Health Indifference and the Inverse Association Between Symptom Burden and Healthcare Use: A Longitudinal Cohort Study from the Japan Society and New Tobacco/Infodemic Survey

Health Indifference and the Inverse Association Between Symptom Burden and Healthcare Use: A Longitudinal Cohort Study from the Japan Society and New Tobacco/Infodemic Survey | 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 Health Indifference and the Inverse Association Between Symptom Burden and Healthcare Use: A Longitudinal Cohort Study from the Japan Society and New Tobacco/Infodemic Survey Hikaru Oba¹, Tomohiro Mitoma², Takahiro Tabuchi³, Jota Maki¹ This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7584212/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Approximately 30% of adults delay or avoid healthcare despite experiencing symptoms, leading to preventable morbidities and substantial economic losses. Understanding the psychological mechanisms, particularly health indifference, that drive this paradox is critical for developing effective interventions to address this issue. Methods This longitudinal cohort study analyzed data from the Japan Society and New Tobacco/Infodemic Survey, a nationwide online panel survey conducted annually from February 2021 to February 2023. Among the 26,000 baseline participants from all 47 Japanese prefectures, 15,519 adults completed all three survey waves. Health indifference was measured at wave 2 (February 2022) using the validated 13-item Health Interest Scale (score range, 13–52), analyzed as a continuous standardized variable. Primary outcome was self-reported all-cause hospitalization during the 12 months preceding wave 3. Secondary outcomes included 14 moderate-to-severe physical symptoms and 14 physician-diagnosed chronic diseases. Results Among 15,519 participants, those with higher health indifference exhibited a paradoxical pattern: despite reporting more moderate-to-severe symptoms (including fever, chest pain, and dyspnea), they had significantly lower healthcare utilization. Each standard deviation increase in health indifference was associated with 18% lower hospitalization (adjusted risk ratio, 0.82; 95% CI, 0.77–0.88) and fewer diagnoses of chronic conditions typically detected through screening. This inverse relationship between symptom burden and healthcare utilization was consistent across all analyses. Conclusions Health indifference predicts paradoxical dissociation, wherein individuals experience and report more symptoms but utilize less healthcare. Lower chronic disease diagnoses likely reflect detection bias from avoided screening rather than improved health. Health indifference Healthcare utilization Symptom burden Healthcare avoidance Longitudinal cohort study Japan Preventive care Detection bias Health behavior Screening participation Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Approximately 30% of adults worldwide delay or avoid healthcare despite experiencing symptoms that warrant medical attention [ 1 , 2 ]. This paradox results in preventable mortality [ 3 ], disease progression [ 4 ], and annual economic losses exceeding $ 100 billion [ 5 ]. While structural barriers such as financial constraints [ 6 ], geographic distance [ 7 ], and provider shortages [ 8 ] explain underutilization [ 9 ], 15–25% of individuals in universal healthcare systems avoid necessary care despite minimal access barriers [ 10 ]. This persistent underutilization across diverse healthcare contexts suggests that psychological factors constitute independent, yet poorly understood, determinants of healthcare avoidance that current health system interventions fail to address. Health indifference is characterized by low health awareness [ 11 ], diminished health management motivation [ 12 ] and reduced health valuation [ 13 ]. This concept represents a previously underexamined psychological barrier distinct from established healthcare utilization determinants. Unlike health literacy deficits [ 14 ], which educational interventions successfully address [ 15 ], health indifference involves motivational disengagement resistant to informational approaches. While health consciousness [ 16 ] reflects active monitoring among engaged individuals, health indifference captures fundamental disinterest among those who avoid healthcare, despite symptoms. This differs from clinical apathy in depression [ 17 ], representing a stable trait in otherwise healthy populations [ 18 ]. Dominant behavioral theories, such as the Health Belief Model [ 19 ] and Theory of Planned Behavior [ 20 ], assume rational actors who value health and respond predictably to barrier removal. Health indifference violates this core assumption, potentially explaining why educational campaigns [ 21 ] and access improvements [ 22 ] systematically fail to reach populations that remain persistently disengaged. The validation of the Health Interest Scale [ 23 ] finally enables a rigorous empirical investigation of this construct. No longitudinal studies have examined whether health indifference predicts the paradoxical pattern of increased symptoms with decreased healthcare utilization. Existing cross-sectional studies [ 24 , 25 ] cannot establish causality or temporal precedence. Research on health consciousness [ 26 ] and beliefs [ 27 ] examines positive motivators rather than disengagement barriers. Studies on systems with structural barriers [ 28 ] conflict with psychological and access factors. Individuals experience symptoms but systematically avoid care due to psychological rather than structural factors. This study represents the first longitudinal examination of whether baseline health indifference predicts subsequent healthcare utilization patterns. We hypothesized that health-indifferent individuals would report more symptoms yet utilize less healthcare, independent of access and socioeconomic factors. The inverse symptom-utilization relationship would identify a high-risk population currently invisible to healthcare systems. Methods Study Design and Data Source We analyzed data from the Japan Society and New Tobacco/Infodemic Survey (JASTIS), a nationwide longitudinal online panel survey conducted annually from 2021 to 2023 [ 29 ]. JASTIS recruits participants from a commercial research panel of over 2.2 million registered monitors across Japan, using quota sampling stratified by 5-year age bands, sex, and prefecture to approximate the Japanese census distribution. Participants received credit points as compensation. The study protocol was approved by the Okayama University Hospital Review Board (No. 2507-014) with waiver of informed consent for retrospective analysis of de-identified data. Participants The study included three annual survey waves conducted in February 2021 (baseline), February 2022 (exposure assessment), and February 2023 (outcome assessment). Of the 26,000 individuals who participated in the baseline survey, we included adults aged 16 years or older who completed all three waves. The online survey system required responses to all questions, minimizing item-level missing data within each wave. Participants with missing information on exposure or outcomes or who provided invalid responses were excluded. Exposure Assessment Health indifference was measured in the second wave using the 13-item Health Interest Scale, which has been previously validated in Japanese populations [ 23 ]. The scale comprises eight items assessing health consciousness and five items assessing health-neglecting attitudes, each rated on a 4-point Likert scale from strongly disagree to strongly agree. After reverse-coding negative items, we summed responses to create scores ranging from 13 to 52, with higher scores indicating greater health indifference. We analyzed health indifference as a continuous standardized variable (z-score) to estimate per-standard-deviation effects. Outcome Measures The primary outcome was all-cause hospitalization during the 12 months preceding the third wave assessment, ascertained through self-report. Secondary outcomes included 14 physician-diagnosed chronic diseases (hypertension, diabetes mellitus, dyslipidemia, asthma, periodontitis, dental caries, angina or myocardial infarction, stroke, chronic obstructive pulmonary disease, chronic kidney disease, chronic liver disease, cancer, chronic pain, and depression), 14 physical symptoms rated as moderate-to-severe on 5-point scales, and healthcare utilization measures including COVID-19 and influenza vaccination uptake. Covariates We selected baseline covariates based on the established determinants of healthcare utilization in Japan [ 30 , 31 ]. Demographic variables included age and sex. Socioeconomic factors comprised education level (categorized as low if less than high school completion) and annual household income (categorized as low if less than 4 million Japanese yen). Social factors included living arrangement (alone versus with others) and perceived social support (lacking if participants disagreed with having someone to consult when troubled). Health behaviors encompassed current smoking status, alcohol consumption (heavy drinking defined as consuming alcohol at least 5 days per week with 3 or more drinks per occasion), physical activity level (low if walking 2 days or fewer per week with no vigorous exercise), and health checkup attendance in the past year. Health status variables included body mass index calculated from self-reported height and weight, and self-rated health categorized as poor if rated as poor or very poor on a 5-point scale. The details of the questionnaire are provided in Additional File 1. Statistical Analysis We calculated that 13,476 participants would provide 80% power to detect a relative risk of 0.80 for the primary outcome with a two-sided alpha of 0.05, assuming a 5% baseline hospitalization rate [ 32 ]. To mitigate potential attrition bias arising from participant dropout between waves, we employed inverse probability of censoring weights (IPCW) [ 33 ]. This method adjusts for the selective loss of participants by upweighting individuals who remained in the cohort but shared baseline characteristics with those who were lost to follow-up, thereby aligning the analytical cohort's composition with that of the original baseline population. We computed the stabilized IPCW from a logistic regression model predicting complete follow-up, trimming weights at the 1st and 99th percentiles [ 34 ]. To handle missing data in baseline covariates while maximizing statistical power and minimizing selection bias, we performed multiple imputations using chained equations (MICE) [ 35 ]. This approach assumes that data are missing at random (MAR) [ 36 ] and generates multiple plausible complete datasets by inputting missing values based on the observed relationships between variables, with the results subsequently pooled according to Rubin's rules [ 37 ]. To directly estimate adjusted relative risks (aRR) for binary outcomes, we used modified Poisson regression with robust standard errors [ 38 ], incorporating IPCW weights through variance weighting. This approach is preferred over logistic regression for cohort studies when the outcome is common, as it avoids the overestimation of effects inherent in the odds ratios. To provide absolute effect measures, we calculated standardized risk differences using g-computation [ 39 ], expressing the results as percentage points with 95% confidence intervals (CIs) obtained via the delta method. For the primary hospitalization outcome, we examined the dose-response relationship across the full range of health indifference scores (13–52) using restricted cubic splines with three degrees of freedom [ 40 ]. For secondary outcomes, we applied the Benjamini-Hochberg procedure to control the false discovery rate at 0.05. [ 41 ]. We conducted four prespecified sensitivity analyses to assess the robustness of our primary findings to potential biases from missing data, alternative exposure specifications, and confounding factors. First, we performed a complete case analysis [ 42 ] to examine whether the distribution changed substantially because of attrition. Second, we conducted a categorical exposure analysis using quartiles to ascertain whether there was a monotonic trend by exposure level. Third, we calculated the E-value to quantify the potential impact of unmeasured confounding on our primary findings [ 43 ]. The E-value represents the minimum strength of association on the risk ratio scale that an unmeasured confounder would need to have with both the exposure and the outcome to fully explain the observed association, thus serving as a sensitivity analysis for residual confounding. Finally, to generate hypotheses regarding the underlying data-generating structure and potential mechanistic pathways, we applied a suite of four complementary causal discovery algorithms (DirectLiNGAM [ 44 ], GOLEM [ 45 ], DAGMA [ 46 ], and CORL [ 47 ]). These algorithms attempt to map out plausible cause-and-effect relationships from the data, with the critical rule that an event in the future cannot temporally cause something in the past. It is important to note that these analyses were exploratory and did not establish causality from observational data. Rather, their purpose is to identify plausible directed acyclic graphs (DAGs) that are consistent with the observed data and pre-specified temporal constraints. We used ten-fold cross-validation and retained the edges that appeared in at least six folds. Although these exploratory analyses cannot establish causality from observational data, they can infer relationships between variables. Details of these analyses are provided in Additional File 2. All analyses were performed using Python version 3.10.0 (Python Software Foundation, Wilmington, DE, USA). Results Of the 26,000 individuals enrolled in the baseline survey (February 2021), 15,519 (59.7%) completed all three annual waves and comprised the analytical cohort for this study. The details of recruitment are described in Figure S1 (Additional file 3).Attrition occurred primarily between Waves 1 and 2 (23.4%), with an additional 15.6% loss between Waves 2 and 3. Participants lost to follow-up were younger (mean age 48.2 vs. 50.0 years), less likely to have attended health checkups (51.3% vs. 60.8%), and more likely to smoke (28.6% vs. 19.6%, all P < .001). After applying the inverse probability of censoring weights, the weighted sample characteristics approximated the baseline population. The analytic cohort had a mean (SD) age of 50.0 (16.1) years, with 8,230 (51.0%) males. The Health Interest Scale demonstrated good internal consistency (Cronbach α = 0.828), with scores approximately normally distributed (mean [SD], 29.1 [6.3]; median, 29; Fig. 1 ). Using the median split, 8,033 participants (51.8%) were classified as having high health indifference, with mean scores of 33.7 (SD 4.1) versus 23.7 (SD 3.8) in the low indifference group (standardized mean difference [SMD], 2.54; P < .001). Participants with high health indifference differed markedly from those with low indifference in multiple domains (Table 1 ). They were, on average, 10 years younger (45.4 vs. 55.4 years; standardized mean difference [SMD], 0.66), more likely to be male (55.1% vs. 46.2%; SMD, 0.18), and had a higher rate of modifiable risk factors, including current smoking (23.8% vs. 14.6%; SMD, 0.24), absence of recent health checkups (44.9% vs. 32.4%; SMD, 0.26), and low physical activity (26.5% vs. 18.6%; SMD, 0.19). Social disadvantage was also more common, with 36.3% versus 23.3% of participants reporting a lack of social support (SMD, 0.28) and 21.9% versus 17.0% of participants living alone (SMD, 0.12). Multivariate analysis identified several baseline predictors of subsequent health indifference. Current smoking showed the strongest association (aRR, 1.31; 95% CI, 1.27–1.36), followed by absence of health checkups (aRR, 1.21; 95% CI, 1.17–1.25) and low physical activity (aRR, 1.21; 95% CI, 1.17–1.25). The complete results are presented in Table S1 (Additional file 4). Table 1 Baseline Characteristics by Health Index Categoryᵃ Variable All Participants (n = 15,519) Low HI (n = 7,486) High HI (n = 8,033) SMD P Value Demographic Characteristics HI score (2022), mean (SD) 29.1 (6.3) 23.7 (3.8) 33.7 (4.1) 2.54 < .001 Age (2021), mean (SD), y 50.0 (16.1) 55.4 (15.7) 45.4 (14.9) 0.66 < .001 Male sex, No. (%) 8,230 (51.0) 3,618 (46.2) 4,612 (55.1) 0.18 < .001 Social Determinants Low education (< high school), No. (%) 1,251 (8.3) 533 (7.2) 718 (9.3) 0.07 < .001 Low household income, No. (%) 1,348 (8.6) 589 (7.8) 759 (9.4) 0.06 < .001 Living alone, No. (%) 3,005 (19.6) 1,258 (17.0) 1,747 (21.9) 0.12 < .001 Lack of social support, No. (%) 4,549 (30.4) 1,688 (23.3) 2,861 (36.3) 0.28 < .001 Health Behaviors Current smoking, No. (%) 3,119 (19.6) 1,109 (14.6) 2,010 (23.8) 0.24 < .001 Heavy drinking, No. (%) 1,200 (10.6) 558 (10.1) 642 (11.1) 0.04 .032 Low physical activity, No. (%) 3,562 (22.9) 1,403 (18.6) 2,159 (26.5) 0.19 < .001 No health checkup, No. (%) 5,838 (39.2) 2,340 (32.4) 3,498 (44.9) 0.26 < .001 Poor self-rated health, No. (%) 1,912 (11.9) 799 (10.2) 1,113 (13.4) 0.10 < .001 BMI (2021), mean (SD), kg/m² 22.4 (3.9) 22.2 (3.5) 22.6 (4.1) 0.12 < .001 Health Outcomes (2023) Hospitalization past year, No. (%) 899 (5.5) 523 (6.7) 376 (4.5) 0.09 < .001 COVID-19 infection, No. (%) 2,632 (18.3) 1,101 (16.1) 1,531 (20.3) 0.11 < .001 COVID-19 vaccination, No. (%) 13,788 (88.0) 6,765 (89.6) 7,023 (86.7) 0.09 < .001 Influenza vaccination, No. (%) 6,139 (37.9) 3,533 (45.6) 2,606 (31.4) 0.29 < .001 Chronic Conditions (2023) Hypertension, No. (%) 3,935 (22.6) 2,175 (26.3) 1,760 (19.5) 0.16 < .001 Diabetes mellitus, No. (%) 1,242 (7.2) 675 (8.1) 567 (6.4) 0.07 < .001 Dyslipidemia, No. (%) 2,618 (15.4) 1,478 (18.3) 1,140 (12.9) 0.15 < .001 Asthma, No. (%) 525 (3.4) 251 (3.4) 274 (3.4) 0.00 .88 Periodontitis, No. (%) 2,441 (14.7) 1,226 (15.5) 1,215 (14.1) 0.04 .034 Dental caries, No. (%) 1,921 (12.1) 796 (10.4) 1,125 (13.6) 0.10 < .001 Angina/MI, No. (%) 368 (2.2) 183 (2.2) 185 (2.2) 0.00 .60 Stroke, No. (%) 222 (1.4) 87 (1.1) 135 (1.7) 0.05 .008 COPD, No. (%) 184 (1.2) 75 (0.9) 109 (1.4) 0.04 .049 CKD, No. (%) 277 (1.7) 143 (1.8) 134 (1.7) 0.01 .28 Chronic liver disease, No. (%) 203 (1.3) 84 (1.1) 119 (1.4) 0.03 .058 Cancer, No. (%) 391 (2.4) 226 (2.8) 165 (2.0) 0.06 < .001 Chronic pain, No. (%) 2,487 (15.4) 1,268 (16.5) 1,219 (14.5) 0.05 .003 Depression, No. (%) 683 (4.5) 241 (3.4) 442 (5.5) 0.10 < .001 Symptoms (2023) GI discomfort, No. (%) 3,928 (26.1) 1,607 (22.1) 2,321 (29.5) 0.17 < .001 Back pain, No. (%) 5,255 (33.8) 2,414 (32.3) 2,841 (35.2) 0.06 < .001 Joint pain, No. (%) 4,284 (27.2) 2,022 (26.5) 2,262 (27.7) 0.03 .11 Headache, No. (%) 3,067 (21.3) 1,166 (17.0) 1,901 (24.9) 0.19 < .001 Chest pain, No. (%) 1,352 (9.3) 422 (5.9) 930 (12.2) 0.22 < .001 Dyspnea, No. (%) 1,785 (11.8) 646 (8.7) 1,139 (14.4) 0.18 < .001 Dizziness, No. (%) 1,781 (12.1) 659 (9.3) 1,122 (14.5) 0.16 < .001 Sleep disturbance, No. (%) 3,959 (25.9) 1,712 (23.2) 2,247 (28.2) 0.12 < .001 Memory disorder, No. (%) 1,592 (10.4) 611 (8.1) 981 (12.4) 0.14 < .001 Concentration decline, No. (%) 2,374 (15.8) 900 (12.3) 1,474 (18.8) 0.18 < .001 Reduced libido, No. (%) 2,202 (13.6) 985 (12.4) 1,217 (14.7) 0.07 < .001 Fatigue, No. (%) 3,088 (20.4) 1,229 (16.8) 1,859 (23.3) 0.16 < .001 Cough, No. (%) 1,658 (10.9) 657 (8.8) 1,001 (12.6) 0.12 < .001 Fever, No. (%) 728 (5.2) 190 (2.8) 538 (7.2) 0.20 < .001 BMI, body mass index; CKD, chronic kidney disease; COPD, chronic obstructive pulmonary disease; GI, gastrointestinal; HI, health index; MI, myocardial infarction; SMD, standardized mean difference. ᵃPercentages were calculated based on the available data for each variable. During the 12-month follow-up period, 899 participants (5.5%) reported hospitalization, with rates significantly lower among those with high versus low health indifference (376/8,033 [4.5%] vs. 523/7,486 [6.7%]; risk difference, -2.2 percentage points; 95% CI, -3.0 to -1.5; P < .001). This association persisted after multivariable adjustment, with an adjusted relative risk (aRR) of 0.82 (95% CI, 0.77–0.88) per standard deviation increase in the health indifference score, representing an 18% relative reduction in hospitalization (Fig. 2 ). Table S2 (Additional file 4) presents the complete effect estimates with the E-values. Restricted cubic spline analysis confirmed this monotonic relationship across the full score range without evidence of threshold effects (Fig. 3 ). Despite lower healthcare utilization, participants with high health indifference reported substantially higher symptom rates. Per-standard deviation increase in health indifference was associated with increased risk of fever (aRR, 1.12; 95% CI, 1.05–1.19), chest pain (aRR, 1.10; 95% CI, 1.05–1.16), and dyspnea (aRR, 1.08; 95% CI, 1.03–1.13). Among the 14 symptoms assessed, 11 remained statistically significant after false discovery rate correction. In contrast, diagnosed chronic conditions showed an inverse pattern. Per-standard deviation increase in health indifference was associated with lower cancer diagnoses (aRR, 0.81; 95% CI, 0.74–0.89). Hypertension (19.5% vs. 26.3%), dyslipidemia (12.9% vs. 18.3%), and diabetes mellitus (6.4% vs. 8.1%) showed similar inverse patterns. The exceptions were dental caries (aRR, 1.15; 95% CI, 1.10–1.21) and periodontitis (aRR, 1.06; 95% CI, 1.02–1.11), which showed a higher reporting rate per SD increase, likely reflecting both increased risk and potential detection through symptomatic presentation rather than preventive care. Preventive care engagement showed marked reductions with increasing health indifference scores. Per-standard deviation increase was associated with reduced influenza vaccination (aRR, 0.90; 95% CI, 0.88–0.92). COVID-19 vaccination showed minimal association per SD (aRR, 1.00; 95% CI, 1.00-1.01), possibly reflecting intensive public health campaigns that reduced individual choice factors. The robustness of the findings was confirmed through multiple sensitivity analyses. The complete case analysis excluding participants with any missing covariate data (n = 10,876) yielded similar results (aRR for hospitalization, 0.83; 95% CI, 0.77–0.90). Details of the aRR for each outcome are provided in Figure S2 (Additional file 3) and Table S3 (Additional file 4). A dose-response relationship was evident across the health indifference quartiles. The absolute risk of hospitalization decreased progressively from 6.9% in the lowest to 3.7% in the highest quartiles. For a detailed comparison of the quartiles, refer to Table S4 (Additional file 4). Compared with Q1, the adjusted relative risks were 1.14 (95% CI, 0.99–1.32) for Q2, 0.96 (95% CI, 0.82–1.13) for Q3, and 0.64 (95% CI, 0.53–0.78) for Q4. The E-value for hospitalization was 1.73 (95% CI lower bound, 1.53), indicating that unmeasured confounding would need to be associated with both exposure and outcome by risk ratios exceeding 1.73 to explain the observed association between exposure and outcome. The causal discovery analysis consistently identified a directed edge from the baseline to subsequent health indifference. This finding suggests a potential pathway through which inappropriate behavior contributes to the development of health indifference over time. In turn, health indifference appeared to be a key intermediate step leading to outcomes such as lower healthcare use and a higher number of reported symptoms. Four complementary algorithms identified 554 directed edges that met the cross-validation thresholds. Of these, 40 (7.2%) involved health indifference, with 28 paths originating from baseline factors to health indifference (primarily from smoking, health checkup absence, social support lack, and younger age) and 12 paths from health indifference to outcomes (primarily reduced healthcare utilization, vaccination, and increased symptoms). The algorithms showed varying detection rates, with DirectLiNGAM identifying the most edges (258 total, 21 involving health indifference) and CORL identifying the fewest edges (13 total, none involving health indifference). The detailed results are presented in Fig. 4 , Figure S3 (Additional file 3), and Table S5 (Additional file 4). Discussion In this longitudinal cohort study of 15,519 Japanese adults, we observed a paradoxical inverse relationship between health indifference and healthcare utilization. Each standard deviation increase in the health indifference score (measured on a 13–52 scale) was associated with an 18% reduction in hospitalization (aRR 0.82; 95% CI, 0.77–0.88), despite a concurrent increase in symptom reporting, including fever (aRR 1.12), chest pain (aRR 1.10), and dyspnea (aRR 1.08). This dose-response relationship was monotonic, with hospitalization rates declining from 6.9% in the lowest quartile to 3.7% in the highest quartile of health indifference (P for trend < .001). The robustness of this finding was confirmed through multiple sensitivity analyses, including complete case analysis (n = 10,876; aRR, 0.83), E-value (1.73; 95% CI lower bound, 1.53), and causal discovery analysis. This paradox aligns with models in which symptom interpretation and help-seeking can dissociate: the Common-Sense Model specifies that lay representations of illness shape appraisal and action, allowing symptoms to be noticed but not acted upon [ 48 ]. Qualitative synthesis in oncology likewise depicts iterative symptom appraisal that can delay help-seeking, even when the clinical risk is non-trivial [ 49 ]. Selective engagement is also consistent with Japan's COVID-19 vaccination experience, in which several campaigns reached unsure or unwilling groups [ 50 ]. A one-year follow-up study further characterized the reversal of hesitancy amid shifting informational and social contexts [ 51 ]. Social capital has also been linked to vaccination decisions in Japan, suggesting that community trust and networks modulate participation in public health [ 52 ]. Another clinical implication of our findings is that the lower incidence of diagnosed chronic conditions among those indifferent to health should not be mistaken for improved health. This likely indicates a detection bias. These individuals may have undiagnosed illnesses because they do not undergo screenings. Evidence from the pandemic era indicates a decrease in screening rates and a short-term reduction in the number of newly diagnosed cancers [ 53 ]. Japanese data likewise document a decline in screening participation with subgroup variations [ 54 ]. Population-based analyses in the US further demonstrated that incident cancer diagnoses fell below the expected levels during 2020, consistent with under-detection rather than a reduced incidence [ 55 ]. Several constraints biased the estimates towards null, suggesting that our findings may be conservative lower-bound estimates. First, hospitalization, diagnoses, screening, and vaccination were self-reported; Japanese validation studies show only moderate agreement with administrative sources and variability by condition, so non-differential misclassification could attenuate associations [ 56 ]. Disease-specific validation also indicates that the positive predictive value depends on the context and may be modest without corroboration [ 57 ]. Second, our window overlapped with COVID-19 waves, during which screening volumes fell and newly detected cancers temporarily declined; such conditions raise detection bias that depresses screening-detected diagnoses, irrespective of incidence [ 53 ]. Third, participant attrition may induce selection bias. Although we applied inverse probability-of-censoring weights and confirmed the results in the complete case analyses, weighting approaches are sensitive to response model specifications and may not fully remove bias. [ 58 ]. Fourth, the online non-probability panel with quota sampling limits generalizability despite adjustment [ 59 ]. Fifth, health indifference was measured once; single-time-point measurements invite regression-dilution-type errors that would also tend to attenuate true effects. Taken together, these features would more often dampen rather than inflate the observed associations. Conclusions Health indifference is linked to a paradoxical split between symptom awareness and healthcare use. The lower number of diagnoses among health-indifferent adults is more consistent with missed detection than with actual protection. These findings identify a behaviorally distinct group that experiences symptoms but routinely avoids care. Abbreviations aRR adjusted risk ratio BMI body mass index CI confidence interval CKD chronic kidney disease COPD chronic obstructive pulmonary disease CORL causal ordering via reinforcement learning COVID-19 coronavirus disease 2019 DAG directed acyclic graph DAGMA directed acyclic graphs via M-matrices and acyclicity characterization DirectLiNGAM direct linear non-Gaussian acyclic model GI gastrointestinal GOLEM gradient-based optimization learning for estimation of DAGs with continuous optimization HI health indifference IPCW inverse probability of censoring weights JASTIS Japan Society and New Tobacco/Infodemic Survey KDE kernel density estimation MAR missing at random MI myocardial infarction MICE multiple imputations using chained equations SD standard deviation SMD standardized mean difference Declarations Ethics approval and consent to participate The study protocol was approved by the Okayama University Hospital Review Board (No. 2507-014) with waiver of informed consent for retrospective analysis of de-identified data. This study complied with the Declaration of Helsinki for human research and provided written informed consent for the use of the data. Consent for publication Not applicable. Clinical trial number Not applicable. Availability of data and materials The research datasets contain personally identifiable information and are governed by Japanese privacy regulations. Anonymized individual-level data supporting the published findings will be accessible to qualified investigators upon justified request for replication. Data access requests should be directed to the corresponding author, accompanied by a comprehensive research proposal detailing the analytical plan and intended research application. Data transfer will occur through secure channels, following institutional review board approval and the execution of appropriate data use agreements. A comprehensive data dictionary describing all dataset variables and the complete statistical analysis code will be provided with approved data requests. The full analytical code repository is publicly accessible at https://github.com/OHikaru/HealthIndifference. Dataset availability commences six months post-publication and continues for five years after the article publication. Data usage is limited to non-commercial research applications and requires institutional review board clearance from the requesting institution prior to use. Competing interests The authors declare no competing interests. Funding HO received funding from the MEXT/JSPS KAKENHI (Grant Number: 25K20627). TT received financial support from the MEXT/JSPS KAKENHI Grant Numbers 18H03062 and 19K22788 and the Japan Health Research Promotion Bureau Number 2020-B-09. The funding organizations were not involved in the study conception, data gathering, statistical analysis, result interpretation, manuscript preparation, or publication decisions. Authors' contributions HO: Study conceptualization and methodology, data acquisition, statistical analysis and interpretation, and initial manuscript preparation. TM: Technical and logistical support, manuscript review, and intellectual input. TT: Data management and integrity, maintained complete access to all study data, assumed responsibility for data integrity and analytical accuracy, technical and logistical support, manuscript review, and intellectual input. JM: Project oversight and supervision, technical and logistical support, manuscript review, and intellectual input. All the authors have read and approved the final manuscript. Acknowledgements The authors thank all participants of the Japan Society and New Tobacco/Infodemic Survey (JASTIS) for their participation in the study. 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1","display":"","copyAsset":false,"role":"figure","size":66079,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of Health Indifference Score: Before vs After IPCW\u003c/p\u003e\n\u003cp\u003eHistograms and density plots showing the distribution of Health Interest Scale scores (range 13-52) before and after applying inverse probability of censoring weights. Blue histogram bars and solid lines represent the unweighted distribution (n = 15,519); red histogram bars and solid lines represent the IPCW-weighted distribution (n = 15,519). The black dashed line indicates the normal distribution fit for the IPCW-weighted data. IPCW, inverse probability of censoring weights; KDE, kernel density estimation; n, number of participants; SD, standard deviation; Δ, change.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7584212/v1/48e2d6b85de6f88366e7b869.png"},{"id":94138747,"identity":"9cfd1714-4639-40df-a897-33a9a01bd840","added_by":"auto","created_at":"2025-10-22 19:30:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":100880,"visible":true,"origin":"","legend":"\u003cp\u003eHealth Indifference and Health Outcomes (per 1 SD increase in HI score)\u003c/p\u003e\n\u003cp\u003eThe forest plot shows the adjusted relative risks and 95% confidence intervals for the association between health indifference (per standard deviation increase in the Health Interest Scale score) and various health outcomes. Blue circles with horizontal lines indicate statistically significant associations (P \u0026lt; .05); gray circles indicate non-significant associations (P ≥ .05). The vertical red dashed line at 1.0 indicates the null effect. The outcomes were ordered by effect magnitude. All models were adjusted for baseline demographic factors (age, sex, education level, household income), social factors (living arrangement, social support), health behaviors (smoking status, alcohol consumption, physical activity level, health checkup attendance), and health status (body mass index and self-rated health). P-values were adjusted for multiple comparisons using the Benjamini-Hochberg false discovery rate correction at 0.05 significance level. aRR, adjusted risk ratio; CI, confidence interval; CKD, chronic kidney disease; COPD, chronic obstructive pulmonary disease; COVID-19, coronavirus disease 2019; GI, gastrointestinal; HI, health indifference; MI, myocardial infarction; SD, standard deviation.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7584212/v1/986193335b8074b565e03e77.png"},{"id":94138749,"identity":"b5a5a98e-9a97-4ad0-bad2-0155f594e78d","added_by":"auto","created_at":"2025-10-22 19:30:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":76115,"visible":true,"origin":"","legend":"\u003cp\u003eSpline: Hospitalization vs Health Indifference\u003c/p\u003e\n\u003cp\u003eRestricted cubic spline analysis displaying the dose-response relationship between the Health Interest Scale scores (range 13-52) and the adjusted relative risk of hospitalization. The blue solid line represents the point estimates, with the 95% confidence interval shown as the light blue shaded area. The green vertical dashed line indicates the reference value set at the median score (HI = 29). The orange horizontal dashed line at aRR = 1.0 represents a null effect. The spline was fitted with three degrees of freedom, with knots placed at the 5th, 35th, 65th, and 95th percentiles of the score distribution. The model was adjusted for baseline demographic, social, behavioral, and health status covariates. aRR, adjusted risk ratio; HI, health indifference.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7584212/v1/ab8ae440bb3fc9d2578c76c4.png"},{"id":94140197,"identity":"d4c4b270-4834-4a0f-a5c2-1a588b48fa9d","added_by":"auto","created_at":"2025-10-22 19:38:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":254184,"visible":true,"origin":"","legend":"\u003cp\u003eCausal Network: Hospitalization, Disease and Vaccination Outcomes\u003c/p\u003e\n\u003cp\u003eDirected acyclic graph illustrating temporal relationships between baseline characteristics (2021, left column), health indifference exposure (2022, center column), and disease/vaccination outcomes (2023, right column). Edge colors indicate the level of consensus across the four causal discovery algorithms: orange (all four methods: DirectLiNGAM, GOLEM, DAGMA, and CORL), purple (three methods), pink (two methods), light blue (DirectLiNGAM only), light green (GOLEM only), black (DAGMA only), and blue (CORL only). Solid lines represent pathways involving health indifference, and dashed lines represent pathways not involving health indifference. BMI, body mass index; COPD, chronic obstructive pulmonary disease; CORL, causal ordering via reinforcement learning; COVID, coronavirus disease; DAGMA, directed acyclic graphs via M-matrices and acyclicity characterization; DirectLiNGAM, direct linear non-Gaussian acyclic model; GOLEM, gradient-based optimization learning for estimation of DAGs with continuous optimization; HI, health indifference.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7584212/v1/2da068732d25a7d7ca56d578.png"},{"id":94141170,"identity":"5ec7dc21-33bc-4856-ab4d-d064c3bcf458","added_by":"auto","created_at":"2025-10-22 19:54:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1229105,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7584212/v1/bb97bb92-0af5-47c9-9c22-efdf0e234c5a.pdf"},{"id":94138754,"identity":"742e3bcb-4a15-4343-afad-81cd5a487400","added_by":"auto","created_at":"2025-10-22 19:30:34","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":24040,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7584212/v1/ea495505263f33d233a18587.docx"},{"id":94140195,"identity":"a42f17c8-786a-4c6b-8392-3a388edfb34a","added_by":"auto","created_at":"2025-10-22 19:38:34","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":24838,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7584212/v1/737abc57e5200fd8e0d073eb.docx"},{"id":94138757,"identity":"fdb341ee-9630-4692-ae95-7accc0f8aa4a","added_by":"auto","created_at":"2025-10-22 19:30:34","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":697957,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7584212/v1/81ad9d8d747038e93e42fa87.docx"},{"id":94138768,"identity":"b602ef46-8262-4a17-b72b-51b8b3c35055","added_by":"auto","created_at":"2025-10-22 19:30:34","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":82194,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile4.docx","url":"https://assets-eu.researchsquare.com/files/rs-7584212/v1/6ba74ab8d53c201e73038915.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Health Indifference and the Inverse Association Between Symptom Burden and Healthcare Use: A Longitudinal Cohort Study from the Japan Society and New Tobacco/Infodemic Survey","fulltext":[{"header":"Background","content":"\u003cp\u003eApproximately 30% of adults worldwide delay or avoid healthcare despite experiencing symptoms that warrant medical attention [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This paradox results in preventable mortality [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], disease progression [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], and annual economic losses exceeding \u003cspan\u003e$\u003c/span\u003e100\u0026nbsp;billion [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. While structural barriers such as financial constraints [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], geographic distance [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and provider shortages [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] explain underutilization [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], 15\u0026ndash;25% of individuals in universal healthcare systems avoid necessary care despite minimal access barriers [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This persistent underutilization across diverse healthcare contexts suggests that psychological factors constitute independent, yet poorly understood, determinants of healthcare avoidance that current health system interventions fail to address.\u003c/p\u003e\u003cp\u003eHealth indifference is characterized by low health awareness [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], diminished health management motivation [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and reduced health valuation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This concept represents a previously underexamined psychological barrier distinct from established healthcare utilization determinants. Unlike health literacy deficits [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], which educational interventions successfully address [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], health indifference involves motivational disengagement resistant to informational approaches. While health consciousness [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] reflects active monitoring among engaged individuals, health indifference captures fundamental disinterest among those who avoid healthcare, despite symptoms. This differs from clinical apathy in depression [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], representing a stable trait in otherwise healthy populations [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Dominant behavioral theories, such as the Health Belief Model [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and Theory of Planned Behavior [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], assume rational actors who value health and respond predictably to barrier removal. Health indifference violates this core assumption, potentially explaining why educational campaigns [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and access improvements [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] systematically fail to reach populations that remain persistently disengaged. The validation of the Health Interest Scale [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] finally enables a rigorous empirical investigation of this construct.\u003c/p\u003e\u003cp\u003eNo longitudinal studies have examined whether health indifference predicts the paradoxical pattern of increased symptoms with decreased healthcare utilization. Existing cross-sectional studies [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] cannot establish causality or temporal precedence. Research on health consciousness [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and beliefs [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] examines positive motivators rather than disengagement barriers. Studies on systems with structural barriers [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] conflict with psychological and access factors. Individuals experience symptoms but systematically avoid care due to psychological rather than structural factors. This study represents the first longitudinal examination of whether baseline health indifference predicts subsequent healthcare utilization patterns. We hypothesized that health-indifferent individuals would report more symptoms yet utilize less healthcare, independent of access and socioeconomic factors. The inverse symptom-utilization relationship would identify a high-risk population currently invisible to healthcare systems.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy Design and Data Source\u003c/p\u003e\u003cp\u003eWe analyzed data from the Japan Society and New Tobacco/Infodemic Survey (JASTIS), a nationwide longitudinal online panel survey conducted annually from 2021 to 2023 [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. JASTIS recruits participants from a commercial research panel of over 2.2\u0026nbsp;million registered monitors across Japan, using quota sampling stratified by 5-year age bands, sex, and prefecture to approximate the Japanese census distribution. Participants received credit points as compensation. The study protocol was approved by the Okayama University Hospital Review Board (No. 2507-014) with waiver of informed consent for retrospective analysis of de-identified data.\u003c/p\u003e\u003cp\u003eParticipants\u003c/p\u003e\u003cp\u003eThe study included three annual survey waves conducted in February 2021 (baseline), February 2022 (exposure assessment), and February 2023 (outcome assessment). Of the 26,000 individuals who participated in the baseline survey, we included adults aged 16 years or older who completed all three waves. The online survey system required responses to all questions, minimizing item-level missing data within each wave. Participants with missing information on exposure or outcomes or who provided invalid responses were excluded.\u003c/p\u003e\u003cp\u003eExposure Assessment\u003c/p\u003e\u003cp\u003eHealth indifference was measured in the second wave using the 13-item Health Interest Scale, which has been previously validated in Japanese populations [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The scale comprises eight items assessing health consciousness and five items assessing health-neglecting attitudes, each rated on a 4-point Likert scale from strongly disagree to strongly agree. After reverse-coding negative items, we summed responses to create scores ranging from 13 to 52, with higher scores indicating greater health indifference. We analyzed health indifference as a continuous standardized variable (z-score) to estimate per-standard-deviation effects.\u003c/p\u003e\u003cp\u003eOutcome Measures\u003c/p\u003e\u003cp\u003eThe primary outcome was all-cause hospitalization during the 12 months preceding the third wave assessment, ascertained through self-report. Secondary outcomes included 14 physician-diagnosed chronic diseases (hypertension, diabetes mellitus, dyslipidemia, asthma, periodontitis, dental caries, angina or myocardial infarction, stroke, chronic obstructive pulmonary disease, chronic kidney disease, chronic liver disease, cancer, chronic pain, and depression), 14 physical symptoms rated as moderate-to-severe on 5-point scales, and healthcare utilization measures including COVID-19 and influenza vaccination uptake.\u003c/p\u003e\u003cp\u003eCovariates\u003c/p\u003e\u003cp\u003eWe selected baseline covariates based on the established determinants of healthcare utilization in Japan [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Demographic variables included age and sex. Socioeconomic factors comprised education level (categorized as low if less than high school completion) and annual household income (categorized as low if less than 4\u0026nbsp;million Japanese yen). Social factors included living arrangement (alone versus with others) and perceived social support (lacking if participants disagreed with having someone to consult when troubled). Health behaviors encompassed current smoking status, alcohol consumption (heavy drinking defined as consuming alcohol at least 5 days per week with 3 or more drinks per occasion), physical activity level (low if walking 2 days or fewer per week with no vigorous exercise), and health checkup attendance in the past year. Health status variables included body mass index calculated from self-reported height and weight, and self-rated health categorized as poor if rated as poor or very poor on a 5-point scale. The details of the questionnaire are provided in Additional File 1.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eWe calculated that 13,476 participants would provide 80% power to detect a relative risk of 0.80 for the primary outcome with a two-sided alpha of 0.05, assuming a 5% baseline hospitalization rate [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. To mitigate potential attrition bias arising from participant dropout between waves, we employed inverse probability of censoring weights (IPCW) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This method adjusts for the selective loss of participants by upweighting individuals who remained in the cohort but shared baseline characteristics with those who were lost to follow-up, thereby aligning the analytical cohort's composition with that of the original baseline population. We computed the stabilized IPCW from a logistic regression model predicting complete follow-up, trimming weights at the 1st and 99th percentiles [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. To handle missing data in baseline covariates while maximizing statistical power and minimizing selection bias, we performed multiple imputations using chained equations (MICE) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. This approach assumes that data are missing at random (MAR) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and generates multiple plausible complete datasets by inputting missing values based on the observed relationships between variables, with the results subsequently pooled according to Rubin's rules [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. To directly estimate adjusted relative risks (aRR) for binary outcomes, we used modified Poisson regression with robust standard errors [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], incorporating IPCW weights through variance weighting. This approach is preferred over logistic regression for cohort studies when the outcome is common, as it avoids the overestimation of effects inherent in the odds ratios. To provide absolute effect measures, we calculated standardized risk differences using g-computation [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], expressing the results as percentage points with 95% confidence intervals (CIs) obtained via the delta method. For the primary hospitalization outcome, we examined the dose-response relationship across the full range of health indifference scores (13\u0026ndash;52) using restricted cubic splines with three degrees of freedom [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. For secondary outcomes, we applied the Benjamini-Hochberg procedure to control the false discovery rate at 0.05. [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWe conducted four prespecified sensitivity analyses to assess the robustness of our primary findings to potential biases from missing data, alternative exposure specifications, and confounding factors. First, we performed a complete case analysis [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] to examine whether the distribution changed substantially because of attrition. Second, we conducted a categorical exposure analysis using quartiles to ascertain whether there was a monotonic trend by exposure level. Third, we calculated the E-value to quantify the potential impact of unmeasured confounding on our primary findings [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The E-value represents the minimum strength of association on the risk ratio scale that an unmeasured confounder would need to have with both the exposure and the outcome to fully explain the observed association, thus serving as a sensitivity analysis for residual confounding. Finally, to generate hypotheses regarding the underlying data-generating structure and potential mechanistic pathways, we applied a suite of four complementary causal discovery algorithms (DirectLiNGAM [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], GOLEM [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], DAGMA [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], and CORL [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]). These algorithms attempt to map out plausible cause-and-effect relationships from the data, with the critical rule that an event in the future cannot temporally cause something in the past. It is important to note that these analyses were exploratory and did not establish causality from observational data. Rather, their purpose is to identify plausible directed acyclic graphs (DAGs) that are consistent with the observed data and pre-specified temporal constraints. We used ten-fold cross-validation and retained the edges that appeared in at least six folds. Although these exploratory analyses cannot establish causality from observational data, they can infer relationships between variables. Details of these analyses are provided in Additional File 2. All analyses were performed using Python version 3.10.0 (Python Software Foundation, Wilmington, DE, USA).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eOf the 26,000 individuals enrolled in the baseline survey (February 2021), 15,519 (59.7%) completed all three annual waves and comprised the analytical cohort for this study. The details of recruitment are described in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e (Additional file 3).Attrition occurred primarily between Waves 1 and 2 (23.4%), with an additional 15.6% loss between Waves 2 and 3. Participants lost to follow-up were younger (mean age 48.2 vs. 50.0 years), less likely to have attended health checkups (51.3% vs. 60.8%), and more likely to smoke (28.6% vs. 19.6%, all P\u0026thinsp;\u0026lt;\u0026thinsp;.001). After applying the inverse probability of censoring weights, the weighted sample characteristics approximated the baseline population. The analytic cohort had a mean (SD) age of 50.0 (16.1) years, with 8,230 (51.0%) males. The Health Interest Scale demonstrated good internal consistency (Cronbach α\u0026thinsp;=\u0026thinsp;0.828), with scores approximately normally distributed (mean [SD], 29.1 [6.3]; median, 29; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Using the median split, 8,033 participants (51.8%) were classified as having high health indifference, with mean scores of 33.7 (SD 4.1) versus 23.7 (SD 3.8) in the low indifference group (standardized mean difference [SMD], 2.54; P\u0026thinsp;\u0026lt;\u0026thinsp;.001). Participants with high health indifference differed markedly from those with low indifference in multiple domains (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). They were, on average, 10 years younger (45.4 vs. 55.4 years; standardized mean difference [SMD], 0.66), more likely to be male (55.1% vs. 46.2%; SMD, 0.18), and had a higher rate of modifiable risk factors, including current smoking (23.8% vs. 14.6%; SMD, 0.24), absence of recent health checkups (44.9% vs. 32.4%; SMD, 0.26), and low physical activity (26.5% vs. 18.6%; SMD, 0.19). Social disadvantage was also more common, with 36.3% versus 23.3% of participants reporting a lack of social support (SMD, 0.28) and 21.9% versus 17.0% of participants living alone (SMD, 0.12). Multivariate analysis identified several baseline predictors of subsequent health indifference. Current smoking showed the strongest association (aRR, 1.31; 95% CI, 1.27\u0026ndash;1.36), followed by absence of health checkups (aRR, 1.21; 95% CI, 1.17\u0026ndash;1.25) and low physical activity (aRR, 1.21; 95% CI, 1.17\u0026ndash;1.25). The complete results are presented in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e (Additional file 4).\u003c/p\u003e\u003cp\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\u003eBaseline Characteristics by Health Index Categoryᵃ\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll Participants (n\u0026thinsp;=\u0026thinsp;15,519)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow HI (n\u0026thinsp;=\u0026thinsp;7,486)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh HI (n\u0026thinsp;=\u0026thinsp;8,033)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSMD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP Value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDemographic Characteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHI score (2022), mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29.1 (6.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23.7 (3.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33.7 (4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (2021), mean (SD), y\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e50.0 (16.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55.4 (15.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45.4 (14.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale sex, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8,230 (51.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3,618 (46.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4,612 (55.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSocial Determinants\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow education (\u0026lt;\u0026thinsp;high school), No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,251 (8.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e533 (7.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e718 (9.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow household income, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,348 (8.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e589 (7.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e759 (9.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiving alone, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3,005 (19.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,258 (17.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,747 (21.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLack of social support, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4,549 (30.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,688 (23.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,861 (36.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHealth Behaviors\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent smoking, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3,119 (19.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,109 (14.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,010 (23.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeavy drinking, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,200 (10.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e558 (10.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e642 (11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.032\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow physical activity, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3,562 (22.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,403 (18.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,159 (26.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo health checkup, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5,838 (39.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2,340 (32.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3,498 (44.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoor self-rated health, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,912 (11.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e799 (10.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,113 (13.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (2021), mean (SD), kg/m\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22.4 (3.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.2 (3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22.6 (4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHealth Outcomes (2023)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospitalization past year, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e899 (5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e523 (6.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e376 (4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOVID-19 infection, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,632 (18.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,101 (16.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,531 (20.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOVID-19 vaccination, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13,788 (88.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6,765 (89.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7,023 (86.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfluenza vaccination, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6,139 (37.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3,533 (45.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,606 (31.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eChronic Conditions (2023)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3,935 (22.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2,175 (26.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,760 (19.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes mellitus, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,242 (7.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e675 (8.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e567 (6.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDyslipidemia, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,618 (15.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,478 (18.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,140 (12.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsthma, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e525 (3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e251 (3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e274 (3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeriodontitis, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,441 (14.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,226 (15.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,215 (14.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.034\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDental caries, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,921 (12.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e796 (10.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,125 (13.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAngina/MI, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e368 (2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e183 (2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e185 (2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStroke, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e222 (1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e87 (1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e135 (1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOPD, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e184 (1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e75 (0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e109 (1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.049\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCKD, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e277 (1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e143 (1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e134 (1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic liver disease, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e203 (1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e84 (1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e119 (1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.058\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCancer, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e391 (2.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e226 (2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e165 (2.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic pain, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,487 (15.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,268 (16.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,219 (14.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepression, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e683 (4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e241 (3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e442 (5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSymptoms (2023)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGI discomfort, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3,928 (26.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,607 (22.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,321 (29.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBack pain, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5,255 (33.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2,414 (32.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,841 (35.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJoint pain, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4,284 (27.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2,022 (26.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,262 (27.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeadache, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3,067 (21.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,166 (17.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,901 (24.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChest pain, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,352 (9.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e422 (5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e930 (12.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDyspnea, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,785 (11.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e646 (8.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,139 (14.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDizziness, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,781 (12.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e659 (9.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,122 (14.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep disturbance, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3,959 (25.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,712 (23.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,247 (28.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMemory disorder, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,592 (10.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e611 (8.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e981 (12.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConcentration decline, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,374 (15.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e900 (12.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,474 (18.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReduced libido, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,202 (13.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e985 (12.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,217 (14.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFatigue, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3,088 (20.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,229 (16.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,859 (23.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCough, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,658 (10.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e657 (8.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,001 (12.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFever, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e728 (5.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e190 (2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e538 (7.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eBMI, body mass index; CKD, chronic kidney disease; COPD, chronic obstructive pulmonary disease; GI, gastrointestinal; HI, health index; MI, myocardial infarction; SMD, standardized mean difference. ᵃPercentages were calculated based on the available data for each variable.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eDuring the 12-month follow-up period, 899 participants (5.5%) reported hospitalization, with rates significantly lower among those with high versus low health indifference (376/8,033 [4.5%] vs. 523/7,486 [6.7%]; risk difference, -2.2 percentage points; 95% CI, -3.0 to -1.5; P\u0026thinsp;\u0026lt;\u0026thinsp;.001). This association persisted after multivariable adjustment, with an adjusted relative risk (aRR) of 0.82 (95% CI, 0.77\u0026ndash;0.88) per standard deviation increase in the health indifference score, representing an 18% relative reduction in hospitalization (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e (Additional file 4) presents the complete effect estimates with the E-values. Restricted cubic spline analysis confirmed this monotonic relationship across the full score range without evidence of threshold effects (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eDespite lower healthcare utilization, participants with high health indifference reported substantially higher symptom rates. Per-standard deviation increase in health indifference was associated with increased risk of fever (aRR, 1.12; 95% CI, 1.05\u0026ndash;1.19), chest pain (aRR, 1.10; 95% CI, 1.05\u0026ndash;1.16), and dyspnea (aRR, 1.08; 95% CI, 1.03\u0026ndash;1.13). Among the 14 symptoms assessed, 11 remained statistically significant after false discovery rate correction. In contrast, diagnosed chronic conditions showed an inverse pattern. Per-standard deviation increase in health indifference was associated with lower cancer diagnoses (aRR, 0.81; 95% CI, 0.74\u0026ndash;0.89). Hypertension (19.5% vs. 26.3%), dyslipidemia (12.9% vs. 18.3%), and diabetes mellitus (6.4% vs. 8.1%) showed similar inverse patterns. The exceptions were dental caries (aRR, 1.15; 95% CI, 1.10\u0026ndash;1.21) and periodontitis (aRR, 1.06; 95% CI, 1.02\u0026ndash;1.11), which showed a higher reporting rate per SD increase, likely reflecting both increased risk and potential detection through symptomatic presentation rather than preventive care. Preventive care engagement showed marked reductions with increasing health indifference scores. Per-standard deviation increase was associated with reduced influenza vaccination (aRR, 0.90; 95% CI, 0.88\u0026ndash;0.92). COVID-19 vaccination showed minimal association per SD (aRR, 1.00; 95% CI, 1.00-1.01), possibly reflecting intensive public health campaigns that reduced individual choice factors. The robustness of the findings was confirmed through multiple sensitivity analyses. The complete case analysis excluding participants with any missing covariate data (n\u0026thinsp;=\u0026thinsp;10,876) yielded similar results (aRR for hospitalization, 0.83; 95% CI, 0.77\u0026ndash;0.90). Details of the aRR for each outcome are provided in Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e (Additional file 3) and Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e (Additional file 4). A dose-response relationship was evident across the health indifference quartiles. The absolute risk of hospitalization decreased progressively from 6.9% in the lowest to 3.7% in the highest quartiles. For a detailed comparison of the quartiles, refer to Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e (Additional file 4). Compared with Q1, the adjusted relative risks were 1.14 (95% CI, 0.99\u0026ndash;1.32) for Q2, 0.96 (95% CI, 0.82\u0026ndash;1.13) for Q3, and 0.64 (95% CI, 0.53\u0026ndash;0.78) for Q4. The E-value for hospitalization was 1.73 (95% CI lower bound, 1.53), indicating that unmeasured confounding would need to be associated with both exposure and outcome by risk ratios exceeding 1.73 to explain the observed association between exposure and outcome.\u003c/p\u003e\u003cp\u003eThe causal discovery analysis consistently identified a directed edge from the baseline to subsequent health indifference. This finding suggests a potential pathway through which inappropriate behavior contributes to the development of health indifference over time. In turn, health indifference appeared to be a key intermediate step leading to outcomes such as lower healthcare use and a higher number of reported symptoms. Four complementary algorithms identified 554 directed edges that met the cross-validation thresholds. Of these, 40 (7.2%) involved health indifference, with 28 paths originating from baseline factors to health indifference (primarily from smoking, health checkup absence, social support lack, and younger age) and 12 paths from health indifference to outcomes (primarily reduced healthcare utilization, vaccination, and increased symptoms). The algorithms showed varying detection rates, with DirectLiNGAM identifying the most edges (258 total, 21 involving health indifference) and CORL identifying the fewest edges (13 total, none involving health indifference). The detailed results are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e(Additional file 3), and Table S5 (Additional file 4).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this longitudinal cohort study of 15,519 Japanese adults, we observed a paradoxical inverse relationship between health indifference and healthcare utilization. Each standard deviation increase in the health indifference score (measured on a 13\u0026ndash;52 scale) was associated with an 18% reduction in hospitalization (aRR 0.82; 95% CI, 0.77\u0026ndash;0.88), despite a concurrent increase in symptom reporting, including fever (aRR 1.12), chest pain (aRR 1.10), and dyspnea (aRR 1.08). This dose-response relationship was monotonic, with hospitalization rates declining from 6.9% in the lowest quartile to 3.7% in the highest quartile of health indifference (P for trend\u0026thinsp;\u0026lt;\u0026thinsp;.001). The robustness of this finding was confirmed through multiple sensitivity analyses, including complete case analysis (n\u0026thinsp;=\u0026thinsp;10,876; aRR, 0.83), E-value (1.73; 95% CI lower bound, 1.53), and causal discovery analysis.\u003c/p\u003e\u003cp\u003eThis paradox aligns with models in which symptom interpretation and help-seeking can dissociate: the Common-Sense Model specifies that lay representations of illness shape appraisal and action, allowing symptoms to be noticed but not acted upon [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Qualitative synthesis in oncology likewise depicts iterative symptom appraisal that can delay help-seeking, even when the clinical risk is non-trivial [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Selective engagement is also consistent with Japan's COVID-19 vaccination experience, in which several campaigns reached unsure or unwilling groups [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. A one-year follow-up study further characterized the reversal of hesitancy amid shifting informational and social contexts [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Social capital has also been linked to vaccination decisions in Japan, suggesting that community trust and networks modulate participation in public health [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Another clinical implication of our findings is that the lower incidence of diagnosed chronic conditions among those indifferent to health should not be mistaken for improved health. This likely indicates a detection bias. These individuals may have undiagnosed illnesses because they do not undergo screenings. Evidence from the pandemic era indicates a decrease in screening rates and a short-term reduction in the number of newly diagnosed cancers [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Japanese data likewise document a decline in screening participation with subgroup variations [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Population-based analyses in the US further demonstrated that incident cancer diagnoses fell below the expected levels during 2020, consistent with under-detection rather than a reduced incidence [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSeveral constraints biased the estimates towards null, suggesting that our findings may be conservative lower-bound estimates. First, hospitalization, diagnoses, screening, and vaccination were self-reported; Japanese validation studies show only moderate agreement with administrative sources and variability by condition, so non-differential misclassification could attenuate associations [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Disease-specific validation also indicates that the positive predictive value depends on the context and may be modest without corroboration [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Second, our window overlapped with COVID-19 waves, during which screening volumes fell and newly detected cancers temporarily declined; such conditions raise detection bias that depresses screening-detected diagnoses, irrespective of incidence [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Third, participant attrition may induce selection bias. Although we applied inverse probability-of-censoring weights and confirmed the results in the complete case analyses, weighting approaches are sensitive to response model specifications and may not fully remove bias. [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Fourth, the online non-probability panel with quota sampling limits generalizability despite adjustment [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Fifth, health indifference was measured once; single-time-point measurements invite regression-dilution-type errors that would also tend to attenuate true effects. Taken together, these features would more often dampen rather than inflate the observed associations.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eHealth indifference is linked to a paradoxical split between symptom awareness and healthcare use. The lower number of diagnoses among health-indifferent adults is more consistent with missed detection than with actual protection. These findings identify a behaviorally distinct group that experiences symptoms but routinely avoids care.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eaRR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eadjusted risk ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ebody mass index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003econfidence interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCKD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003echronic kidney disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCOPD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003echronic obstructive pulmonary disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCORL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ecausal ordering via reinforcement learning\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCOVID-19\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ecoronavirus disease 2019\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDAG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003edirected acyclic graph\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDAGMA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003edirected acyclic graphs via M-matrices and acyclicity characterization\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDirectLiNGAM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003edirect linear non-Gaussian acyclic model\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003egastrointestinal\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGOLEM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003egradient-based optimization learning for estimation of DAGs with continuous optimization\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ehealth indifference\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIPCW\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003einverse probability of censoring weights\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eJASTIS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eJapan Society and New Tobacco/Infodemic Survey\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eKDE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ekernel density estimation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMAR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emissing at random\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emyocardial infarction\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMICE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emultiple imputations using chained equations\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003estandard deviation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSMD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003estandardized mean difference\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Okayama University Hospital Review Board (No. 2507-014) with waiver of informed consent for retrospective analysis of de-identified data. This study complied with the Declaration of Helsinki for human research and provided written informed consent for the use of the data.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eClinical trial number\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe research datasets contain personally identifiable information and are governed by Japanese privacy regulations. Anonymized individual-level data supporting the published findings will be accessible to qualified investigators upon justified request for replication. Data access requests should be directed to the corresponding author, accompanied by a comprehensive research proposal detailing the analytical plan and intended research application. Data transfer will occur through secure channels, following institutional review board approval and the execution of appropriate data use agreements. A comprehensive data dictionary describing all dataset variables and the complete statistical analysis code will be provided with approved data requests. The full analytical code repository is publicly accessible at https://github.com/OHikaru/HealthIndifference. Dataset availability commences six months post-publication and continues for five years after the article publication. Data usage is limited to non-commercial research applications and requires institutional review board clearance from the requesting institution prior to use.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHO received funding from the MEXT/JSPS KAKENHI (Grant Number: 25K20627). TT received financial support from the MEXT/JSPS KAKENHI Grant Numbers 18H03062 and 19K22788 and the Japan Health Research Promotion Bureau Number 2020-B-09. The funding organizations were not involved in the study conception, data gathering, statistical analysis, result interpretation, manuscript preparation, or publication decisions.\u003c/p\u003e\n\u003cp\u003eAuthors' contributions\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHO: Study conceptualization and methodology, data acquisition, statistical analysis and interpretation, and initial manuscript preparation. TM: Technical and logistical support, manuscript review, and intellectual input. TT: Data management and integrity, maintained complete access to all study data, assumed responsibility for data integrity and analytical accuracy, technical and logistical support, manuscript review, and intellectual input. JM: Project oversight and supervision, technical and logistical support, manuscript review, and intellectual input. All the authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors thank all participants of the Japan Society and New Tobacco/Infodemic Survey (JASTIS) for their participation in the study.\u003c/p\u003e\n\u003cp\u003eAuthors' information\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTaber JM, Leyva B, Persoskie A. Why do people avoid medical care? A qualitative study using national data. J Gen Intern Med. 2015;30(3):290\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSmith KT, Monti D, Mir N, et al. Access is necessary but not sufficient: factors influencing delay and avoidance of health care services. MDM Policy Pract. 2018;3(1):2381468318760298.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGBD 2019 Healthcare Access and Quality Collaborators. 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Lancet Reg Health West Pac. 2022;27:100541.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOkubo T, Noy I. Vaccination decisions and social capital in Japan. SSM Popul Health. 2025;30:101769.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi T, Nickel B, Ngo P, et al. A systematic review of the impact of the COVID-19 pandemic on breast cancer screening and diagnosis. Breast. 2023;67:78\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKawamura C, Iwagami M, Komiyama J, et al. Change in Breast Cancer Screening Participation during COVID-19 Based on the 2019 and 2022 Comprehensive Survey of Living Conditions in Japan. JMA J. 2025;8(1):183\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBurus T, Lei F, Huang B, et al. Undiagnosed Cancer Cases in the US During the First 10 Months of the COVID-19 Pandemic. JAMA Oncol. 2024;10(4):500\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKurabayashi T, Ideno Y, Nagai K, et al. Validity of Self-Reported Diagnosis of Osteoporosis in Japan Nurses' Health Study. Clin Epidemiol. 2021;13:237\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMetten MA, Costet N, Viel JF, Chauvet G. Inverse probability weighting to handle attrition in cohort studies: some guidance and a call for caution. BMC Med Res Methodol. 2022;22:45.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaker R, Brick JM, Bates N, et al. Summary Report of the AAPOR Task Force on Non-probability Sampling. J Surv Stat Methodol. 2013;1(2):90\u0026ndash;143.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAmerican Association for Public Opinion Research (AAPOR). Report of the AAPOR Task Force on Non-probability Sampling. Oakbrook Terrace, IL: AAPOR; 2013.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Health indifference, Healthcare utilization, Symptom burden, Healthcare avoidance, Longitudinal cohort study, Japan, Preventive care, Detection bias, Health behavior, Screening participation","lastPublishedDoi":"10.21203/rs.3.rs-7584212/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7584212/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eApproximately 30% of adults delay or avoid healthcare despite experiencing symptoms, leading to preventable morbidities and substantial economic losses. Understanding the psychological mechanisms, particularly health indifference, that drive this paradox is critical for developing effective interventions to address this issue.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis longitudinal cohort study analyzed data from the Japan Society and New Tobacco/Infodemic Survey, a nationwide online panel survey conducted annually from February 2021 to February 2023. Among the 26,000 baseline participants from all 47 Japanese prefectures, 15,519 adults completed all three survey waves. Health indifference was measured at wave 2 (February 2022) using the validated 13-item Health Interest Scale (score range, 13\u0026ndash;52), analyzed as a continuous standardized variable. Primary outcome was self-reported all-cause hospitalization during the 12 months preceding wave 3. Secondary outcomes included 14 moderate-to-severe physical symptoms and 14 physician-diagnosed chronic diseases.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong 15,519 participants, those with higher health indifference exhibited a paradoxical pattern: despite reporting more moderate-to-severe symptoms (including fever, chest pain, and dyspnea), they had significantly lower healthcare utilization. Each standard deviation increase in health indifference was associated with 18% lower hospitalization (adjusted risk ratio, 0.82; 95% CI, 0.77\u0026ndash;0.88) and fewer diagnoses of chronic conditions typically detected through screening. This inverse relationship between symptom burden and healthcare utilization was consistent across all analyses.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eHealth indifference predicts paradoxical dissociation, wherein individuals experience and report more symptoms but utilize less healthcare. Lower chronic disease diagnoses likely reflect detection bias from avoided screening rather than improved health.\u003c/p\u003e","manuscriptTitle":"Health Indifference and the Inverse Association Between Symptom Burden and Healthcare Use: A Longitudinal Cohort Study from the Japan Society and New Tobacco/Infodemic Survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-22 19:30:29","doi":"10.21203/rs.3.rs-7584212/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-10-28T05:18:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-23T01:46:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"120345443956848108836230814453975445906","date":"2025-10-12T16:50:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"332311867326208546695565292798351046225","date":"2025-10-09T09:24:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-09T05:33:40+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-15T07:32:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-12T14:04:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-12T14:03:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-09-10T14:54:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"87a083dd-53a2-4ae6-8695-15690bb0a63d","owner":[],"postedDate":"October 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-22T19:30:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-22 19:30:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7584212","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7584212","identity":"rs-7584212","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}