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Validation of the symptom pattern method for analyzing verbal autopsy data.

Christopher J L Murray1, Alan D Lopez, Dennis M Feehan

  • 1Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, United States of America. cjlm@u.washington.edu

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A new symptom pattern (SP) method for verbal autopsy (VA) data significantly improves cause of death accuracy compared to physician-coded verbal autopsy (PCVA). SP is more reliable, especially in low-resource settings lacking medical records.

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Area of Science:

  • Public Health
  • Epidemiology
  • Biostatistics

Background:

  • Cause of death data are essential for public health policy.
  • Verbal autopsy (VA) is used when vital registration data are unreliable.
  • Physician-coded verbal autopsy (PCVA) is costly and its accuracy is questionable, particularly in resource-limited settings.

Purpose of the Study:

  • To develop and validate a statistical strategy for analyzing VA data that overcomes PCVA limitations.
  • To introduce the symptom pattern (SP) method, combining advantages of existing approaches.
  • To improve the accuracy of cause-specific mortality fraction (CSMF) estimation and individual cause of death assignment.

Main Methods:

  • The symptom pattern (SP) method utilizes two VA datasets: one with known causes of death (e.g., hospital deaths) and a representative population sample.
  • Symptom properties (probability of a symptom given a cause of death) are computed from the hospital data.
  • These properties are used to estimate population CSMFs and assign individual causes of death, with iterative refinement of CSMFs.

Main Results:

  • In China, SP achieved lower average relative error (16%) and absolute error (0.7%) in estimating population CSMFs compared to PCVA (27% and 1.1%).
  • SP correctly assigned individual causes of death in 83% of cases, outperforming PCVA's 69% accuracy.
  • Without access to medical record recall, SP's accuracy remained high (78% individual, 14% population relative error), while PCVA accuracy dropped significantly (38% individual, 70% population relative error).

Conclusions:

  • The SP method demonstrates superior performance to PCVA for both population and individual cause of death assignment in the studied dataset.
  • SP's effectiveness is not dependent on medical record recall, making it suitable for low-resource settings where PCVA is limited.
  • Further validation across diverse datasets is recommended to assess SP's generalizability across different cultures and languages.