Hospitalizations for mental disorders among public assistance recipients and the general population in Japan: A repeated cross-sectional study using national government statistics

Hospitalizations for mental disorders among public assistance recipients and the general population in Japan: A repeated cross-sectional study using national government statistics | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Hospitalizations for mental disorders among public assistance recipients and the general population in Japan: A repeated cross-sectional study using national government statistics Hideyuki Watanabe, Kotaro Kuwaki, Yuito Hosaka, Masaaki Matsunaga, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8437618/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background In Japan, hospital stays for patients with mental disorders are longer than those in many countries, leading to employment difficulties and reliance on public assistance. Mental disorders are common among public assistance recipients (PARs), but category-specific differences in hospitalization rates relative to the general population remain unclear. Using national government statistics, we compared hospitalization rates by subcategories of mental disorder between PARs and the general population, and assessed age-standardized differences to inform mental health policies. Methods We conducted a repeated cross-sectional study using data obtained from national government statistics for 2014, 2017, 2020, and 2023. We compared hospitalization rates (H-rates; inpatients per 100000 on a given day) for mental disorders between PARs and the general population. Diagnoses followed the International Classification of Diseases, Tenth Edition (ICD-10), focusing on “V Mental and behavioural disorders” and its three subcategories (defined for this study): V-1 Schizophrenia disorders, V-2 Mood disorders, and V-3 Neurotic disorders. We calculated crude hospitalization ratios (CHRs; PARs/general population H-rate) and standardized hospitalization ratios (SHR) using indirect age adjustments with 95% confidence intervals (CIs). Results From 2014 to 2023, both PARs and the total population declined. By 2023, PARs and the total population were about 2.0 and 124.4 million, with 52.6% and 29.1% aged ≥ 65, respectively. Across the subcategories, H-rates were consistently higher in PARs than in the general population and were the highest for V-1 Schizophrenia disorders. H-rates declined overall, with smaller decreases for V-2 Mood and V-3 Neurotic disorders. Regarding SHRs, men generally exceeded women, whereas for V-1 the sex difference was minimal across the years. Across the survey years, SHRs for all subcategories exceeded 1 and surpassed those for “All diseases”; the highest by sex were V-3 in men and V-1 in women. Conclusions Among PARs, H-rates were higher than those in the general population not only for V-1 but also for V-2 and V-3. In the SHR trends, the decline in V-1 likely reflected an apparent improvement due to fewer older PARs, with no clear improvement in V-2 or V-3. Policies should move beyond V-1 emphasis and strengthen post-discharge and employment-transition support. age adjusted hospitalization mental disorders public assistance Japan Figures Figure 1 Figure 2 Text box 1. Contributions to the Literature ･Using Japanese administrative data, this study reveals that mental health hospitalization risks for public assistance recipients extend beyond schizophrenia to mood and neurotic disorders. ･Comparing crude and age-standardized trends shows that apparent declines in schizophrenia hospitalizations largely reflect demographic change rather than substantive improvement. ･These findings suggest that mental health policies focusing narrowly on schizophrenia may overlook the persistent burden of other mental disorders among public assistance recipients. ･The study underscores the need for integrated health, welfare, and employment policies to support individuals facing poverty-related mental health risks. Background Hospital stays for patients with mental disorders have been noted to be longer in Japan than in other countries. According to OECD data [ 1 ], the average length of hospital stay for patients with diseases classified under the International Classification of Diseases, Tenth Edition (ICD-10) Chapter V Mental and behavioural disorders (V Mental disorders) was 31.5 days across 30 OECD countries with 2020 data available (Japan excluded). In comparison, the corresponding average hospital stay in Japan was markedly higher at 294.2 days, followed by Korea (200.4 days) and Spain (60.8 days). Such prolonged hospitalizations in Japan are associated with difficulties in obtaining or retaining employment, thereby increasing the likelihood of qualifying for and relying on social welfare programs. Japan’s public assistance system aims to guarantee a minimum standard of living for people in financial hardship and is implemented under the Public Assistance Act. Recipients under this program are referred to as public assistance recipients (PARs), and as part of the program, “Medical Assistance” is provided, under which the costs of necessary medical care are covered using public funds [ 2 – 4 ]. When examining actual national data, substantial differences in Medical Assistance coverage are observed across disease categories. According to the Patient Survey (2023), which contains national government statistics on healthcare utilization in Japan, among discharged inpatients, the proportion whose hospitalization costs were paid via Medical Assistance was 7.4% for “All diseases”, whereas it reached 16.5% for V Mental disorders—the highest share among the major ICD-10 diagnostic categories [ 5 ]. This indicates that PARs constitute an exceptionally high proportion of inpatients hospitalized for mental disorders. Although previous studies conducted in Japan suggest that mental disorders are associated with economic hardship [ 6 – 8 ], the extent to which the prevalence of mental disorders and hospitalization rates among PARs differ from those among the general population has not been fully elucidated. Moreover, in 2023, the proportion aged ≥ 65 years was 52.6% in PAR versus 29.1% in the total population [ 9 , 10 ]. Using publicly available government statistics, this study calculated and compared hospitalization rates by subcategories of mental disorders between PARs and the general population, and evaluated age-standardized differences. It further aimed to identify which subcategories of mental disorders are more strongly associated with receiving public assistance and to examine how differences in age structure affect hospitalization rates, thereby providing foundational evidence to inform mental health policies. Methods ・Study design and setting This repeated cross-sectional study used publicly available government statistics. The study years were 2014, 2017, 2020, and 2023, selected to align with the Patient Survey (a national government survey conducted every three years). ・Data sources and study population Age-group-specific hospitalization rates and population counts were used for the PARs and the general population, respectively. For PARs, hospitalization rates were obtained from the Fact-finding Survey on Medical Assistance, and age-group-specific population counts were obtained from the National Survey on Public Assistance Recipients. The Fact-finding Survey on Medical Assistance is a government statistical survey compiled by the Ministry of Health, Labour and Welfare that aggregates claim-level data on medical services provided under Medical Assistance [11]. The National Survey on Public Assistance Recipients is administered by the same ministry to monitor households receiving protection under the Public Assistance Act; prefectures and welfare offices serve as reporting entities, and the survey is conducted annually as a census of all recipients [12]. For the general population, hospitalization rates were obtained from the Patient Survey and age-group-specific population counts from Population Estimates. The Patient Survey conducted in 2023 collected data from 96% of sampled medical institutions and provides information on outpatient and inpatient care, including distributions by disease category [13]. Given this high response rate, it is considered to reasonably reflect national patterns of health-care utilization. Population Estimates is a core national statistic widely used as the standard source for population figures [10]. ・Variable s The hospitalization rate (H-Rate) was the primary outcome, defined following the Patient Survey, as the number of inpatients per 100000 population on the survey day. We defined the crude hospitalization ratio (CHR) as the H-Rate in PARs divided by the H-Rate in the general population, as follows: ( ) To account for differences in age structure, we computed the standardized hospitalization ratio (SHR) through indirect standardization, using PARs as the study population and the general population as the reference. The SHR was defined as the ratio of the observed number of inpatients in PARs to the expected number obtained by applying age-specific hospitalization rates in the general population to the age distribution of PARs. Therefore, the SHR was computed as follows: ( ) Both ratios use the general population as the reference: for the CHR, the denominator is the H-Rate in the general population; for the SHR, the denominator is the expected inpatient count derived from age-specific H-Rates in the general population. That enabled assessment of how differences in age structure influence H-Rates. ・Quantitative variable s Diagnostic categories were defined according to the ICD-10. The analyses focused on three subcategories of mental disorders for which comparison was possible using publicly available government statistics: Schizophrenia, schizotypal and delusional disorders (ICD-10 codes F20–F29); Mood [affective] disorders (ICD-10 codes F30–F39); and Neurotic, stress-related and somatoform disorders (ICD-10 codes F40–F48). Hereafter, these are referred to as V-1 Schizophrenia disorders, V-2 Mood disorders, and V-3 Neurotic disorders, respectively. For comparison, we also computed the values for “All diseases”, which includes conditions other than mental disorders. For indirect age standardization, age groups followed those used in the Patient Survey: 0 years; 1–4 years; five-year bands from 5 through 89 years; and ≥90 years (20 groups in total). ・Statistical method The H-Rate was defined as the number of inpatients per 100000 population on the survey day. For the general population, H-Rates were obtained from the Patient Survey. For the PARs, inpatient counts were derived from the Fact-finding Survey on Medical Assistance, which aggregated claims data for April–May by disease category (total number of claims, total inpatient care days [patient days], and total medical fee points). Because inpatient claims are generally billed once per hospitalization per month, the number of claims cannot be used to directly estimate the number of inpatients. In inpatient care, a hospitalized patient can accrue one care day per calendar day at most. Therefore, we divided the total inpatient-care days by 61 (the combined number of days in April and May) to estimate the average daily number of inpatients for April–May and used this value as the numerator to compute the H-Rates. For the CHR, 95% confidence intervals (CIs) were estimated under a Poisson assumption for observed inpatient counts using standard errors on the log scale (Wald method). For the SHR, 95% CIs were computed using the exact Poisson method based on the chi-square distribution. All analyses were conducted using Stata/MP 19.0 (StataCorp LLC, College Station, TX, USA). Results ・Participants Table 1 shows the number of individuals by sex and the distribution of age groups (0–4, 15–64, ≥ 65 years, and ≥ 75 years) among the PARs and the total population during the study period. The number of PARs declined steadily from approximately 2.1 million in 2014 to 2.0 million in 2023 (–6.9%). The total population decreased modestly from approximately 127 million in 2014 to 124 million in 2023 (–2.2%). The proportion of individuals aged ≥ 65 years among the PARs was consistently high and increased further, reaching 52.6% in 2023. In the total population, the proportion of those aged ≥ 65 years also increased, but only modestly, reaching 29.1% in 2023. Table 1 Trends in population size and age distribution of PARs and total population, Japan, 2014–2023 PAR Total population 2014 2017 2020 2023 2014 2017 2020 2023 n ( x 1000) Total 2128 2096 2026 1989 127083 126711 126153 124356 Men 1054 1039 1003 987 61800 61655 61348 60493 Women 1074 1057 1023 1003 65283 65056 64805 63863 Age Group (%) 0–14 9.5% 8.0% 6.7% 5.7% 12.8% 12.3% 11.9% 11.4% 15–64 47.0% 42.9% 41.3% 41.7% 61.3% 60.0% 59.5% 59.5% 65≤ 43.5% 49.1% 52.0% 52.6% 26.0% 27.7% 28.6% 29.1% 75≤༊ 20.0% 24.2% 27.8% 30.8% 12.5% 13.8% 14.7% 16.1% *A subset of individuals aged 65 years and over. Population numbers are expressed in thousands and rounded to one decimal place. The proportions of each age group represent the percentage within the respective population for each year and were rounded at the second decimal place (to one decimal place). ・Descriptive data Table 2 shows the temporal trends in H-Rates by mental-disorder subcategory and sex. Across all subcategories and both sexes, the H-Rates were consistently higher among the PARs than in the total population, with the highest rates observed for V-1 Schizophrenia disorders. In 2023, the H-Rate for this category was 551 versus 97 for men and 533 versus 106 for women (PARs versus total population, respectively). Overall, the H-Rates declined in both PARs and the total population, although the decreases were smaller for V-2 Mood disorders and V-3 Neurotic disorders. Table 2 H-Rates by sex and disease category among PARs and total population, Japan, 2014–2023. Sex Category* PAR Total population 2014 2017 2020 2023 2014 2017 2020 2023 Men All diseases 2530 2489 2387 2028 977 972 910 893 V-1 Schizophrenia disorders 824 757 707 551 135 121 112 97 V-2 Mood disorders 74 76 73 64 16 18 16 15 V-3 Neurotic disorders 17 20 17 15 3 3 3 3 Women All diseases 2050 2082 2036 1844 1095 1096 1007 995 V-1 Schizophrenia disorders 709 683 653 533 126 121 114 106 V-2 Mood disorders 91 92 88 80 29 29 28 27 V-3 Neurotic disorders 24 29 26 23 6 6 6 6 *Category definitions (ICD-10): “V-1 Schizophrenia disorders” corresponds to Schizophrenia, schizotypal and delusional disorders (F20–F29); “V-2 Mood disorders” corresponds to Mood [affective] disorders (F30–F39); and “V-3 Neurotic disorders” corresponds to Neurotic, stress-related and somatoform disorders (F40–F48). “All diseases” denotes all ICD-10 disease categories, including those outside mental disorders. All values were rounded to the first decimal place. ・Main result Figure 1 shows the scatter plots of the CHR and SHR. In all subcategories, the points were located below the diagonal line, indicating that CHR = SHR, with the CHR exceeding the SHR. When comparing each mental disorder subcategory by sex, the points for men were generally positioned above and to the right of those for women, showing higher values for both the CHR and SHR. However, for V-1 Schizophrenia disorders, the sex differences were minimal. In particular, the SHR values were 4.31, 4.31, 4.29, and 3.92 among men and 4.45, 4.38, 4.31, and 3.78 among women. When comparing subcategories, the influence of age adjustment varied. The discrepancy between the CHR and SHR was large for V-1 Schizophrenia disorders and V-2 Mood disorders, whereas for V-3 Neurotic disorders the points were closer to the diagonal line of equality, indicating a relatively small impact of age adjustment. Corresponding values are provided in Additional file1 (Table S1 ). The dashed line indicates equality between the CHR and SHR (CHR = SHR). Figure 2 shows annual trends in the SHR with 95% CIs by sex. Across all subcategories, the SHR values exceeded those for “All diseases” and were consistently greater than 1. The highest values were observed for V-3 Neurotic disorders among men, with values of 5.20, 6.18, 5.45, and 4.53 from 2014 to 2023. The highest values were also observed for V-1 Schizophrenia disorders among women, with values of 4.45, 4.38, 4.31, and 3.78 over the same period. A notable feature was the temporary increase in V-3 Neurotic disorders between 2014 and 2017 in both sexes, where values rose from 5.20 to 6.18 among men and from 3.53 to 4.29 among women, before returning to approximately previous levels in subsequent years. Corresponding values for the SHRs and their 95% CIs shown in Fig. 2 are provided in Additional file1 (Table S1 ). Discussion In this study, we calculated the H-Rates for mental disorders by diagnostic subcategory among PARs and the general population, and compared them using the CHR and SHR. Both indicators were consistently elevated, highlighting the substantial risk of hospitalization for mental disorders among PARs. In Japan, few studies have comprehensively compared and analyzed hospitalizations for mental disorders among PARs by diagnostic subcategories. Previous research has reported that, in cross-sectional studies on older PARs, the prevalence of depressive symptoms was higher than that among non-PARs [ 14 ]. In addition, Kawauchi et al. indicated that the prevalence of mental disorders, including anxiety and depressive disorders, was consistently higher among PARs [ 15 ]. A key strength of the present study is that it evaluated all diagnostic subcategories using the same governmental statistical datasets, rather than estimating risks separately from heterogeneous data sources. Furthermore, by comparing the results before and after age standardization, we examined the hospitalization risks among PARs in greater detail. This analysis revealed that hospitalizations for V-1 Schizophrenia disorders were frequent, but those for V-3 Neurotic disorders were equally or even more common. The H-Rate for mental disorders showed an overall downward trend in both the general population and among PARs. Prior studies suggested that the decline in the number of inpatients occupying psychiatric beds is largely attributable to a decrease in the number of older patients with schizophrenia, whereas mood disorders are expected to increase [ 16 ]. In our analysis restricted to PARs, the H-Rate for V-1 Schizophrenia disorders declined substantially, whereas the corresponding SHR decreased only modestly. Regarding V-2 Mood disorders and V-3 Neurotic disorders, both the H-Rate and SHR remained essentially unchanged, with no clear improvement. For V-1 Schizophrenia disorders, long-stay older inpatients likely contributed to the high H-Rate, suggesting that the observed decline may represent only an apparent improvement and may not reflect substantive changes in the underlying conditions. In Japan, efforts have primarily focused on reducing long-term hospitalization among patients with schizophrenia and on employment support through “Type B Continuous Employment Support” (a welfare-based program providing simple work or productive activities for individuals for whom regular employment is difficult) [ 17 ]. However, our findings indicate a lag in measures addressing mood and neurotic disorders. This pattern aligns with international evidence showing higher frequencies of depression and anxiety among socioeconomically disadvantaged groups. According to an OECD report, severe depressive symptoms are approximately 3.5 times more prevalent in low-income groups than in high-income groups [ 18 ], and Ridley et al. have highlighted bidirectional causal links between poverty and mental illness for depression and anxiety [ 19 ]. Furthermore, reports from Japan and other countries indicate insufficient medical and social support for mood disorders, which may contribute to prolonged hospitalization and social isolation [ 20 ]. Therefore, regardless of the diagnostic category, strengthening community-based systems that support post-discharge life reconstruction and phased transitions to competitive employment is essential. A notable increase in hospitalizations for V-3 Neurotic disorders among PAR was observed exclusively in 2017, both before and after age standardization. Consistent with this finding, the Fact-finding Survey on Medical Assistance [ 11 ] also reported an increase in this subcategory in 2017, particularly among men aged 35–54 years and women aged 6–34 years and those aged ≥ 75 years. In contrast, outpatient visits showed no comparable increase. These results suggest that the 2017 surge likely reflected external influences, such as policy revisions or reimbursement changes, rather than a true increase in incidence. However, the specific factors underlying this anomaly, could not be determined within the scope of this study. This study had several limitations. First, the timing of data collection differed between the PARs and the general population. Hospitalization data for the general population were collected in October, whereas those for the PARs were fcollected from April–May claims. Mental disorders are subject to seasonal variation [ 21 ], and in Japan depressive symptoms are reported to worsen during winter [ 22 – 24 ]. To account for such fluctuations, more refined analyses would ideally require using claims data for PARs and the National Database (NDB) [ 25 ] for the general population, aligned to the same calendar months. However, these data are not publicly available and could not be used in this study. This remains a topic for future research. Second, the government statistics used in this study may not fully capture all hospitalizations among PARs. Under Japan’s Medical Assistance system, conditions for which medical costs are already covered in full by other public schemes are excluded. For example, some infectious diseases are subject to separate government-funded medical programs, and hospitalizations are therefore not billed under Medical Assistance. This also applied to COVID-19 hospitalizations, which were fully publicly funded until May 2023. Consequently, certain hospitalizations among PARs may have been omitted from the statistics, leading to a potential underestimation of hospitalizations for mental disorders. This study demonstrated that hospitalizations for mental disorders among PARs remain high, with no improvement in schizophrenia, mood, or neurotic disorders. Although Japan’s current mental health policy seeks to reduce long-term psychiatric hospitalizations, its primary focus remains on schizophrenia, leaving support for mood and neurotic disorders insufficient. Hospitalizations for these conditions cannot be addressed by medical or welfare interventions alone, and difficulties in employment and poverty after discharge may trigger deterioration and readmission, creating a vicious cycle. Such interactions between poverty and mental illness represent a global challenge, and the findings of this study underscore the need for policies that promote collaboration across health care, welfare, and employment sectors to support the mental health and social participation of disadvantaged populations. Furthermore, the decline in hospitalizations for schizophrenia is likely only an “apparent improvement” associated with a reduction in older PARs, rather than a genuine change. Additionally, the increase in hospitalizations for neurotic disorders observed in 2017 was likely driven by external factors such as reimbursement revisions or changes in diagnostic classification, rather than by an actual increase in incidence. These findings suggest that simple trends in hospitalization statistics alone are insufficient to capture underlying realities. Government statistics require extensive processing, which result in a publication lag of several years. Consequently, it is difficult to capture annual changes in detail. Future research should use national databases to enable the continuous and timely monitoring of the effects of policy reforms and classification changes. Conclusion This study demonstrated that hospitalizations for mental disorders among PAR remain high not only for V-1 Schizophrenia disorders but also for V-2 Mood disorders and V-3 Neurotic disorders. Age-standardized analyses indicated that the observed decline in V-1 Schizophrenia disorders likely represented only an apparent improvement, whereas no clear improvement was evident for other disorders. These findings suggest that the current policies are disproportionately focused on schizophrenia, with insufficient attention paid to other mental disorders. Moving forward, it is essential to strengthen community-based support systems that facilitate post-discharge life reconstruction across all diagnostic categories and establish nationwide databases that enable ongoing and timely monitoring of hospitalization trends. Abbreviations CHR Crude Hospitalization Ratio CI Confidence Interval H-Rate Hospitalization Rate ICD-10 International Classification of Diseases, Tenth Edition PARs Public Assistance Recipients SHR Standardized Hospitalization Ratio V Mental disorders Chapter V Mental and behavioural disorders V-1 Schizophrenia disorders Schizophrenia, schizotypal and delusional disorders V-2 Mood disorders Mood [affective] disorders V-3 Neurotic disorders Neurotic, stress-related and somatoform disorders Declarations Ethics approval and consent to participate This study used only publicly available government statistics that do not allow for the identification of individuals. In accordance with the Ethical Guidelines for Life Sciences and Medical Research Involving Human Subjects (2021), ethics approval and informed consent were not required for this type of research. All study procedures were conducted in accordance with the principles of the Declaration of Helsinki. Consent for publication Not applicable. Availability of data and materials The dataset supporting the conclusions of this study was generated from multiple publicly available government statistics and is provided in Additional file2.xls. Competing interests TK and KS declare no conflict of interest concerning this study. However, they disclose the following financial competing interests that have arisen in the last three years to ensure full transparency. TK reports receiving speaker honoraria from Eisai, Daiichi Sankyo , Janssen, Boehringer Ingelheim, Meiji, Otsuka, Sumitomo, Takeda, Mitsubishi-Tanabe, Kyowa, Yoshitomi, and Viatris; research grants from the Fujita Health University School of Medicine Research Grant, JSPS KAKENHI (23K06998 and 25K10874), Japan Agency for Medical Research and Development (JP22dk0307107, JP22wm0525024, JP23dk0307122, and 24dk0307129), and the Japanese Ministry of Health, Labour and Welfare (21GC1018). KS received speaker honoraria from Janssen, Kyowa, Meiji, Otsuka, Sumitomo, and Takeda, and research grants from JSPS KAKENHI (19K17099 and 23K06998) and the Japan Agency for Medical Research and Development (JP22dk0307107 and JP23dk0307122). The other authors declare that they have no competing interests. Funding This study was funded by a Health and Labour Sciences Research grant from the Ministry of Health, Labour and Welfare, Japan (JPMH21GC1018). Authors’ contributions HW, KK, and ST conceptualized and designed the study. HW and KK analyzed the data. HW prepared the manuscript. KK, YH, MM, LY, KS, TK, NI, AO, and ST reviewed the manuscript. HW, KK, YH, MM, LY, KS, TK, NI, AO, and ST finalized the manuscript. All the authors have read and approved the final version of the manuscript. Acknowledgements We thank Editage (www.editage.jp) for the English language editing. References OECD. Hospital average length of stay by diagnostic categories. https://data-explorer.oecd.org/vis?tm=Hospital%20average&pg=0&snb=13&vw=tb&df[ds]=dsDisseminateFinalDMZ&df[id]=DSD_HEALTH_PROC%40DF_HOSP_AV_LENGTH&df[ag]=OECD.ELS.HD&df[vs]=1.1&dq=…DICDA500… … … … &pd=2020%2C2020&to[TIME_PERIOD]=false. Accessed 24 Jul 2025. Ministry of Health, Labour and Welfare. Outline of the Public Assistance System. 2010. https://www.mhlw.go.jp/english/topics/social_welfare/dl/outline_of_the_public_assistance_system_20101004.pdf . Accessed 24 Jul 2025. 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Mental health atlas 2020. https://apps.who.int/iris/handle/10665/345946 . Accessed 25 Jul 2025. Rizavas I, Gournellis R, Douzenis P, Efstathiou V, Bali P, Lagouvardos K, Douzenis A. A systematic review on the impact of seasonality on severe mental illness admissions: Does seasonal variation affect coercion? Healthc (Basel). 2023;11(15):2155. 10.3390/healthcare11152155 . Takahashi K, Asano Y, Kohsaka M, Okawa M, Sasaki M, Honda Y, et al. Multi-center study of seasonal affective disorders in Japan. A preliminary report. J Affect Disord. 1991;21(1):57–65. 10.1016/0165-0327(91)90019-O . Okawa M, Shirakawa S, Uchiyama M, Oguri M, Kohsaka M, Mishima K, et al. Seasonal variation of mood and behaviour in a healthy middle-aged population in Japan. Acta Psychiatr Scand. 1996;94(4):211–6. 10.1111/j.1600-0447.1996.tb09851.x . Imai M, Kayukawa Y, Ohta T, Li L, Nakagawa T. Cross-regional survey of seasonal affective disorders in adults and high-school students in Japan. J Affect Disord. 2003;77(2):127–33. 10.1016/S0165-0327(02)00110-6 . Hirose N, Ishimaru M, Morita K, Yasunaga H. A review of studies using the Japanese National Database of Health Insurance Claims and Specific Health Checkups. Ann Clin Epidemiol. 2020;2(1):13–26. 10.37737/ace.2.1_13 . Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.xls Name: Additional file1 Format: .xls Title of data: Table S1. CHRs and SHRs with 95% CIs, by sex, year, and mental disease, Japan, 2014–2023. Description of data: This table provides detailed statistical results, including Crude Hospitalization Ratios (CHRs) and Standardized Hospitalization Rates (SHRs) with 95% Confidence Intervals, which serve as the numerical basis for the trends illustrated in Figures 1 and 2 of the main manuscript. The underlying data were extracted and calculated from public government statistics of Japan, as described in the Methods section. Additionalfile2.xls Name: Additional file2 Format: .xls Title of data:Dataset Description of data: The dataset was calculated using governmental statistical sources, as described in the Methods section. The variable definitions are as follows: Category = disease classification; Year = survey year; Sex (0 = Men, 1 = Women); Patients_Obs_PAR = observed number of hospitalized patients among public assistance recipients; Patients_Exp_PAR = expected number of hospitalized patients among public assistance recipients (indirect method); SHR = standardized hospitalization ratio; H-Rate_PAR = hospitalization rate among public assistance recipients; H-Rate_Gen = hospitalization rate in the general population; CHR = crude hospitalization ratio. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8437618","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":570655561,"identity":"9e97e41e-6fe5-45e2-b899-34f18975e2f4","order_by":0,"name":"Hideyuki 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1","display":"","copyAsset":false,"role":"figure","size":92871,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of the CHRs and SHRs by sex and mental disorder categories in Japan.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCorresponding values are provided in Additional file1 (Table S1).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe dashed line indicates equality between the CHR and SHR (CHR = SHR).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8437618/v1/e2399d9d598f827cd6bfa09d.jpg"},{"id":100361525,"identity":"354294ab-5b89-4ab6-af0a-9e8d5c7a3d2f","added_by":"auto","created_at":"2026-01-16 07:45:15","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":171658,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTrends in SHRs (95% CIs) by categories among men (Panel A) and women (Panel B)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCorresponding values for the SHRs and their 95% CIs shown in Fig. 2 are provided in Additional file1 (Table S1).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8437618/v1/66d2df8457d26554201f04e4.jpg"},{"id":104880956,"identity":"3c2353af-8417-4853-929f-b3ebde4ffcbe","added_by":"auto","created_at":"2026-03-18 09:14:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1031596,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8437618/v1/37be814e-640f-4e29-a6e2-341ff937d1a4.pdf"},{"id":100007880,"identity":"b0b4685f-5a5a-49ed-a7c6-63863b2fa02a","added_by":"auto","created_at":"2026-01-12 05:51:14","extension":"xls","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":26112,"visible":true,"origin":"","legend":"\u003cp\u003eName: Additional file1\u003c/p\u003e\n\u003cp\u003eFormat: .xls\u003c/p\u003e\n\u003cp\u003eTitle of data: Table S1. CHRs and SHRs with 95% CIs, by sex, year, and mental disease, Japan, 2014–2023.\u003c/p\u003e\n\u003cp\u003eDescription of data: This table provides detailed statistical results, including Crude Hospitalization Ratios (CHRs) and Standardized Hospitalization Rates (SHRs) with 95% Confidence Intervals, which serve as the numerical basis for the trends illustrated in Figures 1 and 2 of the main manuscript. The underlying data were extracted and calculated from public government statistics of Japan, as described in the Methods section.\u003c/p\u003e","description":"","filename":"Additionalfile1.xls","url":"https://assets-eu.researchsquare.com/files/rs-8437618/v1/b9176b11970b4d121464c34c.xls"},{"id":100007881,"identity":"9086f29b-29ae-4a65-9eb3-b678da037299","added_by":"auto","created_at":"2026-01-12 05:51:14","extension":"xls","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":25600,"visible":true,"origin":"","legend":"\u003cp\u003eName: Additional file2\u003c/p\u003e\n\u003cp\u003eFormat: .xls\u003c/p\u003e\n\u003cp\u003eTitle of data:Dataset\u003c/p\u003e\n\u003cp\u003eDescription of data: The dataset was calculated using governmental statistical sources, as described in the Methods section. The variable definitions are as follows: Category = disease classification; Year = survey year; Sex (0 = Men, 1 = Women); Patients_Obs_PAR = observed number of hospitalized patients among public assistance recipients; Patients_Exp_PAR = expected number of hospitalized patients among public assistance recipients (indirect method); SHR = standardized hospitalization ratio; H-Rate_PAR = hospitalization rate among public assistance recipients; H-Rate_Gen = hospitalization rate in the general population; CHR = crude hospitalization ratio.\u003c/p\u003e","description":"","filename":"Additionalfile2.xls","url":"https://assets-eu.researchsquare.com/files/rs-8437618/v1/c5336f2def6c208946ced8b6.xls"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hospitalizations for mental disorders among public assistance recipients and the general population in Japan: A repeated cross-sectional study using national government statistics","fulltext":[{"header":"Text box 1. Contributions to the Literature","content":"\u003cp\u003e･Using Japanese administrative data, this study reveals that mental health hospitalization risks for public assistance recipients extend beyond schizophrenia to mood and neurotic disorders.\u003c/p\u003e\n\u003cp\u003e･Comparing crude and age-standardized trends shows that apparent declines in schizophrenia hospitalizations largely reflect demographic change rather than substantive improvement.\u003c/p\u003e\n\u003cp\u003e･These findings suggest that mental health policies focusing narrowly on schizophrenia may overlook the persistent burden of other mental disorders among\u0026nbsp;public assistance recipients.\u003c/p\u003e\n\u003cp\u003e･The study underscores the need for integrated health, welfare, and employment policies to support individuals facing poverty-related mental health risks.\u003c/p\u003e"},{"header":"Background","content":"\u003cp\u003eHospital stays for patients with mental disorders have been noted to be longer in Japan than in other countries. According to OECD data [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], the average length of hospital stay for patients with diseases classified under the International Classification of Diseases, Tenth Edition (ICD-10) Chapter V Mental and behavioural disorders (V Mental disorders) was 31.5 days across 30 OECD countries with 2020 data available (Japan excluded). In comparison, the corresponding average hospital stay in Japan was markedly higher at 294.2 days, followed by Korea (200.4 days) and Spain (60.8 days). Such prolonged hospitalizations in Japan are associated with difficulties in obtaining or retaining employment, thereby increasing the likelihood of qualifying for and relying on social welfare programs.\u003c/p\u003e \u003cp\u003eJapan\u0026rsquo;s public assistance system aims to guarantee a minimum standard of living for people in financial hardship and is implemented under the Public Assistance Act. Recipients under this program are referred to as public assistance recipients (PARs), and as part of the program, \u0026ldquo;Medical Assistance\u0026rdquo; is provided, under which the costs of necessary medical care are covered using public funds [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. When examining actual national data, substantial differences in Medical Assistance coverage are observed across disease categories. According to the Patient Survey (2023), which contains national government statistics on healthcare utilization in Japan, among discharged inpatients, the proportion whose hospitalization costs were paid via Medical Assistance was 7.4% for \u0026ldquo;All diseases\u0026rdquo;, whereas it reached 16.5% for V Mental disorders\u0026mdash;the highest share among the major ICD-10 diagnostic categories [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This indicates that PARs constitute an exceptionally high proportion of inpatients hospitalized for mental disorders.\u003c/p\u003e \u003cp\u003eAlthough previous studies conducted in Japan suggest that mental disorders are associated with economic hardship [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], the extent to which the prevalence of mental disorders and hospitalization rates among PARs differ from those among the general population has not been fully elucidated. Moreover, in 2023, the proportion aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years was 52.6% in PAR versus 29.1% in the total population [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUsing publicly available government statistics, this study calculated and compared hospitalization rates by subcategories of mental disorders between PARs and the general population, and evaluated age-standardized differences. It further aimed to identify which subcategories of mental disorders are more strongly associated with receiving public assistance and to examine how differences in age structure affect hospitalization rates, thereby providing foundational evidence to inform mental health policies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e・Study design and setting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis\u0026nbsp;repeated cross-sectional study used publicly available government statistics. The study years were 2014, 2017, 2020, and 2023, selected to align with the Patient Survey (a national government survey conducted every three years).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e・Data sources and study population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAge-group-specific hospitalization rates and population counts were used for\u0026nbsp;the\u0026nbsp;PARs\u0026nbsp;and the general population, respectively. For PARs, hospitalization rates were obtained from\u0026nbsp;the\u0026nbsp;Fact-finding Survey on Medical Assistance, and age-group-specific population counts\u0026nbsp;were obtained from the\u0026nbsp;National Survey on Public Assistance Recipients.\u0026nbsp;The\u0026nbsp;Fact-finding Survey on Medical Assistance is a government statistical survey\u0026nbsp;compiled by the Ministry of Health, Labour and Welfare that aggregates claim-level data on medical services provided under Medical Assistance [11].\u0026nbsp;The\u0026nbsp;National Survey on Public Assistance Recipients is\u0026nbsp;administered by the same ministry to monitor households receiving protection under the Public Assistance Act; prefectures and welfare offices serve as reporting entities, and the survey is conducted annually as a census of all recipients [12]. For the general population, hospitalization rates were\u0026nbsp;obtained\u0026nbsp;from\u0026nbsp;the\u0026nbsp;Patient Survey\u0026nbsp;and age-group-specific population counts from Population Estimates.\u0026nbsp;The\u0026nbsp;Patient Survey conducted in 2023\u0026nbsp;collected data\u0026nbsp;from 96% of sampled\u0026nbsp;medical institutions and provides information on\u0026nbsp;outpatient and inpatient care, including\u0026nbsp;distributions by disease category [13]. Given this high response rate, it is considered to reasonably reflect national patterns of health-care utilization. Population Estimates is a core national statistic widely used as the standard source for population figures [10].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e・Variable\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe hospitalization rate (H-Rate)\u0026nbsp;was the primary outcome, defined\u0026nbsp;following\u0026nbsp;the\u0026nbsp;Patient Survey,\u0026nbsp;as the number of inpatients per 100000 population on the survey day. We defined the crude hospitalization ratio (CHR) as the H-Rate in PARs\u0026nbsp;divided by the H-Rate in the general population, as follows:\u003c/p\u003e\n\u003cp\u003e(\u003cimg width=\"239\" height=\"49\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e)\u003c/p\u003e\n\u003cp\u003eTo account for differences in age structure, we computed the standardized hospitalization ratio (SHR)\u0026nbsp;through\u0026nbsp;indirect standardization, using PARs\u0026nbsp;as the study population and the general population as the reference.\u0026nbsp;The\u0026nbsp;SHR was defined as the ratio of the observed number of inpatients in PARs\u0026nbsp;to the expected number obtained by applying age-specific hospitalization rates in the general population to the age distribution of PARs. Therefore, the SHR was computed as follows:\u003c/p\u003e\n\u003cp\u003e(\u003cimg width=\"240\" height=\"49\" 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\" alt=\"image\"\u003e)\u003c/p\u003e\n\u003cp\u003eBoth ratios use the general population as the reference: for\u0026nbsp;the\u0026nbsp;CHR, the denominator is the H-Rate in the general population; for\u0026nbsp;the\u0026nbsp;SHR,\u0026nbsp;the denominator\u0026nbsp;is the expected inpatient count derived from age-specific H-Rates in the general population.\u0026nbsp;That enabled assessment of how differences in age structure influence H-Rates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e・Quantitative variable\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDiagnostic categories were defined according to\u0026nbsp;the\u0026nbsp;ICD-10. The analyses focused on three subcategories of mental disorders for which comparison\u0026nbsp;was\u0026nbsp;possible using publicly available government statistics: Schizophrenia, schizotypal and delusional disorders (ICD-10 codes F20\u0026ndash;F29);\u0026nbsp;Mood [affective] disorders (ICD-10 codes F30\u0026ndash;F39);\u0026nbsp;and Neurotic, stress-related and somatoform disorders (ICD-10 codes F40\u0026ndash;F48). Hereafter, these are referred to as V-1 Schizophrenia disorders, V-2 Mood disorders, and V-3 Neurotic disorders, respectively. For comparison, we also computed\u0026nbsp;the\u0026nbsp;values for\u0026nbsp;\u0026ldquo;All diseases\u0026rdquo;, which includes conditions other than mental disorders.\u003c/p\u003e\n\u003cp\u003eFor indirect age standardization, age groups followed\u0026nbsp;those used in the\u0026nbsp;Patient Survey: 0 years; 1\u0026ndash;4 years; five-year bands from 5 through 89 years; and \u0026ge;90 years (20 groups in total).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e・Statistical method\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe H-Rate was defined as the number of inpatients per 100000 population on the survey day. For the general population, H-Rates were obtained from\u0026nbsp;the\u0026nbsp;Patient Survey. For\u0026nbsp;the\u0026nbsp;PARs, inpatient counts were derived from the Fact-finding Survey on Medical Assistance, which aggregated\u0026nbsp;claims data for April\u0026ndash;May by disease category (total number of claims, total inpatient\u0026nbsp;care days [patient\u0026nbsp;days], and total medical fee points). Because inpatient claims are generally billed once per hospitalization per month, the number of claims cannot be used to directly estimate the number of inpatients. In inpatient care, a hospitalized patient can accrue one care day per calendar day\u0026nbsp;at most.\u0026nbsp;Therefore, we divided the total inpatient-care days by 61 (the combined number of days in April and May) to estimate the average daily number of inpatients for April\u0026ndash;May and used this value as the numerator to compute\u0026nbsp;the\u0026nbsp;H-Rates.\u003c/p\u003e\n\u003cp\u003eFor the CHR, 95% confidence intervals (CIs) were estimated under a Poisson assumption for observed inpatient counts using standard errors on the log scale (Wald method). For the SHR, 95% CIs were computed using the exact Poisson method based on the chi-square distribution. All analyses were conducted using Stata/MP 19.0 (StataCorp LLC, College Station, TX, USA).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e・Participants\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the number of individuals by sex and the distribution of age groups (0\u0026ndash;4, 15\u0026ndash;64, \u0026ge;\u0026thinsp;65 years, and \u0026ge;\u0026thinsp;75 years) among the PARs and the total population during the study period. The number of PARs declined steadily from approximately 2.1\u0026nbsp;million in 2014 to 2.0\u0026nbsp;million in 2023 (\u0026ndash;6.9%). The total population decreased modestly from approximately 127\u0026nbsp;million in 2014 to 124\u0026nbsp;million in 2023 (\u0026ndash;2.2%). The proportion of individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years among the PARs was consistently high and increased further, reaching 52.6% in 2023. In the total population, the proportion of those aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years also increased, but only modestly, reaching 29.1% in 2023.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTrends in population size and age distribution of PARs and total population, Japan, 2014\u0026ndash;2023\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003ePAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eTotal population\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003en ( x 1000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e127083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e126711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e126153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e124356\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e61800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e61655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e61348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e60493\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e65283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e65056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e64805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e63863\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAge Group\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e61.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e60.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e59.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e59.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65\u0026le;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e28.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e29.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75\u0026le;༊\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e*A subset of individuals aged 65 years and over.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003ePopulation numbers are expressed in thousands and rounded to one decimal place.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eThe proportions of each age group represent the percentage within the respective population for each year and were rounded at the second decimal place (to one decimal place).\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e・Descriptive data\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the temporal trends in H-Rates by mental-disorder subcategory and sex. Across all subcategories and both sexes, the H-Rates were consistently higher among the PARs than in the total population, with the highest rates observed for V-1 Schizophrenia disorders. In 2023, the H-Rate for this category was 551 versus 97 for men and 533 versus 106 for women (PARs versus total population, respectively). Overall, the H-Rates declined in both PARs and the total population, although the decreases were smaller for V-2 Mood disorders and V-3 Neurotic disorders.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eH-Rates by sex and disease category among PARs and total population, Japan, 2014\u0026ndash;2023.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003ePAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eTotal population\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e893\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eV-1 Schizophrenia disorders\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eV-2 Mood disorders\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eV-3 Neurotic disorders\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e995\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eV-1 Schizophrenia disorders\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eV-2 Mood disorders\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eV-3 Neurotic disorders\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e*Category definitions (ICD-10): \u0026ldquo;V-1 Schizophrenia disorders\u0026rdquo; corresponds to Schizophrenia, schizotypal and delusional disorders (F20\u0026ndash;F29); \u0026ldquo;V-2 Mood disorders\u0026rdquo; corresponds to Mood [affective] disorders (F30\u0026ndash;F39); and \u0026ldquo;V-3 Neurotic disorders\u0026rdquo; corresponds to Neurotic, stress-related and somatoform disorders (F40\u0026ndash;F48). \u0026ldquo;All diseases\u0026rdquo; denotes all ICD-10 disease categories, including those outside mental disorders.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eAll values were rounded to the first decimal place.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e・Main result\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the scatter plots of the CHR and SHR. In all subcategories, the points were located below the diagonal line, indicating that CHR\u0026thinsp;=\u0026thinsp;SHR, with the CHR exceeding the SHR. When comparing each mental disorder subcategory by sex, the points for men were generally positioned above and to the right of those for women, showing higher values for both the CHR and SHR. However, for V-1 Schizophrenia disorders, the sex differences were minimal. In particular, the SHR values were 4.31, 4.31, 4.29, and 3.92 among men and 4.45, 4.38, 4.31, and 3.78 among women. When comparing subcategories, the influence of age adjustment varied. The discrepancy between the CHR and SHR was large for V-1 Schizophrenia disorders and V-2 Mood disorders, whereas for V-3 Neurotic disorders the points were closer to the diagonal line of equality, indicating a relatively small impact of age adjustment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eCorresponding values are provided in Additional file1 (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eThe dashed line indicates equality between the CHR and SHR (CHR\u0026thinsp;=\u0026thinsp;SHR).\u003c/em\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows annual trends in the SHR with 95% CIs by sex. Across all subcategories, the SHR values exceeded those for \u0026ldquo;All diseases\u0026rdquo; and were consistently greater than 1. The highest values were observed for V-3 Neurotic disorders among men, with values of 5.20, 6.18, 5.45, and 4.53 from 2014 to 2023. The highest values were also observed for V-1 Schizophrenia disorders among women, with values of 4.45, 4.38, 4.31, and 3.78 over the same period. A notable feature was the temporary increase in V-3 Neurotic disorders between 2014 and 2017 in both sexes, where values rose from 5.20 to 6.18 among men and from 3.53 to 4.29 among women, before returning to approximately previous levels in subsequent years.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eCorresponding values for the SHRs and their 95% CIs shown in\u003c/em\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cem\u003eare provided in Additional file1 (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we calculated the H-Rates for mental disorders by diagnostic subcategory among PARs and the general population, and compared them using the CHR and SHR. Both indicators were consistently elevated, highlighting the substantial risk of hospitalization for mental disorders among PARs. In Japan, few studies have comprehensively compared and analyzed hospitalizations for mental disorders among PARs by diagnostic subcategories. Previous research has reported that, in cross-sectional studies on older PARs, the prevalence of depressive symptoms was higher than that among non-PARs [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In addition, Kawauchi et al. indicated that the prevalence of mental disorders, including anxiety and depressive disorders, was consistently higher among PARs [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA key strength of the present study is that it evaluated all diagnostic subcategories using the same governmental statistical datasets, rather than estimating risks separately from heterogeneous data sources. Furthermore, by comparing the results before and after age standardization, we examined the hospitalization risks among PARs in greater detail. This analysis revealed that hospitalizations for V-1 Schizophrenia disorders were frequent, but those for V-3 Neurotic disorders were equally or even more common.\u003c/p\u003e \u003cp\u003eThe H-Rate for mental disorders showed an overall downward trend in both the general population and among PARs. Prior studies suggested that the decline in the number of inpatients occupying psychiatric beds is largely attributable to a decrease in the number of older patients with schizophrenia, whereas mood disorders are expected to increase [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In our analysis restricted to PARs, the H-Rate for V-1 Schizophrenia disorders declined substantially, whereas the corresponding SHR decreased only modestly. Regarding V-2 Mood disorders and V-3 Neurotic disorders, both the H-Rate and SHR remained essentially unchanged, with no clear improvement. For V-1 Schizophrenia disorders, long-stay older inpatients likely contributed to the high H-Rate, suggesting that the observed decline may represent only an apparent improvement and may not reflect substantive changes in the underlying conditions. In Japan, efforts have primarily focused on reducing long-term hospitalization among patients with schizophrenia and on employment support through \u0026ldquo;Type B Continuous Employment Support\u0026rdquo; (a welfare-based program providing simple work or productive activities for individuals for whom regular employment is difficult) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, our findings indicate a lag in measures addressing mood and neurotic disorders. This pattern aligns with international evidence showing higher frequencies of depression and anxiety among socioeconomically disadvantaged groups. According to an OECD report, severe depressive symptoms are approximately 3.5 times more prevalent in low-income groups than in high-income groups [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and Ridley et al. have highlighted bidirectional causal links between poverty and mental illness for depression and anxiety [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Furthermore, reports from Japan and other countries indicate insufficient medical and social support for mood disorders, which may contribute to prolonged hospitalization and social isolation [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Therefore, regardless of the diagnostic category, strengthening community-based systems that support post-discharge life reconstruction and phased transitions to competitive employment is essential.\u003c/p\u003e \u003cp\u003eA notable increase in hospitalizations for V-3 Neurotic disorders among PAR was observed exclusively in 2017, both before and after age standardization. Consistent with this finding, the Fact-finding Survey on Medical Assistance [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] also reported an increase in this subcategory in 2017, particularly among men aged 35\u0026ndash;54 years and women aged 6\u0026ndash;34 years and those aged\u0026thinsp;\u0026ge;\u0026thinsp;75 years. In contrast, outpatient visits showed no comparable increase. These results suggest that the 2017 surge likely reflected external influences, such as policy revisions or reimbursement changes, rather than a true increase in incidence. However, the specific factors underlying this anomaly, could not be determined within the scope of this study.\u003c/p\u003e \u003cp\u003eThis study had several limitations. First, the timing of data collection differed between the PARs and the general population. Hospitalization data for the general population were collected in October, whereas those for the PARs were fcollected from April\u0026ndash;May claims. Mental disorders are subject to seasonal variation [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and in Japan depressive symptoms are reported to worsen during winter [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. To account for such fluctuations, more refined analyses would ideally require using claims data for PARs and the National Database (NDB) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] for the general population, aligned to the same calendar months. However, these data are not publicly available and could not be used in this study. This remains a topic for future research. Second, the government statistics used in this study may not fully capture all hospitalizations among PARs. Under Japan\u0026rsquo;s Medical Assistance system, conditions for which medical costs are already covered in full by other public schemes are excluded. For example, some infectious diseases are subject to separate government-funded medical programs, and hospitalizations are therefore not billed under Medical Assistance. This also applied to COVID-19 hospitalizations, which were fully publicly funded until May 2023. Consequently, certain hospitalizations among PARs may have been omitted from the statistics, leading to a potential underestimation of hospitalizations for mental disorders.\u003c/p\u003e \u003cp\u003eThis study demonstrated that hospitalizations for mental disorders among PARs remain high, with no improvement in schizophrenia, mood, or neurotic disorders. Although Japan\u0026rsquo;s current mental health policy seeks to reduce long-term psychiatric hospitalizations, its primary focus remains on schizophrenia, leaving support for mood and neurotic disorders insufficient. Hospitalizations for these conditions cannot be addressed by medical or welfare interventions alone, and difficulties in employment and poverty after discharge may trigger deterioration and readmission, creating a vicious cycle. Such interactions between poverty and mental illness represent a global challenge, and the findings of this study underscore the need for policies that promote collaboration across health care, welfare, and employment sectors to support the mental health and social participation of disadvantaged populations.\u003c/p\u003e \u003cp\u003eFurthermore, the decline in hospitalizations for schizophrenia is likely only an \u0026ldquo;apparent improvement\u0026rdquo; associated with a reduction in older PARs, rather than a genuine change. Additionally, the increase in hospitalizations for neurotic disorders observed in 2017 was likely driven by external factors such as reimbursement revisions or changes in diagnostic classification, rather than by an actual increase in incidence. These findings suggest that simple trends in hospitalization statistics alone are insufficient to capture underlying realities. Government statistics require extensive processing, which result in a publication lag of several years. Consequently, it is difficult to capture annual changes in detail. Future research should use national databases to enable the continuous and timely monitoring of the effects of policy reforms and classification changes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrated that hospitalizations for mental disorders among PAR remain high not only for V-1 Schizophrenia disorders but also for V-2 Mood disorders and V-3 Neurotic disorders. Age-standardized analyses indicated that the observed decline in V-1 Schizophrenia disorders likely represented only an apparent improvement, whereas no clear improvement was evident for other disorders. These findings suggest that the current policies are disproportionately focused on schizophrenia, with insufficient attention paid to other mental disorders. Moving forward, it is essential to strengthen community-based support systems that facilitate post-discharge life reconstruction across all diagnostic categories and establish nationwide databases that enable ongoing and timely monitoring of hospitalization trends.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCrude Hospitalization Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eH-Rate\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHospitalization Rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICD-10\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Classification of Diseases, Tenth Edition\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePARs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePublic Assistance Recipients\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandardized Hospitalization Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eV Mental disorders\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChapter V Mental and behavioural disorders\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eV-1 Schizophrenia disorders\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSchizophrenia, schizotypal and delusional disorders\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eV-2 Mood disorders\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMood [affective] disorders\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eV-3 Neurotic disorders\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNeurotic, stress-related and somatoform disorders\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used only publicly available government statistics that do not allow\u0026nbsp;for the\u0026nbsp;identification of individuals. In accordance with the Ethical Guidelines for Life Sciences and Medical Research Involving Human Subjects (2021), ethics\u0026nbsp;approval\u0026nbsp;and informed consent were not required for this type of research. All study procedures were conducted in accordance with the principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset supporting the conclusions of this study was generated from multiple publicly available government statistics and is provided in Additional file2.xls.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTK and KS declare no conflict of interest concerning this study. However, they disclose the following financial competing interests that have arisen in the last three years to ensure full transparency.\u003c/p\u003e\n\u003cp\u003eTK reports receiving speaker honoraria from Eisai, Daiichi Sankyo , Janssen, Boehringer Ingelheim, Meiji, Otsuka, Sumitomo, Takeda, Mitsubishi-Tanabe, Kyowa, Yoshitomi, and Viatris; research grants from the Fujita Health University School of Medicine Research Grant, JSPS KAKENHI (23K06998 and 25K10874), Japan Agency for Medical Research and Development (JP22dk0307107, JP22wm0525024, JP23dk0307122, and 24dk0307129), and the Japanese Ministry of Health, Labour and Welfare (21GC1018).\u003c/p\u003e\n\u003cp\u003eKS received speaker honoraria from Janssen, Kyowa, Meiji, Otsuka, Sumitomo, and Takeda, and research grants from JSPS KAKENHI (19K17099 and 23K06998) and the Japan Agency for Medical Research and Development (JP22dk0307107 and JP23dk0307122).\u003c/p\u003e\n\u003cp\u003eThe other authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by a Health and Labour Sciences Research grant from the Ministry of Health, Labour and Welfare, Japan (JPMH21GC1018).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHW, KK, and ST conceptualized and designed the study. HW and KK analyzed the data. HW prepared the manuscript. KK, YH, MM, LY, KS, TK, NI, AO, and ST reviewed the manuscript. HW, KK, YH, MM, LY, KS, TK, NI, AO, and ST finalized the manuscript. All the authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Editage (www.editage.jp) for the English language editing.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eOECD. Hospital average length of stay by diagnostic categories. https://data-explorer.oecd.org/vis?tm=Hospital%20average\u0026amp;pg=0\u0026amp;snb=13\u0026amp;vw=tb\u0026amp;df[ds]=dsDisseminateFinalDMZ\u0026amp;df[id]=DSD_HEALTH_PROC%40DF_HOSP_AV_LENGTH\u0026amp;df[ag]=OECD.ELS.HD\u0026amp;df[vs]=1.1\u0026amp;dq=\u0026hellip;DICDA500\u0026hellip; \u0026hellip; \u0026hellip; \u0026hellip; \u0026amp;pd=2020%2C2020\u0026amp;to[TIME_PERIOD]=false. 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Ann Clin Epidemiol. 2020;2(1):13\u0026ndash;26. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.37737/ace.2.1_13\u003c/span\u003e\u003cspan address=\"10.37737/ace.2.1_13\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"age adjusted, hospitalization, mental disorders, public assistance, Japan","lastPublishedDoi":"10.21203/rs.3.rs-8437618/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8437618/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eIn Japan, hospital stays for patients with mental disorders are longer than those in many countries, leading to employment difficulties and reliance on public assistance. Mental disorders are common among public assistance recipients (PARs), but category-specific differences in hospitalization rates relative to the general population remain unclear. Using national government statistics, we compared hospitalization rates by subcategories of mental disorder between PARs and the general population, and assessed age-standardized differences to inform mental health policies.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a repeated cross-sectional study using data obtained from national government statistics for 2014, 2017, 2020, and 2023. We compared hospitalization rates (H-rates; inpatients per 100000 on a given day) for mental disorders between PARs and the general population. Diagnoses followed the International Classification of Diseases, Tenth Edition (ICD-10), focusing on \u0026ldquo;V Mental and behavioural disorders\u0026rdquo; and its three subcategories (defined for this study): V-1 Schizophrenia disorders, V-2 Mood disorders, and V-3 Neurotic disorders. We calculated crude hospitalization ratios (CHRs; PARs/general population H-rate) and standardized hospitalization ratios (SHR) using indirect age adjustments with 95% confidence intervals (CIs).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFrom 2014 to 2023, both PARs and the total population declined. By 2023, PARs and the total population were about 2.0 and 124.4\u0026nbsp;million, with 52.6% and 29.1% aged\u0026thinsp;\u0026ge;\u0026thinsp;65, respectively. Across the subcategories, H-rates were consistently higher in PARs than in the general population and were the highest for V-1 Schizophrenia disorders. H-rates declined overall, with smaller decreases for V-2 Mood and V-3 Neurotic disorders. Regarding SHRs, men generally exceeded women, whereas for V-1 the sex difference was minimal across the years. Across the survey years, SHRs for all subcategories exceeded 1 and surpassed those for \u0026ldquo;All diseases\u0026rdquo;; the highest by sex were V-3 in men and V-1 in women.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAmong PARs, H-rates were higher than those in the general population not only for V-1 but also for V-2 and V-3. In the SHR trends, the decline in V-1 likely reflected an apparent improvement due to fewer older PARs, with no clear improvement in V-2 or V-3. Policies should move beyond V-1 emphasis and strengthen post-discharge and employment-transition support.\u003c/p\u003e","manuscriptTitle":"Hospitalizations for mental disorders among public assistance recipients and the general population in Japan: A repeated cross-sectional study using national government statistics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-12 05:51:10","doi":"10.21203/rs.3.rs-8437618/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6c5119b2-288b-46f2-ab9d-36f8776706ff","owner":[],"postedDate":"January 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-18T09:13:58+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-12 05:51:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8437618","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8437618","identity":"rs-8437618","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}