Risk factors for catastrophic healthcare expenditure and high economic burden for children with anorectal malformations in Southwestern Uganda
Bibliographic record
Abstract
BACKGROUND: Anorectal malformations (ARMs) are common congenital anomalies in low-and middle-income countries (LMICs), and they are often repaired in a staged manner. High out-of-pocket (OOP) payment for surgical care in many LMICs makes households vulnerable to catastrophic health expenditures (CHE). ARM patients often require multiple operations and hospitalizations, which may make them vulnerable to CHE. In this study, we sought to determine the prevalence of CHE and the factors driving these costs among families of children with ARMs in southwestern Uganda. METHODS: This was a combined retrospective and prospective cohort study of the OOP and CHE among families of children with ARMs at a Regional Referral Hospital between June 2021 and July 2023. CHE was defined as a cost exceeding 10% of annual income. Patient characteristics were compared, and multivariable modeling with best subset analysis was used to determine which factors were significantly associated with CHE and total OOP expenditure. RESULTS: There were 236 study participants with a median age at diagnosis of 6 days, 51% were male, 71% lived in rural areas, and the median distance traveled was 175 km. 64% of patients experienced CHE, with almost all families incurring travel costs (99%). Following best subset analysis, distance traveled (OR 1.06, 95% CI: 1.03-1.08) and rurality (OR 1.83, 95% CI: 0.96-3.48) were significantly associated with CHE, suggesting that for every additional 10 km, a patient traveled for care, there were 6% higher odds of incurring CHE. In examining total cost, patients who had a two-stage repair incurred more than twice the costs compared to those who had a single-stage repair, and education level and repair type were also significantly associated. CONCLUSION: Identifying methods to provide financial protection from CHE is essential for all children. ARM patients are at particularly high risk for CHE and high OPP expenditures, especially those living far from healthcare services and in rural areas.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".