Incompleteness and Misclassification of Maternal Deaths in Zimbabwe: Data from Two Reproductive Age Mortality Surveys, 2007-2008 and 2018-2019.

Autor: Musarandega R; School of Health Systems and Public Health, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa. rmusara@gmail.com., Nystrom L; Department of Epidemiology and Global Health, Umea University, Umea, Sweden., Murewanhema G; Unit of Obstetrics and Gynaecology, Faculty of Medical and Health Sciences, University of Zimbabwe, Harare, Zimbabwe., Gwanzura C; Unit of Obstetrics and Gynaecology, Faculty of Medical and Health Sciences, University of Zimbabwe, Harare, Zimbabwe., Ngwenya S; Department of Obstetrics and Gynaecology, Mpilo Central Hospital, National University of Science and Technology, Bulawayo, Zimbabwe., Pattinson R; Research Centre for Maternal, Fetal, Newborn and Child Health Care Strategies, University of Pretoria, Pretoria, South Africa., Machekano R; Biostatistics and Epidemiology Department, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa., Munjanja SP; Unit of Obstetrics and Gynaecology, Faculty of Medical and Health Sciences, University of Zimbabwe, Harare, Zimbabwe.
Jazyk: angličtina
Zdroj: Journal of epidemiology and global health [J Epidemiol Glob Health] 2024 Nov 12. Date of Electronic Publication: 2024 Nov 12.
DOI: 10.1007/s44197-024-00318-1
Abstrakt: Introduction: We implemented two cross-sectional reproductive age mortality surveys in 2007-2008 and 2018-2019 to assess changes in the MMR and causes of death in Zimbabwe. We collected data from health institutions, civil registration and vital statistics, the community, and surveillance. This paper analyses missingness and misclassification of deaths in the two surveys.
Methods: We compared proportions of missed and misclassified deaths in the surveys using Chi-square or Fisher's exact tests. Using log-linear regression models, we calculated and compared risk ratios of missed deaths in the data sources. We assessed the effect on MMRs of misclassifying deaths and analysed the sensitivity and specificity of identifying deaths in the surveys using the six-box method and risk ratios calculated through Binomial exact tests.
Results: All data sources missed and misclassified the deaths. The community survey was seven times [RR 7.1 (5.1-9.7)] and CRVS three times [RR 3.4 (2.4-4.7)] more likely to identify maternal deaths than health records in 2007-08. In 2018-19, CRVS [RR 0.8 (0.7-0.9)] and surveillance [RR 0.7 (0.6-0.9)] were less likely to identify maternal deaths than health records. Misclassification of causes of death significantly reduced MMRs in health records [RR 1.4 (1.2-1.5)]; CRVS [RR 1.3 (1.1-1.5)] and the community survey/surveillance [RR 1.4 (1.2-1.6)].
Conclusion: Incompleteness and misclassification of maternal deaths are still high in Zimbabwe. Maternal mortality studies must triangulate data sources to improve the completeness of data while efforts to reduce misclassification of deaths continue.
Competing Interests: Declarations Conflict of Interest The authors declare no competing interests. Ethics Approval and Consent to Participate The University of Pretoria and the Medical Research Council of Zimbabwe institutional review boards (IRBs) approved the study including collection of clinical notes for obstetricians to use to recode the causes of death. The MoHCC and RG’s office approved the study including collecting person identifying data used to link and de-duplicate data across sources. Verbal autopsy informants gave written informed consent to be interviewed in the 2007–08 survey. Informed consent was waived in the 2018–19 survey when all data were collected from existing records. Consent for Publication Not applicable.
(© 2024. The Author(s).)
Databáze: MEDLINE
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