Implementation of patient charges at primary care facilities in Kenya: implications of low adherence to user fee policy for users and facility revenue
Bibliographic record
Abstract
With user fees now seen as a major hindrance to universal health coverage, many countries have introduced fee reduction or elimination policies, but there is growing evidence that adherence to reduced fees is often highly imperfect. In 2004, Kenya adopted a reduced and uniform user fee policy providing fee exemptions to many groups. We present data on user fee implementation, revenue and expenditure from a nationally representative survey of Kenyan primary health facilities. Data were collected from 248 randomly selected public health centres and dispensaries in 2010, comprising an interview with the health worker in charge, exit interviews with curative outpatients, and a financial record review. Adherence to user fee policy was assessed for eight tracer conditions based on health worker reports, and patients were asked about actual amounts paid. No facilities adhered fully to the user fee policy across all eight tracers, with adherence ranging from 62.2% for an adult with tuberculosis to 4.2% for an adult with malaria. Three quarters of exit interviewees had paid some fees, with a median payment of US dollars (USD) 0.39, and a quarter of interviewees were required to purchase additional medical supplies at a later stage from a private drug retailer. No consistent pattern of association was identified between facility characteristics and policy adherence. User fee revenues accounted for almost all facility cash income, with average revenue of USD 683 per facility per year. Fee revenue was mainly used to cover support staff, non-drug supplies and travel allowances. Adherence to user fee policy was very low, leading to concerns about the impact on access and the financial burden on households. However, the potential to ensure adherence was constrained by the facilities' need for revenue to cover basic operating costs, highlighting the need for alternative funding strategies for peripheral health facilities.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".