Evidence map›Paper›PMID 25609079›Full record

ArticleBMC health services research2015

Early detection of maternal deaths in Senegal through household-based death notification integrating verbal and social autopsy: a community-level case study.

Mosa Moshabela, Massamba Sene, Ingrid Nanne, Yombo Tankoano, Jennifer Schaefer, Oumulkhairy Niang, Sonia Ehrlich Sachs

Open access · goldAbstract read
In one paragraph

Article in BMC health services research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 3 pooled it
4.0field-weighted citation impact, top 7% 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

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

13 citing papers in PubMed, 3 syntheses or guidelines pooled it, 22 citations in OpenAlex.

  1. Pooled it
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  7. Sisters in Birth: Improving Birth Outcomes in Mississippi.The Journal of perinatal education · 2020
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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

7 authors at 5 institutions in 4 countries.

Mosa MoshabelaDepartment of Rural Health, School of Nursing and Public Health, University of KwaZulu-Natal, Science Drive, Howard College, Fourth Floor, George Campbell Building, Durban, 4001, South Africa. moshabela@ukzn.ac.za.
Massamba SeneMillennium Promise, Columbia University, Dakar, Senegal. massamba.sene@millenniumpromise.org.
Ingrid NanneMillennium Development Goal Centre, Columbia University, Dakar, Senegal. inanne87@gmail.com.
Yombo TankoanoEarth Institute, Columbia University, New York City, USA. yt2147@columbia.edu.
Jennifer SchaeferSchool of Public Health, Tropical Medicine and Rehabilitation Sciences, James Cook University, Townsville, Australia. jenschae@fastmail.fm.
Oumulkhairy NiangMillennium Promise, Columbia University, Dakar, Senegal. oumul.khairy.niang@millenniumpromise.org.
Sonia Ehrlich SachsEarth Institute, Columbia University, New York City, USA. ssachs@ei.columbia.edu.
Columbia University · USDigital Promise · USCentre de Suivi Ecologique · SNJames Cook University · AUUniversity of KwaZulu-Natal · ZA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundReliable detection of maternal deaths is an essential prerequisite for successful diagnosis of barriers to care and formulation of relevant targeted interventions. In a community-level case study, the use of household-level surveillance in Senegal unveiled an apparent increase in maternal deaths, which triggered a rapid-cycle collaborative response to implement a multipronged set of quick-win and sustained interventions intended to improve quality care.

methodsPart of a multi-country effort, the Millennium Villages Project is implementing a routine community-level information system in Senegal, able to detect maternal deaths in real-time and uncover clinical and social factors contributing to mortality. Within this geographically demarcated area of approximately 32 000 inhabitants, with a well-structured health system with patient referral services, deaths were registered and notified by community health workers, followed by timely verbal and social autopsies. Using the Pathway to Survival conceptual framework, case analysis and mortality reviews were conducted for evaluation and quality improvement purposes.

resultsThe estimated maternal mortality rates rose from 67/100000 births in 2009 (1 death), to 202/100000 births in 2010 (3 deaths) and 392/100000 births (5 deaths) in 2011. Although absolute numbers of maternal deaths remained too small for robust statistical analysis, following verbal autopsy analyses in 2011, it became evident that an unexpectedly high proportion of maternal deaths were occurring at the referral hospital, mostly post-Caesarian section. Inadequate case management of post-partum haemorrhage at the referral hospital was the most frequently identified probable cause of death. A joint task team systematically identified several layers of inefficiencies, with a potential negative impact on a larger catchment area than the study community.

conclusionsIn this study, routine community-based surveillance identified inefficiencies at a tertiary level of care. Community-level surveillance systems that include pregnancy, birth and death tracking through household visits by community health workers , combined with verbal and social autopsy can identify barriers within the continuum of maternal care. Use of mHealth data collection tools sensitive enough to detect small changes in community-level mortality trends in real-time, can facilitate rapid-cycle quality improvement interventions, particularly when associated with social accountability structures of mortality reviews.

Indexed as

AutopsyCause of DeathCommunity Health WorkersData CollectionEarly DiagnosisFemaleHumansMaternal DeathMaternal MortalityParturitionPostpartum HemorrhagePregnancyRetrospective StudiesSenegal

Identifiers

PMID25609079
PMCPMC4323233
OpenAlexW1983798849

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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.