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Record W2996192244 · doi:10.3390/nu12010049

Effects of Preventive Nutrition Interventions among Adolescents on Health and Nutritional Status in Low- and Middle-Income Countries: A Systematic Review and Meta-Analysis

2019· review· en· W2996192244 on OpenAlexaff
Rehana A Salam, Jai K Das, Wardah Ahmed, Omar Irfan, Sana Sheikh, Zulfiqar A Bhutta

Bibliographic record

VenueNutrients · 2019
Typereview
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsHospital for Sick Children
FundersBill and Melinda Gates Foundation
KeywordsMeta-analysisLow and middle income countriesEnvironmental healthPsychological interventionMedicineLow incomeGerontologyDeveloping countrySocioeconomicsEconomic growthNursingEconomics

Abstract

fetched live from OpenAlex

The objective of this review was to assess the impact of preventive nutrition interventions on health and nutritional status of adolescents aged 10–19 years in low- and middle-income countries (LMICs). We searched the databases until 5 February 2019 without any restrictions on publication, date, language, or publication status. A total of 10 studies (15 papers) including 10,802 participants assessing the impact of micronutrient supplementation/fortification were included in this review. We did not find any study assessing the impact of nutrition education and counseling or macronutrient supplementation among adolescents. Among primary outcomes, we are uncertain of the effect of iron supplementation with or without folic acid on anemia (daily supplementation; relative risk (RR): 1.04, 95% confidence interval (CI) 0.42, 2.57; one study; 1160 participants; low-quality evidence; weekly supplementation; RR: 1.07, 95% CI: 0.46, 2.52; one study; 1247 participants; low-quality evidence). We are also uncertain of the effect of various micronutrient supplementation/fortification on body mass index (BMI) (calcium/vitamin D supplementation; (MD: −0.01 kg/m2; 95% CI: −1.20, 1.17; two studies; 730 participants; I2 94%; very-low-quality evidence, iron supplementation with or without folic acid; MD: 0.47 kg/m2; 95% CI: −0.17, 1.11; two studies; 652 participants; I2 37%; very-low-quality evidence, zinc supplementation; MD: 0.35 kg/m2; 95% CI: −0.15, 0.85; one study; 382 participants; very-low-quality evidence) and multiple micronutrient (MMN) fortification; MD: 0.23 kg/m2, 95% CI: −0.11, 0.57; two studies; 943 participants; I2 22%; very-low-quality evidence). None of the included studies reported any other primary outcomes including morbidity or adverse effects. Among secondary outcomes, iron supplementation with or without folic acid may improve hemoglobin concentrations, and calcium/vitamin D supplementation may improve serum 25(OH)D levels, while calcium only supplementation and calcium and vitamin D supplementation may marginally improve total body bone mineral density (BMD). We are uncertain of the effect of MMN fortification on hemoglobin concentrations, calcium supplementation on total body bone mineral content (BMC), calcium + vitamin D supplementation on total body BMC, and zinc supplementation on zinc levels. There is limited evidence of micronutrient supplementation/fortification among adolescents, especially adolescent boys, on health and nutritional status in LMICs. These findings should be interpreted with caution due to the low quality and limited number of studies.

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 imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.023
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.034
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0230.037
Bibliometrics0.0060.006
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.049
GPT teacher head0.358
Teacher spread0.308 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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

Citations49
Published2019
Admission routes1
Has abstractyes

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