Analyzing Disparity in Geographical Accessibility to Home Medical Care Using a Claims Database and Geographical Information System: Simulation Study
Bibliographic record
Abstract
Background: The demand for home medical care services has increased in aging societies. Therefore, allocating health care resources optimally to meet the needs of each community is essential. Geographical accessibility is an important factor affecting access to home medical care services; however, little research has been conducted on regional disparities in geographical accessibility. Objective: This study aims to analyze the regional disparities in geographical accessibility to home medical care services using the Kokuho database (KDB), a comprehensive medical claims database for a prefecture in Japan. Methods: This study included 39 municipalities in Nara Prefecture, Japan. Using a geographical information system, accessibility to home medical care services, that is, travel distance and time from hospitals and clinics to hypothetical patients, was analyzed in two scenarios: (1) an ideal scenario, where we assumed that all hospitals or clinics in Nara Prefecture provided those services and (2) an actual scenario, where hospitals or clinics in Nara Prefecture that actually provided home medical care services, identified from KDB data analysis, were used in the analysis. Hypothetical patients were randomly distributed on the geographical information system in accordance with the usage rates of home medical care services and with the distributions of the population aged ≥75 years. The usage rate by municipalities was aggregated from the analysis of KDB data of Nara Prefecture in FY2019. Results: The median travel distance was longer than 16 km, the reference limit value specified in the Japanese fee table, and the median travel time exceeded 30 min in certain rural municipalities in the southern part of Nara Prefecture, in the actual scenario, whereas the travel distance and time were improved in the ideal scenario. The differences in travel time between the ideal and actual scenarios were the largest in the depopulated municipalities in the southern part, such as Totsukawa (32.6 vs 5.8 min), Kawakami (30.1 vs 11.8 min), Kurotaki (21.3 vs 5.2 min), and Kamikitayama (20.7 vs 3.5 min). The usage rates were also lower in rural municipalities in the southern part. Conclusions: The results revealed that geographical accessibility was lower in depopulated municipalities in the southern part, and the disparity could be partly solved in the ideal scenario, especially in that area, highlighting the necessity of increasing supply in the southern areas. KDB is a comprehensive database that includes medical claims information for home medical care patients and details of the provision of medical institutions, enabling geographical analysis that reflects actual health care usage.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".