SynthesisBMC women's health2026
Health systems interventions for prevention of maternal peripartum infection in low and middle-income countries: a systematic review.
Synthesis in BMC women's health, 2026. 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGlobally, there is growing recognition that a functional health system is key to improving maternal health and reducing maternal mortality. Maternal peripartum infection remains a major cause of maternal morbidity and mortality, particularly in low- and middle-income countries. This infection is a multifaceted problem with many players. Adopting a health systems approach could help avert this preventable infection by addressing the gaps across all the components of care.
objectiveThe aim of this systematic review was to identify and evaluate interventions to prevent maternal peripartum infection in LMICs.
methodsA systematic search was conducted in Cochrane Library, CINAHL, MEDLINE (via PubMed) and Scopus, World Health Organization (WHO), and the National Institute for Health and Care Excellence (NICE) websites to identify primary articles reporting on interventions for preventing maternal peripartum infection using the PRISMA model. The article search was conducted for a period of 3 months (01/08/2022 to 30/10/2022). Search terms were "Peripartum", "Infection", "Genital tract", and their MESH terms. The inclusion criteria were primary interventional studies that reported interventions for the prevention of maternal peripartum infection, studies from LMICs, and those written in the English language. Cochrane Risk of Bias tools were used to appraise the quality of the studies.
resultsOut of 1662 articles, 24 studies met the inclusion criteria comprising of 55,982 participants. The interventions were grouped into five domains: antibiotic prophylaxis 10(42%) of studies), self-care training 6 (25%), skin preparation 5 (21%), Traditional Birth Attendant training 2(2%) and use of Clean Delivery Kit 1(1%)0.11 studies reported a significantly lower risk of infection. Four studies reported a significantly improved knowledge and practice of women regarding maternal peripartum infection. While six studies reported no change in the risk of infection with the intervention.
conclusionA range of interventions targeting maternal peripartum infection prevention in LMICs were identified in this review. They were all targeting a single intervention without integrating other system components. The review highlights the need for integrated health systems interventions and further research to generate stronger evidence and inform effective implementation. STUDY REGISTRATION: PROSPERO CRD42022342550.
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.