Analyzing spatial clustering and the spatiotemporal nature and trends of HIV/AIDS prevalence using GIS: the case of Malawi, 1994-2010

Abstract

BackgroundAlthough local spatiotemporal analysis can improve understanding of geographic variation of the HIV epidemic, its drivers, and the search for targeted interventions, it is limited in sub-Saharan Africa. Despite recent declines, Malawi’s estimated 10.0% HIV prevalence (2011) remained among the highest globally. Using data on pregnant women in Malawi, this study 1) examines spatiotemporal trends in HIV prevalence 1994-2010, and 2) for 2010, identifies and maps the spatial variation/clustering of factors associated with HIV prevalence at district level.MethodsInverse distance weighting was used within ArcGIS Geographic Information Systems (GIS) software to generate continuous surfaces of HIV prevalence from point data (1994, 1996, 1999, 2001, 2003, 2005, 2007, and 2010) obtained from surveillance antenatal clinics. From the surfaces prevalence estimates were extracted at district level and the results mapped nationally. Spatial dependency (autocorrelation) and clustering of HIV prevalence were also analyzed. Correlation and multiple regression analyses were used to identify factors associated with HIV prevalence for 2010 and their spatial variation/clustering mapped and compared to HIV clustering.ResultsAnalysis revealed wide spatial variation in HIV prevalence at regional, urban/rural, district and sub-district levels. However, prevalence was spatially leveling out within and across ‘sub-epidemics’ while declining significantly after 1999. Prevalence exhibited statistically significant spatial dependence nationally following initial (1995-1999) localized, patchy low/high patterns as the epidemic spread rapidly. Locally, HIV “hotspots” clustered among eleven southern districts/cities while a “coldspot” captured configurations of six central region districts. Preliminary multiple regression of 2010 HIV prevalence produced a model with four significant explanatory factors (adjusted R2 = 0.688): mean distance to main roads, mean travel time to nearest transport, percentage that had taken an HIV test ever, and percentage attaining a senior primary education. Spatial clustering linked some factors to particular subsets of high HIV-prevalence districts.ConclusionsSpatial analysis enhanced understanding of local spatiotemporal variation in HIV prevalence, possible underlying factors, and potential for differentiated spatial targeting of interventions. Findings suggest that intervention strategies should also emphasize improved access to health/HIV services, basic education, and syphilis management, particularly in rural hotspot districts, as further research is done on drivers at finer scale.

Risk factors associated with HIV prevalence in pregnant women in Burkina Faso, from 2006 to 2014

“…However, Burkina Faso remains one of the countries with "moderate prevalence" globally (1% -3.9%) and a generalized epidemic among pregnant women (>1%) according to the WHO classification (14). Such a decrease was also reported in the 15 to 49 years old pregnant women in other studies, particularly in West African countries from 4.3% to 2.9%, and Eastern African countries from 3.6% to 2.9% (15), in Malawi from 15.0% to 10.6% (16), and in Uganda from 28.3% to 25.1% (17) even though most of these countries had higher national prevalence than Burkina Faso.…”

Using the Kulldorff’s scan statistical analysis to detect spatio-temporal clusters of tuberculosis in Qinghai Province, China, 2009–2016

“…However the Global Moran's I spatial autocorrelation analysis only evaluates the distribution characteristics of the disease in several specific time points. Moreover, this method can not estimate the risk level of high-risk cluster areas [8][9][10][11]. It is a known fact that time is a critical confounder that might directly bias the determination of the high-risk regions of TB.…”

Application opportunities of geographic information systems analysis to support achievement of the UNAIDS 90-90-90 targets in South Africa

“…In an effort to achieve epidemic control, the Joint United Nations Programme on HIV/ AIDS (UNAIDS) has proposed ambitious 909090 targets to be achieved by 2020, whereby 90% of people infected with HIV will have received a diagnosis, 90% of those diagnosed will be receiving sustained antiretroviral therapy (ART), and 90% of those on treatment will be virally suppressed. [3] GIS analyses, often involving complex spatial statistics, have been used to investigate various aspects of the HIV epidemic in a number of countries including SA, for example to examine the distribution of HIV infection, including hotspots or clusters of high or low prevalence; [417] to assess the spatial distribution of factors contributing to HIV infection; [4,14,18] to describe the spatial distribution of the HIV care continuum (HIV testing, uptake of ART, viral suppression) and the factors that affect provision of this care, including location of services; [1927] to investigate HIV infection, prevention services and location/density of key populations, including injection drug users, men who have sex with men and sex workers; [2833] to guide or map prevention efforts such as male circumcision or condom distribution; [34,35] to understand patterns of HIV knowledge; [36] and to examine distribution of funding for HIV services. [37] Although some of these applications can inform efforts towards achieving the 909090 objectives, to our knowledge no systematic approach has been presented to define the role of GIS in achieving these targets.…”

“…A further use of GIS to guide progress towards the first 90 is mapping of highrisk populations to direct testing for higher yield. Explanatory variables associated with HIV prevalence, many of which have been mapped in SA and LMICs, [4,12,14,18] can be simply visualised with pie charts to identify locations with populations at high risk of contracting HIV (Fig. 2).…”