Individual, community and region level predictors of insecticide-treated net use among women in Uganda: a multilevel analysis
Bibliographic record
Abstract
BACKGROUND: Use of insecticide-treated net (ITN) has been identified by the World Health Organization as an effective approach for malaria prevention. The government of Uganda has instituted measures to enhance ITN supply over the past decade, however, the country ranks third towards the global malaria burden. As a result, this study investigated how individual, community and region level factors affect ITN use among women of reproductive age in Uganda. METHODS: The 2018-2019 Malaria Indicator Survey of Uganda involving 7798 women aged 15-49 was utilized. The descriptive summaries of ITN use were analysed by individual, community and region level factors. Based on the hierarchical nature of the data, four distinct binomial multilevel logistic regression models were fitted using the MLwiN 3.05 module in Stata. The parameters were estimated using the Markov Chain Monte Carlo (MCMC) estimation procedure and Bayesian Deviance Information Criterion was used to identify the model with a better fit. RESULTS: The proportion of women who use ITN was 78.2% (n = 6097). Poor household wealth status [aOR = 1.66, Crl = 1.55-1.80], knowing that sleeping under ITN prevents malaria [aOR = 1.11, Crl = 1.05-1.24] and that destroying mosquito breeding sites can prevent malaria [aOR = 1.85, Crl = 1.75-1.98] were associated with higher odds of ITN use. ITN use attributable to regional and community level random effects was 39.1% and 45.2%, respectively. CONCLUSION: The study has illustrated that ITN policies and interventions in Uganda need to be sensitive to community and region level factors that affect usage. Also, strategies to enhance women's knowledge on malaria prevention is indispensable in improving ITN use.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".