ArticleConflict and health2024
Maternal mortality estimation methodologies: a scoping review and evaluation of suitability for use in humanitarian settings.
Article in Conflict and health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAround the world, a maternal death occurs approximately every two minutes-most of these deaths are preventable. The maternal mortality ratio is a key indicator for the Sustainable Development Goals, yet we have no reliable way to estimate maternal deaths in refugee or internally displaced persons (IDP) camps and settlements. The goal of this study was to understand the methodologies most suited for adaptation for use to estimate the proportion of maternal mortality due to abortion complications in these settings.
methodsWe conducted a scoping review of methodologies to estimate maternal mortality and evaluated them using a predetermined set of criteria. We evaluated nine original methodologies using eleven categories related to implementation in refugee or IDP camps and settlements: data sources, definitions, sample size, timing of point estimate relative to data collection, bias, human resources, time needed for implementation, data collection training, statistical training, digitalization, and cost. Each category could be assigned zero to four points, for a total score of 44 points. After evaluating each original methodology, we reviewed the original publication's citations or searched for other implementations through October 2022. We revised the original scores and developed a rank-order list of the methodologies according to their suitability for implementation in refugee and IDP camps.
resultsWe identified 124 publications that estimated maternal mortality. The Maternal Deaths from Informants/Maternal Death Follow on Review (MADE-IN/MADE-FOR) (33.5), hospital- or facility-based (33.5), and community informant-based (32.5) methodologies ranked highest due to low costs, short time interval needed for implementation, small sample sizes and close timing of point estimate relative to data collection, easy digitalization, and the need for no statistical training. DISCUSSION: Similar to the lack of a "perfect" methodology to estimate maternal mortality in stable settings, there are compromises to consider when applying these methodologies to humanitarian settings. The most promising methodologies are adaptable to practical constraints in refugee and IDP camps and settlements. New methodologies that adapt and strengthen the MADE-IN/MADE-FOR, hospital- or facility-based, and community informant-based methodology show promise and must be further developed.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.