Inpatient healthcare utilization among people with disabilities in Iran: determinants and inequality patterns
Bibliographic record
Abstract
BACKGROUND: People with disabilities (PWD) have different health service needs and different factors affect the utilization of these services. Therefore, the aim of this present study was to identify determinants of inpatient healthcare utilization among PWDs in Iran. METHODS: This research was a secondary data analysis of a cross-sectional study. The present study used data gathered for 766 PWDs (aged 18 years and older) within the Iranian Society with Disabilities (ISD) between September and December 2020. Multiple logistic regression models calculated adjusted odds ratios (aOR) and 95% confidence intervals in order to identify determinants of inpatient healthcare utilization among PWDs. RESULTS: Data for 766 people with disabilities were analyzed. A large number of participants were over 28 years of age (70.94%), male (64.36%), and single (54.02%). In the present study, more than 71% of participants had no history of hospitalization during the last year. In this study, males [aOR 2.11(1.14-3.91), participants with Civil Servants health insurance coverage [aOR 3.44 (1.16 - 10.17)] and individuals in the 3th quartile of disability severity [aOR 2.13 (1.01 - 4.51)] had greater odds of inpatient healthcare utilization compared to the other groups. The value of the concentration index (C) for inpatient healthcare utilization was - 0.084 (P.value = 0.046). The decomposition analysis indicated that gender was the greatest contributor (21.92%) to the observed inequality in inpatient healthcare utilization among participants. CONCLUSION: Our findings suggested that the likelihood of hospitalization among the study participants could be significantly influenced by factors such as gender, the health insurance scheme, and the degree of disability severity. These results underscore the imperative for enhanced access to outpatient services, affordable insurance coverage, and reduced healthcare expenditures for this vulnerable population. Addressing these issues has the potential to mitigate the burden of hospitalization and promote better health outcomes for disadvantaged individuals.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".