Comparar métodos

	Verbal Autopsy	Small-Area Health Estimation
Campo	Social Epidemiology	Social Epidemiology
Familia	Process / pipeline	Regression model
Año de origen	2012	1979
Autor original	Peter Byass et al. (InterVA); Christopher Murray et al. / PHMRC (Tariff, SmartVA)	Robert E. Fay & Roger A. Herriot; J. N. K. Rao & Isabel Molina
Tipo	Survey-based cause-of-death measurement pipeline	Model-based estimator for reliable indicators in data-sparse areas
Fuente seminal	Byass, P., Chandramohan, D., Clark, S. J., D'Ambruoso, L., Fottrell, E., Graham, W. J., et al. (2012). Strengthening standardised interpretation of verbal autopsy data: the new InterVA-4 tool. Global Health Action, 5, 19281.	Fay, R. E., & Herriot, R. A. (1979). Estimates of Income for Small Places: An Application of James-Stein Procedures to Census Data. Journal of the American Statistical Association, 74(366), 269-277.
Alias	VA, Automated Verbal Autopsy, InterVA, Tariff / SmartVA	Small Area Estimation for Health, Fay-Herriot Health Estimation, Model-Based Small-Area Prevalence, Local Health Indicator Estimation
Relacionados	3	3
Resumen	Verbal autopsy is a method for assigning a probable cause of death by interviewing the caregivers or relatives of a person who died, used where medical certification and vital registration are weak or absent. A trained interviewer administers a structured questionnaire about the signs, symptoms, and circumstances preceding death, and the resulting symptom profile is converted into a cause of death — historically by physician review, and increasingly by automated tools. Two computer-based approaches dominate: the probabilistic InterVA model, formalized for InterVA-4 by Peter Byass and colleagues in 2012 and aligned with the WHO instrument, and the Tariff method behind SmartVA, developed and validated by Christopher Murray and the Population Health Metrics Research Consortium (PHMRC) in 2014. Verbal autopsy supplies cause-of-death data for roughly the majority of the world's deaths that occur without medical attendance.	Small-area estimation produces reliable health indicators for places where the survey sample is too thin to support a trustworthy direct estimate. A national health survey may interview only a handful of people in a given county or census tract, so a county-level prevalence computed straight from the data swings wildly from area to area. The model-based solution, pioneered by Robert Fay and Roger Herriot in 1979 for estimating income in small places, is to borrow strength: combine each area's noisy direct estimate with a regression prediction built from auxiliary variables that are known for every area, weighting the two by their relative reliability. Rao and Molina's comprehensive treatment codified this area-level mixed model and its variants as the foundation of small area estimation. Applied to public health, the approach underpins local prevalence maps for chronic disease and health behaviors, such as the CDC PLACES project, that decision-makers use to target resources at neighborhood and county scale.