Associating the Scale-Up of Insecticide-Treated Nets and Use With the Decline in All-Cause Child Mortality in the Democratic Republic of Congo From 2005 to 2014
Bibliographic record
Abstract
Abstract Background To reduce the malaria burden and improve the socioeconomic status of its citizens, the Democratic Republic of Congo scaled up key malaria control interventions, especially insecticide-treated nets (ITNs), between 2005 and 2014. Since then, the effects of these interventions on malaria mortality and morbidity have not been assessed. This study aims to measure the impact of the National Malaria Control Programme’s efforts and to inform future control strategies.Methods The authors used data from the Demographic and Health Surveys 2007 and 2013-2014 to assess trends in all-cause childhood mortality (ACCM) against trends in coverage of malaria interventions at national and subnational levels. The authors used the plausibility argument to assess the impact of the malaria control interventions and used Kaplan-Meier survival probability and Cox proportional hazard models to examine the effect of ITN ownership on child survival. Contextual factor trends affecting child survival were also considered.Results Countrywide, household ownership of at least one ITN increased, from 9% in 2007 to 70% in 2013-2014. All provinces experienced similar increases, with some greater than the national level. ITN use increased between 2007 and 2013-2014 among children under five (6% to 55%). Severe anaemia (haemoglobin concentration <8g/dl) prevalence among children aged 6–59 months significantly decreased, from 11% (95% confidence interval [CI]: 9–13%) in 2007 to 6% (95% CI: 5–7%) in 2013-2014. During the same period, ACCM declined, from 148 (95% CI: 132-163) to 104 (95% CI: 97-112) deaths per 1,000 live births. The decline in ACCM was greater among children aged 6–23 months (relative reduction of 36%), compared to children aged 24–59 months (relative reduction of 12%). Cox regression indicated that household ownership of at least one ITN reduced the risk of mortality by 24% among children under five (risk ratio=0.76, 95% CI: 0.64–0.90). Contextual factor analysis revealed marginal improvements in socioeconomic indicators and other health interventions.Conclusions Given the patterns of the coverage of malaria control interventions, patterns in ACCM by province, and marginal improvements in contextual factors, the authors conclude that the malaria control interventions have plausibly contributed to the decrease in ACCM in the Democratic Republic of Congo from 2005 to 2014.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".