Evidence map›Paper›PMID 32527253›Full record

ArticleBMC medicine2020

Using health facility deaths to estimate population causes of neonatal and child mortality in four African countries.

Henry D Kalter, Jamie Perin, Agbessi Amouzou, Gift Kwamdera, Wasilat Adeyinka Adewemimo, Félicitée Nguefack, Abdoulaye-Mamadou Roubanatou, Robert E Black

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Henry D KalterInstitute for International Programs, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. hkalter1@jhu.edu.
Jamie PerinInstitute for International Programs, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Agbessi AmouzouInstitute for International Programs, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Gift KwamderaQueen Elizabeth Central Hospital, Ministry of Health, Blantyre, Malawi.
Wasilat Adeyinka AdewemimoDepartment of Health Planning, Research & Statistics, Federal Ministry of Health, FCT, Abuja, Nigeria.
Félicitée NguefackDepartment of Pediatrics, Faculty of Medicine and Biomedical Sciences, University of Yaoundé I, Yaoundé, Cameroon.
Abdoulaye-Mamadou RoubanatouMinistry of Public Health, Niamey, Niger.
Robert E BlackInstitute for International Programs, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.

Funding

Bill and Melinda Gates Foundation OPP1096225United States Agency for International Development GHS-A-00-09-00004
6 · The paper itself

Abstract

backgroundVerbal autopsy is the main method used in countries with weak civil registration systems for estimating community causes of neonatal and 1-59-month-old deaths. However, validation studies of verbal autopsy methods are limited and assessment has been dependent on hospital-based studies, with uncertain implications for its validity in community settings. If the distribution of community deaths by cause was similar to that of facility deaths, or could be adjusted according to related demographic factors, then the causes of facility deaths could be used to estimate population causes.

methodsCauses of neonatal and 1-59-month-old deaths from verbal/social autopsy (VASA) surveys in four African countries were estimated using expert algorithms (EAVA) and physician coding (PCVA). Differences between facility and community deaths in individual causes and cause distributions were examined using chi-square and cause-specific mortality fractions (CSMF) accuracy, respectively. Multinomial logistic regression and random forest models including factors from the VASA studies that are commonly available in Demographic and Health Surveys were built to predict population causes from facility deaths.

resultsLevels of facility and community deaths in the four countries differed for one to four of 10 EAVA or PCVA neonatal causes and zero to three of 12 child causes. CSMF accuracy for facility compared to community deaths in the four countries ranged from 0.74 to 0.87 for neonates and 0.85 to 0.95 for 1-59-month-olds. Crude CSMF accuracy in the prediction models averaged 0.86 to 0.88 for neonates and 0.93 for 1-59-month-olds. Adjusted random forest prediction models increased average CSMF accuracy for neonates to, at most, 0.90, based on small increases in all countries.

conclusionsThere were few differences in facility and community causes of neonatal and 1-59-month-old deaths in the four countries, and it was possible to project the population CSMF from facility deaths with accuracy greater than the validity of verbal autopsy diagnoses. Confirmation of these findings in additional settings would warrant research into how medical causes of deaths in a representative sample of health facilities can be utilized to estimate the population causes of child death.

Indexed as

AfricaChild MortalityChild, PreschoolFemaleHealth FacilitiesHumansInfantInfant MortalityInfant, NewbornMaleMortalityPregnancySurveys and QuestionnairesCause-specific mortality fractionChild mortalityCommunityHealth facilityNeonatal mortalityVerbal autopsy

Identifiers

PMID32527253
PMCPMC7291588

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Registered trials

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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.