Incompleteness and Misclassification of Maternal Deaths in Zimbabwe: Data from Two Reproductive Age Mortality
Reuben Musarandega1, Lennarth Nystrom2, Grant Murewanhema3
1School of Health Systems and Public Health, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa. rmusara@gmail.com.
Journal of Epidemiology and Global Health
|November 12, 2024
Summary
Maternal deaths in Zimbabwe remain high due to incomplete data and misclassification. Improving data collection and reducing misclassification are crucial for accurate maternal mortality studies.
Area of Science:
- Reproductive Health
- Public Health Surveillance
- Mortality Studies
Background:
- Two cross-sectional reproductive age mortality surveys were conducted in Zimbabwe (2007-2008 and 2018-2019) to assess changes in maternal mortality ratio (MMR) and causes of death.
- Data were collected from health institutions, civil registration and vital statistics (CRVS), community surveillance, and other sources.
- This study specifically analyzes the missingness and misclassification of maternal deaths within these data sources.
Purpose of the Study:
- To evaluate changes in the maternal mortality ratio (MMR) and causes of death in Zimbabwe between two survey periods.
- To analyze the extent of missingness and misclassification of maternal deaths across different data sources.
- To assess the impact of misclassification on MMR estimates and the effectiveness of data triangulation methods.
Main Methods:
- Comparative analysis of missed and misclassified deaths using Chi-square or Fisher's exact tests.
- Log-linear regression models to compare risk ratios of missed deaths across data sources.
- Sensitivity and specificity analysis of death identification using the six-box method and Binomial exact tests.
Main Results:
- All data sources exhibited both missingness and misclassification of maternal deaths.
- In 2007-08, community surveys and CRVS were more effective in identifying maternal deaths than health records; this trend reversed in 2018-19.
- Misclassification of causes of death significantly reduced estimated MMRs across all data sources, including health records, CRVS, and community/surveillance data.
Conclusions:
- High levels of incompleteness and misclassification of maternal deaths persist in Zimbabwe.
- Maternal mortality studies require triangulation of multiple data sources to enhance data completeness.
- Continued efforts are necessary to reduce the misclassification of causes of death in mortality data.
Keywords:
IncompletenessMaternal deathsMaternal mortalityMisclassificationMissingnessPregnancy-related deathsMore Related Videos
Related Concept Videos
02:57Meiosis vs. Mitosis
53.0K
Cell division is necessary for growth and reproduction in organisms. Mitosis aids cell growth and development by dividing somatic cells. In contrast, meiosis causes the division of germ cells and plays an essential role in sexual reproduction. Due to their unique functional requirements, mitosis and meiosis differ from each other in multiple aspects.
Before the start of mitosis and meiosis I, the cell synthesizes DNA, resulting in two homologous copies of each chromosome. DNA synthesis is...
Before the start of mitosis and meiosis I, the cell synthesizes DNA, resulting in two homologous copies of each chromosome. DNA synthesis is...
53.0K
01:24Kaplan-Meier Approach
100
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
100
01:22Life Tables
79
A life table is a statistical tool that summarizes the mortality and survival patterns of a population, providing detailed insights into the likelihood of survival or death across different age intervals within a cohort. By organizing data on survival probabilities and mortality rates, life tables offer a clear snapshot of population dynamics over time. They are extensively used in demography, public health, actuarial science, and ecology to analyze life expectancy, design health interventions,...
79
01:15Assumptions of Survival Analysis
97
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
97
01:22Applications of Life Tables
50
Life tables are versatile across various fields, providing a quantitative basis for analyzing mortality and survival rates. Whether used by demographers, actuaries, epidemiologists, or sociologists, life tables offer valuable insights into the dynamics of life and death, facilitating informed decisions in public health, insurance, conservation, and beyond. Their broad applicability highlights the interconnectedness of demographic data with practical outcomes in everyday life and strategic...
50
01:15Systematic Error: Methodological and Sampling Errors
1.4K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
1.4K