Abstract
BackgroundIn Geographical Information Systems issues of scale are of an increasing interest in storing health data and using these in policy support. National and international policies on treating HIV (Human Immunodeficiency Virus) positive women in India are based on case counts at Voluntary Counseling and Testing Centers (VCTCs). In this study, carried out in the Indian state of Andhra Pradesh, these centers are located in subdistricts called mandals, serving for both registration and health facility policies. This study hypothesizes that people may move to a mandal different than their place of residence for being tested for reasons of stigma. Counts of a single mandal therefore may include cases from inside and outside a mandal. HIV counts were analyzed on the presence of outside cases and the most likely explanations for movement. Counts of women being tested on a practitioners' referral (REFs) and those directly walking-in at testing centers (DWs) were compared and with counts of pregnant women.ResultsAt the mandal level incidence among REFs is on the average higher than among DWs. For both groups incidence is higher in the South-Eastern coastal zones, being an area with a dense highway network and active port business. A pattern on the incidence maps was statistically confirmed by a cluster analysis. A spatial regression analysis to explain the differences in incidence among pregnant women and REFs shows a negative relation with the number of facilities and a positive relation with the number of roads in a mandal. Differences in incidence among pregnant women and DWs are explained by the same variables, and by a negative relation with the number of neighboring mandals. Based on the assumption that pregnant women are tested in their home mandal, this provides a clear indication that women move for testing as well as clues for explanations why.ConclusionsThe spatial analysis shows that women in India move towards a different mandal for getting tested on HIV. Given the scale of study and different types of movements involved, it is difficult to say where they move to and what the precise effect is on HIV registration. Better recording the addresses of tested women may help to relate HIV incidence to population present within a mandal. This in turn may lead to a better incidence count and therefore add to more reliable policy making, e.g. for locating or expanding health facilities.
Highlights
In Geographical Information Systems issues of scale are of an increasing interest in storing health data and using these in policy support
Patterns of spread displayed by IREF and IDW are largely similar, both showing a higher incidence in the coastal edge of Andhra Pradesh and around the state capital Hyderabad than in the rural areas within the state
Assuming that incidence for pregnant women IP is generally lower than incidence for the general population, as they are a subsection of the whole female population, it is noted that HIV-positive females are apparently moving from mandals with negative values and values up to 2 to other mandals for getting tested
Summary
In Geographical Information Systems issues of scale are of an increasing interest in storing health data and using these in policy support. According to Pandey et al [9], the earlier HIV estimates in India were based on HIV sentinel surveillance (HSS) data. The absence of HIV surveillance among female sex workers and men having sex with men was a weakness of this system Those two groups were later included in the estimation but sexually transmitted disease clinics were not discarded. Community based HIV prevalence measured by the National Family Health Survey-3 [10] provided an opportunity to replace earlier assumptions, validate the HSS data and improve the HIV estimate. Calculations and estimates in Pandey et al [9] reverse the number of total HIV prevalence in India They quote that the current estimate is a revision based on improved data and methodological changes. The difference between the current estimate and previously published estimates does not represent a true decline at the population level
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