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Investigating spatial disparities in high-risk women and HIV infections using generalized additive models: Results from a cohort of South African women.

ABSTRACT: OBJECTIVE:We identified the geographical clustering of HIV as well as those at highest risk of infection using a decade long data (2002-2012) from KwaZulu-Natal, South Africa. METHODS:A total of 5,776 women who enrolled in several HIV prevention trials were included in the study. Geo-coded individual-level data were linked to the community-level characteristics using the South African Census. High-risk women were identified using a risk scoring algorithm. Generalized additive models were used to identify the significant geographical clustering of high-risk women and HIV. RESULTS:Overall, 60% of the women were classified as high risk of HIV. HIV infection rates were estimated as high as 10 to 15 per 100 person year. Areas with high rates of HIV infections were spatially clustered and overlapped particularly in the Northern part of Durban. CONCLUSION:Targeting multifactorial and complex nature of the epidemic is urgently needed to identify the "high transmission" areas.

SUBMITTER: Wand H

PROVIDER: S-EPMC6914769 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Investigating spatial disparities in high-risk women and HIV infections using generalized additive models: Results from a cohort of South African women.

Wand Handan H Reddy Tarylee T Ramjee Gita G

Spatial and spatio-temporal epidemiology 20190529

<h4>Objective</h4>We identified the geographical clustering of HIV as well as those at highest risk of infection using a decade long data (2002-2012) from KwaZulu-Natal, South Africa.<h4>Methods</h4>A total of 5,776 women who enrolled in several HIV prevention trials were included in the study. Geo-coded individual-level data were linked to the community-level characteristics using the South African Census. High-risk women were identified using a risk scoring algorithm. Generalized additive mode ...[more]

PMID: 31421797

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Project description:BackgroundA common, important problem in spatial epidemiology is measuring and identifying variation in disease risk across a study region. In application of statistical methods, the problem has two parts. First, spatial variation in risk must be detected across the study region and, second, areas of increased or decreased risk must be correctly identified. The location of such areas may give clues to environmental sources of exposure and disease etiology. One statistical method applicable in spatial epidemiologic settings is a generalized additive model (GAM) which can be applied with a bivariate LOESS smoother to account for geographic location as a possible predictor of disease status. A natural hypothesis when applying this method is whether residential location of subjects is associated with the outcome, i.e. is the smoothing term necessary? Permutation tests are a reasonable hypothesis testing method and provide adequate power under a simple alternative hypothesis. These tests have yet to be compared to other spatial statistics.ResultsThis research uses simulated point data generated under three alternative hypotheses to evaluate the properties of the permutation methods and compare them to the popular spatial scan statistic in a case-control setting. Case 1 was a single circular cluster centered in a circular study region. The spatial scan statistic had the highest power though the GAM method estimates did not fall far behind. Case 2 was a single point source located at the center of a circular cluster and Case 3 was a line source at the center of the horizontal axis of a square study region. Each had linearly decreasing logodds with distance from the point. The GAM methods outperformed the scan statistic in Cases 2 and 3. Comparing sensitivity, measured as the proportion of the exposure source correctly identified as high or low risk, the GAM methods outperformed the scan statistic in all three Cases.ConclusionsThe GAM permutation testing methods provide a regression-based alternative to the spatial scan statistic. Across all hypotheses examined in this research, the GAM methods had competing or greater power estimates and sensitivities exceeding that of the spatial scan statistic.

Dataset Information

Investigating spatial disparities in high-risk women and HIV infections using generalized additive models: Results from a cohort of South African women.

Publications

Investigating spatial disparities in high-risk women and HIV infections using generalized additive models: Results from a cohort of South African women.

Similar Datasets

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