Spatial and socio-behavioral patterns of HIV prevalence in the Democratic Republic of Congo

Messina, Jane P.; Emch, Michael; Muwonga, Jérémie; Mwandagalirwa, Kashamuka; Edidi, Samuel; Mama, Nicaise; Okenge, Augustin; Meshnick, Steven R.

doi:10.1016/j.socscimed.2010.07.025

Cited by 43 publications

(44 citation statements)

References 37 publications

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“…At the individual level, several population and behavioral factors were found to be significant, with pregnancy unsurprisingly being the single largest risk factor for being anemic. While the relatively low prevalence of HIV in our study population (1.3%, (Messina et al, 2010)) may explain its lack of association with anemia, it is remarkable that malaria parasitemia was not a significant predictor of anemia outcome even at high parasite levels. Although the bivariate analyses in Table 3 suggest that malaria parasitemia may be associated with anemia, its lack of statistical significance in the multivariate models indicates that other factors are more important in accounting for anemia prevalence.…”

Section: Discussionmentioning

confidence: 54%

Spatial and social factors drive anemia in Congolese women

et al. 2013

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Anemia is common in women of child-bearing age in the Democratic Republic of the Congo (DRC). As part of the 2007 DRC Demographic and Health Survey (DHS), 4,638 women of childbearing age (including 526 pregnant women) were tested for HIV and had the hemoglobin content of their blood recorded. We assessed malaria prevalence using laboratory methods. The DHS provided extensive information for individuals, as well as household cluster coordinates which enabled us to derive several spatial variables. Multilevel analyses were conducted to determine individual and contextual risk factors for anemia. Prevalence varied geographically and was associated with both one's ethnic group and the amount and type of nearby agriculture. In contrast, prevalence was not affected by HIV or malaria status.

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Section: Discussionmentioning

confidence: 54%

Spatial and social factors drive anemia in Congolese women

et al. 2013

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“…This may enable modeling the systematic misclassification error of facility sampling, leading to better control of this error in regression analysis. More sophisticated analytic methods, such as using master facility lists to calibrate facility sample data for small area estimation, a method demonstrated by researchers linking population-based survey data with census data and facility data with population census data, warrants further study [9,15,32,44]. …”

Section: Discussionmentioning

confidence: 99%

Geographically linking population and facility surveys: methodological considerations

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et al. 2013

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BackgroundThe relationship between health services and population outcomes is an important area of public health research that requires bringing together data on outcomes and the relevant service environment. Linking independent, existing datasets geographically is potentially an efficient approach; however, it raises a number of methodological issues which have not been extensively explored. This sensitivity analysis explores the potential misclassification error introduced when a sample rather than a census of health facilities is used and when household survey clusters are geographically displaced for confidentiality.MethodsUsing the 2007 Rwanda Service Provision Assessment (RSPA) of all public health facilities and the 2007–2008 Rwanda Interim Demographic and Health Survey (RIDHS), five health facility samples and five household cluster displacements were created to simulate typical SPA samples and household cluster datasets. Facility datasets were matched with cluster datasets to create 36 paired datasets. Four geographic techniques were employed to link clusters with facilities in each paired dataset. The links between clusters and facilities were operationalized by creating health service variables from the RSPA and attaching them to linked RIDHS clusters. Comparisons between the original facility census and undisplaced clusters dataset with the multiple samples and displaced clusters datasets enabled measurement of error due to sampling and displacement.ResultsFacility sampling produced larger misclassification errors than cluster displacement, underestimating access to services. Distance to the nearest facility was misclassified for over 50% of the clusters when directly linked, while linking to all facilities within an administrative boundary produced the lowest misclassification error. Measuring relative service environment produced equally poor results with over half of the clusters assigned to the incorrect quintile when linked with a sample of facilities and more than one-third misclassified due to displacement.ConclusionsAt low levels of geographic disaggregation, linking independent facility samples and household clusters is not recommended. Linking facility census data with population data at the cluster level is possible, but misclassification errors associated with geographic displacement of clusters will bias estimates of relationships between service environment and health outcomes. The potential need to link facility and population-based data requires consideration when designing a facility survey.

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“…While HIV has circulated in the DRC for decades, the epidemic there is still relatively youthful, and little research has examined HIV prevalence outside of urban areas (Mulanga-Kabeya et al, 1998). However, in 2007 increased distance from a city was shown to be protective against HIV, following the typical spatial patterns of HIV in a low prevalence context (Messina et al, 2010). …”

Section: Introductionmentioning

confidence: 98%

Changing spatial patterns and increasing rurality of HIV prevalence in the Democratic Republic of the Congo between 2007 and 2013

et al. 2016

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The Democratic Republic of the Congo (DRC) has one of the lowest HIV prevalence in sub-Saharan Africa, estimated at 1.1% [0.9-1.3] of adults aged 15-49 in 2013 (UNAIDS). Within the 2 million km2 country, however, there exists spatial variation in HIV prevalence, with the highest HIV prevalence observed in the large cities of Kinshasa and Lubumbashi. Globally, HIV is an increasingly rural disease, diffusing outwards from urban centers of high HIV prevalence to places where HIV was previously absent or present at very low levels. Utilizing data collected during Demographic and Health Surveillance (DHS) in 2007 and 2013 in the DRC, we sought to update the map of HIV prevalence in the DRC as well as to explore whether HIV in the DRC is an increasingly rural disease or remains confined to urban areas. Bayesian kriging and regression indicate that HIV prevalence in rural areas of the DRC is higher in 2013 than in 2007 and that increased distance to an urban area is no longer protective against HIV as it was in 2007. These findings suggest that HIV education, testing and prevention efforts need to diffuse from urban to rural areas just as HIV is doing.

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Spatial and socio-behavioral patterns of HIV prevalence in the Democratic Republic of Congo

Cited by 43 publications

References 37 publications

Spatial and social factors drive anemia in Congolese women

Spatial and social factors drive anemia in Congolese women

Geographically linking population and facility surveys: methodological considerations

Changing spatial patterns and increasing rurality of HIV prevalence in the Democratic Republic of the Congo between 2007 and 2013

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