Verbal Autopsy Methodologies in Population Health Analytics

Verbal autopsy (VA) is a structured interview method used to determine probable causes of death in populations where medical certification is incomplete or absent. By gathering information from caregivers or witnesses about symptoms, circumstances and medical history preceding death, VA bridges critical gaps in global mortality data. Recent advances have standardised questionnaires, harmonised cause categories and enabled incorporation of VA into routine civil registration and vital statistics (CRVS) systems. Automated diagnostic algorithms now complement or replace physician review, offering rapid, reproducible assignment of causes at scale. Integration with digital platforms and machine-learning techniques has further enhanced timeliness, consistency and comparability of VA data across diverse settings. These methodologies support national and international health monitoring, inform policy decisions and guide resource allocation by producing reliable cause-specific mortality fractions for distinct age groups, regions and disease categories.

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Verbal Autopsy Methodologies in Population Health Analytics publication trend

The graph below shows the total number of articles in verbal autopsy methodologies in population health analytics across all publications each year (not limited to Nature Index journals).

Technical terms

Verbal autopsy (VA): A structured interview method used to determine probable causes of death through systematic collection of symptom and circumstance data from non-medical respondents.

Civil Registration and Vital Statistics (CRVS): A system for continuous, standardised recording of vital events, including births and deaths, and their causes, underpinning national health statistics.

Cause-specific mortality fraction (CSMF): The proportion of deaths in a population attributed to a particular cause, derived from VA or certification data.

Automated diagnostic algorithm: A computer-based method that processes VA questionnaire data to assign probable causes of death using statistical or machine-learning techniques.

InterVA-5 model: A probabilistic algorithm aligned with the WHO 2016 VA instrument that harmonises multiple input formats to estimate cause-specific mortality profiles at population level.

References

1. Cardiovascular disease mortality based on verbal autopsy in low- and middle-income countries: a systematic review. Bulletin of the World Health Organization (2023).
2. Under-5 mortality surveillance in low-income and middle-income countries: insights from two Health and Demographic Surveillance Systems in rural Gambia. BMJ Global Health (2024).
3. An integrated approach to processing WHO-2016 verbal autopsy data: the InterVA-5 model. BMC Medicine (2019).
4. Integrating community-based verbal autopsy into civil registration and vital statistics (CRVS): system-level considerations. Global Health Action (2017).
5. The WHO 2016 verbal autopsy instrument: An international standard suitable for automated analysis by InterVA, InSilicoVA, and Tariff 2.0. PLOS Medicine (2018).