ABSTRACT
Background
In the Chinese aging population characterized by rising chronic comorbidities, this study investigated under‐utilization of inpatient services (hospitalization) among elderly individuals with multimorbidities, aiming to elucidate its health impacts and contributing factors so as to inform policy.
Methods
A national household investigation was conducted using cross‐sectional and prospective methods. The study included 3636 adults aged ≥ 60 with multimorbidities from the China Health and Retirement Longitudinal Study (CHARLS 2015), with follow‐up health assessments in 2018. Cox regression was employed to analyze the health consequences of non‐hospitalization; logistic regression analysis identified contributing factors.
Results
Overall, 8.7% of elderly adults with multimorbidities declined hospitalization despite medical necessity. After adjusting for demographics and lifestyle factors, we found that this refusal was significantly linked to a deterioration in self‐rated health (hazard ratio [HR]: 1.273; 95% confidence interval [CI]: 1.091–1.484) and inability to work normally (HR: 1.244; 95% CI: 1.060–1.459) and inability to perform normal household tasks (HR: 1.579; 95% CI: 1.250–1.996) after 3 years. In the fully adjusted model, non‐hospitalization despite medical necessity remained a statistically significant risk factor for a decreased ability to perform normal household chores (HR: 1.403; 95% CI: 1.064–1.851), particularly affecting families with poor economic status (HR: 1.529; 95% CI: 1.003–2.332). Key independent factors for non‐hospitalization despite medical needs included physical dysfunctions (odds ratio [OR]: 2.646; 95% CI: 1.644–4.260), inadequacy of long‐term care (OR: 1.341; 95% CI: 1.006–1.787), being very dissatisfied with marriage (OR: 2.629; 95% CI: 1.137–6.079), and increased number of chronic multimorbidities (OR: 1.292; 95% CI: 1.190–1.402).
Conclusions
Inadequate inpatient service utilization among multimorbid elderly individuals exacerbates health risks, particularly for those with physical limitations, marital distress, care deficits, and high chronic disease burdens. Therefore, targeted interventions are urgently required.
Keywords: association analysis, health impact assessment, inpatient service under‐utilization, older adult multimorbidity
Our analysis of 3636 participants from the nationally representative CHARLS cohort revealed that 8.7% of older adults with multimorbidities declined hospitalization despite medical necessity. This under‐utilization was associated with significant long‐term health deterioration, including reduced self‐rated health, impaired work capacity, and diminished ability to perform household tasks.
Abbreviations
- CHARLS
China Health and Retirement Longitudinal Study
- CI
confidence interval
- HR
hazard ratio
- OR
odds ratio
- SRH
self‐rated health
1. Background
Although life expectancy has increased, multiple chronic conditions have emerged as a major challenge for older adults [1] and include asthma, cancer, cardiovascular disease, diabetes, musculoskeletal conditions, and stroke [2]. Multimorbidity refers to the coexistence of multiple chronic conditions in a single individual [3].
A systematic review has reported markedly elevated multimorbidity prevalence among Chinese older adults (≥ 32.4% in the elderly vs. 25.4% in adults) [4]. The escalating trend of multimorbidity poses a major challenge to healthcare policymaking and resource allocation decisions [5]. This condition is associated with functional status decline, poorer quality of life, polypharmacy, increased hospitalizations, increased mortality risk, greater healthcare utilization, and higher costs [6]. Aging is closely associated with an increased risk of multimorbidity [7], while multimorbidity increases the risk of hospitalization and hospital readmission among older adults [8]. The probability of hospitalization was two times, and that of hospital stay duration was 5.6 times higher among older adults with multimorbidity [9]. The underutilization of inpatient care by older adults with multimorbidity remains conspicuous.
Underutilization of health services refers to a failure to access an affordable health service that is highly likely to improve the quality or quantity of life [10, 11]. It is manifested behaviorally as not seeking medical care when feeling ill [12]. Data from the National Health Services Survey revealed that hospitalization rates increased significantly from 6.8% (2008) to 9.0% (2013). Meanwhile, although the proportion of adults who declined medically necessary hospitalization showed a gradual decline from 25.1% to 17.1%, the rate remained high [13]. Such non‐hospitalization rates pose significant challenges, as evidenced by studies linking them to adverse outcomes. For instance, one study found that uncontrolled asthma led to decreased productivity among working‐age individuals [14]. Among the elderly with chronic diseases requiring hospitalization within 1 year, 41.8% did not receive inpatient care [15]. According to China's Fifth National Health Service Survey, the 2‐week prevalence rate among residents was 24.1%, with urban and rural rates at 28.2% and 20.2%, respectively [16]. It has been reported that even when recommended by doctors, 18.2% of elderly migrants in China prefer not to be hospitalized [17].
Another study noted that numerous patients who initially deemed hospitalization unnecessary revised their opinions post‐discharge, acknowledging the need for inpatient treatment [18]. Declining hospitalization is often linked to interactions with the healthcare system [19]. Predictors of health services under‐utilization among patients with hypertension and diabetes include predisposing factors such as age, enabling factors such as income and a regular source of care, and need factors including self‐reported health scores [20]. Older age was shown to protect against under‐utilization of health services in 540 patients with hypertension in Dar es Salaam [21]. American women with diabetes were more prone to undertreatment compared with men [22], while financial constraints and reluctance influenced outpatient and inpatient service utilization among middle‐aged and elderly individuals [23].
From a macro perspective, “declined medically necessary hospitalization” is not an isolated individual choice, but has cascading, long‐term impacts on family finances and the public health system. For families, saving on short‐term health costs leads to a financial trap where “minor illnesses worsen into serious ones.” Combined with patient disability and lost income from family caregivers taking time off work, this creates a vicious cycle of “higher medical expenses and lower income,” [24]. For the public health system, it disrupts the “prevention‐treatment‐rehabilitation” cycle: a large number of non‐hospitalized chronic patients occupy primary care resources with recurring symptoms (causing “resource waste and worsening conditions”), while deteriorating cases flood secondary/tertiary hospitals as emergencies, occupying intensive care units and other critical resources and driving up public health costs [25].
Research focused specifically on Chinese older adults with multimorbidity remains scarce. This patient cohort encounters considerable barriers in accessing necessary hospitalizations. Hence, there is a pressing need for further research to enhance our understanding of health service under‐utilization among this vulnerable group. We aimed to address this knowledge gap by specifically delving into the factors impacting under‐utilization of inpatient services among Chinese older adults with multimorbidity. We employed cross‐sectional and prospective methodologies to gain a clearer picture of the complexities surrounding this particular demographic challenge.
2. Methods
2.1. Study Design and Participants
Older patients (aged ≥ 60 years) with chronic multimorbidities were enrolled in this study. Data were obtained from the 2015 China Health and Retirement Longitudinal Study (CHARLS). CHARLS is an ongoing health survey of the Chinese population conducted by the National School for Development (China Center for Economic Research), Peking University, which is a large‐scale, multistage, and nationally representative survey of 150 counties and 450 communities (villages) in 30 provincial‐level administrative divisions in China. The sample covered 23,000 respondents from 12,400 households in 2015 and was investigated face‐to‐face in their homes using questionnaires via computer‐assisted personal interview technology. All the participants signed an informed consent form. Ethical Approval for CHARLS data collection was obtained from the Biomedical Ethics Review Committee of Peking University (IRB00001052‐11015) [26].
CHARLS conducted a comprehensive study encompassing 14 chronic diseases prevalent among older adults [27], including hypertension, dyslipidemia, diabetes or elevated blood glucose, cancers or other malignant tumors, chronic lung diseases, liver diseases, heart diseases, stroke, kidney diseases, gastric or digestive diseases, emotional or mental disorders, memory‐related diseases, arthritis or rheumatism, and asthma. Multimorbidity, defined as the concurrent presence of two or more of these chronic diseases in an individual, was the focus of this study [2]. We analyzed 3636 participants aged ≥ 60 years out of 5977 individuals with multimorbidity. This sample size was sufficient for a thorough influencing factors analysis.
We also investigated the health‐related variables of participants analyzed in 2015 from CHARLS 2018, to analyze the health consequences 3 years after non‐hospitalization despite medical necessity.
2.2. Patient and Public Involvement
The participants were not involved in the development of the research questions or the design of this study. However, we disseminated the results of this analysis through public conferences, including summarized statements and open access to published reports.
2.3. Non‐Hospitalization Despite Medical Need
Drawing upon relevant definitions from studies in the Lancet [10, 11] that address under‐utilization of health services from the perspective of the patient, such as lack of access to healthcare and patients who do not seek medical resources or innovations, we defined “non‐hospitalization despite medical need” in this study as situations where individuals require hospitalization due to their physical health conditions but do not receive it due to various barriers [12].
The phenomenon of “non‐hospitalization despite medical need” was explored through the inquiry: “In the past year, did a doctor recommend that you required inpatient care but you declined hospitalization?” The responses were limited to “yes” or “no” [23]. Further, the underlying reasons were investigated using the question, “What was the primary reason for not pursuing inpatient care?” with options including “Not enough money,” “Not willing to go to the hospital,” “ Felt that the hospital was unlikely to cure the problem/hospital quality poor,” “Felt that care was unlikely to cure the problem due to its severity,” “No ward available,” and “Other” as possible answers.
2.4. Health Impact Indicators of Non‐Hospitalization Despite Medical Need
In the 2018 assessment, we examined the potential health consequences for patients who were not hospitalized despite requiring medical attention. To quantify these effects, we utilized four health indicators: death (yes or no), deterioration of self‐rated health (SRH) (yes or no), normal work capacity (yes or no), and normal household chore capability (yes or no). Fluctuations in SRH were clarified by asking the question “Compared to your health status during our previous assessment, would you say your health is now better, roughly the same, or worse?”, where “better now” and “about the same” were categorized as indicating no deterioration in SRH, whereas the response of “worse” was classified as representing a deterioration in SRH.
2.5. Covariate Assessment
Based on previous analyses, our study encompassed 23 pertinent covariates. These included: age, sex, marital status (married, divorced/widowed, single), living place (family, nursing facility, and others), smoking status (smoking, quitting smoking, never smoked), alcohol consumption (more than once a month, less than once a month, never consumed alcohol), social activity(inactive group, active group), sleep duration (hours) at night, Nap duration (minutes), depression, physical dysfunction(yes or no), disability (yes or no), feasibility of long term care (yes or no), satisfaction levels in various domains (medical, health, marriage, children, life), multimorbidity number, income, retirement pension, pension insurance, and medical insurance. All these variables were gathered using standardized questionnaires, in which the respondents completed the answers in accordance with their own situations.
The regional economic level is a binary variable determined based on the location of the survey participants, referencing the per capita gross domestic product level of each region in 2015 from the 2016 China Statistical Yearbook. Subsequently, we ranked the 28 survey sites according to their economic development level and divided them into two groups of 14 regions each. For social activity, the inactive group was defined as those with no reported physical activity, interaction with friends such as playing chess or cards, providing help to people who do not live with them, engaging in sports, belonging to a community‐related organization, charity work, caring for a sick or disabled adult, attending courses, or use of the internet in the previous month [2]. Depression was assessed using the Center of Epidemiology Studies Depression Scale, with a score of 10 or higher indicating depression [19]. Physical dysfunction was defined as individuals experiencing one or more difficulties in performing the following activities: jogging 1 km, walking 1 km, walking 100 m, sitting up for a long time, climbing stairs, bending or squatting, extending the upper arms, lifting items weighing 5 kg, and picking up a small coin from a table [28]. Disability was determined based on the ability of an individual to perform the following six tasks: dressing, bathing, eating, getting in and out of bed, going to the toilet, and controlling bowel movements. If there were difficulties in completing any of these tasks, the individual was considered to have a disability [29].
2.6. Statistical Analysis
In this study, summary statistics for continuous variables are presented as , and categorical variables are presented as numbers (percentage). To investigate the associations between the impact of declining hospitalization despite medical need on health outcomes, the following Cox proportional hazards regression models were applied. Model 1 adjusted for demographics (age, sex, marital status, living place), lifestyle factors (smoking status, alcohol consumption, social activity, sleep duration at night, and Nap duration). Model 2 extended Model 1 by incorporating health‐related variables (physical dysfunction, feasibility of long‐term care, disability, depression, and multimorbidity number). Model 3 extended Model 2 by the addition of satisfaction with medical care, satisfaction with health, satisfaction with marriage, satisfaction with children, satisfaction with life, retirement pension, pension insurance, and medical insurance. Model 4 took into account the impact of regional economic standards, extending Model 3 by the regional economic level. To identify the factors influencing inpatient service underutilization, a multivariate logistic regression analysis was performed, considering only variables that showed statistical significance in the univariate analysis. When analyzing variables with missing values, individuals containing the missing value for that specific variable were excluded from the analysis. However, for variables with a substantial proportion of missing values (such as “Normal household chores capability”) we applied multiple imputation methods to address the issue of missing data. All statistical analyses were performed using SPSS Statistics for Windows software, Version 20.0 (SPSS Inc., Chicago, USA), and two‐sided p values < 0.05 were considered statistically significant.
3. Results
A total of 3636 participants aged ≥ 60 years with multiple chronic conditions were included in the study. The age of participants ranged between 60 and 93 years, with a mean age of 68.75 ± 6.62 years. There were 1565 (43.0%) men and 2071 (57.0%) women in the study. Of all participants, 734 (20.2%) were hospitalized in the past year, of which 517 (70.4%) were hospitalized once, 161 (21.9%) were hospitalized twice, and 56 (7.7%) were hospitalized three or more times.
Further, 315 people (8.7%) were not hospitalized even when necessary; 314 of them stated the following reasons: 181 (57.6%) refused inpatient service due to financial problems, 61 (19.4%) were unwilling to go to the hospital, 17 (5.4%) were dissatisfied with the medical conditions in the hospital or found the hospitalization outcomes unsatisfactory, five (1.6%) claimed that their condition was so severe that hospitalization was useless, four (1.3%) indicated insufficient beds, and 46 (14.6%) claimed other reasons. The baseline characteristics of the study participants are presented in Table 1. Among the participants, significant differences in the situation of non‐hospitalization despite medical needs were observed among different characteristic groups: In terms of physical function, 10.1% of the participants with physical dysfunction did not get hospitalized when they should have, which was significantly higher than 3.6% of those without physical dysfunction (p < 0.001). Regarding chronic diseases, the median number of chronic diseases among participants who did not get hospitalized despite medical needs was 4, which was greater than the median value of 3 for those who were hospitalized as needed (p < 0.001). At the psychological health level, the median depression score of this group was 22, which was significantly higher than the median value of 18 among those who were hospitalized (p < 0.001). In terms of life satisfaction, participants who refused hospitalization when medically needed displayed lower satisfaction levels for medical care, health, marriage, and life compared with those who opted for hospitalization (p < 0.05).
Table 1.
Baseline characteristics of the study participants in 2015 and health impact indicators in 2018.
| Variables | n | Non‐hospitalization despite medical need | Statistic | p | |
|---|---|---|---|---|---|
| No | Yes | ||||
| Basic description in 2015 | |||||
| Agea | 3636 | 68.87 ± 6.67 | 67.45 ± 5.87 | 3.637 | < 0.001 |
| Sex | 3636 | 0.614 | 0.433 | ||
| Men | 1565 | 1436 (91.8) | 129 (8.2) | ||
| Women | 2071 | 1885 (91.0) | 186 (9.0) | ||
| Marital status | 3636 | 1.732 | 0.421 | ||
| Married | 2814 | 2561 (91.0) | 253 (9.0) | ||
| Divorced/widowed | 777 | 718 (92.4) | 59 (7.6) | ||
| Single | 45 | 42 (93.3) | 3 (6.7) | ||
| Living place | 3636 | 0.190 | 0.909 | ||
| Family | 3594 | 3283 (91.3) | 311 (8.7) | ||
| Nursing facility | 14 | 13 (92.9) | 1 (7.1) | ||
| Others | 28 | 25 (89.3) | 3 (10.7) | ||
| Smoking status | 3636 | 3.454 | 0.178 | ||
| Smoking | 832 | 772 (92.8) | 60 (7.2) | ||
| Quitting smoking | 601 | 551 (91.7) | 50 (8.3) | ||
| Never smoking | 2203 | 1998 (90.7) | 205 (9.3) | ||
| Alcohol consumption | 3632 | 1.189 | 0.552 | ||
| More than once a month | 756 | 697 (92.2) | 59 (7.8) | ||
| Less than once a month | 488 | 448 (91.8) | 40 (8.2) | ||
| Never drinking | 2388 | 2173 (91) | 215 (9) | ||
| Social activity | 3636 | 0.042 | 0.838 | ||
| Inactive group | 1873 | 1709 (91.2) | 164 (8.8) | ||
| Active group | 1763 | 1612 (91.4) | 151 (8.6) | ||
| Physical dysfunction | 3636 | 33.241 | < 0.001 | ||
| No | 803 | 774 (96.4) | 29 (3.6) | ||
| Yes | 2833 | 2547 (89.9) | 286 (10.1) | ||
| Disability | 3636 | 22.011 | < 0.001 | ||
| No | 2247 | 2091 (93.1) | 156 (6.9) | ||
| Yes | 1389 | 1230 (88.6) | 159 (11.4) | ||
| Sleep durationb | 3535 | 6 (5–8) | 6 (4–7) | 2.286 | 0.022 |
| Nap durationb | 3591 | 30 (0–60) | 30 (0–60) | 0.248 | 0.804 |
| Multimorbidity numberb | 3636 | 3 (2–4) | 4 (3–5) | 8.035 | < 0.001 |
| Depressionb | 3636 | 18 (14–23) | 22 (17–27) | 7.577 | < 0.001 |
| Incomeb | 3633 | 1500 (0–5000) | 2000 (0–5400) | 1.219 | 0.223 |
| Regional economic levels | 3636 | 1.065 | 0.302 | ||
| High level | 1309 | 1204 (92.0) | 105 (8.0) | ||
| Low level | 2327 | 2117 (91.0) | 210 (9.0) | ||
| Feasibility of long‐term care | 3378 | 18.378 | < 0.001 | ||
| Yes | 2126 | 1972 (92.8) | 154 (7.2) | ||
| No | 1252 | 1107 (88.4) | 145 (11.6) | ||
| Satisfaction with medical care | 3496 | 9.815 | 0.044 | ||
| Very satisfied | 577 | 538 (93.2) | 39 (6.8) | ||
| Somewhat satisfied | 937 | 864 (92.2) | 73 (7.8) | ||
| Neutral | 1191 | 1085 (91.1) | 106 (8.9) | ||
| Somewhat dissatisfied | 496 | 438 (88.3) | 58 (11.7) | ||
| Very dissatisfied | 295 | 266 (90.2) | 29 (9.8) | ||
| Satisfaction with health | 3569 | 39.942 | < 0.001 | ||
| Very satisfied | 98 | 93 (94.9) | 5 (5.1) | ||
| Somewhat satisfied | 597 | 558 (93.5) | 39 (6.5) | ||
| Neutral | 1623 | 1512 (93.2) | 111 (6.8) | ||
| Somewhat dissatisfied | 875 | 778 (88.9) | 97 (11.1) | ||
| Very dissatisfied | 376 | 318 (84.6) | 58 (15.4) | ||
| Satisfaction with marriage | 2766 | 16.513 | 0.002 | ||
| Very satisfied | 174 | 162 (93.1) | 12 (6.9) | ||
| Somewhat satisfied | 1055 | 970 (91.9) | 85 (8.1) | ||
| Neutral | 1216 | 1115 (91.7) | 101 (8.3) | ||
| Somewhat dissatisfied | 221 | 193 (87.3) | 28 (12.7) | ||
| Very dissatisfied | 100 | 82 (82) | 18 (18) | ||
| Satisfaction with children | 3507 | 7.000 | 0.136 | ||
| Very satisfied | 318 | 284 (89.3) | 34 (10.7) | ||
| Somewhat satisfied | 1651 | 1519 (92) | 132 (8) | ||
| Neutral | 1322 | 1212 (91.7) | 110 (8.3) | ||
| Somewhat dissatisfied | 160 | 139 (86.9) | 21 (13.1) | ||
| Very dissatisfied | 56 | 52 (92.9) | 4 (7.1) | ||
| Satisfaction with life | 3585 | 25.738 | < 0.001 | ||
| Very satisfied | 209 | 191 (91.4) | 18 (8.6) | ||
| Somewhat satisfied | 1180 | 1102 (93.4) | 78 (6.6) | ||
| Neutral | 1834 | 1673 (91.2) | 161 (8.8) | ||
| Somewhat dissatisfied | 274 | 232 (84.7) | 42 (15.3) | ||
| Very dissatisfied | 88 | 75 (85.2) | 13 (14.8) | ||
| Retirement pension | 3636 | 0.804 | 0.370 | ||
| No | 3525 | 3217 (91.3) | 308 (8.7) | ||
| Yes | 111 | 104 (93.7) | 7 (6.3) | ||
| Pension insurance | 3636 | 0.273 | 0.601 | ||
| No | 580 | 533 (91.9) | 47 (8.1) | ||
| Yes | 3056 | 2788 (91.2) | 268 (8.8) | ||
| Medical insurance | 3636 | 0.018 | 0.892 | ||
| No | 350 | 319 (91.1) | 31 (8.9) | ||
| Yes | 3286 | 3002 (91.4) | 284 (8.6) | ||
| Health impact indicators in 2018 | |||||
| Death | 3362 | 0.219 | 0.640 | ||
| No | 3144 | 2875 (93.5) | 269 (94.6) | ||
| Yes | 218 | 201 (6.5) | 17 (5.4) | ||
| Deterioration of self‐rated health (SRH) | 2947 | 23.243 | < 0.001 | ||
| No | 1133 | 1073 (39.8) | 60 (24.2) | ||
| Yes | 1814 | 1626 (60.2) | 188 (75.8) | ||
| Normal work capacity | 2949 | 12.982 | < 0.001 | ||
| Yes | 1164 | 1093 (40.5) | 71 (28.7) | ||
| No | 1785 | 1609 (59.5) | 176 (71.3) | ||
| Normal household chores capability | 3636 | 12.839 | < 0.001 | ||
| Yes | 2800 | 2583 (77.8) | 217 (68.9) | ||
| No | 836 | 738 (22.2) | 98 (31.1) | ||
Note: The statistic for categorical variables is the Chi‐square value, while for continuous variables, arepresents the t‐test and brepresents the rank‐sum test.
Abbreviation: SRH, self‐rated health.
Among the 3636 participants investigated for the year 2015 data, 3362 participants were followed up in 2018. Among the 3362 respondents, 218 (6.5%) died, with 191 missing data points/values. Among the 3144 survivors, self‐rated health (SRH) scores decreased in 1814 (57.7%) participants, and 197 (6.3%) had missing data points/values; 1785 (56.8%) could not work because of disability or health problems, and there were 195 (6.2%) participants with missing data points/values; 707 (22.5%) participants could not do house chores because of disability or health problems, and 1020 (32.4%) participants had missing data points/values (see Table 1). For participants who did not get hospitalized despite medical needs in 2015, the proportion with deteriorated SRH scores in 2018 was 59.68%, which was significantly higher (p < 0.001) compared with those who had normal hospitalization utilization (48.96%). Regarding participants who did not get hospitalized despite medical need in 2015, 55.87% had impaired working capacity and 25.71% exhibited deficient household chore capability in 2018; both values were significantly higher (p < 0.001) than those of the counterparts with normal hospitalization utilization (48.45% and 18.85%, respectively). After imputing missing values for the ability to carry out house chores, a sensitivity analysis revealed a consistent finding: among individuals who should have been hospitalized but did not get hospitalized, 47.9% (137 individuals) retained the ability to perform household chores 3 years later. In contrast, among those who opted for hospitalization when medically needed, 60.6% (1864 individuals) maintained their ability to perform normal household chores 3 years later, a statistically significant different outcome (Chi‐square: 17.506, p < 0.001).
3.1. Health Influence of Non‐Hospitalization Despite Medical Need
Considering the impact of household economic status, this study stratified participants based on the median value of their annual per capita household disposable income (income + earnings), which was set at 1500 yuan. The analysis investigated the effect of not getting hospitalized despite medical necessity on four health‐related variables: death, deterioration of SRH score, reduction in work capacity, and reduction in household chore capabilities, all examined in 2018. The results of the Cox regression analysis are presented in Table 2.
Table 2.
Health influence of non‐hospitalization despite medical need.
| Variables | Unadjusted | Model 1 | Model 2 | Model 3 | Model 4 | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| B | HR (95% CI) | B | HR (95% CI) | B | HR (95% CI) | B | HR (95% CI) | B | HR (95% CI) | |
| All participants | ||||||||||
| Death | −0.095 | 0.910 (0.554–1.492) | 0.162 | 1.175 (0.713–1.938) | −0.078 | 0.925 (0.540–1.583) | −0.220 | 0.803 (0.409–1.578) | −0.204 | 0.816 (0.415–1.602) |
| Deterioration of self‐rated health | 0.230 | 1.258 (1.082–1.463) | 0.241 | 1.273 (1.091–1.484) | 0.150 | 1.161 (0.987–1.366) | 0.149 | 1.161 (0.964–1.398) | 0.154 | 1.167 (0.969–1.405) |
| Normal work capacity | 0.179 | 1.197 (1.024–1.398) | 0.218 | 1.244 (1.060–1.459) | 0.066 | 1.068 (0.902–1.264) | 0.105 | 1.111 (0.917–1.345) | 0.110 | 1.116 (0.922–1.352) |
| Normal household chores capability | 0.423 | 1.526 (1.211–1.924) | 0.457 | 1.579 (1.250–1.996) | 0.268 | 1.307 (1.024–1.668) | 0.301 | 1.351 (1.023–1.782) | 0.339 | 1.403 (1.064–1.851) |
| Economic status is poor | ||||||||||
| Death | −0.043 | 0.958 (0.466–1.967) | 0.271 | 1.312 (0.634–2.714) | 0.309 | 1.362 (0.646–2.872) | 0.485 | 1.623 (0.620–4.254) | 0.263 | 1.301 (0.452–3.745) |
| Deterioration of self‐rated health | 0.269 | 1.308 (1.052–1.628) | 0.273 | 1.314 (1.047–1.649) | 0.191 | 1.21 (0.954–1.535) | 0.222 | 1.248 (0.947–1.645) | 0.230 | 1.259 (0.951–1.668) |
| Normal work capacity | 0.167 | 1.181 (0.940–1.484) | 0.182 | 1.199 (1.004–1.523) | 0.069 | 1.071 (0.833–1.379) | 0.113 | 1.119 (0.838–1.495) | 0.102 | 1.107 (0.825–1.487) |
| Normal household chores capability | 0.446 | 1.562 (1.102–2.214) | 0.447 | 1.563 (1.093–2.235) | 0.275 | 1.317 (0.907–1.911) | 0.453 | 1.574 (1.042–2.377) | 0.425 | 1.529 (1.003–2.332) |
| Economic status is good | ||||||||||
| Death | −0.136 | 0.873 (0.442–1.727) | 0.115 | 1.121 (0.562–2.238) | −0.413 | 0.661 (0.302–1.447) | −0.633 | 0.531 (0.209–1.352) | −0.441 | 0.643 (0.250–1.653) |
| Deterioration of self‐rated health | 0.205 | 1.228 (0.997–1.513) | 0.217 | 1.242 (1.007–1.533) | 0.117 | 1.124 (0.898–1.406) | 0.048 | 1.05 (0.823–1.339) | 0.087 | 1.091 (0.846–1.406) |
| Normal work capacity | 0.194 | 1.214 (0.980–1.504) | 0.243 | 1.275 (1.027–1.582) | 0.053 | 1.055 (0.839–1.326) | 0.087 | 1.091 (0.845–1.410) | 0.072 | 1.075 (0.830–1.392) |
| Normal household chores capability | 0.424 | 1.528 (1.121–2.083) | 0.461 | 1.586 (1.157–2.175) | 0.22 | 1.246 (0.893–1.739) | 0.216 | 1.241 (0.850–1.813) | 0.195 | 1.215 (0.826–1.789) |
Note: Values are regression coefficient or weighted hazard ratio (95% confidence interval). Model 1: adjusted for age, sex, marital status, living place, smoking status, alcohol consumption, social activity, sleep duration (hours) at night, and nap duration (minutes). Model 2: Model 1 + physical dysfunction, feasibility of long‐term care, disability, depression, and multimorbidity number. Model 3: Model 2 + satisfaction with medical care, health, marriage, children, life, retirement pension, pension insurance, and medical insurance. Model 4: Model 3 + regional economic level.
Abbreviations: B, regression coefficient; CI, confidence interval; HR, hazard ratio.
Either considering all survey participants or those groups that were stratified by economic status, refusal to get hospitalization did not exhibit a significant correlation with death, both in the unadjusted and the adjusted models. After adjusting for demographics and lifestyle factors, a significant association was observed between not getting hospitalized despite medical necessity and the deterioration of SRH score as well as a reduction in the ability to work normally and perform normal household tasks, regardless of whether individuals belonged to economically disadvantaged or economically advantaged groups. Among patients with poor economic status, the risk of SRH score decline among those who did not get hospitalized despite medical need was 1.314 times higher (HR: 1.314; 95% CI: 1.047–1.649) than among those who were appropriately hospitalized. Similarly, among patients with a good economic status, the risk of SRH from not getting hospitalized despite medical need was 1.242 times higher (HR: 1.242; 95% CI: 1.007–1.533) compared with those who got hospitalized when medically indicated. Regarding the ability to work normally, the risk of disability was 1.199 times higher (HR: 1.199; 95% CI: 1.004–1.523) for patients with poor economic status who should have been hospitalized but were not compared with those who were hospitalized appropriately. For patients with a good economic status, this risk was 1.275 times higher (HR: 1.275; 95% CI: 1.027–1.582). In terms of performing normal household tasks, the risk of disability was significantly elevated in patients who should have been hospitalized but were not. Specifically, for patients with poor economic status, this risk was 1.563 times higher (HR: 1.563; 95% CI: 1.093–2.235), whereas for patients with good economic status, it was 1.586 times higher (HR: 1.586; 95% CI: 1.157–2.175) compared with those who underwent hospitalization as recommended. After considering variables like physical function, chronic disease burden, satisfaction ratings, and insurance coverage, the statistical significance of the correlations between not being hospitalized despite medical necessity and both SRH score deterioration and reduction in the ability to work normally underwent decline.
It is noteworthy that after adjusting for demographic factors, lifestyle factors, satisfaction levels, retirement pension status, pension insurance, and regional economic level, non‐hospitalization despite medical need remained a statistically significant risk factor for a lowered ability to perform normal household chores (HR: 1.403; 95% CI: 1.064–1.851). This effect was particularly pronounced in families with poor economic status (HR: 1.529; 95% CI: 1.003–2.332), whereas it did not reach statistical significance in families with better economic status (HR: 1.215; 95% CI: 0.826–1.789). After imputing the missing values for household chores ability and conducting a sensitivity analysis, no statistical difference was observed in the impact of non‐hospitalization despite medical need on household chore capability in the fully adjusted model (Model 4) for all participants (B = 0.392, HR: 1.480; 95% CI: 0.881–2.488) and those with high economic income levels (B = 0.352, HR: 1.423; 95% CI: 0.678–2.984). However, for those with low economic income levels, the fully adjusted model still indicated that non‐hospitalization despite medical need was a risk factor for reduced household chores capability (B = 1.107, HR: 3.024; 95% CI: 1.030–8.877).
3.2. Analysis of Factors Associated With Non‐Hospitalization Despite Medical Need
Potential influencing factors, including age, sex, marital status, living place, smoking status, alcohol consumption, social activity, sleep duration (hours) at night, Nap duration (minutes), physical dysfunction, feasibility of long‐term care, disability, depression, multimorbidity number, satisfaction with medical care, satisfaction with health, satisfaction with marriage, satisfaction with children, satisfaction with life, retirement pension, pension insurance, medical insurance, income, and regional economic levels were comprehensively considered to analyze the correlates of non‐hospitalization despite medical need.
After applying the forward conditional logistic regression method, we identified physical dysfunction, feasibility of long‐term care, number of multimorbidities, and satisfaction with marriage as independent factors associated with non‐hospitalization despite medical need (Table 3). Elderly individuals with physical dysfunctions and lacking the feasibility of long‐term care were 2.646 and 1.341 times more likely to refuse inpatient services than those without physical dysfunction and with the feasibility of long‐term care. Those who were very dissatisfied with their marital status were 2.629 times more likely to refuse hospitalization than those who were satisfied with their marriage. A higher number of chronic conditions correlated with an increased risk of non‐hospitalization, with each additional comorbidity increasing the non‐hospitalization rate by 29.2%.
Table 3.
Association analysis of variables for participants who did not get hospitalized despite medical need.
| Variables | B | p | OR | 95% CI |
|---|---|---|---|---|
| Physical dysfunction | 0.973 | < 0.001 | 2.646 | 1.644–4.260 |
| Feasibility of long‐term care | 0.293 | 0.046 | 1.341 | 1.006–1.787 |
| Multimorbidity number | 0.256 | < 0.001 | 1.292 | 1.19–1.402 |
| Satisfaction with marriage | 0.031 | |||
| Somewhat satisfied | 0.071 | 0.835 | 1.073 | 0.552–2.088 |
| Neutral | 0.140 | 0.678 | 1.150 | 0.595–2.222 |
| Somewhat dissatisfied | 0.486 | 0.207 | 1.625 | 0.765–3.454 |
| Very dissatisfied | 0.967 | 0.024 | 2.629 | 1.137–6.079 |
Abbreviations: B, regression coefficient; CI, confidence interval; OR, odds ratio.
4. Discussion
We employed rigorous methodology and data from CHARLS to investigate inpatient service utilization status among elderly Chinese with multimorbidity and the variables contributing to under‐utilization of these services by this patient population. First, we utilized a probability proportionate‐to‐size sampling method to recruit participants across China and minimize selection bias. Second, we incorporated 24 influencing factors into our analysis to improve the explanatory power and relative accuracy of our regression models.
The present study observed that “declined hospitalization despite medical necessity” was a manifestation of the under‐utilization of inpatient health services. Notably, 57.6% of older adults refused hospitalization due to financial constraints. This finding highlights the significant impact of economic factors on the under‐utilization of inpatient health services, which is consistent with previous studies reporting that household income is an important contributor to inequalities in health service utilization [30]. However, among older adults with multimorbidities, our study found no statistically significant association between income level and refusal of hospitalization, a result that differs from those reported in studies of the general population [31]. Comorbidities have been reported to markedly contribute to pro‐poor inequality in health services utilization [32]. We found that 8.7% of Chinese older adults with multimorbidity were not hospitalized despite their medical needs. The proportion of patients with comorbidities who should be hospitalized, but are not, is lower than that in other populations. One study indicated that the proportion of non‐hospitalization among home‐dwelling, disabled, and older adults in China was 11.83% [33]. Further, according to the Fifth National Health Service Survey, the overall proportion of Chinese residents who needed but were not hospitalized was 17.1%, with urban rates of 20.8% in the central region, 17.5% in the western region, and 13.7% in the eastern region, and rural rates of 19.0% in the western region, 16.6% in the central region, and 13.0% in the eastern region [16]; notably, the proportion was markedly higher among vulnerable groups, with 27.6% of rural older adults refusing hospitalization [34]. The slightly lower proportion of non‐hospitalization among older adults with chronic diseases compared with the general population can be partly attributed to non‐comparable definitions and inconsistent disease lists. This, to a certain extent, suggests that multimorbidity in this group may result in more severe disability and physical decline, thereby having direct implications on their healthcare utilization [35]. Older adults with multimorbidity face a high risk of adverse health outcomes, particularly those who refrain from hospitalization despite medical necessity [36]. Our study revealed that death was not significantly associated with refusing hospitalization and may be attributed to two factors: first, Chinese emergency green channels ensure prompt hospitalization in emergencies; second, for patients with less severe, electively treatable conditions, short‐term delays in hospitalization do not pose a threat to life in the majority of cases. In the fully adjusted model, declining hospitalization despite medical necessity among elderly patients with multimorbidity was significantly associated with deterioration of self‐rated health and inability to perform household tasks after 3 years when adjusted for basic information and lifestyle factors.
We identified physical dysfunction, lack of access to long‐term care, marital dissatisfaction, and the number of chronic comorbidities as variables influencing non‐hospitalization among Chinese older adults. Older adults with physical dysfunction had a 2.646‐fold increased likelihood of refusing hospitalization compared with those without such limitations. Moreover, those without long‐term care support faced a greater risk of not being hospitalized. Additionally, we observed that older adults dissatisfied with their marriage had a 2.629‐fold greater chance of avoiding hospitalization than those satisfied with their marriage. A previous study concurred with our findings, showing that good marital status was positively associated with higher utilization of community health services [37]. Further, a higher multimorbid burden and greater dissatisfaction with marital status were significantly associated with higher non‐hospitalization rates in older adults. This finding reveals that the underutilization of inpatient services among the elderly is not driven by a single factor, but rather a convergence of multidimensional issues, including physical limitations, lack of emotional support, and accumulated disease burden, which provide a precise population profile for the future development of targeted intervention strategies. Therapeutic education should be strengthened for these individuals to increase their acceptance of hospitalization.
Although this study used a representative sample of the Chinese population, it has some limitations. First, the dependent variable in this study relied on self‐reported and recalled data spanning 1 year, introducing potential recall and information biases. Second, participants may have underreported certain chronic conditions, particularly those related to psychiatric or neurocognitive disorders. Third, although we incorporated 14 self‐reported chronic diseases, this approach may still be prone to information errors [2].
5. Conclusions
The under‐utilization of inpatient services by elderly individuals with multimorbidities is risky for future SRH and housework capabilities. The elderly with physical dysfunction, reduced SRH status, marital dissatisfaction, high chronic disease burden, and lack of long‐term care should be prioritized to enhance therapeutic education and hospitalization acceptance. Inpatient service under‐utilization among elderly Chinese with multimorbidity can guide future policymaking and healthcare service delivery.
Author Contributions
Yunlian Xue: conceptualization (lead), methodology (lead), formal analysis (lead), project administration (lead), writing – original draft (lead), writing – review and editing (lead), funding acquisition (lead). Zhuomin Huang: data curation (lead), writing – review and editing (supporting). Jinqi Ye: conceptualization (supporting), writing – original draft (supporting). Guihao Liu: conceptualization (lead), methodology (lead), project administration (lead), writing – review and editing (lead), funding acquisition (lead).
Ethics Statement
The medical ethics committee of Guangdong Provincial People's Hospital approved the study. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. Ethics approval for data was obtained from the Biomedical Ethics Review Committee of Guangdong Provincial People's Hospital (KY2024‐367‐02).
Consent
All the participants signed an informed consent form. Ethical Approval for CHARLS data collection was obtained from the Biomedical Ethics Review Committee of Peking University (IRB00001052‐11015).
Conflicts of Interest
The authors declare no conflicts of interest.
Acknowledgments
We are very grateful to the participants and staff of the China Health and Retirement Longitudinal Study (CHARLS) team for their valuable contributions.
Data Availability Statement
The datasets used or analyzed during the current study are available upon reasonable request from the corresponding author.
References
- 1. eBioMedicine, “Multimorbidity: A Complex Challenge to Support and Optimise Individual Health,” EBioMedicine 99 (2024): 104973, 10.1016/j.ebiom.2024.104973. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Skou S. T., Mair F. S., Fortin M., et al., “Multimorbidity,” Nature Reviews Disease Primers 8, no. 1 (2022): 48, 10.1038/s41572-022-00376-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Liu G., Xue Y., Liu Y., Wang S., and Geng Q., “Multimorbidity in Cardiovascular Disease and Association With Life Satisfaction: A Chinese National Cross‐Sectional Study,” BMJ Open 10, no. 12 (2020): e042950, 10.1136/bmjopen-2020-042950. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Hu Y., Wang Z., He H., Pan L., Tu J., and Shan G., “Prevalence and Patterns of Multimorbidity in China During 2002–2022: A Systematic Review and Meta‐Analysis,” Ageing Research Reviews 93 (2024): 102165, 10.1016/j.arr.2023.102165. [DOI] [PubMed] [Google Scholar]
- 5. Stanley J., Semper K., Millar E., and Sarfati D., “Epidemiology of Multimorbidity in New Zealand: A Cross‐Sectional Study Using National‐Level Hospital and Pharmaceutical Data,” BMJ Open 8, no. 5 (2018): e021689, 10.1136/bmjopen-2018-021689. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Barnett K., Mercer S. W., Norbury M., Watt G., Wyke S., and Guthrie B., “Epidemiology of Multimorbidity and Implications for Health Care, Research, and Medical Education: A Cross‐Sectional Study,” Lancet 380, no. 9836 (2012): 37–43, 10.1016/S0140-6736(12)60240-2. [DOI] [PubMed] [Google Scholar]
- 7. Beard J. R., Officer A., de Carvalho I. A., et al., “The World Report on Ageing and Health: A Policy Framework for Healthy Ageing,” Lancet 387, no. 10033 (2016): 2145–2154, 10.1016/S0140-6736(15)00516-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Rodrigues L. P., de Oliveira Rezende A. T., Delpino F. M., et al., “Association Between Multimorbidity and Hospitalization in Older Adults: Systematic Review and Meta‐Analysis,” Age and Ageing 51, no. 7 (2022): afac155, 10.1093/ageing/afac155. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Bähler C., Huber C. A., Brüngger B., and Reich O., “Multimorbidity, Health Care Utilization and Costs in An Elderly Community‐Dwelling Population: A Claims Data Based Observational Study,” BMC Health Services Research 15, no. 1 (2015): 23, 10.1186/s12913-015-0698-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Elshaug A. G., Rosenthal M. B., Lavis J. N., et al., “Levers for Addressing Medical Underuse and Overuse: Achieving High‐Value Health Care,” Lancet 390, no. 10090 (2017): 191–202, 10.1016/S0140-6736(16)32586-7. [DOI] [PubMed] [Google Scholar]
- 11. Glasziou P., Straus S., Brownlee S., et al., “Evidence for Underuse of Effective Medical Services Around the World,” Lancet 390, no. 10090 (2017): 169–177, 10.1016/S0140-6736(16)30946-1. [DOI] [PubMed] [Google Scholar]
- 12. Taber J. M., Leyva B., and Persoskie A., “Why Do People Avoid Medical Care? A Qualitative Study Using National Data,” Journal of General Internal Medicine 30, no. 3 (2015): 290–297, 10.1007/s11606-014-3089-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Xuefei G., “ Said That China Is Moving Forward Towards a More Mature Universal Medical Security System ,” (National Health Commission of the People's Republic of China, published March 6, 2017), https://www.nhc.gov.cn/xcs/2017mtbd/201703/2309ea14581740998e2a98efb4979a25.shtml. [Google Scholar]
- 14. Williams S. A., Wagner S., Kannan H., and Bolge S. C., “The Association Between Asthma Control and Health Care Utilization, Work Productivity Loss and Health‐Related Quality of Life,” Journal of Occupational & Environmental Medicine 51, no. 7 (2009): 780–785, 10.1097/JOM.0b013e3181abb019. [DOI] [PubMed] [Google Scholar]
- 15. He L. F., Liao S. M., and Zheng Y. R., “Prevalence of Chronic Diseases and the Status of Health Care Service Utilization Among the Elderly With Chronic Diseases,” Journal of Nursing Science 23, no. 7 (2008): 78–80, 10.3969/j.issn.1001-4152.2008.07.038. [DOI] [Google Scholar]
- 16.“An Analysis Report of National Health Services Survey in China, 2013,” Center for Health Statistics and Information, NHFPC, published October 26, 2016, https://www.nhc.gov.cn/mohwsbwstjxxzx/s8211/201610/9fdb5a2e63ac40a08f211a5926396206.shtml.
- 17. Zhang X., Yu B., He T., and Wang P., “Status and Determinants of Health Services Utilization Among Elderly Migrants in China,” Global Health Research and Policy 3, no. 1 (2018): 8, 10.1186/s41256-018-0064-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Gardner W., Lidz C. W., Hoge S. K., et al., “Patients' Revisions of Their Beliefs About the Need for Hospitalization,” American Journal of Psychiatry 156, no. 9 (1999): 1385–1391, 10.1176/ajp.156.9.1385. [DOI] [PubMed] [Google Scholar]
- 19. Barry P. P., Crescenzi C. A., Radovsky L., Kern D. C., and Steel K., “Why Elderly Patients Refuse Hospitalization,” Journal of the American Geriatrics Society 36, no. 5 (1988): 419–424, 10.1111/j.1532-5415.1988.tb02380.x. [DOI] [PubMed] [Google Scholar]
- 20. Hu H., Jian W., Fu H., Zhang H., Pan J., and Yip W., “Health Service Underutilization and Its Associated Factors for Chronic Diseases Patients in Poverty‐Stricken Areas in China: A Multilevel Analysis,” BMC Health Services Research 21, no. 1 (2021): 707, 10.1186/s12913-021-06725-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Bovet P., Gervasoni J.‐P., Mkamba M., Balampama M., Lengeler C., and Paccaud F., “Low Utilization of Health Care Services Following Screening for Hypertension in Dar Es Salaam (Tanzania): A Prospective Population‐Based Study,” BMC Public Health 8, no. 1 (2008): 407, 10.1186/1471-2458-8-407. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Newman J. D., Berger J. S., and Ladapo J. A., “Underuse of Medications and Lifestyle Counseling to Prevent Cardiovascular Disease in Patients With Diabetes,” Diabetes Care 42, no. 5 (2019): e75–e76, 10.2337/dc18-1554. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Zeng Z. C., Deng Q. W., Lu J. H., Zheng Y. H., and Liu W. B., “Comparative Study on the Influencing Factors of Outpatient and Inpatient Service Utilization of Middle‐Aged and Elderly People,” Journal of Nanjing Medical University (Social Sciences) 21, no. 2 (2021): 165–170, 10.7655/NYDXBSS20210213. [DOI] [Google Scholar]
- 24. Wisk L. E. and Witt W. P., “Predictors of Delayed or Forgone Needed Health Care for Families With Children,” Pediatrics 130, no. 6 (2012): 1027–1037, 10.1542/peds.2012-0668. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Reddy K. P., Mehta S., Eberly L. A., et al., “Delayed Or Forgone Medical Care Associated With Increased Resource Utilization and Health Care Expenditures Among Patients With Peripheral Artery Disease in the United States,” Journal of Vascular Surgery 81, no. 5 (2025): 1172–1182.e5, 10.1016/j.jvs.2024.12.132. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Liu G., Xue Y., Wang S., Zhang Y., and Geng Q., “Association Between Hand Grip Strength and Stroke in China: A Prospective Cohort Study,” Aging 13, no. 6 (2021): 8204–8213, 10.18632/aging.202630. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Yang G., Wang Y., Zeng Y., et al., “Rapid Health Transition in China, 1990 –2010: Findings From the Global Burden of Disease Study 2010,” Lancet 381, no. 9882 (2013): 1987–2015, 10.1016/S0140-6736(13)61097-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Yan Y., Du Y., Li X., Ping W., and Chang Y., “Physical Function, ADL, and Depressive Symptoms in Chinese Elderly: Evidence From the CHARLS,” Frontiers in Public Health 11 (2023): 1017689, 10.3389/fpubh.2023.1017689. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Yang F., Hao X., Bo T., and Sun J., “Analysis on Status Quo and Influencing Factors of Elderly Disability in China‐An Empirical Analysis Using CHARLS Data,” Health Economics Research 23, no. 11 (2016): 7–10, 10.1405/j.cnki.33-1056/f.20161103.019. [DOI] [Google Scholar]
- 30. Guo B., Xie X., Wu Q., et al., “Inequality in the Health Services Utilization in Rural and Urban China: A Horizontal Inequality Analysis,” Medicine 99, no. 2 (2020): e18625, 10.1097/MD.0000000000018625. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Wang N., Shi S. H., Fan J. X., and Zhu Y. J., “Unused Inpatient Service and Its Influencing Factors of Residents in Henan Province,” Journal of Zhengzhou University (Medical Sciences) 51, no. 3 (2016): 368–372, 10.13705/j.issn.1671-6825.2016.03.021. [DOI] [Google Scholar]
- 32. Crespo‐Cebada E. and Urbanos‐Garrido R. M., “Equity and Equality in the Use of Gp Services for Elderly People: The Spanish Case,” Health Policy 104, no. 2 (2012): 193–199, 10.1016/j.healthpol.2011.10.007. [DOI] [PubMed] [Google Scholar]
- 33. Liu J., Dai T., and Huang J., “Analysis of the Medical Service Needs and Utilization of Disabled Elderly at Home in China From 2011 to 2015,” Chinese Journal of Public Health 34, no. 5 (2018): 687–689, 10.11847/zgggws1118582. [DOI] [Google Scholar]
- 34. Gu Y., Liu P., Wang Y., et al., “Health Status and Healthcare Service Needs for the Rural Elderly Population in Northern Jiangsu,” Chinese Journal of Geriatrics 37, no. 6 (2018): 697–701, 10.3760/cma.j.issn.0254-9026.2018.06.022. [DOI] [Google Scholar]
- 35. Tyagi S., Koh V., Koh G. C. H., Low L. L., and Lee E. S., “Defining and Measuring Multimorbidity in Primary Care in Singapore: Results of an Online Delphi Study,” PLoS One 17, no. 12 (2022): e0278559, 10.1371/journal.pone.0278559. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Yuan Y., Li J., Fu P., Zhou C., and Li S., “Association Between Frailty and Inpatient Services Utilization Among Older Adults in Rural China: The Mediating Role of Multimorbidity,” Frontiers in Medicine 9 (2022): 818482, 10.3389/fmed.2022.818482. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37. Peng Q., Xue Z., and Cao X. O., “Investigate on the Factors Which Influence Community Health Services Utilization and Awareness of Rural Residents in Nanhai District of Foshan City,” Chinese and Foreign Medical Research 13, no. 36 (2015): 68–71, 10.14033/j.cnki.cfmr.2015.36.031. [DOI] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used or analyzed during the current study are available upon reasonable request from the corresponding author.