Background

The rural-urban disparity in healthcare quality is a global issue. Compared with living in urban areas, living in rural areas is associated with poorer healthcare outcomes. Moreover, the shortage of healthcare providers in rural areas is a worldwide concern. This scoping review aims to map existing evidence regarding rural-urban disparities in access and quality of healthcare in Japan using the Donabedian model as a theoretical framework and to identify conceptual and measurement gaps.

Methods

This review targeted published articles and gray literature. We included documents that (1) were based on Japanese populations and (2) compared the quality of care between defined rural and urban areas. We excluded articles if they (1) were published during or before 2005 since the Japanese government amended the Medical Care Law in 2006; (2) focused exclusively on urban or rural areas; or (3) were not published in English or Japanese. This study employed PubMed, EMBASE, Web of Science, the Japanese medical literature database, ICHUSHI, and CiNii Research. We extracted quality indicators (structure, process, and outcomes) based on the Donabedian model. We recorded the definitions or indicators of rurality described by the studies.

Results

Out of 5,020 articles, 15 were included. Only one study was conducted in a primary care setting. Moreover, no study evaluated the “outcomes” of the Donabedian model in a primary care setting. Regarding the definitions or indices of rurality, the most commonly used indicator of rurality was population size, followed by population density. The cutoff values or descriptions of rurality using these indicators differed across studies.

Conclusion

This study mapped rural-urban disparities in access and quality of healthcare in Japan. These findings highlight the need to evaluate rural-urban disparities in the “outcomes” of care in primary care settings in Japan and the lack of common indicators of rurality.

Makoto Kaneko
Associate Professor
Graduate School of Data Science, Department of Health Data Science, Yokohama City University