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Record W2401090187 · doi:10.1016/s2214-109x(16)30045-6

Level of evidence of verbal autopsy – Authors' reply

2016· letter· en· W2401090187 on OpenAlexaff
Usha Ram, Rajesh Dikshit, Prabhat Jha

Bibliographic record

VenueThe Lancet Global Health · 2016
Typeletter
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsPublic Health OntarioUniversity of TorontoCentre for Global Health ResearchSt. Michael's Hospital
Fundersnot available
KeywordsVerbal autopsyAutopsyMedicinePsychologyPathologyCause of death

Abstract

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Most of the 60 million annual global deaths occur in low-income and middle-income countries and most of these occur without medical attention at the time of death, hence the causes of death are unknown.1Jha P Reliable direct measurement of causes of death in low- and middle-income countries.BMC Med. 2014; 12: 19Crossref PubMed Scopus (66) Google Scholar Thus, alternative systems are needed to measure causes of death especially to understand nationally representative patterns of deaths. Verbal autopsies have been used to determine childhood causes of death for decades, and we have extended such measurement for adults in India using careful quality-control efforts and dual physician-coding in the Million Death Study (MDS).2Aleksandrowicz L Malhotra V Dikshit R et al.Performance criteria for verbal autopsy-based systems to estimate national causes of death: development and application to the Indian Million Death Study.BMC Med. 2014; 12: 21Crossref PubMed Scopus (50) Google Scholar A key lesson from the MDS2Aleksandrowicz L Malhotra V Dikshit R et al.Performance criteria for verbal autopsy-based systems to estimate national causes of death: development and application to the Indian Million Death Study.BMC Med. 2014; 12: 21Crossref PubMed Scopus (50) Google Scholar is the need to avoid the fallacy of a “gold standard“of hospital-based causes of death. Hospital-based deaths are a poor choice to validate verbal autopsies since variables such as the age and education of the person, pathogen distribution, and causes of death differ greatly between rural unattended deaths and hospital-based deaths. The findings from the MDS2Aleksandrowicz L Malhotra V Dikshit R et al.Performance criteria for verbal autopsy-based systems to estimate national causes of death: development and application to the Indian Million Death Study.BMC Med. 2014; 12: 21Crossref PubMed Scopus (50) Google Scholar show sharp differences in the proportion of deaths from various causes between hospital and home deaths at ages 5–69 years, even after adjustment for differences in education and age (table). Similarly, deaths caused by malaria rarely occur in hospitals, since malaria is mostly cured when treatment is given. By contrast, malaria remains a common cause of rural, unattended fever deaths among adults in India and in Africa.3Registrar General of India and Centre for Global Health ResearchCauses of death in India, 2010–2013: Sample Registration System. Government of India, New Delhi2016Google Scholar, 4Dhingra N Jha P Sharma VP et al.Adult and child malaria mortality in India: a nationally representative mortality survey.Lancet. 2010; 376: 1768-1774Summary Full Text Full Text PDF PubMed Scopus (203) Google ScholarTableCause-specific mortality fractions for original RGI surveyors versus independent random re-sample teams, and for home versus hospital deaths, with p value tests differences, Million Death Study deaths 2001–03 at ages 5–69 years800 RGI surveyors70 random re-sample teamsp value for differences: re-sample vs RGIHome deaths*This analysis excludes 6549 deaths occurring in other places and 1834 with unknown location. NS=not significant. RGI=Registrar General of India.Hospital deaths*This analysis excludes 6549 deaths occurring in other places and 1834 with unknown location. NS=not significant. RGI=Registrar General of India.p value for differences: hospital vs homeTotal number of deaths63 139181143 97910 779Heart attacks7557 (12·0%)239 (13·2%)NS4964 (11·3%)1590 (14·8%)<0·01Other infectious diseases7005 (11·1%)234 (12·9%)<0·016430 (14·6%)1153 (10·7%)<0·01Tuberculosis5714 (9·0%)139 (7·7%)NS4741 (10·8%)636 (5·9%)<0·01Cancer5511 (8·7%)152 (8·4%)NS4183 (9·5%)984 (9·1%)<0·01Chronic lung disease5494 (8·7%)134 (7·4%)NS4646 (10·6%)515 (4·8%)<0·01Stroke4526 (7·2%)137 (7·6%)NS3290 (7·5%)906 (8·4%)<0·01Suicides2647 (4·2%)60 (3·3%)NS1467 (3·3%)506 (4·7%)NSRenal2511 (4·0%)67 (3·7%)NS848 (1·9%)285 (2·6%)<0·01Liver cirrhosis2463 (3·9%)60 (3·3%)NS1707 (3·9%)545 (5·1%)NSMalaria2094 (3·3%)40 (2·2%)NS1618 (3·7%)326 (3·0%)NSRoad traffic accidents1864 (3·0%)71 (3·9%)NS230 (0·5%)484 (4·5%)<0·01Maternal conditions1053 (1·7%)14 (0·8%)<0·01522 (1·2%)343 (3·2%)<0·01HIV and other sexually transmitted infections439 (0·7%)8 (0·4%)NS359 (0·8%)50 (0·5%)<0·01Ill defined3766 (6·0%)108 (6·0%)NS2576 (5·9%)370 (3·4%)<0·01All other causes10 496 (16·6%)348 (19·2%)<0·016398 (14·5%)2086 (19·4%)<0·01All p values adjusted for age, rural or urban status, and education level.* This analysis excludes 6549 deaths occurring in other places and 1834 with unknown location. NS=not significant. RGI=Registrar General of India. Open table in a new tab All p values adjusted for age, rural or urban status, and education level. Evidence for verbal autopsy should consider whether independent resampling yields similar results. The proportions of major cause of death at ages 5–69 years, and their rank order are very similar between the approximately 3% of deaths that were independently and randomly resampled and those from the main MDS (table). This finding suggests stability and reproducibility of the MDS methods, but makes no specious claim to validity. We have published other performance criteria,2Aleksandrowicz L Malhotra V Dikshit R et al.Performance criteria for verbal autopsy-based systems to estimate national causes of death: development and application to the Indian Million Death Study.BMC Med. 2014; 12: 21Crossref PubMed Scopus (50) Google Scholar including the fact that the MDS is able to make use of more International Classification of Diseases (ICD)-10 codes than alternative classifier systems. As well, the proportion of ill-defined deaths before age 70 years is quite low, but verbal autopsy is less accurate after this age.1Jha P Reliable direct measurement of causes of death in low- and middle-income countries.BMC Med. 2014; 12: 19Crossref PubMed Scopus (66) Google Scholar This finding is consistent with broad public health goals to reduce premature deaths and disability, which in most low-income and middle-income countries arises mostly from mortality before age 70 years.5Norheim OF Jha P Admasu K et al.Avoiding 40% of the premature deaths in each country, 2010–30: review of national mortality trends to help quantify the UN sustainable development goal for health.Lancet. 2015; 385: 239-252Summary Full Text Full Text PDF PubMed Scopus (184) Google Scholar Thus, the evidence is strong for the use of well standardised and reproducible verbal autopsy methods to improve public health planning in countries where medically certified causes of death remain a distant reality. We declare no competing interests. Level of evidence of verbal autopsyVerbal autopsy determines probable causes of death when neither medical records nor formal medical attention are given. Usha Ram and colleagues (December, 2015)1 extrapolated cause-specific mortality risks for men and women aged 15–69 years on the basis of deaths accessed by verbal autopsy during 2001–06 in India.1 But many changes have occurred in the past decade. For example, the maternal mortality ratio for India in 1992 was 437 deaths per 100 000 livebirths; it decreased to 212 deaths per 100 000 livebirths during 2007–09, and it was expected to reach 135 deaths by 2015. Full-Text PDF Open Access

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.019
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.306
GPT teacher head0.457
Teacher spread0.151 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations5
Published2016
Admission routes1
Has abstractyes

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