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Mapping HIV prevalence using population and antenatal sentinel-based HIV surveys: a multi-stage approach.

Manda S, Masenyetse L, Cai B, Meyer R - Popul Health Metr (2015)

Bottom Line: Inverse Probability Weighting combined with an appropriate HIV prediction model can be a useful tool to correct for non-response to HIV testing, especially if the number of tested individuals is very minimal at subnational levels.In populations where most know their HIV status, population-based HIV prevalence estimates can be heavily biased.High-coverage antenatal clinics' surveillance HIV data would then be the only important HIV data information sources.

View Article: PubMed Central - PubMed

Affiliation: Biostatistics Unit, South African Medical Research Council, Pretoria, South Africa ; School of Mathematics, Statistics & Computer Science, University of KwaZulu-Natal, Pietermaritzburg, South Africa.

ABSTRACT

Background: Sound public health policy on HIV/AIDS depends on accurate prevalence and incidence statistics for the epidemic at both local and national levels. However, HIV statistics derived from epidemiological extrapolation models and data sources have a number of limitations that may lead to under- or overestimation of the epidemic. Thus, adjustment techniques need to be employed to correctly estimate the size of the HIV burden.

Methods: A multi-stage methodological approach is proposed to obtain HIV statistics at subnational levels by combining nationally population-based and antenatal clinic HIV data. The stages range from computing inverse probability weighting (IPW) for consenting to HIV testing, to HIV status prediction modelling, to the recently developed Bayesian multivariate spatial models to jointly model and map multiple HIV risks. The 2010 Malawi Demographic and Health Survey (MDHS 2010) and the 2010 Malawi Antenatal Clinic (ANC 2010) Sentinel HIV data were used for analyses. Gender, residence, employment, marital status, ethnicity, condom use, and multiple sex partners were considered when estimating HIV prevalence.

Results: The observed MDHS 2010 HIV prevalence among people aged 15-49 years was 10.15 %, with 95 % confidence interval (CI) of (9.66, 10.67 %). The ANC 2010 site HIV prevalence had a median of 10.63 %, with 95 % CI ranging from 1.85-24.09 %. The MDHS 2010 prevalence was 10.61 % (9.9, 11.33 %) and 10.19 % (9.69, 10.71 %) using the HIV weight and IPW, respectively. After predicting the HIV status for the non-tested subjects, the overall MDHS 2010 HIV prevalence was 11.05 % (10.80, 11.30 %). Higher HIV prevalence rates were observed in the mostly Southern districts, where poverty and population density levels are also comparatively high. The excess risk attributable to ANC HIV was much larger in the central-eastern and northern parts of the country.

Conclusions: Inverse Probability Weighting combined with an appropriate HIV prediction model can be a useful tool to correct for non-response to HIV testing, especially if the number of tested individuals is very minimal at subnational levels. In populations where most know their HIV status, population-based HIV prevalence estimates can be heavily biased. High-coverage antenatal clinics' surveillance HIV data would then be the only important HIV data information sources.

No MeSH data available.


Related in: MedlinePlus

a Contextual Factor: Incidence of Poverty-Level (%): Malawi,2011. b Contextual Factor Population Density Levels: Malawi, 2008
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Fig4: a Contextual Factor: Incidence of Poverty-Level (%): Malawi,2011. b Contextual Factor Population Density Levels: Malawi, 2008

Mentions: Maps in Fig. 3a-b show DHS unweighted and total weighted (all 31,139 subjects) HIV prevalence, and the antenatal HIV prevalence in Malawi 2010. There appear to be more districts, especially in the central western district, where after adjustments the HIV rates have increased. The distributions of the two contextual factors are shown in Fig. 4a-b. Higher MDHS 2010 HIV prevalence rates were observed in the mostly southern districts, which mirrors the ANC HIV that is mostly concentrated in the southeastern districts. Poverty level is evenly spread, but southern districts bear the most burden of poverty. The same goes for population density. Thus districts with high HIV prevalence have high levels of poverty and population density.Fig. 3


Mapping HIV prevalence using population and antenatal sentinel-based HIV surveys: a multi-stage approach.

Manda S, Masenyetse L, Cai B, Meyer R - Popul Health Metr (2015)

a Contextual Factor: Incidence of Poverty-Level (%): Malawi,2011. b Contextual Factor Population Density Levels: Malawi, 2008
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4557594&req=5

Fig4: a Contextual Factor: Incidence of Poverty-Level (%): Malawi,2011. b Contextual Factor Population Density Levels: Malawi, 2008
Mentions: Maps in Fig. 3a-b show DHS unweighted and total weighted (all 31,139 subjects) HIV prevalence, and the antenatal HIV prevalence in Malawi 2010. There appear to be more districts, especially in the central western district, where after adjustments the HIV rates have increased. The distributions of the two contextual factors are shown in Fig. 4a-b. Higher MDHS 2010 HIV prevalence rates were observed in the mostly southern districts, which mirrors the ANC HIV that is mostly concentrated in the southeastern districts. Poverty level is evenly spread, but southern districts bear the most burden of poverty. The same goes for population density. Thus districts with high HIV prevalence have high levels of poverty and population density.Fig. 3

Bottom Line: Inverse Probability Weighting combined with an appropriate HIV prediction model can be a useful tool to correct for non-response to HIV testing, especially if the number of tested individuals is very minimal at subnational levels.In populations where most know their HIV status, population-based HIV prevalence estimates can be heavily biased.High-coverage antenatal clinics' surveillance HIV data would then be the only important HIV data information sources.

View Article: PubMed Central - PubMed

Affiliation: Biostatistics Unit, South African Medical Research Council, Pretoria, South Africa ; School of Mathematics, Statistics & Computer Science, University of KwaZulu-Natal, Pietermaritzburg, South Africa.

ABSTRACT

Background: Sound public health policy on HIV/AIDS depends on accurate prevalence and incidence statistics for the epidemic at both local and national levels. However, HIV statistics derived from epidemiological extrapolation models and data sources have a number of limitations that may lead to under- or overestimation of the epidemic. Thus, adjustment techniques need to be employed to correctly estimate the size of the HIV burden.

Methods: A multi-stage methodological approach is proposed to obtain HIV statistics at subnational levels by combining nationally population-based and antenatal clinic HIV data. The stages range from computing inverse probability weighting (IPW) for consenting to HIV testing, to HIV status prediction modelling, to the recently developed Bayesian multivariate spatial models to jointly model and map multiple HIV risks. The 2010 Malawi Demographic and Health Survey (MDHS 2010) and the 2010 Malawi Antenatal Clinic (ANC 2010) Sentinel HIV data were used for analyses. Gender, residence, employment, marital status, ethnicity, condom use, and multiple sex partners were considered when estimating HIV prevalence.

Results: The observed MDHS 2010 HIV prevalence among people aged 15-49 years was 10.15 %, with 95 % confidence interval (CI) of (9.66, 10.67 %). The ANC 2010 site HIV prevalence had a median of 10.63 %, with 95 % CI ranging from 1.85-24.09 %. The MDHS 2010 prevalence was 10.61 % (9.9, 11.33 %) and 10.19 % (9.69, 10.71 %) using the HIV weight and IPW, respectively. After predicting the HIV status for the non-tested subjects, the overall MDHS 2010 HIV prevalence was 11.05 % (10.80, 11.30 %). Higher HIV prevalence rates were observed in the mostly Southern districts, where poverty and population density levels are also comparatively high. The excess risk attributable to ANC HIV was much larger in the central-eastern and northern parts of the country.

Conclusions: Inverse Probability Weighting combined with an appropriate HIV prediction model can be a useful tool to correct for non-response to HIV testing, especially if the number of tested individuals is very minimal at subnational levels. In populations where most know their HIV status, population-based HIV prevalence estimates can be heavily biased. High-coverage antenatal clinics' surveillance HIV data would then be the only important HIV data information sources.

No MeSH data available.


Related in: MedlinePlus