Disparity of secondary prevention among patients with rheumatic heart disease: A longitudinal study in Uganda
Bibliographic record
Abstract
Abstract Background Rheumatic heart disease (RHD) is most prevalent in socially disadvantaged settings, placing a severe burden on patients and their households. The study aims to investigate the disparity of healthcare costs, including financial and time costs, among RHD patients in Uganda. Methods We enrolled 54 RHD households from the Uganda National RHD Registry between June 2019 and February 2021. The patients were interviewed in baseline and 12-month follow-up surveys. A random-effect model was applied to examine the disparity of RHD financial and time costs. Our primary outcomes are the total outpatient costs for RHD patients’ most recent visit, consisting of direct medical costs, direct non-medical costs, and time costs. Results Following the COVID-19 pandemic, the total financial cost of outpatient visits for RHD patients increased by 9 USD on average ( P <0.01), with the change primarily driven by non-medical costs such as transportation and food (5.8 USD, P <0.05). Direct medical costs also increased significantly in the pandemic, with an average increase of 3.2 USD ( P <0.1). Compared with their counterparts, non-medical costs were higher for patients with poor infrastructure, with less education, who were older, and who were male. Patients with employment experienced a higher time cost than those without (3.3 hours, P <0.01). Conclusions The COVID-19 pandemic significantly increased RHD outpatient costs, mainly caused by the increase in non-medical costs. Our study implies that improving infrastructure, investing in education, and providing employees with time to seek care have the potential to reduce non-medical barriers to RHD secondary prevention.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".