Patient costs associated with accessing HIV/AIDS care in Malawi
Bibliographic record
Abstract
INTRODUCTION: The decentralization of HIV services has been shown to improve equity in access to care for the rural poor of sub-Saharan Africa. This study aims to contribute to our understanding of the impact of decentralization on costs borne by patients. Such information is valuable for economic evaluations of anti-retroviral therapy programmes that take a societal perspective. We compared costs reported by patients who received care in an urban centralized programme to those in the same district who received care through rural decentralized care (DC). METHODS: A cross-sectional survey on patient characteristics and costs associated with accessing HIV care was conducted, in May 2010, on 120 patients in centralized care (CC) at a tertiary referral hospital and 120 patients in DC at five rural health centres in Zomba District, Malawi. Differences in costs borne by each group were compared using χ2 and t-tests, and a regression model was developed to adjust for confounders, using bootstrapping to address skewed cost data. RESULTS: There was no significant difference between the groups with respect to sex and age. However, there were significant differences in socio-economic status, with higher educational attainment (p<0.001), personal income (p=0.007) and household income per person (p=0.005) in CC. Travel times were similar (p=0.65), as was time waiting at the clinic (p=0.63) and total time spent seeking care (p=0.65). There was a significant difference in travel-related expenses (p<0.001) related to the type of travel participants noted that they used. In CC, 60% of participants reported using a mini-bus to reach the clinic; in DC only 4% reported using a mini-bus, and the remainder reported travelling on foot or by bicycle. There were no significant differences between the groups in the amount of lost income reported or other out-of-pocket costs. Approximately 91 Malawi Kwacha (95% confidence intervals: 1-182 MKW) or US$0.59 represents the adjusted difference in total costs per visit between CC and DC. CONCLUSIONS: Even within a system of HIV/AIDS care where patients do not pay to see clinicians or for most medications, they still incur costs. We found that most costs are travel related. This has important implications for poorer patients who live at a distance from health facilities for whom these costs may be significant.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".