Publication Type
Journal Article
Version
publishedVersion
Publication Date
7-2024
Abstract
HIV estimation using data from the demographic and health surveys (DHS) is limited by the presence of non-response and test refusals. Conventional adjustments such as imputation require the data to be missing at random. Methods that use instrumental variables allow the possibility that prevalence is different between the respondents and non-respondents, but their performance depends critically on the validity of the instrument. Using Manski's partial identification approach, we form instrumental variable bounds for HIV prevalence from a pool of candidate instruments. Our method does not require all candidate instruments to be valid. We use a simulation study to evaluate and compare our method against its competitors. We illustrate the proposed method using DHS data from Zambia, Malawi and Kenya. Our simulations show that imputation leads to seriously biased results even under mild violations of non-random missingness. Using worst case identification bounds that do not make assumptions about the non-response mechanism is robust but not informative. By taking the union of instrumental variable bounds balances informativeness of the bounds and robustness to inclusion of some invalid instruments. Non-response and refusals are ubiquitous in population based HIV data such as those collected under the DHS. Partial identification bounds provide a robust solution to HIV prevalence estimation without strong assumptions. Union bounds are significantly more informative than the worst case bounds without sacrificing credibility.
Keywords
demographic and health surveys, HIV, instrumental variable, non-response, partial identification
Discipline
Econometrics | Health Economics
Research Areas
Econometrics
Publication
Statistics in Medicine
Volume
43
Issue
16
First Page
3005
Last Page
3019
ISSN
0277-6715
Identifier
10.1002/sim.10108
Publisher
Wiley
Citation
ADEGBOYE, Oyelola A.; FUJII, Tomoki; LEUNG, Denis H. Y.; and LI, Siyu.
HIV estimation using population-based surveys with non-response: A partial identification approach. (2024). Statistics in Medicine. 43, (16), 3005-3019.
Available at: https://ink.library.smu.edu.sg/soe_research/2749
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Additional URL
https://doi.org/10.1002/sim.10108
