Assessing and coping with the financial burden of Computed Tomography utilization in Limbe, Cameroon: a sequential explanatory mixed-methods study.
Bibliographic record
Abstract
Abstract Background: Out-of-pocket (OOP) payments for healthcare services leads to unequal access to care with many not able to seek care or suffer catastrophic health expenditure and impoverishment. The cost of healthcare is on the rise and technological innovations in medical imaging are partly responsible. In this study we assess the risk of financial hardship after Computed Tomography (CT) utilization in a health facility in Cameroon and elaborate on how users adapt and cope.Methods: We carried out a sequential explanatory mixed methods study with a quantitative hospital-based survey of CT users followed by an in-depth interview of some purposively selected participants who reported risk of financial hardship after CT utilization. Data was summarized using frequencies, percentages and 95% confidence intervals. Logistic regression was used in multivariable analysis to determine predictors of risk of financial hardship. Identified themes from in-depth interviews were categorized. Quantitative and qualitative data were integrated.Results: A total of 372 participants were surveyed with a male to female sex ratio of 1:1.2. The mean age (standard deviation) was 52(17) years. CT scans of the head and facial bones accounted for 63% (95%CI: 59%, 68%) and the top three indications were suspected stroke (27% [95%CI: 22%, 32%]), trauma (14% [95%CI: 10%, 18%]) and persistent headaches with blurred vision (14% [95%CI: 10%, 18%]). Seventy-two percent (95%CI: 67%, 76%) of respondents declared being at risk of financial hardship after CT utilization and predictors in the multivariable analysis were low socioeconomic status (aOR: 0.19 [95%CI: 0.10, 0.38]; p<0.001) and not having any form of financial risk protection (aOR: 3.59 [95%CI: 1.31, 9.85]; p=0.013). Coping strategies included relying on family members and friends for financial assistance, lobbying hospital administration, social services and healthcare staff for a reduction of the direct cost, and borrowing of money.Conclusion: Lack of financial risk protection and a low socioeconomic status are associated with risk of financial hardship after CT utilization and diverse coping strategies are engaged to minimize the financial burden. Reducing OOP payments for CT scans and/or the direct cost will reduce this hardship and improve access to CT.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".