MétaCan
Menu
Back to cohort
Record W2971289387 · doi:10.1371/journal.pmed.1002892

Impact on child acute malnutrition of integrating small-quantity lipid-based nutrient supplements into community-level screening for acute malnutrition: A cluster-randomized controlled trial in Mali

2019· article· en· W2971289387 on OpenAlexfundno aff
Lieven Huybregts, Agnès Le Port, Elodie Becquey, Amanda Zongrone, Francisco M. Barba, Rahul Rawat, Jef L Leroy, Marie T. Ruel

Bibliographic record

VenuePLoS Medicine · 2019
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsnot available
FundersConsortium of International Agricultural Research CentersGlobal Affairs CanadaInternational Fine Particle Research Institute
KeywordsMalnutritionSevere Acute MalnutritionMedicineRandomized controlled trialProtein–energy malnutritionCluster (spacecraft)Malnutrition in childrenEnvironmental healthPediatricsInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Community-based management of acute malnutrition (CMAM) has been widely adopted to treat childhood acute malnutrition (AM), but its effectiveness in program settings is often limited by implementation constraints, low screening coverage, and poor treatment uptake and adherence. This study addresses the problem of low screening coverage by testing the impact of distributing small-quantity lipid-based nutrient supplements (SQ-LNSs) at monthly screenings held by community health volunteers (CHVs). Screening sessions included behavior change communication (BCC) on nutrition, health, and hygiene practices (both study arms) and SQ-LNSs (one study arm). Impact was assessed on AM screening and treatment coverage and on AM incidence and prevalence. METHODS AND FINDINGS: A two-arm cluster-randomized controlled trial in 48 health center catchment areas in the Bla and San health districts in Mali was conducted from February 2015 to April 2017. In both arms, CHVs led monthly AM screenings in children 6-23 months of age and provided BCC to caregivers. The intervention arm also received a monthly supply of SQ-LNSs to stimulate caregivers' participation and supplement children's diet. We used two study designs: i) a repeated cross-sectional study (n = approximately 2,300) with baseline and endline surveys to examine impacts on AM screening and treatment coverage and prevalence (primary study outcomes) and ii) a longitudinal study of children enrolled at 6 months of age (n = 1,132) and followed monthly for 18 months to assess impact on AM screening and treatment coverage and incidence (primary study outcomes). All analyses were done by intent to treat. The intervention significantly increased AM screening coverage (cross-sectional study: +40 percentage points [pp], 95% confidence interval [CI]: 32, 49, p < 0.001; longitudinal study: +28 pp, 95% CI: 23, 33, p < 0.001). No impact on treatment coverage or AM prevalence was found. Children in the intervention arm, however, were 29% (95% CI: 8, 46; p = 0.017) less likely to develop a first AM episode (incidence) and, compared to children in comparison arm, their overall risk of AM (longitudinal prevalence) was 30% (95% CI: 12, 44; p = 0.002) lower. The intervention lowered CMAM enrollment by 10 pp (95% CI: 1.9, 18; p = 0.016), an unintended negative impact likely due to CHVs handing out preventive SQ-LNSs to caregivers of AM children instead of referring them to the CMAM program. Study limitations were i) the referral of AM cases by our research team (for ethical reasons) during monthly measurements in the longitudinal study might have interfered with usual CMAM activities and ii) the outcomes presented by child age also reflect seasonal variations because of the closed cohort design. CONCLUSIONS: Incorporating SQ-LNSs into monthly community-level AM screenings and BCC sessions was highly effective at improving screening coverage and reducing AM incidence, but it did not improve AM prevalence or treatment coverage. Future evaluation and implementation research on CMAM should carefully assess and tackle the remaining barriers that prevent AM cases from being correctly diagnosed, referred, and adequately treated. TRIAL REGISTRATION: ClinicalTrials.gov NCT02323815.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.043
GPT teacher head0.348
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designRandomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations50
Published2019
Admission routes1
Has abstractyes

Explore more

Same venuePLoS MedicineSame topicChild Nutrition and Water AccessFrench-language works237,207