Multimorbidity, health service use, and health insurance by socioeconomic groups in 31 countries: a multi-cohort study

Multimorbidity, health service use, and health insurance by socioeconomic groups in 31 countries: a multi-cohort study | 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 Article Multimorbidity, health service use, and health insurance by socioeconomic groups in 31 countries: a multi-cohort study Ping He, Yanshang Wang, Chang Cai, Zhenyu Shi, Qian Gao, Alex Bottle, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6379381/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The prevalence of physical, psychological, and cognitive multimorbidity is marked by socioeconomic status (SES) inequalities. However, the relationship between multimorbidity patterns—particularly those involving cognitive conditions—and healthcare utilization, as well as the role of health insurance, remains poorly understood. This is the first study to explore healthcare-seeking behaviour among individuals with multimorbidity and assess whether these vary by SES and health insurance coverage. This multicohort study analyzed data from six longitudinal studies across 31 countries, including participants aged 50 years and older. Multimorbidity was defined as the coexistence of two or more disorders across physical, psychological, or cognitive disorders. Outpatient and inpatient healthcare utilization were measured. Random-effects logistic regression models were employed to assess associations with healthcare utilization, and random-effects Poisson regression models analyzed visit frequencies. Country-specific analyses were aggregated via multinational meta-analyses using random-effects models to generate overall effect sizes. We included a total of 1450209 individuals. Compared with individuals without any conditions, those with the most complex multimorbidity pattern had higher outpatient care utilization (OR 3.13, 95% CI [2.21–4.05]) but not as high as those with physical-psychological multimorbidity (OR 7.83, 95% CI [6.59–9.07]). Additionally, the association varied across socioeconomic groups, with lower SES individuals experiencing more pronounced disparities in care use. In contrast, the association between multimorbidity and inpatient care utilization was less pronounced. Health insurance coverage weakened the association between multimorbidity and outpatient care use, especially for individuals with physical-psychological-cognitive multimorbidity. Those with insurance had a stronger likelihood of utilizing outpatient care (OR 6.14, 95% CI [5.26, 7.16]) compared with those without insurance (OR 2.98, 95% CI [2.65, 3.36]). Cognitive disorders further complicate multimorbidity, indicating unmet healthcare needs, especially among individuals with lower SES. Our study highlights a potential role of health insurance in mitigating disparities in healthcare utilization related to multimorbidity. Health sciences/Health care/Health services Health sciences/Health care/Public health Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Physical, psychological, and cognitive multimorbidity pose substantial challenges to individuals, healthcare systems and societies. This growing burden of multimorbidity is further amplified by global population ageing and shifts in health behaviours, which have contributed to the increasing prevalence of multimorbidity 1–3 . Studies have reported overall global prevalence of multimorbidity was 37.2%, making multimorbidity a major global health concern with its impact expected to intensify in the coming decades 4 . Moreover, the complexity of multimorbidity extends beyond physical conditions to frequently involve psychological and cognitive disorders, which complicates management and further strains healthcare systems. While the intersection of physical and psychological multimorbidity has been explored in previous studies 5,6 , only limited studies have analyzed cognitive disorders as part of the multimorbidity spectrum 7,8 . Most existing research focuses on investigating how physical and psychological multimorbidity contributes to the risk of developing cognitive impairment or dementia 9,10 , or examines its association with healthcare utilization 5,11 . However, these studies have not fully analyzed the consequences of cognitive related multimorbidity, which hinders a comprehensive understanding of their impact on healthcare utilization patterns, particularly given the known complexity of healthcare utilisation in those with cognitive impairment even without multimorbidity 12 . The inclusion of cognitive disorders also reveals even more pronounced socioeconomic inequalities in multimorbidity prevalence, particularly in low- and middle-income countries (LMICs) 7 . However, there remains a gap in the literature, as no studies have fully described the relationship between healthcare use and the interplay of physical, psychological, and cognitive multimorbidity, nor have they thoroughly examined the role of SES. In the process of obtaining healthcare services, health insurance plays a crucial role, serving as a vital mechanism in advancing Universal Health Coverage (UHC). Individuals with multimorbidity experience significant treatment and illness burdens, which often result in increased healthcare utilization and costs 13 . In addition, they frequently face unmet healthcare needs and inadequate access to appropriate care, particularly in healthcare systems that prioritize single disease management over the complex care coordination required for their multifaceted health need 14,15 . Health insurance, as a key instrument of financial risk protection, has the potential to mitigate the economic burden of multimorbidity management. Existing research on the impact of health insurance on healthcare utilization among individuals with multimorbidity is limited, often focusing on single countries and narrowly defining multimorbidity as either physical conditions alone or combinations of physical and mental disorders 16,17 . With the ongoing trends of population aging and the increased vulnerability of patients with cognitive disorders to unmet healthcare needs 18,19 , it is crucial to consider the complexities introduced by cognitive disorders in multimorbidity patterns. By measuring and characterizing these associations, policymakers could develop targeted interventions and strategies that address the multimorbidity disparities in health care access. Utilizing panel data from six longitudinal studies across 31 countries, this study is the first study to examine the association between healthcare utilization and physical, psychological, and cognitive multimorbidity, as well as the modifiable role of health insurance in this context, particularly concerning socioeconomic status. Methods Study design and data sources This multicohort study utilizes data from six longitudinal studies across 31 countries: the China Health and Retirement Longitudinal Study (CHARLS), the Japanese Study of Aging and Retirement (JSTAR), the Korean Longitudinal Study of Aging (KLoSA), the Mexican Health and Aging Study (MHAS) and the Survey of Health, Ageing and Retirement in Europe (SHARE) 20–24 . These studies use consistent survey protocols, allowing for effective cross-regional comparisons. All studies are adhering to the same biennial design and standardized measures of economic status, lifestyle, and health. Data were drawn from waves 1–4 of CHARLS (2011–2018), waves 1–3 of JSTAR (2007-2011), waves 1–8 of KLoSA (2006–2020), waves 1–5 of MHAS (2001–2018) and waves 4–9 of SHARE (2008–2019). All studies were ethically approved, and participants provided written informed consent. Sample construction For this study, we included data from participants who were aged 50 years and older for consistency across all datasets, and we excluded participants who had missing values of dependent or independent variables. Measure (1) Multimorbidity In this study, multimorbidity was defined as the co-occurrence of two or more disorders across physical, psychological, or cognitive domains 7 . Participants were classified as having a physical disorder if they self-reported at least one of the following seven chronic conditions: hypertension, diabetes, cancer, lung disease, heart disease, stroke, or arthritis. The presence of a psychological disorder was determined through study-specific psychological assessments (e.g., the Center for Epidemiologic Studies Depression scale), using the same cut-off values as in previous studies (Appendix Table 1). Potential cognitive disorders were assessed using study-specific cognitive tests (e.g., the Mini-Mental State Examination), with cut-off values used from prior research or determined empirically (Appendix Table 1). [Appendix Table 1. The definition of physical, psychological and cognitive disorder.] (2) Outcome Healthcare utilization was measured using two indicators: outpatient visits and inpatient admissions. We harmonized the measure of healthcare use across databases and categorized it as binary, indicating whether the participant had any experience of outpatient or inpatient care. Furthermore, to assess the intensity of healthcare use, we calculated the number of visits. Due to differences in measurement units across the datasets, we standardized outpatient service utilization over a one-year period and inpatient service utilization over a two-year period, reflecting the number of outpatient visits in the past year and hospital admissions in the past two years. The detailed definitions and measurement of variables are provided in Appendix Table 2. [Appendix Table 2. The definition of health care use.] (3) Health insurance Given the differences in health insurance systems across countries, we treated health insurance as a binary variable (covered or not covered). This classification encompassed various types of insurance, including public health insurance programs, private health insurance programs, or any other form of health insurance. The definitions and measurement of this variable are detailed in Appendix Table 3. [Appendix Table 3. The definition of health insurance.] (4) Covariates In each study, covariates were assessed at the selected wave of the survey. These included basic demographic information: age, gender (male vs female), educational attainment (middle school or below, high school, and college or above), work status (unemployed vs employed), marital status (not married vs married), and household wealth level. Additionally, we considered lifestyle factors such as BMI, alcohol use (yes vs no), tobacco use (yes vs no), and physical exercise (yes vs no), as potential confounders in the association between healthcare utilization and multimorbidity. BMI was calculated as weight (kg) divided by height squared (m²) and categorized into underweight (<18.5 kg/m²), normal weight (18.5–24.9 kg/m²), and overweight and obesity (≥25 kg/m²). The definitions and measurement methods for alcohol use, tobacco use, and physical exercise are provided in Appendix Table 4. [Appendix Table 4. The definition of lifestyles.] We analyzed differences across socioeconomic groups using annual household wealth as a proxy for socioeconomic status (SES). Socioeconomic groups were categorized into quartiles based on household wealth within each country, with the quartile assignment recalculated for each survey wave to account for changes over time. Statistical analysis First, we conducted descriptive analyses of the prevalence of multimorbidity, stratified by country and age group. We then analyzed the probability of healthcare utilization across different multimorbidity patterns, focusing on outpatient and inpatient services. These analyses were further stratified by SES to provide a detailed understanding of SES-related variations in healthcare use. To assess the associations between multimorbidity and healthcare utilization, we used a panel data approach with random-effects logistic regression, accounting for individual differences as random variations over time. This allowed us to estimate the likelihood of outpatient visits and inpatient admissions in relation to multimorbidity patterns. To analyze the frequency of healthcare use, including the number of outpatient visits and inpatient hospital days, we used random-effects negative binomial models. Specifically, we conducted country-specific models to estimate associations within each country. These results were then aggregated into multinational meta-analyses using the random effects model to obtain overall effect sizes across all countries. To examine the differential impact across population subgroups, we performed subgroup analyses stratified by SES and health insurance coverage. These analyses followed the same regression approach but excluded the stratification variable. For these models, data from all countries were pooled, with country-level variations accounted for as control variables. For the logistic regression analyses, we reported associations as odds ratio (OR) adjusted for relevant covariates, with 95% confidence interval (CI). For the random-effects Poisson regression analysis, we presented incident rate ratio (IRR) adjusted for covariates, also with 95% CIs. Statistical significance was set at a p-value of less than 0.05. All analyses were performed using Stata (version 15.0). The data were all publicly available. The use of public, secondary, de-identified data made the present study exempt from review by an institutional review board. Results We included a total of 1450209 individuals from 31 countries. The average age of respondents across countries ranged from 62.8 to 70.2 years, with a slightly higher proportion of females in the study population, ranging from 50.7% to 62.8%. [Appendix Table 5. Basic demographics by country.] [Appendix Figure 1: Prevalence of physical, psychological, and cognitive multimorbidity by country.] In terms of multimorbidity patterns, physical-mental multimorbidity showed the highest prevalence, ranging from 7.5% in China to 32.6% in Lithuania. The prevalence of physical-mental-cognitive multimorbidity ranged from 0.4% in Finland to 17.6% in South Korea. Regarding healthcare utilization, outpatient care usage varied widely. In China (CHARLS), where the measurement period was one month, outpatient utilization was lowest at 18.9%. For all other databases, measured over 12 months (24 months in KLoSA), Germany reported the highest outpatient utilization at 94.4%. The median outpatient visit within one year was 3 (IQR 1–7.5). For inpatient care, the overall pooled utilization rate was 14.5%. The complexity of multimorbidity patterns increased with age, with slight gender differences observed (Figure 1). As age increased, the proportion of individuals with physical-psychological-cognitive multimorbidity rose, from 1.8% among individuals aged 50–59 years to 17.4% among those aged 90 years or older. In men, the multimorbidity pattern changed with age in a manner comparable to that observed in women. An analysis of the relationship between multimorbidity patterns and healthcare utilization (Figure 2) revealed notable findings. For outpatient care, individuals with cognitive disorders alone had the lowest utilization rate at 34.0%, while those with only physical conditions had the highest utilization rate at 88.0%. Those with the most complex multimorbidity pattern—physical-psychological-cognitive multimorbidity—had an outpatient care utilization rate of 82.1%. For inpatient services, a trend was observed where utilization rates increased with the complexity of multimorbidity patterns. Individuals with physical-psychological multimorbidity had the highest hospitalization rate at 23.0%, followed closely by those with physical-psychological-cognitive multimorbidity at 22.8% (Figure 2). The association between multimorbidity and healthcare utilization The association between multimorbidity patterns and outpatient care utilization reveals a complex relationship (Figure 3). Compared with individuals without any conditions, those with cognitive disorders had significantly lower outpatient utilization (OR = 0.53, 95% CI [0.39, 0.67]). Surprisingly, individuals with the most complex multimorbidity pattern—physical-psychological-cognitive multimorbidity—did not exhibit the highest outpatient care utilization (OR = 3.08, 95% CI [2.22, 3.94]), which was lower than that of individuals with a single physical condition (OR = 5.15, 95% CI [4.46, 5.85]). The highest outpatient utilization was observed among those with physical-psychological multimorbidity, who had a 7.80-fold (95% CI [6.62, 8.97]) increase in utilization compared with those without any conditions. However, when analyzing outpatient visits (as opposed to binary outpatient care utilization), the trends were similar but less pronounced. We observed that individuals with cognitive disorders used outpatient services less frequently (IRR = 0.89 [95% CI 0.75–1.04]) compared with those without conditions. Additionally, the most complex multimorbidity pattern—physical-psychological-cognitive multimorbidity—did not result in the highest visit frequency (IRR = 2.21, 95% CI [1.99–2.43]). For inpatient care, a positive association was found between the complexity of multimorbidity patterns and hospitalization rates. However, we observed that the addition of a cognitive disorder to an existing physical or psychological condition did not lead to a significant increase in inpatient utilization. Similarly, for inpatient visits, the pattern mirrored the trend seen with the binary inpatient care variable. Individuals with cognitive disorders had lower inpatient service utilization compared with those without any conditions (IRR = 0.67, 95% CI [0.40–0.94]). Among all multimorbidity patterns, individuals with physical-psychological multimorbidity had the highest inpatient utilization (IRR = 3.79, 95% CI [3.51–4.07]). Moreover, the associations between multimorbidity and healthcare utilization showed a subtle gradient in relation to socioeconomic status (SES), particularly in outpatient care, where the association weakened as SES increased (Figure 4). Appendix table 6-Appendix table 9 reported the estimates for each country, and Appendix table 10 provides the details of meta-analysis. [Appendix Table 6. The association between multimorbidity and outpatient by country.] [Appendix Table 7. The association between multimorbidity and outpatient visits by country.] [Appendix Table 8. The association between multimorbidity and inpatient by country.] [Appendix Table 9. The association between multimorbidity and inpatient visits by country.] [Appendix Table 10: The meta-analysis details.] The role of health insurance Stratified analyses by health insurance status suggested different pattern in the relationship between multimorbidity and healthcare use, especially for outpatient care (Figure 5). Specifically, for individuals with health insurance coverage, the association between multimorbidity and outpatient care use was generally weaker. This was particularly evident for those with physical-psychological-cognitive multimorbidity (with health insurance: OR = 6.22, 95% CI [5.33, 7.25]; without health insurance: OR = 3.40, 95% CI [3.03, 3.82]), physical-psychological multimorbidity (with health insurance: OR = 8.32, 95% CI [7.76, 8.93]; without health insurance: OR = 3.01, 95% CI [2.76, 3.29]), and single physical conditions (with health insurance: OR = 5.67, 95% CI [5.39, 5.97]; without health insurance: OR = 3.53, 95% CI [3.23, 3.85]). Similar trends were observed for inpatient care utilization, although the differences were less pronounced compared with outpatient care. Further analyses of the differences in OR and IRR between those with and without health insurance across SES quartiles for each multimorbidity pattern revealed that, for outpatient care—both binary and visit frequency measures—most differences were greater than zero (Appendix Figure 3). This effect was more pronounced in lower SES groups, demonstrating a negative correlation between SES and the impact of health insurance. However, for inpatient care, the relationship with SES exhibited the opposite trend. [Appendix Figure 2: The OR and IRR differences between individuals with and without health insurance (grouped by multimorbidity and SES).] Sensitivity analyses We conducted three sensitivity analyses to validate our findings. First, to address potential measurement unit discrepancies, we reanalyzed our data. For outpatient care, we excluded CHARLS, and for inpatient care, we excluded MAHS (Appendix Figure 4), yielding results consistent with our primary findings, confirming the complex associations between multimorbidity and outpatient care use. Second, we presented random-effects Poisson regression models for healthcare visits. The results remained consistent, reinforcing the robustness of our findings across different model specifications. Finally, we tested an alternative SES measure by replacing household wealth with education attainment for stratified analyses, and observed similar trends (Appendix Figure 4). Lastly, the multiple imputation (with 20 sets) was employed to handle the missing values, and the results remained consistent (Appendix Figure 5). [Appendix Figure 3. The association between health utilization and multimorbidity (sensitivity analyses).] [Appendix Figure 4. The association between health utilization and multimorbidity (grouped by education attainment).] [Appendix Figure 5. The association between health utilization and multimorbidity (multiple imputation).] Discussion This study indicates a significant but “non-monotonic” relationship between healthcare utilization and multimorbidity using the cross-national data. Specifically, the presence of cognitive disorders was associated with reduced likelihood of outpatient service use in individuals with co-occurring conditions. Additionally, this negative association demonstrated a gradient effect across socioeconomic groups, weakening as SES increased, at least when measured using household wealth as a proxy. Furthermore, we found that health insurance exerts a buffering effect on the relationship between healthcare utilization and multimorbidity, alleviating some of the SES-related differences in use of, and potentially access to care. The cognitive-related multimorbidity pattern appears to be associated with suppressed healthcare utilization compared with physical-only multimorbidity. Unlike previous studies, which have generally reported that healthcare utilization increases with the number of chronic conditions or the complexity of multimorbidity patterns 3 , 13 , 25 – 27 —primarily focusing on physical or mental multimorbidity, our findings demonstrate a significant reduction in healthcare use among individuals with cognitive disorder-related multimorbidity. This underscores the nuanced nature of healthcare utilization patterns as the burden and complexity of multimorbidity evolve, challenging the assumption that greater complexity invariably results in higher service use. Two possible explanations may account for this result. First, from the demand-side perspective, individuals with cognitive disorders may receive much informal care from family and social networks, which is not captured in current healthcare utilization measurement 18 , 28 , 29 . However, our findings show that individuals with the most complex multimorbidity patterns (physical-mental-cognitive multimorbidity) exhibit even lower healthcare utilization than those with a single physical condition, suggesting that unmet healthcare needs could play a substantial role. While direct evidence for this is lacking, this interpretation aligns with previous research on cognitive disorders that demonstrates high levels of unmet healthcare needs in such populations 18 . Second, from the supply-side perspective, we observed differing results between outpatient care (binary) and outpatient visits (count), offering an alternative explanation for the findings. Outpatient care as a binary indicator measures access to this type of care, while the number of outpatient visits measure the intensity of healthcare utilization. The lack of an increase in the likelihood of obtaining care as multimorbidity patterns become more complex likely reflects existing inequalities in healthcare delivery and the fragmented health care systems, which may fail to meet the needs of patients with multimorbidity in an equitable and accessible manner 30 . However, once patients are able to access healthcare services, the diverse and multiple needs associated with multimorbidity are revealed, as evidenced by the increased intensity of care utilization. These complex findings as to cognitive disorder-related multimorbidity was associated with decreased healthcare use, differing from some previous studies 31 – 33 . Prior studies, primarily based on health records or hospitalization cohorts, cannot have fully captured the complex multimorbidity pattern and actual healthcare use. The relationship between multimorbidity and healthcare utilization indicates significant SES-related inequalities, indicating that the above finding may be particularly pronounced in those with low SES. This may be because individuals with higher SES often have better access to healthcare resources 34 . This finding extends previous research, which had documented SES inequalities in the prevalence of physical-psychological-cognitive multimorbidity 7 , 8 . It suggests that disadvantaged groups face a dual challenge from both the illness burden and the treatment burden associated with multimorbidity. Health insurance potentially plays a critical role in narrowing healthcare utilization disparities, particularly in mitigating SES inequalities. As an essential mechanism for achieving UHC, health insurance is designed to protect individuals and families from financial strain due to high healthcare expenditures 17 . Our findings reveal that the relationship between multimorbidity and healthcare use is less pronounced among individuals with health insurance compared with those without, especially regarding outpatient services. This reflects that health insurance helps reduce disparities in healthcare utilization caused by multimorbidity, potentially lowering the risk of unmet healthcare needs. However, this buffering effect is less pronounced for inpatient services, which can be attributed to the inherent nature of multimorbidity and the relatively lower elasticity of demand for hospital care compared with outpatient care 35 . Multimorbidity often involves chronic conditions that necessitate ongoing management, making the demand for outpatient services more salient. Further analysis suggests that this protective effect of health insurance for outpatient care is more noticeable among the lower SES group. This finding highlights the essential role of health insurance in promoting equitable access to necessary health services for vulnerable populations. Therefore, this study enhances our understanding of the moderating role of health insurance ownership in mitigating the adverse effects of multimorbidity on healthcare utilization 16 . By demonstrating how insurance mitigates barriers to care, these findings emphasize the importance of expanding health insurance coverage, particularly for socioeconomically disadvantaged groups, to address healthcare access inequities and ensure that patients with multimorbidity receive appropriate care. Our study is the first to examine physical, psychological, and cognitive multimorbidity, healthcare utilisation and how this is moderated by SES and health insurance, which points to significant policy implications: First, we underscore the urgent need for person-centered integrated care systems for patients with multimorbidity. Our findings demonstrate that when cognitive disorders are part of multimorbidity patterns, healthcare needs become significantly more complex. Addressing these complexities requires not only integration across different levels of healthcare but also the inclusion of family and social care support systems. This approach is essential to address the unmet needs of this population and to provide targeted, comprehensive, and continuous care, yet current clinical guidelines are insufficient in meeting the complex needs of patients with multimorbidity 3 , 30 . Secondly, expanding health insurance coverage is critical for promoting health equity among patients with multimorbidity. As the prevalence of multimorbidity rises, accompanied by increasingly complex patterns and significant economic and social burdens, the progress toward achieving UHC will face significant challenges. While health insurance has shown buffering effects, sustained impact requires a transformation of both financing and service delivery systems to better manage the complexities multimorbidity beyond traditional single-disease models. Lastly, our study highlights the urgent need to address SES inequalities in healthcare services. Equity-oriented policies and programs are essential to reduce social disparities in healthcare access for multimorbidity. Such efforts may necessitate a targeted approach that allocates health resources and interventions to disadvantaged groups. This study has two notable strengths. First, to our knowledge, it is the first panel data analysis using cross-national data to examine healthcare utilization differences among individuals with physical, psychological, and cognitive multimorbidity, addressing limitations in prior studies—such as challenges with cross-cultural generalizability, statistical conclusion validity, and measurement validity. Second, the study offers a detailed and nuanced analysis of the role of health insurance, exploring not only its buffering effects but also its interaction with socioeconomic inequalities. Limitations This study also had several limitations that should be acknowledged. First, the information on physical, psychological, and cognitive disorders, as well as healthcare use, was self-reported by participants, which may have introduced recall bias and led to underestimation, especially among older individuals and those from lower SES backgrounds, who might be more likely to under-report such factors 13 . Secondly, the measurement of psychological and cognitive multimorbidity may have varied across datasets. To assess depression and cognitive disorders, we used cohort-specific cut-offs for psychological and cognitive assessments, which could have increased heterogeneity in the cohort-specific results. However, this approach has been considered an accepted method for cross-cohort comparisons 7 , 8 . Thirdly, due to data limitations, we were unable to account for supply-side factors. Our study only adjusted for individual characteristics and did not consider healthcare system-related factors such as the type of health insurance scheme and physician access, which might also influence healthcare use. Lastly, no sample weights were applied in our analysis due to the multi-cohort design and inconsistent methods of constructing sample weights across the different studies. The inexorable aging of populations necessitates a critical examination of the complexities inherent in multimorbidity patterns, particularly those including by cognitive disorders. These complexities significantly affect healthcare use behaviour and underscore the diverse healthcare needs. To address these challenges, it is imperative to establish a person-centred healthcare system that includes family and social support networks. Furthermore, advancing multimorbidity-specific health insurance system to promote equity and accessibility in healthcare services is essential. Such efforts are vital not only for enhancing individual health outcomes but also for advancing the broader objective of achieving UHC and Sustainable Development Goals. Declarations Competing interests The authors declare that they have no competing interests. Funding This work was supported by the Major Project of the National Social Science Fund of China (21&ZD187). Author contribution Ping He: Conceptualization and design, Supervision, Writing - Review; Benedict Hayhoe: Supervision, Writing – Review; Yanshang Wang: Formal analysis, Writing - original draft; Chang Cai: Writing – Review; Zhenyu Shi: Writing – Review; Qian Gao: Writing – Review; Alex Bottle: Writing – Review, Mansour Taghavi Azar Sharabiani: Writing – Review. All authors critically revised successive drafts of the paper and approved the final version. The corresponding author attests that listed author meet authorship criteria and that no others meeting the criteria have been omitted. Acknowledgments The authors gratefully acknowledge the funding support from the Major Project of the National Social Science Fund of China (21&ZD187). Data availability The data is publicly available. References Pathirana, T. I. & Jackson, C. A. Socioeconomic status and multimorbidity: a systematic review and meta-analysis. Australian and New Zealand Journal of Public Health 42, 186–194 (2018). 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Poorer outcomes and greater healthcare costs for hospitalised older people with dementia and delirium: A retrospective cohort study. Int. J. Geriatr. Psychiatry 32, 539–547 (2017). Edmans, B. G. et al. Inpatient psychiatric care for patients with dementia at four sites in the United Kingdom. Int. J. Geriatr. Psychiatry 37, (2022). Guijarro, R. et al. Impact of dementia on hospitalization. Neuroepidemiology 35, 101–108 (2010). Kuo, R. N. & Lai, M.-S. The influence of socio-economic status and multimorbidity patterns on healthcare costs: a six-year follow-up under a universal healthcare system. International Journal for Equity in Health 12, 69 (2013). Duarte, F. Price elasticity of expenditure across health care services. Journal of Health Economics 31, 824–841 (2012). Additional Declarations There is NO Competing Interest. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6379381","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":440614958,"identity":"b4a6393f-1fe6-4292-81f6-7d5f21cdcdb9","order_by":0,"name":"Ping He","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqElEQVRIiWNgGAWjYDACCQbGBwwHQKwE4rUwG5CshU2CNC3m0s3HqgvOHGbgZ88xYPi5gwgtlnOOpd2eceMwg2TPGwPG3jNEaDG4kWN2m+fDYRDDgJmxjSgt+d+KQVrsSdCSw8bMA3SYgQSxWixnpBlLzziTziNx5lnBwV5itJhLJD/8XHDMWo6/PXnjg59EOQyImYGYB8Q5QIQGhJZRMApGwSgYBbgBANn9Nhf0DzCGAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-5040-5012","institution":"Peking University, China","correspondingAuthor":true,"prefix":"","firstName":"Ping","middleName":"","lastName":"He","suffix":""},{"id":440614959,"identity":"3a6688af-58df-4a98-aedb-63c8211fa276","order_by":1,"name":"Yanshang Wang","email":"","orcid":"https://orcid.org/0000-0001-6110-6296","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Yanshang","middleName":"","lastName":"Wang","suffix":""},{"id":440614960,"identity":"cf9088e3-bce3-40e4-8134-5301a5f290c0","order_by":2,"name":"Chang Cai","email":"","orcid":"","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Chang","middleName":"","lastName":"Cai","suffix":""},{"id":440614961,"identity":"ecae2315-6858-4ce8-9b49-271b498c6e3a","order_by":3,"name":"Zhenyu Shi","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Zhenyu","middleName":"","lastName":"Shi","suffix":""},{"id":440614962,"identity":"2690e8da-eefc-40c2-8899-e982d8037f69","order_by":4,"name":"Qian Gao","email":"","orcid":"","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Gao","suffix":""},{"id":440614963,"identity":"0f563acd-d1d0-4bde-ae8c-a11118ba9f99","order_by":5,"name":"Alex Bottle","email":"","orcid":"","institution":"Imperial College London, Department of Primary Care and Public Health","correspondingAuthor":false,"prefix":"","firstName":"Alex","middleName":"","lastName":"Bottle","suffix":""},{"id":440614964,"identity":"f6d19712-a3e6-405e-ad7e-e79abcdfbedb","order_by":6,"name":"Mansour Taghavi Azar Sharabiani","email":"","orcid":"","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Mansour","middleName":"Taghavi Azar","lastName":"Sharabiani","suffix":""},{"id":440614965,"identity":"d9c1e2cc-e748-48d6-8b48-0e78ef8dcd35","order_by":7,"name":"Joshua Stott","email":"","orcid":"","institution":"University College London","correspondingAuthor":false,"prefix":"","firstName":"Joshua","middleName":"","lastName":"Stott","suffix":""},{"id":440614966,"identity":"b3939cca-a6d1-48bc-9dd4-6b13aec2a502","order_by":8,"name":"Benedict Hayhoe","email":"","orcid":"","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Benedict","middleName":"","lastName":"Hayhoe","suffix":""}],"badges":[],"createdAt":"2025-04-05 02:30:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6379381/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6379381/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80306293,"identity":"8c7116de-e837-4e90-a606-452187ec4b83","added_by":"auto","created_at":"2025-04-10 10:21:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":692175,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of physical, psychological, and cognitive multimorbidity by gender and age.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: \u003c/em\u003eCog: cognitive disorder; Psy: psychological disorder; Phy: physical condition; Psy + Cog: psychological-cognitive multimorbidity; Phy + Cog: physical-cognitive multimorbidity; Phy + Psy: physical- psychological multimorbidity; Phy + Psy + Cog: physical-psychological-cognitive multimorbidity.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6379381/v1/50d38c22dac787072e2dbb88.png"},{"id":80307249,"identity":"180bb3fb-2979-400d-9487-1ffd66c6dfbe","added_by":"auto","created_at":"2025-04-10 10:29:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1285460,"visible":true,"origin":"","legend":"\u003cp\u003eProbability of healthcare utilization across different multimorbidity patterns.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: \u003c/em\u003eCog: cognitive disorder; Psy: psychological disorder; Phy: physical condition; Psy + Cog: psychological-cognitive multimorbidity; Phy + Cog: physical-cognitive multimorbidity; Phy + Psy: physical- psychological multimorbidity; Phy + Psy + Cog: physical-psychological-cognitive multimorbidity.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6379381/v1/b32646d31c68fb0d282d00e7.png"},{"id":80306292,"identity":"134a0e07-c83a-49bf-92dd-f33da7bb4b1c","added_by":"auto","created_at":"2025-04-10 10:21:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":0,"visible":true,"origin":"","legend":"\u003cp\u003eThe association between health utilization and multimorbidity (result of meta-analysis).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: \u003c/em\u003eCog: cognitive disorder; Psy: psychological disorder; Phy: physical condition; Psy + Cog: psychological-cognitive multimorbidity; Phy + Cog: physical-cognitive multimorbidity; Phy + Psy: physical- psychological multimorbidity; Phy + Psy + Cog: physical-psychological-cognitive multimorbidity. The reference group is population without any conditions.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6379381/v1/03f8bc376d160a197e872d5b.png"},{"id":80306301,"identity":"8dee3592-f79f-46aa-9821-f0cc5132b0aa","added_by":"auto","created_at":"2025-04-10 10:21:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":5417544,"visible":true,"origin":"","legend":"\u003cp\u003eThe associations between health utilization and multimorbidity (grouped by SES).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: \u003c/em\u003eCog: cognitive disorder; Psy: psychological disorder; Phy: physical condition; Psy + Cog: psychological-cognitive multimorbidity; Phy + Cog: physical-cognitive multimorbidity; Phy + Psy: physical- psychological multimorbidity; Phy + Psy + Cog: physical-psychological-cognitive multimorbidity. Population are categorized into 4 groups based on quartiles.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6379381/v1/45241715fac675e8724ac4c1.png"},{"id":80306305,"identity":"95998a5a-cba3-4d41-a494-d0fc10388595","added_by":"auto","created_at":"2025-04-10 10:21:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3207337,"visible":true,"origin":"","legend":"\u003cp\u003eThe associations between health utilization and multimorbidity (grouped by health insurance).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: \u003c/em\u003eCog: cognitive disorder; Psy: psychological disorder; Phy: physical condition; Psy + Cog: psychological-cognitive multimorbidity; Phy + Cog: physical-cognitive multimorbidity; Phy + Psy: physical- psychological multimorbidity; Phy + Psy + Cog: physical-psychological-cognitive multimorbidity.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6379381/v1/a3b55db8dd7dd3c0067eadb8.png"},{"id":83780985,"identity":"3fd6c8d0-d64b-4dab-b683-92b75083a7ab","added_by":"auto","created_at":"2025-06-02 15:23:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11090275,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6379381/v1/96336671-f7ad-4d8c-b6c9-cf4f375e21f0.pdf"},{"id":80307252,"identity":"8c044714-7318-43cf-9a11-0a5128bbe277","added_by":"auto","created_at":"2025-04-10 10:29:46","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":11325152,"visible":true,"origin":"","legend":"Appendix tables and figures","description":"","filename":"4SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-6379381/v1/7ba3872c0bbedccaf36e3aa6.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Multimorbidity, health service use, and health insurance by socioeconomic groups in 31 countries: a multi-cohort study","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePhysical, psychological, and cognitive multimorbidity pose substantial challenges to individuals, healthcare systems and societies. This growing burden of multimorbidity is further amplified by global population ageing and shifts in health behaviours, which have contributed to the increasing prevalence of multimorbidity\u0026nbsp;\u003csup\u003e1–3\u003c/sup\u003e. Studies have reported overall global prevalence of multimorbidity was 37.2%, making multimorbidity a major global health concern with its impact expected to intensify in the coming decades\u0026nbsp;\u003csup\u003e4\u003c/sup\u003e. Moreover, the complexity of multimorbidity extends beyond physical conditions to frequently involve psychological and cognitive disorders, which complicates management and further strains healthcare systems.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile the intersection of physical and psychological multimorbidity has been explored in previous studies\u0026nbsp;\u003csup\u003e5,6\u003c/sup\u003e, only limited studies have analyzed cognitive disorders as part of the multimorbidity spectrum\u0026nbsp;\u003csup\u003e7,8\u003c/sup\u003e.\u0026nbsp;Most existing research focuses on investigating how physical and psychological multimorbidity contributes to the risk of developing cognitive impairment or dementia\u0026nbsp;\u003csup\u003e9,10\u003c/sup\u003e, or examines its association with healthcare utilization\u0026nbsp;\u003csup\u003e5,11\u003c/sup\u003e. However, these studies have not fully analyzed the consequences of cognitive related multimorbidity, which hinders a comprehensive understanding of their impact on healthcare utilization patterns, particularly given the known complexity of healthcare utilisation in those with cognitive impairment even without multimorbidity\u0026nbsp;\u003csup\u003e12\u003c/sup\u003e. The inclusion of cognitive disorders also reveals even more pronounced socioeconomic inequalities in multimorbidity prevalence, particularly in low- and middle-income countries (LMICs)\u0026nbsp;\u003csup\u003e7\u003c/sup\u003e. However, there remains a gap in the literature, as no studies have fully described the relationship between healthcare use and the interplay of physical, psychological, and cognitive multimorbidity, nor have they thoroughly examined the role of SES.\u003c/p\u003e\n\u003cp\u003eIn the process of obtaining healthcare services, health insurance plays a crucial role, serving as a vital mechanism in advancing Universal Health Coverage (UHC). Individuals with multimorbidity experience significant treatment and illness burdens, which often result in increased healthcare utilization and costs\u0026nbsp;\u003csup\u003e13\u003c/sup\u003e. In addition, they frequently face unmet healthcare needs and inadequate access to appropriate care, particularly in healthcare systems that prioritize single disease management over the complex care coordination required for their multifaceted health need\u0026nbsp;\u003csup\u003e14,15\u003c/sup\u003e. Health insurance, as a key instrument of financial risk protection, has the potential to mitigate the economic burden of multimorbidity management. Existing research on the impact of health insurance on healthcare utilization among individuals with multimorbidity is limited, often focusing on single countries and narrowly defining multimorbidity as either physical conditions alone or combinations of physical and mental disorders\u0026nbsp;\u003csup\u003e16,17\u003c/sup\u003e. With the ongoing trends of population aging and the increased vulnerability of patients with cognitive disorders to unmet healthcare needs\u0026nbsp;\u003csup\u003e18,19\u003c/sup\u003e, it is crucial to consider the complexities introduced by cognitive disorders in multimorbidity patterns. By measuring and characterizing these associations, policymakers could develop targeted interventions and strategies that address the multimorbidity disparities in health care access.\u003c/p\u003e\n\u003cp\u003eUtilizing panel data from six longitudinal studies across 31 countries, this study is the first study to examine the association between healthcare utilization and physical, psychological, and cognitive multimorbidity, as well as the modifiable role of health insurance in this context, particularly concerning socioeconomic status.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy design and data sources\u003c/p\u003e\n\u003cp\u003eThis multicohort study utilizes data from six longitudinal studies across 31 countries: the China Health and Retirement Longitudinal Study (CHARLS), the Japanese Study of Aging and Retirement (JSTAR), the Korean Longitudinal Study of Aging (KLoSA), the Mexican Health and Aging Study (MHAS) and the Survey of Health, Ageing and Retirement in Europe (SHARE)\u003csup\u003e20–24\u003c/sup\u003e. These studies use consistent survey protocols, allowing for effective cross-regional comparisons. All studies are adhering to the same biennial design and standardized measures of economic status, lifestyle, and health. Data were drawn from waves 1–4 of CHARLS (2011–2018), waves 1–3 of JSTAR (2007-2011), waves 1–8 of KLoSA (2006–2020), waves 1–5 of MHAS (2001–2018) and waves 4–9 of SHARE (2008–2019).\u003c/p\u003e\n\u003cp\u003eAll studies were ethically approved, and participants provided written informed consent.\u003c/p\u003e\n\u003cp\u003eSample construction\u003c/p\u003e\n\u003cp\u003eFor this study, we included data from participants who were aged 50 years and older for consistency across all datasets, and we excluded participants who had missing values of dependent or independent variables.\u003c/p\u003e\n\u003cp\u003eMeasure\u003c/p\u003e\n\u003cp\u003e(1) Multimorbidity\u003c/p\u003e\n\u003cp\u003eIn this study, multimorbidity was defined as the co-occurrence of two or more disorders across physical, psychological, or cognitive domains\u0026nbsp;\u003csup\u003e7\u003c/sup\u003e. Participants were classified as having a physical disorder if they self-reported at least one of the following seven chronic conditions: hypertension, diabetes, cancer, lung disease, heart disease, stroke, or arthritis. The presence of a psychological disorder was determined through study-specific psychological assessments (e.g., the Center for Epidemiologic Studies Depression scale), using the same cut-off values as in previous studies (Appendix Table 1). Potential cognitive disorders were assessed using study-specific cognitive tests (e.g., the Mini-Mental State Examination), with cut-off values used from prior research or determined empirically (Appendix Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[Appendix Table 1. The definition of physical, psychological and cognitive disorder.]\u003c/p\u003e\n\u003cp\u003e(2) Outcome\u003c/p\u003e\n\u003cp\u003eHealthcare utilization was measured using two indicators: outpatient visits and inpatient admissions. We harmonized the measure of healthcare use across databases and categorized it as binary, indicating whether the participant had any experience of outpatient or inpatient care. Furthermore, to assess the intensity of healthcare use, we calculated the number of visits. Due to differences in measurement units across the datasets, we standardized outpatient service utilization over a one-year period and inpatient service utilization over a two-year period, reflecting the number of outpatient visits in the past year and hospital admissions in the past two years. The detailed definitions and measurement of variables are provided in Appendix Table 2.\u003c/p\u003e\n\u003cp\u003e[Appendix Table 2. The definition of health care use.]\u003c/p\u003e\n\u003cp\u003e(3) Health insurance\u003c/p\u003e\n\u003cp\u003eGiven the differences in health insurance systems across countries, we treated health insurance as a binary variable (covered or not covered). This classification encompassed various types of insurance, including public health insurance programs, private health insurance programs, or any other form of health insurance. The definitions and measurement of this variable are detailed in Appendix Table 3.\u003c/p\u003e\n\u003cp\u003e[Appendix Table 3. The definition of health insurance.]\u003c/p\u003e\n\u003cp\u003e(4) Covariates\u003c/p\u003e\n\u003cp\u003eIn each study, covariates were assessed at the selected wave of the survey. These included basic demographic information: age, gender (male vs female), educational attainment (middle school or below, high school, and college or above), work status (unemployed vs employed), marital status (not married vs married), and household wealth level. Additionally, we considered lifestyle factors such as BMI, alcohol use (yes vs no), tobacco use (yes vs no), and physical exercise (yes vs no), as potential confounders in the association between healthcare utilization and multimorbidity. BMI was calculated as weight (kg) divided by height squared (m²) and categorized into underweight (\u0026lt;18.5 kg/m²), normal weight (18.5–24.9 kg/m²), and overweight and obesity (≥25 kg/m²). The definitions and measurement methods for alcohol use, tobacco use, and physical exercise are provided in Appendix Table 4.\u003c/p\u003e\n\u003cp\u003e[Appendix Table 4. The definition of lifestyles.]\u003c/p\u003e\n\u003cp\u003eWe analyzed differences across socioeconomic groups using annual household wealth as a proxy for socioeconomic status (SES). Socioeconomic groups were categorized into quartiles based on household wealth within each country, with the quartile assignment recalculated for each survey wave to account for changes over time.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStatistical analysis\u003c/p\u003e\n\u003cp\u003eFirst, we conducted descriptive analyses of the prevalence of multimorbidity, stratified by country and age group. We then analyzed the probability of healthcare utilization across different multimorbidity patterns, focusing on outpatient and inpatient services. These analyses were further stratified by SES to provide a detailed understanding of SES-related variations in healthcare use. To assess the associations between multimorbidity and healthcare utilization, we used a panel data approach with random-effects logistic regression, accounting for individual differences as random variations over time. This allowed us to estimate the likelihood of outpatient visits and inpatient admissions in relation to multimorbidity patterns.\u003c/p\u003e\n\u003cp\u003eTo analyze the frequency of healthcare use, including the number of outpatient visits and inpatient hospital days, we used random-effects negative binomial models. Specifically, we conducted country-specific models to estimate associations within each country. These results were then aggregated into multinational meta-analyses\u0026nbsp;using the random effects model to obtain overall effect sizes across all countries.\u003c/p\u003e\n\u003cp\u003eTo examine the differential impact across population subgroups, we performed subgroup analyses stratified by SES and health insurance coverage. These analyses followed the same regression approach but excluded the stratification variable. For these models, data from all countries were pooled, with country-level variations accounted for as control variables.\u003c/p\u003e\n\u003cp\u003eFor the logistic regression analyses, we reported associations as odds ratio (OR) adjusted for relevant covariates, with 95% confidence interval (CI). For the random-effects Poisson regression analysis, we presented incident rate ratio (IRR) adjusted for covariates, also with 95% CIs. Statistical significance was set at a p-value of less than 0.05. All analyses were performed using Stata (version 15.0).\u003c/p\u003e\n\u003cp\u003eThe data were all publicly available. The use of public, secondary, de-identified data made the present study exempt from review by an institutional review board.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eWe included a total of 1450209 individuals from 31 countries. The average age of respondents across countries ranged from 62.8 to 70.2 years, with a slightly higher proportion of females in the study population, ranging from 50.7% to 62.8%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[Appendix Table 5. Basic demographics by country.]\u003c/p\u003e\n\u003cp\u003e[Appendix Figure 1: Prevalence of physical, psychological, and cognitive multimorbidity by country.]\u003c/p\u003e\n\u003cp\u003eIn terms of multimorbidity patterns, physical-mental multimorbidity showed the highest prevalence, ranging from 7.5% in China to 32.6% in Lithuania. The prevalence of physical-mental-cognitive multimorbidity ranged from 0.4% in Finland to 17.6% in South Korea.\u003c/p\u003e\n\u003cp\u003eRegarding healthcare utilization, outpatient care usage varied widely. In China (CHARLS), where the measurement period was one month, outpatient utilization was lowest at 18.9%. For all other databases, measured over 12 months (24 months in KLoSA), Germany reported the highest outpatient utilization at 94.4%. The median outpatient visit within one year was 3 (IQR 1\u0026ndash;7.5). For inpatient care, the overall pooled utilization rate was 14.5%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe complexity of multimorbidity patterns increased with age, with slight gender differences observed (Figure 1). As age increased, the proportion of individuals with physical-psychological-cognitive multimorbidity rose, from 1.8% among individuals aged 50\u0026ndash;59 years to 17.4% among those aged 90 years or older.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn men, the multimorbidity pattern changed with age in a manner comparable to that observed in women.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAn analysis of the relationship between multimorbidity patterns and healthcare utilization (Figure 2) revealed notable findings. For outpatient care, individuals with cognitive disorders alone had the lowest utilization rate at 34.0%, while those with only physical conditions had the highest utilization rate at 88.0%. Those with the most complex multimorbidity pattern\u0026mdash;physical-psychological-cognitive multimorbidity\u0026mdash;had an outpatient care utilization rate of 82.1%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor inpatient services, a trend was observed where utilization rates increased with the complexity of multimorbidity patterns. Individuals with physical-psychological multimorbidity had the highest hospitalization rate at 23.0%, followed closely by those with physical-psychological-cognitive multimorbidity at 22.8% (Figure 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe association between multimorbidity and healthcare utilization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe association between multimorbidity patterns and outpatient care utilization reveals a complex relationship (Figure 3). Compared with individuals without any conditions, those with cognitive disorders had significantly lower outpatient utilization (OR = 0.53, 95% CI [0.39, 0.67]). Surprisingly, individuals with the most complex multimorbidity pattern\u0026mdash;physical-psychological-cognitive multimorbidity\u0026mdash;did not exhibit the highest outpatient care utilization (OR = 3.08, 95% CI [2.22, 3.94]), which was lower than that of individuals with a single physical condition (OR = 5.15, 95% CI [4.46, 5.85]). The highest outpatient utilization was observed among those with physical-psychological multimorbidity, who had a 7.80-fold (95% CI [6.62, 8.97]) increase in utilization compared with those without any conditions.\u003c/p\u003e\n\u003cp\u003eHowever, when analyzing outpatient visits (as opposed to binary outpatient care utilization), the trends were similar but less pronounced. We observed that individuals with cognitive disorders used outpatient services less frequently (IRR = 0.89 [95% CI 0.75\u0026ndash;1.04]) compared with those without conditions. Additionally, the most complex multimorbidity pattern\u0026mdash;physical-psychological-cognitive multimorbidity\u0026mdash;did not result in the highest visit frequency (IRR = 2.21, 95% CI [1.99\u0026ndash;2.43]).\u003c/p\u003e\n\u003cp\u003eFor inpatient care, a positive association was found between the complexity of multimorbidity patterns and hospitalization rates. However, we observed that the addition of a cognitive disorder to an existing physical or psychological condition did not lead to a significant increase in inpatient utilization. Similarly, for inpatient visits, the pattern mirrored the trend seen with the binary inpatient care variable. Individuals with cognitive disorders had lower inpatient service utilization compared with those without any conditions (IRR = 0.67, 95% CI [0.40\u0026ndash;0.94]). Among all multimorbidity patterns, individuals with physical-psychological multimorbidity had the highest inpatient utilization (IRR = 3.79, 95% CI [3.51\u0026ndash;4.07]). Moreover, the associations between multimorbidity and healthcare utilization showed a subtle gradient in relation to socioeconomic status (SES), particularly in outpatient care, where the association weakened as SES increased (Figure 4). Appendix table 6-Appendix table 9 reported the estimates for each country, and Appendix table 10 provides the details of meta-analysis.\u003c/p\u003e\n\u003cp\u003e[Appendix Table 6. The association between multimorbidity and outpatient by country.]\u003c/p\u003e\n\u003cp\u003e[Appendix Table 7. The association between multimorbidity and outpatient visits by country.]\u003c/p\u003e\n\u003cp\u003e[Appendix Table 8. The association between multimorbidity and inpatient by country.]\u003c/p\u003e\n\u003cp\u003e[Appendix Table 9. The association between multimorbidity and inpatient visits by country.]\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;[Appendix Table 10: The meta-analysis details.]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe role of health insurance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStratified analyses by health insurance status suggested different pattern in the relationship between multimorbidity and healthcare use, especially for outpatient care (Figure 5). Specifically, for individuals with health insurance coverage, the association between multimorbidity and outpatient care use was generally weaker. This was particularly evident for those with physical-psychological-cognitive multimorbidity (with health insurance: OR = 6.22, 95% CI [5.33, 7.25]; \u0026nbsp; \u0026nbsp; \u0026nbsp;without health insurance: \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;OR = 3.40, 95% CI [3.03, \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 3.82]), physical-psychological multimorbidity (with health insurance: OR = 8.32, 95% CI [7.76, 8.93]; without health insurance: OR = 3.01, 95% CI [2.76, 3.29]), and single physical conditions (with health insurance: OR = 5.67, 95% CI [5.39, 5.97]; without health insurance: OR = 3.53, 95% CI [3.23, 3.85]). Similar trends were observed for inpatient care utilization, although the differences were less pronounced compared with outpatient care.\u003c/p\u003e\n\u003cp\u003eFurther analyses of the differences in OR and IRR between those with and without health insurance across SES quartiles for each multimorbidity pattern revealed that, for outpatient care\u0026mdash;both binary and visit frequency measures\u0026mdash;most differences were greater than zero (Appendix Figure 3). This effect was more pronounced in lower SES groups, demonstrating a negative correlation between SES and the impact of health insurance.\u0026nbsp;However, for inpatient care, the relationship with SES exhibited the opposite trend.\u003c/p\u003e\n\u003cp\u003e[Appendix Figure 2: The OR and IRR differences between individuals with and without health insurance (grouped by multimorbidity and SES).]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSensitivity analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted three sensitivity analyses to validate our findings. First, to address potential measurement unit discrepancies, we reanalyzed our data. For outpatient care, we excluded CHARLS, and for inpatient care, we excluded MAHS (Appendix Figure 4), yielding results consistent with our primary findings, confirming the complex associations between multimorbidity and outpatient care use. Second, we presented random-effects Poisson regression models for healthcare visits. The results remained consistent, reinforcing the robustness of our findings across different model specifications. Finally, we tested an alternative SES measure by replacing household wealth with education attainment for stratified analyses, and observed similar trends (Appendix Figure 4). Lastly, the multiple imputation (with 20 sets) was employed to handle the missing values, and the results remained consistent (Appendix Figure 5).\u003c/p\u003e\n\u003cp\u003e[Appendix Figure 3. The association between health utilization and multimorbidity (sensitivity analyses).]\u003c/p\u003e\n\u003cp\u003e[Appendix Figure 4. The association between health utilization and multimorbidity (grouped by education attainment).]\u003c/p\u003e\n\u003cp\u003e[Appendix Figure 5. The association between health utilization and multimorbidity (multiple imputation).]\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study indicates a significant but \u0026ldquo;non-monotonic\u0026rdquo; relationship between healthcare utilization and multimorbidity using the cross-national data. Specifically, the presence of cognitive disorders was associated with reduced likelihood of outpatient service use in individuals with co-occurring conditions. Additionally, this negative association demonstrated a gradient effect across socioeconomic groups, weakening as SES increased, at least when measured using household wealth as a proxy. Furthermore, we found that health insurance exerts a buffering effect on the relationship between healthcare utilization and multimorbidity, alleviating some of the SES-related differences in use of, and potentially access to care.\u003c/p\u003e \u003cp\u003eThe cognitive-related multimorbidity pattern appears to be associated with suppressed healthcare utilization compared with physical-only multimorbidity. Unlike previous studies, which have generally reported that healthcare utilization increases with the number of chronic conditions or the complexity of multimorbidity patterns \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e \u0026mdash;primarily focusing on physical or mental multimorbidity, our findings demonstrate a significant reduction in healthcare use among individuals with cognitive disorder-related multimorbidity. This underscores the nuanced nature of healthcare utilization patterns as the burden and complexity of multimorbidity evolve, challenging the assumption that greater complexity invariably results in higher service use.\u003c/p\u003e \u003cp\u003eTwo possible explanations may account for this result. First, from the demand-side perspective, individuals with cognitive disorders may receive much informal care from family and social networks, which is not captured in current healthcare utilization measurement \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. However, our findings show that individuals with the most complex multimorbidity patterns (physical-mental-cognitive multimorbidity) exhibit even lower healthcare utilization than those with a single physical condition, suggesting that unmet healthcare needs could play a substantial role. While direct evidence for this is lacking, this interpretation aligns with previous research on cognitive disorders that demonstrates high levels of unmet healthcare needs in such populations \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Second, from the supply-side perspective, we observed differing results between outpatient care (binary) and outpatient visits (count), offering an alternative explanation for the findings. Outpatient care as a binary indicator measures access to this type of care, while the number of outpatient visits measure the intensity of healthcare utilization. The lack of an increase in the likelihood of obtaining care as multimorbidity patterns become more complex likely reflects existing inequalities in healthcare delivery and the fragmented health care systems, which may fail to meet the needs of patients with multimorbidity in an equitable and accessible manner \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. However, once patients are able to access healthcare services, the diverse and multiple needs associated with multimorbidity are revealed, as evidenced by the increased intensity of care utilization. These complex findings as to cognitive disorder-related multimorbidity was associated with decreased healthcare use, differing from some previous studies \u003csup\u003e\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Prior studies, primarily based on health records or hospitalization cohorts, cannot have fully captured the complex multimorbidity pattern and actual healthcare use.\u003c/p\u003e \u003cp\u003eThe relationship between multimorbidity and healthcare utilization indicates significant SES-related inequalities, indicating that the above finding may be particularly pronounced in those with low SES. This may be because individuals with higher SES often have better access to healthcare resources \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. This finding extends previous research, which had documented SES inequalities in the prevalence of physical-psychological-cognitive multimorbidity \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. It suggests that disadvantaged groups face a dual challenge from both the illness burden and the treatment burden associated with multimorbidity.\u003c/p\u003e \u003cp\u003eHealth insurance potentially plays a critical role in narrowing healthcare utilization disparities, particularly in mitigating SES inequalities. As an essential mechanism for achieving UHC, health insurance is designed to protect individuals and families from financial strain due to high healthcare expenditures \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Our findings reveal that the relationship between multimorbidity and healthcare use is less pronounced among individuals with health insurance compared with those without, especially regarding outpatient services. This reflects that health insurance helps reduce disparities in healthcare utilization caused by multimorbidity, potentially lowering the risk of unmet healthcare needs. However, this buffering effect is less pronounced for inpatient services, which can be attributed to the inherent nature of multimorbidity and the relatively lower elasticity of demand for hospital care compared with outpatient care \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Multimorbidity often involves chronic conditions that necessitate ongoing management, making the demand for outpatient services more salient. Further analysis suggests that this protective effect of health insurance for outpatient care is more noticeable among the lower SES group. This finding highlights the essential role of health insurance in promoting equitable access to necessary health services for vulnerable populations. Therefore, this study enhances our understanding of the moderating role of health insurance ownership in mitigating the adverse effects of multimorbidity on healthcare utilization \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. By demonstrating how insurance mitigates barriers to care, these findings emphasize the importance of expanding health insurance coverage, particularly for socioeconomically disadvantaged groups, to address healthcare access inequities and ensure that patients with multimorbidity receive appropriate care.\u003c/p\u003e \u003cp\u003eOur study is the first to examine physical, psychological, and cognitive multimorbidity, healthcare utilisation and how this is moderated by SES and health insurance, which points to significant policy implications: First, we underscore the urgent need for person-centered integrated care systems for patients with multimorbidity. Our findings demonstrate that when cognitive disorders are part of multimorbidity patterns, healthcare needs become significantly more complex. Addressing these complexities requires not only integration across different levels of healthcare but also the inclusion of family and social care support systems. This approach is essential to address the unmet needs of this population and to provide targeted, comprehensive, and continuous care, yet current clinical guidelines are insufficient in meeting the complex needs of patients with multimorbidity \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Secondly, expanding health insurance coverage is critical for promoting health equity among patients with multimorbidity. As the prevalence of multimorbidity rises, accompanied by increasingly complex patterns and significant economic and social burdens, the progress toward achieving UHC will face significant challenges. While health insurance has shown buffering effects, sustained impact requires a transformation of both financing and service delivery systems to better manage the complexities multimorbidity beyond traditional single-disease models. Lastly, our study highlights the urgent need to address SES inequalities in healthcare services. Equity-oriented policies and programs are essential to reduce social disparities in healthcare access for multimorbidity. Such efforts may necessitate a targeted approach that allocates health resources and interventions to disadvantaged groups.\u003c/p\u003e \u003cp\u003eThis study has two notable strengths. First, to our knowledge, it is the first panel data analysis using cross-national data to examine healthcare utilization differences among individuals with physical, psychological, and cognitive multimorbidity, addressing limitations in prior studies\u0026mdash;such as challenges with cross-cultural generalizability, statistical conclusion validity, and measurement validity. Second, the study offers a detailed and nuanced analysis of the role of health insurance, exploring not only its buffering effects but also its interaction with socioeconomic inequalities.\u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eThis study also had several limitations that should be acknowledged. First, the information on physical, psychological, and cognitive disorders, as well as healthcare use, was self-reported by participants, which may have introduced recall bias and led to underestimation, especially among older individuals and those from lower SES backgrounds, who might be more likely to under-report such factors \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Secondly, the measurement of psychological and cognitive multimorbidity may have varied across datasets. To assess depression and cognitive disorders, we used cohort-specific cut-offs for psychological and cognitive assessments, which could have increased heterogeneity in the cohort-specific results. However, this approach has been considered an accepted method for cross-cohort comparisons \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Thirdly, due to data limitations, we were unable to account for supply-side factors. Our study only adjusted for individual characteristics and did not consider healthcare system-related factors such as the type of health insurance scheme and physician access, which might also influence healthcare use. Lastly, no sample weights were applied in our analysis due to the multi-cohort design and inconsistent methods of constructing sample weights across the different studies.\u003c/p\u003e \u003cp\u003eThe inexorable aging of populations necessitates a critical examination of the complexities inherent in multimorbidity patterns, particularly those including by cognitive disorders. These complexities significantly affect healthcare use behaviour and underscore the diverse healthcare needs. To address these challenges, it is imperative to establish a person-centred healthcare system that includes family and social support networks. Furthermore, advancing multimorbidity-specific health insurance system to promote equity and accessibility in healthcare services is essential. Such efforts are vital not only for enhancing individual health outcomes but also for advancing the broader objective of achieving UHC and Sustainable Development Goals.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the Major Project of the National Social Science Fund of China (21\u0026amp;ZD187).\u003c/p\u003e\u003ch2\u003eAuthor contribution\u003c/h2\u003e \u003cp\u003ePing He: Conceptualization and design, Supervision, Writing - Review; Benedict Hayhoe: Supervision, Writing \u0026ndash; Review; Yanshang Wang: Formal analysis, Writing - original draft; Chang Cai: Writing \u0026ndash; Review; Zhenyu Shi: Writing \u0026ndash; Review; Qian Gao: Writing \u0026ndash; Review; Alex Bottle: Writing \u0026ndash; Review, Mansour Taghavi Azar Sharabiani: Writing \u0026ndash; Review. All authors critically revised successive drafts of the paper and approved the final version. The corresponding author attests that listed author meet authorship criteria and that no others meeting the criteria have been omitted.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThe authors gratefully acknowledge the funding support from the Major Project of the National Social Science Fund of China (21\u0026amp;ZD187).\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe data is publicly available.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePathirana, T. I. \u0026amp; Jackson, C. A. Socioeconomic status and multimorbidity: a systematic review and meta-analysis. 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The influence of socio-economic status and multimorbidity patterns on healthcare costs: a six-year follow-up under a universal healthcare system. International Journal for Equity in Health 12, 69 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuarte, F. Price elasticity of expenditure across health care services. Journal of Health Economics 31, 824\u0026ndash;841 (2012).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6379381/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6379381/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe prevalence of physical, psychological, and cognitive multimorbidity is marked by socioeconomic status (SES) inequalities. However, the relationship between multimorbidity patterns—particularly those involving cognitive conditions—and healthcare utilization, as well as the role of health insurance, remains poorly understood. This is the first study to explore healthcare-seeking behaviour among individuals with multimorbidity and assess whether these vary by SES and health insurance coverage.\u003c/p\u003e\n\u003cp\u003eThis multicohort study analyzed data from six longitudinal studies across 31 countries, including participants aged 50 years and older. Multimorbidity was defined as the coexistence of two or more disorders across physical, psychological, or cognitive disorders. Outpatient and inpatient healthcare utilization were measured. Random-effects logistic regression models were employed to assess associations with healthcare utilization, and random-effects Poisson regression models analyzed visit frequencies. Country-specific analyses were aggregated via multinational meta-analyses using random-effects models to generate overall effect sizes.\u003c/p\u003e\n\u003cp\u003eWe included a total of 1450209 individuals. Compared with individuals without any conditions, those with the most complex multimorbidity pattern had higher outpatient care utilization (OR 3.13, 95% CI [2.21–4.05]) but not as high as those with physical-psychological multimorbidity (OR 7.83, 95% CI [6.59–9.07]). Additionally, the association varied across socioeconomic groups, with lower SES individuals experiencing more pronounced disparities in care use. In contrast, the association between multimorbidity and inpatient care utilization was less pronounced. Health insurance coverage weakened the association between multimorbidity and outpatient care use, especially for individuals with physical-psychological-cognitive multimorbidity. Those with insurance had a stronger likelihood of utilizing outpatient care (OR 6.14, 95% CI [5.26, 7.16]) compared with those without insurance (OR 2.98, 95% CI [2.65, 3.36]).\u003c/p\u003e\n\u003cp\u003eCognitive disorders further complicate multimorbidity, indicating unmet healthcare needs, especially among individuals with lower SES. Our study highlights a potential role of health insurance in mitigating disparities in healthcare utilization related to multimorbidity.\u003c/p\u003e","manuscriptTitle":"Multimorbidity, health service use, and health insurance by socioeconomic groups in 31 countries: a multi-cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-10 10:21:41","doi":"10.21203/rs.3.rs-6379381/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"96c7471c-89a9-4459-a399-a2d85bca269f","owner":[],"postedDate":"April 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":46910659,"name":"Health sciences/Health care/Health services"},{"id":46910660,"name":"Health sciences/Health care/Public health"}],"tags":[],"updatedAt":"2025-06-02T15:15:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-10 10:21:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6379381","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6379381","identity":"rs-6379381","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}