Delivering non-communicable disease interventions to women and children in conflict settings: a systematic review
Bibliographic record
Abstract
BACKGROUND: Non-communicable diseases (NCDs) are the leading cause of death worldwide. In the context of conflict settings, population displacement, disrupted treatment, infrastructure damage and other factors impose serious NCD intervention delivery challenges, but relatively little attention has been paid to addressing these challenges. Here we synthesise the available indexed and grey literature reporting on the delivery of NCD interventions to conflict-affected women and children in low- and middle-income countries (LMICs). METHODS: A systematic search in MEDLINE, Embase, CINAHL and PsycINFO databases for indexed articles published between 1 January 1990 and 31 March 2018 was conducted, and publications reporting on NCD intervention delivery to conflict-affected women or children in LMICs were included. A grey literature search of 10 major humanitarian organisation websites for publications dated between 1 January 2013 and 30 November 2018 was also conducted. We extracted and synthesised information on intervention delivery characteristics and delivery barriers and facilitators. RESULTS: Of 27 included publications, most reported on observational research studies, half reported on studies in the Middle East and North Africa region and 80% reported on interventions targeted to refugees. Screening and medication for cardiovascular disease and diabetes were the most commonly reported interventions, with most publications reporting facility-based delivery and very few reporting outreach or community approaches. Doctors were the most frequently reported delivery personnel. No publications reported on intervention coverage or on the effectiveness of interventions among women or children. Limited population access and logistical constraints were key delivery barriers reported, while innovative technology use, training of workforce and multidisciplinary care were reported to have facilitated NCD intervention delivery. CONCLUSION: Large and persistent gaps in information and evidence make it difficult to recommend effective strategies for improving the reach of quality NCD care among conflict-affected women and children. More rigorous research and reporting on effective strategies for delivering NCD care in conflict contexts is urgently needed. PROSPERO REGISTRATION NUMBER: CRD42019125221.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".