Time and Money
Bibliographic record
Abstract
BACKGROUND: HIV and tuberculosis (TB) services are provided free of charge in many sub-Saharan African countries, but patients still incur costs. METHODS: Patient-exit interviews were conducted in primary health care clinics in rural South Africa with representative samples of 200 HIV-infected patients enrolled in a pre-antiretroviral treatment (pre-ART) program, 300 patients receiving antiretroviral treatment (ART), and 300 patients receiving TB treatment. For each group, we calculated health expenditures across different spending categories, time spent traveling to and using services, and how patients financed their spending. Associations between patient group and costs were assessed in multivariate regression models. RESULTS: Total monthly health expenditures [1 USD = 7.3 South African Rand (ZAR)] were ZAR 171 [95% confidence interval (CI): 134 to 207] for pre-ART, ZAR 164 (95% CI: 141 to 187) for ART, and ZAR 122 (95% CI: 105 to 140) for TB patients (P = 0.01). Total monthly time costs (in hours) were 3.4 (95% CI: 3.3 to 3.5) for pre-ART, 5.0 (95% CI: 4.7 to 5.3) for ART, and 3.2 (95% CI: 2.9 to 3.4) for TB patients (P < 0.01). Although overall patient costs were similar across groups, pre-ART patients spent on average ZAR 29.2 more on traditional healers and ZAR 25.9 more on chemists and private doctors than ART patients, whereas ART patients spent ZAR 34.0 more than pre-ART patients on transport to clinics (P < 0.05 for all results). Thirty-one percent of pre-ART, 39% of ART, and 41% of TB patients borrowed money or sold assets to finance health care. CONCLUSIONS: Patients receiving nominally free care for HIV/TB face large private costs, commonly leading to financial distress. Subsidized transport, fewer clinic visits, and drug pick-up points closer to home could reduce costs for ART patients, potentially improving retention and adherence. Large expenditure on alternative care among pre-ART patients suggests that transitioning patients to ART earlier, as under HIV treatment-as-prevention policies, may not substantially increase patients' financial burden.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.100 | 0.022 |
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".