Association between rurality and mortality from major diseases in Japan: an ecological study

Abstract

Objectives

This study aimed to examine rural–urban disparities in healthcare outcomes in Japan using the Rurality Index for Japan (RIJ). It evaluated the association between rurality and mortality from five major diseases prioritised by the Japanese government, accounting for socioeconomic and demographic factors.

Design

An ecological study using publicly available data at municipal and administrative district levels.

Setting

All municipalities and administrative districts within government-designated cities in Japan.

Participants

A total of 1897 municipalities and administrative districts were analysed, excluding areas with zero population. The total number of the population was approximately 126 million.

Exposure

Rurality was measured using RIJ.

Primary outcome measures

Standardised mortality ratios (SMRs) were used for acute myocardial infarction (AMI), cerebrovascular diseases (stroke and haemorrhage), cancer and suicide. The standardised claim ratio (SCR) for diabetes outpatient care served as a proxy measure due to the unavailability of mortality data.

Results

Greater rurality, as quantified by RIJ, was associated with higher SMRs for cerebrovascular diseases and male suicide. A dose-response relationship was observed among SMRs for cerebrovascular disease and male suicide, whereas AMI mortality was higher in rural areas but lacked a strict dose-dependent trend. No significant association was found between rurality and cancer mortality or diabetes in outpatient SCR. Additionally, RIJ was positively correlated with the proportion of older adults (Spearman’s ρ=0.67) and the Arial Deprivation Index (ρ=0.55).

Conclusion

These findings highlight the need for targeted rural health policies that improve access to emergency care and mental health services.

STRENGTHS AND LIMITATIONS OF THIS STUDY.

• This ecological study examined the rural–urban disparity in the five major diseases prioritised by the Japanese government.

• This study employed a validated measure of rurality in Japanese healthcare.

• Given the cross-sectional and ecological nature of this study, further research is warranted to investigate the relationship between rurality and individual-level healthcare outcomes.

Introduction

Disparities in health status, healthy behaviours and access to care between rural and urban populations are well-documented globally.1,9 Compared with urban residents, individuals in rural areas have a higher prevalence of obesity-related chronic diseases and experience poorer physical and social functioning,1,47 mental health,⁷ self-reported health status,⁷ cancer survival rates⁸ and overall quality of life.⁹ They are also less likely to engage in healthy behaviours1,4 and have fewer consultations with family physicians and specialists than their urban counterparts.^{5 6} Additionally, rural communities face enormous challenges in recruiting and retaining healthcare providers.¹⁰

In Japan, approximately 900 000 people reside on 1 of its 400 remote islands,¹¹ while an additional 11 million people live in ‘underpopulated areas, as defined by municipalities based on income levels, population decline rates and local demand’.¹² Furthermore, approximately 130 000 individuals live in ‘districts without a doctor’, where access to healthcare is severely limited.¹¹ As examples of rural–urban disparity in Japan, rural areas exhibit higher suicide rates among young and middle-aged male populations and lower average life expectancies.^{13 14} In-hospital mortality due to severe traumatic injury and acute myocardial infarction is higher in rural settings.^{15 16}

Although assessing rurality is key to evaluating rural–urban disparities,¹⁷ previous studies in Japan have used various indices to define rurality, leading to inconsistencies in measurements. To date, no study has examined multiple health outcomes and rural–urban disparities using a standardised rurality index. Under such a situation, the Rurality Index for Japan (RIJ) was developed in 2023 for assessing rurality in healthcare research in Japan.¹⁴ RIJ comprises four components—population density, distance to the nearest secondary or tertiary care hospital, the presence of remote islands and designated heavy snowfall areas.¹⁴ The validated index ranges from 1 to 100, where higher values represent greater rurality. The previous studies have demonstrated that RIJ is associated with poorer healthcare outcomes, such as average life expectancy in men and functional outcomes in patients with acute stroke.^{14 18} However, the association between other major diseases and RIJ has not been investigated.

Aims

This study aimed to examine rural–urban disparities in the five diseases prioritised by the Japanese government (acute myocardial infarction, brain stroke/haemorrhage, cancer, diabetes and suicide: a proxy of psychiatric diseases) and identify key areas for future interventions. Furthermore, it explores the association between the rurality index and ageing rate/socioeconomic status. Assessing rural–urban disparities using a validated measure and examining the relationship between the index and ageing rate/socioeconomic status are fundamental for promoting rural healthcare research in Japan.

Methods

Study design

An ecological study. Publicly available data were used to evaluate the correlation between rurality and healthcare outcomes in Japan. The unit of observation was a municipality and administrative district within a government-designated city. Municipalities, the smallest administrative unit within Japan, are responsible for primary care, while government-designated cities with a population of over 500 000 include administrative districts. Municipalities are responsible for providing primary care to residents. Furthermore, municipalities and administrative districts serve as the smallest units for analysing population census data, socioeconomic information and health statistics.

Settings

In the Japanese healthcare system, the Ministry of Health, Labour and Welfare oversees healthcare administration.¹⁹ Under its jurisdiction, local governments are responsible for healthcare.¹⁹ These local governments comprised 47 prefectures and approximately 1724 municipalities, including cities, towns and villages, as of March 2025.²⁰ Prefectures manage secondary and tertiary care service areas, which are divided into 330 secondary medical areas (SMAs) and 52 tertiary medical areas.²¹ Each SMA comprises multiple municipalities responsible for delivering primary care services to residents.¹⁹

Because Japan does not have a formal gatekeeping system, patients can access hospitals and specialist services directly.¹⁹ As a result, hospitals frequently provide first-contact care for common and minor conditions, which overlaps with services typically considered primary care.^{19 21} This lack of system-level differentiation contributes to unclear boundaries regarding the roles and responsibilities of primary care providers.^{19 21} In Japan, private clinics and hospitals operate independently, funded by their owners rather than by the government, with revenue primarily generated through a fee-for-service payment system.

Participants

A total of 1897 municipalities and administrative districts were included in the analysis after excluding those with zero population. The study population represented approximately 126 million residents, according to the 2020 national census.²⁰

Exposure

This study used RIJ as a validated indicator of rurality—a tool developed by a team including the authors of this study and previously published elsewhere.¹⁴ RIJ consists of four components: population density, distance to the nearest secondary or tertiary care hospital, presence of remote islands and heavy snowfall areas.¹⁴ The index was developed based on a scoping review of rurality indices worldwide and consensus among stakeholders using a modified Delphi method.¹⁴

The development of RIJ involved standardising the included factors, and min–max normalisation was applied. Additionally, an exploratory factor analysis with Promax rotation was conducted based on the four identified factors to examine the factor structure. Factor loadings were used to determine the weight assigned to each factor.¹⁴ In our prior study, the convergent validity was examined by assessing its correlation with the index of physician distribution, and criterion-related validity was evaluated based on its correlation with the average life expectancy.¹⁴ The final RIJ score was calculated by summing weighted factors and ranged from 1 to 100 across all zip codes, municipalities and SMAs in Japan, with higher scores indicating more rural areas.¹⁴

Outcomes

For healthcare outcomes, this study employed the standardised mortality ratio (SMR) of the five diseases prioritised by the Japanese government. SMR is a measure used in epidemiology and public health to compare the observed number of deaths in a specific population with the expected number of deaths based on a reference population. These five diseases are acute myocardial infarction (AMI), brain stroke/haemorrhage, cancer, diabetes and suicide (a proxy of psychiatric diseases).²² For psychiatric conditions, suicide was selected as a proxy indicator because a previous nationwide psychological autopsy study reported that 65.3% of individuals who died by suicide had a diagnosable mental disorder.²³ In that study, mood disorders, anxiety disorders and alcohol-related disorders were among the conditions most strongly associated with suicide.²³ The Japanese government identified five priority diseases based on patient prevalence and their significance for public health. In this study, we used SMRs as indicators for four diseases: AMI, brain stroke/haemorrhage, cancer and psychiatric disorders, represented by SMRs for AMI, brain stroke/haemorrhage, cancer and suicide (male and female), respectively. SMRs were obtained from the government statistics. However, because the SMR for diabetes is not publicly available, we used the standardised claim ratio (SCR) for outpatient care as a proxy measure. SCR is a metric that adjusts the number of medical claims for sex and age using the formula (observed value/expected value), where 100 represents the national average level of medical service provision. An SCR >100 indicates a higher-than-expected level of medical service provision relative to sex-adjusted and age-adjusted population size. An SCR of <100 indicates a lower-than-expected level of medical service provision. This adjustment allowed for direct regional comparisons by accounting for variations in population size and age structure. SCR is also available from the government census.

Additionally, to assess the association between RIJ and ageing rate/socioeconomic variables, we examined its correlation with the proportion of people aged 65 years and over and the Arial Deprivation Index (ADI) as an indicator of deprivation.²⁴

Covariates

We adjusted for covariates to examine the association between RIJ and healthcare outcomes, including SMRs of AMI, brain stroke/haemorrhage, cancer, suicide and SCR of diabetes. The covariates were selected based on previous literature: the proportion of individuals aged 65 years and older and ADI.^{24 25} This adjustment was made because rural residents are considered one of the social determinants of health26,28 and are associated with lower socioeconomic status and increased exposure to unsafe environments, including unstable employment, poor neighbourhood quality, food insecurity, lower educational attainment and exposure to violence.²⁸ Therefore, this study used ADI.²⁴ ADI consists of the proportion of people living alone, rental housing, agriculture/grey/blue-collar workers and non-working persons in each municipality.²⁴ ADI has been shown to be associated with all-cause mortality and has been widely used in Japanese healthcare research.29,32

Data source

The study relied solely on publicly available data, except for RIJ. However, as our team provides the RIJ dataset for research and policymaking purposes, other researchers can access it on request.

Data on SMRs are obtained from e-Stat, a database maintained by the Ministry of Internal Affairs and Communications of Japan.²⁰ Data on SCR is publicly available on the Cabinet Office’s website.³³ These data were derived from the National Database (NDB), which compiles nearly all administrative claims and health check-up data collected from all insurers across Japan.³³ Due to the public disclosure rules of the NDB, data cannot be displayed for towns and villages with a population of 2000 or fewer.³³

Other variables, including the proportion of individuals aged 65 years and older and ADI, were also obtained from e-Stat.²⁰ The National Statistics Center manages and operates the database, which is compiled based on the national census conducted every 5 years, with the prefectural governments responsible for data collection.²⁰ Each municipality in Japan has a unique code that is used to merge the datasets. The publicly available datasets used in this study generally have no missing data, except for towns and villages with populations of 2000 or fewer for SCR. All municipalities including remote island regions were included in the SMR analyses without exclusion. We employed data from 2020 for SMRs, 2021 for SCR and 2020 for the other variables.

Statistical analysis

The units of analysis in this study were the municipality and administrative district within a government-designated city. Of the 1727 municipalities and 175 administrative districts, areas with a population of zero were excluded. SCR was derived from claims data electronically submitted by medical institutions within a municipality. If a municipality lacks medical facilities, has a high proportion of residents seeking healthcare services in other municipalities, or if healthcare facilities predominantly use paper-based claims, SCR for that municipality is zero. Categorical variables are presented as frequencies and proportions, whereas continuous variables are summarised as medians and IQRs. We visually confirmed the relationships between RIJ and outcomes, covariates and outcomes. Scatter plots were generated, and Spearman’s correlation coefficients were calculated to assess the relationships between RIJ and each indicator. We conducted multiple linear regression analysis to examine the association between RIJ and SMRs of the four prioritised and SCR of diabetes after adjusting for covariates. SMRs and SCR as outcomes were continuous variables. RIJ score was categorised into quartiles (Q1–Q4) based on its distribution to allow comparison across levels of rurality. This approach was selected because preliminary visual inspection of scatter plots indicated that the associations between RIJ and the outcomes were not strictly linear. Therefore, treating RIJ as quartiles was considered more appropriate than modelling it as a continuous variable. Covariates were included as continuous variables in the model. We demonstrated p values for each RIJ category. Also, we calculated the p for trend using a multivariable linear regression model, with RIJ assigned as Q1, Q2, Q3 and Q4. We conducted a complete case analysis for missing variables. Thus, if the outcome variable was zero, the municipality was excluded from the analysis. Regression coefficients are interpreted as the adjusted absolute differences in mortality between groups. For categorical RIJ, coefficients indicate the difference relative to the least rural group (Q1). Data management and analysis were performed using Stata Statistical Software Release 19 (StataCorp, College Station, Texas, USA).

Patient and public involvement

This study was conducted without any patient involvement. The patients were not invited to comment on the study design, consulted for the development of relevant patient outcomes or asked to interpret the results. They were not asked to contribute to the writing or editing of the documents for readability or accuracy.

Ethics approval

This study did not undergo ethical committee review, as it relied solely on publicly available data without personal information or human biological specimens. All data, except for RIJ, are publicly accessible and RIJ was developed by our team. The dataset did not contain any patients’ personal information. The email address of the institutional representative responsible for research ethics is rinri@yokohama-cu.ac.jp.

Results

Of the 1898 municipalities and administrative districts, 1 was excluded because its population was zero. Table 1 presents the distribution of outcomes and covariates stratified by RIJ quartile. SMRs for stroke/haemorrhage and suicide among men were higher in areas with higher RIJ categories. Similarly, the proportion of older adults and ADI scores also increased with higher RIJ categories. Online supplemental table S1 presents the crude and adjusted coefficients of RIJ for the outcomes. In SMRs for AMI, RIJ in the second (Q2), third (Q3) and fourth quartiles (Q4) was higher than that in the first quartile (Q1). A higher RIJ was associated with an increased SMR in cerebrovascular events, including stroke/haemorrhage. Regarding SMR for brain stroke/haemorrhage in male, compared with Q1, the adjusted coefficient in Q2 was 11.5 (95% CI 9.1 to 13.2), 12.7 (95% CI 9.9 to 15.6) for Q3 and 18.4 (95% CI 11.1 to 25.7) for Q4. SMR for brain stroke/haemorrhage in female has a similar trend. Additionally, higher RIJ was linked to an elevated SMR for suicide in males: 8.7 (95% CI 6.2 to 11.2) in Q2, 12.9 (95% CI 10.0 to 15.9) in Q3 and 14.1 (95% CI 11.0 to 17.2) in Q4, respectively. However, no significant association was observed between RIJ and SMRs in cancer or SCR in diabetes. The p for trend was statistically significant for AMI and for stroke/haemorrhage and suicide among men. We assessed multicollinearity in the multiple regression analysis and confirmed that the variance inflation factor was less than four for all variables. Figures1 2 show the scatter plot between RIJ and ageing rate/socioeconomic status. The figures indicate that RIJ exhibits a linear relationship with both the proportion of older adults and ADI. Spearman’s correlation coefficient between RIJ and the proportion of older adults was 0.67, and that between RIJ and ADI was 0.55.

Table 1. Distribution of outcomes and covariates stratified by RIJ quartile.

Variables	Total	RIJ Q1	RIJ Q2	RIJ Q3	RIJ Q4
	n=1887	n=476	n=476	n=475	N=469
Outcomes
SMRs
AMI male, median (IQR)	96.6 (74–127.7)	80.9 (66.5–100.5)	103.3 (78.5–131.4)	106 (79.7–137.4)	100.9 (76.2–132.6)
Missing	10 (0.5)	0 (0)	0 (0)	1 (0.2)	9 (1.9)
AMI female, median (IQR)	96.5 (74.8–129.5)	78.3 (65.5–98.4)	104.6 (80.4–137.1)	106.3 (82.6–140.7)	102.6 (78.1–134.8)
Missing	10 (0.5)	0 (0)	0 (0)	1 (0.2)	9 (1.9)
Brain stroke and haemorrhage male, median (IQR)	100.5 (90.3–113.8)	90.4 (83.0–100.0)	102.4 (92.0–116.2)	103.3 (93.4–115.9)	106.5 (96.5–120.3)
Missing	10 (0.5)	0 (0)	0 (0)	1 (0.2)	9 (1.9)
Brain stroke and haemorrhage female, median (IQR)	100.6 (89.6–115.1)	89.3 (81.9–99.1)	104.5 (92.3–117.4)	103.2 (92.9–116.5)	107.4 (95.9–12.8)
Missing	10 (0.5)	0 (0)	0 (0)	1 (0.2)	9 (1.9)
Cancer male, median (IQR)	99.7 (94.5–105.8)	98.4 (94.1–105.2)	99.1 (94.1–103.3)	98.8 (93.4–104.3)	103.4 (96.6–111.2)
Missing	10 (0.5)	0 (0)	0 (0)	1 (0.2)	9 (1.9)
Cancer female, median (IQR)	98.8 (94.3–104.2)	99.9 (96.1–105.1)	98.1 (94–102.1)	97.2 (92.7–102)	101.0 (94.7–111.5)
Missing	10 (0.5)	0 (0)	0 (0)	1 (0.2)	9 (1.9)
Suicide male, median (IQR)	104.8 (93.4–117.3)	93.0 (84.0–102.9)	104.7 (94.6–115.1)	108.9 (99.9–122.7)	113.2 (100.5–127.4)
Missing	10 (0.5)	0 (0)	0 (0)	1 (0.2)	9 (1.9)
Suicide female, median (IQR)	98.99 (88.5–109.7)	99.9 (90.6–109.5)	96.9 (85.8–107.4)	96.5 (86.5–109.6)	101.1 (90.4–113.2)
Missing	10 (0.5)	0 (0)	0 (0)	1 (0.2)	9 (1.9)
SCR
Diabetes, median (IQR)	69.5 (9.5–11.5)	94.9 (67.4–123.3)	86.0 (38.2–134.3)	33.3 (0–90.8)	3.1 (0–91.1)
Missing	94 (5.0)	0 (0)	0 (0)	15 (3.2)	79 (16.8)
Covariates
Proportion of individuals aged 65 years and older (%), median (IQR)	33.6 (28.0–39.5)	26.4 (23.2–29.4)	31.4 (28.1–35.4)	38.2 (34.5–42.4)	39.5 (35.5–44.3)
Missing	2 (0.1)	0 (0)	0 (0)	1 (0.2)	1 (0.2)
ADI, median (IQR)	5.68 (5.27–6.21)	5.2 (4.8–5.5)	5.5 (5.2–5.9)	5.9 (5.6–6.3)	6.2 (5.7–6.7)
Missing	2 (0.1)	0 (0)	0 (0)	1 (0.2)	1 (0.2)

ADI, Arial Deprivation Index; AMI, acute myocardial infarction; RIJ, Rurality Index for Japan; SCR, standardised claim ratio; SMR, standardised mortality ratio.

Figure 1. Scatter plot of RIJ and proportion of people aged 65 years and over. RIJ, Rurality Index for Japan.

Figure 2. Scatter plot of RIJ and ADI. ADI, Arial Deprivation Index; RIJ, Rurality Index for Japan.

Discussion

Summary of the findings

This study examined rural–urban disparities in healthcare outcomes in Japan using RIJ and found that the association between rurality and health outcomes varies across diseases. For AMI, mortality was higher in rural areas (RIJ Q2-Q4) than in urban areas (RIJ Q1), although the relationship was not strictly dose-dependent. For brain stroke/haemorrhage and suicide, a dose-response relationship was observed, with higher rurality correlating with a higher SMR, whereas no significant association was found for suicide in females. However, cancer-related mortality was not significantly associated with overall mortality. Finally, in diabetes, RIJ was not significantly associated with SCR in outpatient care.

These findings suggest that rurality is an associated factor of certain health outcomes, particularly for AMI, brain stroke/haemorrhage and male suicide, but not necessarily for all diseases. RIJ was also found to be strongly correlated with key health indicators, including the ageing rate and ADI. While RIJ captures aspects of rurality that overlap with these health indicators, it remains distinct, as some healthcare outcomes are independently associated with rurality rather than with indicators.

Comparison with existing literature

The results of this study align with those of previous studies demonstrating poorer AMI and brain stroke/haemorrhage outcomes in rural Japanese areas^{16 18 34} and in other countries.35,37 The results can be attributed to limited access to initial treatments, such as percutaneous coronary intervention for AMI or intravenous thrombolysis for stroke.^{16 18 37} Geographical and workforce-related factors may help explain the observed association between rurality and mortality. Prior studies have shown that travel time to emergency and tertiary care facilities is substantially longer in municipalities with smaller populations and limited transport infrastructure.³⁸ In addition, the uneven distribution of specialists and the tendency of physicians to migrate from rural to urban regions may further constrain access to timely cardiovascular and acute care.³⁹ These system-level barriers are particularly relevant for time-sensitive conditions such as AMI and stroke, where delayed access to reperfusion or specialist intervention is associated with poorer outcomes.³⁸ Studies using different rurality indices have reported similar trends. However, our study adds granularity by examining the extent of disparities across multiple rurality levels.

The absence of a strict dose-response pattern in AMI mortality may reflect the influence of factors beyond rurality alone. Prior studies in Japan have demonstrated that access to percutaneous coronary intervention–capable hospitals is unevenly distributed, and patients in rural regions are less likely to receive direct transport to such facilities, resulting in longer reperfusion delays and poorer outcomes.³⁴ Furthermore, aeromedical transport systems, such as doctor-helicopter services, have been shown to reduce prehospital delays and improve access to emergency cardiovascular care in remote settings, which may partially mitigate mortality in the most rural (Q4) areas.⁴⁰ These contextual system-level factors may therefore obscure a linear rurality–mortality gradient when using aggregated population-level indicators such as SMR.

Prior research has identified higher suicide rates among men in rural areas in Japan¹³ and other countries⁴¹; however, previous studies in Japan have not fully explored the dose-dependent effect observed in our findings for males. This highlights the need for targeted mental health interventions in rural areas.⁴² The association between rurality and suicide observed in this study suggests the need for targeted mental health strategies in rural areas. In Japan, access to psychiatric services is uneven, with psychiatric specialists disproportionately concentrated in metropolitan regions, while rural areas face persistent workforce shortages.⁴³ Moreover, rural regions have a higher proportion of older adults living alone, and social isolation has been identified as an independent risk factor for suicide. Although national and municipal mental health programmes exist, their availability and uptake remain limited in remote areas. Tailored interventions such as telepsychiatry, integration of mental health into primary care and community-based suicide prevention programmes may therefore be essential to reduce suicide risk in high-rurality settings.

Regarding cancer, past Japanese studies have focused on specific cancer types rather than overall cancer mortality.^{44 45} In this study, total cancer mortality was not associated with RIJ. These findings differ from those reported in other countries.^{45 46} One possible explanation for this is that cancer treatment is mainly non-urgent, allowing for the centralisation of care.^{47 48} Also, the most common cancer types in Japan—lung, colorectal, stomach and pancreatic cancers⁴⁹—are conditions for which nationwide screening programmes and diagnostic imaging, including CT scans and endoscopic examinations, are widely available. Japan is known to have relatively high access to cross-sectional imaging compared with other high-income countries,²¹ which may have mitigated geographical differences in early detection and treatment pathways. Therefore, unlike acute time-sensitive conditions such as AMI or stroke, disparities in healthcare accessibility captured by RIJ may exert a weaker influence on cancer mortality.

Regarding rurality and socioeconomic status, the trends were similar to those of previous studies conducted in other countries.^{50 51} Regions with a higher RIJ were found to have limited access to secondary and tertiary emergency care, a higher ageing population rate and a tendency toward a lower socioeconomic status. Because the term ‘rural’ encompasses various aspects, the characteristics of regions with higher RIJ should be considered when using RIJ in healthcare research or policy development.

Limitations of the study

Despite these important findings, this study had several limitations. First, as an ecological study, our analysis was conducted at the municipal level, which might not have fully captured individual-level health disparities. These results should be interpreted as population-level associations rather than direct causal relationships. Second, owing to NDB disclosure restrictions, data for towns and villages with populations below 2000 were unavailable, potentially underestimating the disparities in the most remote areas. The potential impact of this exclusion on the main mortality findings is expected to be minimal. However, these excluded areas represent some of the most remote and medically underserved regions in Japan; therefore, our results may underestimate disparities in healthcare utilisation, and caution is needed when generalising SCR-related findings to isolated or very small population settings. Third, the SCR measurement used in this study does not necessarily reflect healthcare needs or healthcare-seeking behaviours because it is influenced by patient migration across municipalities for treatment. Additionally, unmeasured confounders, such as variability in healthcare provider distribution, may not have been directly included in the analysis.

Implication of the study

The findings of this study have important policy implications. Given the significantly worse outcomes of AMI and brain stroke/haemorrhage in rural areas, strengthening emergency transport systems and improving access to specialist care could be crucial for reducing mortality disparities. The observed association between rurality and male suicide rates suggests that expanding mental health services in rural areas, including telepsychiatry and community-based support programmes, may be beneficial. In contrast, because cancer mortality is not directly linked to rurality, centralised and specialised cancer treatment centres may be more effective than decentralised care. These findings highlight the need for disease-specific strategies to address the rural health disparities in Japan.

Additionally, the Japanese Ministry of Health, Labour and Welfare is currently considering the use of RIJ as a metric to guide physician workforce allocation,⁵² and the observed associations between higher rurality and poorer outcomes in time-sensitive conditions such as AMI and stroke may further support the relevance of incorporating rurality measures into policy frameworks. In particular, the relationship between RIJ and SMRs for these acute conditions suggests that geographical constraints may delay timely access to emergency care, highlighting the need to prioritise resource planning, including strategic placement of emergency care facilities and specialist services, in high-rurality regions. These insights may contribute to ongoing discussions on strengthening equitable healthcare delivery and reducing geographical disparities in Japan.

Strengthening primary care systems is critical to reducing avoidable mortality and addressing health inequities.⁵³ In Japan, multimorbidity has received increasing attention alongside rapid population ageing⁵⁴; however, care delivery remains predominantly specialist-oriented, and fragmented access may hinder coordinated management.²¹ Our findings indicate that rural areas with higher RIJ scores may be particularly vulnerable due to a higher proportion of older adults and limited availability of continuous primary and specialist care. Although recent national policy initiatives aim to expand the number of board-certified primary care physicians and enhance community-based care, the current workforce remains insufficient, particularly in rural and remote settings.⁵⁴ Improving access to continuous and coordinated primary care may therefore be an essential strategy to reduce mortality among older adults with multimorbidity in high-rurality regions.

Conclusions

In conclusion, this study provides novel insights into rural–urban healthcare disparities in Japan using a validated rurality index. Clear rural disparities exist in diseases such as AMI, brain stroke/haemorrhage and suicide, whereas others, such as cancer, were not associated with rurality. These findings suggest that rural healthcare policies should prioritise emergency care accessibility, mental health interventions and context-specific healthcare strategies tailored to disease burden.

Data availability statement

Data are available upon reasonable request.