HIV estimation using population based surveys with non-response: a partial identification approach
Oyelola A. Adegboye (),
Tomoki Fujii,
Denis Heng-Yan Leung () and
Siyu Li ()
Additional contact information
Oyelola A. Adegboye: Menzies School of Health Research, Charles Darwin University, Australia
Denis Heng-Yan Leung: School of Economics, Singapore Management University, Singapore
Siyu Li: School of Economics, Singapore Management University, Singapore
No 4-2024, Economics and Statistics Working Papers from Singapore Management University, School of Economics
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 boundsare significantly more informative than the worst case bounds without sacrificing credibility
Pages: 72 pages
Date: 2024-11-07
New Economics Papers: this item is included in nep-hea and nep-sea
References: Add references at CitEc
Citations:
Downloads: (external link)
https://ink.library.smu.edu.sg/soe_research/2748/ Full text (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:ris:smuesw:2024_004
Access Statistics for this paper
More papers in Economics and Statistics Working Papers from Singapore Management University, School of Economics 90 Stamford Road, Sigapore 178903. Contact information at EDIRC.
Bibliographic data for series maintained by Cheong Pei Qi ( this e-mail address is bad, please contact ).