Nationwide Implementation of Non-Mandatory Preventive Medicine Programmes in Japanese Municipalities: A Descriptive Cross-Sectional Survey and Evidence–Practice Gap Analysis

Nationwide Implementation of Non-Mandatory Preventive Medicine Programmes in Japanese Municipalities: A Descriptive Cross-Sectional Survey and Evidence–Practice Gap Analysis | 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 Nationwide Implementation of Non-Mandatory Preventive Medicine Programmes in Japanese Municipalities: A Descriptive Cross-Sectional Survey and Evidence–Practice Gap Analysis Hideki Mori, Kazuhiro Shimomura, Kei Miyazaki, Kazuya Honda, Ayako Shibata, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9674474/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, much preventive medicine outside mandated national programmes is left to municipal discretion, yet the nationwide alignment between these locally administered programmes and graded evidence remains unexamined. We mapped implementation of non-mandatory preventive medicine programmes across Japanese municipalities and quantified evidence–practice gaps. Methods. A nationwide cross-sectional survey was administered to all 1,741 Japanese municipalities between March and August 2025. Implementation (excluding the five mandated cancer screenings under the Health Promotion Act) was defined as municipal provision or subsidisation and calculated on municipality-count and population-weighted bases. Each programme was benchmarked against USPSTF recommendation grades, and an Evidence–Practice Alignment (EPA) score was derived for each municipality (weights: A = + 2, B = + 1, D = − 2, I or ungraded = − 1). Non-response bias was assessed by comparing responding and non-responding municipalities using standardised mean differences derived from national statistical databases. Results. Valid responses were received from 467 municipalities (response rate, 26.8%) across all 47 prefectures. Implementation rates ranged from 0.2% to 92.1% (municipality-count) and 0.0% to 94.4% (population-weighted). Ten Grade A/B programmes had implementation below 50%, including folic acid supplementation (3.2%), syphilis screening (9.6%), and abdominal aortic aneurysm screening (16.1%). Conversely, hepatitis B/C (92.1%, 91.4%) and osteoporosis screening (68.5%) were widely implemented. Several Grade D or ungraded programmes showed appreciable uptake, notably young adult health check-ups (86.3%; ungraded), brain/carotid screening (41.1%; Grade D), and frailty screening (26.1%; ungraded). Population-weighted coverage exceeded municipality-count rates for HIV (48.1% vs 10.1%) and syphilis (48.3% vs 9.6%) screening, indicating concentration in larger municipalities. Prefectural EPA scores ranged from − 4 to 1, with heterogeneity and no clear geographic gradient. Conclusions. Substantial evidence–practice gaps and equity concerns coexist in Japan’s municipal preventive medicine programmes. Our findings support strengthened dissemination of graded evidence to municipal decision-makers and critical reassessment of low-value screening. Preventive Medicine Health Policy preventive medicine municipal health policy evidence–practice gap Japan cross-sectional survey non-mandatory screening evidence-based prevention Health policy Implementation research Municipal health services USPSTF Screening Cross-sectional study Overdiagnosis Figures Figure 1 Figure 2 Background Ageing populations and rising healthcare expenditures have placed preventive healthcare at the centre of health policy agendas worldwide [ 1 , 2 ], yet the translation of evidence-based recommendations into consistent population-level practice remains an enduring challenge across healthcare systems. Japan represents a particularly illuminating case. Preventive healthcare in Japan is organised through a layered structure involving national legislation, medical insurers, and municipal governments, with the Ministry of Health, Labour and Welfare (MHLW) playing a central role in setting policy frameworks and guidelines [ 3 ]. Some programmes are mandated by national law: the Specific Health Check-up and Specific Health Guidance (Tokutei Kenshin), for example, are required under the Act on Assurance of Medical Care for Elderly People and are delivered by medical insurers to individuals aged 40–74 enrolled in public health insurance schemes [ 4 ]. Notably, even some components of these nationally mandated programmes do not always align with contemporary international evidence standards, suggesting that questions of evidence–practice alignment extend across the full spectrum of preventive services in Japan, both mandated and non-mandated. Other services are delegated to municipalities under the Health Promotion Act, the Maternal and Child Health Act, and related legislation. For these municipally delivered services—which particularly serve residents not covered by employer-based health check-ups—national policies provide general guidance, but the actual implementation, including the types of screenings offered, target populations, and outreach strategies, varies substantially across municipalities. Such locally administered programmes fall broadly into two types: those for which national guidelines exist but implementation authority rests with municipalities (as in cancer screening), and those developed entirely at the local level without national policy guidance (as in carotid artery stenosis or COPD screening). While cancer screening programmes are relatively well-monitored through national surveillance data on uptake [ 5 , 6 ], a systematic cross-sectional evaluation of the full spectrum of municipally administered preventive medicine programmes—beyond the five nationally designated cancers—and their alignment with current scientific evidence remains lacking. One widely referenced benchmark is the US Preventive Services Task Force (USPSTF) in the United States, which provides one of the world’s most structured and methodologically rigorous evidence-grading frameworks for clinical preventive services [ 7 ]. However, even within the United States, substantial evidence gaps, implementation barriers, and health inequities persist, particularly among underserved populations, indicating that a centralized evidence-grading framework alone does not guarantee equitable or optimal preventive care delivery [ 8 ]. In contrast, Japanese municipalities lack a comparable cross-cutting evidence-grading framework for non-mandatory preventive services, which may contribute to heterogeneous implementation [ 3 ]. Against this background, this study had two objectives: first, to describe how non-mandatory preventive medicine programmes are implemented by municipalities across Japan; and second, to evaluate how well these programmes align with a structured evidence-based benchmark, and to identify gaps between evidence and practice. Methods Study design A descriptive cross-sectional study was conducted using a questionnaire survey administered to all 1,741 municipalities in Japan. Implementation of non-mandatory preventive medicine programmes was described based on data from responding municipalities. We excluded the five cancer screenings mandated under the Health Promotion Act (gastric, colorectal, lung, breast, and cervical), but included non-mandatory cancer-related screenings such as thyroid ultrasound and positron emission tomography (PET) cancer screening that are delivered or subsidised at municipal discretion. Reporting followed STROBE and CHERRIES guidelines [ 16 , 17 ]. Study population The study population comprised all municipalities in Japan (n = 1,741), defined as cities, towns, villages, and the 23 special wards of Tokyo. Administrative wards within designated cities were excluded, as they function as internal subdivisions rather than independent local government units. Questionnaires were distributed to the health check-up and screening divisions (or equivalent responsible department) of all municipalities as a census survey. The analytical sample consisted of those returning completed responses. Municipalities were included if they submitted responses via web-based form, Microsoft Word document, or PDF, and were excluded if identification was not possible or if more than 50% of items were missing or duplicated. Data collection Data collection was conducted from 10 March to 31 August 2025. Questionnaires were distributed by postal mail to the health check-up and screening divisions of all municipalities, and participation was voluntary. Respondents could complete the survey via a password-protected web-based form (Google Forms), or by returning a completed PDF or Word document by mail. No financial compensation was provided; a summary of findings was offered to all responding municipalities upon study completion. Responses were managed at the municipal level using password and municipality identifier systems to prevent duplicate submissions; cookie- or IP-based verification was not performed. The majority of web-based items were designated as required fields. Where PDF responses contained missing data, municipalities were contacted and data supplemented where feasible. Survey items The survey covered the implementation status of non-mandatory preventive medicine programmes, excluding vaccinations and the five mandated cancer screenings. Target items were selected based on programmes with documented implementation records in Japan and USPSTF recommendations. The questionnaire comprised 60 items across 11 pages and used primarily mandatory multiple-choice formats to ensure completeness and reduce respondent burden, supplemented by open-ended fields. Definition of implementation Implementation was defined as municipal provision or subsidisation of a given screening test or service, irrespective of whether the target age, sex, or risk criteria specified in USPSTF recommendations were met. Detailed eligibility criteria could not be collected for most items—exceptions included osteoporosis screening and brain dock—and implementation was therefore recorded on a binary basis. This approach was adopted to maintain response rates and enable consistent classification across municipalities. External data Municipal baseline characteristics were obtained from public governmental databases—including e-Stat (System of Social and Demographic Statistics), the Geospatial Information Authority of Japan, and Ministry of Health, Labour and Welfare sources—as well as official municipal websites. Derived indicators included total population, area, population density, ageing rate, Rurality Index for Japan, age-adjusted mortality rate, fiscal capacity index, income per capita, proportion of residents exempt from resident tax, and numbers of hospitals, clinics, and physicians [ 9 – 13 ]. Data management All response data were centrally managed by the research team. Web-based responses were processed via automatically generated spreadsheets; postal responses were manually entered according to a standardised protocol. Open-ended responses were standardised against predefined definitions and coded as categorical variables where applicable. Data cleaning was performed according to pre-established criteria to ensure analytical reproducibility. Variable definitions Each preventive medicine programme was coded as a binary variable (1 = implemented or subsidised; 0 = otherwise). USPSTF grades (A, B, D, or I) were assigned to each item based on recommendations current as of March 2025, the time of survey administration [ 7 ]. We selected the USPSTF framework as a structured external benchmark because, although Japan has multiple domain-specific preventive health policies and guidelines, it lacks a single cross-cutting, nationally authoritative evidence-grading system for many non-mandatory preventive services. The USPSTF was chosen because it provides one of the most methodologically rigorous and internationally recognized frameworks for evaluating preventive services across diverse domains using explicit assessments of net benefit and harm. Our use of USPSTF grades was not intended to imply direct transferability of US recommendations to the Japanese context, but rather to provide a transparent and standardized reference framework for comparative evaluation of municipal policy alignment across heterogeneous preventive programmes. Items not evaluated by the USPSTF were classified as ungraded. For Grade A or B items, a municipality was classified as implementing the programme if it provided or subsidised the service in any form. Evidence–Practice Alignment (EPA) score The EPA score was calculated as a weighted sum of implementation status across all items. Weights were assigned a priori by the study team to reflect the relative strength of evidence conveyed by each USPSTF grade: A = + 2, B = + 1, D = − 2, and I or ungraded = − 1; non-implementation of a programme was assigned 0 points. No prior validated scoring instrument exists for this purpose; the weighting scheme was therefore developed de novo to penalise implementation of services with evidence of net harm (Grade D) while treating insufficient-evidence items (Grade I) and ungraded items conservatively. Higher scores indicate greater implementation of recommended services alongside appropriate restraint regarding non-recommended ones; lower scores reflect the inverse. Descriptive analysis and evidence–practice gap Implementation rates for each programme were calculated descriptively by municipality count and by population-weighted proportion, then compared against USPSTF grades to identify programmes with low uptake among Grade A/B items and high uptake among Grade D, I, or ungraded items, thereby characterising evidence–practice gaps. Non-response bias assessment To evaluate self-selection bias, baseline characteristics of responding and non-responding municipalities were compared using external data obtained from public governmental databases. Although baseline characteristics are presented in Table 2 as medians and interquartile ranges to reflect the skewed distributions of municipal-level variables, standardised mean differences (SMDs) were calculated using the mean and standard deviation of each variable, in accordance with the conventional formula. Between-group differences were quantified using SMDs, with |SMD| 0.20 meaningful imbalance [ 14 , 15 ]. Given the descriptive intent of the study and the impossibility of fully characterising the non-response mechanism from observed covariates, we did not apply statistical adjustments for non-response. Instead, the assessment of potential bias was limited to comparisons of observed characteristics between responding and non-responding municipalities. Missing data Missing data were minimised by designating the majority of web-based items as required fields. Residual missing values in postal responses were addressed through direct follow-up with municipalities. Use of Large Language Models During the preparation of this manuscript, the authors used ChatGPT (OpenAI, GPT-4) and Claude (Anthropic) to assist with translation from Japanese to English and to improve the clarity and readability of draft text. All AI-assisted outputs were critically reviewed, edited, and verified by the authors, who take full responsibility for the content of the manuscript. No AI tool was used to generate original scientific content, perform data analysis, draw conclusions, or generate references. Results Respondent municipality profile Response rate and geographic distribution Responses were received from 468 of 1,741 municipalities; one municipality with a wholly missing questionnaire was excluded, yielding 467 valid responses (response rate, 26.8%). Responding municipalities were distributed across all 47 prefectures, encompassing urban, peri-urban, and rural areas. In terms of population size, five municipalities (1.1%) had populations of 1,000,000 or more, 78 (16.7%) had 100,000–999,999, 248 (53.1%) had 10,000–99,999, and 136 (29.1%) had fewer than 10,000 residents; municipalities with fewer than 1,000,000 residents accounted for 462 (98.9%) of respondents (Table 1 ). Table 1 Population size distribution of respondent municipalities (n = 467) Population size (total population) n (%) ≥ 1,000,000 5 (1.1) 100,000–999,999 78 (16.7) 10,000–99,999 248 (53.1) < 10,000 136 (29.1) Total 467 (100.0) Data are presented as number (percentage). Municipalities include cities, towns, villages, and the special wards of Tokyo. Administrative wards of designated cities were excluded as they constitute internal administrative subdivisions rather than independent basic local government units. Prefecture-level response rates were calculated with the number of responding municipalities as the numerator and the total number of municipalities within each prefecture as the denominator; 95% confidence intervals were estimated using the Wilson score method (Additional file 1: Table S1). Response rates by region (8-region classification) are shown as % (n/N) in Additional file 2: Table S2. Prefecture-level response rates ranged from 9.3% to 48.8%, with a median of 26.7% (interquartile range [IQR] 20.8–31.9%). The geographic distribution of response rates was visualised using a choropleth map (Additional file 3: Figure S1). A funnel plot with the overall response rate (26.8%) as the reference line and 95% and 99.8% control limits, shown in Additional file 4: Figure S2, demonstrated that between-prefecture variation in response rates persisted even after accounting for statistical fluctuation associated with denominator size. Characteristics of responding and non-responding municipalities Baseline characteristics of responding (n = 467) and non-responding municipalities were compared using external data, and standardised mean differences (SMDs) were calculated (Table 2 ). The Rurality Index for Japan (RIJ) and ageing rate were well balanced between groups (RIJ: 56 [IQR 34–79] vs 54 [30–77]; ageing rate: 36.4% [30.1–40.9] vs 35.5% [29.8–41.7]), suggesting no substantial imbalance. Only two variables exceeded the prespecified threshold for adequate balance: per-capita resident income (3,088,363 JPY [2,894,575–3,354,483] vs 3,121,105 JPY [2,883,839–3,399,850]; SMD = 0.124) and general clinic physician density (809.64 [332.73–2,154.56] vs 1,147.11 [462.28–3,004.44]; SMD = 0.104), indicating that responding municipalities tended to be slightly lower-income and have fewer clinic physicians than non-responding municipalities. All other variables, including the fiscal capacity index, total population, and hospital density, were adequately balanced (all |SMD| < 0.10). Table 2 Characteristics of respondent and non-respondent municipalities and standardised mean differences Characteristic Responding municipalities (n = 467) Non-responding municipalities (n = 1,274) SMD Aging rate (%) 36.4 (30.1–40.9) 35.5 (29.8–41.7) 0.018 Fiscal capacity index 0.43 (0.28–0.65) 0.44 (0.26–0.66) 0.013 Per-capita resident income (JPY) 3,088,363 (2,894,575–3,354,483) 3,121,105 (2,883,839–3,399,850) 0.124 Hospitals per 100,000 population [n = 358] 8.42 (5.19–12.82) 8.01 (5.23–12.86) [n = 914] 0.001 Physicians per 100,000 population (general clinics) [n = 458] 809.64 (332.73–2,154.56) 1,147.11 (462.28–3,004.44) [n = 1,263] 0.104 Rurality Index for Japan (RIJ, 1–100) 56 (34–79) 54 (30–77) 0.076 Total population 26,694 (7,901–65,217) 21,078 (7,009–59,728) 0.086 Values are presented as median (interquartile range [IQR]). Standardised mean differences (SMDs) were calculated using the mean and standard deviation of each variable, although central tendency and dispersion are displayed as median and IQR to reflect the skewed distributions of municipal-level variables. Bracketed n indicates the number of municipalities with non-missing data for that variable. Baseline characteristics were compared between responding and non-responding municipalities using data obtained from public governmental databases. |SMD| 0.20 meaningful imbalance. Main analysis Implementation rates of non-mandatory preventive medicine programmes Implementation rates of non-mandatory preventive medicine programmes were calculated on both a municipality-count basis (programme penetration) and a population-weighted basis (population coverage) (Table 3 ). Table 3 Implementation of non-mandatory preventive medicine programmes by Japanese municipalities, by disease category and USPSTF recommendation grade Preventive programme USPSTF grade Municipalities implementing, % (n/N) Population-weighted (%) Population covered, n Infectious diseases HBV screening B 92.1 (430/467) 93.6 36,554,094 HCV screening B 91.4 (427/467) 94.4 36,863,939 HIV screening A 10.1 (47/467) 48.1 18,778,645 Syphilis screening A 9.6 (45/467) 48.3 18,858,018 Chlamydia screening B 7.7 (36/467) 36.6 14,293,149 Gonorrhoea screening B 2.1 (10/467) 9.9 3,876,682 Cardiovascular, cerebrovascular, and respiratory diseases Young adult health check-up No grade 86.3 (403/467) 78.8 30,772,901 Brain/carotid screening D 41.1 (192/467) 35.6 13,889,906 AAA screening B 16.1 (75/467) 14.5 5,664,715 PAD screening (ABI) I 3.6 (17/467) 0.9 369,971 COPD screening D 3.4 (16/467) 8.2 3,217,836 Cardiac screening (echocardiography) No grade 2.4 (11/467) 0.3 114,850 Cancer screening Thyroid screening (ultrasonography) D 3.4 (16/467) 5.5 2,138,848 Positron emission tomography (PET) screening No grade 6.2 (29/467) 3.1 1,193,793 Musculoskeletal health and functional assessment in older adults Osteoporosis screening B 68.5 (320/467) 70.2 27,420,935 Frailty screening No grade 26.1 (122/467) 25.8 10,068,792 Fall prevention programme B 17.1 (80/467) 22.7 8,868,164 Mental health and substance use Depression/anxiety screening B 3.2 (15/467) 1.9 751,605 Unhealthy drug use screening B 0.2 (1/467) 0.0 12,782 Perinatal and reproductive health Folic acid supplementation A 3.2 (15/467) 4.9 1,895,353 IPV screening B 1.7 (8/467) 0.8 303,580 Perinatal depression screening B 1.5 (7/467) 1.2 456,802 This table summarises the implementation rates of non-mandated preventive medicine programmes delivered by Japanese municipalities, stratified by disease category and USPSTF recommendation grade. Implementation was defined as provision or subsidisation of the programmes by a municipality, irrespective of eligibility criteria. Population-weighted rates reflect the proportion of the responding-municipality population living in those implementing municipalities. USPSTF, United States Preventive Services Task Force. Grade A, recommended with high certainty of substantial net benefit; Grade B, recommended with high certainty of moderate net benefit; Grade D, recommended against; Grade I, insufficient evidence; No grade, not evaluated by the USPSTF. HBV, hepatitis B virus; HCV, hepatitis C virus; HIV, human immunodeficiency virus; AAA, abdominal aortic aneurysm; PAD, peripheral artery disease; ABI, ankle–brachial index; COPD, chronic obstructive pulmonary disease; IPV, intimate partner violence. Population covered was estimated using municipal population data. Municipality-count-based (unweighted) implementation rates Unweighted implementation rates varied substantially across programmes, ranging from 0.2% to 92.1%. HBV and HCV screening were implemented by more than 90%, and young adult health check-ups by 86.3%, whereas unhealthy drug use screening, IPV screening, and gonorrhoea screening were rarely implemented. Osteoporosis screening was also relatively widespread (68.5%), while depression/anxiety screening remained uncommon (3.2%). Population-weighted implementation rates Population-weighted implementation rates ranged from 0.0% to 94.4%. Programmes with the highest population coverage were HCV screening (94.4%), HBV screening (93.6%), young adult health check-ups (78.8%), and osteoporosis screening (70.2%). Programmes with the lowest population coverage were unhealthy drug use screening (0.0%), cardiac screening by echocardiography (0.3%), IPV screening (0.8%), PAD screening by ankle–brachial index (0.9%), and perinatal depression screening (1.2%). Divergence between municipality-count and population-weighted rates For certain programmes, population-weighted coverage was substantially higher than municipality-count-based rates. HIV screening (10.1% by municipality count vs 48.1% population-weighted), syphilis screening (9.6% vs 48.3%), and chlamydia screening (7.7% vs 36.6%) were implemented by relatively few municipalities, yet population-weighted coverage was comparatively high, suggesting that these services tend to be provided in larger municipalities. Conversely, for brain/carotid screening (41.1% vs 35.6%) and young adult health check-ups (86.3% vs 78.8%), municipality-count-based penetration exceeded population-weighted coverage, suggesting that these programmes are implemented disproportionately in smaller municipalities. Evidence–practice gaps against USPSTF recommendation grades Among programmes classified as USPSTF Grade A or B, 10 had municipality-count-based implementation rates below 50%. In contrast, HBV and HCV screening and osteoporosis screening showed relatively high implementation rates. Among programmes classified as USPSTF Grade D, I, or no grade, three had municipality-count-based implementation rates of 10% or above, suggesting gaps between evidence and practice. To highlight these gaps explicitly, programmes with Grade A or B recommendations but implementation rates below 50% are presented in Table 4 , and programmes with Grade D, I, or no grade but implementation rates of 10% or above are presented in Table 5 . Implementation rates on both a municipality-count and population-weighted basis, stratified by disease category and USPSTF recommendation grade, are visualised as a dumbbell plot (Fig. 1 ). Table 4 Preventive programmes with USPSTF Grade A or B recommendation but municipality-level implementation below 50% This table presents programmes for which the USPSTF assigned a Grade A or B recommendation but fewer than half of responding municipalities reported implementation. Programmes are ordered by ascending implementation rate. This pattern represents a potential evidence-to-practice gap, where evidence-based recommendations have not translated into widespread municipal adoption. USPSTF, United States Preventive Services Task Force. Grade A, recommended with high certainty of substantial net benefit; Grade B, recommended with high certainty of moderate net benefit. IPV, intimate partner violence; AAA, abdominal aortic aneurysm. Implementation was defined as provision or subsidisation of the programme by a municipality, irrespective of eligibility criteria. Preventive programme USPSTF grade Municipalities implementing, % (n/N) Unhealthy Drug Use Screening B 0.2 (1/467) IPV Screening B 1.7 (8/467) Gonorrhoea Screening B 2.1 (10/467) Depression/Anxiety Screening B 3.2 (15/467) Folic Acid Supplementation A 3.2 (15/467) Chlamydia Screening B 7.7 (36/467) Syphilis Screening A 9.6 (45/467) HIV Screening A 10.1 (47/467) AAA Screening B 16.1 (75/467) Fall Prevention Programme B 17.1 (80/467) Table 5 Preventive programmes with USPSTF Grade D, I, or No grade but municipality-level implementation of 10% or above Preventive programme USPSTF grade Municipalities implementing, % (n/N) Young Adult Check-up No grade 86.3 (403/467) Brain/Carotid Screening D 41.1 (192/467) Frailty Screening No grade 26.1 (122/467) This table presents programmes for which the USPSTF assigned a Grade D or no grade, yet 10% or more of responding municipalities reported implementation. Programmes are ordered by descending implementation rate. This pattern represents a potential evidence-to-practice gap, where municipal adoption has outpaced or diverged from current evidence-based guidance. USPSTF, United States Preventive Services Task Force. Grade D, recommended against with moderate or high certainty that the service has no net benefit or that harms outweigh benefits; No grade, not evaluated by the USPSTF. Implementation was defined as provision or subsidisation of the programme by a municipality, irrespective of eligibility criteria. Each programme is shown as a dumbbell: the closed circle indicates the percentage of responding municipalities that reported implementation (n = 467), and the open circle indicates the population-weighted coverage. Programmes are grouped by disease category and labelled with the USPSTF recommendation grade (A, B, D, I, or N/A for ungraded). A wider gap between the two circles indicates greater divergence between municipality-level penetration and population-level coverage; population-weighted coverage exceeding municipality-count rates typically reflects concentration of the service in larger municipalities (as observed for HIV, syphilis, and chlamydia screening), whereas the reverse pattern suggests that the programme is disproportionately implemented in smaller municipalities. USPSTF, US Preventive Services Task Force; HBV, hepatitis B virus; HCV, hepatitis C virus; HIV, human immunodeficiency virus; AAA, abdominal aortic aneurysm; PAD, peripheral artery disease; ABI, ankle–brachial index; US, ultrasonography; COPD, chronic obstructive pulmonary disease; PET, positron emission tomography; IPV, intimate partner violence; N/A, not evaluated by the USPSTF. Evidence–Practice Alignment (EPA) score EPA scores were calculated for each municipality and visualised at the prefectural level using a choropleth map (Fig. 2 ). Scores ranged from − 4 to 1, demonstrating marked heterogeneity in evidence–practice alignment across regions. Prefectures with lower EPA scores, indicating relatively greater implementation of non-recommended or insufficiently evidenced programmes, were widely distributed across the country. In contrast, prefectures with higher scores, reflecting better alignment with evidence-based recommendations, were fewer and appeared to be geographically clustered. Overall, no clear geographic gradient was observed nationwide, suggesting that variation in EPA scores is not solely explained by regional location but may reflect local policy decisions and implementation practices. Choropleth map displaying the mean EPA score of responding municipalities within each prefecture. The EPA score for each municipality was calculated as a weighted sum of implementation status across all non-mandatory preventive medicine programmes, with weights assigned a priori to reflect the strength of evidence conveyed by each USPSTF grade: A = + 2, B = + 1, D = − 2, and I or ungraded = − 1. Higher scores (blue) indicate greater implementation of recommended services alongside appropriate restraint regarding non-recommended services; lower scores (red) reflect the inverse. Prefectural scores ranged from − 4 to 1. A diverging colour palette centred at zero is used to distinguish prefectures above and below the neutral reference value. Discussion What this study adds In this nationwide descriptive cross-sectional survey of Japanese municipalities, we provide the first nationwide municipal-level mapping of implementation patterns for non-mandatory preventive medicine programmes, revealing substantial heterogeneity across domains. Among 1,741 municipalities contacted, 467 returned valid responses (response rate 26.8%). We assessed implementation using two complementary measures: the proportion of municipalities that provided or subsidised each programme and the corresponding population coverage. When we compared municipal practices with USPSTF evidence grades, we found a gap between evidence and implementation. Notably, several programmes graded A or B by the USPSTF were implemented by fewer than half of municipalities—for example, folic acid supplementation (3.2%), syphilis screening (9.6%), chlamydia screening (7.7%), depression/anxiety screening (3.2%), and abdominal aortic aneurysm screening (16.1%). Conversely, a number of programmes graded D or “no grade” were implemented at appreciable levels, including brain/carotid screening (41.1%), young adult health check-ups (86.3%; no grade), and frailty screening (26.1%; no grade). A further insight was the divergence between municipality-count and population-weighted implementation. Even when relatively few municipalities offered certain STI/HIV services, population-weighted coverage was substantially higher (e.g., HIV screening 10.1% by municipality count vs 48.1% population-weighted), suggesting concentration of such services in larger municipalities. To visualise municipality-level alignment with evidence-based prevention, we constructed an Evidence–Practice Alignment (EPA) score that summarises implementation patterns across domains. Comparison with prior work and interpretation Internationally, the gap between evidence-based recommendations and real-world adoption is well recognised; simply producing evidence or guidelines rarely ensures consistent uptake without active implementation strategies [ 18 ]. In our context, we interpret the under-implementation of multiple A/B-graded services as potentially reflecting not only operational constraints but also limited and uneven dissemination of evidence-based prioritisation into municipal decision-making. Specifically, in Japan it is difficult to argue that a cross-cutting, USPSTF-like centralisation of evidence-graded preventive recommendations is sufficiently institutionalised and disseminated; consequently, municipalities may be less likely to consistently reference graded evidence when selecting preventive medicine programmes. This is a plausible hypothesis rather than a causal conclusion, because our survey did not directly measure municipal decision processes. Nonetheless, it offers a testable explanation for why some highly recommended services remain uncommon while other programmes persist despite weak evidence or potential net harm. The latter pattern is consistent with concerns about overdiagnosis and low-value screening. Large-scale screening can increase detection of indolent disease without proportional mortality benefit, as illustrated by thyroid cancer overdiagnosis associated with widespread screening in Korea [ 19 ] and by evidence that favourable shifts in breast tumour size distributions may be driven largely by additional detection of small tumours with substantial overdiagnosis [ 20 ]. At the system level, these dynamics align with the broader challenge of reducing “waste” in healthcare by limiting low-value services rather than cutting beneficial care [ 21 ]. Limitations This study has several limitations. First, the response rate was modest (26.8%), and non-response bias is possible. Responding municipalities tended to have lower per-capita resident income and fewer general-clinic physicians per 100,000 population; accordingly, results may over-represent municipalities with lower per-capita income and fewer clinic physicians, and under-represent urban or higher-resource municipalities. Second, “implementation” was defined as municipal provision or subsidisation, and we could not fully capture eligibility criteria, intensity, quality assurance, or participation rates. Therefore, implementation does not necessarily indicate guideline-concordant delivery at the individual level. Third, USPSTF recommendations are stratified by age, sex, and risk, and are derived from evidence bases reflecting the epidemiological context of the United States. Applying these grades as a benchmark for Japanese municipal programmes involves two important caveats: population-level policy classification does not map directly onto individual-level eligibility criteria, and the prevalence of relevant conditions diverges between the two countries in ways that affect the appropriateness of universal screening—in some cases overstating and in others understating the urgency of implementation. These discordances limit the direct applicability of USPSTF grades, and future studies should incorporate domestically developed evidence-graded frameworks where available. Furthermore, USPSTF grades reflect recommendations current as of March 2025 and may have been updated subsequently; findings should be interpreted with reference to the grades in effect at the time of the survey. Finally, as a cross-sectional descriptive study, we cannot infer causal determinants of adoption or quantify downstream benefits, harms, or cost consequences. Conclusions In a nationwide municipal survey, we observed substantial variation and clear evidence–practice gaps in Japan’s non-mandatory preventive medicine programmes. Under-implementation of multiple A/B-graded services coexisted with notable implementation of Grade D or ungraded programmes. Population-weighted results further suggested that some evidence-aligned services are concentrated in larger municipalities, raising potential equity concerns. Our findings support a dual agenda. First, there is a need to strengthen dissemination and implementation support for high-value preventive medicine services, including evaluation of whether limited awareness or uptake of graded evidence contributes to municipal choices in Japan. Second, low-value screening programmes—where harms may outweigh benefits—warrant critical reassessment and, where appropriate, active de-implementation [ 22 ]. A structural gap underlying these findings is the absence of a nationally authoritative, evidence-grading body for preventive medicine in Japan analogous to the USPSTF. Establishing such an institution would provide municipalities with a unified and regularly updated reference standard, and represents a necessary policy priority for reducing evidence–practice gaps in Japanese preventive medicine. Future research should examine municipal decision-making to identify modifiable barriers to evidence-aligned prevention. Abbreviations AAA: Abdominal aortic aneurysm; ABI: Ankle–brachial index; COPD: Chronic obstructive pulmonary disease; EPA: Evidence–Practice Alignment; HBV: Hepatitis B virus; HCV: Hepatitis C virus; HIV: Human immunodeficiency virus; IQR: Interquartile range; IPV: Intimate partner violence; MHLW: Ministry of Health, Labour and Welfare; PAD: Peripheral artery disease; PET: Positron emission tomography; RIJ: Rurality Index for Japan; SMD: Standardised mean difference; USPSTF: US Preventive Services Task Force. Declarations Ethics approval and consent to participate This study was assessed by the institutional review board office of the National Hospital Organization Nagasaki Medical Center and determined not to require formal ethics committee review, as it involved no personal identifying information and constituted a descriptive survey of municipal administrative entities rather than individual human subjects, in accordance with the “Ethical Guidelines for Medical and Health Research Involving Human Subjects” (Ministry of Health, Labour and Welfare / Ministry of Education, Culture, Sports, Science and Technology, Japan, 2021). All participating municipalities were informed of the study objectives, the intended use of data, and the assurance that individual responses would not be used for evaluative purposes; return of the completed questionnaire was accepted as indication of consent to participate. Data were used exclusively for research purposes. Consent for publication Not applicable. Availability of data and materials The datasets generated and analysed during the current study are not publicly available due to the risk of identifying individual municipalities but are available from the corresponding author on reasonable request. Competing interests Dr. Sakata is employed in the Department of Neurodevelopmental Medicine, Nagoya City University Graduate School of Medical Sciences, which is an endowment department supported by the City of Nagoya. He has received a personal fee from SONY and Daiichi-Sankyo outside the submitted work. Dr. Tsugawa receives funding from the National Institutes of Health (NIH)/National Institute on Aging (R01AG068633 and R01AG082991), NIH/National Institute on Minority Health and Health Disparities (R01MD013913), and Gregory Annenberg Weingarten, GRoW @ Annenberg for work not related to this study, and serves on the board of directors of M3, Inc. The other authors declare no competing interests. Funding This study was supported by EVIDENCE STUDIO (a general incorporated association in Japan). The funder had no involvement in study design, data collection, analysis, interpretation of data, writing of the manuscript, or the decision to submit the manuscript for publication. Authors’ contributions HM conceived and designed the study, led data collection, performed statistical analyses, and drafted the manuscript. KS and KMur planned and conducted the statistical analyses. KMi and AS contributed to study design, interpretation of findings, and critical revision of the manuscript. MS and KMuk contributed to study design, critical revision of the manuscript, and supervised the study. MF contributed to critical revision of the manuscript. KH and YT contributed to interpretation of findings and critical revision of the manuscript. All authors read and approved the final version of the manuscript and agree to be accountable for all aspects of the work. Acknowledgements The authors thank Knowledge Database Co., Ltd. for their assistance with the internet-based questionnaire survey. This work was conducted as part of the JPPSTF Project, which was commissioned by EVIDENCE STUDIO, a general incorporated association whose purposes include optimising public healthcare expenditures in Japan. The contract was formally established with Kurume University, with which Kei Mukohara, the chair of the JPPSTF, is affiliated. References Jamison DT, Summers LH, Alleyne G, Arrow KJ, Berkley S, Binagwaho A et al (2013) Global health 2035: a world converging within a generation. Lancet 382:1898–1955 Hanson K, Brikci N, Erlangga D, Alebachew A, De Allegri M, Balabanova D et al (2022) The Lancet Global Health Commission on financing primary health care: putting people at the centre. Lancet Glob Health 10:e715–e772 Organisation for Economic Co-operation and Development (2019) Japan: a healthier tomorrow. OECD, Paris Cedex, France Ministry of Health, Labour and Welfare (Japan) Specific Health Check-ups and Specific Health Guidance. https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/0000161103.html . Accessed 2 Mar 2026 National Cancer Center, Cancer Information Service Cancer screening implementation and process indicators by municipality. https://ganjoho.jp/reg_stat/statistics/stat/screening/dl_screening.html . Accessed 27 Feb 2026 Ministry of Health, Labour and Welfare (Japan). Overview of the FY2022 Regional Health and Health Promotion Project Report (2024) https://www.mhlw.go.jp/toukei/saikin/hw/c-hoken/22/dl/R04gaikyo.pdf . Accessed 5 May 2026 U.S. Preventive Services Task Force About the USPSTF. https://www.uspreventiveservicestaskforce.org/uspstf/about-uspstf . Accessed 27 Feb 2026 Mangione CM, Nicholson W, Davidson KW (2022) Addressing gaps in research to reduce disparities and advance health equity: the USPSTF incorporation of the NASEM taxonomy on closing evidence gaps in clinical prevention. JAMA 328(18):1803–1804 Statistics Bureau of Japan, Ministry of Internal Affairs and Communications (Japan). Statistical Observations of Municipalities (2025) https://www.stat.go.jp/data/s-sugata/pdf/all_shi.pdf . Accessed 2 Mar 2026 e-Stat (Portal Site of Official Statistics of Japan). System of Social and Demographic Statistics: Statistical Observations of Municipalities 2025 (Basic Data) (2025) https://www.e-stat.go.jp/en/stat-search/files?tclass1=000001229546&toukei=00200502&tstat=000001229545 . Accessed 2 Mar 2026 Geospatial Information Authority of Japan (GSI). National land area survey by prefecture and municipality (as of 1 July 2025) (2025) https://www.gsi.go.jp/KOKUJYOHO/MENCHO/backnumber/GSI-menseki20250701.pdf . Accessed 2 Mar 2026 Kaneko M, Ikeda T, Inoue M, Sugiyama K, Saito M, Ohta R et al (2023) Development and validation of a rurality index for healthcare research in Japan: a modified Delphi study. BMJ Open 13:e068800 Ministry of Health, Labour and Welfare (Japan). Survey of Medical Institutions (Government Statistics Code: 00450021) (2025) https://www.e-stat.go.jp/en/statistics/00450021 . Accessed 2 Mar 2026 Austin PC (2009) Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Stat Med 28:3083–3107 Zhang Z, Kim HJ, Lonjon G, Zhu Y, written on behalf of AME Big-Data Clinical Trial Collaborative Group (2019) Balance diagnostics after propensity score matching. Ann Transl Med 7:16 von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP et al (2007) The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. PLoS Med 4:e296 Eysenbach G (2004) Improving the quality of Web surveys: the Checklist for Reporting Results of Internet E-Surveys (CHERRIES). J Med Internet Res 6:e34 Grol R, Grimshaw J (2003) From best evidence to best practice: effective implementation of change in patients’ care. Lancet 362:1225–1230 Ahn HS, Kim HJ, Welch HG (2014) Korea’s thyroid-cancer epidemic—screening and overdiagnosis. N Engl J Med 371:1765–1767 Welch HG, Prorok PC, O’Malley AJ, Kramer BS (2016) Breast-cancer tumor size, overdiagnosis, and mammography screening effectiveness. N Engl J Med 375:1438–1447 Berwick DM, Hackbarth AD (2012) Eliminating waste in US health care. JAMA 307:1513–1516 Niven DJ, Mrklas KJ, Holodinsky JK, Straus SE, Hemmelgarn BR, Jeffs LP et al (2015) Towards understanding the de-adoption of low-value clinical practices: a scoping review. BMC Med 13:255 Additional Declarations The authors declare potential competing interests as follows: Dr. Sakata is employed in the Department of Neurodevelopmental Medicine, Nagoya City University Graduate School of Medical Sciences, which is an endowment department supported by the City of Nagoya. He has received a personal fee from SONY and Daiichi-Sankyo outside the submitted work. Dr. Tsugawa receives funding from the National Institutes of Health (NIH)/National Institute on Aging (R01AG068633 and R01AG082991), NIH/National Institute on Minority Health and Health Disparities (R01MD013913), and Gregory Annenberg Weingarten, GRoW @ Annenberg for work not related to this study, and serves on the board of directors of M3, Inc. The other authors declare no competing interests. Supplementary Files Additionalfileslegends.docx Additionalfile1.docx Table S1. Prefecture-level response rates across Japan This table presents the survey response rates for all 47 prefectures of Japan. Response rates were calculated as the proportion of municipalities within each prefecture that returned completed questionnaires. 95% confidence intervals were estimated using the Wilson score method. Additionalfile2.docx Table S2. Regional distribution of responding municipalities across Japan (8-region grouping) Response rates are shown by region based on Japan's standard 8-region classification. Data are presented as response rate % (n/N), where n is the number of responding municipalities and N is the total number of municipalities in each region. Additionalfile3.tiff Additional file 3: Figure S1. Geographic distribution of survey response rates by prefecture. Choropleth map of Japan showing the proportion of municipalities within each prefecture that returned a valid questionnaire, calculated as the number of responding municipalities divided by the total number of municipalities in the prefecture. Darker shading indicates higher response rates. Prefecture-level response rates ranged from 9.3% to 48.8% (median 26.7%, interquartile range 20.8–31.9%). See Additional file 1: Table S1 for underlying counts and Wilson 95% confidence intervals. Additionalfile3.tiff Additional file 3: Figure S1. Geographic distribution of survey response rates by prefecture. Choropleth map of Japan showing the proportion of municipalities within each prefecture that returned a valid questionnaire, calculated as the number of responding municipalities divided by the total number of municipalities in the prefecture. Darker shading indicates higher response rates. Prefecture-level response rates ranged from 9.3% to 48.8% (median 26.7%, interquartile range 20.8–31.9%). See Additional file 1: Table S1 for underlying counts and Wilson 95% confidence intervals. Additionalfile4.tiff Additional file 4: Figure S2. Funnel plot of prefectural response rates against the total number of municipalities. Each point represents one of the 47 prefectures. The horizontal reference line indicates the overall response rate (26.8%). Inner dashed lines and outer dotted lines represent 95% and 99.8% control limits, respectively, based on exact binomial variation around the overall rate. Points falling outside the control limits indicate variation between prefectures that exceeds what would be expected from statistical fluctuation alone given the denominator size. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-9674474","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":637892086,"identity":"b9776904-5e77-4b83-9b8f-962194f049d0","order_by":0,"name":"Hideki 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Health","correspondingAuthor":false,"prefix":"","firstName":"Yusuke","middleName":"","lastName":"Tsugawa","suffix":""},{"id":637892094,"identity":"360b5562-570f-4be2-b17e-19f5af0846a7","order_by":8,"name":"Kenta Murotani","email":"","orcid":"https://orcid.org/0000-0003-0623-9365","institution":"Kurume University","correspondingAuthor":false,"prefix":"","firstName":"Kenta","middleName":"","lastName":"Murotani","suffix":""},{"id":637892095,"identity":"108b3146-5ee6-4ac7-9752-4af96c63f29a","order_by":9,"name":"Kei Mukohara","email":"","orcid":"https://orcid.org/0000-0002-1980-1487","institution":"Kurume University","correspondingAuthor":false,"prefix":"","firstName":"Kei","middleName":"","lastName":"Mukohara","suffix":""}],"badges":[],"createdAt":"2026-05-11 04:35:18","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9674474/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9674474/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109052623,"identity":"7061d5a8-44d7-4299-8387-14f66610a749","added_by":"auto","created_at":"2026-05-12 07:02:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":351453,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMunicipality-count and population-weighted implementation rates of non-mandatory preventive medicine programmes, stratified by disease category and USPSTF recommendation grade.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEach programme is shown as a dumbbell: the closed circle indicates the percentage of responding municipalities that reported implementation (n = 467), and the open circle indicates the population-weighted coverage. Programmes are grouped by disease category and labelled with the USPSTF recommendation grade (A, B, D, I, or N/A for ungraded). A wider gap between the two circles indicates greater divergence between municipality-level penetration and population-level coverage; population-weighted coverage exceeding municipality-count rates typically reflects concentration of the service in larger municipalities (as observed for HIV, syphilis, and chlamydia screening), whereas the reverse pattern suggests that the programme is disproportionately implemented in smaller municipalities. USPSTF, US Preventive Services Task Force; HBV, hepatitis B virus; HCV, hepatitis C virus; HIV, human immunodeficiency virus; AAA, abdominal aortic aneurysm; PAD, peripheral artery disease; ABI, ankle–brachial index; US, ultrasonography; COPD, chronic obstructive pulmonary disease; PET, positron emission tomography; IPV, intimate partner violence; N/A, not evaluated by the USPSTF.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9674474/v1/5e1093bfcd7d4130324e1b00.png"},{"id":109067770,"identity":"9739843c-e69c-458b-9130-b5a4832e0675","added_by":"auto","created_at":"2026-05-12 10:00:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":76039,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrefectural Evidence–Practice Alignment (EPA) scores across Japan.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChoropleth map displaying the mean EPA score of responding municipalities within each prefecture. The EPA score for each municipality was calculated as a weighted sum of implementation status across all non-mandatory preventive medicine programmes, with weights assigned a priori to reflect the strength of evidence conveyed by each USPSTF grade: A = +2, B = +1, D = −2, and I or ungraded = −1. Higher scores (blue) indicate greater implementation of recommended services alongside appropriate restraint regarding non-recommended services; lower scores (red) reflect the inverse. Prefectural scores ranged from −4 to 1. A diverging colour palette centred at zero is used to distinguish prefectures above and below the neutral reference value.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9674474/v1/f29b8637d7a8e92d658ffdce.png"},{"id":109206647,"identity":"fa9ed370-cb1b-4683-87c2-5b2412eae3e5","added_by":"auto","created_at":"2026-05-13 15:14:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":639254,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9674474/v1/6f334ef8-92d2-43ec-9332-c6c1417736a5.pdf"},{"id":109068160,"identity":"9d66e937-79d2-4e44-9870-33e9ef8787cf","added_by":"auto","created_at":"2026-05-12 10:04:10","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14459,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfileslegends.docx","url":"https://assets-eu.researchsquare.com/files/rs-9674474/v1/e42e16a86d3a488803318367.docx"},{"id":109052624,"identity":"435f15eb-ee92-4d9e-8adb-96bdd534b519","added_by":"auto","created_at":"2026-05-12 07:02:46","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16397,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S1. Prefecture-level response rates across Japan\u003c/strong\u003e\u003cbr\u003e\nThis table presents the survey response rates for all 47 prefectures of Japan. Response rates were calculated as the proportion of municipalities within each prefecture that returned completed questionnaires. 95% confidence intervals were estimated using the Wilson score method.\u003c/p\u003e","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9674474/v1/93ac360874a9a4311d9ccba5.docx"},{"id":109052627,"identity":"33ee2508-d08d-4040-a5f1-e35f92424893","added_by":"auto","created_at":"2026-05-12 07:02:46","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":13388,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S2. Regional distribution of responding municipalities across Japan (8-region grouping)\u003c/strong\u003e\u003cbr\u003e\nResponse rates are shown by region based on Japan's standard 8-region classification. Data are presented as response rate % (n/N), where n is the number of responding municipalities and N is the total number of municipalities in each region.\u003c/p\u003e","description":"","filename":"Additionalfile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-9674474/v1/a3a76fc00f2b5123ed66d2eb.docx"},{"id":109222192,"identity":"3946f894-9573-489f-94f2-9bb986e802b1","added_by":"auto","created_at":"2026-05-13 21:04:09","extension":"tiff","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":89244,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 3: Figure S1.\u003c/strong\u003e Geographic distribution of survey response rates by prefecture. Choropleth map of Japan showing the proportion of municipalities within each prefecture that returned a valid questionnaire, calculated as the number of responding municipalities divided by the total number of municipalities in the prefecture. Darker shading indicates higher response rates. Prefecture-level response rates ranged from 9.3% to 48.8% (median 26.7%, interquartile range 20.8–31.9%). See Additional file 1: Table S1 for underlying counts and Wilson 95% confidence intervals.\u003c/p\u003e","description":"","filename":"Additionalfile3.tiff","url":"https://assets-eu.researchsquare.com/files/rs-9674474/v1/727909a57531a6aa9d9d3090.tiff"},{"id":109102220,"identity":"a1474fb4-18ce-4bcf-ad84-c494762fa991","added_by":"auto","created_at":"2026-05-12 14:31:43","extension":"tiff","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":89244,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 3: Figure S1.\u003c/strong\u003e Geographic distribution of survey response rates by prefecture. Choropleth map of Japan showing the proportion of municipalities within each prefecture that returned a valid questionnaire, calculated as the number of responding municipalities divided by the total number of municipalities in the prefecture. Darker shading indicates higher response rates. Prefecture-level response rates ranged from 9.3% to 48.8% (median 26.7%, interquartile range 20.8–31.9%). See Additional file 1: Table S1 for underlying counts and Wilson 95% confidence intervals.\u003c/p\u003e","description":"","filename":"Additionalfile3.tiff","url":"https://assets-eu.researchsquare.com/files/rs-9674474/v1/4344552e5dadc8bdb92e12f1.tiff"},{"id":109068130,"identity":"bf98228b-2010-48ee-a8de-33291460adce","added_by":"auto","created_at":"2026-05-12 10:03:51","extension":"tiff","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":113308,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 4: Figure S2.\u003c/strong\u003e Funnel plot of prefectural response rates against the total number of municipalities. Each point represents one of the 47 prefectures. The horizontal reference line indicates the overall response rate (26.8%). Inner dashed lines and outer dotted lines represent 95% and 99.8% control limits, respectively, based on exact binomial variation around the overall rate. Points falling outside the control limits indicate variation between prefectures that exceeds what would be expected from statistical fluctuation alone given the denominator size.\u003c/p\u003e","description":"","filename":"Additionalfile4.tiff","url":"https://assets-eu.researchsquare.com/files/rs-9674474/v1/13763a609efc6bd9def97ac4.tiff"}],"financialInterests":"The authors declare potential competing interests as follows: Dr. Sakata is employed in the Department of Neurodevelopmental Medicine, Nagoya City University Graduate School of Medical Sciences, which is an endowment department supported by the City of Nagoya. He has received a personal fee from SONY and Daiichi-Sankyo outside the submitted work.\nDr. Tsugawa receives funding from the National Institutes of Health (NIH)/National Institute on Aging (R01AG068633 and R01AG082991), NIH/National Institute on Minority Health and Health Disparities (R01MD013913), and Gregory Annenberg Weingarten, GRoW @ Annenberg for work not related to this study, and serves on the board of directors of M3, Inc.\nThe other authors declare no competing interests.","formattedTitle":"\u003cp\u003eNationwide Implementation of Non-Mandatory Preventive Medicine Programmes in Japanese Municipalities: A Descriptive Cross-Sectional Survey and Evidence–Practice Gap Analysis\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eAgeing populations and rising healthcare expenditures have placed preventive healthcare at the centre of health policy agendas worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], yet the translation of evidence-based recommendations into consistent population-level practice remains an enduring challenge across healthcare systems. Japan represents a particularly illuminating case. Preventive healthcare in Japan is organised through a layered structure involving national legislation, medical insurers, and municipal governments, with the Ministry of Health, Labour and Welfare (MHLW) playing a central role in setting policy frameworks and guidelines [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Some programmes are mandated by national law: the Specific Health Check-up and Specific Health Guidance (Tokutei Kenshin), for example, are required under the Act on Assurance of Medical Care for Elderly People and are delivered by medical insurers to individuals aged 40\u0026ndash;74 enrolled in public health insurance schemes [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Notably, even some components of these nationally mandated programmes do not always align with contemporary international evidence standards, suggesting that questions of evidence\u0026ndash;practice alignment extend across the full spectrum of preventive services in Japan, both mandated and non-mandated. Other services are delegated to municipalities under the Health Promotion Act, the Maternal and Child Health Act, and related legislation. For these municipally delivered services\u0026mdash;which particularly serve residents not covered by employer-based health check-ups\u0026mdash;national policies provide general guidance, but the actual implementation, including the types of screenings offered, target populations, and outreach strategies, varies substantially across municipalities. Such locally administered programmes fall broadly into two types: those for which national guidelines exist but implementation authority rests with municipalities (as in cancer screening), and those developed entirely at the local level without national policy guidance (as in carotid artery stenosis or COPD screening). While cancer screening programmes are relatively well-monitored through national surveillance data on uptake [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], a systematic cross-sectional evaluation of the full spectrum of municipally administered preventive medicine programmes\u0026mdash;beyond the five nationally designated cancers\u0026mdash;and their alignment with current scientific evidence remains lacking.\u003c/p\u003e \u003cp\u003eOne widely referenced benchmark is the US Preventive Services Task Force (USPSTF) in the United States, which provides one of the world\u0026rsquo;s most structured and methodologically rigorous evidence-grading frameworks for clinical preventive services [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, even within the United States, substantial evidence gaps, implementation barriers, and health inequities persist, particularly among underserved populations, indicating that a centralized evidence-grading framework alone does not guarantee equitable or optimal preventive care delivery [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In contrast, Japanese municipalities lack a comparable cross-cutting evidence-grading framework for non-mandatory preventive services, which may contribute to heterogeneous implementation [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Against this background, this study had two objectives: first, to describe how non-mandatory preventive medicine programmes are implemented by municipalities across Japan; and second, to evaluate how well these programmes align with a structured evidence-based benchmark, and to identify gaps between evidence and practice.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eA descriptive cross-sectional study was conducted using a questionnaire survey administered to all 1,741 municipalities in Japan. Implementation of non-mandatory preventive medicine programmes was described based on data from responding municipalities. We excluded the five cancer screenings mandated under the Health Promotion Act (gastric, colorectal, lung, breast, and cervical), but included non-mandatory cancer-related screenings such as thyroid ultrasound and positron emission tomography (PET) cancer screening that are delivered or subsidised at municipal discretion. Reporting followed STROBE and CHERRIES guidelines [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cp\u003eThe study population comprised all municipalities in Japan (n\u0026thinsp;=\u0026thinsp;1,741), defined as cities, towns, villages, and the 23 special wards of Tokyo. Administrative wards within designated cities were excluded, as they function as internal subdivisions rather than independent local government units. Questionnaires were distributed to the health check-up and screening divisions (or equivalent responsible department) of all municipalities as a census survey. The analytical sample consisted of those returning completed responses. Municipalities were included if they submitted responses via web-based form, Microsoft Word document, or PDF, and were excluded if identification was not possible or if more than 50% of items were missing or duplicated.\u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eData collection was conducted from 10 March to 31 August 2025. Questionnaires were distributed by postal mail to the health check-up and screening divisions of all municipalities, and participation was voluntary. Respondents could complete the survey via a password-protected web-based form (Google Forms), or by returning a completed PDF or Word document by mail. No financial compensation was provided; a summary of findings was offered to all responding municipalities upon study completion. Responses were managed at the municipal level using password and municipality identifier systems to prevent duplicate submissions; cookie- or IP-based verification was not performed. The majority of web-based items were designated as required fields. Where PDF responses contained missing data, municipalities were contacted and data supplemented where feasible.\u003c/p\u003e\n\u003ch3\u003eSurvey items\u003c/h3\u003e\n\u003cp\u003eThe survey covered the implementation status of non-mandatory preventive medicine programmes, excluding vaccinations and the five mandated cancer screenings. Target items were selected based on programmes with documented implementation records in Japan and USPSTF recommendations. The questionnaire comprised 60 items across 11 pages and used primarily mandatory multiple-choice formats to ensure completeness and reduce respondent burden, supplemented by open-ended fields.\u003c/p\u003e\n\u003ch3\u003eDefinition of implementation\u003c/h3\u003e\n\u003cp\u003eImplementation was defined as municipal provision or subsidisation of a given screening test or service, irrespective of whether the target age, sex, or risk criteria specified in USPSTF recommendations were met. Detailed eligibility criteria could not be collected for most items\u0026mdash;exceptions included osteoporosis screening and brain dock\u0026mdash;and implementation was therefore recorded on a binary basis. This approach was adopted to maintain response rates and enable consistent classification across municipalities.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eExternal data\u003c/h2\u003e \u003cp\u003eMunicipal baseline characteristics were obtained from public governmental databases\u0026mdash;including e-Stat (System of Social and Demographic Statistics), the Geospatial Information Authority of Japan, and Ministry of Health, Labour and Welfare sources\u0026mdash;as well as official municipal websites. Derived indicators included total population, area, population density, ageing rate, Rurality Index for Japan, age-adjusted mortality rate, fiscal capacity index, income per capita, proportion of residents exempt from resident tax, and numbers of hospitals, clinics, and physicians [\u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData management\u003c/h3\u003e\n\u003cp\u003eAll response data were centrally managed by the research team. Web-based responses were processed via automatically generated spreadsheets; postal responses were manually entered according to a standardised protocol. Open-ended responses were standardised against predefined definitions and coded as categorical variables where applicable. Data cleaning was performed according to pre-established criteria to ensure analytical reproducibility.\u003c/p\u003e\n\u003ch3\u003eVariable definitions\u003c/h3\u003e\n\u003cp\u003eEach preventive medicine programme was coded as a binary variable (1\u0026thinsp;=\u0026thinsp;implemented or subsidised; 0\u0026thinsp;=\u0026thinsp;otherwise). USPSTF grades (A, B, D, or I) were assigned to each item based on recommendations current as of March 2025, the time of survey administration [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. We selected the USPSTF framework as a structured external benchmark because, although Japan has multiple domain-specific preventive health policies and guidelines, it lacks a single cross-cutting, nationally authoritative evidence-grading system for many non-mandatory preventive services. The USPSTF was chosen because it provides one of the most methodologically rigorous and internationally recognized frameworks for evaluating preventive services across diverse domains using explicit assessments of net benefit and harm. Our use of USPSTF grades was not intended to imply direct transferability of US recommendations to the Japanese context, but rather to provide a transparent and standardized reference framework for comparative evaluation of municipal policy alignment across heterogeneous preventive programmes. Items not evaluated by the USPSTF were classified as ungraded. For Grade A or B items, a municipality was classified as implementing the programme if it provided or subsidised the service in any form.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEvidence\u0026ndash;Practice Alignment (EPA) score\u003c/h2\u003e \u003cp\u003eThe EPA score was calculated as a weighted sum of implementation status across all items. Weights were assigned a priori by the study team to reflect the relative strength of evidence conveyed by each USPSTF grade: A\u0026thinsp;=\u0026thinsp;+\u0026thinsp;2, B\u0026thinsp;=\u0026thinsp;+\u0026thinsp;1, D\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2, and I or ungraded\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1; non-implementation of a programme was assigned 0 points. No prior validated scoring instrument exists for this purpose; the weighting scheme was therefore developed de novo to penalise implementation of services with evidence of net harm (Grade D) while treating insufficient-evidence items (Grade I) and ungraded items conservatively. Higher scores indicate greater implementation of recommended services alongside appropriate restraint regarding non-recommended ones; lower scores reflect the inverse.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive analysis and evidence\u0026ndash;practice gap\u003c/h2\u003e \u003cp\u003eImplementation rates for each programme were calculated descriptively by municipality count and by population-weighted proportion, then compared against USPSTF grades to identify programmes with low uptake among Grade A/B items and high uptake among Grade D, I, or ungraded items, thereby characterising evidence\u0026ndash;practice gaps.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eNon-response bias assessment\u003c/h2\u003e \u003cp\u003eTo evaluate self-selection bias, baseline characteristics of responding and non-responding municipalities were compared using external data obtained from public governmental databases. Although baseline characteristics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e as medians and interquartile ranges to reflect the skewed distributions of municipal-level variables, standardised mean differences (SMDs) were calculated using the mean and standard deviation of each variable, in accordance with the conventional formula. Between-group differences were quantified using SMDs, with |SMD| \u0026lt; 0.10 indicating adequate balance, 0.10\u0026ndash;0.20 minor imbalance, and \u0026gt;\u0026thinsp;0.20 meaningful imbalance [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Given the descriptive intent of the study and the impossibility of fully characterising the non-response mechanism from observed covariates, we did not apply statistical adjustments for non-response. Instead, the assessment of potential bias was limited to comparisons of observed characteristics between responding and non-responding municipalities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMissing data\u003c/h2\u003e \u003cp\u003eMissing data were minimised by designating the majority of web-based items as required fields. Residual missing values in postal responses were addressed through direct follow-up with municipalities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eUse of Large Language Models\u003c/h2\u003e \u003cp\u003eDuring the preparation of this manuscript, the authors used ChatGPT (OpenAI, GPT-4) and Claude (Anthropic) to assist with translation from Japanese to English and to improve the clarity and readability of draft text. All AI-assisted outputs were critically reviewed, edited, and verified by the authors, who take full responsibility for the content of the manuscript. No AI tool was used to generate original scientific content, perform data analysis, draw conclusions, or generate references.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eRespondent municipality profile\u003c/h2\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003eResponse rate and geographic distribution\u003c/h2\u003e \u003cp\u003eResponses were received from 468 of 1,741 municipalities; one municipality with a wholly missing questionnaire was excluded, yielding 467 valid responses (response rate, 26.8%). Responding municipalities were distributed across all 47 prefectures, encompassing urban, peri-urban, and rural areas. In terms of population size, five municipalities (1.1%) had populations of 1,000,000 or more, 78 (16.7%) had 100,000\u0026ndash;999,999, 248 (53.1%) had 10,000\u0026ndash;99,999, and 136 (29.1%) had fewer than 10,000 residents; municipalities with fewer than 1,000,000 residents accounted for 462 (98.9%) of respondents (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003ePopulation size distribution of respondent municipalities (n\u0026thinsp;=\u0026thinsp;467)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation size (total population)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1,000,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (1.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100,000\u0026ndash;999,999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78 (16.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10,000\u0026ndash;99,999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e248 (53.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e136 (29.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e467 (100.0)\u003c/b\u003e\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\u003eData are presented as number (percentage). Municipalities include cities, towns, villages, and the special wards of Tokyo. Administrative wards of designated cities were excluded as they constitute internal administrative subdivisions rather than independent basic local government units.\u003c/em\u003e \u003c/p\u003e \u003cp\u003ePrefecture-level response rates were calculated with the number of responding municipalities as the numerator and the total number of municipalities within each prefecture as the denominator; 95% confidence intervals were estimated using the Wilson score method (Additional file 1: Table S1). Response rates by region (8-region classification) are shown as % (n/N) in Additional file 2: Table S2. Prefecture-level response rates ranged from 9.3% to 48.8%, with a median of 26.7% (interquartile range [IQR] 20.8\u0026ndash;31.9%). The geographic distribution of response rates was visualised using a choropleth map (Additional file 3: Figure S1). A funnel plot with the overall response rate (26.8%) as the reference line and 95% and 99.8% control limits, shown in Additional file 4: Figure S2, demonstrated that between-prefecture variation in response rates persisted even after accounting for statistical fluctuation associated with denominator size.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of responding and non-responding municipalities\u003c/h2\u003e \u003cp\u003eBaseline characteristics of responding (n\u0026thinsp;=\u0026thinsp;467) and non-responding municipalities were compared using external data, and standardised mean differences (SMDs) were calculated (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The Rurality Index for Japan (RIJ) and ageing rate were well balanced between groups (RIJ: 56 [IQR 34\u0026ndash;79] vs 54 [30\u0026ndash;77]; ageing rate: 36.4% [30.1\u0026ndash;40.9] vs 35.5% [29.8\u0026ndash;41.7]), suggesting no substantial imbalance. Only two variables exceeded the prespecified threshold for adequate balance: per-capita resident income (3,088,363 JPY [2,894,575\u0026ndash;3,354,483] vs 3,121,105 JPY [2,883,839\u0026ndash;3,399,850]; SMD\u0026thinsp;=\u0026thinsp;0.124) and general clinic physician density (809.64 [332.73\u0026ndash;2,154.56] vs 1,147.11 [462.28\u0026ndash;3,004.44]; SMD\u0026thinsp;=\u0026thinsp;0.104), indicating that responding municipalities tended to be slightly lower-income and have fewer clinic physicians than non-responding municipalities. All other variables, including the fiscal capacity index, total population, and hospital density, were adequately balanced (all |SMD| \u0026lt; 0.10).\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\u003eCharacteristics of respondent and non-respondent municipalities and standardised mean differences\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResponding municipalities (n\u0026thinsp;=\u0026thinsp;467)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-responding municipalities (n\u0026thinsp;=\u0026thinsp;1,274)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSMD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAging rate (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.4 (30.1\u0026ndash;40.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.5 (29.8\u0026ndash;41.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFiscal capacity index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.43 (0.28\u0026ndash;0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.44 (0.26\u0026ndash;0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePer-capita resident income (JPY)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,088,363 (2,894,575\u0026ndash;3,354,483)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,121,105 (2,883,839\u0026ndash;3,399,850)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospitals per 100,000 population [n\u0026thinsp;=\u0026thinsp;358]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.42 (5.19\u0026ndash;12.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.01 (5.23\u0026ndash;12.86) [n\u0026thinsp;=\u0026thinsp;914]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysicians per 100,000 population (general clinics) [n\u0026thinsp;=\u0026thinsp;458]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e809.64 (332.73\u0026ndash;2,154.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,147.11 (462.28\u0026ndash;3,004.44) [n\u0026thinsp;=\u0026thinsp;1,263]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRurality Index for Japan (RIJ, 1\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56 (34\u0026ndash;79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (30\u0026ndash;77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26,694 (7,901\u0026ndash;65,217)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21,078 (7,009\u0026ndash;59,728)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.086\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\u003eValues are presented as median (interquartile range [IQR]). Standardised mean differences (SMDs) were calculated using the mean and standard deviation of each variable, although central tendency and dispersion are displayed as median and IQR to reflect the skewed distributions of municipal-level variables. Bracketed n indicates the number of municipalities with non-missing data for that variable. Baseline characteristics were compared between responding and non-responding municipalities using data obtained from public governmental databases. |SMD| \u0026lt; 0.10 indicates adequate balance, 0.10\u0026ndash;0.20 minor imbalance, and \u0026gt;\u0026thinsp;0.20 meaningful imbalance.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eMain analysis\u003c/h2\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003eImplementation rates of non-mandatory preventive medicine programmes\u003c/h2\u003e \u003cp\u003eImplementation rates of non-mandatory preventive medicine programmes were calculated on both a municipality-count basis (programme penetration) and a population-weighted basis (population coverage) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eImplementation of non-mandatory preventive medicine programmes by Japanese municipalities, by disease category and USPSTF recommendation grade\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreventive programme\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSPSTF grade\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMunicipalities implementing, % (n/N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePopulation-weighted (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePopulation covered, n\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eInfectious diseases\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBV screening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.1 (430/467)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36,554,094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCV screening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91.4 (427/467)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36,863,939\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHIV screening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.1 (47/467)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18,778,645\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSyphilis screening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.6 (45/467)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18,858,018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChlamydia screening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.7 (36/467)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14,293,149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGonorrhoea screening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1 (10/467)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,876,682\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCardiovascular, cerebrovascular, and respiratory diseases\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYoung adult health check-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.3 (403/467)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30,772,901\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain/carotid screening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.1 (192/467)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13,889,906\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAAA screening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.1 (75/467)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,664,715\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePAD screening (ABI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.6 (17/467)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e369,971\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD screening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4 (16/467)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,217,836\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac screening (echocardiography)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.4 (11/467)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e114,850\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCancer screening\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThyroid screening (ultrasonography)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4 (16/467)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,138,848\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositron emission tomography (PET) screening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.2 (29/467)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,193,793\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMusculoskeletal health and functional assessment in older adults\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsteoporosis screening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.5 (320/467)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27,420,935\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrailty screening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.1 (122/467)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10,068,792\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFall prevention programme\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.1 (80/467)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,868,164\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMental health and substance use\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression/anxiety screening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.2 (15/467)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e751,605\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnhealthy drug use screening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2 (1/467)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12,782\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePerinatal and reproductive health\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFolic acid supplementation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.2 (15/467)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,895,353\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIPV screening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7 (8/467)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e303,580\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerinatal depression screening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5 (7/467)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e456,802\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\u003eThis table summarises the implementation rates of non-mandated preventive medicine programmes delivered by Japanese municipalities, stratified by disease category and USPSTF recommendation grade. Implementation was defined as provision or subsidisation of the programmes by a municipality, irrespective of eligibility criteria. Population-weighted rates reflect the proportion of the responding-municipality population living in those implementing municipalities. USPSTF, United States Preventive Services Task Force. Grade A, recommended with high certainty of substantial net benefit; Grade B, recommended with high certainty of moderate net benefit; Grade D, recommended against; Grade I, insufficient evidence; No grade, not evaluated by the USPSTF. HBV, hepatitis B virus; HCV, hepatitis C virus; HIV, human immunodeficiency virus; AAA, abdominal aortic aneurysm; PAD, peripheral artery disease; ABI, ankle\u0026ndash;brachial index; COPD, chronic obstructive pulmonary disease; IPV, intimate partner violence. Population covered was estimated using municipal population data.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eMunicipality-count-based (unweighted) implementation rates\u003c/h2\u003e \u003cp\u003eUnweighted implementation rates varied substantially across programmes, ranging from 0.2% to 92.1%. HBV and HCV screening were implemented by more than 90%, and young adult health check-ups by 86.3%, whereas unhealthy drug use screening, IPV screening, and gonorrhoea screening were rarely implemented. Osteoporosis screening was also relatively widespread (68.5%), while depression/anxiety screening remained uncommon (3.2%).\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003ePopulation-weighted implementation rates\u003c/h2\u003e \u003cp\u003ePopulation-weighted implementation rates ranged from 0.0% to 94.4%. Programmes with the highest population coverage were HCV screening (94.4%), HBV screening (93.6%), young adult health check-ups (78.8%), and osteoporosis screening (70.2%). Programmes with the lowest population coverage were unhealthy drug use screening (0.0%), cardiac screening by echocardiography (0.3%), IPV screening (0.8%), PAD screening by ankle\u0026ndash;brachial index (0.9%), and perinatal depression screening (1.2%).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eDivergence between municipality-count and population-weighted rates\u003c/h2\u003e \u003cp\u003eFor certain programmes, population-weighted coverage was substantially higher than municipality-count-based rates. HIV screening (10.1% by municipality count vs 48.1% population-weighted), syphilis screening (9.6% vs 48.3%), and chlamydia screening (7.7% vs 36.6%) were implemented by relatively few municipalities, yet population-weighted coverage was comparatively high, suggesting that these services tend to be provided in larger municipalities. Conversely, for brain/carotid screening (41.1% vs 35.6%) and young adult health check-ups (86.3% vs 78.8%), municipality-count-based penetration exceeded population-weighted coverage, suggesting that these programmes are implemented disproportionately in smaller municipalities.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eEvidence\u0026ndash;practice gaps against USPSTF recommendation grades\u003c/h2\u003e \u003cp\u003eAmong programmes classified as USPSTF Grade A or B, 10 had municipality-count-based implementation rates below 50%. In contrast, HBV and HCV screening and osteoporosis screening showed relatively high implementation rates. Among programmes classified as USPSTF Grade D, I, or no grade, three had municipality-count-based implementation rates of 10% or above, suggesting gaps between evidence and practice. To highlight these gaps explicitly, programmes with Grade A or B recommendations but implementation rates below 50% are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, and programmes with Grade D, I, or no grade but implementation rates of 10% or above are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Implementation rates on both a municipality-count and population-weighted basis, stratified by disease category and USPSTF recommendation grade, are visualised as a dumbbell plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePreventive programmes with USPSTF Grade A or B recommendation but municipality-level implementation below 50% \u003cem\u003eThis table presents programmes for which the USPSTF assigned a Grade A or B recommendation but fewer than half of responding municipalities reported implementation. Programmes are ordered by ascending implementation rate. This pattern represents a potential evidence-to-practice gap, where evidence-based recommendations have not translated into widespread municipal adoption. USPSTF, United States Preventive Services Task Force. Grade A, recommended with high certainty of substantial net benefit; Grade B, recommended with high certainty of moderate net benefit. IPV, intimate partner violence; AAA, abdominal aortic aneurysm. Implementation was defined as provision or subsidisation of the programme by a municipality, irrespective of eligibility criteria.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreventive programme\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSPSTF grade\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMunicipalities implementing, % (n/N)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnhealthy Drug Use Screening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2 (1/467)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIPV Screening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.7 (8/467)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGonorrhoea Screening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.1 (10/467)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression/Anxiety Screening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.2 (15/467)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFolic Acid Supplementation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.2 (15/467)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChlamydia Screening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.7 (36/467)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSyphilis Screening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.6 (45/467)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHIV Screening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.1 (47/467)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAAA Screening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.1 (75/467)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFall Prevention Programme\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.1 (80/467)\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 \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePreventive programmes with USPSTF Grade D, I, or No grade but municipality-level implementation of 10% or above\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreventive programme\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSPSTF grade\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMunicipalities implementing, % (n/N)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYoung Adult Check-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86.3 (403/467)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain/Carotid Screening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.1 (192/467)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrailty Screening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.1 (122/467)\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\u003eThis table presents programmes for which the USPSTF assigned a Grade D or no grade, yet 10% or more of responding municipalities reported implementation. Programmes are ordered by descending implementation rate. This pattern represents a potential evidence-to-practice gap, where municipal adoption has outpaced or diverged from current evidence-based guidance. USPSTF, United States Preventive Services Task Force. Grade D, recommended against with moderate or high certainty that the service has no net benefit or that harms outweigh benefits; No grade, not evaluated by the USPSTF. Implementation was defined as provision or subsidisation of the programme by a municipality, irrespective of eligibility criteria.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEach programme is shown as a dumbbell: the closed circle indicates the percentage of responding municipalities that reported implementation (n\u0026thinsp;=\u0026thinsp;467), and the open circle indicates the population-weighted coverage. Programmes are grouped by disease category and labelled with the USPSTF recommendation grade (A, B, D, I, or N/A for ungraded). A wider gap between the two circles indicates greater divergence between municipality-level penetration and population-level coverage; population-weighted coverage exceeding municipality-count rates typically reflects concentration of the service in larger municipalities (as observed for HIV, syphilis, and chlamydia screening), whereas the reverse pattern suggests that the programme is disproportionately implemented in smaller municipalities. USPSTF, US Preventive Services Task Force; HBV, hepatitis B virus; HCV, hepatitis C virus; HIV, human immunodeficiency virus; AAA, abdominal aortic aneurysm; PAD, peripheral artery disease; ABI, ankle\u0026ndash;brachial index; US, ultrasonography; COPD, chronic obstructive pulmonary disease; PET, positron emission tomography; IPV, intimate partner violence; N/A, not evaluated by the USPSTF.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eEvidence\u0026ndash;Practice Alignment (EPA) score\u003c/h2\u003e \u003cp\u003eEPA scores were calculated for each municipality and visualised at the prefectural level using a choropleth map (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Scores ranged from \u0026minus;\u0026thinsp;4 to 1, demonstrating marked heterogeneity in evidence\u0026ndash;practice alignment across regions. Prefectures with lower EPA scores, indicating relatively greater implementation of non-recommended or insufficiently evidenced programmes, were widely distributed across the country. In contrast, prefectures with higher scores, reflecting better alignment with evidence-based recommendations, were fewer and appeared to be geographically clustered. Overall, no clear geographic gradient was observed nationwide, suggesting that variation in EPA scores is not solely explained by regional location but may reflect local policy decisions and implementation practices.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eChoropleth map displaying the mean EPA score of responding municipalities within each prefecture. The EPA score for each municipality was calculated as a weighted sum of implementation status across all non-mandatory preventive medicine programmes, with weights assigned a priori to reflect the strength of evidence conveyed by each USPSTF grade: A\u0026thinsp;=\u0026thinsp;+\u0026thinsp;2, B\u0026thinsp;=\u0026thinsp;+\u0026thinsp;1, D\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2, and I or ungraded\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1. Higher scores (blue) indicate greater implementation of recommended services alongside appropriate restraint regarding non-recommended services; lower scores (red) reflect the inverse. Prefectural scores ranged from \u0026minus;\u0026thinsp;4 to 1. A diverging colour palette centred at zero is used to distinguish prefectures above and below the neutral reference value.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eWhat this study adds\u003c/h2\u003e \u003cp\u003eIn this nationwide descriptive cross-sectional survey of Japanese municipalities, we provide the first nationwide municipal-level mapping of implementation patterns for non-mandatory preventive medicine programmes, revealing substantial heterogeneity across domains. Among 1,741 municipalities contacted, 467 returned valid responses (response rate 26.8%). We assessed implementation using two complementary measures: the proportion of municipalities that provided or subsidised each programme and the corresponding population coverage. When we compared municipal practices with USPSTF evidence grades, we found a gap between evidence and implementation. Notably, several programmes graded A or B by the USPSTF were implemented by fewer than half of municipalities\u0026mdash;for example, folic acid supplementation (3.2%), syphilis screening (9.6%), chlamydia screening (7.7%), depression/anxiety screening (3.2%), and abdominal aortic aneurysm screening (16.1%). Conversely, a number of programmes graded D or \u0026ldquo;no grade\u0026rdquo; were implemented at appreciable levels, including brain/carotid screening (41.1%), young adult health check-ups (86.3%; no grade), and frailty screening (26.1%; no grade).\u003c/p\u003e \u003cp\u003eA further insight was the divergence between municipality-count and population-weighted implementation. Even when relatively few municipalities offered certain STI/HIV services, population-weighted coverage was substantially higher (e.g., HIV screening 10.1% by municipality count vs 48.1% population-weighted), suggesting concentration of such services in larger municipalities.\u003c/p\u003e \u003cp\u003eTo visualise municipality-level alignment with evidence-based prevention, we constructed an Evidence\u0026ndash;Practice Alignment (EPA) score that summarises implementation patterns across domains.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eComparison with prior work and interpretation\u003c/h2\u003e \u003cp\u003eInternationally, the gap between evidence-based recommendations and real-world adoption is well recognised; simply producing evidence or guidelines rarely ensures consistent uptake without active implementation strategies [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our context, we interpret the under-implementation of multiple A/B-graded services as potentially reflecting not only operational constraints but also limited and uneven dissemination of evidence-based prioritisation into municipal decision-making. Specifically, in Japan it is difficult to argue that a cross-cutting, USPSTF-like centralisation of evidence-graded preventive recommendations is sufficiently institutionalised and disseminated; consequently, municipalities may be less likely to consistently reference graded evidence when selecting preventive medicine programmes. This is a plausible hypothesis rather than a causal conclusion, because our survey did not directly measure municipal decision processes. Nonetheless, it offers a testable explanation for why some highly recommended services remain uncommon while other programmes persist despite weak evidence or potential net harm.\u003c/p\u003e \u003cp\u003eThe latter pattern is consistent with concerns about overdiagnosis and low-value screening. Large-scale screening can increase detection of indolent disease without proportional mortality benefit, as illustrated by thyroid cancer overdiagnosis associated with widespread screening in Korea [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and by evidence that favourable shifts in breast tumour size distributions may be driven largely by additional detection of small tumours with substantial overdiagnosis [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. At the system level, these dynamics align with the broader challenge of reducing \u0026ldquo;waste\u0026rdquo; in healthcare by limiting low-value services rather than cutting beneficial care [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Limitations","content":"\u003cp\u003eThis study has several limitations. First, the response rate was modest (26.8%), and non-response bias is possible. Responding municipalities tended to have lower per-capita resident income and fewer general-clinic physicians per 100,000 population; accordingly, results may over-represent municipalities with lower per-capita income and fewer clinic physicians, and under-represent urban or higher-resource municipalities.\u003c/p\u003e \u003cp\u003eSecond, \u0026ldquo;implementation\u0026rdquo; was defined as municipal provision or subsidisation, and we could not fully capture eligibility criteria, intensity, quality assurance, or participation rates. Therefore, implementation does not necessarily indicate guideline-concordant delivery at the individual level.\u003c/p\u003e \u003cp\u003eThird, USPSTF recommendations are stratified by age, sex, and risk, and are derived from evidence bases reflecting the epidemiological context of the United States. Applying these grades as a benchmark for Japanese municipal programmes involves two important caveats: population-level policy classification does not map directly onto individual-level eligibility criteria, and the prevalence of relevant conditions diverges between the two countries in ways that affect the appropriateness of universal screening\u0026mdash;in some cases overstating and in others understating the urgency of implementation. These discordances limit the direct applicability of USPSTF grades, and future studies should incorporate domestically developed evidence-graded frameworks where available. Furthermore, USPSTF grades reflect recommendations current as of March 2025 and may have been updated subsequently; findings should be interpreted with reference to the grades in effect at the time of the survey.\u003c/p\u003e \u003cp\u003eFinally, as a cross-sectional descriptive study, we cannot infer causal determinants of adoption or quantify downstream benefits, harms, or cost consequences.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn a nationwide municipal survey, we observed substantial variation and clear evidence\u0026ndash;practice gaps in Japan\u0026rsquo;s non-mandatory preventive medicine programmes. Under-implementation of multiple A/B-graded services coexisted with notable implementation of Grade D or ungraded programmes. Population-weighted results further suggested that some evidence-aligned services are concentrated in larger municipalities, raising potential equity concerns.\u003c/p\u003e \u003cp\u003eOur findings support a dual agenda. First, there is a need to strengthen dissemination and implementation support for high-value preventive medicine services, including evaluation of whether limited awareness or uptake of graded evidence contributes to municipal choices in Japan. Second, low-value screening programmes\u0026mdash;where harms may outweigh benefits\u0026mdash;warrant critical reassessment and, where appropriate, active de-implementation [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. A structural gap underlying these findings is the absence of a nationally authoritative, evidence-grading body for preventive medicine in Japan analogous to the USPSTF. Establishing such an institution would provide municipalities with a unified and regularly updated reference standard, and represents a necessary policy priority for reducing evidence\u0026ndash;practice gaps in Japanese preventive medicine. Future research should examine municipal decision-making to identify modifiable barriers to evidence-aligned prevention.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAAA: Abdominal aortic aneurysm; ABI: Ankle\u0026ndash;brachial index; COPD: Chronic obstructive pulmonary disease; EPA: Evidence\u0026ndash;Practice Alignment; HBV: Hepatitis B virus; HCV: Hepatitis C virus; HIV: Human immunodeficiency virus; IQR: Interquartile range; IPV: Intimate partner violence; MHLW: Ministry of Health, Labour and Welfare; PAD: Peripheral artery disease; PET: Positron emission tomography; RIJ: Rurality Index for Japan; SMD: Standardised mean difference; USPSTF: US Preventive Services Task Force.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThis study was assessed by the institutional review board office of the National Hospital Organization Nagasaki Medical Center and determined not to require formal ethics committee review, as it involved no personal identifying information and constituted a descriptive survey of municipal administrative entities rather than individual human subjects, in accordance with the \u0026ldquo;Ethical Guidelines for Medical and Health Research Involving Human Subjects\u0026rdquo; (Ministry of Health, Labour and Welfare / Ministry of Education, Culture, Sports, Science and Technology, Japan, 2021). All participating municipalities were informed of the study objectives, the intended use of data, and the assurance that individual responses would not be used for evaluative purposes; return of the completed questionnaire was accepted as indication of consent to participate. Data were used exclusively for research purposes.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe datasets generated and analysed during the current study are not publicly available due to the risk of identifying individual municipalities but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eDr. Sakata is employed in the Department of Neurodevelopmental Medicine, Nagoya City University Graduate School of Medical Sciences, which is an endowment department supported by the City of Nagoya. He has received a personal fee from SONY and Daiichi-Sankyo outside the submitted work.\u003c/p\u003e\n\u003cp\u003eDr. Tsugawa receives funding from the National Institutes of Health (NIH)/National Institute on Aging (R01AG068633 and R01AG082991), NIH/National Institute on Minority Health and Health Disparities (R01MD013913), and Gregory Annenberg Weingarten, GRoW @ Annenberg for work not related to this study, and serves on the board of directors of M3, Inc.\u003c/p\u003e\n\u003cp\u003eThe other authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis study was supported by EVIDENCE STUDIO (a general incorporated association in Japan). The funder had no involvement in study design, data collection, analysis, interpretation of data, writing of the manuscript, or the decision to submit the manuscript for publication.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026rsquo; contributions\u003c/h2\u003e\n\u003cp\u003eHM conceived and designed the study, led data collection, performed statistical analyses, and drafted the manuscript. KS and KMur planned and conducted the statistical analyses. KMi and AS contributed to study design, interpretation of findings, and critical revision of the manuscript. MS and KMuk contributed to study design, critical revision of the manuscript, and supervised the study. MF contributed to critical revision of the manuscript. KH and YT contributed to interpretation of findings and critical revision of the manuscript. All authors read and approved the final version of the manuscript and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors thank Knowledge Database Co., Ltd. for their assistance with the internet-based questionnaire survey.\u003c/p\u003e\n\u003cp\u003eThis work was conducted as part of the JPPSTF Project, which was commissioned by EVIDENCE STUDIO, a general incorporated association whose purposes include optimising public healthcare expenditures in Japan. The contract was formally established with Kurume University, with which Kei Mukohara, the chair of the JPPSTF, is affiliated.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJamison DT, Summers LH, Alleyne G, Arrow KJ, Berkley S, Binagwaho A et al (2013) Global health 2035: a world converging within a generation. Lancet 382:1898\u0026ndash;1955\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHanson K, Brikci N, Erlangga D, Alebachew A, De Allegri M, Balabanova D et al (2022) The Lancet Global Health Commission on financing primary health care: putting people at the centre. Lancet Glob Health 10:e715\u0026ndash;e772\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrganisation for Economic Co-operation and Development (2019) Japan: a healthier tomorrow. OECD, Paris Cedex, France\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinistry of Health, Labour and Welfare (Japan) Specific Health Check-ups and Specific Health Guidance. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mhlw.go.jp/stf/seisakunitsuite/bunya/0000161103.html\u003c/span\u003e\u003cspan address=\"https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/0000161103.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 2 Mar 2026\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Cancer Center, Cancer Information Service Cancer screening implementation and process indicators by municipality. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ganjoho.jp/reg_stat/statistics/stat/screening/dl_screening.html\u003c/span\u003e\u003cspan address=\"https://ganjoho.jp/reg_stat/statistics/stat/screening/dl_screening.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 27 Feb 2026\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinistry of Health, Labour and Welfare (Japan). Overview of the FY2022 Regional Health and Health Promotion Project Report (2024) \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mhlw.go.jp/toukei/saikin/hw/c-hoken/22/dl/R04gaikyo.pdf\u003c/span\u003e\u003cspan address=\"https://www.mhlw.go.jp/toukei/saikin/hw/c-hoken/22/dl/R04gaikyo.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 5 May 2026\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eU.S. Preventive Services Task Force About the USPSTF. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.uspreventiveservicestaskforce.org/uspstf/about-uspstf\u003c/span\u003e\u003cspan address=\"https://www.uspreventiveservicestaskforce.org/uspstf/about-uspstf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 27 Feb 2026\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMangione CM, Nicholson W, Davidson KW (2022) Addressing gaps in research to reduce disparities and advance health equity: the USPSTF incorporation of the NASEM taxonomy on closing evidence gaps in clinical prevention. JAMA 328(18):1803\u0026ndash;1804\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStatistics Bureau of Japan, Ministry of Internal Affairs and Communications (Japan). Statistical Observations of Municipalities (2025) \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.stat.go.jp/data/s-sugata/pdf/all_shi.pdf\u003c/span\u003e\u003cspan address=\"https://www.stat.go.jp/data/s-sugata/pdf/all_shi.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 2 Mar 2026\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ee-Stat (Portal Site of Official Statistics of Japan). System of Social and Demographic Statistics: Statistical Observations of Municipalities 2025 (Basic Data) (2025) \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.e-stat.go.jp/en/stat-search/files?tclass1=000001229546\u0026amp;toukei=00200502\u0026amp;tstat=000001229545\u003c/span\u003e\u003cspan address=\"https://www.e-stat.go.jp/en/stat-search/files?tclass1=000001229546\u0026amp;toukei=00200502\u0026amp;tstat=000001229545\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 2 Mar 2026\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeospatial Information Authority of Japan (GSI). National land area survey by prefecture and municipality (as of 1 July 2025) (2025) \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsi.go.jp/KOKUJYOHO/MENCHO/backnumber/GSI-menseki20250701.pdf\u003c/span\u003e\u003cspan address=\"https://www.gsi.go.jp/KOKUJYOHO/MENCHO/backnumber/GSI-menseki20250701.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 2 Mar 2026\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaneko M, Ikeda T, Inoue M, Sugiyama K, Saito M, Ohta R et al (2023) Development and validation of a rurality index for healthcare research in Japan: a modified Delphi study. BMJ Open 13:e068800\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinistry of Health, Labour and Welfare (Japan). Survey of Medical Institutions (Government Statistics Code: 00450021) (2025) \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.e-stat.go.jp/en/statistics/00450021\u003c/span\u003e\u003cspan address=\"https://www.e-stat.go.jp/en/statistics/00450021\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 2 Mar 2026\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAustin PC (2009) Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Stat Med 28:3083\u0026ndash;3107\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Z, Kim HJ, Lonjon G, Zhu Y, written on behalf of AME Big-Data Clinical Trial Collaborative Group (2019) Balance diagnostics after propensity score matching. Ann Transl Med 7:16\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evon Elm E, Altman DG, Egger M, Pocock SJ, G\u0026oslash;tzsche PC, Vandenbroucke JP et al (2007) The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. PLoS Med 4:e296\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEysenbach G (2004) Improving the quality of Web surveys: the Checklist for Reporting Results of Internet E-Surveys (CHERRIES). J Med Internet Res 6:e34\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrol R, Grimshaw J (2003) From best evidence to best practice: effective implementation of change in patients\u0026rsquo; care. Lancet 362:1225\u0026ndash;1230\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhn HS, Kim HJ, Welch HG (2014) Korea\u0026rsquo;s thyroid-cancer epidemic\u0026mdash;screening and overdiagnosis. N Engl J Med 371:1765\u0026ndash;1767\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWelch HG, Prorok PC, O\u0026rsquo;Malley AJ, Kramer BS (2016) Breast-cancer tumor size, overdiagnosis, and mammography screening effectiveness. N Engl J Med 375:1438\u0026ndash;1447\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerwick DM, Hackbarth AD (2012) Eliminating waste in US health care. JAMA 307:1513\u0026ndash;1516\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiven DJ, Mrklas KJ, Holodinsky JK, Straus SE, Hemmelgarn BR, Jeffs LP et al (2015) Towards understanding the de-adoption of low-value clinical practices: a scoping review. BMC Med 13:255\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Nagasaki Medical Center","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":"preventive medicine, municipal health policy, evidence–practice gap, Japan, cross-sectional survey, non-mandatory screening, evidence-based prevention, Health policy, Implementation research, Municipal health services, USPSTF, Screening, Cross-sectional study, Overdiagnosis","lastPublishedDoi":"10.21203/rs.3.rs-9674474/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9674474/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn Japan, much preventive medicine outside mandated national programmes is left to municipal discretion, yet the nationwide alignment between these locally administered programmes and graded evidence remains unexamined. We mapped implementation of non-mandatory preventive medicine programmes across Japanese municipalities and quantified evidence\u0026ndash;practice gaps.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA nationwide cross-sectional survey was administered to all 1,741 Japanese municipalities between March and August 2025. Implementation (excluding the five mandated cancer screenings under the Health Promotion Act) was defined as municipal provision or subsidisation and calculated on municipality-count and population-weighted bases. Each programme was benchmarked against USPSTF recommendation grades, and an Evidence\u0026ndash;Practice Alignment (EPA) score was derived for each municipality (weights: A\u0026thinsp;=\u0026thinsp;+\u0026thinsp;2, B\u0026thinsp;=\u0026thinsp;+\u0026thinsp;1, D\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2, I or ungraded\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1). Non-response bias was assessed by comparing responding and non-responding municipalities using standardised mean differences derived from national statistical databases.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eValid responses were received from 467 municipalities (response rate, 26.8%) across all 47 prefectures. Implementation rates ranged from 0.2% to 92.1% (municipality-count) and 0.0% to 94.4% (population-weighted). Ten Grade A/B programmes had implementation below 50%, including folic acid supplementation (3.2%), syphilis screening (9.6%), and abdominal aortic aneurysm screening (16.1%). Conversely, hepatitis B/C (92.1%, 91.4%) and osteoporosis screening (68.5%) were widely implemented. Several Grade D or ungraded programmes showed appreciable uptake, notably young adult health check-ups (86.3%; ungraded), brain/carotid screening (41.1%; Grade D), and frailty screening (26.1%; ungraded). Population-weighted coverage exceeded municipality-count rates for HIV (48.1% vs 10.1%) and syphilis (48.3% vs 9.6%) screening, indicating concentration in larger municipalities. Prefectural EPA scores ranged from \u0026minus;\u0026thinsp;4 to 1, with heterogeneity and no clear geographic gradient.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eSubstantial evidence\u0026ndash;practice gaps and equity concerns coexist in Japan\u0026rsquo;s municipal preventive medicine programmes. Our findings support strengthened dissemination of graded evidence to municipal decision-makers and critical reassessment of low-value screening.\u003c/p\u003e","manuscriptTitle":"Nationwide Implementation of Non-Mandatory Preventive Medicine Programmes in Japanese Municipalities: A Descriptive Cross-Sectional Survey and Evidence–Practice Gap Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-12 07:02:41","doi":"10.21203/rs.3.rs-9674474/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":"3b93793d-2326-4992-9d44-f9a00128ac5e","owner":[],"postedDate":"May 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":67891458,"name":"Preventive Medicine"},{"id":67891459,"name":"Health Policy"}],"tags":[],"updatedAt":"2026-05-12T07:02:41+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-12 07:02:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9674474","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9674474","identity":"rs-9674474","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}