Assessing and coping with the financial burden of computed tomography utilization in Limbe, Cameroon: a sequential explanatory mixed-methods study
Bibliographic record
Abstract
BACKGROUND: There has been a significant increase in computed tomography (CT) utilization over the past two decades with the major challenges being a high exposure to ionizing radiation and rising cost. In this study we assess the risk of financial hardship after CT utilization and elaborate on how users adapt and cope in a sub-Saharan context with user fee for services and no national health insurance policy. METHODS: We carried out a sequential explanatory mixed methods study with a quantitative hospital-based survey of CT users followed by in-depth interviews 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 findings 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 (14% [95%CI: 10-18%]). Seventy-two percent (95%CI: 67-76%) of the respondents reported being at risk of financial hardship after CT utilization and predictors in the multivariable analysis were a low socioeconomic status (aOR: 0.19 [95%CI: 0.10-0.38]; p < 0.001), being unemployed or retired (aOR: 11.75 [95%CI: 2.59-53.18]; p = 0.001) and not having any form of health insurance (aOR: 3.59 [95%CI: 1.31-9.85]; p = 0.013). Coping strategies included getting financial support from family and friends, borrowing money and obtaining discounts from the hospital administration and staff. CONCLUSION: No health insurance ownership, being unemployed or retired and a low socioeconomic status are associated with financial hardship after CT utilization. Diverse coping strategies are utilized to lessen the financial burden, some with negative consequences. Minimizing out-of-pocket payments and/or the direct cost of CT can reduce this financial burden and improve CT access.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".