Regional Variation in National Healthcare Expenditure and Health System Performance in Central Cities and Suburbs in Japan

Abstract

The increasing national healthcare expenditure (NHE) with the aging rate is a significant social problem in Japan, and efficient distribution and use of NHE is an urgent issue. It is assumed that comparisons in subregions would be important to explore the regional variation in NHE and health system performance in targeted municipalities of the metropolitan area of Tokyo (central cities) and the neighboring municipalities of Chiba Prefecture (suburbs). This study aimed to clarify the differences of the socioeconomic factors affecting NHE and the health system performances between subregions. A multiple regression analysis was performed to extract the factors affecting the total medical expenses of NHE (Total), comprising the medical expenses of inpatients (MEI), medical expenses of outpatients (MEO), and consultation rates of inpatients (CRI) and outpatients (CRO). Using the stepwise method, dependent variables were selected from three categories: health service, socioeconomic, and lifestyle. Then, health system performance analysis was performed, and the differences between regions were clarified using the Mann–Whitney U test. The test was applied to 18 indicators, classified into five dimensions referred to in the OECD indicators: health status, risk factors for health, access to care, quality of care, and health system capacity and resources. In the central cities, the number of persons per household was the primary factor affecting Total, MEI, MEO, and CRO, and the number of persons per household and the percentage of the entirely unemployed persons primarily affected CRI. In the suburbs, the ratio of the population aged 65–74 and the number of hospital beds were significantly positively related to Total, MEI, and CRI, but the number of workers employed in primary industries was negatively related to Total and MEI. The ratio of the population aged 65–74 was significantly positively related to MEO and CRO. Regarding health system performance, while risk factors for health was high in the central cities, the others, including access to care, quality of care, and health system capacity and resources, were superior in the suburbs, suggesting that the health system might be well developed to compensate for the risks. In the suburbs, while risk factors for health were lower than those in the central cities, access to care, quality of care, and health system capacity and resources were also lower, suggesting that the healthcare system might be poorer. These results indicate a need to prioritize mitigating healthcare disparities in the central cities and promoting the health of the elderly in the suburbs by expanding the suburbs’ healthcare systems and resources. This study clarified that the determinants of NHE and health system performance are drastically varied among subregional levels and suggested the importance of precise regional moderation of the healthcare system.

Vza Süper Etki̇nli̇k Modeli̇ İle Türki̇ye'deki̇ Şehi̇rleri̇n Sağlik Hi̇zmeti̇ Etki̇nli̇kleri̇ni̇n İncelenmesi̇

“…Literatür incelendiğinde, sağlık alanında etkinlik ölçümüne ilişkin çalışmaların bir kısmı ülkelerin (Konca ve Demirci 2019;Pekkaya ve Dökmen, 2019;Stefko vd., 2018;Tchouaket vd., 2012), bir kısmı bölge ve şehirlerin (Çarıkçı ve Akbulut, 2019;Ömürbek vd., 2021;Pehlivan ve Yiğit, 2022;Seo ve Takikawa 2022;Şenol ve Gençtürk, 2017;Yiğit, 2016), bir kısmı ise belirli sağlık kurumlarının (Araújo vd., 2014;Çakmak vd., 2009;Doğan ve Gencan, 2014;Yılmaz ve Şenel, 2019) verimlilik düzeylerinin karşılaştırılması yoluyla yapılan değerlendirmeler olduğu görülmektedir.…”

Analysis of the Forces Driving Public Hospitals’ Operating Costs Using LMDI Decomposition: The Case of Japan

“…Among tens of indicators, many are mentioned as key factors, such as inpatient unit price, average daily number of inpatients, and outpatient unit price [32] (regression analysis), and outpatient number and inpatient unit price [33] (regression analysis). Geographically, factors such as the number of persons per household, the unemployment rate, and the ratio of the elderly in the population were also mentioned as key factors [34]. In conclusion, the results are inconsistent because of different perspectives and research designs regarding variable selection, while the approaches have been unable to track the impacts from historical events and quantitatively evaluate the contribution from driving forces.…”

Geographic variation in inpatient medical expenditure among older adults aged 75 years and above in Japan: a three-level multilevel analysis of nationwide data

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