Exposure to pollutants for household cooking and lighting and pediatric post-discharge mortality following a severe infection in Uganda
Bibliographic record
Abstract
BACKGROUND: Particulate matter from household air pollution (HAP) is linked to half of all lower respiratory infection deaths among children under 5 years of age. In rural Uganda, similar number of children die 6-months post discharge as during hospitalization for severe infections. However, it is unclear whether exposure to HAP contributes to poor health and death after discharge. We investigated the association between cooking and household lighting practices and mortality 6-months post-discharge among children under 5 years of age treated for severe infection in rural Uganda. METHODS: We conducted a secondary analysis of data from observational cohort studies, conducted between July 2017 to July 2021, among 6,955 children 0 to 5 years admitted to one of six Ugandan hospitals for a severe infectious illness. Clinical signs and symptoms, sociodemographic variables, and mortality up to 6-months post-discharge was collected for all participants, and follow-up rates were >95%. Exposure variables included type of cooking fuel used, location of cooking (e.g. indoors, outdoors), and primary source of household lighting. We assessed post-discharge mortality using simple and multivariate Poisson regression. RESULTS: The unadjusted risk ratio of 6-month post-discharge mortality by dual or single exposure to pollutant fuel sources for cooking indoors and household lighting, when compared to minimal exposure, was 1.57 (95%CI 1.17, 2.11) and 1.20 (95%CI 0.94, 1.54), respectively. Adjusting for age, sex, distance to hospital, maternal education, and maternal HIV status, the adjusted risk ratios for dual and single exposure became 1.30 (95%CI 0.96, 1.76] and 1.08 (95%CI 0.84, 1.38). There was no significant interaction between exposure and age, sex, maternal education, or anemia status. DISCUSSION: This analysis did not find a statistically significant association between HAP and 6-month post-discharge mortality. However, HAP cannot be ruled out as a contributor in this population where malnutrition, comorbidities and sociodemographic vulnerabilities are common.
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 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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".