Strengthening Cause of Death Statistics in Selected Districts of 3 States in India: Protocol for an Uncontrolled, Before-After, Mixed Method Study
Bibliographic record
Abstract
BACKGROUND: Mortality statistics are vital for health policy development, epidemiological research, and health care service planning. A robust surveillance system is essential for obtaining vital information such as cause of death (CoD) information. OBJECTIVE: This study aims to develop a comprehensive model to strengthen the CoD information in the selected study sites. The specific objectives are (1) to identify the best practices and challenges in the functioning of the Civil Registration and Vital Statistics (CRVS) system with respect to mortality statistics and CoD information; (2) to develop and implement interventions to strengthen the CoD information; (3) to evaluate the quality improvement of the Medical Certification of Cause of Death (MCCD); and (4) to improve the CoD information at the population level through verbal autopsy for noninstitutional deaths in the selected study sites. METHODS: An uncontrolled, before-after, mixed method study will be conducted in 3 blocks located in the districts of 3 states (Madhya Pradesh, Uttar Pradesh, and Odisha) in India. A baseline assessment to identify the best practices and challenges in the functioning of the CRVS system, along with a quality assessment of the MCCD, will be conducted. An intervention informed by existing literature and the baseline assessment will be developed and implemented in the study sites. The major components of intervention will include a Training of Trainers workshop, orientation of stakeholders in the functioning of the CRVS system, training of physicians and medical officers in the MCCD, and training of community health workers in World Health Organization Verbal Autopsy 2022 instrument. Postintervention evaluation will be carried out to assess the impact made by the intervention on the availability and quality improvement of CoD information in the selected study sites. The outcome will be measured in terms of the quality improvement of the MCCD and the availability of CoD information at population level through verbal autopsy in the selected study sites. RESULTS: The project has been funded, and regulatory approval has been obtained from the Institutional Ethics Committee. The data collection process began in May 2023. The duration of the study will be for 24 months. CONCLUSIONS: Our study is expected to provide a valuable contribution toward strengthening CoD information, which could be helpful for policy making and further research. The intervention model will be developed in collaboration with the existing functionaries of the health and CRVS systems in the selected study sites that are engaged in reporting and recording CoD information; this will ensure sustainability and provide lessons for upscaling, with the aim to improve the reporting of CoD information in the country. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/51493.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".