The economic burden of cervical cancer on women in Uganda: Findings from a cross-sectional study conducted at two public cervical cancer clinics
Bibliographic record
Abstract
There is limited research on how a cervical cancer diagnosis financially impacts women and their families in Uganda. This analysis aimed to describe the economic impact of cervical cancer treatment, including how it differs by socio-economic status (SES) in Uganda. We conducted a cross-sectional study from September 19, 2022 to January 17, 2023. Women were recruited from the Uganda Cancer Institute and Jinja Regional Referral Hospital, and were eligible if they were ≥ of 18 years and being treated for cervical cancer. Participants completed a survey that included questions about their out-of-pocket costs, unpaid labor, and family's economic situation. A wealth index was constructed to determine their SES. Descriptive statistics were reported. Of the 338 participants, 183 were from the lower SES. Women from the lower SES were significantly more likely to be older, have ≤ primary school education, and have a more advanced stage of cervical cancer. Over 90% of participants in both groups reported paying out-of-pocket for cervical cancer. Only 15 participants stopped treatment because they could not afford it. Women of a lower SES were significantly more likely to report borrowing money (higher SES n = 47, 30.5%; lower SES n = 84, 46.4%; p-value = 0.004) and selling possessions (higher SES n = 47, 30.5%; lower SES n = 90, 49.7%; p-value = 0.006) to pay for care. Both SES groups reported a decrease in the amount of time that they spent caring for their children since their cervical cancer diagnosis (higher SES n = 34, 31.2%; lower SES n = 36, 29.8%). Regardless of their SES, women in Uganda incur out-of-pocket costs related to their cervical cancer treatment. However, there are inequities as women from the lower SES groups were more likely to borrow funds to afford treatment. Alternative payment models and further economic support could help alleviate the financial burden of cervical cancer care in Uganda.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 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".