**3. Results**

From November 27, 2008 to February 22, 2016, 221 individuals showed up for HIV screening to know their status, 91.4% of them were farmers and this revealed how agriculturally dependent this population is.


N = number sampled, I = number infected, P = prevalence, *χ2* = Chi-square, df = degree of freedom, P-value is level of significance (P < 0.05).

**Table 1.** HIV prevalence with sex.

with consulting/counseling senior nurse and laboratory technician. In addition, questionnaires were administered and group discussions were organized. Home visits were also made to know the conditions of individuals living with the disease while collecting vital information. All patients coming to carry out the test were considered, but note was taken when studying past data to ensure that the same kit previously used for diagnosis was the same with that presently used for diagnosis. Diagnosis was supervised by a senior researcher to ensure that protocol for testing using the test kit was in accordance with manufacturer's instructions. Confidentiality of test results following the test was confidential, and only a code

Rapid diagnostic tests using standard commercially sourced 'Determine' and Uni-Gold test kits were used to determine the HIV status of individuals who come for the test. 'Determine 'HIV rapid test kit (www.who.int/diagnostics laboratory) used with whole blood, serum or plasma) as pre-test. Uni-Gold test kits (The trinity Biotech Uni-Gold™ HIV test) are kits that pick or react only with HIV in blood sample and was used for confirmation. The protocol for the usage of the above kits was as outlined by Olusi and Abe [14]. Storage conditions and protocols according to manufacturers of kits were strictly

An authorization was given by the Chief medical officer at the Dschang health district. Based on the fact that we were working on hospital registers in collaboration with laboratory technicians and nurses following instructions of the head of health unit on patients showing up for the test, ethical clearance was not required since we were not recruiting individuals for HIV screening. All clinical investigations were conducted according to the Declaration of Helsinki

Data were analyzed using the SPSS statistical software of version 22.0, graphs and pie chart were constructed using MS excel software of version 2010. Chi-square test was used to compare HIV prevalence with, sex, age cohort, years of screening, marital status, motif of test and

From November 27, 2008 to February 22, 2016, 221 individuals showed up for HIV screening to know their status, 91.4% of them were farmers and this revealed how agriculturally

was designated for each test and not the patient's identity.

120 Current Topics in Tropical Emerging Diseases and Travel Medicine

**2.3. HIV testing**

followed.

principles.

profession.

**3. Results**

dependent this population is.

**2.5. Data analysis**

**2.4. Ethical consideration**


N = number sampled, I = number infected, P = prevalence, *χ2* = Chi-square, df = degree of freedom, P-value is level of significance (P < 0.05).

**Table 2.** Prevalence with age cohorts.

Prevalence with sex revealed that male (14.3%) were more infected than female (4.0%) with a statistical significant difference (*χ*<sup>2</sup> = 4.251, *df* = 1, P = 0.039) (**Table 1**). Prevalence recorded with respect to age showed that the highest cases were signaled in individuals of ages ≥36, followed by 14–24 and lastly by 25–35 years, even though such discrepancies in prevalence existed with age, there was no significant difference (*χ*<sup>2</sup> = 3.096, *df* = 3, P = 0.377), recorded with age cohorts (**Table 2**).

Evolution of the disease in this area since 2008 till date was monitored. Prevalence based on the year of screening showed that the years between 2014 and 2016 (30.0%), recorded highest infected and 2012–2013 (4.1%) presented the least number of cases. Statistically, there was a significant difference (*χ*<sup>2</sup> = 27.373, *df* = 8, P = 0.002) in HIV prevalence with years of testing (**Table 3**).

Prevalence based on profession showed that traders (20.0%) presented the highest prevalence, followed by farmers (14.8%) while students and teachers had zero prevalence, despite the difference in HIV prevalence registered in various occupations, there still existed no statistical significant difference (*χ*<sup>2</sup> = 9.531, *df* = 6, P = 0.146) (**Table 4**). The high HIV prevalence recorded by farmers in this community is an indicator of a possible decrease in agricultural work force in this agriculture-dependent community if serious measures are not taken to prevent spread of the disease among farmers. Traders recorded the highest infection rate among others, and this is due to their high mobility rates exposing them to high risks of contracting the disease.


N = number sampled, I = number infected, P = prevalence, *χ2* = Chi-square, df = degree of freedom, P-value is level of significance (P < 0.05).

**Table 3.** Prevalence of HIV with year of testing.


N = number sampled, I = number infected, P = prevalence, *χ2 =* Chi-square, df = degree of freedom, P-value is level of significance (P < 0.05).

**Table 4.** Prevalence based on profession.


Prevalence based on marital status indicated that single (unmarried) (16.0%) recorded high cases of the disease than their married counterparts (3.6%), with a statistical significant difference (*χ*<sup>2</sup> = 7.421, *df* = 1, P = 0.007) (**Table 6**). It was observed that married people showed up

**Marital status N I P (I/N × 100)%** *χ2 df* **P-value**

Married 196 7 3.6 7.241 1 0.007

N = number sampled, I = number infected, P = prevalence, *χ2* = Chi-square, df = degree of freedom, P-value is level of

HIV/AIDS in a Community of Western Cameroon http://dx.doi.org/10.5772/intechopen.77086 123

The most frequent control measure for sexually transmitted diseases (STDs) in this community is the use of condoms. During community visits, it was discovered that 100% of shops sold condom, and when shop sellers were interviewed on which age group frequently purchased it, the response was students in secondary and high schools and rarely parents. From the results of our group discussions and questionnaires analysis, we realized that 80% of adolescent population used condom for safe sex as a preventive tool from STDs while 20% preferred abstinence (**Figure 1**). A further analysis of the effect of condom usage by youths of this village as a prevention option for HIV showed that 60% of individuals who came for screening and were diagnosed/confirmed positive did not practice safe sex (use condom); meanwhile, the other fraction who practiced safe sex with condom recorded 38% HIV-AIDS

for the test than single persons.

**Figure 1.** HIV prevention strategies.

Single 25 4 16.0

Total 221 11 19.6

**Table 6.** Prevalence based on marital status.

significance (P < 0.05).

prevalence (**Figure 2**).

N = number sampled, I = number infected, P = prevalence, *χ2* = Chi-square, df = degree of freedom, P-value is level of significance (P < 0.05).

**Table 5.** Prevalence based on reason of test.

Apparently, there are two reasons why people in this study area go in for HIV screening, one being pregnancy and the other is sickness for both men and women. From data recorded, pregnant women frequently showed up for this test than those who choose to make HIV test when they come to consult because they are sick. From the prevalence results, those who diagnosed because they were sick (10.9%) as reason recorded the highest number of cases as compared to women who did for pregnancy reasons (3.0%), with a statistical significant difference (*χ*<sup>2</sup> = 5.44, *df* = 1, P = 0.020) (**Table 5**).


N = number sampled, I = number infected, P = prevalence, *χ2* = Chi-square, df = degree of freedom, P-value is level of significance (P < 0.05).

**Table 6.** Prevalence based on marital status.

**Figure 1.** HIV prevention strategies.

Apparently, there are two reasons why people in this study area go in for HIV screening, one being pregnancy and the other is sickness for both men and women. From data recorded, pregnant women frequently showed up for this test than those who choose to make HIV test when they come to consult because they are sick. From the prevalence results, those who diagnosed because they were sick (10.9%) as reason recorded the highest number of cases as compared to women who did for pregnancy reasons (3.0%), with a statistical significant

N = number sampled, I = number infected, P = prevalence, *χ2* = Chi-square, df = degree of freedom, P-value is level of

**Year N I P (I/N × 100)%** *χ2 df* **P-value**

2014–2016 72 3 30.0 27.373 8 0.002

**Profession N I P (I/N × 100)%** *χ2 df* **P-value**

Trader 5 1 20.0 9.531 6 0.146

**Motif N I P (I/N × 100)%** *χ2 df* **P-value**

Sick 55 6 10.9 5.44 1 0.020

N = number sampled, I = number infected, P = prevalence, *χ2 =* Chi-square, df = degree of freedom, P-value is level of

N = number sampled, I = number infected, P = prevalence, *χ2* = Chi-square, df = degree of freedom, P-value is level of

2008–2009 12 1 14.3 2010–2011 44 5 21.2 2012–2013 93 2 4.1

122 Current Topics in Tropical Emerging Diseases and Travel Medicine

Total 221 11 69.6

**Table 3.** Prevalence of HIV with year of testing.

Farmer 27 4 14.8 House wife 175 6 3.4 Student 12 0 0.0 Teacher 2 0 0.0

Total 221 11 38.2

Pregnancy 166 5 3.0

Total 221 11 13.9

**Table 5.** Prevalence based on reason of test.

**Table 4.** Prevalence based on profession.

significance (P < 0.05).

significance (P < 0.05).

significance (P < 0.05).

difference (*χ*<sup>2</sup> = 5.44, *df* = 1, P = 0.020) (**Table 5**).

Prevalence based on marital status indicated that single (unmarried) (16.0%) recorded high cases of the disease than their married counterparts (3.6%), with a statistical significant difference (*χ*<sup>2</sup> = 7.421, *df* = 1, P = 0.007) (**Table 6**). It was observed that married people showed up for the test than single persons.

The most frequent control measure for sexually transmitted diseases (STDs) in this community is the use of condoms. During community visits, it was discovered that 100% of shops sold condom, and when shop sellers were interviewed on which age group frequently purchased it, the response was students in secondary and high schools and rarely parents. From the results of our group discussions and questionnaires analysis, we realized that 80% of adolescent population used condom for safe sex as a preventive tool from STDs while 20% preferred abstinence (**Figure 1**). A further analysis of the effect of condom usage by youths of this village as a prevention option for HIV showed that 60% of individuals who came for screening and were diagnosed/confirmed positive did not practice safe sex (use condom); meanwhile, the other fraction who practiced safe sex with condom recorded 38% HIV-AIDS prevalence (**Figure 2**).

of individuals from 35 years and above living in this village did not go to school and 95% of youths below 30 years have at least attended primary school. Lack of education among parents in this village has led to their unawareness of the transmission and prevention of the disease as well as lack of parental doctrine about the disease to their children. Parents in this community are equally high consumers of alcohol (beer or palm wine) which renders them senseless, exposing them to risky behaviors and disease. The years between 2014 till date recorded the highest HIV-positive cases in this community. This can be justified by referring to some socio-economic reasons which involve relocation of youths from cities during festive periods into this community and introduction of risky habits brought from the town, exposing the community to more danger. In addition, the opening up of the Santchou-Fondonera road has increased accessibility by indigenes of this village and visitors based in towns to frequently visit this area as compared to past years. From our frequency table analysis, 75.1% of individuals coming for HIV test constitute pregnant women and only 24.9% carry out the test because they are sick. It is clear from these figures that HIV test is not a priority of sick patients in this village and they prefer routine tests like stool, typhoid, and malaria. The high screening percentage for pregnant women is because HIV test is obligatory for them through ANC teachings. Even though a greater fraction consulting is made of pregnant women, they rather recorded low (3.0%) prevalence as compared to 10% in cases testing for sick reasons. This low prevalence in pregnant women in this community is still epidemiologically significant because a seropositive pregnant woman being mainly married have a far-reaching implication to the family as well as the socio-economic life of the people [17]. Based on the marital status and the frequency of consultations, free persons (single, divorced, widows, and widowers) recorded an 11.3% testing frequency as compared to 88.7% in married persons. From the prevalence results, free persons were infected than married, and this finding is contrary to that of Manu et al. [4] who reported that married were more infected than unmarried. It is logical that free persons have multiple sex partners to a greater extent than married people in a village setting like our study community. Such risky habits expose those free individual to HIV infection than those legally married; therefore, the present result was expectant. Fondonera community is an agriculture-dominated area with a greater population of indigenes resident in the villages of this community being farmers. This is portrayed from the global hospital statistics of patients consulting yearly according occupation, with 91.4% of them consulting as farmers. It was interesting even though vexing to know that farmers had the second highest consultation frequency after traders than any other occupation with no association. This finding is similar to that of Nyambi et al. [7] who reported that there is no association of HIV infection with occupation of participants in rural areas of Cameroon. Traders were highly infected because their mobility is the highest hence confirming the risk of mobile populations in the contraction of the disease. This finding is similar to that of Njukeng et al. [17] who reported highest cases with traders. The consequences of high farmer infection in this rural agriculturedependent society can be deduced from the report of Gillespie and Kadiyala [18] in Rwanda who said that 60–80% reduction rates witnessed in farm labor are due to illness and death of infected households. It was noted from respondents about prevention strategies that 80% of them used condom for safe sex and only 20% preferred abstinence to the use of condoms. A further analysis was made on HIV prevalence among users and nonusers of condom as prevention strategy, and it revealed that 62% of infected cases did not use condom during sex

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**Figure 2.** Sexual practice based on the use and nonusage of condom for safe sex.
