eprintid: 25103
rev_number: 10
eprint_status: archive
userid: 1589
dir: disk0/00/02/51/03
datestamp: 2018-07-18 06:43:27
lastmod: 2018-07-23 10:59:43
status_changed: 2018-07-18 06:43:27
type: article
metadata_visibility: show
creators_name: Cuadros, Diego F.
creators_name: Sartorius, Benn
creators_name: Hall, Chris
creators_name: Akullian, Adam
creators_name: Bärnighausen, Till
creators_name: Tanser, Frank
title: Capturing the spatial variability of HIV epidemics in South Africa and Tanzania using routine healthcare facility data
subjects: 610
divisions: 912800
abstract: Background: Large geographical variations in the intensity of the HIV epidemic in sub-Saharan Africa call for geographically targeted resource allocation where burdens are greatest. However, data available for mapping the geographic variability of HIV prevalence and detecting HIV ‘hotspots’ is scarce, and population-based surveillance data are not always available. Here, we evaluated the viability of using clinic-based HIV prevalence data to measure the spatial variability of HIV in South Africa and Tanzania.   

Methods: Population-based and clinic-based HIV data from a small HIV hyper-endemic rural community in South Africa as well as for the country of Tanzania were used to map smoothed HIV prevalence using kernel interpolation techniques. Spatial variables were included in clinic-based models using co-kriging methods to assess whether cofactors improve clinic-based spatial HIV prevalence predictions. Clinic- and population-based smoothed prevalence maps were compared using partial rank correlation coefficients and residual local indicators of spatial autocorrelation.

Results: Routinely-collected clinic-based data captured most of the geographical heterogeneity described by population-based data but failed to detect some pockets of high prevalence. Analyses indicated that clinic-based data could accurately predict the spatial location of so-called HIV ‘hotspots’ in > 50% of the high HIV burden areas.

Conclusion: Clinic-based data can be used to accurately map the broad spatial structure of HIV prevalence and to identify most of the areas where the burden of the infection is concentrated (HIV ‘hotspots’). Where population-based data are not available, HIV data collected from health facilities may provide a second-best option to generate valid spatial prevalence estimates for geographical targeting and resource allocation.
date: 2018
publisher: BioMed Central
id_scheme: DOI
id_number: https://doi.org/10.1186/s12942-018-0146-8
ppn_swb: 1654668184
own_urn: urn:nbn:de:bsz:16-heidok-251037
language: eng
bibsort: CUADROSDIECAPTURINGT2018
full_text_status: public
publication: International journal of health geographics
volume: 17
number: 27
place_of_pub: London
pagerange: 1-9
issn: 1476-072X
citation:   Cuadros, Diego F. ; Sartorius, Benn ; Hall, Chris ; Akullian, Adam ; Bärnighausen, Till ; Tanser, Frank  (2018) Capturing the spatial variability of HIV epidemics in South Africa and Tanzania using routine healthcare facility data.  International journal of health geographics, 17 (27).  pp. 1-9.  ISSN 1476-072X     
document_url: https://archiv.ub.uni-heidelberg.de/volltextserver/25103/1/12942_2018_Article_146.pdf