Healthcare Utilization Among Japanese Older Adults During Later Stage of Prolonged Pandemic

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This period was characterized by the Omicron variant, widespread vaccination coverage, and adapted public health measures. Using a comprehensive dataset of 189,841,257 medical claims linked with income tax records, we analyze how healthcare utilization correlates with public health measures, pandemic severity, and income levels. Our findings reveal distinct utilization patterns: moderate decreases in healthcare visits during periods of public health measures, with the association between pandemic severity and healthcare use varying based on whether these measures are in place. Despite fluctuations in visit frequency, healthcare costs remain stable, indicating consistent service intensity. While income-related differences in general healthcare access are modest, dental care shows more pronounced socioeconomic variations. These patterns suggest a transition from initial widespread healthcare avoidance to more stable healthcare engagement, indicating adaptation to prolonged crisis conditions. Our findings provide insights for maintaining healthcare access during extended public health emergencies, particularly in aging societies where balancing healthcare needs with public health measures is crucial. Health sciences/Health care/Health policy Health sciences/Health care/Health services Health sciences/Health care/Public health Figures Figure 1 Figure 2 Figure 3 Introduction Public health crises, from infectious disease outbreaks to natural disasters, can profoundly disrupt healthcare systems and alter how people access healthcare. These disruptions often disproportionately affect vulnerable populations, particularly older population who typically require regular medical attention yet may be most hesitant to seek care during emergencies. The COVID-19 pandemic, with its unprecedented duration and global reach 1 – 3 , offers a unique opportunity to examine how healthcare systems and vulnerable populations adapt to prolonged public health emergencies. Initial responses to the pandemic are well-documented, revealing widespread healthcare avoidance 4 – 6 . Studies highlight significant declines in hospital admissions at the pandemic onset, alongside decreases in both urgent/emergency and routine care 7 – 9 . This global trend of reduced healthcare utilization, coupled with increased mortality rates from non-COVID conditions, highlighted the severe consequences of delayed medical care during crises 4 , 10 . Although these studies effectively capture immediate responses to an acute crisis, less is known about long-term adaptation in healthcare-seeking patterns, particularly among vulnerable populations who must balance ongoing healthcare needs with crisis-related risk 11 , 12 . For understanding these long-term adaptation patterns, Japan’s experience during the pandemic offers particularly valuable insights. As the world’s most aged society with a universal healthcare system 13 , Japan represents an important case study of how healthcare systems can balance sustained access with public health risk management. The lessons from Japan’s experience may become increasingly relevant as other countries also face demographic aging and the growing challenge of protecting healthcare access for older population during emergencies 11 , 12 , 16 . This study examines healthcare utilization patterns among Japan’s oldest-old population (i.e., aged 75 and above) during the later phase of COVID-19 pandemic. Using a comprehensive dataset linking medical claim records with income tax information, we analyze how healthcare utilization correlates with public health measures, crisis severity, and socioeconomic status. We observe moderate decreases in healthcare visits during periods with public health measures, with the relationship between pandemic severity and healthcare utilization varying by the presence of these measures. Despite fluctuations in visit frequency, overall healthcare costs remain stable, suggesting adaptations in how care is accessed rather than reductions in necessary treatment. We also find that while income-related differences in general healthcare access are modest, dental care show pronounced socioeconomic variations, indicating differential adaptation patterns across healthcare services. These findings contribute to our understanding of healthcare system resilience during public health emergencies. By examining how vulnerable populations modify healthcare access during a prolonged crisis, our study provides insights that may help inform efforts to maintain essential healthcare access during future emergencies, particularly in aging societies. Methods Study Context Japan reported its first COVID-19 case on January 16, 2020, marking the beginning of unprecedented challenges to the nation’s public health infrastructure 17 . Over the next three years, Japan experienced eight distinct infection surges, each prompting evolving governmental and public health responses. [Figure 1 ] Figure 1 illustrates Japan’s COVID-19 case trends and emergency responses from January 2020 through May 2023. The early waves (I-V) were characterized by the original SARS-CoV-2 strain and its Alpha and Delta variants 18 . During this period, Japan implemented rigorous measures at the prefecture level, including States of Emergency (SoE) and States of Precautionary Emergency (SoPE). SoE involved stringent but non-binding regulations to curb public gatherings and reinforce social distancing, while SoPE aimed to control the virus’s spread through less severe public health measures 19 . Notably, unlike many countries that imposed mandatory lockdowns, these measures represented strong recommendations rather than legally enforced restrictions, reflecting Japan’s distinctive strategy of encouraging voluntary behavioral changes rather than imposing strict lockdowns. The later waves (VI-VIII) were primarily driven by the Omicron variant, which, despite its high transmissibility, was typically associated with milder clinical outcomes 20 . This shift was reflected in Japan’s public health response, with the government implementing less stringent measures SoPE and eventually lifting all emergency declarations after Wave VI. In the early stages of the pandemic, widespread uncertainty and fear dominated. Public health messages urging people to shelter in place 19 , combined with the fear of contracting the virus in healthcare setting 21 , led to significant healthcare avoidance, especially among the older population. As the pandemic progressed, several factors may have influenced their healthcare utilization patterns, including widespread vaccine distribution 22 , enhanced infection control measures in healthcare facilities 23 , and more targeted public health messages 24 . These factors suggest that healthcare utilization patterns among Japan’s older population may have undergone a significant shift, moving from widespread avoidance in the pandemic’s early stages to more stable engagement later. Understanding these evolving patterns is valuable for developing effective healthcare strategies and policies for this vulnerable population during prolonged health crises. Data Sources and Sample Construction Our primary data source is the Medical Claims Data with Income Tax Information for the Oldest-Old in Japan (MCD-Tx), a comprehensive dataset of Japan’s Latter-Stage Elderly Healthcare System (LSEH) collected by the Ministry of Health, Labour and Welfare (MHLW). The LSEH is Japan’s universal healthcare system for the 75 + population, and as of September 2022, it covered 18.52 million individuals, comprising 98.6% of Japan’s 75 + population. The MCD-Tx, spanning from November 2021 to November 2022, captures all LSEH enrollees, regardless of their healthcare service utilization. For each individual, the dataset provides detailed monthly healthcare utilization and cost information from medical claims, linked with individual demographic and socioeconomic data including income information. This dataset represents the first instance in Japan where income information is integrated with medical claims data. Informed consent was waived by the Ethics Review Committee for Research Involving Human Subjects at Waseda University (Approval No.: 2022-HN038). As this study used anonymized secondary data, the requirement for informed consent was waived by the ethics committee. Furthermore, all methods used in this study were conducted in accordance with relevant guidelines and regulations regarding using data of human participants. To complement the MCD-Tx, we incorporated three additional datasets. The first tracks daily new COVID-19 cases at the secondary medical region (SMR) level—administrative units organized around specialized healthcare facilities that provide more granular geographical resolution than prefectures—from the onset of Japan’s first reported case until September 2022. The second dataset provides biweekly records of hospital beds designated for COVID-19 patients and actual admissions at the SMR level, from December 2021 to April 2023. The third dataset covers the daily implementation status of SoE and SoPE at the prefecture level, from March 2020 to September 2022. In constructing our sample, we first convert these three supplementary datasets to a monthly format. We then link them with the MCD-Tx claim records based on the SMR or prefecture of residence of the oldest-old (detailed linkage procedures are described in Appendix A). The final sample covers an 11-month period from November 2021 to September 2022, capturing the later phase of the pandemic (Waves VI and VII). This sample includes 1,769,537 individuals aged 75 + and 189,841,257 associated health insurance claim records. Measurements [Table 1 ] Table 1 Basic Statistics N Mean S.D. A: Extensive Healthcare Utilization 198,952,929 0.842 0.365 Hospital Admission 198,952,929 0.054 0.226 Outpatient Visit 198,952,929 0.785 0.411 Dental Care 198,952,929 0.208 0.406 B: Intensive Total Costs 167,437,977 86.60 230.64 Inpatient Costs 10,702,466 651.32 573.54 Outpatient Costs 156,186,553 43.37 87.50 Dental Costs 41,424,441 14.63 22.01 C: COVID-19 severity and SoPE measures Cases 198,952,929 0.013 0.021 SoPE 198,952,929 0.260 0.438 D: Individual characteristics and healthcare capacity Male 79,190,454 0.398 Age Q1 44,545,622 75.56 1.75 Q2 43,403,979 78.98 0.82 Q3 33,774,418 81.91 0.81 Q4 44,054,906 85.81 1.39 Q5 33,174,004 92.25 3.04 Income Q1 40,199,764 37.42 20.69 Q2 39,478,624 76.41 7.64 Q3 39,854,201 117.13 21.00 Q4 39,801,160 256.35 55.31 Q5 39,542,644 554.22 754.53 Primary non-COVID-19 diagnosis for hospital admission Infectious 10,265,787 0.014 Neoplasms 10,265,787 0.111 Hematologic 10,265,787 0.006 Endocrine 10,265,787 0.039 Mental 10,265,787 0.079 Neurological 10,265,787 0.087 Ophthalmic 10,265,787 0.027 Otologic 10,265,787 0.003 Cardiac 10,265,787 0.221 Respiratory 10,265,787 0.069 Digestive 10,265,787 0.075 Dermatologic 10,265,787 0.010 Musculoskeletal 10,265,787 0.071 Urogenital 10,265,787 0.055 Trauma 10,265,787 0.131 Primary non-COVID-19 diagnosis for outpatient visit Infectious 155,177,275 0.005 Neoplasms 155,177,275 0.018 Hematologic 155,177,275 0.002 Endocrine 155,177,275 0.077 Mental 155,177,275 0.013 Neurological 155,177,275 0.028 Ophthalmic 155,177,275 0.034 Otologic 155,177,275 0.005 Cardiac 155,177,275 0.267 Respiratory 155,177,275 0.024 Digestive 155,177,275 0.196 Dermatologic 155,177,275 0.034 Musculoskeletal 155,177,275 0.179 Urogenital 155,177,275 0.056 Trauma 155,177,275 0.063 COVID-19 Bed Occupancy Q1 37,953,684 0.050 0.043 Q2 30,949,872 0.164 0.025 Q3 29,851,806 0.257 0.030 Q4 31,479,306 0.396 0.048 Q5 34,678,415 0.686 0.176 Notes: “S.D.” refers to the standard deviation, indicating the extent of variation or dispersion in the data set. “Q1-Q5” represent the quintiles of the variable in question, arranged in ascending order, where Q1 denotes the lowest quintile and Q5 the highest. Our analysis examines both extensive and intensive margins of healthcare utilization. For the extensive margin, we construct four binary indicators associated with different healthcare services: overall healthcare utilization, hospital admission, outpatient visits, and dental care. Each indicator takes a value of one if an individual use the respective service within a given month, and zero otherwise. As shown in Table 1 , 84.2% of our sample access some form of healthcare during the study period, with outpatient visits being the most common (78.5%), followed by dental services (20%), while hospital admissions are less frequent (5.4%). For the intensive margin, we analyze four variables representing monthly medical costs: total costs, inpatient costs, outpatient costs, and dental costs (measured in 10,000 Japanese Yen, JPY). Among those who utilize services, average monthly inpatient care costs (651,320 JPY) are substantially higher than outpatient care (43,370 JPY) and dental care costs (14,630 JPY). To measure pandemic conditions, we construct two variables. The first captures pandemic severity through the monthly aggregated number of new COVID-19 cases per million people within each SMR, averaging 0.013 during our study period. The second indicates the presence of SoPE measures, taking a value of one for months with active measures in the resident’s prefecture (present in 26% of our study period). Notably, no prefecture implemented SoE measures during our study timeframe. Our models include several control variables to account for individual characteristics and healthcare capacity. Individual-level controls comprise age quintiles (ranging from mean age 75.56 years in Q1 to 92.25 years in Q5), income quintiles (from mean 37.42 million JPY in Q1 to 554.22 million JPY in Q5), and gender (39.8% male). We also include indicators for non-COVID-19 main diagnoses in both inpatient and outpatient care, with cardiac conditions being most prevalent (22.1% of hospitalizations and 26.7% of outpatient visits). Healthcare capacity is measured through COVID-19 hospital bed occupancy quintiles, ranging from 5.0–68.6%, reflecting regional variations in healthcare system strain during the pandemic. Analytical Approach We use two complementary models to examine healthcare utilization patterns. Our first model analyzes the association between SoPE measures and healthcare utilization: $$\:{Y}_{ijt}=\:{\beta\:}_{0}+\:{\beta\:}_{1}SoP{E}_{jt}+\:{\beta\:}_{2}f\left(COVI{D}_{jt}\right)+\:{X}_{ijt}^{{\prime\:}}\gamma\:\:+\:{\delta\:}_{t}+\:{\eta\:}_{j}+\:{\tau\:}_{j}t\:+\:{\epsilon\:}_{ijt}.$$ 1 Here, \(\:{Y}_{ijt}\) represents healthcare utilization and costs for individual \(\:i\) in SMR \(\:j\) at time \(\:t\) . \(\:SoP{E}_{jt}\) is the indicator of SoPE measures, our primary variable of interest. We anticipate healthcare utilization to be negatively associated with SoPE implementation, consistent with documented healthcare avoidance patterns. The relationship between medical costs and SoPE measures may be more complex, potentially showing either negative associations due to fewer visits or positive associations if delayed care leads to more intensive treatment needs. \(\:f\left(COVI{D}_{jt}\right)\) represents a quadratic polynomial function of new COVID-19 cases. The quadratic specification captures potential non-linear patterns in the relationship between case numbers and healthcare utilization, allowing for diminishing marginal correlations at higher case levels. \(\:{X}_{ijt}\) represents individual characteristics and healthcare capacity controls. The model incorporates year-month fixed effects \(\:{\delta\:}_{t}\) , geographic fixed effects at the SMR level \(\:{\eta\:}_{j}\) , and geographic linear trends \(\:{\tau\:}_{j}t\) to control for unobserved temporal and regional variations. \(\:{\epsilon\:}_{ijt}\) denotes the error term, with standard errors clustered at the SMR level. Our second model examines how the association between healthcare utilization and pandemic severity varies with public health measures: $$\:{Y}_{ijt}=\:{\theta\:}_{0}+\:{\theta\:}_{1}SoP{E}_{jt}+\:{\theta\:}_{2}f\left(COVI{D}_{jt}\right)+\:{\theta\:}_{3}\left[SoP{E}_{jt}\times\:\:f\left(COVI{D}_{jt}\right)\right]+\:{X}_{ijt}^{{\prime\:}}\gamma\:\:+\:{\delta\:}_{t}+\:{\eta\:}_{j}\:\:\:\:$$ $$\:+\:{\tau\:}_{j}t\:+\:{\epsilon\:}_{ijt}.\:\:$$ 2 This model fully interacts the SoPE indicator with the quadratic function of case numbers, allowing us to examine whether the relationship between pandemic severity and healthcare utilization differs during periods with and without public health measures. To interpret these relationships, we calculate average marginal effects of changes in case numbers at different pandemic severity levels, ranging from low (below 0.04 cases per million) to high (above 0.14 cases per million). To assess the robustness of our approaches, we conduct additional analyses (see Appendix B). We estimate models with a linear specification of pandemic severity as an alternative to our quadratic specification. We also perform subgroup analyses by age groups (75–84 and 85+) and gender to examine potential heterogeneity in patterns. These analyses reveal patterns consistent with our main findings, suggesting that the observed relationships between healthcare utilization, public health measures, and pandemic severity are robust across specifications and subpopulations. Results Healthcare Utilization During the SoPE Period We first examine patterns in healthcare service utilization among Japan’s oldest-old during periods with and without SoPE measures. Table 2 presents four models based on Eq. ( 1 ) with increasing complexity, from basic controls in Model 1 to a full consideration of the pandemic’s nonlinear patterns, individual attributes, and healthcare system capacity in Model 4. The observed relationships remain consistent across all models in both direction and magnitude. We focus our discussion on the most comprehensive Model 4. Table 2 Relationship Between SoPE Measures and Healthcare Utilization Patterns Mean Model 1 Model 2 Model 3 Model 4 (1) (2) (3) (4) (5) Extensive Margins Healthcare Utilization 0.842 -0.0109 *** -0.0121 *** -0.0074 ** -0.0073 ** (0.001) (0.005) (0.003) (0.003) Hospital Admission 0.054 -0.0001 -0.0008 *** -0.0004 * -0.0004 * (0.000) (0.000) (0.000) (0.000) Outpatient Visit 0.785 -0.0131 *** -0.0137 *** -0.0078 ** -0.0077 ** (0.001) (0.005) (0.003) (0.003) Dental Care 0.208 -0.0070 *** -0.0037 *** -0.0020 *** -0.0016 ** (0.001) (0.001) (0.001) (0.001) Intensive Margins Total Costs 86.60 1.825 1.730 0.287 0.272 (1.13) (1.07) (0.18) (0.18) Inpatient Costs 651.32 14.108 * 10.648 2.448 * 2.006 (7.42) (7.14) (1.42) (1.40) Outpatient Costs 43.37 0.753 0.953 -0.231 *** -0.217 *** (0.55) (0.59) (0.08) (0.08) Dental Costs 14.63 -0.117 -0.006 0.026 0.020 (0.20) (0.21) (0.05) (0.05) Covariates and Fixed Effects #Cases Yes Yes Yes Yes Individual characteristics and healthcare capacity No Yes Yes Yes Fixed effects and linear trends No No Yes Yes Squared #Cases No No No Yes Notes: Column (1) lists the means of the outcome variables. Columns (2) through (5) each represent the results from a separate regression as specified in Eq. ( 1 ). Standard errors in parentheses are clustered at the SMR level. *Inference: *** p < 0.01; ** p < 0.05; * p < 0.1. [Table 2 ] During SoPE periods, we observe lower overall healthcare engagement by 0.73 percentage points (0.86% below the mean). Similar patterns emerge for outpatient visits and dental care, which are lower by 0.77 and 0.16 percentage points respectively (0.98% and 0.77% below their respective means). For healthcare costs, outpatient costs during SoPE periods are lower by 2,170 JPY (0.50% below the mean). Notably, we find no systematic differences in inpatient and dental costs during SoPE periods, suggesting that while fewer people may seek these services, the intensity of care among those who do remains stable. Pandemic Severity and Healthcare Utilization [Figure 2 ] Figure 2 illustrates how healthcare utilization patterns relate to pandemic severity based on Eq. ( 2 ), revealing distinct patterns during periods with and without SoPE measures. For overall healthcare utilization, periods without SoPE show a negative relationship with rising COVID-19 cases, with an average marginal effect (AME) of -2.5 percentage points (2.97% below the mean). In contrast, during SoPE periods, we observe a positive relationship with an AME of 4.2 percentage points (4.99% above the mean). As pandemic severity increases, we observe an upward trend in healthcare utilization on the extensive margin regardless of SoPE measures, while the intensive margin generally trends downward. Two exceptions are hospital admission and outpatient costs. Hospital admissions show a positive relationship with case numbers at lower severity levels during SoPE periods, transitioning to negative at higher severity levels. Similarly, outpatient costs show an initial negative correlation with case numbers that shifts to positive as severity increases. Socioeconomic Patterns in Healthcare Utilization [Figure 3 ] Our analyses of income-stratified samples based on Eq. ( 1 ) reveal modest differences in healthcare utilization patterns across income levels. Specifically, Fig. 3 shows similar moderate decreases in healthcare utilization across all income quintiles during SoPE periods. We observe consistent patterns where healthcare utilization shows negative correlations with rising COVID-19 cases during non-SoPE periods and positive correlations during SoPE periods, regardless of income levels. Distinctive Patterns in Dental Care Dental care exhibits notably different patterns compared to other healthcare services. Figure 2 shows that rising COVID-19 cases associates with larger decreases in dental care utilization—18.3 percentage points (87.98% below the mean) during the non-SoPE periods. These decreases substantially exceed those observed in other healthcare services. Figure 3 further reveals income-related variations unique to dental care: utilization decreases by 21.7 percentage points (104.33% below the mean) in the lowest income quintile compared to 14 percentage points (67.31% below the mean) in the highest income quintile. Discussion This study examines healthcare utilization patterns among Japan’s oldest-old during the later phase of the COVID-19 pandemic, providing insights into how healthcare systems and vulnerable populations adapt to prolonged public health crises. Our analysis reveals a correlation between SoPE measures and moderate reductions in healthcare service utilization, particularly in outpatient care. This pattern differs from the sharp decreases documented in earlier pandemic stages, potentially reflecting adaptation in the later phase of the crisis 15 . Notably, while healthcare visit frequency shows modest decreases, healthcare costs remain relatively stable, suggesting that elderly people who continue seeking care maintain similar levels of service intensity. This pattern of adaptation—from acute avoidance to more stable utilization patterns—may represent a broader phenomenon in how healthcare systems and populations adjust to extended emergency conditions. Healthcare utilization patterns show distinct relationships with pandemic severity during periods with and without SoPE measures. During periods without SoPE measures, higher COVID-19 cases correlate with decreased healthcare utilization. However, this pattern reverses during SoPE periods, possibly reflecting factors such as enhanced safety protocols 24 or increased public confidence in healthcare facilities 25 . These patterns point to the dynamic nature of healthcare utilization during crises 26 , though we note that our approach cannot establish clear causal relationships between these factors. The relatively modest differences in healthcare utilization patterns across income levels among Japan’s oldest-old contrast with more pronounced income-based disparities reported in other countries during the pandemic 27 . This observation aligns with the role of Japan’s universal health insurance in maintaining healthcare access, suggesting that structural features of health systems may significantly influence their resilience during prolonged emergencies. Dental care utilization shows notably different patterns, marked by significant declines irrespective of whether SoPE measures are implemented. This trend likely reflects multiple factors, including the perceived high-risk nature of dental procedures during the pandemic 28 . The observed income-related variations in dental care utilization, even within Japan’s universal health insurance system, suggest that socioeconomic factors may influence oral health decisions during crises. These patterns raise concerns about potential long-term health implications, given the established connections between oral health and various systemic conditions among older population 29 , 30 . This study has several limitations. First, our sample faces selection bias as it necessarily includes only individuals who survived through the study period. Those who did not survive may have had different healthcare utilization patterns, potentially leading to underestimation of crisis-related healthcare challenges. Additionally, our analysis does not account for unobserved factors that might influence healthcare decisions, such as individual risk perceptions. The absence of data on specific health outcomes also limits our ability to assess the broader health implications of the observed utilization patterns. Future research could address these limitations by incorporating mortality data, examining specific health outcomes associated with utilization changes, and applying quasi-experimental designs. The patterns observed among Japan’s older population under the universal healthcare system during this prolonged crisis suggest several considerations for maintaining healthcare access during emergencies. Healthcare systems might benefit from developing integrated monitoring mechanisms to identify emerging care gaps, particularly in essential preventive services such as dental care. Combining enhanced safety protocols with flexible delivery models, including mobile services and remote care options, could help maintain service accessibility. Early warning systems to track utilization patterns among vulnerable groups, coupled with targeted support for high-risk but essential services, may help maintain healthcare access during various types of extended emergencies. These considerations may become increasingly relevant as healthcare systems worldwide address challenges from aging populations and various forms of public health crises. Concluding Remarks This study examines healthcare utilization patterns during a prolonged public health emergency, with particular focus on vulnerable older populations. While our observational study design precludes causal conclusions, the documented patterns contribute to our understanding of healthcare system resilience beyond the specific context of COVID-19. The findings reveal how healthcare utilization evolves during extended crises, showing both successes and challenges in maintaining access to healthcare services. As populations age globally, and healthcare systems face various challenges—from infectious disease outbreaks to natural disasters—these insights into healthcare adaptation patterns may inform efforts to protect vulnerable populations during future prolonged emergencies. Declarations Acknowledgements. We express our gratitude to Hidetake Endou, Kenji Suzuki, Kousuke Kashimura, and Takashi Ninishioka in the Ministry of Health, Labour and Welfare (MHLW) for their valuable feedback. This research received support from the Health Labour Sciences Research Grant (Research on Policy Planning and Evaluation) provided by the MHLW under Grant Number 22AA1002, with Haruko Noguchi serving as the Principal Investigator. Ethical statement. The study was reviewed and deemed exempt from ethical review by the Ethics Review Committee for Research Involving Human Subjects at Waseda University (Approval No.: 2022-HN038; Approval Date: November 25, 2022, please see the website: https://www.waseda.jp/inst/ore/procedures/). The data provided contains no personally identifiable information. Author contributions. R.F. conceived the study, conducted the analysis, and drafted the manuscript. S.L. assisted with data colletcion. M.O. contributed to the study design and interpretation of results. H.N. supervised the project and provided critical revisions. A.K. contributed to the interpretation of results and manuscript revision. All authors reviewed and approved the final manuscript. Data availability . This study used anonymized individual data from the Medical Claims Data with Income Tax Information for the Oldest-Old in Japan (MCD-Tx), collected and maintained by the Research Division of the Health Insurance Bureau, MHLW. 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Daly, B. et al. Evidence summary: the relationship between oral health and dementia. British Dental Journal 2017 223:11 223, 846–853 (2017). Petersen, P. E. & Yamamoto, T. Improving the oral health of older people: the approach of the WHO Global Oral Health Programme. Community Dent. Oral Epidemiol. 33 , 81–92 (2005). MHLW. Visualizing the data: information on COVID-19 infections. (2023). https://covid19.mhlw.go.jp/extensions/public/en/index.html Cabinet Agency for Infectious Diseases Crisis Management. COVID-19 Countermeasures. (2023). https://corona.go.jp/emergency/ Additional Declarations No competing interests reported. Supplementary Files HealthcareAppendix.docx Cite Share Download PDF Status: Published Journal Publication published 22 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Accepted 15 Apr, 2025 Reviews received at journal 12 Apr, 2025 Reviewers agreed at journal 27 Mar, 2025 Reviewers invited by journal 25 Mar, 2025 Submission checks completed at journal 21 Mar, 2025 First submitted to journal 14 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5086827","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":433969581,"identity":"d56c7123-6896-480e-bebe-9c580e2cc290","order_by":0,"name":"Rong Fu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYLCCBwYM/AzsDSAmMxAfIKQeqCjBgEGygecASVoYgFokEmBaCABz9vMHPyQU2EgY3Hxj+PFHhbU8A+NZ/NZY9iQzSyQYpEkY3M4xluY5k27YwHAuAa8WgwPJDEAth+sMbudukGZsO8zYwHDGAL+W84+ZfyQY/Ac67Ozmnz//HbYnrOVGMhvQlgMSBjd4t0nwNhxOJELLYzOLBINkCckz+d+seY6lJ7cR9Mv5xMc3Pvyxk+A7fiz55o8aa9t+CQIhhgnYJM6QqIOBgb+HZC2jYBSMglEwvAEA5wBL9ZEI2joAAAAASUVORK5CYII=","orcid":"","institution":"Waseda University","correspondingAuthor":true,"prefix":"","firstName":"Rong","middleName":"","lastName":"Fu","suffix":""},{"id":433969582,"identity":"c17ae519-4f84-485a-b118-0219f9684e7e","order_by":1,"name":"Sizhe Liu","email":"","orcid":"","institution":"Waseda University","correspondingAuthor":false,"prefix":"","firstName":"Sizhe","middleName":"","lastName":"Liu","suffix":""},{"id":433969583,"identity":"7f00d590-2289-48dd-a5c4-29ba5d3a7d73","order_by":2,"name":"Masato Oikawa","email":"","orcid":"","institution":"Waseda University","correspondingAuthor":false,"prefix":"","firstName":"Masato","middleName":"","lastName":"Oikawa","suffix":""},{"id":433969584,"identity":"f223e6c3-364d-4942-b304-68c2e33fddb6","order_by":3,"name":"Haruko Noguchi","email":"","orcid":"","institution":"Waseda University","correspondingAuthor":false,"prefix":"","firstName":"Haruko","middleName":"","lastName":"Noguchi","suffix":""},{"id":433969585,"identity":"a1b6191c-e744-46e7-9b07-ddad2ddbd234","order_by":4,"name":"Akira Kawamura","email":"","orcid":"","institution":"Waseda University","correspondingAuthor":false,"prefix":"","firstName":"Akira","middleName":"","lastName":"Kawamura","suffix":""}],"badges":[],"createdAt":"2024-09-14 03:51:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5086827/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5086827/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-98908-x","type":"published","date":"2025-04-22T15:57:29+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79328902,"identity":"f82dd9fa-9ba3-4a8d-a26e-cc2de0985c09","added_by":"auto","created_at":"2025-03-27 06:06:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":297475,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNewly confirmed COVID-19 cases and governmental emergency responses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNotes: COVID-19 case data were obtained from the official governmental website . The data collection ended on May 7, 2023, due to the reclassification of COVID-19 to a Category V Infectious Disease. Information regarding the enforcement of SoE and SoPE policies was gathered from the official website of the Cabinet Agency for Infectious Diseases Crisis Management. The gray shaded area in both graphs denotes the study period.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5086827/v1/0bd996ee33692ea410737fa0.png"},{"id":79328897,"identity":"3661a3f3-620a-42f2-b54a-a6279be7dd30","added_by":"auto","created_at":"2025-03-27 06:06:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":542991,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePandemic Severity and Healthcare Utilization Patterns\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNotes: The figure presents eight regression analyses based on Equation (2), examining healthcare utilization and costs. Each graph displays two key elements: the estimated marginal effects across different pandemic severity levels shown by the trend lines with confidence bands, and the Average Marginal Effects (AME) both with and without SoPE shown at the bottom of each panel along with their standard errors in parentheses. The AMEs are displayed as dashed lines, with red lines corresponding to estimates with SoPE and blue lines representing estimates without SoPE. In addition to the AME, marginal effects by the severity of the pandemic are presented with shaded confidence intervals at 90% for the inner band and 95% for the outer band. The x-axis measures pandemic severity in cases per million people, with values ranging from 0 to 0.18. The difference in slopes between red and blue lines indicates how the SoPE policy moderated the relationship between case numbers and healthcare outcomes.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5086827/v1/a05b91862cb7b7bdd7d77b3b.png"},{"id":79329611,"identity":"5e116f82-9584-4cef-a2fb-8af2d4f80d8d","added_by":"auto","created_at":"2025-03-27 06:14:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":257077,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIncome-Related Disparities in Healthcare Utilization Patterns\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNotes: \u003c/em\u003eEach graph in Figure 3 is divided into three panels: “SoPE,” “Cases w/o SoPE,” and “Cases w/ SoPE.” The “SoPE” panel illustrates the marginal effect of SoPE measures as described in Equation (1). The panels “Cases w/o SoPE” and “Cases w/ SoPE” represent the marginal effects of changes in COVID-19 case numbers without and with SoPE measures, respectively, following the specification of Equation (2). Point estimate is presented for each marker, accompanied by bars depicting confidence intervals: the thicker bar indicates a 95% confidence interval, and the thinner white line indicates a 90% confidence interval.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5086827/v1/22e52daf1e305dd283ea13bc.png"},{"id":81569863,"identity":"2c569925-b167-403c-bcfa-0c4e1eba3dbe","added_by":"auto","created_at":"2025-04-28 16:12:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2085817,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5086827/v1/3921a55d-843d-4e2a-84d7-f3ae5c03e4be.pdf"},{"id":79328923,"identity":"94b3721a-c14b-44ba-8286-bf91e650aa75","added_by":"auto","created_at":"2025-03-27 06:06:40","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":606502,"visible":true,"origin":"","legend":"","description":"","filename":"HealthcareAppendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-5086827/v1/24026e837dbfcf5039749beb.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Healthcare Utilization Among Japanese Older Adults During Later Stage of Prolonged Pandemic","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePublic health crises, from infectious disease outbreaks to natural disasters, can profoundly disrupt healthcare systems and alter how people access healthcare. These disruptions often disproportionately affect vulnerable populations, particularly older population who typically require regular medical attention yet may be most hesitant to seek care during emergencies. The COVID-19 pandemic, with its unprecedented duration and global reach \u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, offers a unique opportunity to examine how healthcare systems and vulnerable populations adapt to prolonged public health emergencies.\u003c/p\u003e \u003cp\u003eInitial responses to the pandemic are well-documented, revealing widespread healthcare avoidance\u003csup\u003e\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Studies highlight significant declines in hospital admissions at the pandemic onset, alongside decreases in both urgent/emergency and routine care\u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. This global trend of reduced healthcare utilization, coupled with increased mortality rates from non-COVID conditions, highlighted the severe consequences of delayed medical care during crises\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Although these studies effectively capture immediate responses to an acute crisis, less is known about long-term adaptation in healthcare-seeking patterns, particularly among vulnerable populations who must balance ongoing healthcare needs with crisis-related risk\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFor understanding these long-term adaptation patterns, Japan\u0026rsquo;s experience during the pandemic offers particularly valuable insights. As the world\u0026rsquo;s most aged society with a universal healthcare system\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, Japan represents an important case study of how healthcare systems can balance sustained access with public health risk management. The lessons from Japan\u0026rsquo;s experience may become increasingly relevant as other countries also face demographic aging and the growing challenge of protecting healthcare access for older population during emergencies\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study examines healthcare utilization patterns among Japan\u0026rsquo;s oldest-old population (i.e., aged 75 and above) during the later phase of COVID-19 pandemic. Using a comprehensive dataset linking medical claim records with income tax information, we analyze how healthcare utilization correlates with public health measures, crisis severity, and socioeconomic status. We observe moderate decreases in healthcare visits during periods with public health measures, with the relationship between pandemic severity and healthcare utilization varying by the presence of these measures. Despite fluctuations in visit frequency, overall healthcare costs remain stable, suggesting adaptations in how care is accessed rather than reductions in necessary treatment. We also find that while income-related differences in general healthcare access are modest, dental care show pronounced socioeconomic variations, indicating differential adaptation patterns across healthcare services.\u003c/p\u003e \u003cp\u003eThese findings contribute to our understanding of healthcare system resilience during public health emergencies. By examining how vulnerable populations modify healthcare access during a prolonged crisis, our study provides insights that may help inform efforts to maintain essential healthcare access during future emergencies, particularly in aging societies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Context\u003c/h2\u003e \u003cp\u003eJapan reported its first COVID-19 case on January 16, 2020, marking the beginning of unprecedented challenges to the nation\u0026rsquo;s public health infrastructure\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Over the next three years, Japan experienced eight distinct infection surges, each prompting evolving governmental and public health responses.\u003c/p\u003e \u003cp\u003e[Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates Japan\u0026rsquo;s COVID-19 case trends and emergency responses from January 2020 through May 2023. The early waves (I-V) were characterized by the original SARS-CoV-2 strain and its Alpha and Delta variants\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. During this period, Japan implemented rigorous measures at the prefecture level, including States of Emergency (SoE) and States of Precautionary Emergency (SoPE). SoE involved stringent but non-binding regulations to curb public gatherings and reinforce social distancing, while SoPE aimed to control the virus\u0026rsquo;s spread through less severe public health measures\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Notably, unlike many countries that imposed mandatory lockdowns, these measures represented strong recommendations rather than legally enforced restrictions, reflecting Japan\u0026rsquo;s distinctive strategy of encouraging voluntary behavioral changes rather than imposing strict lockdowns. The later waves (VI-VIII) were primarily driven by the Omicron variant, which, despite its high transmissibility, was typically associated with milder clinical outcomes\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. This shift was reflected in Japan\u0026rsquo;s public health response, with the government implementing less stringent measures SoPE and eventually lifting all emergency declarations after Wave VI.\u003c/p\u003e \u003cp\u003eIn the early stages of the pandemic, widespread uncertainty and fear dominated. Public health messages urging people to shelter in place\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, combined with the fear of contracting the virus in healthcare setting\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, led to significant healthcare avoidance, especially among the older population. As the pandemic progressed, several factors may have influenced their healthcare utilization patterns, including widespread vaccine distribution\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, enhanced infection control measures in healthcare facilities\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, and more targeted public health messages\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. These factors suggest that healthcare utilization patterns among Japan\u0026rsquo;s older population may have undergone a significant shift, moving from widespread avoidance in the pandemic\u0026rsquo;s early stages to more stable engagement later. Understanding these evolving patterns is valuable for developing effective healthcare strategies and policies for this vulnerable population during prolonged health crises.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Sources and Sample Construction\u003c/h3\u003e\n\u003cp\u003eOur primary data source is the Medical Claims Data with Income Tax Information for the Oldest-Old in Japan (MCD-Tx), a comprehensive dataset of Japan\u0026rsquo;s Latter-Stage Elderly Healthcare System (LSEH) collected by the Ministry of Health, Labour and Welfare (MHLW). The LSEH is Japan\u0026rsquo;s universal healthcare system for the 75\u0026thinsp;+\u0026thinsp;population, and as of September 2022, it covered 18.52\u0026nbsp;million individuals, comprising 98.6% of Japan\u0026rsquo;s 75\u0026thinsp;+\u0026thinsp;population.\u003c/p\u003e \u003cp\u003eThe MCD-Tx, spanning from November 2021 to November 2022, captures all LSEH enrollees, regardless of their healthcare service utilization. For each individual, the dataset provides detailed monthly healthcare utilization and cost information from medical claims, linked with individual demographic and socioeconomic data including income information. This dataset represents the first instance in Japan where income information is integrated with medical claims data. Informed consent was waived by the Ethics Review Committee for Research Involving Human Subjects at Waseda University (Approval No.: 2022-HN038). As this study used anonymized secondary data, the requirement for informed consent was waived by the ethics committee. Furthermore, all methods used in this study were conducted in accordance with relevant guidelines and regulations regarding using data of human participants.\u003c/p\u003e \u003cp\u003eTo complement the MCD-Tx, we incorporated three additional datasets. The first tracks daily new COVID-19 cases at the secondary medical region (SMR) level\u0026mdash;administrative units organized around specialized healthcare facilities that provide more granular geographical resolution than prefectures\u0026mdash;from the onset of Japan\u0026rsquo;s first reported case until September 2022. The second dataset provides biweekly records of hospital beds designated for COVID-19 patients and actual admissions at the SMR level, from December 2021 to April 2023. The third dataset covers the daily implementation status of SoE and SoPE at the prefecture level, from March 2020 to September 2022.\u003c/p\u003e \u003cp\u003eIn constructing our sample, we first convert these three supplementary datasets to a monthly format. We then link them with the MCD-Tx claim records based on the SMR or prefecture of residence of the oldest-old (detailed linkage procedures are described in Appendix A). The final sample covers an 11-month period from November 2021 to September 2022, capturing the later phase of the pandemic (Waves VI and VII). This sample includes 1,769,537 individuals aged 75\u0026thinsp;+\u0026thinsp;and 189,841,257 associated health insurance claim records.\u003c/p\u003e\n\u003ch3\u003eMeasurements\u003c/h3\u003e\n\u003cp\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBasic Statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS.D.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eA: Extensive\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealthcare Utilization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e198,952,929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.365\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital Admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e198,952,929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutpatient Visit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e198,952,929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.411\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDental Care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e198,952,929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.406\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eB: Intensive\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Costs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e167,437,977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e230.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInpatient Costs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,702,466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e651.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e573.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutpatient Costs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e156,186,553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDental Costs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41,424,441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC: COVID-19 severity and SoPE measures\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e198,952,929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoPE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e198,952,929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eD: Individual characteristics and healthcare capacity\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e79,190,454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44,545,622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43,403,979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33,774,418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44,054,906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33,174,004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40,199,764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39,478,624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39,854,201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e117.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39,801,160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e256.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39,542,644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e554.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e754.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary non-COVID-19 diagnosis for hospital admission\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfectious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,265,787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeoplasms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,265,787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematologic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,265,787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndocrine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,265,787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,265,787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeurological\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,265,787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOphthalmic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,265,787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOtologic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,265,787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,265,787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,265,787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigestive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,265,787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDermatologic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,265,787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMusculoskeletal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,265,787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrogenital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,265,787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrauma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,265,787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary non-COVID-19 diagnosis for outpatient visit\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfectious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e155,177,275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeoplasms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e155,177,275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematologic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e155,177,275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndocrine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e155,177,275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e155,177,275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeurological\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e155,177,275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOphthalmic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e155,177,275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOtologic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e155,177,275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e155,177,275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e155,177,275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigestive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e155,177,275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDermatologic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e155,177,275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMusculoskeletal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e155,177,275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrogenital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e155,177,275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrauma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e155,177,275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOVID-19 Bed Occupancy\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37,953,684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30,949,872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29,851,806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31,479,306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34,678,415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNotes: \u0026ldquo;S.D.\u0026rdquo; refers to the standard deviation, indicating the extent of variation or dispersion in the data set. \u0026ldquo;Q1-Q5\u0026rdquo; represent the quintiles of the variable in question, arranged in ascending order, where Q1 denotes the lowest quintile and Q5 the highest.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOur analysis examines both extensive and intensive margins of healthcare utilization. For the extensive margin, we construct four binary indicators associated with different healthcare services: overall healthcare utilization, hospital admission, outpatient visits, and dental care. Each indicator takes a value of one if an individual use the respective service within a given month, and zero otherwise. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, 84.2% of our sample access some form of healthcare during the study period, with outpatient visits being the most common (78.5%), followed by dental services (20%), while hospital admissions are less frequent (5.4%).\u003c/p\u003e \u003cp\u003eFor the intensive margin, we analyze four variables representing monthly medical costs: total costs, inpatient costs, outpatient costs, and dental costs (measured in 10,000 Japanese Yen, JPY). Among those who utilize services, average monthly inpatient care costs (651,320 JPY) are substantially higher than outpatient care (43,370 JPY) and dental care costs (14,630 JPY).\u003c/p\u003e \u003cp\u003eTo measure pandemic conditions, we construct two variables. The first captures pandemic severity through the monthly aggregated number of new COVID-19 cases per million people within each SMR, averaging 0.013 during our study period. The second indicates the presence of SoPE measures, taking a value of one for months with active measures in the resident\u0026rsquo;s prefecture (present in 26% of our study period). Notably, no prefecture implemented SoE measures during our study timeframe.\u003c/p\u003e \u003cp\u003eOur models include several control variables to account for individual characteristics and healthcare capacity. Individual-level controls comprise age quintiles (ranging from mean age 75.56 years in Q1 to 92.25 years in Q5), income quintiles (from mean 37.42\u0026nbsp;million JPY in Q1 to 554.22\u0026nbsp;million JPY in Q5), and gender (39.8% male). We also include indicators for non-COVID-19 main diagnoses in both inpatient and outpatient care, with cardiac conditions being most prevalent (22.1% of hospitalizations and 26.7% of outpatient visits). Healthcare capacity is measured through COVID-19 hospital bed occupancy quintiles, ranging from 5.0\u0026ndash;68.6%, reflecting regional variations in healthcare system strain during the pandemic.\u003c/p\u003e\n\u003ch3\u003eAnalytical Approach\u003c/h3\u003e\n\u003cp\u003eWe use two complementary models to examine healthcare utilization patterns. Our first model analyzes the association between SoPE measures and healthcare utilization:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{Y}_{ijt}=\\:{\\beta\\:}_{0}+\\:{\\beta\\:}_{1}SoP{E}_{jt}+\\:{\\beta\\:}_{2}f\\left(COVI{D}_{jt}\\right)+\\:{X}_{ijt}^{{\\prime\\:}}\\gamma\\:\\:+\\:{\\delta\\:}_{t}+\\:{\\eta\\:}_{j}+\\:{\\tau\\:}_{j}t\\:+\\:{\\epsilon\\:}_{ijt}.$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eHere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{ijt}\\)\u003c/span\u003e\u003c/span\u003e represents healthcare utilization and costs for individual \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e in SMR \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\)\u003c/span\u003e\u003c/span\u003e at time \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e\u003c/span\u003e. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:SoP{E}_{jt}\\)\u003c/span\u003e\u003c/span\u003e is the indicator of SoPE measures, our primary variable of interest. We anticipate healthcare utilization to be negatively associated with SoPE implementation, consistent with documented healthcare avoidance patterns. The relationship between medical costs and SoPE measures may be more complex, potentially showing either negative associations due to fewer visits or positive associations if delayed care leads to more intensive treatment needs. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:f\\left(COVI{D}_{jt}\\right)\\)\u003c/span\u003e\u003c/span\u003e represents a quadratic polynomial function of new COVID-19 cases. The quadratic specification captures potential non-linear patterns in the relationship between case numbers and healthcare utilization, allowing for diminishing marginal correlations at higher case levels. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{ijt}\\)\u003c/span\u003e\u003c/span\u003e represents individual characteristics and healthcare capacity controls. The model incorporates year-month fixed effects \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\delta\\:}_{t}\\)\u003c/span\u003e\u003c/span\u003e, geographic fixed effects at the SMR level \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{j}\\)\u003c/span\u003e\u003c/span\u003e, and geographic linear trends \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\tau\\:}_{j}t\\)\u003c/span\u003e\u003c/span\u003e to control for unobserved temporal and regional variations. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{ijt}\\)\u003c/span\u003e\u003c/span\u003e denotes the error term, with standard errors clustered at the SMR level.\u003c/p\u003e \u003cp\u003eOur second model examines how the association between healthcare utilization and pandemic severity varies with public health measures:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{Y}_{ijt}=\\:{\\theta\\:}_{0}+\\:{\\theta\\:}_{1}SoP{E}_{jt}+\\:{\\theta\\:}_{2}f\\left(COVI{D}_{jt}\\right)+\\:{\\theta\\:}_{3}\\left[SoP{E}_{jt}\\times\\:\\:f\\left(COVI{D}_{jt}\\right)\\right]+\\:{X}_{ijt}^{{\\prime\\:}}\\gamma\\:\\:+\\:{\\delta\\:}_{t}+\\:{\\eta\\:}_{j}\\:\\:\\:\\:$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:+\\:{\\tau\\:}_{j}t\\:+\\:{\\epsilon\\:}_{ijt}.\\:\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThis model fully interacts the SoPE indicator with the quadratic function of case numbers, allowing us to examine whether the relationship between pandemic severity and healthcare utilization differs during periods with and without public health measures. To interpret these relationships, we calculate average marginal effects of changes in case numbers at different pandemic severity levels, ranging from low (below 0.04 cases per million) to high (above 0.14 cases per million).\u003c/p\u003e \u003cp\u003eTo assess the robustness of our approaches, we conduct additional analyses (see Appendix B). We estimate models with a linear specification of pandemic severity as an alternative to our quadratic specification. We also perform subgroup analyses by age groups (75\u0026ndash;84 and 85+) and gender to examine potential heterogeneity in patterns. These analyses reveal patterns consistent with our main findings, suggesting that the observed relationships between healthcare utilization, public health measures, and pandemic severity are robust across specifications and subpopulations.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eHealthcare Utilization During the SoPE Period\u003c/h2\u003e \u003cp\u003eWe first examine patterns in healthcare service utilization among Japan\u0026rsquo;s oldest-old during periods with and without SoPE measures. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents four models based on Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) with increasing complexity, from basic controls in Model 1 to a full consideration of the pandemic\u0026rsquo;s nonlinear patterns, individual attributes, and healthcare system capacity in Model 4. The observed relationships remain consistent across all models in both direction and magnitude. We focus our discussion on the most comprehensive Model 4.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelationship Between SoPE Measures and Healthcare Utilization Patterns\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExtensive Margins\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealthcare Utilization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.0073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital Admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutpatient Visit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.0077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDental Care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.0016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIntensive Margins\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Costs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInpatient Costs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e651.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(7.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(7.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutpatient Costs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDental Costs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCovariates and Fixed Effects\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#Cases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividual characteristics and healthcare capacity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed effects and linear trends\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSquared #Cases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eNotes: Column (1) lists the means of the outcome variables. Columns (2) through (5) each represent the results from a separate regression as specified in Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Standard errors in parentheses are clustered at the SMR level. *Inference: *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eDuring SoPE periods, we observe lower overall healthcare engagement by 0.73 percentage points (0.86% below the mean). Similar patterns emerge for outpatient visits and dental care, which are lower by 0.77 and 0.16 percentage points respectively (0.98% and 0.77% below their respective means). For healthcare costs, outpatient costs during SoPE periods are lower by 2,170 JPY (0.50% below the mean). Notably, we find no systematic differences in inpatient and dental costs during SoPE periods, suggesting that while fewer people may seek these services, the intensity of care among those who do remains stable.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePandemic Severity and Healthcare Utilization\u003c/h3\u003e\n\u003cp\u003e[Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates how healthcare utilization patterns relate to pandemic severity based on Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), revealing distinct patterns during periods with and without SoPE measures. For overall healthcare utilization, periods without SoPE show a negative relationship with rising COVID-19 cases, with an average marginal effect (AME) of -2.5 percentage points (2.97% below the mean). In contrast, during SoPE periods, we observe a positive relationship with an AME of 4.2 percentage points (4.99% above the mean).\u003c/p\u003e \u003cp\u003eAs pandemic severity increases, we observe an upward trend in healthcare utilization on the extensive margin regardless of SoPE measures, while the intensive margin generally trends downward. Two exceptions are hospital admission and outpatient costs. Hospital admissions show a positive relationship with case numbers at lower severity levels during SoPE periods, transitioning to negative at higher severity levels. Similarly, outpatient costs show an initial negative correlation with case numbers that shifts to positive as severity increases.\u003c/p\u003e\n\u003ch3\u003eSocioeconomic Patterns in Healthcare Utilization\u003c/h3\u003e\n\u003cp\u003e[Figure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOur analyses of income-stratified samples based on Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) reveal modest differences in healthcare utilization patterns across income levels. Specifically, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows similar moderate decreases in healthcare utilization across all income quintiles during SoPE periods. We observe consistent patterns where healthcare utilization shows negative correlations with rising COVID-19 cases during non-SoPE periods and positive correlations during SoPE periods, regardless of income levels.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDistinctive Patterns in Dental Care\u003c/h2\u003e \u003cp\u003eDental care exhibits notably different patterns compared to other healthcare services. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that rising COVID-19 cases associates with larger decreases in dental care utilization\u0026mdash;18.3 percentage points (87.98% below the mean) during the non-SoPE periods. These decreases substantially exceed those observed in other healthcare services. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e further reveals income-related variations unique to dental care: utilization decreases by 21.7 percentage points (104.33% below the mean) in the lowest income quintile compared to 14 percentage points (67.31% below the mean) in the highest income quintile.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examines healthcare utilization patterns among Japan\u0026rsquo;s oldest-old during the later phase of the COVID-19 pandemic, providing insights into how healthcare systems and vulnerable populations adapt to prolonged public health crises.\u003c/p\u003e \u003cp\u003eOur analysis reveals a correlation between SoPE measures and moderate reductions in healthcare service utilization, particularly in outpatient care. This pattern differs from the sharp decreases documented in earlier pandemic stages, potentially reflecting adaptation in the later phase of the crisis \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Notably, while healthcare visit frequency shows modest decreases, healthcare costs remain relatively stable, suggesting that elderly people who continue seeking care maintain similar levels of service intensity. This pattern of adaptation\u0026mdash;from acute avoidance to more stable utilization patterns\u0026mdash;may represent a broader phenomenon in how healthcare systems and populations adjust to extended emergency conditions.\u003c/p\u003e \u003cp\u003eHealthcare utilization patterns show distinct relationships with pandemic severity during periods with and without SoPE measures. During periods without SoPE measures, higher COVID-19 cases correlate with decreased healthcare utilization. However, this pattern reverses during SoPE periods, possibly reflecting factors such as enhanced safety protocols\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e or increased public confidence in healthcare facilities\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. These patterns point to the dynamic nature of healthcare utilization during crises\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, though we note that our approach cannot establish clear causal relationships between these factors.\u003c/p\u003e \u003cp\u003eThe relatively modest differences in healthcare utilization patterns across income levels among Japan\u0026rsquo;s oldest-old contrast with more pronounced income-based disparities reported in other countries during the pandemic\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. This observation aligns with the role of Japan\u0026rsquo;s universal health insurance in maintaining healthcare access, suggesting that structural features of health systems may significantly influence their resilience during prolonged emergencies.\u003c/p\u003e \u003cp\u003eDental care utilization shows notably different patterns, marked by significant declines irrespective of whether SoPE measures are implemented. This trend likely reflects multiple factors, including the perceived high-risk nature of dental procedures during the pandemic\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. The observed income-related variations in dental care utilization, even within Japan\u0026rsquo;s universal health insurance system, suggest that socioeconomic factors may influence oral health decisions during crises. These patterns raise concerns about potential long-term health implications, given the established connections between oral health and various systemic conditions among older population \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, our sample faces selection bias as it necessarily includes only individuals who survived through the study period. Those who did not survive may have had different healthcare utilization patterns, potentially leading to underestimation of crisis-related healthcare challenges. Additionally, our analysis does not account for unobserved factors that might influence healthcare decisions, such as individual risk perceptions. The absence of data on specific health outcomes also limits our ability to assess the broader health implications of the observed utilization patterns. Future research could address these limitations by incorporating mortality data, examining specific health outcomes associated with utilization changes, and applying quasi-experimental designs.\u003c/p\u003e \u003cp\u003eThe patterns observed among Japan\u0026rsquo;s older population under the universal healthcare system during this prolonged crisis suggest several considerations for maintaining healthcare access during emergencies. Healthcare systems might benefit from developing integrated monitoring mechanisms to identify emerging care gaps, particularly in essential preventive services such as dental care. Combining enhanced safety protocols with flexible delivery models, including mobile services and remote care options, could help maintain service accessibility. Early warning systems to track utilization patterns among vulnerable groups, coupled with targeted support for high-risk but essential services, may help maintain healthcare access during various types of extended emergencies. These considerations may become increasingly relevant as healthcare systems worldwide address challenges from aging populations and various forms of public health crises.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eConcluding Remarks\u003c/h2\u003e \u003cp\u003eThis study examines healthcare utilization patterns during a prolonged public health emergency, with particular focus on vulnerable older populations. While our observational study design precludes causal conclusions, the documented patterns contribute to our understanding of healthcare system resilience beyond the specific context of COVID-19. The findings reveal how healthcare utilization evolves during extended crises, showing both successes and challenges in maintaining access to healthcare services. As populations age globally, and healthcare systems face various challenges\u0026mdash;from infectious disease outbreaks to natural disasters\u0026mdash;these insights into healthcare adaptation patterns may inform efforts to protect vulnerable populations during future prolonged emergencies.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements.\u003c/strong\u003e We express our gratitude to Hidetake Endou, Kenji Suzuki, Kousuke Kashimura, and Takashi Ninishioka in the Ministry of Health, Labour and Welfare (MHLW) for their valuable feedback. This research received support from the Health Labour Sciences Research Grant (Research on Policy Planning and Evaluation) provided by the MHLW under Grant Number 22AA1002, with Haruko Noguchi serving as the Principal Investigator.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical statement.\u0026nbsp;\u003c/strong\u003eThe study was reviewed and deemed exempt from ethical review by the Ethics Review Committee for Research Involving Human Subjects at Waseda University (Approval No.: 2022-HN038; Approval Date: November 25, 2022, please see the website: https://www.waseda.jp/inst/ore/procedures/). The data provided contains no personally identifiable information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions.\u003c/strong\u003e R.F. conceived the study, conducted the analysis, and drafted the manuscript. S.L. assisted with data colletcion. M.O. contributed to the study design and interpretation of results. H.N. supervised the project and provided critical revisions. A.K. contributed to the interpretation of results and manuscript revision. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eThis study used anonymized individual data from the Medical Claims Data with Income Tax Information for the Oldest-Old in Japan (MCD-Tx), collected and maintained by the Research Division of the Health Insurance Bureau, MHLW.\u0026nbsp;The data can be obtained by applying to the MHLW (https://www.mhlw.go.jp/toukei/sonota/chousahyo.html).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional Information.\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCutler, D. M. \u0026amp; Summers, L. H. 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(2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://covid19.mhlw.go.jp/extensions/public/en/index.html\u003c/span\u003e\u003cspan address=\"https://covid19.mhlw.go.jp/extensions/public/en/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCabinet Agency for Infectious Diseases Crisis Management. COVID-19 Countermeasures. (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://corona.go.jp/emergency/\u003c/span\u003e\u003cspan address=\"https://corona.go.jp/emergency/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5086827/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5086827/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines healthcare utilization patterns among Japan\u0026rsquo;s older population (aged 75 and above) during a prolonged public health emergency, focusing on the later phase of the COVID-19 pandemic (November 2021-September 2022). This period was characterized by the Omicron variant, widespread vaccination coverage, and adapted public health measures. Using a comprehensive dataset of 189,841,257 medical claims linked with income tax records, we analyze how healthcare utilization correlates with public health measures, pandemic severity, and income levels. Our findings reveal distinct utilization patterns: moderate decreases in healthcare visits during periods of public health measures, with the association between pandemic severity and healthcare use varying based on whether these measures are in place. Despite fluctuations in visit frequency, healthcare costs remain stable, indicating consistent service intensity. While income-related differences in general healthcare access are modest, dental care shows more pronounced socioeconomic variations. These patterns suggest a transition from initial widespread healthcare avoidance to more stable healthcare engagement, indicating adaptation to prolonged crisis conditions. Our findings provide insights for maintaining healthcare access during extended public health emergencies, particularly in aging societies where balancing healthcare needs with public health measures is crucial.\u003c/p\u003e","manuscriptTitle":"Healthcare Utilization Among Japanese Older Adults During Later Stage of Prolonged Pandemic","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-27 06:06:36","doi":"10.21203/rs.3.rs-5086827/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2025-04-15T09:59:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-12T16:59:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"259347589658416770459208804422674096870","date":"2025-03-28T02:51:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-25T21:36:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-21T06:26:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-14T14:09:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cf451b18-c795-43a6-8804-f543ab29a968","owner":[],"postedDate":"March 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":46210786,"name":"Health sciences/Health care/Health policy"},{"id":46210787,"name":"Health sciences/Health care/Health services"},{"id":46210788,"name":"Health sciences/Health care/Public health"}],"tags":[],"updatedAt":"2025-04-28T16:05:25+00:00","versionOfRecord":{"articleIdentity":"rs-5086827","link":"https://doi.org/10.1038/s41598-025-98908-x","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-04-22 15:57:29","publishedOnDateReadable":"April 22nd, 2025"},"versionCreatedAt":"2025-03-27 06:06:36","video":"","vorDoi":"10.1038/s41598-025-98908-x","vorDoiUrl":"https://doi.org/10.1038/s41598-025-98908-x","workflowStages":[]},"version":"v1","identity":"rs-5086827","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5086827","identity":"rs-5086827","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}