Combining national survey with facility-based HIV testing data to obtain more accurate estimate of HIV prevalence in districts in Uganda.
Bias
Combining
District Health Information System
Health Information System
Hybrid Prevalence Estimate
Population survey
Journal
BMC public health
ISSN: 1471-2458
Titre abrégé: BMC Public Health
Pays: England
ID NLM: 100968562
Informations de publication
Date de publication:
23 Mar 2020
23 Mar 2020
Historique:
received:
10
01
2020
accepted:
28
02
2020
entrez:
16
4
2020
pubmed:
16
4
2020
medline:
25
8
2020
Statut:
epublish
Résumé
National or regional population-based HIV prevalence surveys have small sample sizes at district or sub-district levels; this leads to wide confidence intervals when estimating HIV prevalence at district level for programme monitoring and decision making. Health facility programme data, collected during service delivery is widely available, but since people self-select for HIV testing, HIV prevalence estimates based on it, is subject to selection bias. We present a statistical annealing technique, Hybrid Prevalence Estimation (HPE), that combines a small population-based survey sample with a facility-based sample to generate district level HIV prevalence estimates with associated confidence intervals. We apply the HPE methodology to combine the 2011 Uganda AIDS indicator survey with the 2011 health facility HIV testing data to obtain HIV prevalence estimates for districts in Uganda. Multilevel logistic regression was used to obtain the propensity of testing for HIV in a health facility, and the propensity to test was used to combine the population survey and health facility HIV testing data to obtain the HPEs. We assessed comparability of the HPEs and survey-based estimates using Bland Altman analysis. The estimates ranged from 0.012 to 0.178 and had narrower confidence intervals compared to survey-based estimates. The average difference between HPEs and population survey estimates was 0.00 (95% CI: - 0.04, 0.04). The HPE standard errors were 28.9% (95% CI: 23.4-34.4) reduced, compared to survey-based standard errors. Overall reduction in HPE standard errors compared survey-based standard errors ranged from 5.4 to 95%. Facility data can be combined with population survey data to obtain more accurate HIV prevalence estimates for geographical areas with small population survey sample sizes. We recommend use of the methodology by district level managers to obtain more accurate HIV prevalence estimates to guide decision making without incurring additional data collection costs.
Sections du résumé
BACKGROUND
BACKGROUND
National or regional population-based HIV prevalence surveys have small sample sizes at district or sub-district levels; this leads to wide confidence intervals when estimating HIV prevalence at district level for programme monitoring and decision making. Health facility programme data, collected during service delivery is widely available, but since people self-select for HIV testing, HIV prevalence estimates based on it, is subject to selection bias. We present a statistical annealing technique, Hybrid Prevalence Estimation (HPE), that combines a small population-based survey sample with a facility-based sample to generate district level HIV prevalence estimates with associated confidence intervals.
METHODS
METHODS
We apply the HPE methodology to combine the 2011 Uganda AIDS indicator survey with the 2011 health facility HIV testing data to obtain HIV prevalence estimates for districts in Uganda. Multilevel logistic regression was used to obtain the propensity of testing for HIV in a health facility, and the propensity to test was used to combine the population survey and health facility HIV testing data to obtain the HPEs. We assessed comparability of the HPEs and survey-based estimates using Bland Altman analysis.
RESULTS
RESULTS
The estimates ranged from 0.012 to 0.178 and had narrower confidence intervals compared to survey-based estimates. The average difference between HPEs and population survey estimates was 0.00 (95% CI: - 0.04, 0.04). The HPE standard errors were 28.9% (95% CI: 23.4-34.4) reduced, compared to survey-based standard errors. Overall reduction in HPE standard errors compared survey-based standard errors ranged from 5.4 to 95%.
CONCLUSIONS
CONCLUSIONS
Facility data can be combined with population survey data to obtain more accurate HIV prevalence estimates for geographical areas with small population survey sample sizes. We recommend use of the methodology by district level managers to obtain more accurate HIV prevalence estimates to guide decision making without incurring additional data collection costs.
Identifiants
pubmed: 32293367
doi: 10.1186/s12889-020-8436-z
pii: 10.1186/s12889-020-8436-z
pmc: PMC7092592
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
379Subventions
Organisme : Wellcome Trust (GB)
ID : 107754/Z/15/Z
Références
J Int AIDS Soc. 2015 Nov 26;18:19954
pubmed: 26613900
Sex Transm Infect. 2008 Aug;84 Suppl 1:i71-i77
pubmed: 18647870
AIDS. 2014 Nov;28 Suppl 4:S469-76
pubmed: 25406750
J Acquir Immune Defic Syndr. 2000 Apr 15;23(5):410-7
pubmed: 10866234
AIDS. 1998 Jul 9;12(10):1227-34
pubmed: 9677172
JMIR Public Health Surveill. 2018 Apr 03;4(2):e36
pubmed: 29615387
Sex Transm Infect. 2008 Aug;84 Suppl 1:i17-i23
pubmed: 18647861
Bull World Health Organ. 2017 Oct 1;95(10):683-694
pubmed: 29147041
Proc Natl Acad Sci U S A. 2018 Dec 18;115(51):13063-13068
pubmed: 30518561
AIDS. 2003 Feb 14;17(3):399-405
pubmed: 12556694
J Acquir Immune Defic Syndr. 2009 May 1;51(1):78-84
pubmed: 19325495
Biochem Med (Zagreb). 2015 Jun 05;25(2):141-51
pubmed: 26110027
BMC Med Inform Decis Mak. 2014 May 13;14:40
pubmed: 24886567
AIDS. 2001 Sep 7;15(13):1717-25
pubmed: 11546948
AIDS. 2017 Apr;31 Suppl 1:S95-S102
pubmed: 28296805
HIV AIDS (Auckl). 2015 Jul 13;7:215-22
pubmed: 26203282
AIDS. 2001 May 4;15(7):907-16
pubmed: 11399963
Popul Health Metr. 2015 Sep 02;13:22
pubmed: 26336361
AIDS. 2002 Mar 8;16(4):643-52
pubmed: 11873009
Lancet. 1986 Feb 8;1(8476):307-10
pubmed: 2868172
J Int Assoc Provid AIDS Care. 2017 Nov/Dec;16(6):546-554
pubmed: 29117777
BMC Med Res Methodol. 2009 Jul 14;9:49
pubmed: 19602263
Front Public Health. 2018 Aug 14;6:192
pubmed: 30155454
AIDS. 2017 Apr;31 Suppl 1:S87-S94
pubmed: 28296804
AIDS. 2000 Dec 1;14(17):2741-50
pubmed: 11125893
Stat Med. 2011 Feb 28;30(5):560-8
pubmed: 21290401
AIDS. 2015 Sep 10;29(14):1845-53
pubmed: 26372390
PLoS Med. 2010 Jan 26;7(1):e1000223
pubmed: 20126260