Socio‐economic inequality in health service utilisation: Does accounting for seasonality in health‐seeking behaviour matter?
Bibliographic record
Abstract
Seasonal variation exists in disease incidence. The variation could occur across the different regions in a country. This paper argues that using national household data that are not adjusted for seasonal and regional variations in disease incidence may not be directly suitable for assessing socio-economic inequality in annual outpatient service utilisation, including for cross-country comparison. In fact, annual health service utilisation may be understated or overstated depending on the period of data collection. This may lead to miss-estimation of socio-economic inequality in health service utilisation depending, among other things, on how health service utilisation, across geographical areas, varies by socio-economic status. Using a nationally representative dataset from South Africa, the paper applies a seasonality index that is constructed from the District Health Information System, an administrative dataset, to annualise public outpatient health service visits. Using the concentration index, socio-economic inequality in health service visits, after accounting for seasonal variations, was compared with that when seasonal variations are ignored. It was found that, in some cases, socio-economic inequality in outpatient health service visits depends on the socio-economic distribution of the seasonality index. This may justify the need to account for seasonal and geographical variations.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".