Analytical methods used in estimating the prevalence of HIV/AIDS from demographic and cross-sectional surveys with missing data : a systematic review
Date
2020-03-14
Journal Title
Journal ISSN
Volume Title
Publisher
BMC (part of Springer Nature)
Abstract
Background: Sero- prevalence studies often have a problem of missing data. Few studies report the proportion of
missing data and even fewer describe the methods used to adjust the results for missing data. The objective of this
review was to determine the analytical methods used for analysis in HIV surveys with missing data.
Methods: We searched for population, demographic and cross-sectional surveys of HIV published from January
2000 to April 2018 in Pub Med/Medline, Web of Science core collection, Latin American and Caribbean Sciences
Literature, Africa-Wide Information and Scopus, and by reviewing references of included articles. All potential
abstracts were imported into Covidence and abstracts screened by two independent reviewers using pre-specified
criteria. Disagreements were resolved through discussion. A piloted data extraction tool was used to extract data
and assess the risk of bias of the eligible studies. Data were analysed through a quantitative approach; variables
were presented and summarised using figures and tables.
Results: A total of 3426 citations where identified, 194 duplicates removed, 3232 screened and 69 full articles were
obtained. Twenty-four studies were included. The response rate for an HIV test of the included studies ranged from 32 to
96% with the major reason for the missing data being refusal to consent for an HIV test. Complete case analysis was the
primary method of analysis used, multiple imputations 11(46%) was the most advanced method used, followed by the
Heckman’s selection model 9(38%). Single Imputation and Instrumental variables method were used in only two studies
each, with 13(54%) other different methods used in several studies. Forty-two percent of the studies applied more than
two methods in the analysis, with a maximum of 4 methods per study. Only 6(25%) studies conducted a sensitivity
analysis, while 11(46%) studies had a significant change of estimates after adjusting for missing data.
Conclusion: Missing data in survey studies is still a problem in disease estimation. Our review outlined a number of
methods that can be used to adjust for missing data on HIV studies; however, more information and awareness are needed
to allow informed choices on which method to be applied for the estimates to be more reliable and representative.
Description
CITATION: Mosha, N. R., et al. 2020. Analytical methods used in estimating the prevalence of HIV/AIDS from demographic and cross-sectional surveys with missing data : a systematic review. BMC Medical Research Methodology, 20:65, doi:10.1186/s12874-020-00944-w.
The original publication is available at https://bmcmedresmethodol.biomedcentral.com
The original publication is available at https://bmcmedresmethodol.biomedcentral.com
Keywords
HIV (Viruses) -- Incidence -- Analysis, Systematic reviews (Medical research)
Citation
Mosha, N. R., et al. 2020. Analytical methods used in estimating the prevalence of HIV/AIDS from demographic and cross-sectional surveys with missing data : a systematic review. BMC Medical Research Methodology, 20:65, doi:10.1186/s12874-020-00944-w