Evidence map›Paper›PMID 25377339›Full record

ArticleGlobal health action2014

Cause of death during 2009-2012, using a probabilistic model (InterVA-4): an experience from Ballabgarh Health and Demographic Surveillance System in India.

Sanjay K Rai, Shashi Kant, Puneet Misra, Rahul Srivastava, Chandrakant S Pandav

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 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
3.5field-weighted citation impact, top 8% 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

4 citing papers in PubMed, 10 citations in OpenAlex.

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4 · The record

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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

5 authors at 2 institutions in 2 countries.

Sanjay K RaiINDEPTH Network, Accra, Ghana; All India Institute of Medical Sciences, New Delhi, India.
Shashi KantINDEPTH Network, Accra, Ghana; All India Institute of Medical Sciences, New Delhi, India.
Puneet MisraINDEPTH Network, Accra, Ghana; All India Institute of Medical Sciences, New Delhi, India.
Rahul SrivastavaINDEPTH Network, Accra, Ghana; All India Institute of Medical Sciences, New Delhi, India; drsrivastava.rahul@gmail.com.
Chandrakant S PandavAll India Institute of Medical Sciences, New Delhi, India.
Indepth Network · GHAll India Institute of Medical Sciences · IN

Funding

Wellcome Trust
6 · The paper itself

Abstract

objectivesThe present study aimed to estimate the age and cause-specific mortality in Ballabgarh Health and Demographic Surveillance System (HDSS) site for the years 2009 to 2012, using a probabilistic model (InterVA-4).

methodsAll Deaths in Ballabgarh HDSS from January 1, 2009, to December 31, 2012, were included in the study. InterVA-4 model (version 4.02) was used for assigning cause of death (COD). Data from the verbal autopsy (VA) tool were extracted and processed with the InterVA-4 model. Cause-specific mortality rate (CSMR) per 1,000 person-years was calculated.

resultsA total of 2,459 deaths occurred in the HDSS during the year 2009 to 2012. Among them, 2,174 (88.4%) valid VA interviews were conducted. Crude death rate ranged from 7.1 (2009) to 6.4 (2012) per 1,000 population. The CSMR per 1,000 person-years over the years (2009-2012) for non-communicable diseases, communicable diseases, trauma, neoplasm, and maternal and neonatal diseases were 1.78, 1.68, 0.68, 0.49, and 0.48, respectively. The most common causes of death among children, adults, and the elderly were infectious diseases, trauma, and non-communicable diseases, respectively.

conclusionsOverall, non-communicable diseases constituted the largest proportion of mortality, whereas trauma was the most common COD among adults at Ballabgarh HDSS. Policy-makers ought to focus on prevention of premature CODs, especially prevention of infectious diseases in children, and intentional self-harm and road traffic accidents in the adult population.

Indexed as

Cause of DeathAdolescentAdultAgedAutopsyBayes TheoremChildChild, PreschoolData CollectionFemaleHumansIndiaInfantInfant, NewbornMaleMiddle AgedBallabgarhcause of deathHDSSInterVA-4mortalityrural Indiaverbal autopsy

Identifiers

PMID25377339
PMCPMC4220166
OpenAlexW2056781678

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LicenceCC BY
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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.