Inequalities in health services utilization and catastrophic health expenditures among patients with chronic respiratory diseases in China: An empirical analysis based on the CHARLS, 2013—2020

Inequalities in health services utilization and catastrophic health expenditures among patients with chronic respiratory diseases in China: An empirical analysis based on the CHARLS, 2013—2020 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Inequalities in health services utilization and catastrophic health expenditures among patients with chronic respiratory diseases in China: An empirical analysis based on the CHARLS, 2013—2020 Xueting Wang, Ziqi Cai, Yamin Lu, Fuzhi Ai, weiguang Yao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8091430/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Chronic respiratory disease (CRD) is a global priority in preventing and controlling non-communicable diseases (NCDs). Its disease burden and health inequalities significantly undermine population health outcomes. This study examines inequalities in health service utilization and CHE incidence among middle-aged/elderly CRD patients in China, identifying key drivers. Methods Using 2013–2020 China Health and Retirement Longitudinal Study(CHARLS) data, this study analyzed outpatient, inpatient, and physical examination utilization rates and CHE incidence in CRD patients ≥ 45 years. The degree of inequality and its main contributing factors were measured using indirect standardization, the concentration index (CI), and decomposition analysis. Results All service utilization rates increased from 2013 to 2020. CIs declined but remained positive in 2020 (outpatient: 0.020; inpatient: 0.059; physical examination: 0.017). Health behaviors reduced outpatient inequality, while age 65 and above drove inpatient and physical examination disparities. CHE incidence rose from 10.40% (2013) to 11.67% (2020), with CIs worsening (-0.027 to -0.048). Urban residence (13.55%) and non-agricultural hukou (8.82%) were the main drivers of inequality at CHE. Conclusions Although equity improved in outpatient, inpatient, and physical examination service utilization during the study period, persistent pro-rich inequalities remain. CHE incidence demonstrated widening pro-poor disparities. Policy priorities should target unmet needs and economic burdens among socioeconomically disadvantaged groups through strengthened health education, behavioral interventions, health insurance reforms, and enhancements to the primary care system. Inequit Health service utilization Catastrophic health expenditure Chronic respiratory disease Introduction Under the global public health governance framework, the United Nations Sustainable Development Goals (SDGs) set a clear target: by 2030, reduce premature mortality from non-communicable diseases (NCDs) by one-third through prevention, treatment, and mental health promotion. Chronic respiratory diseases (CRD), including chronic obstructive pulmonary disease, asthma, and emphysema, represent a significant category of NCDs with high prevalence among middle-aged and elderly populations. Characterized by elevated morbidity, disability rates, mortality, and disease burden, CRDs have emerged as the world's third-leading cause of death [ 1 ]. Recent years have witnessed rising CRD prevalence globally, driven by factors such as air pollution and smoking [ 2 ]. The disease’s chronic nature and frequent exacerbations require costly lifelong care, impairing patients’ quality of life. Additionally, it strains economic and social systems due to workforce health losses and rising expenditures. To alleviate the NCD disease burden and achieve SDG targets, the Chinese government has prioritized CRD prevention and control. It has integrated CRD management into national strategies, including the Healthy China 2030 blueprint and the Medium- and Long-Term Plan for Chronic Disease Prevention and Treatment (2017–2025), while establishing the National CRD Prevention and Control Initiative. Despite these policy efforts, CRD control continues to face persistent challenges. According to epidemiological data, China reported 75.27 million cases of CRD in 2021, representing a 34.29% increase from 1990. These numbers show a substantial discrepancy between preventative outcomes and policy goals, with 1.33 million CRD-related deaths (or 29.56% of global CRD mortality). More importantly, the main obstacle to managing CRD is the rise in health inequalities, particularly in relation to health outcomes, medical expenditure burdens, and health service utilization. Health inequalities are primarily shaped by socioeconomic status (SES), as demonstrated by Link and Phelan in the 1990s [ 3 ]. Socioeconomic status (SES) groups differ significantly in terms of disease control rates and quality of survival. In addition to facing greater risks of catastrophic health expenditures (CHE), low SES individuals, rural dwellers, and communities in less developed regions are severely disadvantaged in terms of early screening rates and access to standardized treatment [ 4 ]. Although China has the largest basic medical insurance network in the world and has implemented medical assistance mechanisms and differential reimbursement policies to protect vulnerable groups, these measures are still insufficient. Pro-rich disparities persist in the likelihood and frequency of older individuals using health services [ 5 ]. This systematic health inequality not only weakens the overall prevention and control of public health problems such as CRD, but also creates a vicious cycle of “poverty caused by disease, poverty caused by disease” [ 6 ]. The construction of an efficient and equitable health care delivery system is centered on ensuring equitable access to and utilization of health services, which should be provided to all individuals based on the principle of vertical equity. Within the framework of this principle, differences in healthcare service utilization triggered by individual health needs are reasonable. At the same time, inequalities directly caused by SES are objective, real, and should be avoided [ 7 ]. Therefore, further evidence is required to narrow the irrational health service utilization and economic burden inequality brought on by the SES disparity and to comprehend the current state of these issues as well as their evolving trends among China's middle-aged and older CRD population. Most previous studies on health service utilization disparities have primarily focused on outpatient and inpatient services [ 8 , 9 , 10 ], with relatively limited attention to physical examination service utilization. This oversight, to a certain extent, underestimates the importance of preventive healthcare services in promoting health equity. Inequality in the utilization of physical examination, as an effective means of early detection and intervention of CRD, may exacerbate the pressure of demand for subsequent treatment services, creating a more serious cycle of health inequality. Based on the four-phase cross-section of the China Health and Retirement Longitudinal Study (CHARLS) from 2013 to 2020, this study examines the current state, level of inequality, and dynamic trends in the use of three different health services, such as inpatien, outpatient, and physical examination services, as well as the incidence of catastrophic health expenditures among the middle-aged and elderly CRD population in China. It also examines the main factors of these disparities to provide evidence for bettering CRD prevention and control strategies and for developing targeted health equity policies. Methods Data sources This study uses data from the China Health and Retirement Longitudinal Study (CHARLS), which was conducted by Peking University's National Development Research Institute (NDRI). Researchers employed multi-stage probability proportional to size (PPS) sampling to administer structured questionnaires. The surveys targeted residents aged 45 and above in 450 villages and communities across 150 counties in 28 provincial-level regions. The questionnaires covered basic information about elderly residents, including family conditions, socioeconomic background, health status, physical functioning, access to medical services, and insurance coverage. The questionnaire covers the basic status of the elderly and their families, including socioeconomic background and family structure, health status and functioning, as well as medical services and insurance. The survey has the breadth and depth of data needed for social science research, and its validity and authority have been widely recognized by the academic community. The project conducted a baseline survey in 2011, enrolling a total of 17,708 residents. This was followed by four follow-up surveys in 2013, 2015, 2018, and 2020, which allowed for the dynamic addition and subtraction of people entering the cohort. All CHARLS data were approved by the Ethics Review Board of Peking University (approval number: IRB00001052-11015), and all participants signed an informed consent form. Participants The CHARLS provided nationally representative tracking data for four periods in 2013, 2015, 2018, and 2020, which served as the basis for this investigation. After removing patients under 45, those with diagnosed mental or memory disorders, and those lacking essential variables, the final sample sizes included in the analysis were 11,392 cases in 2013, 12,746 cases in 2015, 15,031 cases in 2018, and 13,088 cases in 2020. The study population was defined as individuals aged 45 years or older. Patients who self-reported a doctor's diagnosis of "chronic lung disease (e.g., chronic bronchitis, emphysema, or pulmonary heart disease, etc., excluding tumors)" or "asthma" were considered to have CRD for this study. Outcome variables The outcome variables of this study include three health service utilization rates and the incidence rates of catastrophic health expenditure (CHE). Health service utilization was measured by outpatient service utilization in the past month, inpatient service utilization over the past year, and physical examination service utilization over the past two years. In the CHARLS questionnaire, respondents were asked to answer the question, “In the past month, have you visited a health care facility for an outpatient visit or received in-home health care services? (excluding physical exams)”, “Have you been hospitalized in the past year?” and “When was your last routine physical examination since your last visit? (Note: does not include a CHARLS physical exam).” The results of the responses were used to determine whether respondents utilized outpatient services, inpatient services, and physical examination services, and the percentage of people who did so was calculated. This study's definition of CHE is based on the World Health Organization's (WHO) criteria, which state that a household is deemed to be experiencing CHE if, after covering basic subsistence expenses, medical costs account for at least 40% of its disposable income. Household disposable income equals household income minus household food expenditure. According to the definition of household income in the National Statistical Yearbook and information from the CHARLS database, household income in this study involves the calculation of variables including 11 sources of income, such as wage income, net business income, and transfer income. Household food expenditures include food expenditures and the value of agricultural products consumed from the household. Household health expenditures include direct and indirect out-of-pocket expenditures. Independent variable The independent variable in this study is socioeconomic status (SES). The following factors led to the selection of annual per capita household consumption expenditures (PCE) as the primary indicator of SES: (1) The quality of reported consumption data is higher among middle-aged and older individuals, and (2) consumer expenditure is a more consistent indicator of long-term economic resources. Respondents' annual PCE were ranked according to the customary international income quintiles, and divided into five groups according to the level of the average, which were Group I (low expenditure group), Group II (medium-low expenditure group), Group III (medium expenditure group), Group IV (medium-high expenditure group), and Group V (high-expenditure group) in the order of Group I (low-expenditure group), II (medium-low expenditure group), III (medium-expenditure group), IV (medium-high expenditure group), and V (high-expenditure group). To thoroughly evaluate the mechanisms of SES influence on Health services utilization and financial burden while controlling for potential confounders, multidimensional parameters such as sociodemographic traits and population-related health behaviors were included in the selection of covariates. According to Andersen's health service use behavior model, the contributing elements to health service utilization behavior in this study were categorized into dispositional features, enabling factors, and demand factors[ 11 ]. Table 1 provides exact definitions, measurements, and assignment criteria for each of the study variables. Table 1 Definition of research variables and assignment criteria based on Andersen's behavioral model of health services utilization and financial burden Variables Specific definition and assignment Dispositional characteristic Age If 45 = < age < 55, age = 1 If 55 = < age < 65, age = 2 If 65 = < age = 75, age = 4 Gender Male = 1, Female = 2 Marital status Married or with spouse = 1, Unmarried and other = 0 Education Junior high School and below = 1 Senior high school/technical secondary school = 2 University and above = 3 Household registration Agricultural registration = 1, Non-agricultural registration = 2 Drinking history Yes = 1, No = 2 Smoking history Yes = 1, No = 2 Enabling factors Area of residence Rural areas = 1, Urban areas = 2 Availability of medical insurance Yes = 1, No = 2 Availability of pension insurance Yes = 1, No = 2 Socioeconomic status Annual PCE is categorized into five subgroups from lowest to highest. Group I (low spending group) = 1 Group II (low and medium spending group) = 2 Group III (middle disbursement group) = 3 Group IV (medium-high spending group) = 4 Group V (high spending group) = 5 Demand factors Self-assessed health status Good = 1, Fair = 2, Poor = 3 Chronic respiratory diseases ①self-reported doctor diagnosed chronic lung disease (e.g., chronic bronchitis, emphysema, or pulmonary heart disease, excluding tumors) ②self-reported doctor diagnosed “asthma”. If 1 = YES AND/OR 2 = YES: With chronic respiratory disease = 1 If 1 = No AND 2 = No: Without chronic respiratory disease = 0 Outcome variables Health services utilization One-month outpatient service utilization rate Visited a health care facility for an outpatient visit or received in-home health care services in the past month (not including having a physical exam). Yes = 1, No = 0 One-year inpatient service utilization rate Hospitalized within the past year. Yes = 1, No = 0 Two-year physical examination service utilization rate Within the past two years, a routine physical examination was performed. Yes = 1, No = 0 Incidence of catastrophic health expenditures OOP medical expenditures equal or exceed 40% of the household's ability to pay. Yes = 1, No = 0 Statistical Analysis Chi-square test In this study, the Pearson chi-square test was used to detect between-group differences in the prevalence of hypertension, health services utilization, and CHE incidence among patients with chronic respiratory diseases. Indirect standardization method The indirect standardization method of health services utilization was used in this study to categorize the factors influencing health service utilization and the incidence of CHE into two groups: "need variables" (which include factors that reflect the population's health status, such as age, gender, and self-assessed health status) and "non-need variables" (socioeconomic factors that affect health services utilization that are not need variables, such as marital status, education level, place of residence, household registration, smoking and drinking histories, health insurance, and pension insurance). To explain the expected distribution of fairness in health service use and catastrophic health costs among patients with chronic respiratory diseases in China, when socioeconomic covariates are not considered, we employed a regression model in this study. This model adjusts for the effects of non-need variables and predicts the demand for standardized health service utilization driven by individuals' own health needs and the standardized rate of catastrophic health expenditures. [ 12 ]. The specific formula is: $$\:{\widehat{y}}_{i}^{IS}={y}_{i}-{\widehat{y}}_{i}^{x}+\stackrel{-}{y}$$ Where \(\:{\widehat{\text{y}}}_{\text{i}}^{\text{I}\text{S}\:}\) is the standardized health services utilization estimate, \(\:{\text{y}}_{\text{i}}\) is the actual utilization value, \(\:{\widehat{\text{y}}}_{\text{i}}^{\text{x}}\) is the utilization value after controlling for non-need variables, and \(\:\stackrel{-}{\text{y}}\) is the sample mean. It should be noted that smoking history and drinking history mainly reflect socioeconomic status differences and individual behavioral choices rather than direct health service needs in this study, so they were included in the analysis as non-needed variables. Concentration index method Based on the standardized analysis, this study followed the health equity measurement framework proposed by Wagstaff et al. (1991) [ 13 ]. It used the concentration index (CI) to quantify equity in health services utilization and CHE incidence. The CI calculation formula is as follows: \(\:CI=\frac{2}{\mu\:}cov（{y}_{i}\:,{R}_{i}\) ) Where µ is the sample mean of health services utilization, cov is the covariance, \(\:{y}_{i}\) is the health services utilization indicator for individual i, and \(\:{R}_{i}\) is the relative rank of individual i's annual PCE in the overall population (in descending order, taking values from 0–1). CI takes the range of [-1, 1], with CI = 0 indicating absolute fairness; and CI > 0 indicating that service utilization is biased in favor of the high SES group, while CI < 0 indicates a bias towards lower SES groups. Concentration index decomposing method The concentration index (CI) decomposing method proposed by Wagstaff in 2003 was used to decompose the CI of health services utilization and CHE incidence [ 14 ]. This method can decompose the CI and demonstrate how each component contributes to the inequality of results. We chose Probit regression instead of linear regression for the decomposition because the primary outcome indicators in our study are dichotomous variables [ 15 ]. The linear approximation of the regression formula is as follows: $$\:{y}_{i}\approx\:\alpha\:+\sum\:_{k}{\beta\:}_{k}^{m}{x}_{i}^{k}+{u}_{i}$$ Where \(\:{y}_{i}\) is the health outcome variable (whether or not health services are utilized and whether or not CHE are incurred), \(\:\alpha\:\:\) is the intercept, \(\:{x}_{i}^{k}\:\) is the kth explanatory variable, \(\:{\beta\:}_{k}^{m}\) is the marginal effect of the kth explanatory variable in the Probit model, which explains the change in the predicted probability associated with a unit change in the independent variable, and \(\:{\:u}_{i}\) is the implied error term. The concentration index decomposition considers the effects of both need and non-need variables, with the contribution of each factor assessed through the decomposition. The CI decomposition formula is as follows: $$\:C=\sum\:_{K}\left(\frac{{\beta\:}_{k}^{m}{\stackrel{-}{x}}_{k}}{\mu\:}\right)C{I}_{k}+\frac{G{C}_{u}}{\mu\:}$$ Where \(\:C\) is the unscaled concentration index, \(\:{\beta\:}_{k}^{m}\) is the marginal effect of the kth explanatory variable, \(\:{\stackrel{-}{x}}_{k}\) is the sample mean of the kth explanatory variable, \(\:\mu\:\) is the mean (i.e., average probability) of the health variable \(\:{y}_{i}\) , \(\:C{I}_{k}\) is the concentration index of the kth explanatory variable, \(\:G{C}_{u}\) is the generalized concentration index of the residual term \(\:{u}_{i}\) . Results Descriptive statistics Table 2 shows the demographic characteristics and prevalence of CRD in the Chinese study population. The gender ratio of all participants from 2013 to 2020 was comparatively balanced based on their demographics, with a slightly higher percentage of girls (50.57%–52.01%) than males (47.99%–49.96%). Most participants lived in rural areas (59.89%-61.93%), were married or had a spouse (86.13%-89.72%), were aged between 55 and 64 (33.51%-38.33%), had a household registration in agriculture (70.05%-75.54%), and had an education level of junior high school or less (86.48%-88.38%). The average level of health was between 48.70% and 52.48%. Between 15.82% and 28.96% of individuals reported a history of smoking during the research period, while nearly 35% reported a history of alcohol use. Most participants (80.03% to 97.10%) had some form of health insurance, and the percentage of people with pension coverage increased from 42.44% in 2013 to 85.92% in 2020. In terms of participant prevalence, the prevalence of CRD among Chinese adults aged 45 years and above was 11.82%, 13.46%, 17.27%, and 15.92% for the four waves from 2013 to 2020. During the study period, statistically significant differences ( p < 0.05) were observed in the prevalence of CRD among the various groups, based on gender, age, marital status, education level, place of residence, self-rated health status, and history of alcohol use. In particular, males aged 55–64, those who were unmarried, divorced, or widowed, those who had completed junior high school or less, those who lived in rural areas, those who had a history of alcohol use, and those who had poor self-rated health were more likely to be diagnosed with CRD than those in the other groups. The prevalence of CRD in the 65–74 age group was steadily increasing and was more comparable to that of the 55–64 age group. In the 65–74 age range, the prevalence of CRD was consistently growing and was more similar to that of the 55–64 age group. Lower SES was associated with higher incidences of CRD. Compared to 17.89% of participants in the highest socioeconomic group, 20.13% of those in the lowest socioeconomic group had a diagnosis of a chronic respiratory condition in 2013. These rates shifted to 22.30% and 19.59%, respectively, by 2020. Table 2 Demographic characteristics and prevalence of CRD in the Chinese study population, 2013–2020 [n(%)]. Variables 2013wave 2015wave 2018wave 2020wave Total CRD Total CRD Total CRD Total CRD Total 11392 1346(11.82%) 12746 1716(13.46%) 15031 2596(17.27%) 13088 2083(15.92%) Sex Male 5631(49.43%) 775(57.58%) 6368(49.96%) 977(56.93%) 7242(48.18%) 1389(53.51%) 6281(47.99%) 1091(52.38%) Female 5761(50.57%) 571(42.42%) 6378(50.04%) 739(43.07%) 7789(51.82%) 1207(46.49%) 6807(52.01%) 992(47.62%) χ 2 40.5446*** 38.5776*** 35.6382*** 19.0916*** Age 45–54 4010(35.20%) 295(21.92%) 4774(37.45%) 390(22.73%) 4656(30.98%) 493(18.99%) 3975(30.37%) 411(19.73%) 55–64 4366(38.33%) 515(38.26%) 4450(34.91%) 621(36.19%) 5037(33.51%) 824(31.74%) 4598(35.13%) 704(33.80%) 65–74 2254(19.79%) 379(28.16%) 2678(21.01%) 495(28.84%) 3880(25.81%) 908(34.98%) 3379(25.82%) 701(33.65%) 75and above 762(6.69%) 157(11.66%) 844(6.62%) 210(12.24%) 1458(9.70%) 371(14.29%) 1136(8.68%) 267(12.82%) χ 2 187.0634*** 268.1581*** 318.728*** 201.3926*** Marital status Unmarried and other 1201(10.54%) 210(15.60%) 1310(10.28%) 246(14.34%) 1933(12.86%) 428(16.48%) 1815(13.87%) 352(16.90%) Married or with spouse 10191(89.46%) 1136(84.40%) 11436(89.72%) 1470(85.66%) 13098(87.14%) 2168(83.51%) 11273(86.13%) 1731(83.10%) χ 2 41.4259*** 35.4101*** 36.8329*** 19.0540*** Education Junior high School and below 9852(86.48%) 1210(89.90%) 9608(87.48%) 1541(89.80%) 13285(88.38%) 2363(91.02%) 11344(86.67%) 1874(89.97%) Senior high school/technical secondary school 1313(11.53%) 114(8.47%) 1336(10.48%) 152(8.86%) 1479(9.84%) 211(8.13%) 1452(11.09%) 185(8.88%) University and above 227(1.99%) 22(1.63%) 260(2.04%) 23(1.34%) 267(1.78%) 22(0.85%) 292(2.23%) 24(1.15%) χ 2 15.4182*** 10.9772** 27.0620*** 26.9534*** Residence status Rural 6940(60.92%) 883(65.60%) 7633(59.89%) 1158(67.48%) 9164(60.97%) 1724(66.41%) 8105(61.93%) 1372(65.87%) Urban 4452(39.08%) 463(34.40%) 5113(40.11%) 558(32.52%) 5867(39.03%) 872(33.59%) 4983(38.07%) 711(34.13%) χ 2 14.053*** 47.6413*** 39.0591*** 16.3071*** Household registration Agricultural registration 8606(75.54%) 1017(75.56%) 8929(70.05%) 1274(74.24%) 10900(72.52%) 2074(79.89%) 9749(74.49%) 1585(76.09%) Non-agricultural registration 2776(24.36%) 329(24.44%) 3817(29.95%) 442(25.76%) 4131(27.48%) 522(20.11%) 3339(25.51%) 498(23.91%) χ 2 0.0018 38.8189*** 0.9931 3.3543 Self-assessed health status Good 2713(23.81%) 169(12.56%) 3247(25.47%) 230(13.40%) 3782(25.16%) 281(10.82%) 3307(25.27%) 217(10.42%) Fair 5979(52.48%) 601(44.65%) 6770(53.11%) 842(49.07%) 7320(48.70%) 1157(44.57%) 6525(49.85%) 953(45.75%) Poor 2700(23.70%) 576(42.79%) 2729(21.41%) 644(37.53%) 3929(26.14%) 1158(44.61%) 3256(24.88%) 913(43.83%) χ 2 333.8519*** 360.1654*** 676.7743*** 582.2729*** Smoking history Yes 1802(15.82%) 225(16.72%) 3691(28.96%) 538(31.35%) 4153(27.63%) 747(28.78%) 3440(26.28%) 580(27.84%) No 9590(84.18%) 1121(83.28%) 9055(71.04%) 1178(68.65%) 10878(72.37%) 1849(71.22%) 9648(73.72%) 1503(72.16%) χ 2 0.9245 5.5238* 2.0591 3.1149 Drinking history Yes 4088(35.88%) 433(32.17%) 4733(37.13%) 587(34.21%) 5235(34.84%) 821(31.63%) 4872(37.22%) 710(34.09%) No 7294(64.02%) 910(67.61%) 8013(62.87%) 1129(65.79%) 9789(65.16%) 1775(68.37%) 8216(62.78%) 1373(65.91%) χ 2 8.9503** 7.4287** 14.0912*** 10.4488* Health insurance Yes 10944(96.07%) 1301(96.66%) 10200(80.03%) 1576(91.84%) 14595(97.10%) 2618(97.00%) 12522(95.68%) 1991(95.58%) No 448(3.93%) 45(3.34%) 2546(19.97%) 140(8.16%) 436(2.90%) 78(3.00%) 566(4.32%) 92(4.42%) χ 2 1.4033 173.2105*** 0.1204 0.0508 Pension insurance Yes 4835(42.44%) 758(56.32%) 4888(61.65%) 836(48.72%) 8130(54.09%) 1592(61.33%) 11245(85.92%) 1778(85.36%) No 6557(57.56%) 588(43.68%) 7858(38.35%) 880(51.28%) 6901(45.91%) 1004(38.67%) 1843(14.08%) 305(14.64%) χ 2 120.2506*** 90.1708*** 66.1797*** 0.6438 Socioeconomic status Group I (lowest) 2280(20.01%) 271(20.13%) 2550(20.01%) 376(21.91%) 3007(20.01%) 579(22.30%) 2618(20.00%) 433(20.79%) Group II 2277(19.99%) 275(20.43%) 2550(20.01%) 342(19.93%) 3007(20.01%) 511(19.68%) 2618(20.00%) 412(19.78%) Group Ⅲ 2280(20.01%) 269(19.99%) 2549(20.00%) 364(21.21%) 3006(20.00%) 524(20.18%) 2617(20.00%) 405(19.44%) Group Ⅳ 2282(20.03%) 281(20.88%) 2549(20.00%) 327(19.06%) 3006(20.00%) 489(18.84%) 2619(20.01%) 425(20.40%) Group Ⅴ(highest) 2273(19.95%) 250(18.57%) 2548(19.99%) 307(17.89%) 3005(19.99%) 493(18.99%) 2616(29.99%) 408(19.59%) χ 2 2.1605 10.3198* 12.1933* 1.5915 *** p < 0.001, ** p < 0.01, * p < 0.05 significance test Utilization of health services for patients with CRD in China Table 3 shows changes in utilization rates for patients with CRD diagnoses across the three health services. The findings show that from 2013 to 2020, the utilization rates for all three categories of health services increased, with inpatient service utilization having the highest average annual growth rate at 8.26%. The utilization rate of two-year physical examination services (41.60%-49.58%) is higher than that of one-month outpatient services (29.35%-32.36%) and one-year inpatient services (24.89%-31.59%). The proportion of individuals utilizing outpatient services rose from 29.35% in 2013 to 32.36% in 2020. No significant variation in outpatient service use is observed among different SES groups. The utilization rate of inpatient services increased with SES, and the chi-square test indicated that the utilization rate of inpatient services was higher in the highest socioeconomic group than in the lowest socioeconomic group from 2015 to 2020 (p < 0.05). In 2015, utilization rates for inpatient services were 20.48% in the lowest socioeconomic group and 31.27% in the highest socioeconomic group. By 2020, these rates increased to 31.59% and 36.27%, respectively. Utilization of physical examination services also rose considerably with socioeconomic in the 2013 wave and 2015 wave (p < 0.05). Between 2013 and 2020, over half of the patients with CRD in the highest socioeconomic group utilized physical examination services. Table 3 Health services utilization rate of patients with CDR in China, 2013—2020 Health services utilization Wave Total[n(%)] Socioeconomic status Group I (lowest) Group II Group Ⅲ Group Ⅳ Group Ⅴ (highest) χ 2 One-month outpatient service utilization rate 2013 395(29.35%) 73(26.94%) 71(25.82%) 84(30.11%) 88(31.31%) 78(31.20%) 3.87 2015 479(27.91%) 90(23.94%) 87(25.44%) 107(29.40%) 98(29.97%) 97(31.60%) 7.15 2018 655(25.23%) 133(22.97%) 123(24.07%) 143(27.29%) 128(26.18%) 128(25.96%) 3.48 2020 674(32.36%) 131(30.25%) 136(33.01%) 121(29.88%) 152(35.76%) 134(32.84%) 4.39 AAGR 3.31% 3.77% 8.55% -0.26% 4.52% 1.72% One-year inpatient service utilization rate 2013 335(24.89%) 60(22.14%) 68(24.73%) 71(26.39%) 72(25.62%) 64(25.60%) 1.57 2015 423(24.65%) 77(20.48%) 67(19.59%) 97(26.65%) 86(26.30%) 96(31.27%) 16.74** 2018 751(28.93%) 135(23.32%) 142(27.79%) 146(27.86%) 152(31.08%) 176(35.69%) 21.58*** 2020 658(31.59%) 111(25.64%) 130(31.55%) 127(31.36%) 142(33.41%) 148(36.27%) 11.91* AAGR 8.26% 4.99% 8.45% 5.93% 9.29% 12.28% Two-year physical examination service utilization rate 2013 560(41.60%) 86(31.73%) 97(35.27%) 108(40.15%) 132(46.98%) 137(54.80%) 36.89*** 2015 749(43.65%) 154(40.96%) 131(38.30%) 164(45.05%) 142(43.43%) 158(51.47%) 13.01* 2018 1287(49.58%) 286(49.40%) 245(47.95%) 255(48.66%) 243(49.69%) 258(52.33%) 2.23 2020 1023(49.11%) 202(46.65%) 197(47.82%) 202(49.88%) 217(51.06%) 205(50.25%) 2.28 AAGR 5.59% 13.72% 10.68% 7.49% 2.82% -2.85% Note: AAGR = average annual growth rate; *** p < 0.001,** p < 0.01,* p < 0.05 significance test CHE incidence among patients with CRD in China Table 4 shows the trend of CHE incidence in different socioeconomic groups from 2013 to 2020. With an average annual growth of 1.63%, the CHE incidence of all CRD families generally displayed a varying upward pattern, falling from 10.40% in 2013 to 6.29% in 2015 before rising to 11.67% in 2020. The incidence of CHE varied significantly (p < 0.01) across the various socioeconomic groups in 2015. The low socioeconomic group (Group I, 10.64%) had a considerably greater incidence of CHE than the medium socioeconomic group (Group III, 3.57%) and the medium-high socioeconomic group (Group IV, 4.59%), which served as evidence of this. Meanwhile, the risk of CHE occurrence continued to increase during the study period in the medium-low (Group II) and low socioeconomic groups (Group I), with average annual growth rates of 4.06% and 2.43%, respectively. The medium-low socioeconomic group (Group II) reached the highest in each group at 14.32% in 2020. Although the initial level of the high socioeconomic group (Group V) was the lowest at 8.80%, in contrast, the middle socioeconomic groups (Groups III and IV) showed a declining trend, with average annual growth rates of -0.21% and − 1.73%. However, the growth rate of Group III reached 3.56%, and for Group IV, it increased to 11.27% in 2020. Table 4 Incidence of CHE for Patients with CRD in China, 2013–2020 Wave Total[n(%)] Socioeconomic status Group I (lowest) Group II Group Ⅲ Group Ⅳ Group Ⅴ (highest) χ 2 CHE incidence 2013 140(10.40%) 28(10.33%) 30(10.91%) 31(11.52%) 29(10.32%) 22(8.80%) 1.13 2015 108(6.29%) 40(10.64%) 19(5.56%) 13(3.57%) 15(4.59%) 21(6.84%) 18.69** 2018 326(12.56%) 84(14.51%) 61(11.94%) 49(9.35%) 69(14.11%) 63(12.78%) 8.19 2020 243(11.67%) 53(12.24%) 59(14.32%) 46(11.35%) 39(9.18%) 46(11.27%) 5.61 AAGR 1.63% 2.43% 4.06% -0.21% -1.73% 3.56% Note: AAGR = average annual growth rate *** p < 0.001, ** p < 0.01 ,* p < 0.05 significance test Changes in inequalities in health services utilization among patients with CRD in China Table 5 shows the standardized utilization levels of outpatient, inpatient, and physical examination services, analyzing socioeconomic inequality among patients with chronic respiratory diseases from 2013 to 2020. The findings show that, from 2013 to 2020, the CI for outpatient service utilization remained consistently positive, indicating that pro-rich inequality persisted. The standardized outpatient service utilization rates for each socioeconomic group were roughly similar after controlling for non-need characteristics, and they all displayed a declining trend from 2013 to 2016, followed by a rising trend from 2016 to 2020. After adjusting for socioeconomic inequalities, the post-standardization CI in 2020 showed a significant negative value (-0.011, P < 0.05), indicating that the low-income group required more outpatient services due to their higher health demands. Physical examination services exhibited the highest level of standardized utilization and experienced significant growth, with standardized utilization rates exceeding 40% across all socioeconomic groups. This indicates that, compared to outpatient and inpatient care, physical examination services have the greatest increase in equity. Utilization of physical examination services had a CI of 0.110 in 2013, which was substantially higher than that of outpatient services (0.039) and inpatient services (0.025). However, by 2020, it had steadily declined to 0.017, the lowest among the three health services categories. At the same time, the uniform application of physical examination services was evident. In 2020, however, the standardized CI of physical examination service utilization became slightly negative (-0.006, p < 0.05), suggesting that the initial pro-rich trend in physical examination service utilization has shifted towards a more equitable pattern. Table 5 Standardized utilization of health services for patients with CRD in China (across socioeconomic groups and concentration indexes) Standardized health services utilization rate[n(%)] Outpatient services Inpatient services Physical examination service 2013 2015 2018 2020 2013 2015 2018 2020 2013 2015 2018 2020 Socioeconomic status Group I (lowest) 30.24 27.31 24.62 33.19 25.94 25.18 28.51 32.89 40.36 42.03 49.04 50.02 Group II 29.67 27.50 25.39 32.87 25.07 23.69 28.49 31.40 40.73 41.61 48.09 49.59 Group Ⅲ 28.40 28.24 25.47 32.12 24.89 25.02 29.35 31.11 41.53 43.82 49.67 48.48 Group Ⅳ 29.32 28.35 25.67 32.25 23.72 25.56 29.42 31.42 41.66 44.57 49.71 48.99 Group Ⅴ(highest) 29.08 28.26 25.11 31.31 24.86 23.67 28.94 31.04 43.93 46.71 51.52 48.41 Unstandardized CI 0.039 0.057** 0.027 0.020 0.025 0.090** 0.078*** 0.059** 0.110*** 0.046** 0.012 0.017 Standardized CI -0.007 0.008 0.005 -0.011* -0.012 -0.003 0.005 -0.010 0.015*** 0.022*** 0.010** -0.006* Note: *** p < 0.001, ** p < 0.01, * p < 0.05 significance test Changes in inequality in CHE incidence among households of people with CRD Table 6 presents the results of the socioeconomic inequality analysis of the standardized incidence of CHE among households with CRD in China. The standardized incidence rates for various socioeconomic groups fluctuated over time but stayed largely constant between 2013 and 2020. During 2015, the unstandardized CI was significantly negative (-0.125, P < 0.05), indicating that the burden of health expenditures was concentrated among lower socioeconomic groups. With values of (0.031, P < 0.01) and (-0.016, P < 0.01), respectively, the standardized CI revealed significant differences between 2015 and 2020, indicating that, after controlling for non-need variables, the burden was higher in higher socioeconomic groups in 2015 but shifted to lower socioeconomic groups by 2020. This implies that the overall low-socioeconomic Chinese CRD group will eventually face more pressure on health expenditures, with the economic burden of health expenditures varying substantially over time and between socioeconomic groups. Table 6 Standardized incidence of CHE among patients with CRD in China (across socioeconomic groups and concentration indexes) Standardized CHE incidence[n(%)] 2013wave 2015wave 2018wave 2020wave Socioeconomic status Group I (lowest) 10.50 5.91 12.25 12.14 Group II 10.16 5.84 12.65 11.85 Group Ⅲ 9.95 6.44 12.54 11.61 Group Ⅳ 10.40 6.58 12.88 11.53 Group Ⅴ(highest) 11.05 6.78 12.52 11.69 Unstandardized CI -0.027 -0.125* -0.013 -0.048 Standardized CI 0.010 0.031** 0.005 -0.016** Note: *** p < 0.001, ** p < 0.01, * p < 0.05 significance test Decomposition of health services utilization and CHE concentration index for patients with CRD Table 7 presents the marginal effects of different variables and their contribution to the utilization of the three types of health services for CRD patients in 2020. Positive values indicate an increase in inequality and negative values indicate a decrease in inequality. Smoking, drinking behavior, and self-assessed health status were the factors that had the biggest impact on the inequality in outpatient service utilization among CRD patients. Nondrinking (marginal effect = -0.068, p < 0.01) and nonsmoking (marginal effect = -0.104, p < 0.001) reduce the probability of outpatient services by 6.8 and 10.4 percentage points, respectively. Their negative contribution rates (-22.16% and − 17.03%) indicate that these health behaviors help to mitigate utilization inequalities. However, the likelihood of using outpatient services was raised by 23.8 percentage points (marginal effect = 0.238, p < 0.001), and "fair" health increases it by 9.7 points (marginal effect = 0.097, p < 0.05). These positive contributions (7.85% and 5.15%) indicate that poorer health perceptions tend to worsen inequality. Inequality in utilization of inpatient services was mainly driven by self-assessed health status, gender, age structure, and health behaviors. Among these, "poor" self-assessed health status contributed 2.80% to inpatient care utilization, increasing inpatient services by 24.4 percentage points (marginal impact = 0.244, p < 0.001). Inpatient services would rise by 14.5 and 14.9 percentage points for populations aged 65–74 (marginal effect = 0.145, p < 0.001) and 75+ (marginal effect = 0.149, p < 0.001), respectively. However, the contribution of advanced age to inequality was in the opposite direction, contributing − 21.25 and − 4.36 percentage points, respectively. Inequality in the utilization of physical examination services was notably affected by age, smoking status, and pension insurance enrollment. With contributions of -11.31% and − 10.42%, respectively, quitting smoking (marginal impact = -0.098, P < 0.01) and being 75 years or older (marginal effect = 0.160, P < 0.001) significantly reduced inequality, contributing − 16.58% and − 10.9%, respectively. Conversely, those aged 65–74 (marginal impact = 0.242, P < 0.01) and individuals not enrolled in pension insurance (marginal effect = 0.120, P < 0.001) contributed to a notable increase in inequality, with contributions of- 16.58% and- 10.9%. Table 6 presents the decomposition of inequality factors related to CHE incidence among households with CRD. The results highlight that structural issues are the primary cause of inequality in CHE incidence. Among these, living in an urban area (contribution rate = 13.55%) and being part of a non-agricultural household (contribution rate = 8.82%) significantly contribute to higher disparities in health care costs. The likelihood of CHE increases notably with self-assessed health status as "poor" (marginal effect = 0.055, p < 0.5); however, the overall inequality in CHE incidence is slightly reduced by this same health status, with an estimated contribution of around − 2.11%. This suggests that while poorer health increases individual risk of medical expenses, its widespread presence across the population may help reduce overall inequality. Table 7 Decomposition of Health services utilization and CHE Concentration Index for Patients with CRD in 2020 Variables Outpatient services Inpatient services Physical examination service CHE marginal effect Contribution (%) marginal effect Contribution (%) marginal effect Contribution (%) marginal effect Contribution (%) Need variables Self-assessed health status (Ref = good) Fair 0.097* 5.15 0.071 1.31 0.002 0.008 -0.024 1.46 Poor 0.238*** 7.85 0.244*** 2.80 0.020 0.51 0.055* -2.11 Sex (Ref = male) Female -0.007 0.37 -0.082** 1.54 -0.003 0.11 -0.026 -1.60 Age (Ref = 45–54) 55–64 0.032 7.29 -0.000 -0.02 0.045 7.93 -0.003 0.82 65–74 0.031 -12.97 0.145*** -21.25 0.242*** 16.58 -0.012 -5.63 75 and above 0.026 -21.49 0.149*** -4.36 0.160*** -10.42 -0.018 -1.77 Non-need variables Marital status (Ref = unmarried and other) Married or with spouse 0.010 -0.15 -0.028 0.15 0.035 -0.43 0.033 0.60 Education (Ref = Junior high School and below) Senior high school/technical secondary school 0.006 2.03 0.004 0.50 0.06 17.67 0.000 0.14 University and above 0.109 11.69 0.036 1.33 0.148 12.38 -0.014 1.80 Residence status (Ref = rural) Urban 0.034 -26.87 -0.030 8.23 0.011 -6.55 0.015 13.55 Household registration (Ref = Agricultural registration) Non-agricultural registration 0.040 -37.18 0.018 -5.66 -0.004 2.73 0.008 8.82 Drinking history (Ref = yes) No -0.068** -22.16 -0.110*** -12.40 -0.009 -2.38 -0.023 8.57 Smoking history (Ref = yes) No -0.104*** -17.03 -0.093*** -5.28 -0.089** -11.31 -0.009 1.74 Health insurance (Ref = yes) No 0.005 0.27 -0.018 -0.33 0.072 2.89 0.011 -0.65 Health insurance (Ref = yes) No 0.031 3.66 0.053 2.18 0.120*** 10.9 -0.009 1.24 Note: *** p < 0.001, ** p < 0.01, * p < 0.05 significance test Discussion Based on CHARLS data, this study examined the evolving trends of inequalities in three types of health services utilization and CHE among middle-aged and older adults with CRD in China from 2013 to 2020. It also identified the main factors contributing to these inequalities. Results showed that the prevalence of CRD among Chinese adults aged 45 and above rosed from 11.82% in 2013 to 15.92% in 2020. The prevalence differed significantly across various dimensions, including demographic factors, social determinants, and subjective health perceptions, consistent with findings by Ferrante et al [ 16 , 17 ]. This underscores the importance of developing CRD prevention and control strategies that target population-specific risk factors and include tailored prevention and treatment measures for high-risk groups. It is also vital to address underlying social determinants and biological mechanisms. CRD is more frequently diagnosed in populations with lower SES compared to those with higher SES, reflecting the socioeconomic health gradient. Lower SES groups tend to have poorer health outcomes, indicating a greater objective need for health services in these populations, in line with Larick's study [ 18 ]. From 2013 to 2020, we observed an increase in the utilization of outpatient, inpatient, and physical examination services among middle-aged and older CRD populations in China. The trend in inpatient service utilization exhibited the highest average annual growth rate, aligning with the findings of the China National Health Services Survey (NHSS) [ 19 ]. The Chinese government has been working to establish a universal healthcare system since the start of the century. To promote equitable access to essential medical care, it has introduced three different types of social medical insurance. According to data in this study, the basic medical insurance coverage rate for China's middle-aged and older CRD population has consistently remained above 95%, providing institutional protection for healthcare use and continuously boosting the population's demand for health services. China's Medium- and Long-Term Plan for the Prevention and Treatment of Chronic Diseases (2017–2025) aims to reduce CRD mortality rates and increase pulmonary function tests. It also includes pulmonary function tests as part of routine physical exams for people over 40, promoting greater utilization of preventive health services. To ensure vertical equity in healthcare, the principle of "distribution according to need" should be followed, prioritising patients with higher health service needs and poorer health status for resource access [ 20 ]. After controlling for socioeconomic factors, CRD patients with low SES exhibited higher utilization rates of standardized health services, reflecting their more urgent objective health needs. However, the comparison revealed a discrepancy between the actual utilization rate of the three types of health services among the low SES CRD patient group and their standardized needs. This indicates that the current health service system is still unable to adequately meet the population's health needs, and there is a need to address the unmet demand gap within the low socioeconomic group. Furthermore, regional health planning should include standardized demand measurement. This study employed the concentration index and the standardized concentration index analysis to thoroughly assess changes in health services utilization equity among middle-aged and elderly CRD patients in China from 2013 to 2020. The findings revealed improvements in the equity of outpatient, inpatient, and physical examination services over this period, with the most notable progress seen in health examination service equity. This suggests that China has made substantial advances in enhancing health service accessibility for vulnerable groups and promoting' vertical equity" through initiatives like poverty alleviation via health insurance and hierarchical diagnosis and treatment systems. In particular, the implementation of the annual free physical examination program for the elderly in the National Basic Public Health Service has effectively improved the equity of preventive services for CRD patients. Nevertheless, it is important to note that, even with the improvement in overall equity, the CI of the three types of health service utilization by middle-aged and elderly CRD patients in China was consistently positive throughout the study period. This suggests that, in line with the global trend of low- and middle-income countries, the health service utilization of middle-aged and elderly CRD patients in China continues to be clearly favorable to the rich [ 21 , 22 ]. In addition, against the background of high health insurance coverage, the CI of inpatient service utilization among CRD patients in 2020 was the highest among the three types of health services, and the service utilization rate was positively correlated with SES. Yang Wei demonstrated that although health insurance coverage continues to broaden, the new rural cooperative medical system has contributed to reducing inequities in health service utilization in China, but its contribution to improving access to health services for the poor is limited [ 23 ]. It shows that SES still an important factor affecting access to hospitalization services. Moving forward, it is essential to persist with reforming the health insurance system, implement differentiated health insurance policies for low socioeconomic CRD patients, and simultaneously improve primary healthcare service systems to promote equitable inpatient services. In order to further analyze the contribution of related factors to pro-rich inequality in health service utilization, this study decomposed the concentration index of three types of health service utilization by CRD patients in China in 2020. The decomposition results show that there are significant differences in the mechanisms driving inequality across different health service types. Inequality in outpatient services is mainly influenced by the non-need variables “non-smoking” and “non-drinking”, while inequality in the utilization of inpatient services and health check-ups is more influenced by the need variable “older than 65 years”. For inpatient service utilization, advanced age can increase the probability of inpatient utilization by 14.5 percentage points, but its contribution to inequality is negative. This indicates a reverse buffering effect in health service utilization [ 24 ]: physiological decline and more co-morbidities in the elderly CRD population increase the overall demand for inpatient service utilization. Since this health risk is relatively evenly spread across socioeconomic groups and China's current elderly healthcare policies effectively address these broad health needs, the higher utilization among high-need groups actually helps reduce the socioeconomic gap in access to care. For physical examination services, the senior factor, on the other hand, shows a positive contribution rate, probably mainly due to the stratification of access to preventive services, with high socioeconomic groups more likely to utilize value-added physical examination services. Furthermore, health behavioral factors among the non-need factors significantly promote equity across all three types of health service use. Non-smoking was able to reduce the probability of outpatient, inpatient, and physical examination services utilization by 10.4, 9.3, and 8.9 percentage points, respectively, accounting for 17.03%, 5.28%, and 11.31% of reducing inequality. Additionally, Non-drinking notably lowered outpatient and inpatient utilization probabilities by 6.8 and 11.0 percentage points. These findings suggest that improving equity in service use could be achieved through targeted health education and behavioral interventions. This study also found that CRD patients without pension insurance have nearly an 12% lower probability of physical examination service utilization. This highlights the important role of the social security system in promoting equity in preventive care, aligning with findings by Zhao Y and other researchers [ 25 , 26 ]. The absence of pension coverage often compounds other socioeconomic challenges, such as low income and education, worsening health disparities. Consequently, a recommendation is to create a dedicated subsidy program for preventive services for CRD patients lacking pension insurance and to integrate this with the family doctor contracting system, enhancing collaboration between social security and public health. This study found that, under the 40% health out-of-pocket expenses, the overall risk of CHE in 2020 for Chinese middle-aged and elderly CRD households was 11.67%. This is lower than the 25.5% CHE incidence among Chinese households reported in Yuan Q et al.'s study [ 27 ] and the 14.62% among Chinese elderly CRD households reported by Yao X et al. [ 28 ], but higher than the 4.4% CHE incidence in U.S. households with chronic diseases [ 29 ]. Xu X et al. state that liver disease, stroke, and cancer are the chronic conditions most often associated with higher healthcare costs [ 30 ]. Compared to other high-cost chronic diseases, CRD is less expensive to manage routinely. China has included major CRDs, such as COPD, in outpatient special chronic disease coverage, with reimbursement rates typically between 60% and 70%. This significantly lessens patients' out-of-pocket expenses. However, it is concerning that the incidence of CHE among middle-aged and elderly CRD patients in China increased from 10.40% in 2013 to 11.67% in 2020, with an average annual growth of 1.63%. Even more worrying, limited health payment capacity means the low socioeconomic CRD population faces greater financial risks, with CHE risk continuing to rise in middle-low and low socioeconomic groups. Furthermore, the CI analysis showed that the unstandardized CI remained negative throughout the study period, rising from − 0.027 in 2013 to -0.048 in 2020. This indicates that the burden of health expenditure has a pro-poor distribution, which aligns with the findings of existing studies [ 31 ]. The decomposition study indicates that structural factors account for the majority of CHE inequality, with urban residence and non-agricultural registration accounting for 13.55% and 8.82% of the inequality, respectively. This indicates that the urban-rural divide remains one of the main systemic sources of healthcare burden disparity [ 32 ]. Rapid integration of the basic medical insurance systems in urban and rural areas is necessary, as is the gradual reduction of urban-rural disparities and the unification of outpatient coverage for chronic diseases. Limitations There are some limitations to this study. First, the variables are based on respondents' self-reported data, and there may be reporting bias in assessing health status and recalling healthcare expenditure. Second, because of the large number of variables, insurance status was only categorised dichotomously, which fails to account for the different impacts of various health insurance types and coverage levels, such as those of urban and rural residents or employees, on the study results. Finally, due to data availability limitations, the 2020 CHARLS only collected total out-of-pocket medical expenses and did not include data on total health expenditures or disaggregated expenses for outpatient, inpatient, and physical examinations; therefore, this study did not conduct a detailed analysis of the economic burden associated with different types of health services. Conclusion In conclusion, the prevalence of CRD among people aged ≥ 45 years in China continues to rise, with significant socioeconomic gradient disparities. Throughout the study period, although the utilization of outpatient, inpatient, and physical examination services for CRD patients increased overall and equity improved, a clear pro-rich inequality persisted. Additionally, health behavioural factors showed a significant role in promoting equity in the utilization of all three types of health services. CHE incidence displayed a pro-poor inequality, which has worsened, with low socioeconomic CRD patients experiencing higher health needs yet facing heavier financial burdens. The urban-rural dual structure remains the main factor driving the unequal financial burdens. Abbreviations CRD Chronic respiratory disease NCDs Non-communicable diseases CHE catastrophic health expenditures CHARLS China Health and Retirement Longitudinal Study CI Concentration index SDGs United Nations Sustainable Development Goals SES socioeconomic status NDRI National Development Research Institute of Peking University PPS probability proportional to size WHO World Health Organization OOP Out-of-pocket PCE per capita household consumption AAGR average annual growth rate Declarations Ethics approval and consent to participate The CHARLS was approved by the Ethics Review Board of Peking University (approval number: IRB00001052-11015). All participants signed an informed consent form. All study procedures were conducted in accordance with the Declaration of Helsinki. Consent for publication Not applicable. Clinical trial number Not applicable. Availability of data and materials The data used in this study are available through the China Health and Retirement Longitudinal Study (CHARLS) conducted by the Center for Social Science Research at Peking University. The link to access is: http://charls.pku.edu.cn. Competing interests The authors declare that they have no competing interest. Funding This study was supported by Guangzhou Public Health Service System Construction Research Base Funding Program (Grant NO: 2024-2026) and the 2022 Project of National Topics Cultivation Program, School of Health Administration, Southern Medical University (Grant NO: 2022RFT004). Author contributions XW and WY co-designed the study and developed the main framework of the study; XW was responsible for the data collection and analysis work, and the writing of the final manuscript. XW, ZC, YL, FA and WY commented on and edited the manuscript for important intellectual content. All authors read and approved the final manuscript. 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Analysis of US household catastrophic health care expenditures associated with chronic disease, 2008-2018. JAMA Netw Open. 2022;5:e2214923. Xu X, Yang H. Does elderly chronic disease hinder the sustainability of borderline poor families’ wellbeing: An investigation from catastrophic health expenditure in China. Int J Public Health. 2022;67:1605030. Zhao Y, Atun R, Oldenburg B, McPake B, Tang S, Mercer SW, et al. Physical multimorbidity, health service use, and catastrophic health expenditure by socioeconomic groups in China: An analysis of population-based panel data. Lancet Glob Health. 2020;8:e840–9. Fu X-Z, Sun Q-W, Sun C-Q, Xu F, He J-J. Urban-rural differences in catastrophic health expenditure among households with chronic non-communicable disease patients: Evidence from China family panel studies. BMC Public Health. 2021;21:874. Additional Declarations No competing interests reported. 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07:33:35","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":197930,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8091430/v1/e7a56953ae0b6f41691a4c73.html"},{"id":98631748,"identity":"2bff6ff2-4f19-4314-9ffc-da5997fc4c63","added_by":"auto","created_at":"2025-12-19 17:20:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1743815,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8091430/v1/c668033d-182a-447f-85aa-d55046f1932a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Inequalities in health services utilization and catastrophic health expenditures among patients with chronic respiratory diseases in China: An empirical analysis based on the CHARLS, 2013—2020","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUnder the global public health governance framework, the United Nations Sustainable Development Goals (SDGs) set a clear target: by 2030, reduce premature mortality from non-communicable diseases (NCDs) by one-third through prevention, treatment, and mental health promotion. Chronic respiratory diseases (CRD), including chronic obstructive pulmonary disease, asthma, and emphysema, represent a significant category of NCDs with high prevalence among middle-aged and elderly populations. Characterized by elevated morbidity, disability rates, mortality, and disease burden, CRDs have emerged as the world's third-leading cause of death [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Recent years have witnessed rising CRD prevalence globally, driven by factors such as air pollution and smoking [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The disease\u0026rsquo;s chronic nature and frequent exacerbations require costly lifelong care, impairing patients\u0026rsquo; quality of life. Additionally, it strains economic and social systems due to workforce health losses and rising expenditures.\u003c/p\u003e \u003cp\u003eTo alleviate the NCD disease burden and achieve SDG targets, the Chinese government has prioritized CRD prevention and control. It has integrated CRD management into national strategies, including the Healthy China 2030 blueprint and the Medium- and Long-Term Plan for Chronic Disease Prevention and Treatment (2017\u0026ndash;2025), while establishing the National CRD Prevention and Control Initiative. Despite these policy efforts, CRD control continues to face persistent challenges. According to epidemiological data, China reported 75.27\u0026nbsp;million cases of CRD in 2021, representing a 34.29% increase from 1990. These numbers show a substantial discrepancy between preventative outcomes and policy goals, with 1.33\u0026nbsp;million CRD-related deaths (or 29.56% of global CRD mortality). More importantly, the main obstacle to managing CRD is the rise in health inequalities, particularly in relation to health outcomes, medical expenditure burdens, and health service utilization. Health inequalities are primarily shaped by socioeconomic status (SES), as demonstrated by Link and Phelan in the 1990s [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Socioeconomic status (SES) groups differ significantly in terms of disease control rates and quality of survival. In addition to facing greater risks of catastrophic health expenditures (CHE), low SES individuals, rural dwellers, and communities in less developed regions are severely disadvantaged in terms of early screening rates and access to standardized treatment [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Although China has the largest basic medical insurance network in the world and has implemented medical assistance mechanisms and differential reimbursement policies to protect vulnerable groups, these measures are still insufficient. Pro-rich disparities persist in the likelihood and frequency of older individuals using health services [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This systematic health inequality not only weakens the overall prevention and control of public health problems such as CRD, but also creates a vicious cycle of \u0026ldquo;poverty caused by disease, poverty caused by disease\u0026rdquo; [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe construction of an efficient and equitable health care delivery system is centered on ensuring equitable access to and utilization of health services, which should be provided to all individuals based on the principle of vertical equity. Within the framework of this principle, differences in healthcare service utilization triggered by individual health needs are reasonable. At the same time, inequalities directly caused by SES are objective, real, and should be avoided [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Therefore, further evidence is required to narrow the irrational health service utilization and economic burden inequality brought on by the SES disparity and to comprehend the current state of these issues as well as their evolving trends among China's middle-aged and older CRD population.\u003c/p\u003e \u003cp\u003eMost previous studies on health service utilization disparities have primarily focused on outpatient and inpatient services [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], with relatively limited attention to physical examination service utilization. This oversight, to a certain extent, underestimates the importance of preventive healthcare services in promoting health equity. Inequality in the utilization of physical examination, as an effective means of early detection and intervention of CRD, may exacerbate the pressure of demand for subsequent treatment services, creating a more serious cycle of health inequality. Based on the four-phase cross-section of the China Health and Retirement Longitudinal Study (CHARLS) from 2013 to 2020, this study examines the current state, level of inequality, and dynamic trends in the use of three different health services, such as inpatien, outpatient, and physical examination services, as well as the incidence of catastrophic health expenditures among the middle-aged and elderly CRD population in China. It also examines the main factors of these disparities to provide evidence for bettering CRD prevention and control strategies and for developing targeted health equity policies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData sources\u003c/h2\u003e \u003cp\u003eThis study uses data from the China Health and Retirement Longitudinal Study (CHARLS), which was conducted by Peking University's National Development Research Institute (NDRI). Researchers employed multi-stage probability proportional to size (PPS) sampling to administer structured questionnaires. The surveys targeted residents aged 45 and above in 450 villages and communities across 150 counties in 28 provincial-level regions. The questionnaires covered basic information about elderly residents, including family conditions, socioeconomic background, health status, physical functioning, access to medical services, and insurance coverage. The questionnaire covers the basic status of the elderly and their families, including socioeconomic background and family structure, health status and functioning, as well as medical services and insurance. The survey has the breadth and depth of data needed for social science research, and its validity and authority have been widely recognized by the academic community. The project conducted a baseline survey in 2011, enrolling a total of 17,708 residents. This was followed by four follow-up surveys in 2013, 2015, 2018, and 2020, which allowed for the dynamic addition and subtraction of people entering the cohort. All CHARLS data were approved by the Ethics Review Board of Peking University (approval number: IRB00001052-11015), and all participants signed an informed consent form.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eThe CHARLS provided nationally representative tracking data for four periods in 2013, 2015, 2018, and 2020, which served as the basis for this investigation. After removing patients under 45, those with diagnosed mental or memory disorders, and those lacking essential variables, the final sample sizes included in the analysis were 11,392 cases in 2013, 12,746 cases in 2015, 15,031 cases in 2018, and 13,088 cases in 2020. The study population was defined as individuals aged 45 years or older. Patients who self-reported a doctor's diagnosis of \"chronic lung disease (e.g., chronic bronchitis, emphysema, or pulmonary heart disease, etc., excluding tumors)\" or \"asthma\" were considered to have CRD for this study.\u003c/p\u003e\n\u003ch3\u003eOutcome variables\u003c/h3\u003e\n\u003cp\u003eThe outcome variables of this study include three health service utilization rates and the incidence rates of catastrophic health expenditure (CHE). Health service utilization was measured by outpatient service utilization in the past month, inpatient service utilization over the past year, and physical examination service utilization over the past two years. In the CHARLS questionnaire, respondents were asked to answer the question, \u0026ldquo;In the past month, have you visited a health care facility for an outpatient visit or received in-home health care services? (excluding physical exams)\u0026rdquo;, \u0026ldquo;Have you been hospitalized in the past year?\u0026rdquo; and \u0026ldquo;When was your last routine physical examination since your last visit? (Note: does not include a CHARLS physical exam).\u0026rdquo; The results of the responses were used to determine whether respondents utilized outpatient services, inpatient services, and physical examination services, and the percentage of people who did so was calculated. This study's definition of CHE is based on the World Health Organization's (WHO) criteria, which state that a household is deemed to be experiencing CHE if, after covering basic subsistence expenses, medical costs account for at least 40% of its disposable income. Household disposable income equals household income minus household food expenditure. According to the definition of household income in the National Statistical Yearbook and information from the CHARLS database, household income in this study involves the calculation of variables including 11 sources of income, such as wage income, net business income, and transfer income. Household food expenditures include food expenditures and the value of agricultural products consumed from the household. Household health expenditures include direct and indirect out-of-pocket expenditures.\u003c/p\u003e\n\u003ch3\u003eIndependent variable\u003c/h3\u003e\n\u003cp\u003eThe independent variable in this study is socioeconomic status (SES). The following factors led to the selection of annual per capita household consumption expenditures (PCE) as the primary indicator of SES: (1) The quality of reported consumption data is higher among middle-aged and older individuals, and (2) consumer expenditure is a more consistent indicator of long-term economic resources. Respondents' annual PCE were ranked according to the customary international income quintiles, and divided into five groups according to the level of the average, which were Group I (low expenditure group), Group II (medium-low expenditure group), Group III (medium expenditure group), Group IV (medium-high expenditure group), and Group V (high-expenditure group) in the order of Group I (low-expenditure group), II (medium-low expenditure group), III (medium-expenditure group), IV (medium-high expenditure group), and V (high-expenditure group). To thoroughly evaluate the mechanisms of SES influence on Health services utilization and financial burden while controlling for potential confounders, multidimensional parameters such as sociodemographic traits and population-related health behaviors were included in the selection of covariates. According to Andersen's health service use behavior model, the contributing elements to health service utilization behavior in this study were categorized into dispositional features, enabling factors, and demand factors[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides exact definitions, measurements, and assignment criteria for each of the study variables.\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\u003eDefinition of research variables and assignment criteria based on Andersen's behavioral model of health services utilization and financial burden\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\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\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpecific definition and assignment\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e\u003cb\u003eDispositional characteristic\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIf 45\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;age\u0026thinsp;\u0026lt;\u0026thinsp;55, age\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003cp\u003eIf 55\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;age\u0026thinsp;\u0026lt;\u0026thinsp;65, age\u0026thinsp;=\u0026thinsp;2 \u003c/p\u003e \u003cp\u003eIf 65\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;age\u0026thinsp;\u0026lt;\u0026thinsp;75, age\u0026thinsp;=\u0026thinsp;3 \u003c/p\u003e \u003cp\u003eIf age\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;75, age\u0026thinsp;=\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u0026thinsp;=\u0026thinsp;1, Female\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarried or with spouse\u0026thinsp;=\u0026thinsp;1, Unmarried and other\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJunior high School and below =\u0026thinsp;1 \u003c/p\u003e \u003cp\u003eSenior high school/technical secondary school\u0026thinsp;=\u0026thinsp;2 \u003c/p\u003e \u003cp\u003eUniversity and above =\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHousehold registration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAgricultural registration\u0026thinsp;=\u0026thinsp;1, Non-agricultural registration\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrinking history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u0026thinsp;=\u0026thinsp;1, No\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmoking history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u0026thinsp;=\u0026thinsp;1, No\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eEnabling factors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea of residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRural areas\u0026thinsp;=\u0026thinsp;1, Urban areas\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAvailability of medical insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u0026thinsp;=\u0026thinsp;1, No\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAvailability of pension insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u0026thinsp;=\u0026thinsp;1, No\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocioeconomic status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual PCE is categorized into five subgroups from lowest to highest.\u003c/p\u003e \u003cp\u003eGroup I (low spending group)\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003cp\u003eGroup II (low and medium spending group)\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003cp\u003eGroup III (middle disbursement group)\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e \u003cp\u003eGroup IV (medium-high spending group)\u0026thinsp;=\u0026thinsp;4\u003c/p\u003e \u003cp\u003eGroup V (high spending group)\u0026thinsp;=\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eDemand factors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelf-assessed health status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGood\u0026thinsp;=\u0026thinsp;1, Fair\u0026thinsp;=\u0026thinsp;2, Poor\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChronic respiratory diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e①self-reported doctor diagnosed chronic lung disease (e.g., chronic bronchitis, emphysema, or pulmonary heart disease, excluding tumors)\u003c/p\u003e \u003cp\u003e②self-reported doctor diagnosed \u0026ldquo;asthma\u0026rdquo;.\u003c/p\u003e \u003cp\u003eIf 1\u0026thinsp;=\u0026thinsp;YES AND/OR 2\u0026thinsp;=\u0026thinsp;YES: With chronic respiratory disease\u0026thinsp;=\u0026thinsp;1 \u003c/p\u003e \u003cp\u003eIf 1\u0026thinsp;=\u0026thinsp;No AND 2\u0026thinsp;=\u0026thinsp;No: Without chronic respiratory disease\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOutcome variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHealth services utilization\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\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 \u003cp\u003eOne-month outpatient service utilization rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVisited a health care facility for an outpatient visit or received in-home health care services in the past month (not including having a physical exam).\u003c/p\u003e \u003cp\u003eYes\u0026thinsp;=\u0026thinsp;1, No\u0026thinsp;=\u0026thinsp;0\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 \u003cp\u003eOne-year inpatient service utilization rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHospitalized within the past year. \u003c/p\u003e \u003cp\u003eYes\u0026thinsp;=\u0026thinsp;1, No\u0026thinsp;=\u0026thinsp;0\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 \u003cp\u003eTwo-year physical examination service utilization rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWithin the past two years, a routine physical examination was performed.\u003c/p\u003e \u003cp\u003eYes\u0026thinsp;=\u0026thinsp;1, No\u0026thinsp;=\u0026thinsp;0\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 \u003cp\u003e\u003cb\u003eIncidence of catastrophic health expenditures\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOOP medical expenditures equal or exceed 40% of the household's ability to pay.\u003c/p\u003e \u003cp\u003eYes\u0026thinsp;=\u0026thinsp;1, No\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003eChi-square test\u003c/h2\u003e \u003cp\u003eIn this study, the Pearson chi-square test was used to detect between-group differences in the prevalence of hypertension, health services utilization, and CHE incidence among patients with chronic respiratory diseases.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eIndirect standardization method\u003c/h3\u003e\n\u003cp\u003eThe indirect standardization method of health services utilization was used in this study to categorize the factors influencing health service utilization and the incidence of CHE into two groups: \"need variables\" (which include factors that reflect the population's health status, such as age, gender, and self-assessed health status) and \"non-need variables\" (socioeconomic factors that affect health services utilization that are not need variables, such as marital status, education level, place of residence, household registration, smoking and drinking histories, health insurance, and pension insurance). To explain the expected distribution of fairness in health service use and catastrophic health costs among patients with chronic respiratory diseases in China, when socioeconomic covariates are not considered, we employed a regression model in this study. This model adjusts for the effects of non-need variables and predicts the demand for standardized health service utilization driven by individuals' own health needs and the standardized rate of catastrophic health expenditures. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The specific formula is:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{\\widehat{y}}_{i}^{IS}={y}_{i}-{\\widehat{y}}_{i}^{x}+\\stackrel{-}{y}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\widehat{\\text{y}}}_{\\text{i}}^{\\text{I}\\text{S}\\:}\\)\u003c/span\u003e\u003c/span\u003eis the standardized health services utilization estimate, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{y}}_{\\text{i}}\\)\u003c/span\u003e\u003c/span\u003e is the actual utilization value, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\widehat{\\text{y}}}_{\\text{i}}^{\\text{x}}\\)\u003c/span\u003e\u003c/span\u003e is the utilization value after controlling for non-need variables, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\text{y}}\\)\u003c/span\u003e\u003c/span\u003e is the sample mean. It should be noted that smoking history and drinking history mainly reflect socioeconomic status differences and individual behavioral choices rather than direct health service needs in this study, so they were included in the analysis as non-needed variables.\u003c/p\u003e\n\u003ch3\u003eConcentration index method\u003c/h3\u003e\n\u003cp\u003eBased on the standardized analysis, this study followed the health equity measurement framework proposed by Wagstaff et al. (1991) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. It used the concentration index (CI) to quantify equity in health services utilization and CHE incidence. The CI calculation formula is as follows:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:CI=\\frac{2}{\\mu\\:}cov（{y}_{i}\\:,{R}_{i}\\)\u003c/span\u003e \u003c/span\u003e \u003cem\u003e)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eWhere \u003cem\u003e\u0026micro;\u003c/em\u003e is the sample mean of health services utilization, \u003cem\u003ecov\u003c/em\u003e is the covariance, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the health services utilization indicator for individual i, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the relative rank of individual i's annual PCE in the overall population (in descending order, taking values from 0\u0026ndash;1). CI takes the range of [-1, 1], with CI\u0026thinsp;=\u0026thinsp;0 indicating absolute fairness; and CI\u0026thinsp;\u0026gt;\u0026thinsp;0 indicating that service utilization is biased in favor of the high SES group, while CI\u0026thinsp;\u0026lt;\u0026thinsp;0 indicates a bias towards lower SES groups.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eConcentration index decomposing method\u003c/h2\u003e \u003cp\u003eThe concentration index (CI) decomposing method proposed by Wagstaff in 2003 was used to decompose the CI of health services utilization and CHE incidence [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This method can decompose the CI and demonstrate how each component contributes to the inequality of results. We chose Probit regression instead of linear regression for the decomposition because the primary outcome indicators in our study are dichotomous variables [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The linear approximation of the regression formula is as follows:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{y}_{i}\\approx\\:\\alpha\\:+\\sum\\:_{k}{\\beta\\:}_{k}^{m}{x}_{i}^{k}+{u}_{i}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the health outcome variable (whether or not health services are utilized and whether or not CHE are incurred), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\alpha\\:\\:\\)\u003c/span\u003e\u003c/span\u003eis the intercept, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{i}^{k}\\:\\)\u003c/span\u003e\u003c/span\u003eis the kth explanatory variable, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{k}^{m}\\)\u003c/span\u003e\u003c/span\u003e is the marginal effect of the kth explanatory variable in the Probit model, which explains the change in the predicted probability associated with a unit change in the independent variable, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\:u}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the implied error term.\u003c/p\u003e \u003cp\u003eThe concentration index decomposition considers the effects of both need and non-need variables, with the contribution of each factor assessed through the decomposition. The CI decomposition formula is as follows:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:C=\\sum\\:_{K}\\left(\\frac{{\\beta\\:}_{k}^{m}{\\stackrel{-}{x}}_{k}}{\\mu\\:}\\right)C{I}_{k}+\\frac{G{C}_{u}}{\\mu\\:}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:C\\)\u003c/span\u003e\u003c/span\u003e is the unscaled concentration index, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{k}^{m}\\)\u003c/span\u003e\u003c/span\u003e is the marginal effect of the kth explanatory variable, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\stackrel{-}{x}}_{k}\\)\u003c/span\u003e\u003c/span\u003e is the sample mean of the kth explanatory variable, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mu\\:\\)\u003c/span\u003e\u003c/span\u003e is the mean (i.e., average probability) of the health variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{i}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:C{I}_{k}\\)\u003c/span\u003e\u003c/span\u003e is the concentration index of the kth explanatory variable, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:G{C}_{u}\\)\u003c/span\u003e\u003c/span\u003eis the generalized concentration index of the residual term \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{u}_{i}\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive statistics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the demographic characteristics and prevalence of CRD in the Chinese study population. The gender ratio of all participants from 2013 to 2020 was comparatively balanced based on their demographics, with a slightly higher percentage of girls (50.57%\u0026ndash;52.01%) than males (47.99%\u0026ndash;49.96%). Most participants lived in rural areas (59.89%-61.93%), were married or had a spouse (86.13%-89.72%), were aged between 55 and 64 (33.51%-38.33%), had a household registration in agriculture (70.05%-75.54%), and had an education level of junior high school or less (86.48%-88.38%). The average level of health was between 48.70% and 52.48%. Between 15.82% and 28.96% of individuals reported a history of smoking during the research period, while nearly 35% reported a history of alcohol use. Most participants (80.03% to 97.10%) had some form of health insurance, and the percentage of people with pension coverage increased from 42.44% in 2013 to 85.92% in 2020.\u003c/p\u003e \u003cp\u003eIn terms of participant prevalence, the prevalence of CRD among Chinese adults aged 45 years and above was 11.82%, 13.46%, 17.27%, and 15.92% for the four waves from 2013 to 2020. During the study period, statistically significant differences (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were observed in the prevalence of CRD among the various groups, based on gender, age, marital status, education level, place of residence, self-rated health status, and history of alcohol use. In particular, males aged 55\u0026ndash;64, those who were unmarried, divorced, or widowed, those who had completed junior high school or less, those who lived in rural areas, those who had a history of alcohol use, and those who had poor self-rated health were more likely to be diagnosed with CRD than those in the other groups. The prevalence of CRD in the 65\u0026ndash;74 age group was steadily increasing and was more comparable to that of the 55\u0026ndash;64 age group. In the 65\u0026ndash;74 age range, the prevalence of CRD was consistently growing and was more similar to that of the 55\u0026ndash;64 age group. Lower SES was associated with higher incidences of CRD. Compared to 17.89% of participants in the highest socioeconomic group, 20.13% of those in the lowest socioeconomic group had a diagnosis of a chronic respiratory condition in 2013. These rates shifted to 22.30% and 19.59%, respectively, by 2020.\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\u003eDemographic characteristics and prevalence of CRD in the Chinese study population, 2013\u0026ndash;2020 [n(%)].\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2013wave\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2015wave\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2018wave\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e2020wave\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCRD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCRD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCRD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCRD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1346(11.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1716(13.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2596(17.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2083(15.92%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5631(49.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e775(57.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6368(49.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e977(56.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7242(48.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1389(53.51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6281(47.99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1091(52.38%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5761(50.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e571(42.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6378(50.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e739(43.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7789(51.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1207(46.49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6807(52.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e992(47.62%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.5446***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.5776***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.6382***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19.0916***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4010(35.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e295(21.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4774(37.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e390(22.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4656(30.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e493(18.99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3975(30.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e411(19.73%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4366(38.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e515(38.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4450(34.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e621(36.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5037(33.51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e824(31.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4598(35.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e704(33.80%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65\u0026ndash;74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2254(19.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e379(28.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2678(21.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e495(28.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3880(25.81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e908(34.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3379(25.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e701(33.65%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e762(6.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e157(11.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e844(6.62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e210(12.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1458(9.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e371(14.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1136(8.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e267(12.82%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e187.0634***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e268.1581***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e318.728***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e201.3926***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried and other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1201(10.54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e210(15.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1310(10.28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e246(14.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1933(12.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e428(16.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1815(13.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e352(16.90%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried or with spouse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10191(89.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1136(84.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11436(89.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1470(85.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13098(87.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2168(83.51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11273(86.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1731(83.10%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.4259***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.4101***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36.8329***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19.0540***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior high School and below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9852(86.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1210(89.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9608(87.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1541(89.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13285(88.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2363(91.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11344(86.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1874(89.97%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSenior high school/technical secondary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1313(11.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114(8.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1336(10.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e152(8.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1479(9.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e211(8.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1452(11.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e185(8.88%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e227(1.99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(1.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e260(2.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23(1.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e267(1.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22(0.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e292(2.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e24(1.15%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.4182***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.9772**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27.0620***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26.9534***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eResidence status\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6940(60.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e883(65.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7633(59.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1158(67.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9164(60.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1724(66.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8105(61.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1372(65.87%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4452(39.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e463(34.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5113(40.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e558(32.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5867(39.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e872(33.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4983(38.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e711(34.13%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.053***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.6413***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39.0591***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16.3071***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eHousehold registration\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricultural registration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8606(75.54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1017(75.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8929(70.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1274(74.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10900(72.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2074(79.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9749(74.49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1585(76.09%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-agricultural registration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2776(24.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e329(24.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3817(29.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e442(25.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4131(27.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e522(20.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3339(25.51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e498(23.91%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.8189***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.9931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.3543\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eSelf-assessed health status\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2713(23.81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e169(12.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3247(25.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e230(13.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3782(25.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e281(10.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3307(25.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e217(10.42%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5979(52.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e601(44.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6770(53.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e842(49.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7320(48.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1157(44.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6525(49.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e953(45.75%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2700(23.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e576(42.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2729(21.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e644(37.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3929(26.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1158(44.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3256(24.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e913(43.83%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e333.8519***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e360.1654***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e676.7743***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e582.2729***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eSmoking history\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1802(15.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e225(16.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3691(28.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e538(31.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4153(27.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e747(28.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3440(26.28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e580(27.84%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9590(84.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1121(83.28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9055(71.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1178(68.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10878(72.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1849(71.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9648(73.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1503(72.16%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.5238*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.0591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.1149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eDrinking history\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4088(35.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e433(32.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4733(37.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e587(34.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5235(34.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e821(31.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4872(37.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e710(34.09%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7294(64.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e910(67.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8013(62.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1129(65.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9789(65.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1775(68.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8216(62.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1373(65.91%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.9503**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.4287**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.0912***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.4488*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eHealth insurance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10944(96.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1301(96.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10200(80.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1576(91.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14595(97.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2618(97.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12522(95.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1991(95.58%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e448(3.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45(3.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2546(19.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e140(8.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e436(2.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e78(3.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e566(4.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e92(4.42%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e173.2105***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0508\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003ePension insurance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4835(42.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e758(56.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4888(61.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e836(48.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8130(54.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1592(61.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11245(85.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1778(85.36%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6557(57.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e588(43.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7858(38.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e880(51.28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6901(45.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1004(38.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1843(14.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e305(14.64%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120.2506***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90.1708***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e66.1797***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.6438\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eSocioeconomic status\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup I (lowest)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2280(20.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e271(20.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2550(20.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e376(21.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3007(20.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e579(22.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2618(20.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e433(20.79%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2277(19.99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e275(20.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2550(20.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e342(19.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3007(20.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e511(19.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2618(20.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e412(19.78%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup Ⅲ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2280(20.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e269(19.99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2549(20.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e364(21.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3006(20.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e524(20.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2617(20.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e405(19.44%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup Ⅳ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2282(20.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e281(20.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2549(20.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e327(19.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3006(20.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e489(18.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2619(20.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e425(20.40%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup Ⅴ(highest)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2273(19.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e250(18.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2548(19.99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e307(17.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3005(19.99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e493(18.99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2616(29.99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e408(19.59%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.3198*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.1933*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.5915\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e*** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 significance test\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eUtilization of health services for patients with CRD in China\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows changes in utilization rates for patients with CRD diagnoses across the three health services. The findings show that from 2013 to 2020, the utilization rates for all three categories of health services increased, with inpatient service utilization having the highest average annual growth rate at 8.26%. The utilization rate of two-year physical examination services (41.60%-49.58%) is higher than that of one-month outpatient services (29.35%-32.36%) and one-year inpatient services (24.89%-31.59%).\u003c/p\u003e \u003cp\u003eThe proportion of individuals utilizing outpatient services rose from 29.35% in 2013 to 32.36% in 2020. No significant variation in outpatient service use is observed among different SES groups. The utilization rate of inpatient services increased with SES, and the chi-square test indicated that the utilization rate of inpatient services was higher in the highest socioeconomic group than in the lowest socioeconomic group from 2015 to 2020 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In 2015, utilization rates for inpatient services were 20.48% in the lowest socioeconomic group and 31.27% in the highest socioeconomic group. By 2020, these rates increased to 31.59% and 36.27%, respectively. Utilization of physical examination services also rose considerably with socioeconomic in the 2013 wave and 2015 wave (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Between 2013 and 2020, over half of the patients with CRD in the highest socioeconomic group utilized physical examination services.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHealth services utilization rate of patients with CDR in China, 2013\u0026mdash;2020\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHealth services utilization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWave\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal[n(%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c8\" namest=\"c4\"\u003e \u003cp\u003eSocioeconomic status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup I\u003c/p\u003e \u003cp\u003e(lowest)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGroup II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGroup Ⅲ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGroup Ⅳ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGroup Ⅴ\u003c/p\u003e \u003cp\u003e(highest)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eχ 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eOne-month outpatient service utilization rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e395(29.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73(26.94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71(25.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e84(30.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e88(31.31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e78(31.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e479(27.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90(23.94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87(25.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e107(29.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e98(29.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e97(31.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e655(25.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e133(22.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e123(24.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e143(27.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e128(26.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e128(25.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e674(32.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131(30.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e136(33.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e121(29.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e152(35.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e134(32.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAAGR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.31%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.55%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.26%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.52%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.72%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eOne-year inpatient service utilization rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e335(24.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60(22.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68(24.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71(26.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e72(25.62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e64(25.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e423(24.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77(20.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67(19.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97(26.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e86(26.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e96(31.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16.74**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e751(28.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135(23.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e142(27.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e146(27.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e152(31.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e176(35.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e21.58***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e658(31.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e111(25.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e130(31.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e127(31.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e142(33.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e148(36.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.91*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAAGR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.26%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.45%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.93%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.29%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.28%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eTwo-year physical examination service utilization rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e560(41.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86(31.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97(35.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e108(40.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e132(46.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e137(54.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e36.89***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e749(43.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e154(40.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e131(38.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e164(45.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e142(43.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e158(51.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13.01*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1287(49.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e286(49.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e245(47.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e255(48.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e243(49.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e258(52.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1023(49.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e202(46.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e197(47.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e202(49.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e217(51.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e205(50.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAAGR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.59%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.72%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.68%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.49%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.82%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-2.85%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: AAGR\u0026thinsp;=\u0026thinsp;average annual growth rate;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001,** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01,* p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 significance test\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCHE incidence among patients with CRD in China\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the trend of CHE incidence in different socioeconomic groups from 2013 to 2020. With an average annual growth of 1.63%, the CHE incidence of all CRD families generally displayed a varying upward pattern, falling from 10.40% in 2013 to 6.29% in 2015 before rising to 11.67% in 2020. The incidence of CHE varied significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) across the various socioeconomic groups in 2015. The low socioeconomic group (Group I, 10.64%) had a considerably greater incidence of CHE than the medium socioeconomic group (Group III, 3.57%) and the medium-high socioeconomic group (Group IV, 4.59%), which served as evidence of this. Meanwhile, the risk of CHE occurrence continued to increase during the study period in the medium-low (Group II) and low socioeconomic groups (Group I), with average annual growth rates of 4.06% and 2.43%, respectively. The medium-low socioeconomic group (Group II) reached the highest in each group at 14.32% in 2020. Although the initial level of the high socioeconomic group (Group V) was the lowest at 8.80%, in contrast, the middle socioeconomic groups (Groups III and IV) showed a declining trend, with average annual growth rates of -0.21% and \u0026minus;\u0026thinsp;1.73%. However, the growth rate of Group III reached 3.56%, and for Group IV, it increased to 11.27% in 2020.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIncidence of CHE for Patients with CRD in China, 2013\u0026ndash;2020\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWave\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal[n(%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c8\" namest=\"c4\"\u003e \u003cp\u003eSocioeconomic status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup I\u003c/p\u003e \u003cp\u003e(lowest)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGroup II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGroup Ⅲ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGroup Ⅳ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGroup Ⅴ\u003c/p\u003e \u003cp\u003e(highest)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eχ 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eCHE incidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140(10.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28(10.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30(10.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31(11.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29(10.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22(8.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108(6.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40(10.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19(5.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13(3.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15(4.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21(6.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18.69**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e326(12.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84(14.51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61(11.94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49(9.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e69(14.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e63(12.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e243(11.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53(12.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59(14.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46(11.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39(9.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e46(11.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAAGR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.63%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.43%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.06%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.21%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.73%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.56%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: AAGR\u0026thinsp;=\u0026thinsp;average annual growth rate\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 ,* p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 significance test\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eChanges in inequalities in health services utilization among patients with CRD in China\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the standardized utilization levels of outpatient, inpatient, and physical examination services, analyzing socioeconomic inequality among patients with chronic respiratory diseases from 2013 to 2020. The findings show that, from 2013 to 2020, the CI for outpatient service utilization remained consistently positive, indicating that pro-rich inequality persisted. The standardized outpatient service utilization rates for each socioeconomic group were roughly similar after controlling for non-need characteristics, and they all displayed a declining trend from 2013 to 2016, followed by a rising trend from 2016 to 2020. After adjusting for socioeconomic inequalities, the post-standardization CI in 2020 showed a significant negative value (-0.011, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that the low-income group required more outpatient services due to their higher health demands.\u003c/p\u003e \u003cp\u003ePhysical examination services exhibited the highest level of standardized utilization and experienced significant growth, with standardized utilization rates exceeding 40% across all socioeconomic groups. This indicates that, compared to outpatient and inpatient care, physical examination services have the greatest increase in equity. Utilization of physical examination services had a CI of 0.110 in 2013, which was substantially higher than that of outpatient services (0.039) and inpatient services (0.025). However, by 2020, it had steadily declined to 0.017, the lowest among the three health services categories. At the same time, the uniform application of physical examination services was evident. In 2020, however, the standardized CI of physical examination service utilization became slightly negative (-0.006, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting that the initial pro-rich trend in physical examination service utilization has shifted towards a more equitable pattern.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStandardized utilization of health services for patients with CRD in China (across socioeconomic groups and concentration indexes)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"12\" nameend=\"c13\" namest=\"c2\"\u003e \u003cp\u003eStandardized health services utilization rate[n(%)]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eOutpatient services\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eInpatient services\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e \u003cp\u003ePhysical examination service\u003c/p\u003e \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\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocioeconomic status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup I (lowest)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e32.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e40.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e42.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e49.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e50.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e31.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e40.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e41.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e48.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e49.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup Ⅲ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e29.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e31.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e41.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e43.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e49.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e48.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup Ⅳ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e29.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e31.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e41.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e44.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e49.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e48.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup Ⅴ(highest)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e31.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e43.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e46.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e51.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e48.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnstandardized CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.057**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.090**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.078***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.059**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.110***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.046**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandardized CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.011*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \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 \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.015***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.022***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.010**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.006*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003eNote: *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 significance test\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eChanges in inequality in CHE incidence among households of people with CRD\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the results of the socioeconomic inequality analysis of the standardized incidence of CHE among households with CRD in China. The standardized incidence rates for various socioeconomic groups fluctuated over time but stayed largely constant between 2013 and 2020. During 2015, the unstandardized CI was significantly negative (-0.125, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that the burden of health expenditures was concentrated among lower socioeconomic groups. With values of (0.031, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and (-0.016, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), respectively, the standardized CI revealed significant differences between 2015 and 2020, indicating that, after controlling for non-need variables, the burden was higher in higher socioeconomic groups in 2015 but shifted to lower socioeconomic groups by 2020. This implies that the overall low-socioeconomic Chinese CRD group will eventually face more pressure on health expenditures, with the economic burden of health expenditures varying substantially over time and between socioeconomic groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStandardized incidence of CHE among patients with CRD in China (across socioeconomic groups and concentration indexes)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eStandardized CHE incidence[n(%)]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2013wave\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2015wave\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2018wave\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2020wave\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocioeconomic status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup I (lowest)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup Ⅲ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup Ⅳ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup Ⅴ(highest)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnstandardized CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.125*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandardized CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.031**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.016**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 significance test\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eDecomposition of health services utilization and CHE concentration index for patients with CRD\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e presents the marginal effects of different variables and their contribution to the utilization of the three types of health services for CRD patients in 2020. Positive values indicate an increase in inequality and negative values indicate a decrease in inequality.\u003c/p\u003e \u003cp\u003eSmoking, drinking behavior, and self-assessed health status were the factors that had the biggest impact on the inequality in outpatient service utilization among CRD patients. Nondrinking (marginal effect = -0.068, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and nonsmoking (marginal effect = -0.104, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) reduce the probability of outpatient services by 6.8 and 10.4 percentage points, respectively. Their negative contribution rates (-22.16% and \u0026minus;\u0026thinsp;17.03%) indicate that these health behaviors help to mitigate utilization inequalities. However, the likelihood of using outpatient services was raised by 23.8 percentage points (marginal effect\u0026thinsp;=\u0026thinsp;0.238, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and \"fair\" health increases it by 9.7 points (marginal effect\u0026thinsp;=\u0026thinsp;0.097, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These positive contributions (7.85% and 5.15%) indicate that poorer health perceptions tend to worsen inequality.\u003c/p\u003e \u003cp\u003eInequality in utilization of inpatient services was mainly driven by self-assessed health status, gender, age structure, and health behaviors. Among these, \"poor\" self-assessed health status contributed 2.80% to inpatient care utilization, increasing inpatient services by 24.4 percentage points (marginal impact\u0026thinsp;=\u0026thinsp;0.244, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Inpatient services would rise by 14.5 and 14.9 percentage points for populations aged 65\u0026ndash;74 (marginal effect\u0026thinsp;=\u0026thinsp;0.145, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 75+ (marginal effect\u0026thinsp;=\u0026thinsp;0.149, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively. However, the contribution of advanced age to inequality was in the opposite direction, contributing \u0026minus;\u0026thinsp;21.25 and \u0026minus;\u0026thinsp;4.36 percentage points, respectively.\u003c/p\u003e \u003cp\u003eInequality in the utilization of physical examination services was notably affected by age, smoking status, and pension insurance enrollment. With contributions of -11.31% and \u0026minus;\u0026thinsp;10.42%, respectively, quitting smoking (marginal impact = -0.098, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and being 75 years or older (marginal effect\u0026thinsp;=\u0026thinsp;0.160, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) significantly reduced inequality, contributing \u0026minus;\u0026thinsp;16.58% and \u0026minus;\u0026thinsp;10.9%, respectively. Conversely, those aged 65\u0026ndash;74 (marginal impact\u0026thinsp;=\u0026thinsp;0.242, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and individuals not enrolled in pension insurance (marginal effect\u0026thinsp;=\u0026thinsp;0.120, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) contributed to a notable increase in inequality, with contributions of- 16.58% and- 10.9%.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the decomposition of inequality factors related to CHE incidence among households with CRD. The results highlight that structural issues are the primary cause of inequality in CHE incidence. Among these, living in an urban area (contribution rate\u0026thinsp;=\u0026thinsp;13.55%) and being part of a non-agricultural household (contribution rate\u0026thinsp;=\u0026thinsp;8.82%) significantly contribute to higher disparities in health care costs. The likelihood of CHE increases notably with self-assessed health status as \"poor\" (marginal effect\u0026thinsp;=\u0026thinsp;0.055, p\u0026thinsp;\u0026lt;\u0026thinsp;0.5); however, the overall inequality in CHE incidence is slightly reduced by this same health status, with an estimated contribution of around \u0026minus;\u0026thinsp;2.11%. This suggests that while poorer health increases individual risk of medical expenses, its widespread presence across the population may help reduce overall inequality.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDecomposition of Health services utilization and CHE Concentration Index for Patients with CRD in 2020\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eOutpatient\u003c/p\u003e \u003cp\u003eservices\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eInpatient\u003c/p\u003e \u003cp\u003eservices\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ePhysical examination service\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eCHE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003emarginal effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContribution (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003emarginal effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eContribution (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003emarginal effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eContribution (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003emarginal effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eContribution (%)\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\u003eNeed variables\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eSelf-assessed health status (Ref\u0026thinsp;=\u0026thinsp;good)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.097*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.238***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.244***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.055*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-2.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (Ref\u0026thinsp;=\u0026thinsp;male)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.082**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (Ref\u0026thinsp;=\u0026thinsp;45\u0026ndash;54)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65\u0026ndash;74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-12.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.145***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-21.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.242***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-5.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75 and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-21.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.149***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.160***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-10.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-need variables\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eMarital status (Ref\u0026thinsp;=\u0026thinsp;unmarried and other)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried or with spouse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eEducation (Ref\u0026thinsp;=\u0026thinsp;Junior high School and below)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSenior high school/technical secondary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eResidence status (Ref\u0026thinsp;=\u0026thinsp;rural)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-26.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-6.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eHousehold registration (Ref\u0026thinsp;=\u0026thinsp;Agricultural registration)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-agricultural registration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-37.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking history (Ref\u0026thinsp;=\u0026thinsp;yes)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.068**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-22.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.110***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-12.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history (Ref\u0026thinsp;=\u0026thinsp;yes)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.104***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-17.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.093***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.089**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-11.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth insurance (Ref\u0026thinsp;=\u0026thinsp;yes)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth insurance (Ref\u0026thinsp;=\u0026thinsp;yes)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.120***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 significance test\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eBased on CHARLS data, this study examined the evolving trends of inequalities in three types of health services utilization and CHE among middle-aged and older adults with CRD in China from 2013 to 2020. It also identified the main factors contributing to these inequalities. Results showed that the prevalence of CRD among Chinese adults aged 45 and above rosed from 11.82% in 2013 to 15.92% in 2020. The prevalence differed significantly across various dimensions, including demographic factors, social determinants, and subjective health perceptions, consistent with findings by Ferrante et al [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This underscores the importance of developing CRD prevention and control strategies that target population-specific risk factors and include tailored prevention and treatment measures for high-risk groups. It is also vital to address underlying social determinants and biological mechanisms. CRD is more frequently diagnosed in populations with lower SES compared to those with higher SES, reflecting the socioeconomic health gradient. Lower SES groups tend to have poorer health outcomes, indicating a greater objective need for health services in these populations, in line with Larick's study [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFrom 2013 to 2020, we observed an increase in the utilization of outpatient, inpatient, and physical examination services among middle-aged and older CRD populations in China. The trend in inpatient service utilization exhibited the highest average annual growth rate, aligning with the findings of the China National Health Services Survey (NHSS) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The Chinese government has been working to establish a universal healthcare system since the start of the century. To promote equitable access to essential medical care, it has introduced three different types of social medical insurance. According to data in this study, the basic medical insurance coverage rate for China's middle-aged and older CRD population has consistently remained above 95%, providing institutional protection for healthcare use and continuously boosting the population's demand for health services. China's Medium- and Long-Term Plan for the Prevention and Treatment of Chronic Diseases (2017\u0026ndash;2025) aims to reduce CRD mortality rates and increase pulmonary function tests. It also includes pulmonary function tests as part of routine physical exams for people over 40, promoting greater utilization of preventive health services. To ensure vertical equity in healthcare, the principle of \"distribution according to need\" should be followed, prioritising patients with higher health service needs and poorer health status for resource access [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. After controlling for socioeconomic factors, CRD patients with low SES exhibited higher utilization rates of standardized health services, reflecting their more urgent objective health needs. However, the comparison revealed a discrepancy between the actual utilization rate of the three types of health services among the low SES CRD patient group and their standardized needs. This indicates that the current health service system is still unable to adequately meet the population's health needs, and there is a need to address the unmet demand gap within the low socioeconomic group. Furthermore, regional health planning should include standardized demand measurement.\u003c/p\u003e \u003cp\u003eThis study employed the concentration index and the standardized concentration index analysis to thoroughly assess changes in health services utilization equity among middle-aged and elderly CRD patients in China from 2013 to 2020. The findings revealed improvements in the equity of outpatient, inpatient, and physical examination services over this period, with the most notable progress seen in health examination service equity. This suggests that China has made substantial advances in enhancing health service accessibility for vulnerable groups and promoting' vertical equity\" through initiatives like poverty alleviation via health insurance and hierarchical diagnosis and treatment systems. In particular, the implementation of the annual free physical examination program for the elderly in the National Basic Public Health Service has effectively improved the equity of preventive services for CRD patients. Nevertheless, it is important to note that, even with the improvement in overall equity, the CI of the three types of health service utilization by middle-aged and elderly CRD patients in China was consistently positive throughout the study period. This suggests that, in line with the global trend of low- and middle-income countries, the health service utilization of middle-aged and elderly CRD patients in China continues to be clearly favorable to the rich [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In addition, against the background of high health insurance coverage, the CI of inpatient service utilization among CRD patients in 2020 was the highest among the three types of health services, and the service utilization rate was positively correlated with SES. Yang Wei demonstrated that although health insurance coverage continues to broaden, the new rural cooperative medical system has contributed to reducing inequities in health service utilization in China, but its contribution to improving access to health services for the poor is limited [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. It shows that SES still an important factor affecting access to hospitalization services. Moving forward, it is essential to persist with reforming the health insurance system, implement differentiated health insurance policies for low socioeconomic CRD patients, and simultaneously improve primary healthcare service systems to promote equitable inpatient services.\u003c/p\u003e \u003cp\u003eIn order to further analyze the contribution of related factors to pro-rich inequality in health service utilization, this study decomposed the concentration index of three types of health service utilization by CRD patients in China in 2020. The decomposition results show that there are significant differences in the mechanisms driving inequality across different health service types. Inequality in outpatient services is mainly influenced by the non-need variables \u0026ldquo;non-smoking\u0026rdquo; and \u0026ldquo;non-drinking\u0026rdquo;, while inequality in the utilization of inpatient services and health check-ups is more influenced by the need variable \u0026ldquo;older than 65 years\u0026rdquo;. For inpatient service utilization, advanced age can increase the probability of inpatient utilization by 14.5 percentage points, but its contribution to inequality is negative. This indicates a reverse buffering effect in health service utilization [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]: physiological decline and more co-morbidities in the elderly CRD population increase the overall demand for inpatient service utilization. Since this health risk is relatively evenly spread across socioeconomic groups and China's current elderly healthcare policies effectively address these broad health needs, the higher utilization among high-need groups actually helps reduce the socioeconomic gap in access to care. For physical examination services, the senior factor, on the other hand, shows a positive contribution rate, probably mainly due to the stratification of access to preventive services, with high socioeconomic groups more likely to utilize value-added physical examination services. Furthermore, health behavioral factors among the non-need factors significantly promote equity across all three types of health service use. Non-smoking was able to reduce the probability of outpatient, inpatient, and physical examination services utilization by 10.4, 9.3, and 8.9 percentage points, respectively, accounting for 17.03%, 5.28%, and 11.31% of reducing inequality. Additionally, Non-drinking notably lowered outpatient and inpatient utilization probabilities by 6.8 and 11.0 percentage points. These findings suggest that improving equity in service use could be achieved through targeted health education and behavioral interventions. This study also found that CRD patients without pension insurance have nearly an 12% lower probability of physical examination service utilization. This highlights the important role of the social security system in promoting equity in preventive care, aligning with findings by Zhao Y and other researchers [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The absence of pension coverage often compounds other socioeconomic challenges, such as low income and education, worsening health disparities. Consequently, a recommendation is to create a dedicated subsidy program for preventive services for CRD patients lacking pension insurance and to integrate this with the family doctor contracting system, enhancing collaboration between social security and public health.\u003c/p\u003e \u003cp\u003eThis study found that, under the 40% health out-of-pocket expenses, the overall risk of CHE in 2020 for Chinese middle-aged and elderly CRD households was 11.67%. This is lower than the 25.5% CHE incidence among Chinese households reported in Yuan Q et al.'s study [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and the 14.62% among Chinese elderly CRD households reported by Yao X et al. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], but higher than the 4.4% CHE incidence in U.S. households with chronic diseases [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Xu X et al. state that liver disease, stroke, and cancer are the chronic conditions most often associated with higher healthcare costs [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Compared to other high-cost chronic diseases, CRD is less expensive to manage routinely. China has included major CRDs, such as COPD, in outpatient special chronic disease coverage, with reimbursement rates typically between 60% and 70%. This significantly lessens patients' out-of-pocket expenses. However, it is concerning that the incidence of CHE among middle-aged and elderly CRD patients in China increased from 10.40% in 2013 to 11.67% in 2020, with an average annual growth of 1.63%. Even more worrying, limited health payment capacity means the low socioeconomic CRD population faces greater financial risks, with CHE risk continuing to rise in middle-low and low socioeconomic groups. Furthermore, the CI analysis showed that the unstandardized CI remained negative throughout the study period, rising from \u0026minus;\u0026thinsp;0.027 in 2013 to -0.048 in 2020. This indicates that the burden of health expenditure has a pro-poor distribution, which aligns with the findings of existing studies [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The decomposition study indicates that structural factors account for the majority of CHE inequality, with urban residence and non-agricultural registration accounting for 13.55% and 8.82% of the inequality, respectively. This indicates that the urban-rural divide remains one of the main systemic sources of healthcare burden disparity [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Rapid integration of the basic medical insurance systems in urban and rural areas is necessary, as is the gradual reduction of urban-rural disparities and the unification of outpatient coverage for chronic diseases.\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThere are some limitations to this study. First, the variables are based on respondents' self-reported data, and there may be reporting bias in assessing health status and recalling healthcare expenditure. Second, because of the large number of variables, insurance status was only categorised dichotomously, which fails to account for the different impacts of various health insurance types and coverage levels, such as those of urban and rural residents or employees, on the study results. Finally, due to data availability limitations, the 2020 CHARLS only collected total out-of-pocket medical expenses and did not include data on total health expenditures or disaggregated expenses for outpatient, inpatient, and physical examinations; therefore, this study did not conduct a detailed analysis of the economic burden associated with different types of health services.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, the prevalence of CRD among people aged\u0026thinsp;\u0026ge;\u0026thinsp;45 years in China continues to rise, with significant socioeconomic gradient disparities. Throughout the study period, although the utilization of outpatient, inpatient, and physical examination services for CRD patients increased overall and equity improved, a clear pro-rich inequality persisted. Additionally, health behavioural factors showed a significant role in promoting equity in the utilization of all three types of health services. CHE incidence displayed a pro-poor inequality, which has worsened, with low socioeconomic CRD patients experiencing higher health needs yet facing heavier financial burdens. The urban-rural dual structure remains the main factor driving the unequal financial burdens.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCRD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Chronic respiratory disease\u003c/p\u003e\n\u003cp\u003eNCDs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Non-communicable diseases\u003c/p\u003e\n\u003cp\u003eCHE \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;catastrophic health expenditures\u003c/p\u003e\n\u003cp\u003eCHARLS \u0026nbsp; \u0026nbsp; \u0026nbsp;China Health and Retirement Longitudinal Study\u003c/p\u003e\n\u003cp\u003eCI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Concentration index\u003c/p\u003e\n\u003cp\u003eSDGs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; United Nations Sustainable Development Goals\u003c/p\u003e\n\u003cp\u003eSES \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; socioeconomic status\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNDRI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; National Development Research Institute of Peking University\u003c/p\u003e\n\u003cp\u003ePPS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; probability proportional to size\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWHO \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; World Health Organization\u003c/p\u003e\n\u003cp\u003eOOP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Out-of-pocket\u003c/p\u003e\n\u003cp\u003ePCE \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;per capita household consumption\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAAGR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;average annual growth rate\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe CHARLS was approved by the Ethics Review Board of Peking University (approval number: IRB00001052-11015). All participants signed an informed consent form. All study procedures were conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe data used in this study are available through the China Health and Retirement Longitudinal Study (CHARLS) conducted by the Center for Social Science Research at Peking University. The link to access is: http://charls.pku.edu.cn.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study was supported by Guangzhou Public Health Service System Construction Research Base Funding Program (Grant NO: 2024-2026) and the 2022 Project of National Topics Cultivation Program, School of Health Administration, Southern Medical University (Grant NO: 2022RFT004). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eXW and WY co-designed the study and developed the main framework of the study; XW was responsible for the data collection and analysis work, and the writing of the final manuscript. XW, ZC, YL, FA and WY commented on and edited the manuscript for important intellectual content. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge the China Health and Retirement Longitudinal Study (CHARLS) team for providing the data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; information\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eSchool of Health Management, Southern Medical University, Guangzhou 510515, China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eDepartment of Spine Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eChen S, Kuhn M, Prettner K, Yu F, Yang T, B\u0026auml;rnighausen T, et al. 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Socio-economic inequality and inequity in use of health care services in kenya: Evidence from the fourth kenya household health expenditure and utilization survey. Int J Equity Health. 2019;18:196.\u003c/li\u003e\n \u003cli\u003eYang W. China\u0026rsquo;s new cooperative medical scheme and equity in access to health care: Evidence from a longitudinal household survey. Int J Equity Health. 2013;12:20.\u003c/li\u003e\n \u003cli\u003eHouse JS, Kessler RC, Herzog AR. Age, socioeconomic status, and health. Milbank Q. 1990;68:383\u0026ndash;411.\u003c/li\u003e\n \u003cli\u003eZhao Y, Atun R, Oldenburg B, McPake B, Tang S, Mercer SW, et al. Physical multimorbidity, health service use, and catastrophic health expenditure by socioeconomic groups in China: An analysis of population-based panel data. Lancet Glob Health. 2020;8:e840\u0026ndash;9.\u003c/li\u003e\n \u003cli\u003eWu N, Xie X, Cai M, Han Y, Wu S. Trends in health service needs, utilization, and non-communicable chronic diseases burden of older adults in China: Evidence from the 1993 to 2018 national health service survey. Int J Equity Health. 2023;22:169.\u003c/li\u003e\n \u003cli\u003eYuan Q, Wu Y, Li F, Yang M, Chen D, Zou K. Economic status and catastrophic health expenditures in China in the last decade of health reform: A systematic review and meta-analysis. BMC Health Serv Res. 2021;21:600.\u003c/li\u003e\n \u003cli\u003eYao X, Wang D, Zhang T, Wang Q. Chronic diseases and catastrophic health expenditures in elderly Chinese households: A cohort study. BMC Geriatr. 2025;25:272.\u003c/li\u003e\n \u003cli\u003eHong Y-R, Xie Z, Suk R, Tabriz AA, Turner K, Qiu P. Analysis of US household catastrophic health care expenditures associated with chronic disease, 2008-2018. JAMA Netw Open. 2022;5:e2214923.\u003c/li\u003e\n \u003cli\u003eXu X, Yang H. Does elderly chronic disease hinder the sustainability of borderline poor families\u0026rsquo; wellbeing: An investigation from catastrophic health expenditure in China. Int J Public Health. 2022;67:1605030.\u003c/li\u003e\n \u003cli\u003eZhao Y, Atun R, Oldenburg B, McPake B, Tang S, Mercer SW, et al. Physical multimorbidity, health service use, and catastrophic health expenditure by socioeconomic groups in China: An analysis of population-based panel data. Lancet Glob Health. 2020;8:e840\u0026ndash;9.\u003c/li\u003e\n \u003cli\u003eFu X-Z, Sun Q-W, Sun C-Q, Xu F, He J-J. Urban-rural differences in catastrophic health expenditure among households with chronic non-communicable disease patients: Evidence from China family panel studies. BMC Public Health. 2021;21:874.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Inequit, Health service utilization, Catastrophic health expenditure, Chronic respiratory disease","lastPublishedDoi":"10.21203/rs.3.rs-8091430/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8091430/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eChronic respiratory disease (CRD) is a global priority in preventing and controlling non-communicable diseases (NCDs). Its disease burden and health inequalities significantly undermine population health outcomes. This study examines inequalities in health service utilization and CHE incidence among middle-aged/elderly CRD patients in China, identifying key drivers.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUsing 2013\u0026ndash;2020 China Health and Retirement Longitudinal Study(CHARLS) data, this study analyzed outpatient, inpatient, and physical examination utilization rates and CHE incidence in CRD patients\u0026thinsp;\u0026ge;\u0026thinsp;45 years. The degree of inequality and its main contributing factors were measured using indirect standardization, the concentration index (CI), and decomposition analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAll service utilization rates increased from 2013 to 2020. CIs declined but remained positive in 2020 (outpatient: 0.020; inpatient: 0.059; physical examination: 0.017). Health behaviors reduced outpatient inequality, while age 65 and above drove inpatient and physical examination disparities. CHE incidence rose from 10.40% (2013) to 11.67% (2020), with CIs worsening (-0.027 to -0.048). Urban residence (13.55%) and non-agricultural hukou (8.82%) were the main drivers of inequality at CHE.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAlthough equity improved in outpatient, inpatient, and physical examination service utilization during the study period, persistent pro-rich inequalities remain. CHE incidence demonstrated widening pro-poor disparities. Policy priorities should target unmet needs and economic burdens among socioeconomically disadvantaged groups through strengthened health education, behavioral interventions, health insurance reforms, and enhancements to the primary care system.\u003c/p\u003e","manuscriptTitle":"Inequalities in health services utilization and catastrophic health expenditures among patients with chronic respiratory diseases in China: An empirical analysis based on the CHARLS, 2013—2020","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-18 07:33:30","doi":"10.21203/rs.3.rs-8091430/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-12-26T22:33:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-24T01:06:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"299077107932998956280103145822140563817","date":"2025-12-24T01:00:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"294452875041620658111829123024797239907","date":"2025-12-16T14:19:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-12T14:56:52+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-18T08:02:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-17T05:06:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-17T05:03:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2025-11-12T02:58:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c2584591-bc63-4ded-ba5b-05ebdb3b1658","owner":[],"postedDate":"December 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-18T07:33:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-18 07:33:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8091430","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8091430","identity":"rs-8091430","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}