Supply-side factors related to regional variations in healthcare have been thoroughly documented in the current health economics literature. However, less attention has been given to demand-side factors. To determine the source of variations in patient-side (i.e., demand-side) healthcare decisions, this study explored individual decisions on healthcare utilization related to the screening and treatment of chronic conditions. We found that overall municipal variations in healthcare utilization were mainly explained by individual-level factors and that the contribution of regionally specific factors was less than 5% of total variation after controlling for individual-level factors. However, there is a difference in the magnitude of variations between diagnosis and treatment and by condition. Specifically, the regional variation in diagnoses is far smaller than that in treatment given a diagnosis, and the magnitude of variation is different across chronic conditions. If it is a public health goal to further reduce regional disparities in healthcare utilization, the findings suggest that interventions designed to control chronic conditions should emphasize treatment for a specific condition.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Camenzind, P. A. (2012). Explaining regional variations in health care utilization between Swiss cantons using panel econometric models. BMC Health Services Research,12(1), 62.
Cutler, D., Skinner, J., Stern, A. D., & Wennberg, D. (2018). Physician beliefs and patient preferences: a new look at regional variation in health care spending. NBER Working Paper Series, No. 19320.
Eibich, P., & Ziebarth, N. R. (2014). Examining the structure of spatial health effects in Germany using hierarchical Bayes models. Regional Science and Urban Economics,49, 305–320.
Hashimoto, H., Ikegami, N., Shibuya, K., Izumida, N., Noguchi, H., Yasunaga, H., et al. (2011). Cost containment and quality of care in Japan: Is there a trade-off? The Lancet,378(9797), 1174–1182.
Ichimura, H., Shimizutani, S., & Hashimoto, H. (2009). JSTAR First Results 2009 Report. RIETI Discussion Paper Series 09-E-047.
Ikeda, N., Saito, E., Kondo, N., Inoue, M., Ikeda, S., Satoh, T., et al. (2011). What has made the population of Japan healthy? The Lancet,378(9796), 1094–1105.
Johansson, N., Jakobsson, N., & Svensson, M. (2018). Regional variation in health care utilization in Sweden—the importance of demand-dide factors. BMC Health Services Research, 1–9.
JSTAR dataset. (2007, 2009, 2011, 2013). https://www.rieti.go.jp/en/projects/jstar/.
Ministry of Health, Labour and Welfare. (2014) Nijiiryoken no jokyo ni tsuite (in Japanese). Retrieved June 23, 2019 from https://www.mhlw.go.jp/file/05-Shingikai-10801000-Iseikyoku-Soumuka/0000058300.pdf.
Ministry of Internal Affairs and Communications. (2014) Shicho-son suu no hensen to meiji showa no daigappei not tokucho (in Japanese). Retrieved June 23, 2019 from https://www.soumu.go.jp/gapei/gapei2.html.
Molitor, D. (2018). The evolution of physician practice styles: evidence from cardiologist migration. AEJ Economics Policy,10(1), 326–356.
Nakaya, T., Honjo, K., Hanibuchi, T., Ikeda, A., Iso, H., Inoue, M., et al. (2014). Associations of all-causeKindly provide mortality with census-based neighbourhood deprivation and population density in Japan: a multilevel survival analysis. PLoS One, 9(6), 1–10.
Rice, T., & Unruh, L. (2015). The economics of health, reconsidered. Health Administration Press, 4th edition.
Skinner, J. (2011). Causes and consequences of regional variations in health care. Vol. 2. Elsevier B.V.
Shoji, K., Ibuka, I. (2017) Regional variations in access to healthcare among Japanese individulas over 50 years old: an analysis using JSTAR. RIETI Discussion Paper Series, 17-J-036.
Song, Y., Skinner, J., Bynum, J., Sutherland, J., Wennberg, J. E., & Fisher, E. S. (2010). Regional variations in diagnostic practices. New England Journal of Medicine,363(1), 45–53.
Statistical Bureau of Japan (2010). E-stat, System of Social and Demographic Statistics. Retrieved March 01, 2020 from https://www.e-stat.go.jp/en/regional-statistics/ssdsview.
Williams, H., Gentzkow, M., & Finkelstein, A. (2016). Sources of geographic variation in health care: evidence from patient migration. Quarterly Journal of Economics,131(4), 1681–1726.
Yamamura, E. (2011). Different effects of social capital on health status among residents: evidence from modern Japan. Journal of Socio-Economics,40(5), 475–479.
Conflict of interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Yoko Ibuka received support from the Open Research Areas program, and Yasumasa Matsuda received support from Grant-in-Aid for Scientific Research (B)17H01701, by the Society for the Promotion of Science. The authors declare no conflict of interest. The authors are grateful for the valuable comments we received from participants of the Japan–Singapore Academic Forum on Aging in 2018. JSTAR dataset was provided by the Research Institute of Economy, Trade and Industry. The authors thank Takaki Sato for his assistance in preparing a manuscript. This paper extends a working paper (Shoji and Ibuka 2017) in data and analyses.
See Table 4.
About this article
Cite this article
Ibuka, Y., Matsuda, Y., Shoji, K. et al. Evaluation of regional variations in healthcare utilization. Jpn J Stat Data Sci 3, 349–365 (2020). https://doi.org/10.1007/s42081-020-00082-z
- Regional variations
- Healthcare utilization
- Logistic regression