1 / 15

Comparison between clinicians’ reviews of verbal autopsies and a probabilistic model in Zimbabwe

Comparison between clinicians’ reviews of verbal autopsies and a probabilistic model in Zimbabwe. Ron Mataya, Velda Mushangwe, Edward Fottrell, Stephen Munjanja. Background. Zimbabwe maternal and perinatal mortality study 2007-8 Causes of maternal deaths identified by

delora
Download Presentation

Comparison between clinicians’ reviews of verbal autopsies and a probabilistic model in Zimbabwe

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Comparison between clinicians’ reviews of verbal autopsies and a probabilistic model in Zimbabwe Ron Mataya, Velda Mushangwe, Edward Fottrell, Stephen Munjanja

  2. Background • Zimbabwe maternal and perinatal mortality study 2007-8 • Causes of maternal deaths identified by • Review of medical records • Verbal autopsy

  3. Definitionof verbal autopsy: • Verbal autopsy: interviewing of family members about the circumstances of a death at home to arrive at a cause of death (WHO)

  4. Rationale for the study • Many deliveries and deaths occur at home • Facility-based causes of deaths are an inaccurate reflection of cause-specific mortality • Interventions to prevent maternal deaths should be based on population-based data

  5. Study objective: • To determine causes of death among women of reproductive age dying at home • To compare the causes of death as determined by 3 physicians • To assess the performance of a probabilistic model (InterVA-M, (www.interva.net) against the physicians

  6. Study Design • Population based study of pregnancy outcomes in 11 randomly selected districts in Zimbabwe • All maternal deaths were recruited during the study period

  7. Determination of Pregnancy Status at Death • Pregnant at death • Not pregnant within 6 weeks of death • Pregnancy ended within 6 weeks of death

  8. Physician coding for pregnancy status • In 82/87 of cases (94%) 2 physicians independently reached the same conclusion • Disagreed in 5 cases (6%) • Overall kappa statistic for agreement in relation to pregnancy status was 0.89;

  9. Results: Physician coding for causes • Reached the same cause of death in 38% (n=33) of cases • In 44 cases (51%) 2 out of the 3 reached the same cause of death • In 10 (11%) each reached a different conclusion • Combined kappa for the 3 reviewers is 0.49, representing a fair agreement

  10. Results: Pregnancy status of the 87 deaths as classified by InterVA-M & 2 physicians

  11. Results: InterVA-M; causes • Each death assigned a probability • Probability used in weighting the causes in calculating cause-specific mortality fractions (CSMF) for the whole sample • InterVA-M quoted 2 causes with the highest probability in 66 cases (72%) • Single cause in 15 cases (17%) • 3 causes in 6 cases (7%) • Overall certainty for causes assigned to each case by InterVA-M was 53%

  12. Comparison of major cause categories

  13. Agreement between physicians and InterVA-M • In 80 cases (92%) the InterVA-M determined pregnancy status agreed with at least one of the two coding physicians • In 69% of cases at least one of the InterVA-M causes agreed with at least one of the physician diagnoses • In 27 cases (31%) no agreement between InterVA-M and physician diagnoses

  14. Conclusion • Population-level cause-specific mortality fractions generally compared well between the InterVA-M approach and physicians reviews • The InterVA-M method continues to show promise as an efficient and reliable tool for ascertaining major population-level mortality burdens required for health planning, monitoring and program assessment.

More Related