Socioeconomic inequalities in health care utilization in Paraguay: Description of trends from 1999 to 2018
Bibliographic record
Abstract
OBJECTIVE: Paraguay's health care system is characterized by segmented provision and low public spending, with limited coverage and asymmetries in terms of access and quality of care. The present study provides national estimates of income-related inequality in health care utilization and trends in the country over the past two decades. METHODS: Using data from the Paraguayan Permanent Household Survey, we estimated socioeconomic inequality in health care use during the period 1999-2018. We used poverty-to-income ratio as the socioeconomic stratifier and defined health care use as having reported a health problem and subsequent health care use in the last 90 days before interview. Inequality was summarized by rank- and level-based versions of the Concentration Index for binary outcomes. RESULTS: Inequalities affecting those with lower incomes were present in all years assessed, although the magnitude of these inequalities declined over time. Inequality as expressed by the rank-based index decreased from 0.209 (95%CI 0.164; 0.253) in 1999 to 0.032 (95%CI -0.010; 0.075) in 2018. The level-based index decreased from 0.076 (95%CI -0.029; 0.182) in 1999 to 0.024 (0.002; 0.045) in 2018. Trends in both indices were generally stable from 1999 to 2009, with a noticeable decrease in 2010. The sharpest decreases relative to the 1999 baseline were observed in the period 2010-2018, reflecting changes in health care use and income distribution. Stratification by area, sex and older people suggest similar trends within subgroups. CONCLUSIONS: Decreases in inequality coincide temporally with increments in public health expenditure, removal of user fees in public health care facilities and the expansion of conditional cash-transfer programmes. Future research should disentangle the role of each of these policies in explaining the trends described.
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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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 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 | 0.001 |
| 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".