Evidence map›Paper›PMID 35435349›Full record

ArticleThe Indian journal of medical research2021

Comparative performance of verbal autopsy methods in identifying causes of adult mortality: A case study in India.

Mamta Gupta, P V M Lakshmi, Shankar Prinja, Tarundeep Singh, Titiksha Sirari, Chalapati Rao, Rajesh Kumar

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Article in The Indian journal of medical research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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3 · Its place in the literature

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2 citing papers in PubMed.

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5 · Who and what money

Authors and funding

7 authors.

Mamta GuptaDepartment of Community Medicine & School of Public Health, Post Graduate Institute of Medical Education & Research, Chandigarh, India.
P V M LakshmiDepartment of Community Medicine & School of Public Health, Post Graduate Institute of Medical Education & Research, Chandigarh, India.
Shankar PrinjaDepartment of Community Medicine & School of Public Health, Post Graduate Institute of Medical Education & Research, Chandigarh, India.
Tarundeep SinghDepartment of Community Medicine & School of Public Health, Post Graduate Institute of Medical Education & Research, Chandigarh, India.
Titiksha SirariAdvanced Pediatrics Centre, Post Graduate Institute of Medical Education & Research, Chandigarh, India.
Chalapati RaoDepartment of Global Health, Research School of Population Health, Australian National University, Canberra, Australia.
Rajesh KumarDepartment of Community Medicine & School of Public Health, Post Graduate Institute of Medical Education & Research, Chandigarh, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background & objectives: Cause of death assignment from verbal autopsy (VA) questionnaires is conventionally accomplished through physician review. However, since recently, computer softwares have been developed to assign the cause of death. The present study evaluated the performance of computer software in assigning the cause of death from the VA, as compared to physician review. Methods: VA of 600 adult deaths was conducted using open- and close-ended questionnaires in Nandpur Kalour Block of Punjab, India. Entire VA forms were used by two physicians independently to assign the cause of death using the International Statistical Classification of Diseases and Related Health Problems (ICD)-10 codes. In case of disagreement between them, reconciliation was done, and in cases of persistent disagreements finally, adjudication was done by a third physician. InterVA-4-generated causes from close-ended questionnaires were compared using Kappa statistics with causes assigned by physicians using a questionnaire having both open- and close-ended questions. At the population level, Cause-Specific Mortality Fraction (CSMF) accuracy and P-value from McNemar's paired Chi-square were calculated. CSMF accuracy indicates the absolute deviation of a set of proportions of causes of death out of the total number of deaths between the two methods. Results: The overall agreement between InterVA-4 and physician coding was 'fair' (κ=0.42; 95% confidence interval 0.38, 0.46). CSMF accuracy was found to be 0.71. The differences in proportions from the two methods were statistically different as per McNemar's paired Chi-square test for ischaemic heart diseases, liver cirrhosis and maternal deaths. Interpretation & conclusions: In comparison to physicians, assignment of causes of death by InterVA- 4 was only 'fair'. Hence, it may be appropriate to continue with physician review as the optimal option available in the current scenario.

Indexed as

PhysiciansAdultAutopsyCause of DeathHumansIndiaSurveys and QuestionnairesAdult mortalitycauses of deathcomputer-coded verbal autopsy methodsInterVA-4physician coding of deathverbal autopsy

Identifiers

PMID35435349
PMCPMC9205010

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LicenceCC BY-NC-SA
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.