Spatial Distributions of HIV Infection in an Endemic Area of Western Kenya: Guiding Information for Localized HIV Control and Prevention

Abstract

HIV is still a major health problem in developing countries. Even though high HIV-risk-taking behaviors have been reported in African fishing villages, local distribution patterns of HIV infection in the communities surrounding these villages have not been thoroughly analyzed. The objective of this study was to investigate the geographical distribution patterns of HIV infection in communities surrounding African fishing villages. In 2011, we applied age- and sex-stratified random sampling to collect 1,957 blood samples from 42,617 individuals registered in the Health and Demographic Surveillance System in Mbita, which is located on the shore of Lake Victoria in western Kenya. We used these samples to evaluate existing antibody detection assays for several infectious diseases, including HIV antibody titers. Based on the results of the assays, we evaluated the prevalence of HIV infection according to sex, age, and altitude of participating households. We also used Kulldorff's spatial scan statistic to test for HIV clustering in the study area. The prevalence of HIV at our study site was 25.3%. Compared with the younger age group (15-19 years), adults aged 30-34 years were 6.71 times more likely to be HIV-positive, and the estimated HIV-positive population among women was 1.43 times larger than among men. Kulldorff's spatial scan statistic detected one marginally significant (P = 0.055) HIV-positive and one significant HIV-negative cluster (P = 0.047) in the study area. These results suggest a homogeneous HIV distribution in the communities surrounding fishing villages. In addition to individual behavior, more complex and diverse factors related to the social and cultural environment can contribute to a homogeneous distribution pattern of HIV infection outside of African fishing villages. To reduce rates of transmission in HIV-endemic areas, HIV prevention and control programs optimized for the local environment need to be developed.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1. Study site along the shore of Lake Victoria in Mbita, Kenya.

The HDSS program survey at the study site is managed by a NUITM-KEMRI Project.

Fig 2. HIV prevalence by sub-area of the study site according to age-group and sex.

The horizontal axis indicates age groups in units of five years, and the vertical axis shows the prevalence of HIV positive cases. Red and blue lines with error bars indicating 95% confidence intervals represent females and males, respectively.

Fig 3. The estimated HIV positive population by sub-area of the study site according to age and sex.

The horizontal axis shows five-year age groups, and the vertical axis shows the prevalence of HIV positive cases. Red and blue lines with error bars indicating 95% confidence intervals represent females and males, respectively.

Fig 4. Results of cluster analysis using Kulldorff’s spatial scan statistic for both HIV-positive and negative clusters.

Hexagons in color gradations from white to red represent proportions of HIV-positive households; blue pentagons represent health facilities; green diamonds represent fishing villages; circles with an orange solid line represent clusters of HIV positivity; circles with dotted lines represent clusters of HIV negative tendencies. Fig reused and superimposed the results of the cluster analyses on part of Fig from Fujii et al. [15]