} = \frac{TP + TN}{TP + TN + FP + FN} \tag{2}$$

$$\text{Precision} = \frac{\text{TP}}{\text{TP} + \text{FP}} \tag{3}$$

$$\text{Recall} = \frac{\text{TP}}{\text{TP} + \text{FN}} \tag{4}$$

As shown in Eqs. (2)-(4), where true positive (TP) is the number of HIV-positive persons who are actually positive. The number of predicted negative persons that are actually negative is represented by the true negative (TP). The amount of people who are labeled as positive but are actually negative is known as false positive (FP). The number of labeled negative persons who are actually positive is defined as false negative (FN). These metrics are frequently computed to measure the predictive quality of models.

$$\text{F1} - \text{score} = 2 \ast \frac{Precision \ast Recall}{Precision + Recall} \tag{5}$$

The F1 score may be calculated by dividing the product of recall and precision by the total of recall and precision, as shown in Eq. (5).

$$\mathbf{AUC} = \int\_{-\infty}^{\infty} \mathbf{y}(t) d\mathbf{x}(t) \tag{6}$$

$$\text{Log loss} = \left(\mathbf{x} + \mathbf{a}\right)^{n} = \sum\_{i=0}^{n} y\_i \bullet \left(\log\left(P\_i\right) + \left(\mathbf{1} - \mathbf{y}\right) \bullet \log\left(\mathbf{1} - P\_i\right)\right) \tag{7}$$

As Eq. (7), where *n* is the samples count, yi is the label of the actual class, and pi is the probability of ith sample fits one class. The model performance is measured using log-loss, which computes the prediction as a probability value between "0" and "1". A better predictor must have a lower error value of log-loss, for the goal of lowering it to "0" in the case of a perfect predictor.

#### **4. Experiment setups and result discussion**

This part presents the experimental setting and experimental findings and analysis.

#### **4.1 Experimental setting**

We carried out our model experiments on Microsoft Windows 10 with an Intel® Core™ i7- 9700 CPU running at 3.00 GHz, 8 processors, 16 GB RAM, and a 1 TB hard disc. The Python language version 3.6 tool with Keras [23] and Tenser-flow was utilized.

To evaluate a model's performance, we need some dataset (input) for which we know the ground truth (label). For this problem, we don't know the ground truth for the test set but we do know for the train set. So the idea is to train and evaluate the model performance on HIV/AIDS dataset. One thing we do is to split the train set into two groups, in the case we use the 80:20 ratio, the ratio is done randomly. That means we would train the model on 80% of the training dataset and we reserve the rest 20% for evaluating the model since we know the ground truth for this 20% dataset. Then we compare our model prediction with this ground truth (for 20% dataset). That's we observe how our model would perform on the unseen dataset. This is the first model evaluation technique. This process is used by the sklearn library in a train-test split method [24].

**The parameters setting:** For ANN we have 3 hidden dense layers with 32, 16, and 8 perceptrons and the last layer is activation functions with sigmoid. For CNN we have 2 hidden CNN layers with 512 and 256 perceptrons with MaxPooling1D function followed by 2 fully connected layers with 2048 and 1024 perceptrons. And the last layer is activation functions with the sigmoid. For RNN the input layer is Simple RNN with 512 perceptrons followed by 2 fully connected layers with 2048 and 1024 perceptrons. And the last layer is activation functions with the sigmoid. For LSTM the input layer is LSTM with 512 perceptrons followed by 2 fully connected layers with 2048 and 1024 perceptrons. And the last layer is activation functions with the sigmoid. For CNNLSTM the input layer is Conv1D with 512 perceptrons followed by MaxPooling1D layer, and the output of them is connected to the LSTM layers with 512 perceptrons. And the last hidden layers are dense layers with 2048 and 1024 perceptrons. For CNNRNN the input layer is Conv1D with 512 perceptrons followed by the MaxPooling1D layer, and the output of them is connected to the RNN layers with 512 perceptrons. And the last hidden layers are dense layers with 2048 and 1024 perceptrons. Batch Normalization and Dropout layers have been added to all the

models to improve the accuracy and help to avoid overfitting. For all models the Learning rate is 0.001, the Loss function is Binary Cross entropy, the Decay is 0.0001, and the optimizer is ADAM [25].

#### **4.2 Experimental result analysis**

This subsection presents a detailed analysis of the experimental findings achieved using the proposed approach on HIV/AIDS datasets with standard performance metrics.

As a predictor, six DL models were constructed and used. Predictions were then made, and the performance was assessed. The first experiment is conducted with a train-test split.

For performance evaluation, in terms of goodness-of-fit, the HIV test result prediction model performances are compared. The model compared in this proposed method is; the RNN model, achieving an accuracy of 0.870, the precision of 0.871, recall of 0.876, f1-score of 0.876, and AUC of 0.94. As shown in **Table 2**, all DL models' accuracy results were at least 0.834 or above. With 0.870, the RNN model had the best evaluation performance. RNN was implemented considering several parameters such as dropout, batch-size, epochs, optimizers, etc. The performance of the RNN was based on those parameters. Thus, the performance of RNN is slightly better than the other DL models. The CNNLSTM hybrid model was shown to be the second-best model with 86.2%.

Performance measure metrics values were found to be more than 83.0%. Precision is the proportion of accurately predicted positive findings to the total number of expected positive findings. A perfect precision in information retrieval experiments should be 1. The greatest precision score in this study was obtained using RNN, which was 0.871. A ratio of accurately predicted positive findings to all results is defined as recall. A recall score, like accuracy, must be one for the categorization process to be perfect. With 0.876, the best recall value was attained using the RNN model. F1-score calculated as the weighted average of accuracy and recall scores. This criterion considers both FP and FN. A high F1-score indicates that the predictor has few FP and few FN. In this scenario, the predictor identifies serious threats while avoiding false alarms. When the value of an F1-score is 1, it is deemed perfect. The best F1-score got with RNN was 0.876, as with any other assessment criterion. In classification analysis, The AUC is used to determine the best algorithms used to predict target classes. In general, a score value of AUC 0.5 indicates that no variance, a score between 0.6 and 0.8 is held as allowable, a score of 0.8–0.9 is regarded as excellent, and a value of


**Table 2.**

*The evaluation outcomes of all DL models using the train-test split method.*

greater than 0.9 is regarded as exceptional [26]. The AUC values of all DL models were outstanding since all of the outcomes were more than 0.9. All DL models may be used to predict HIV test results based on their AUC values.

True positive rates are critical in health investigations since recall indicates the percentage of actual positives identified [27]. A recall is a significant assessment criterion in this study since it is computed by dividing the number of properlyrecognized HIV-positive samples by the total number of HIV test results. Besides, the AUC score plays an important role in health research since it has a relevant interpretation for health prediction [28]. Accuracy is a study criterion that indicates how near the sample parameters are to population characteristics. We can demonstrate that the study is generalizable, dependable, and valid by testing the correctness of the models [29]. As a result, just these three assessment indicators were examined in this study. The remaining ones were computed to compare the findings to earlier studies. The AUC values using the train test split strategy are shown in **Figures 2**–**7**.

In addition to the metrics listed above, we calculated prediction accuracy to assess the efficacy of the proposed approach. **Figures 8**–**13** depict the prediction accuracy of the proposed technique on the HIV/AIDS dataset in terms of each DL model. Because of

**Figure 2.** *ANN models AUC using the train-test split strategy.*

**Figure 3.** *CNN models AUC using the train-test split strategy.*

*A Deep Learning Approaches for Modeling and Predicting of HIV Test Results Using EDHS… DOI: http://dx.doi.org/10.5772/intechopen.104224*

**Figure 4.** *RNN models AUC using the train-test split strategy.*

**Figure 5.** *LSTM models AUC using the train-test split Strategy.*

**Figure 6.** *CNNRNN models AUC using the train-test split strategy.*

**Figure 7.** *CNNLSTM models AUC using the train-test split strategy.*

the flawless prediction accuracy of the HIV/AIDS dataset, a substantial difference is not there among lines related to the training and test dataset, as shown in **Figures 8**–**13**.

We also used the log-loss error function to evaluate our work. As shown in **Figures 14**–**19** the training sample loss is near to 0, while the stated loss with the test sample is 0.3707 (refer to **Table 2**) implying that more research on this specific dataset is required to reduce the error. As demonstrated in Loss Figures, the proposed technique outperforms all DL models by scoring the least number of errors in the test instances, with the ANN, CNN, RNN, LSTM, CNNLSTM, and CNNRNN scoring 0.3137, 0.3201, 0.2929, 0.3587, 0.3130, and 0.3707, correspondingly (refer **Table 2**). It is noticed that a distance between the training and the test lines indicates whether or not the model is over-fitting.

The second experimental result for this work is a 10-fold CV. **Table 3** demonstrates the assessment results of all DL models using a 10-fold CV technique.

Concerning the predictive performances, we discovered that the best comprehensive recognized models on AUC score for predicting HIV test status were 89.72 by ANN. The main reason behind ANN outperforming better results is its activation function unlike CNN, RNN, and LSTM. Moreover, ANN works better for numerical datasets unlike CNN, RNN, and LSTM which work on image data and time-series data

**Figure 8.** *The prediction accuracy on the model ANN.*

*A Deep Learning Approaches for Modeling and Predicting of HIV Test Results Using EDHS… DOI: http://dx.doi.org/10.5772/intechopen.104224*

**Figure 9.** *The prediction accuracy on the model CNN.*

**Figure 10.** *The prediction accuracy on the model RNN.*

**Figure 11.** *The prediction accuracy on the model LSTM.*

**Figure 12.** *The prediction accuracy on the model CNNLSTM.*

**Figure 13.** *The prediction accuracy on the model CNNRNN.*

**Figure 14.** *Prediction Loss on the model ANN.*

*A Deep Learning Approaches for Modeling and Predicting of HIV Test Results Using EDHS… DOI: http://dx.doi.org/10.5772/intechopen.104224*

**Figure 15.** *Prediction loss on the model CNN.*

**Figure 16.** *Prediction loss on the model RNN.*

**Figure 17.** *Prediction loss on the model LSTM.*

**Figure 18.** *Prediction loss on the model CNNRNN.*

**Figure 19.** *Prediction loss on the model CNNLSTM.*


#### **Table 3.**

*The outcomes of all DL models were evaluated using a 10-fold cross-validation methodology.*

respectively. It was discovered that predicting HIV test status from the EDHS dataset was considered a difficult activity. Nonetheless, the best HIV test status prediction outcomes using ANN obtained reasonable accuracy of 85.5%, precision of 84.4%, recall of 85.7%, and f1-score of 85.1%.

*A Deep Learning Approaches for Modeling and Predicting of HIV Test Results Using EDHS… DOI: http://dx.doi.org/10.5772/intechopen.104224*


**Table 4.**

*The proposed model comparison with some of the most recent related research works.*

#### **5. Comparison**

This section of the study compares the proposed method to certain selected recent research in terms of performance measures. **Table 4** compares the proposed method's assessment metrics to those of six other recent research works. The hyphen (-) in the table's specific cells indicates that the researchers did not consider metrics in their study. As shown in **Table 4**, the best results were obtained with various models. Nonetheless, we have not employed ML in our research. We created six DL models and achieved higher accuracy, f1-scores, and AUC when compared to earlier similar efforts. In the considered HIV/AIDS dataset, the suggested technique obtains improved prediction performance, with 0.87 in total accuracy and f1-score and 0.94 in AUC score.

#### **6. Conclusion**

In this work, deep learning models based on the EDHS dataset were used to predict HIV test results. Six deep learning models were used to analyze HIV/AIDS dataset. The dataset was normalized in the first stage of the study then utilized as an input for the DL models. Following that, prediction is performed, and the models' results were evaluated using precision, recall, accuracy, AUC, and F1-scores. We used 10 fold CV and train test split techniques to assess the models. In a 10-fold CV technique, the ANN deep learning model produced the most meaningful results, with an accuracy of 85.5%, a recall of 85.7%, and an AUC score of 87.72%. Despite its popularity, this validation did not produce the best validation results. In the train-test split technique, the greatest accuracy, precision, recall, and AUC values were obtained with the RNN model, which was 87%, 87%, 87%, and 94%, respectively. The accuracy of all DL models produced in the study was greater than 83%. Precision and recall values can be inferred in the same way.

Finally, we discovered evidence that DL models may be used to predict HIV test status using demographic and health survey datasets. Our findings on the role of DHS in predicting HIV test status for people improve our knowledge of the consequences of HIV epidemics. Based on the findings of our study, we believe that the health domain should investigate the use of DL models that analyze individual HIV test status to enhance and re-evaluate health policies and intervention mechanisms.

#### **Acknowledgements**

The authors would like to thank anonymous reviewers for their valuable recommendations for improving the article.

#### **Funding**

Not applicable.

#### **Author contributions**

This is a collaborative work with both authors that contribute throughout.

#### **Conflict of interest**

The authors declare that they have no conflict of interest.

#### **Ethical standard**

This article does not contain any studies with human participants or animals performed by any of the authors.

#### **Data availability**

The authors declare that all data supporting the findings of this study are available on https://github.com/danielmesafint/Datasets

*A Deep Learning Approaches for Modeling and Predicting of HIV Test Results Using EDHS… DOI: http://dx.doi.org/10.5772/intechopen.104224*

### **Author details**

Daniel Mesafint Belete\* and Manjaiah D. Huchaiah Department of Computer Science, Mangalore University, India

\*Address all correspondence to: danielmesafint1985@mail.com; drmdhmu@gmail.com

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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