Evidence map›Paper›PMID 25377340›Full record

ArticleGlobal health action2014

Childhood cause-specific mortality in rural Western Kenya: application of the InterVA-4 model.

Nyaguara O Amek, Frank O Odhiambo, Sammy Khagayi, Hellen Moige, Gordon Orwa, Mary J Hamel, Annemieke Van Eijk, John Vulule, Laurence Slutsker, Kayla F Laserson

Open access · goldAbstract read
In one paragraph

Article in Global health action, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
5.6field-weighted citation impact, top 5% of its field
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

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3 · Its place in the literature

Who cites it

20 citing papers in PubMed, 27 citations in OpenAlex.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors at 3 institutions in 2 countries.

Nyaguara O AmekCenter for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya; namek@kemricdc.org.
Frank O OdhiamboCenter for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya.
Sammy KhagayiCenter for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya.
Hellen MoigeCenter for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya.
Gordon OrwaCenter for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya.
Mary J HamelCenter for Global Health, Centers for Disease Control and Prevention, Atlanta, GA, USA.
Annemieke Van EijkCenter for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya.
John VululeCenter for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya.
Laurence SlutskerCenter for Global Health, Centers for Disease Control and Prevention, Atlanta, GA, USA.
Kayla F LasersonCenter for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya; Center for Global Health, Centers for Disease Control and Prevention, Atlanta, GA, USA.
Kenya Medical Research Institute · KECenter for Global Health · USCenters for Disease Control and Prevention · US

Funding

Wellcome Trust
6 · The paper itself

Abstract

backgroundAssessing the progress in achieving the United Nation's Millennium Development Goals in terms of population health requires consistent and reliable information on cause-specific mortality, which is often rare in resource-constrained countries. Health and demographic surveillance systems (HDSS) have largely used medical personnel to review and assign likely causes of death based on the information gathered from standardized verbal autopsy (VA) forms. However, this approach is expensive and time consuming, and it may lead to biased results based on the knowledge and experience of individual clinicians. We assessed the cause-specific mortality for children under 5 years old (under-5 deaths) in Siaya County, obtained from a computer-based probabilistic model (InterVA-4).

designSuccessfully completed VA interviews for under-5 deaths conducted between January 2003 and December 2010 in the Kenya Medical Research Institute/US Centers for Disease Control and Prevention HDSS were extracted from the VA database and processed using the InterVA-4 (version 4.02) model for interpretation. Cause-specific mortality fractions were then generated from the causes of death produced by the model.

resultsA total of 84.33% (6,621) childhood deaths had completed VA data during the study period. Children aged 1-4 years constituted 48.53% of all cases, and 42.50% were from infants. A single cause of death was assigned to 89.18% (5,940) of cases, 8.35% (556) of cases were assigned two causes, and 2.10% (140) were assigned 'indeterminate' as cause of death by the InterVA-4 model. Overall, malaria (28.20%) was the leading cause of death, followed by acute respiratory infection including pneumonia (25.10%), in under-5 children over the study period. But in the first 5 years of the study period, acute respiratory infection including pneumonia was the main cause of death, followed by malaria. Similar trends were also reported in infants (29 days-11 months) and children aged 1-4 years.

conclusionsUnder-5 cause-specific mortality obtained using the InterVA-4 model is consistent with existing knowledge on the burden of childhood diseases in rural western Kenya.

Indexed as

Cause of DeathAutopsyChild, PreschoolData CollectionFemaleHumansInfantInfant, NewbornKenyaMaleModels, StatisticalPopulation SurveillanceRural Populationhealth and demographic surveillanceInterVAunder-5verbal autopsy

Identifiers

PMID25377340
PMCPMC4221497
OpenAlexW1974093960

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LicenceCC BY
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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.