Evidence-based interventions implemented in low-and middle-income countries for sickle cell disease management: A systematic review of randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: Despite ~90% of sickle cell disease (SCD) occurring in low-and middle-income countries (LMICs), the vast majority of people are not receiving evidence-based interventions (EBIs) to reduce SCD-related adverse outcomes and mortality, and data on implementation research outcomes (IROs) and SCD is limited. This study aims to synthesize available data on EBIs for SCD and assess IROs. METHODS: We conducted a systematic review of RCTs reporting on EBIs for SCD management implemented in LMICs. We identified articles from PubMed/Medline, Global Health, PubMed Central, Embase, Web of Science medical subject heading (MeSH and Emtree) and keywords, published from inception through February 23, 2020, and conducted an updated search through December 24, 2020. We provide intervention characteristics for each study, EBI impact on SCD, and evidence of reporting on IROs. MAIN RESULTS: 29 RCTs were analyzed. EBIs identified included disease modifying agents, supportive care agents/analgesics, anti-malarials, systemic treatments, patient/ provider education, and nutritional supplements. Studies using disease modifying agents, nutritional supplements, and anti-malarials reported improvements in pain crisis, hospitalization, children's growth and reduction in severity and prevalence of malaria. Two studies reported on the sustainability of supplementary arginine, citrulline, and daily chloroquine and hydroxyurea for SCD patients. Only 13 studies (44.8%) provided descriptions that captured at least three of the eight IROs. There was limited reporting of acceptability, feasibility, fidelity, cost and sustainability. CONCLUSION: EBIs are effective for SCD management in LMICs; however, measurement of IROs is scarce. Future research should focus on penetration of EBIs to inform evidence-based practice and sustainability in the context of LMICs. CLINICAL TRIAL REGISTRATION: This review is registered in PROSPERO #CRD42020167289.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.030 | 0.006 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".