Factors associated with unmet medical needs among middle-aged and older adults in China during COVID-19 Pandemic

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This cross-sectional study aimed to identify the factors associated with unmet medical needs among middle-aged and older adults in China during the COVID-19 Pandemic. Methods A cross-sectional analysis using data from the 2020 China Health and Retirement Longitudinal Study. The final sample consisted of 19,123 individuals. Multiple imputation was applied to handle missing values. A binary logistic regression was used to examine factors associated with unmet medical needs. Results During the COVID-19 pandemic, 11.18% of middle-aged and older adults did not receive needed medical care. In both urban and rural areas, middle-aged and older adults who were male, with higher educational attainment, rated poor health, suffering from chronic conditions, residing in a residential area completely shut, and often felt fears were more likely to lead to unmet medical needs. In urban areas, middle-aged and older adults with urban employee medical insurance (OR = 2.30, 95% CI: 1.36, 3.56) and urban and rural resident medical insurance (OR = 1.65, 95% CI: 0.96, 2.44) were more likely to have unmet medical needs. In rural areas, middle-aged and older adults over 75 years of age (OR = 0.46, 95% CI: 0.35, 0.62) were less likely to have unmet medical needs, and middle-aged and older adults who knew the preventive measures (OR = 2.16, 95% CI: 1.42, 3.29) had a higher probability of having unmet medical needs. Conclusions The findings indicate gender, age, educational attainment, occupation, living with a spouse, health insurance, number of health technicians per 10,000, self-rated health, chronic conditions, depression, knowing the preventive measures, and fear of pandemic associated with unmet medical needs. The unmet medical needs of vulnerable groups should receive priority attention in the future and facilitate rationalizing the allocation structure of medical resources. COVID-19 Unmet medical needs Middle-aged and older adults Figures Figure 1 Figure 2 Figure 3 Introduction Unmet medical needs represent a significant risk to individuals' health and well-being. The fact that the current healthcare system does not meet individuals' medical requirements can lead to detrimental outcomes such as disease progression, increased severity of illnesses, and compromised quality of life [ 1 ], which would pose a financial burden on patients and their families, escalating medical care costs and placing strain on available health resources [ 2 ]. Recognizing and mitigating unmet medical needs is crucial in ensuring equitable access to healthcare. Among various demographic groups, middle-aged and older adults are particularly vulnerable to unmet medical needs because they are prone to developing chronic conditions and degenerative diseases that require ongoing medical attention [ 3 , 4 ]. However, due to inadequate medical care resources, limited access to medical care, and a lack of health literacy, many middle-aged and older adults may experience significant unmet medical needs [ 5 , 6 ]. This affects their daily functioning and quality of life and increases their vulnerability to adverse health outcomes [ 7 ]. The Coronavirus Disease 2019 (COVID-19) was first identified due to a reported cluster of pneumonia cases in Wuhan in December 2019 [ 8 ]. The infection has rapidly propagated since the day of emergence, spreading globally and becoming a pandemic [ 9 ]. The COVID-19 pandemic led to guidelines to postpone non-essential medical care and screenings [ 10 ]. The pandemic has caused a vast and profound impact on unmet needs, especially for middle-aged and older adults, whose unmet needs have been significantly aggravated in multiple dimensions. The pandemic has not only strained medical resources, making middle-aged and older adults face difficulties in chronic disease management and emergency medical treatment, but also caused them to feel more lonely and isolated in emotional communication and social interaction due to the reduction of social activities and the widening of the digital divide[ 11 ]. At the same time, the economic recession and the instability of the job market have also increased the financial pressure on middle-aged and older adults, and some people are facing the dilemma of insufficient pension and lack of medical security[ 12 ]. These factors work together to make the health, social, emotional, and economic needs of middle-aged and older adults are not fully met, and their quality of life is seriously affected. Unmet medical need refers to the gap between individuals' medical requirements and the services provided by the current healthcare system [ 13 – 17 ]. Most research evidence of unmet medical care needs comes from high-income countries in North America, Europe, and Asia. Studies have investigated the prevalence and characteristics of unmet medical needs among different population groups, such as older adults, children, and individuals with chronic illnesses [ 18 , 19 ]. Furthermore, previous studies have also examined the implications of unmet medical needs on patients' health outcomes, quality of life, and medical care costs [ 20 , 21 ]. However, differences in the national healthcare system and health insurance system may contribute to the incidence of unmet medical needs across countries [ 22 ]. This prevents valuable comparisons between U.S. (or European) studies and Asian studies. In addition, there is less research on the factors influencing unmet medical needs during the COVID-19 pandemic in China. The unique factors contributing to this problem in this demographic group need to be explored more deeply. Therefore, this study aimed to analyze factors associated with unmet medical needs among middle-aged and older adults in China during the COVID-19 pandemic. Methods Theoretical model This study was guided by the Behavioral Model of Health Services Use (BMHSU), created by Dr. Anderson of the University of Chicago School of Public Health in 1968 [ 23 ]. It was initially used to investigate variables influencing the use of family medical care. After five iterations and additions, it has evolved into a dependable framework for medical care research [ 24 ]. By incorporating multi-level factors affecting medical care needs into a relatively mature analytical framework, BMHSU can more fully explain the key characteristics of the sample population and avoid random selection of influencing factors. The theoretical framework of this study includes four parts, as shown in Fig. 1 . (1) External environment: The medical care need is closely related to objective factors such as the level of economic development, the level of medical care, and the policy environment. This study adopted three factors: the number of medical institutions, the number of health technicians, and the number of beds in medical institutions. (2) Individual characteristics: This reflects the individual level of samples needed for medical care. This study discussed and analyzed three aspects: (a) Predisposing factors, defined as individuals' tendency to use health services, reflecting the possibility of individuals using medical care. This study included age, gender, education level, and (b) Enabling factors such as the availability of medical care. Income, medical insurance, and self-rated health status were included in this study; (c) Need factors are usually the most direct reason for individuals to use medical care, that is, personal medical needs. The need factors in this study included chronic conditions, depressive symptoms, drinking, and smoking. (3) Behavioral patterns during the COVID-19 pandemic. This study chose five key behaviors that could impact the medical care needed during the COVID-19 pandemic: quarantined, knowing the preventive measures, residence control, times spent outdoors, and feeling fears or anxiety. (4) Outcome. Whether the medical needs are satisfied by the subjective feelings of the sample population. ---------------Fig. 1 is here--------------- Data source The China Health and Retirement Longitudinal Study Database (CHARLS) is a nationally representative study of Chinese residents aged 45 years and older. First undertaken in 2011, the CHARLS has since conducted four follow-up studies in 2013, 2015, 2018, and 2020 and selected study participants from 150 counties across 28 Chinese provinces using the probability proportional to size (PPS) method. The research content includes population background, socioeconomic status, health status, and psychological status [ 25 ]. Data for this study was extracted from the fifth round of national surveys conducted by the CHARLS in 2020. In addition, this study also extracted three indicators from the open data of the National Bureau of Statistics of China in 2021, including the number of medical institutions, the number of beds in medical institutions per 10,000 people, and the number of health technicians per 10,000 people. A total of 19,123 individuals were included. Measures Dependent variable The unmet medical care needs include unperceived needs and perceived needs. The unperceived needs cannot be empirically studied because they are not recorded. When patients are unwilling to seek delay or even cancel medical care due to barriers, or when medical care providers are unable to provide appropriate care, the perceived needs of patients will not be met [ 26 ]. In this study, unmet medical need refers to people delaying or canceling medical care due to the COVID-19 pandemic so that their perceived medical care need is unmet. Responses to the following survey questions were used to determine unmet medical needs: During the pandemic, have you ever needed to see a doctor, including a dentist, but were forced to postpone or unable to do so because of the COVID-19 pandemic? Response options are yes or no. If the respondent reported ‘yes,’ the respondent was categorized as having unmet medical needs. Independent variables The selection of independent variables was mainly based on the research purpose and literature review. The independent variables can be broadly divided into demographic and socioeconomic characteristics, medical resources, health status and behavior, and behavior patterns during the COVID-19 pandemic. Definitions of the variables are given in Table 1 . Table 1 Definitions of variables Variable Description %/ mean(std) Dependent variable Unmet medical needs 1 If the individual is affected by the COVID-19 pandemic and delays or fails to visit the doctor; 0 others 11.28 Independent variable Demographic and socioeconomic characteristics Male If the individual is male; 0 for female 46.89 Age (years) 45–54 1 if the individual is aged 45–54 years; 0 otherwise 27.74 55–64 1 if the individual is aged 55–64 years; 0 otherwise 34.06 65–74 1 if the individual is aged 65–74 years; 0 otherwise 26.74 ≥ 75 1 if the individual is aged ≥ 75 years; 0 otherwise 11.46 Educational attainment Illiterate 1 if the individual is illiterate; 0 otherwise 22.15 Elementary school 1 if the individual attended elementary school; 0 otherwise 44.89 Middle school 1 if the individual graduated from middle school; 0 otherwise 20.02 High school or vocational school 1 if the individual graduated from high school or vocational school; 0 otherwise 10.67 Above three-years of college 1 if the individual has above three years of college; 0 otherwise 2.28 Urban 1 if the individual lives in urban regions; 0 otherwise 36.57 Farm work 1 if this individual is engaged in farm work; 0 otherwise 63.63 Living with a spouse 1 if the individual is living with a spouse; 0 otherwise 75.29 Household income Low income 1 if the individual’s household income is in the first quartile; 0 otherwise 25.68 Lower middle income 1 if the individual’s household income is in the second quartile; 0 otherwise 23.57 Upper middle income 1 if the individual’s household income is in the third quartile; 0 otherwise 24.44 High income 1 if the individual’s household income is in the highest quartile; 0 otherwise 26.30 Medical insurance No insurance 1 if the individual does not have medical insurance; 0 otherwise 4.79 UEMI 1 if the individual is enrolled in Urban Employee Medical Insurance; 0 otherwise 14.06 URMI 1 if the individual is enrolled in Urban and Rural Resident Medical Insurance; 0 otherwise 78.79 Other insurance 1 if the individual is enrolled in Free Medical Insurance, Private Medical Insurance, Medical Aid, or Other medical insurance; 0 otherwise 2.36 Medical resources Health technicians Number of health technicians per 10,000 people in the province where the individual resides 75.49(7.23) Medical institution Number of medical institutions in the province where the individual resides 48418.01 (23894.71) Bed Number of hospital beds per 10,000 people in the province where the individual resides 65.87 (8.40) Health status and behavior Self-rated health Poor 1 if the individual reports health status to be poor; 0 otherwise 24.54 Fair 1 if the individual reports health status to be fair; 0 otherwise 50.35 Good 1 if the individual reports health status to be good or better; 0 otherwise 25.10 Chronic conditions 1 if the individual has chronic disease; 0 otherwise 64.82 Smoking 1 if the individual is still smoking; 0 otherwise 25.36 Drinking 1 if the individual is still drinking; 0 otherwise 35.81 Depression The sum of the scores of 10 items in the simplified Central Depression Scale (CESD 10). 9.07(6.47) Behavioral patterns during the COVID-19 pandemic Quarantined 1 if the individual is quarantined during the outbreak; 0 otherwise 18.85 Knowing the preventive measures 1 if the individual knows the preventive measures; 0 otherwise 96.26 Residence control No restrictions on entry and exit 1 if the individual faces No Restrictions on Entry and Exit; 0 otherwise 8.42 Residential area completely shut down 1 if the individual's residential area is completely shut down; 0 otherwise 8.61 Restricted entry or exit for residents 1 if the individual faces restricted entry or exit for residents; 0 otherwise 10.25 No entry into residential area for non-residents 1 if the individual faces no entry into the residential area for non-residents; 0 otherwise 26.98 Restricted entry for non-residents 1 if the individual faces restricted entry for non-residents; 0 otherwise 45.73 Times spent outdoors Increased 1 if the individual has a large or small increase in the number of times spent outdoors during the Lunar New Year outbreak; 0 otherwise 1.30 Not changed 1 if the individual’s time spent outdoors does not change during the Lunar New Year outbreak; 0 otherwise 34.75 Decreased 1 if the individual has a large or small decrease in the number of times spent outdoors during the Lunar New Year outbreak; 0 otherwise 63.95 Feeling fears Rarely or never 1 if the individual rarely or never expresses their fears during the Lunar New Year outbreak; 0 otherwise 59.70 Sometimes 1 if the individual sometimes expresses their fears during the Lunar New Year outbreak; 0 otherwise 24.21 Often 1 if the individual often expresses their fears during the Lunar New Year outbreak; 0 otherwise 16.09 Feeling anxiety Rarely or never 1 if the individual rarely or never expresses their anxiety during the Lunar New Year outbreak; 0 otherwise 63.17 Sometimes 1 if the individual Sometimes expresses their anxiety during the Lunar New Year outbreak; 0 otherwise 24.96 Often 1 if the individual often expresses their anxiety during the Lunar New Year outbreak; 0 otherwise 11.87 The CHARLS 2020 has a new COVID module, and the following variables are selected for inclusion in the pattern of behavior during the COVID-19 pandemic: Quarantined, knowing the preventive measures, residence control, times spent outdoors, and feeling fears or anxiety. The question “Have you ever been quarantined or under medical observation due to the following reasons?” was used to measure “Quarantined”, and the options included the following: (1) travels, (2) close contact with COVID cases, (3) building lockdown, (4) after going to a hospital, (5) tested positive, (6) no quarantine experience. If the respondent selected (6), they were not quarantined during the epidemic. “Knowing the preventive measures” was determined by asking, “Do you know that the following practices can reduce the risk of COVID-19 infection? (Multiple choices are allowed)”. The options included (1) washing hands, (2) using disinfectant, (3) avoiding handshaking, (4) masking, (5) avoiding travels, (6) avoiding gatherings, (7) social distancing, (8) others, (9) do not know about the pandemic or preventive measures. If the respondent knows any correct practice, it is considered that they know the precautions against COVID-19. “Residence control” was measured using the following question, “Due to epidemic control measures, have the communities or villages you have lived in since the Spring Festival implemented the following types of restrictions on the entry and exit of internal and external personnel?” There were five levels to the severity of the restrictions: (1) residential area wholly shut down, (2) restricted entry or exit for residents, (3) no entry into a residential area for non-residents, (4) restricted entry for non-residents, (5) no restrictions on entry and exit. “Times spent outdoors” was measured by the following question, “Has the amount of time you spend outside each day increased, decreased, or remained the same compared to what would have happened if the pandemic had not occurred?” The options included (1) incremental, (2) not changed, (3) decreased. “Feeling fears or anxiety” was divided into three levels: rarely or never, sometimes, and often. Statistical analysis To reduce biases when respondents with missing data were excluded, this study used multiple imputation (MI) by chained equation (MICE) to account for missing data in all variables (see Appendix for the non-response rate of the variables used in this study ) [ 27 ]. The variables with missing values were included in the imputation model, and variables with no missing values (age, gender, health technicians, medical institution, bed) were treated as predictors. The current study employed ‘mi impute chained’ in STATA/MP17.0 to create 50 imputed datasets to fill in missing values. This number was large enough to achieve good efficiency [ 28 ]. All statistical analyses were conducted using STATA/MP17.0. Each categorical variable's distribution is described using percentages. Means and standard deviations were used to describe continuous variables. Pie charts showed the reasons and types of unmet medical care needs. This study used binary logistic regression to analyze the factors associated with unmet medical needs. Stratification is essential to prevent potential biases brought on by urban-rural disparities because of the stark differences in medical resources and levels between rural and urban locations. The results of regression analysis were expressed as odds ratios (OR) and their 95% confidence intervals (95% CI). Statistical significance was defined as P < 0.05. ---------------Table 1 is here--------------- Results A descriptive summary of all variables for these respondents is shown in Table 1 . The total sample size was 19,123 middle-aged and older adults, and 11.28% had unmet medical needs. In terms of demographic and socioeconomic characteristics, men accounted for 46.89%, 38.2% of the respondents were over 65 years old, 32.97% had a middle school education or above, 36.57% lived in cities or towns, and 95.21% covered by medical insurance. Regarding medical resources, each province had an average of 75.49 health professionals per 10,000 people, 48,418.01 medical institutions, and 65.87 beds per 10,000 people. Regarding health status and behavior, 75.45% of respondents rated themselves as having good or above health status, and 64.82% of respondents were suffering from chronic diseases. 25.36% and 35.81% of respondents were smoking and drinking, respectively. The average score of CESD 10 was 9.07. In terms of behavior patterns during the COVID-19 pandemic, 18.85% of respondents were quarantined, more than 90% were aware of epidemic prevention measures and had experienced residence control, only 1.30% increased their going out time, and more than 35% felt fears or anxiety. In Fig. 2 , the types of unmet medical services are displayed. The proportion of outpatient service is the highest, accounting for a combined total of 34.46%. Prescriptions for medicine and dental care accounted for 20.22% and 29.48%, respectively. The proportion of small surgery that can be performed in the outpatient department and big surgery requiring in-patient service is relatively low, 1.70% and 3.53%, respectively. The reasons for unmet medical needs are listed in Fig. 3 . “I am afraid to go to hospital” was a significant reason, accounting for 23.25%. “ I decided to visit later”,“unavailable for an appointment,” and “change schedule of the hospital” accounted for 17.51%, 13.34%, and 4.59%, respectively. ---------------Fig. 2 is here--------------- ---------------Fig. 3 is here--------------- Table 2 shows the odds ratios of the binary logistic regression analysis. This study found that men were less likely to have unmet medical needs among middle-aged and older adults than women in urban and rural areas. Age was associated with unmet medical needs in rural areas, and the probability of unmet medical needs decreased with age. For example, middle-aged and older adults over 75 (OR = 0.46, 95% CI: 0.35, 0.62) were 54% less likely to have unmet medical needs than those aged 45–54. The higher the level of education, the higher the probability of unmet medical needs, which occurs in urban and rural locations. Compared to illiterate patients, middle-aged and older adults with a middle school education (OR = 1.69, 95% CI: 1.69, 2.88) in urban areas were 60% more likely to receive unmet medical needs. At the same time, those with more than above three-years of college (OR = 3.50, 95% CI: 3.50, 5.20) were 250% more probable. Middle-aged and older adults engaged in farm work in rural areas (OR = 0.83, 95% CI: 0.71, 0.96) had a lower risk of unmet medical needs. In urban areas, middle-aged and older adults living with their spouses (OR = 0.79, 95% CI: 0.67, 0.93) were less likely to have unmet medical needs. Middle-aged and older adults with UEMI (OR = 2.30, 95% CI: 1.36, 3.56) and URMI (OR = 1.65, 95% CI: 0.96, 2.44) in urban areas had 130% and 65% higher odds of having unmet medical need compared to those without medical insurance, respectively. Table 2 logistic regression results Urban Rural Variable OR 95%CI P OR 95%CI P Male 0.72 (0.59, 0.87) 0.001 0.82 (0.69, 0.97) 0.008 Age 45–54 1.00 Ref. 1.00 Ref. 55–64 1.08 (0.89, 1.31) 0.44 0.80 (0.69, 0.95) < 0.001 65–74 1.04 (0.82, 1.31) 0.75 0.72 (0.61, 0.87) < 0.001 ≥ 75 0.76 (0.54, 1.04) 0.08 0.46 (0.35, 0.62) < 0.001 Educational attainment Illiterate 1.00 Ref. 1.00 Ref. Elementary school 1.41 (1.41, 1.85) 0.036 1.19 (1.08, 1.50) 0.043 Middle school 1.69 (1.69, 2.28) 0.006 1.30 (1.18, 1.80) 0.018 High school or vocational school 1.72 (1.72, 2.36) 0.005 1.40 (1.22, 2.24) 0.032 Above three-years of college 3.50 (3.50, 5.20) < 0.001 1.19 (0.48, 4.25) 0.758 Farm work 1.00 (0.85, 1.21) 0.95 0.83 (0.71, 0.96) 0.014 Living with a spouse 0.79 (0.67, 0.93) 0.006 0.12 (0.69, 0.97) 0.155 Household income Low income 1.00 Ref. 1.00 Ref. Lower middle income 0.83 (0.64, 1.08) 0.173 1.08 (0.90, 1.30) 0.43 Upper middle income 1.02 (0.82, 1.28) 0.812 1.06 (0.86, 1.30) 0.603 High income 0.92 (0.72, 1.17) 0.494 1.07 (0.81, 1.42) 0.63 Medical insurance No insurance 1.00 Ref. 1.00 Ref. UEMI 2.30 (1.36, 3.56) 0.001 1.52 (0.86, 2.17) 0.081 URMI 1.65 (0.96, 2.44) 0.038 1.30 (0.87, 1.67) 0.115 otherwise 1.82 (0.87, 3.05) 0.062 1.05 (0.52, 1.69) 0.866 Health technicians 1.01 (1.00, 1.02) 0.078 1.02 (1.01, 1.03) 0.001 Medical institution 1.00 (0.99, 1.00) 0.001 0.99 (0.99, 1.00) 0.221 Bed 1.00 (1.00, 1.02) 0.04 1.00 (1.00, 1.02) 0.142 Self-rated health Good 1.00 Ref. 1.00 Ref. Fair 1.58 (1.22, 1.95) < 0.001 1.94 (1.51, 2.49) < 0.001 Poor 3.51 (2.62, 4.51) < 0.001 3.75 (2.86, 4.91) < 0.001 Chronic conditions 2.25 (1.72, 2.93) < 0.001 2.52 (1.97, 3.21) < 0.001 Smoking 0.80 (0.65, 1.01) 0.051 0.83 (0.69, 0.99) 0.053 Drinking 1.07 (0.91, 1.28) 0.412 1.05 (0.90, 1.22) 0.513 Depression 1.03 (1.01, 1.03) < 0.001 1.03 (1.02, 1.04) < 0.001 Quarantined 1.47 (0.98, 2.23) 0.066 1.33 (0.85, 2.08) 0.209 Knowing the preventive measures 1.81 (0.71, 4.67) 0.216 2.16 (1.42, 3.29) < 0.001 Residence control No restrictions on entry and exit 1.00 Ref. 1.00 Ref. Residential area completely shut down 1.29 (0.76, 2.23) 0.357 1.57 (1.11, 2.23) 0.011 Restricted entry or exit for residents 1.59 (1.06, 2.39) 0.026 1.79 (1.26, 2.55) 0.001 Entry into residential area for non-residents 1.58 (1.08, 2.33) 0.019 1.78 (1.31, 2.43) < 0.001 Restricted entry for non-residents 1.70 (1.19, 2.47) 0.004 1.98 (1.45, 2.67) < 0.001 Times spent outdoors Increased 1.00 Ref. 1.00 Ref. Not changed 0.85 (0.40, 1.46) 0.656 0.75 (0.43, 1.31) 0.313 Decreased 1.31 (0.72, 2.55) 0.427 1.42 (0.82, 2.47) 0.212 Feeling fears Rarely or never 1.00 Ref. 1.00 Ref. Sometimes 1.56 (1.30, 1.89) < 0.001 1.30 (1.09, 1.55) 0.003 Often 1.36 (1.04, 1.80) 0.025 1.45 (1.18, 1.79) < 0.001 Feeling anxiety Rarely or never 1.00 Ref. 1.00 Ref. Sometimes 1.16 (0.97, 1.41) 0.111 1.22 (1.03, 1.45) 0.021 Often 1.26 (0.93, 1.69) 0.126 1.13 (0.91, 1.43) 0.26 A higher probability of unmet medical needs was observed among middle-aged and older adults with worse self-rated health status. For instance, those who rated their health as “poor” (OR = 3.51, 95% CI: 2.62, 4.51) had a 251% higher likelihood of having unmet medical needs compared to those who rated their health as “good” in rural areas. Middle-aged and older adults with chronic conditions had a higher probability of developing unmet medical needs in the countryside or in the city. Middle-aged and older adults experiencing depression were more likely to have unmet medical needs. In rural areas, the higher the number of health technicians per 10,000 people (OR = 1.02, 95% CI: 1.01, 1.03), the more likely it is to generate unmet medical needs. In rural areas, middle-aged and older adults who knew about COVID-19 prevention measures (OR = 2.16, 95% CI: 1.42, 3.29) were more likely to have unmet medical needs. In both rural and urban areas, “Restricted entry for non-residents” was the control level with the highest likelihood of generating unmet medical needs among the four intensity residency control levels, and middle-aged and older adults who felt fears sometimes or often during the COVID-19 pandemic were more likely to have unmet medical need than those who never feel fears. ---------------Table 2 is here-------------- Discussion The study aimed to explore the factors associated with unmet medical needs among middle-aged and older adults in China during the COVID-19 pandemic. Our findings indicated that 11.28% of the respondent population faced unmet medical needs during the COVID-19 pandemic. With regard to the types of medical care that were delayed or canceled, outpatient services emerged as a significant category. This finding aligns with previous research that has emphasized the importance of outpatient care in managing chronic conditions and providing timely medical attention for various health issues[ 29 ]. The delay or cancellation of these services could have significant implications for the health and well-being of middle-aged and older adults. Additionally, prescription medication and dental care constituted a substantial portion of the delayed or canceled services. These results indicated that even basic medical care needs were not fully met during the pandemic, potentially resulting in worsened health outcomes and increased suffering for this population [ 30 ]. Turning to the reasons for delaying or canceling medical care, fear of visiting hospitals emerged as a significant reason. This finding aligns with previous research that has documented the widespread anxiety and concerns among the elderly population during the pandemic, who may have perceived hospitals as high-risk environments for contracting COVID-19 [ 31 ]. The inability to secure appointments and personal decisions to visit later were also significant reasons, highlighting the challenges faced by individuals in accessing timely medical care during the pandemic [ 32 ]. The present study found that women's medical needs were less likely to be met during the pandemic, which is consistent with Burch [ 33 ] and Yagmur [ 34 ]. Previous studies have discovered that during the pandemic, women are more likely to experience psychological distress, be anxious about infection, and show behavior avoiding medical treatment [ 35 ]. Moreover, women were at greater risk of job loss or engaging in unpaid work, such as home care duties, which could lead to unmet medical needs [ 34 ]. Contrary to the stereotype that younger people have less need for medical care, this research discovered that younger respondents had a greater probability of having unmet medical needs. This is possible because, compared with older adults, most middle-aged people need to work for a living and voluntarily forgo needed medical care to avoid time costs. On the other hand, older adults have more time to spend on their medical care but may voluntarily give up some medical care because of changing hospital schedules and lower income [ 35 , 36 ]. Educational attainment was positively correlated with the probability of unmet medical needs, which is also confirmed by Emiel [ 37 ] and Christina P. [ 38 ], contrary to our past belief that highly educated patients may find it easier to meet medical needs. A possible explanation is that highly educated patients are more knowledgeable about health care and have relatively higher medical needs. They may question doctors' recommendations and have difficulty finding medical services that match their health needs [ 38 , 39 ]. The probability of unmet medical needs of farmers is relatively low. China's rural workers mainly include farmers and migrant workers (workers who are registered in rural areas and engage in non-agricultural industries locally or work outside the home for 6 months or more). From the perspective of industries, the most affected by COVID-19 are service industries such as catering and construction, which are the main areas of employment for migrant workers[ 40 ]. During the COVID-19 pandemic, due to restrictions on epidemic prevention measures, the risk of virus infection, and other reasons, migrant workers could not go out to work and lost their sources of income[ 41 ]. This may lead to a reduction in medical expenditure to a certain extent, and the medical needs of migrant workers cannot be met. Residents with their spouses were less likely to have unmet medical needs. Spouses are one of the most essential providers of care and comfort for older people, and they will meet their health needs by giving companionship and care, reducing loneliness, and improving health [ 42 , 43 ]. Unmet medical needs are more common among urban residents with health insurance, which may reflect variations in care-seeking behavior. It may also be because insured residents have higher expectations for medical care, and due to the more severe epidemic outbreaks in Chinese cities [ 41 ], face-to-face medical care is significantly reduced, and limited medical services or telemedicine services cannot meet their medical needs [ 44 ]. The number of health technicians per 10,000 people is inversely linked to unmet medical needs in rural regions, which may be related to the status of medical resources in China. The abilities of rural health technicians are relatively weak, and the public lacks trust in the skills of grassroots doctors and the quality of diagnostic facilities. This has led many patients to turn to large hospitals for diagnosis and treatment, resulting in a small number of large hospitals being overwhelmed [ 45 ]. Moreover, front-line healthcare workers have a higher risk of infection, and illness and self-isolation of healthcare workers took them away from their jobs, resulting in a shortage of health human resources [ 46 ]. Self-rated health status was another factor of unmet medical needs. The worse the self-rated health status, the more likely there is to be unmet medical needs, which is consistent with the results of most existing studies [ 34 , 47 , 48 ]. People with poor self-rated health are more in need of medical care [ 48 ]. Still, they may avoid physical contact and reduce their frequency of seeking medical treatment due to concerns about the worsening disease caused by COVID-19 infection [ 49 ]. Patients with chronic diseases, as well as those with higher self-test scores for depression, have a higher chance of having unmet medical requirements. Some studies have attributed this to the unique needs of patients with chronic diseases that require additional medical equipment, drugs, diet control, exercise, etc. [ 50 ], which are to some extent limited by epidemic prevention measures [ 51 ]. For example, restricted access to crowded places (such as medical institutions and gyms) makes achieving medical services and exercise opportunities more complex, perhaps leading to increased unmet medical needs. Research has shown that when individuals suffer from depression, their self-evaluation of their health status has a negative impact and can lead to somatization, making diagnosis and treatment difficult [ 52 ]. At the same time, during the COVID-19 pandemic, the government canceled the opening of public places such as schools, restaurants, and sports venues to avoid the spread of the epidemic. Social activities of the public were reduced, leading to social anxiety related to COVID-19 and exacerbating symptoms of depression [ 53 ]. Knowing the epidemic prevention measures indicates a strong awareness of prevention, and they may try to avoid public places with many people and maintain social distance from others, which reduces the possibility of going out to receive medical services. Telemedicine services have replaced some face-to-face medical services [ 54 ]. However, telemedicine services have not been widely used due to limited technical level, doctor-patient communication barriers, data privacy security, and other reasons, and residents are still limited in receiving medical services that meet expectations [ 55 ]. Residence control will increase the probability of unmet medical needs, reducing the opportunities for residents to obtain paid employment, decreasing household income, and thus reducing necessary medical expenses [ 56 ]. Moreover, residency controls restrict residents' movement, meaning they are partly deprived of the medical services they need [ 57 ]. Research indicates that one of the primary causes of unmet medical needs is fear of COVID-19 [ 58 ]. This situation mainly existed in chronic disease patients who believed they were at high risk of COVID-19 infection [ 59 , 60 ]. This study has some limitations. First, self-reported data may contain biases and measurement errors, and there needs to be more self-perceived and actual unmet needs. Second, causality cannot be assumed in this study because it was a cross-sectional study design, and further longitudinal studies are needed to determine the direction of causality of these associations. Third, this study's results are limited to middle-aged and older Chinese adults and may need help to spread to other countries and people. Conclusion In conclusion, this study analyzed the prevalence of unmet medical needs and its influencing factors during the COVID-19 pandemic among middle-aged and older adults in China. The results show that women, middle-aged people, non-agricultural workers, and individuals living alone were more likely to have unmet medical needs. In addition, factors such as poor self-rated health status, chronic conditions, depression, residence control, fear of the epidemic, and awareness of epidemic prevention measures may increase the probability of unmet medical needs. However, this study found that some factors that reduce unmet medical needs may promote the generation of unmet medical needs, including higher levels of education, access to medical insurance, and relatively abundant health human resources. Comprehending the elements that contribute to unmet medical needs during outbreaks of infectious diseases sheds light on policymakers and researchers. The present research suggests that vulnerable populations’ unmet medical needs be prioritized and that suitable health legislation and infrastructure should be implemented to provide targeted help to these groups. In addition, it's critical to reasonably allocate medical resources between urban and rural areas, promote the rationalization of the structure of medical resources, reduce unmet medical needs, and thus enhance the overall health level of the population. Abbreviations COVID-19: Coronavirus Disease 2019; BMHSU: Behavioral Model of Health Services Use; CHARLS: China Health and Retirement Longitudinal Study Database; PPS: probability proportional to size; MICE: multiple imputation by chained equation; CESD: Central Depression Scale; UEMI: Urban Employee Medical Insurance; URMI: Urban and Rural Resident Medical Insurance Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials All data generated or analyzed during this study are included in this published article and its supplementary information files. Competing interests The authors declare that they have no competing interests. Funding Quanzhou “14th Five-Year Plan” Healthcare Reform Programming, Grant Number: 2021B014 Authors' contributions Yiping Zheng, Baoquan Zhang, Jin Wei, Dongyu Xue, Changle Li, and Yue Dai contributed to the conception and design of this study. Yiping Zheng, Dongyu Xue, and Jin Wei were involved in literature screening, data collection, and variables extraction. Yiping Zheng, Jin Wei, Baoquan Zhang, and Changle Li analyzed and interpreted the data. The manuscript was drafted by Yiping Zheng, Baoquan Zhang, Jin Wei, and Changle Li. Yue Dai provided financial support. The final version was reviewed and approved by all authors. Authors' information 1 School of Health Management, Fujian Medical University, Fuzhou 350122, China. 2 Fujian Provincial Maternity and Children’s Hospital, Fuzhou 350122, China References Kortrijk, H.E., A.M. Kamperman, and C.L. Mulder, Changes in individual needs for care and quality of life in Assertive Community Treatment patients: an observational study. 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Harling, G., et al., Impairment in Activities of Daily Living, Care Receipt, and Unmet Needs in a Middle-Aged and Older Rural South African Population: Findings From the HAALSI Study. J Aging Health, 2020. 32 (5-6): p. 296-307. Hoogendijk, E.O., et al., Self-perceived met and unmet care needs of frail older adults in primary care. Arch Gerontol Geriatr, 2014. 58 (1): p. 37-42. Tadiri, C.P., et al., Determinants of perceived health and unmet healthcare needs in universal healthcare systems with high gender equality. BMC Public Health, 2021. 21 (1): p. 1488. Bu, X., et al., Unmet needs of 1210 Chinese breast cancer survivors and associated factors: a multicentre cross-sectional study. BMC Cancer, 2022. 22 (1): p. 135. Pan, D., et al., The influence of COVID-19 on agricultural economy and emergency mitigation measures in China: A text mining analysis. PLoS One, 2020. 15 (10): p. e0241167. Wang, H., et al., Tracking the effects of COVID-19 in rural China over time. Int J Equity Health, 2021. 20 (1): p. 35. Freak-Poli, R., et al., Does social isolation, social support or loneliness influence health or well-being after a cardiovascular disease event? A narrative thematic systematic review. Health Soc Care Community, 2022. 30 (1): p. e16-e38. Huang, D., et al., The unmet needs of older adults living in nursing homes in Mainland China: a nation-wide observational study. BMC Geriatr, 2022. 22 (1): p. 989. Carlson, M.S., M.L. Romo, and E.A. Kelvin, Impact of the First Year of the COVID-19 on Unmet Healthcare Need among New York City Adults: a Universal Healthcare Experiment. Journal of Urban Health, 2023. 100 (5): p. 962-971. Wu, D., et al., Public views towards community health and hospital-based outpatient services and their utilisation in Zhejiang, China: a mixed methods study. BMJ Open, 2017. 7 (11): p. e017611. Sun, S., et al., COVID-19 and healthcare system in China: challenges and progression for a sustainable future. Global Health, 2021. 17 (1): p. 14. Alang, S., et al., Police brutality, medical mistrust and unmet need for medical care. Prev Med Rep, 2021. 22 : p. 101361. Jung, B. and I.-H. Ha, Determining the reasons for unmet healthcare needs in South Korea: a secondary data analysis. Health and Quality of Life Outcomes, 2021. 19 (1): p. 99. Splinter, M.J., et al., Prevalence and determinants of healthcare avoidance during the COVID-19 pandemic: A population-based cross-sectional study. PLoS Med, 2021. 18 (11): p. e1003854. Perry, L., et al., Unmet health-related needs of community-dwelling older adults during COVID-19 lockdown in a diverse urban cohort. J Am Geriatr Soc, 2023. 71 (1): p. 178-187. Kim, S. and J. Hwang, What are the factors affecting older adults' experience of unmet healthcare needs amid the COVID-19 pandemic in Korea? BMC Geriatr, 2023. 23 (1): p. 517. Jung, H., X. Che, and H.J. Park, COVID-19 and Unmet Medical Needs for People With Chronic Diseases: A Cross-Sectional Study. Inquiry, 2022. 59 : p. 469580221133002. Huang, Y. and N. Zhao, Generalized anxiety disorder, depressive symptoms and sleep quality during COVID-19 outbreak in China: a web-based cross-sectional survey. Psychiatry Res, 2020. 288 : p. 112954. Doraiswamy, S., et al., Use of Telehealth During the COVID-19 Pandemic: Scoping Review. J Med Internet Res, 2020. 22 (12): p. e24087. Khoshrounejad, F., et al., Telehealth-Based Services During the COVID-19 Pandemic: A Systematic Review of Features and Challenges. Front Public Health, 2021. 9 : p. 711762. Bose, B., S.A. Alam, and C.C. Pörtner, Impacts of the COVID-19 Lockdown on Healthcare Inaccessibility and Unaffordability in Uganda. Am J Trop Med Hyg, 2023. 109 (3): p. 527-535. Topriceanu, C.C., et al., Evaluating access to health and care services during lockdown by the COVID-19 survey in five UK national longitudinal studies. BMJ Open, 2021. 11 (3): p. e045813. Ponce, S.A., et al., Inability to get needed health care during the COVID-19 pandemic among a nationally representative, diverse population of U.S. adults with and without chronic conditions. BMC Public Health, 2023. 23 (1): p. 1868. Anderson, K.E., et al., Reports of Forgone Medical Care Among US Adults During the Initial Phase of the COVID-19 Pandemic. JAMA Netw Open, 2021. 4 (1): p. e2034882. Ayele, T.A., et al., Effect of COVID-19 pandemic on missed medical appointment among adults with chronic disease conditions in Northwest Ethiopia. PLoS One, 2022. 17 (10): p. e0274190. Additional Declarations No competing interests reported. Supplementary Files Appendix.docx Cite Share Download PDF Status: Published Journal Publication published 09 Oct, 2025 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 30 Jul, 2024 Editor assigned by journal 26 Jul, 2024 Submission checks completed at journal 26 Jul, 2024 First submitted to journal 21 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4775314","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":333509331,"identity":"ce1afd2e-7373-4db2-a244-08fd44aed043","order_by":0,"name":"Yiping Zheng","email":"","orcid":"","institution":"Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yiping","middleName":"","lastName":"Zheng","suffix":""},{"id":333509332,"identity":"d924654e-4414-42f3-a1f9-70e293e85b21","order_by":1,"name":"Baoquan Zhang","email":"","orcid":"","institution":"Fujian Provincial Maternity and Children’s 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2","display":"","copyAsset":false,"role":"figure","size":57003,"visible":true,"origin":"","legend":"\u003cp\u003eThe kind of unmet medical services\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4775314/v1/3639c940807cd05d369a4932.png"},{"id":63032207,"identity":"a2c66e4c-c15d-4f89-9d5e-97e9515174f0","added_by":"auto","created_at":"2024-08-22 09:36:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":52054,"visible":true,"origin":"","legend":"\u003cp\u003eReasons for unmet medical needs\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4775314/v1/28120a62dd3b09438f32a092.png"},{"id":93420090,"identity":"aca0f5aa-1b59-4915-be72-33f3d61c81aa","added_by":"auto","created_at":"2025-10-13 16:09:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1351221,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4775314/v1/258f3f5d-293c-4d6f-a90b-9f71fbb95e53.pdf"},{"id":63032210,"identity":"e69da19c-5a7d-444c-88e7-a0f6aae0b20d","added_by":"auto","created_at":"2024-08-22 09:36:19","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":15184,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-4775314/v1/15b6ecad57c33f316bae5633.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Factors associated with unmet medical needs among middle-aged and older adults in China during COVID-19 Pandemic","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUnmet medical needs represent a significant risk to individuals' health and well-being. The fact that the current healthcare system does not meet individuals' medical requirements can lead to detrimental outcomes such as disease progression, increased severity of illnesses, and compromised quality of life [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], which would pose a financial burden on patients and their families, escalating medical care costs and placing strain on available health resources [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Recognizing and mitigating unmet medical needs is crucial in ensuring equitable access to healthcare. Among various demographic groups, middle-aged and older adults are particularly vulnerable to unmet medical needs because they are prone to developing chronic conditions and degenerative diseases that require ongoing medical attention [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, due to inadequate medical care resources, limited access to medical care, and a lack of health literacy, many middle-aged and older adults may experience significant unmet medical needs [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This affects their daily functioning and quality of life and increases their vulnerability to adverse health outcomes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe Coronavirus Disease 2019 (COVID-19) was first identified due to a reported cluster of pneumonia cases in Wuhan in December 2019 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The infection has rapidly propagated since the day of emergence, spreading globally and becoming a pandemic [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The COVID-19 pandemic led to guidelines to postpone non-essential medical care and screenings [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The pandemic has caused a vast and profound impact on unmet needs, especially for middle-aged and older adults, whose unmet needs have been significantly aggravated in multiple dimensions. The pandemic has not only strained medical resources, making middle-aged and older adults face difficulties in chronic disease management and emergency medical treatment, but also caused them to feel more lonely and isolated in emotional communication and social interaction due to the reduction of social activities and the widening of the digital divide[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. At the same time, the economic recession and the instability of the job market have also increased the financial pressure on middle-aged and older adults, and some people are facing the dilemma of insufficient pension and lack of medical security[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These factors work together to make the health, social, emotional, and economic needs of middle-aged and older adults are not fully met, and their quality of life is seriously affected.\u003c/p\u003e \u003cp\u003eUnmet medical need refers to the gap between individuals' medical requirements and the services provided by the current healthcare system [\u003cspan additionalcitationids=\"CR14 CR15 CR16\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Most research evidence of unmet medical care needs comes from high-income countries in North America, Europe, and Asia. Studies have investigated the prevalence and characteristics of unmet medical needs among different population groups, such as older adults, children, and individuals with chronic illnesses [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Furthermore, previous studies have also examined the implications of unmet medical needs on patients' health outcomes, quality of life, and medical care costs [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, differences in the national healthcare system and health insurance system may contribute to the incidence of unmet medical needs across countries [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This prevents valuable comparisons between U.S. (or European) studies and Asian studies. In addition, there is less research on the factors influencing unmet medical needs during the COVID-19 pandemic in China. The unique factors contributing to this problem in this demographic group need to be explored more deeply. Therefore, this study aimed to analyze factors associated with unmet medical needs among middle-aged and older adults in China during the COVID-19 pandemic.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eTheoretical model\u003c/h2\u003e \u003cp\u003eThis study was guided by the Behavioral Model of Health Services Use (BMHSU), created by Dr. Anderson of the University of Chicago School of Public Health in 1968 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. It was initially used to investigate variables influencing the use of family medical care. After five iterations and additions, it has evolved into a dependable framework for medical care research [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. By incorporating multi-level factors affecting medical care needs into a relatively mature analytical framework, BMHSU can more fully explain the key characteristics of the sample population and avoid random selection of influencing factors.\u003c/p\u003e \u003cp\u003eThe theoretical framework of this study includes four parts, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. (1) External environment: The medical care need is closely related to objective factors such as the level of economic development, the level of medical care, and the policy environment. This study adopted three factors: the number of medical institutions, the number of health technicians, and the number of beds in medical institutions. (2) Individual characteristics: This reflects the individual level of samples needed for medical care. This study discussed and analyzed three aspects: (a) Predisposing factors, defined as individuals' tendency to use health services, reflecting the possibility of individuals using medical care. This study included age, gender, education level, and (b) Enabling factors such as the availability of medical care. Income, medical insurance, and self-rated health status were included in this study; (c) Need factors are usually the most direct reason for individuals to use medical care, that is, personal medical needs. The need factors in this study included chronic conditions, depressive symptoms, drinking, and smoking. (3) Behavioral patterns during the COVID-19 pandemic. This study chose five key behaviors that could impact the medical care needed during the COVID-19 pandemic: quarantined, knowing the preventive measures, residence control, times spent outdoors, and feeling fears or anxiety. (4) Outcome. Whether the medical needs are satisfied by the subjective feelings of the sample population.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e---------------Fig.\u0026nbsp;1 is here---------------\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData source\u003c/h2\u003e \u003cp\u003eThe China Health and Retirement Longitudinal Study Database (CHARLS) is a nationally representative study of Chinese residents aged 45 years and older. First undertaken in 2011, the CHARLS has since conducted four follow-up studies in 2013, 2015, 2018, and 2020 and selected study participants from 150 counties across 28 Chinese provinces using the probability proportional to size (PPS) method. The research content includes population background, socioeconomic status, health status, and psychological status [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Data for this study was extracted from the fifth round of national surveys conducted by the CHARLS in 2020. In addition, this study also extracted three indicators from the open data of the National Bureau of Statistics of China in 2021, including the number of medical institutions, the number of beds in medical institutions per 10,000 people, and the number of health technicians per 10,000 people. A total of 19,123 individuals were included.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eDependent variable\u003c/h2\u003e \u003cp\u003eThe unmet medical care needs include unperceived needs and perceived needs. The unperceived needs cannot be empirically studied because they are not recorded. When patients are unwilling to seek delay or even cancel medical care due to barriers, or when medical care providers are unable to provide appropriate care, the perceived needs of patients will not be met [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In this study, unmet medical need refers to people delaying or canceling medical care due to the COVID-19 pandemic so that their perceived medical care need is unmet. Responses to the following survey questions were used to determine unmet medical needs: During the pandemic, have you ever needed to see a doctor, including a dentist, but were forced to postpone or unable to do so because of the COVID-19 pandemic? Response options are yes or no. If the respondent reported \u0026lsquo;yes,\u0026rsquo; the respondent was categorized as having unmet medical needs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003eIndependent variables\u003c/h2\u003e \u003cp\u003eThe selection of independent variables was mainly based on the research purpose and literature review. The independent variables can be broadly divided into demographic and socioeconomic characteristics, medical resources, health status and behavior, and behavior patterns during the COVID-19 pandemic. Definitions of the variables are given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDefinitions of variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%/\u003c/p\u003e \u003cp\u003emean(std)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDependent variable\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmet medical needs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 If the individual is affected by the COVID-19 pandemic and delays or fails to visit the doctor; 0 others\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent variable\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDemographic and socioeconomic characteristics\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 \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\u003eIf the individual is male; 0 for female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\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 \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\u003e1 if the individual is aged 45\u0026ndash;54 years; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.74\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\u003e1 if the individual is aged 55\u0026ndash;64 years; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.06\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\u003e1 if the individual is aged 65\u0026ndash;74 years; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 if the individual is aged\u0026thinsp;\u0026ge;\u0026thinsp;75 years; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational attainment\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIlliterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 if the individual is illiterate; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElementary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 if the individual attended elementary school; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 if the individual graduated from middle school; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or vocational school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 if the individual graduated from high school or vocational school; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbove three-years of college\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 if the individual has above three years of college; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.28\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\u003e1 if the individual lives in urban regions; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarm work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 if this individual is engaged in farm work; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving with a spouse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 if the individual is living with a spouse; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold income\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 if the individual\u0026rsquo;s household income is in the first quartile; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLower middle income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 if the individual\u0026rsquo;s household income is in the second quartile; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper middle income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 if the individual\u0026rsquo;s household income is in the third quartile; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 if the individual\u0026rsquo;s household income is in the highest quartile; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical insurance\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 if the individual does not have medical insurance; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUEMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 if the individual is enrolled in Urban Employee Medical Insurance; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eURMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 if the individual is enrolled in Urban and Rural Resident Medical Insurance; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 if the individual is enrolled in Free Medical Insurance, Private Medical Insurance, Medical Aid, or Other medical insurance; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedical resources\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth technicians\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of health technicians per 10,000 people in the province where the individual resides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75.49(7.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical institution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of medical institutions in the province where the individual resides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48418.01\u003c/p\u003e \u003cp\u003e(23894.71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of hospital beds per 10,000 people in the province where the individual resides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.87\u003c/p\u003e \u003cp\u003e(8.40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHealth status and behavior\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-rated health\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 \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\u003e1 if the individual reports health status to be poor; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.54\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\u003e1 if the individual reports health status to be fair; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.35\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\u003e1 if the individual reports health status to be good or better; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic conditions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 if the individual has chronic disease; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 if the individual is still smoking; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 if the individual is still drinking; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe sum of the scores of 10 items in the simplified Central Depression Scale (CESD 10).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.07(6.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBehavioral patterns during the COVID-19 pandemic\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuarantined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 if the individual is quarantined during the outbreak; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKnowing the preventive measures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 if the individual knows the preventive measures; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence control\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo restrictions on entry and exit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 if the individual faces No Restrictions on Entry and Exit; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidential area completely shut down\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 if the individual's residential area is completely shut down; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRestricted entry or exit for residents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 if the individual faces restricted entry or exit for residents; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo entry into residential area for non-residents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 if the individual faces no entry into the residential area for non-residents; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRestricted entry for non-residents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 if the individual faces restricted entry for non-residents; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTimes spent outdoors\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncreased\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 if the individual has a large or small increase in the number of times spent outdoors during the Lunar New Year outbreak; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot changed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 if the individual\u0026rsquo;s time spent outdoors does not change during the Lunar New Year outbreak; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecreased\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 if the individual has a large or small decrease in the number of times spent outdoors during the Lunar New Year outbreak; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeeling fears\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRarely or never\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 if the individual rarely or never expresses their fears during the Lunar New Year outbreak; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSometimes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 if the individual sometimes expresses their fears during the Lunar New Year outbreak; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOften\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 if the individual often expresses their fears during the Lunar New Year outbreak; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeeling anxiety\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRarely or never\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 if the individual rarely or never expresses their anxiety during the Lunar New Year outbreak; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSometimes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 if the individual Sometimes expresses their anxiety during the Lunar New Year outbreak; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOften\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 if the individual often expresses their anxiety during the Lunar New Year outbreak; 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe CHARLS 2020 has a new COVID module, and the following variables are selected for inclusion in the pattern of behavior during the COVID-19 pandemic: Quarantined, knowing the preventive measures, residence control, times spent outdoors, and feeling fears or anxiety. The question \u0026ldquo;Have you ever been quarantined or under medical observation due to the following reasons?\u0026rdquo; was used to measure \u0026ldquo;Quarantined\u0026rdquo;, and the options included the following: (1) travels, (2) close contact with COVID cases, (3) building lockdown, (4) after going to a hospital, (5) tested positive, (6) no quarantine experience. If the respondent selected (6), they were not quarantined during the epidemic. \u0026ldquo;Knowing the preventive measures\u0026rdquo; was determined by asking, \u0026ldquo;Do you know that the following practices can reduce the risk of COVID-19 infection? (Multiple choices are allowed)\u0026rdquo;. The options included (1) washing hands, (2) using disinfectant, (3) avoiding handshaking, (4) masking, (5) avoiding travels, (6) avoiding gatherings, (7) social distancing, (8) others, (9) do not know about the pandemic or preventive measures. If the respondent knows any correct practice, it is considered that they know the precautions against COVID-19. \u0026ldquo;Residence control\u0026rdquo; was measured using the following question, \u0026ldquo;Due to epidemic control measures, have the communities or villages you have lived in since the Spring Festival implemented the following types of restrictions on the entry and exit of internal and external personnel?\u0026rdquo; There were five levels to the severity of the restrictions: (1) residential area wholly shut down, (2) restricted entry or exit for residents, (3) no entry into a residential area for non-residents, (4) restricted entry for non-residents, (5) no restrictions on entry and exit. \u0026ldquo;Times spent outdoors\u0026rdquo; was measured by the following question, \u0026ldquo;Has the amount of time you spend outside each day increased, decreased, or remained the same compared to what would have happened if the pandemic had not occurred?\u0026rdquo; The options included (1) incremental, (2) not changed, (3) decreased. \u0026ldquo;Feeling fears or anxiety\u0026rdquo; was divided into three levels: rarely or never, sometimes, and often.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eTo reduce biases when respondents with missing data were excluded, this study used multiple imputation (MI) by chained equation (MICE) to account for missing data in all variables (see Appendix for the non-response rate of the variables used in this study ) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The variables with missing values were included in the imputation model, and variables with no missing values (age, gender, health technicians, medical institution, bed) were treated as predictors. The current study employed \u0026lsquo;mi impute chained\u0026rsquo; in STATA/MP17.0 to create 50 imputed datasets to fill in missing values. This number was large enough to achieve good efficiency [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAll statistical analyses were conducted using STATA/MP17.0. Each categorical variable's distribution is described using percentages. Means and standard deviations were used to describe continuous variables. Pie charts showed the reasons and types of unmet medical care needs. This study used binary logistic regression to analyze the factors associated with unmet medical needs. Stratification is essential to prevent potential biases brought on by urban-rural disparities because of the stark differences in medical resources and levels between rural and urban locations. The results of regression analysis were expressed as odds ratios (OR) and their 95% confidence intervals (95% CI). Statistical significance was defined as P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003e---------------Table\u0026nbsp;1 is here---------------\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA descriptive summary of all variables for these respondents is shown in Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e. The total sample size was 19,123 middle-aged and older adults, and 11.28% had unmet medical needs. In terms of demographic and socioeconomic characteristics, men accounted for 46.89%, 38.2% of the respondents were over 65 years old, 32.97% had a middle school education or above, 36.57% lived in cities or towns, and 95.21% covered by medical insurance. Regarding medical resources, each province had an average of 75.49 health professionals per 10,000 people, 48,418.01 medical institutions, and 65.87 beds per 10,000 people. Regarding health status and behavior, 75.45% of respondents rated themselves as having good or above health status, and 64.82% of respondents were suffering from chronic diseases. 25.36% and 35.81% of respondents were smoking and drinking, respectively. The average score of CESD 10 was 9.07. In terms of behavior patterns during the COVID-19 pandemic, 18.85% of respondents were quarantined, more than 90% were aware of epidemic prevention measures and had experienced residence control, only 1.30% increased their going out time, and more than 35% felt fears or anxiety.\u003c/p\u003e\n\u003cp\u003eIn Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e, the types of unmet medical services are displayed. The proportion of outpatient service is the highest, accounting for a combined total of 34.46%. Prescriptions for medicine and dental care accounted for 20.22% and 29.48%, respectively. The proportion of small surgery that can be performed in the outpatient department and big surgery requiring in-patient service is relatively low, 1.70% and 3.53%, respectively. The reasons for unmet medical needs are listed in Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e. \u0026ldquo;I am afraid to go to hospital\u0026rdquo; was a significant reason, accounting for 23.25%. \u0026ldquo; I decided to visit later\u0026rdquo;,\u0026ldquo;unavailable for an appointment,\u0026rdquo; and \u0026ldquo;change schedule of the hospital\u0026rdquo; accounted for 17.51%, 13.34%, and 4.59%, respectively.\u003c/p\u003e\n\u003cp\u003e---------------Fig.\u0026nbsp;2 is here---------------\u003c/p\u003e\n\u003cp\u003e---------------Fig. 3 is here---------------\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e shows the odds ratios of the binary logistic regression analysis. This study found that men were less likely to have unmet medical needs among middle-aged and older adults than women in urban and rural areas. Age was associated with unmet medical needs in rural areas, and the probability of unmet medical needs decreased with age. For example, middle-aged and older adults over 75 (OR\u0026thinsp;=\u0026thinsp;0.46, 95% CI: 0.35, 0.62) were 54% less likely to have unmet medical needs than those aged 45\u0026ndash;54. The higher the level of education, the higher the probability of unmet medical needs, which occurs in urban and rural locations. Compared to illiterate patients, middle-aged and older adults with a middle school education (OR\u0026thinsp;=\u0026thinsp;1.69, 95% CI: 1.69, 2.88) in urban areas were 60% more likely to receive unmet medical needs. At the same time, those with more than above three-years of college (OR\u0026thinsp;=\u0026thinsp;3.50, 95% CI: 3.50, 5.20) were 250% more probable. Middle-aged and older adults engaged in farm work in rural areas (OR\u0026thinsp;=\u0026thinsp;0.83, 95% CI: 0.71, 0.96) had a lower risk of unmet medical needs. In urban areas, middle-aged and older adults living with their spouses (OR\u0026thinsp;=\u0026thinsp;0.79, 95% CI: 0.67, 0.93) were less likely to have unmet medical needs. Middle-aged and older adults with UEMI (OR\u0026thinsp;=\u0026thinsp;2.30, 95% CI: 1.36, 3.56) and URMI (OR\u0026thinsp;=\u0026thinsp;1.65, 95% CI: 0.96, 2.44) in urban areas had 130% and 65% higher odds of having unmet medical need compared to those without medical insurance, respectively.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003elogistic regression results\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.59, 0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.69, 0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u0026ndash;54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u0026ndash;64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.89, 1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.69, 0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u0026ndash;74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.82, 1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.61, 0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.54, 1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.35, 0.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducational attainment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIlliterate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElementary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.41, 1.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.08, 1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.69, 2.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.18, 1.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh school or vocational school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.72, 2.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.22, 2.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbove three-years of college\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.50, 5.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.48, 4.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.758\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFarm work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.85, 1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.71, 0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiving with a spouse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.67, 0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.69, 0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousehold income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLower middle income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.64, 1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.90, 1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUpper middle income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.82, 1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.86, 1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.603\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.72, 1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.81, 1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedical insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUEMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.36, 3.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.86, 2.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eURMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.96, 2.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.87, 1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eotherwise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.87, 3.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.52, 1.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.866\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealth technicians\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.00, 1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.01, 1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedical institution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.99, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.99, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.221\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.00, 1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.00, 1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-rated health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFair\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.22, 1.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.51, 2.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.62, 4.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.86, 4.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChronic conditions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.72, 2.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.97, 3.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.65, 1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.69, 0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.91, 1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.90, 1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.513\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.01, 1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.02, 1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQuarantined\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.98, 2.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.85, 2.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKnowing the preventive measures\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.71, 4.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.42, 3.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidence control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo restrictions on entry and exit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidential area completely shut down\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.76, 2.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.11, 2.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRestricted entry or exit for residents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.06, 2.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.26, 2.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEntry into residential area for non-residents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.08, 2.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.31, 2.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRestricted entry for non-residents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.19, 2.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.45, 2.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTimes spent outdoors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncreased\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot changed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.40, 1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.43, 1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.313\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDecreased\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.72, 2.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.82, 2.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.212\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFeeling fears\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRarely or never\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSometimes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.30, 1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.09, 1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOften\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.04, 1.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.18, 1.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFeeling anxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRarely or never\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSometimes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.97, 1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.03, 1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOften\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.93, 1.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.91, 1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eA higher probability of unmet medical needs was observed among middle-aged and older adults with worse self-rated health status. For instance, those who rated their health as \u0026ldquo;poor\u0026rdquo; (OR\u0026thinsp;=\u0026thinsp;3.51, 95% CI: 2.62, 4.51) had a 251% higher likelihood of having unmet medical needs compared to those who rated their health as \u0026ldquo;good\u0026rdquo; in rural areas. Middle-aged and older adults with chronic conditions had a higher probability of developing unmet medical needs in the countryside or in the city. Middle-aged and older adults experiencing depression were more likely to have unmet medical needs. In rural areas, the higher the number of health technicians per 10,000 people (OR\u0026thinsp;=\u0026thinsp;1.02, 95% CI: 1.01, 1.03), the more likely it is to generate unmet medical needs.\u003c/p\u003e\n\u003cp\u003eIn rural areas, middle-aged and older adults who knew about COVID-19 prevention measures (OR\u0026thinsp;=\u0026thinsp;2.16, 95% CI: 1.42, 3.29) were more likely to have unmet medical needs. In both rural and urban areas, \u0026ldquo;Restricted entry for non-residents\u0026rdquo; was the control level with the highest likelihood of generating unmet medical needs among the four intensity residency control levels, and middle-aged and older adults who felt fears sometimes or often during the COVID-19 pandemic were more likely to have unmet medical need than those who never feel fears.\u003c/p\u003e\n\u003cp\u003e---------------Table\u0026nbsp;2 is here--------------\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe study aimed to explore the factors associated with unmet medical needs among middle-aged and older adults in China during the COVID-19 pandemic. Our findings indicated that 11.28% of the respondent population faced unmet medical needs during the COVID-19 pandemic.\u003c/p\u003e \u003cp\u003eWith regard to the types of medical care that were delayed or canceled, outpatient services emerged as a significant category. This finding aligns with previous research that has emphasized the importance of outpatient care in managing chronic conditions and providing timely medical attention for various health issues[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The delay or cancellation of these services could have significant implications for the health and well-being of middle-aged and older adults. Additionally, prescription medication and dental care constituted a substantial portion of the delayed or canceled services. These results indicated that even basic medical care needs were not fully met during the pandemic, potentially resulting in worsened health outcomes and increased suffering for this population [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTurning to the reasons for delaying or canceling medical care, fear of visiting hospitals emerged as a significant reason. This finding aligns with previous research that has documented the widespread anxiety and concerns among the elderly population during the pandemic, who may have perceived hospitals as high-risk environments for contracting COVID-19 [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The inability to secure appointments and personal decisions to visit later were also significant reasons, highlighting the challenges faced by individuals in accessing timely medical care during the pandemic [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe present study found that women's medical needs were less likely to be met during the pandemic, which is consistent with Burch [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] and Yagmur [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Previous studies have discovered that during the pandemic, women are more likely to experience psychological distress, be anxious about infection, and show behavior avoiding medical treatment [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Moreover, women were at greater risk of job loss or engaging in unpaid work, such as home care duties, which could lead to unmet medical needs [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Contrary to the stereotype that younger people have less need for medical care, this research discovered that younger respondents had a greater probability of having unmet medical needs. This is possible because, compared with older adults, most middle-aged people need to work for a living and voluntarily forgo needed medical care to avoid time costs. On the other hand, older adults have more time to spend on their medical care but may voluntarily give up some medical care because of changing hospital schedules and lower income [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEducational attainment was positively correlated with the probability of unmet medical needs, which is also confirmed by Emiel [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] and Christina P. [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], contrary to our past belief that highly educated patients may find it easier to meet medical needs. A possible explanation is that highly educated patients are more knowledgeable about health care and have relatively higher medical needs. They may question doctors' recommendations and have difficulty finding medical services that match their health needs [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The probability of unmet medical needs of farmers is relatively low. China's rural workers mainly include farmers and migrant workers (workers who are registered in rural areas and engage in non-agricultural industries locally or work outside the home for 6 months or more). From the perspective of industries, the most affected by COVID-19 are service industries such as catering and construction, which are the main areas of employment for migrant workers[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. During the COVID-19 pandemic, due to restrictions on epidemic prevention measures, the risk of virus infection, and other reasons, migrant workers could not go out to work and lost their sources of income[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. This may lead to a reduction in medical expenditure to a certain extent, and the medical needs of migrant workers cannot be met. Residents with their spouses were less likely to have unmet medical needs. Spouses are one of the most essential providers of care and comfort for older people, and they will meet their health needs by giving companionship and care, reducing loneliness, and improving health [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUnmet medical needs are more common among urban residents with health insurance, which may reflect variations in care-seeking behavior. It may also be because insured residents have higher expectations for medical care, and due to the more severe epidemic outbreaks in Chinese cities [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], face-to-face medical care is significantly reduced, and limited medical services or telemedicine services cannot meet their medical needs [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The number of health technicians per 10,000 people is inversely linked to unmet medical needs in rural regions, which may be related to the status of medical resources in China. The abilities of rural health technicians are relatively weak, and the public lacks trust in the skills of grassroots doctors and the quality of diagnostic facilities. This has led many patients to turn to large hospitals for diagnosis and treatment, resulting in a small number of large hospitals being overwhelmed [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Moreover, front-line healthcare workers have a higher risk of infection, and illness and self-isolation of healthcare workers took them away from their jobs, resulting in a shortage of health human resources [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSelf-rated health status was another factor of unmet medical needs. The worse the self-rated health status, the more likely there is to be unmet medical needs, which is consistent with the results of most existing studies [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. People with poor self-rated health are more in need of medical care [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Still, they may avoid physical contact and reduce their frequency of seeking medical treatment due to concerns about the worsening disease caused by COVID-19 infection [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Patients with chronic diseases, as well as those with higher self-test scores for depression, have a higher chance of having unmet medical requirements. Some studies have attributed this to the unique needs of patients with chronic diseases that require additional medical equipment, drugs, diet control, exercise, etc. [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], which are to some extent limited by epidemic prevention measures [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. For example, restricted access to crowded places (such as medical institutions and gyms) makes achieving medical services and exercise opportunities more complex, perhaps leading to increased unmet medical needs. Research has shown that when individuals suffer from depression, their self-evaluation of their health status has a negative impact and can lead to somatization, making diagnosis and treatment difficult [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. At the same time, during the COVID-19 pandemic, the government canceled the opening of public places such as schools, restaurants, and sports venues to avoid the spread of the epidemic. Social activities of the public were reduced, leading to social anxiety related to COVID-19 and exacerbating symptoms of depression [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eKnowing the epidemic prevention measures indicates a strong awareness of prevention, and they may try to avoid public places with many people and maintain social distance from others, which reduces the possibility of going out to receive medical services. Telemedicine services have replaced some face-to-face medical services [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. However, telemedicine services have not been widely used due to limited technical level, doctor-patient communication barriers, data privacy security, and other reasons, and residents are still limited in receiving medical services that meet expectations [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Residence control will increase the probability of unmet medical needs, reducing the opportunities for residents to obtain paid employment, decreasing household income, and thus reducing necessary medical expenses [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Moreover, residency controls restrict residents' movement, meaning they are partly deprived of the medical services they need [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Research indicates that one of the primary causes of unmet medical needs is fear of COVID-19 [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. This situation mainly existed in chronic disease patients who believed they were at high risk of COVID-19 infection [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study has some limitations. First, self-reported data may contain biases and measurement errors, and there needs to be more self-perceived and actual unmet needs. Second, causality cannot be assumed in this study because it was a cross-sectional study design, and further longitudinal studies are needed to determine the direction of causality of these associations. Third, this study's results are limited to middle-aged and older Chinese adults and may need help to spread to other countries and people.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study analyzed the prevalence of unmet medical needs and its influencing factors during the COVID-19 pandemic among middle-aged and older adults in China. The results show that women, middle-aged people, non-agricultural workers, and individuals living alone were more likely to have unmet medical needs. In addition, factors such as poor self-rated health status, chronic conditions, depression, residence control, fear of the epidemic, and awareness of epidemic prevention measures may increase the probability of unmet medical needs. However, this study found that some factors that reduce unmet medical needs may promote the generation of unmet medical needs, including higher levels of education, access to medical insurance, and relatively abundant health human resources. Comprehending the elements that contribute to unmet medical needs during outbreaks of infectious diseases sheds light on policymakers and researchers. The present research suggests that vulnerable populations\u0026rsquo; unmet medical needs be prioritized and that suitable health legislation and infrastructure should be implemented to provide targeted help to these groups. In addition, it's critical to reasonably allocate medical resources between urban and rural areas, promote the rationalization of the structure of medical resources, reduce unmet medical needs, and thus enhance the overall health level of the population.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCOVID-19: Coronavirus Disease 2019; BMHSU: Behavioral Model of Health Services Use; CHARLS: China Health and Retirement Longitudinal Study Database; PPS: probability proportional to size; MICE: multiple imputation by chained equation; CESD: Central Depression Scale; UEMI: Urban Employee Medical Insurance; URMI: Urban and Rural Resident Medical Insurance\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article and its supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQuanzhou\u0026nbsp;\u0026ldquo;14th Five-Year Plan\u0026rdquo;\u0026nbsp;Healthcare Reform Programming, Grant Number: 2021B014\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYiping Zheng, Baoquan Zhang, Jin Wei, Dongyu Xue, Changle Li, and Yue Dai contributed to the conception and design of this study. Yiping Zheng, Dongyu Xue, and Jin Wei were involved in literature screening, data collection, and variables extraction. Yiping Zheng, Jin Wei, Baoquan Zhang, and Changle Li analyzed and interpreted the data. The manuscript was drafted by Yiping Zheng, Baoquan Zhang, Jin Wei, and Changle Li. Yue Dai provided financial support. The final version was reviewed and approved by all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eSchool of Health Management, Fujian Medical University, Fuzhou 350122, China. \u003csup\u003e2\u003c/sup\u003eFujian Provincial Maternity and Children\u0026rsquo;s Hospital, Fuzhou 350122, China\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKortrijk, H.E., A.M. Kamperman, and C.L. 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This cross-sectional study aimed to identify the factors associated with unmet medical needs among middle-aged and older adults in China during the COVID-19 Pandemic.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional analysis using data from the 2020 China Health and Retirement Longitudinal Study. The final sample consisted of 19,123 individuals. Multiple imputation was applied to handle missing values. A binary logistic regression was used to examine factors associated with unmet medical needs.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eDuring the COVID-19 pandemic, 11.18% of middle-aged and older adults did not receive needed medical care. In both urban and rural areas, middle-aged and older adults who were male, with higher educational attainment, rated poor health, suffering from chronic conditions, residing in a residential area completely shut, and often felt fears were more likely to lead to unmet medical needs. In urban areas, middle-aged and older adults with urban employee medical insurance (OR\u0026thinsp;=\u0026thinsp;2.30, 95% CI: 1.36, 3.56) and urban and rural resident medical insurance (OR\u0026thinsp;=\u0026thinsp;1.65, 95% CI: 0.96, 2.44) were more likely to have unmet medical needs. In rural areas, middle-aged and older adults over 75 years of age (OR\u0026thinsp;=\u0026thinsp;0.46, 95% CI: 0.35, 0.62) were less likely to have unmet medical needs, and middle-aged and older adults who knew the preventive measures (OR\u0026thinsp;=\u0026thinsp;2.16, 95% CI: 1.42, 3.29) had a higher probability of having unmet medical needs.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe findings indicate gender, age, educational attainment, occupation, living with a spouse, health insurance, number of health technicians per 10,000, self-rated health, chronic conditions, depression, knowing the preventive measures, and fear of pandemic associated with unmet medical needs. The unmet medical needs of vulnerable groups should receive priority attention in the future and facilitate rationalizing the allocation structure of medical resources.\u003c/p\u003e","manuscriptTitle":"Factors associated with unmet medical needs among middle-aged and older adults in China during COVID-19 Pandemic","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-22 09:36:14","doi":"10.21203/rs.3.rs-4775314/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-30T04:45:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-27T02:29:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-27T02:29:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-07-21T04:54:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cc353e2c-65d3-4ac0-bec3-8cb7d35eaf59","owner":[],"postedDate":"August 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-13T16:06:49+00:00","versionOfRecord":{"articleIdentity":"rs-4775314","link":"https://doi.org/10.1186/s12889-025-24699-5","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2025-10-09 15:57:47","publishedOnDateReadable":"October 9th, 2025"},"versionCreatedAt":"2024-08-22 09:36:14","video":"","vorDoi":"10.1186/s12889-025-24699-5","vorDoiUrl":"https://doi.org/10.1186/s12889-025-24699-5","workflowStages":[]},"version":"v1","identity":"rs-4775314","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4775314","identity":"rs-4775314","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}