Cargando…

Adjusting HIV Prevalence for Survey Non-Response Using Mortality Rates: An Application of the Method Using Surveillance Data from Rural South Africa

BACKGROUND: The main source of HIV prevalence estimates are household and population-based surveys; however, high refusal rates may hinder the interpretation of such estimates. The study objective was to evaluate whether population HIV prevalence estimates can be adjusted for survey non-response usi...

Descripción completa

Detalles Bibliográficos
Autores principales: Nyirenda, Makandwe, Zaba, Basia, Bärnighausen, Till, Hosegood, Victoria, Newell, Marie-Louise
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2928261/
https://www.ncbi.nlm.nih.gov/pubmed/20811499
http://dx.doi.org/10.1371/journal.pone.0012370
_version_ 1782185845065777152
author Nyirenda, Makandwe
Zaba, Basia
Bärnighausen, Till
Hosegood, Victoria
Newell, Marie-Louise
author_facet Nyirenda, Makandwe
Zaba, Basia
Bärnighausen, Till
Hosegood, Victoria
Newell, Marie-Louise
author_sort Nyirenda, Makandwe
collection PubMed
description BACKGROUND: The main source of HIV prevalence estimates are household and population-based surveys; however, high refusal rates may hinder the interpretation of such estimates. The study objective was to evaluate whether population HIV prevalence estimates can be adjusted for survey non-response using mortality rates. METHODOLOGY/PRINCIPAL FINDINGS: Data come from the longitudinal Africa Centre Demographic Information System (ACDIS), in rural South Africa. Mortality rates for persons tested and not tested in the 2005 HIV surveillance were available from routine household surveillance. Assuming HIV status among individuals contacted but who refused to test (non-response) is missing at random and mortality among non-testers can be related to mortality of those tested a mathematical model was developed. Non-parametric bootstrapping was used to estimate the 95% confidence intervals around the estimates. Mortality rates were higher among untested (16.9 per thousand person-years) than tested population (11.6 per thousand person-years), suggesting higher HIV prevalence in the former. Adjusted HIV prevalence for females (15–49 years) was 31.6% (95% CI 26.1–37.1) compared to observed 25.2% (95% CI 24.0–26.4). For males (15–49 years) adjusted HIV prevalence was 19.8% (95% CI 14.8–24.8), compared to observed 13.2% (95% CI 12.1–14.3). For both sexes (15–49 years) combined, adjusted prevalence was 27.5% (95% CI 23.6–31.3), and observed prevalence was 19.7% (95% CI 19.6–21.3). Overall, observed prevalence underestimates the adjusted prevalence by around 7 percentage points (37% relative difference). CONCLUSIONS/SIGNIFICANCE: We developed a simple approach to adjust HIV prevalence estimates for survey non-response. The approach has three features that make it easy to implement and effective in adjusting for selection bias than other approaches. Further research is needed to assess this approach in populations with widely available HIV treatment (ART).
format Text
id pubmed-2928261
institution National Center for Biotechnology Information
language English
publishDate 2010
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-29282612010-09-01 Adjusting HIV Prevalence for Survey Non-Response Using Mortality Rates: An Application of the Method Using Surveillance Data from Rural South Africa Nyirenda, Makandwe Zaba, Basia Bärnighausen, Till Hosegood, Victoria Newell, Marie-Louise PLoS One Research Article BACKGROUND: The main source of HIV prevalence estimates are household and population-based surveys; however, high refusal rates may hinder the interpretation of such estimates. The study objective was to evaluate whether population HIV prevalence estimates can be adjusted for survey non-response using mortality rates. METHODOLOGY/PRINCIPAL FINDINGS: Data come from the longitudinal Africa Centre Demographic Information System (ACDIS), in rural South Africa. Mortality rates for persons tested and not tested in the 2005 HIV surveillance were available from routine household surveillance. Assuming HIV status among individuals contacted but who refused to test (non-response) is missing at random and mortality among non-testers can be related to mortality of those tested a mathematical model was developed. Non-parametric bootstrapping was used to estimate the 95% confidence intervals around the estimates. Mortality rates were higher among untested (16.9 per thousand person-years) than tested population (11.6 per thousand person-years), suggesting higher HIV prevalence in the former. Adjusted HIV prevalence for females (15–49 years) was 31.6% (95% CI 26.1–37.1) compared to observed 25.2% (95% CI 24.0–26.4). For males (15–49 years) adjusted HIV prevalence was 19.8% (95% CI 14.8–24.8), compared to observed 13.2% (95% CI 12.1–14.3). For both sexes (15–49 years) combined, adjusted prevalence was 27.5% (95% CI 23.6–31.3), and observed prevalence was 19.7% (95% CI 19.6–21.3). Overall, observed prevalence underestimates the adjusted prevalence by around 7 percentage points (37% relative difference). CONCLUSIONS/SIGNIFICANCE: We developed a simple approach to adjust HIV prevalence estimates for survey non-response. The approach has three features that make it easy to implement and effective in adjusting for selection bias than other approaches. Further research is needed to assess this approach in populations with widely available HIV treatment (ART). Public Library of Science 2010-08-25 /pmc/articles/PMC2928261/ /pubmed/20811499 http://dx.doi.org/10.1371/journal.pone.0012370 Text en Nyirenda et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Nyirenda, Makandwe
Zaba, Basia
Bärnighausen, Till
Hosegood, Victoria
Newell, Marie-Louise
Adjusting HIV Prevalence for Survey Non-Response Using Mortality Rates: An Application of the Method Using Surveillance Data from Rural South Africa
title Adjusting HIV Prevalence for Survey Non-Response Using Mortality Rates: An Application of the Method Using Surveillance Data from Rural South Africa
title_full Adjusting HIV Prevalence for Survey Non-Response Using Mortality Rates: An Application of the Method Using Surveillance Data from Rural South Africa
title_fullStr Adjusting HIV Prevalence for Survey Non-Response Using Mortality Rates: An Application of the Method Using Surveillance Data from Rural South Africa
title_full_unstemmed Adjusting HIV Prevalence for Survey Non-Response Using Mortality Rates: An Application of the Method Using Surveillance Data from Rural South Africa
title_short Adjusting HIV Prevalence for Survey Non-Response Using Mortality Rates: An Application of the Method Using Surveillance Data from Rural South Africa
title_sort adjusting hiv prevalence for survey non-response using mortality rates: an application of the method using surveillance data from rural south africa
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2928261/
https://www.ncbi.nlm.nih.gov/pubmed/20811499
http://dx.doi.org/10.1371/journal.pone.0012370
work_keys_str_mv AT nyirendamakandwe adjustinghivprevalenceforsurveynonresponseusingmortalityratesanapplicationofthemethodusingsurveillancedatafromruralsouthafrica
AT zababasia adjustinghivprevalenceforsurveynonresponseusingmortalityratesanapplicationofthemethodusingsurveillancedatafromruralsouthafrica
AT barnighausentill adjustinghivprevalenceforsurveynonresponseusingmortalityratesanapplicationofthemethodusingsurveillancedatafromruralsouthafrica
AT hosegoodvictoria adjustinghivprevalenceforsurveynonresponseusingmortalityratesanapplicationofthemethodusingsurveillancedatafromruralsouthafrica
AT newellmarielouise adjustinghivprevalenceforsurveynonresponseusingmortalityratesanapplicationofthemethodusingsurveillancedatafromruralsouthafrica