Published on in Vol 9 (2023)

Preprints (earlier versions) of this paper are available at, first published .
Association of Catastrophic Health Expenditure With the Risk of Depression in Chinese Adults: Population-Based Cohort Study

Association of Catastrophic Health Expenditure With the Risk of Depression in Chinese Adults: Population-Based Cohort Study

Association of Catastrophic Health Expenditure With the Risk of Depression in Chinese Adults: Population-Based Cohort Study

Original Paper

1Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China

2Vanke School of Public Health, Tsinghua University, Beijing, China

3Institute for Healthy China, Tsinghua University, Beijing, China

4Key Laboratory of Epidemiology of Major Diseases, Peking University, Ministry of Education, Beijing, China

5Institute for Global Health and Development, Peking University, Beijing, China

6Peking University Health Science Center-Weifang Joint Research Center for Maternal and Child Health, Peking University, Beijing, China

Corresponding Author:

Min Liu, PhD

Department of Epidemiology and Biostatistics

School of Public Health

Peking University

No. 38 Xueyuan Road

Haidian District

Beijing, 100191


Phone: 86 10 8 2805146

Fax:86 10 8 2805146


Background: Depression is one of the most common mental illnesses, and it may have a lasting effect on one’s whole life. As a form of financial hardship, catastrophic health expenditure (CHE) may be associated with depression. However, current evidence about the relationship between CHE and the risk of depression is insufficient.

Objective: This study aimed to explore the relationship between CHE and the risk of depression among Chinese adults.

Methods: In this study, we used 3 waves of the China Family Panel Studies (CFPS) from 2012, 2016, and 2018. The CFPS are a nationally representative study covering 25 of 31 provinces in Chinese mainland and representing nearly 94.5% of the total population. We selected eligible household heads as participants, divided them into 2 groups by CHE events at baseline (exposed group: with CHE; unexposed group: without CHE), and followed them up. Households with CHE were defined as having out-of-pocket medical expenditures exceeding 40% of the total household nonfood expenditure, and people with depression were identified by the 8-item Centre for Epidemiological Studies Depression Scale (CES-D). We first described the baseline characteristics and used logistical regression to estimate their effects on CHE events. Then, we used Cox proportional hazard models to estimate adjusted hazard ratios and 95% CIs of depression among participants with CHE compared with those without CHE. Finally, we analyzed the subgroup difference in the association between CHE and depression.

Results: Of a total of 13,315 households, 9629 were eligible for analysis. Among them, 6824 (70.9%) were men. The mean age was 50.15 (SD 12.84) years. Only 987 (10.3%) participants had no medical insurance. The prevalence of CHE at baseline was 12.9% (1393/9629). Participants with a higher family economic level (adjusted odds ratio [aOR] 1.15, 95% CI 1.02-1.31) and with the highest socioeconomic development level (aOR 1.18, 95% CI 1.04-1.34) had a higher prevalence of CHE than reference groups. During a median of 71 (IQR 69-72) person-months of follow-up, the depression incidence of participants with CHE (1.41 per 1000 person-months) was higher than those without CHE (0.73 per 1000 person-months). Multivariable models revealed that the adjusted hazard ratio for the incidence of depression in participants with CHE was 1.33 (95% CI 1.08-1.64), and this association appeared to be greater in participants without outpatient services (for interaction, P=.048).

Conclusions: CHE was significantly associated with increased risk of depression among Chinese adults. Concentrated work should be done to monitor CHE, and more efforts to ensure financial protection need to be made to prevent depression, especially for people with high health care needs.

JMIR Public Health Surveill 2023;9:e42469



There is a strong bidirectional linkage between health and poverty. To cut off this linkage, the United Nation’s Sustainable Development Goals include target 3.8, which aims to achieve universal health coverage (UHC) by 2030 [1]. UHC means that all people can receive the health services they need without experiencing financial hardship [1]. According to the World Health Organization, globally in 2017, almost 1.4 billion people experienced financial hardship due to out-of-pocket (OOP) health payments, among whom nearly 1 billion people were pushed into extreme poverty [2]. Therefore, there is still a long way to go to achieve UHC goals by 2030 considering the additional impact of COVID-19.

One of the 2 essential parts of UHC is providing financial protection for people to pay for health services [3], and the government of China has made great progress on this front. For example, as early as 2009, China reformed a series of health care reforms including three national basic medical insurance programs: (1) Urban Employee Basic Medical Insurance (UEBMI) designed for employed urban residents; (2) the Urban Resident Basic Medical Insurance (URBMI) covering the unemployed, retired, older adults, students, and children in urban areas; and (3) the New Rural Cooperative Medical Scheme (NRCMS) for rural residents [4]. Currently, China’s national basic insurance programs cover over 1.35 billion people, about 97% of the total population [5]. Additionally, to eliminate poverty, the Decision on Winning the Battle Against Poverty policy proposed by China in 2015 achieved substantial results in education, primary medical care, and the basic living needs of people living in poverty. In fact, in 2020, the government of China announced that all low-income counties in China had been lifted out of poverty [6]. Benefitting from policies and measures on financial protection of health, the incidence of catastrophic health expenditure (CHE) in China declined from 14.7% in 2010 to 8.7% in 2018 [7]. However, there are still a few people encountering financial hardship, and the impact on mental and physical health caused by CHE is still worth studying.

Depression is a prevalent mental illness globally that contributes to the global burden of diseases as the leading mental cause of mortality for all ages [8]. Once depression appears, it can have a lasting and profound influence on one’s whole life. The direct outcome of depression is a poor quality of life [9]. A study conducted on patients with schizophrenia indicated that depression has a strong negative effect on all 8 domains of subjective quality of life [10]. Furthermore, a meta-analysis showed that compared to controls, patients with depression had significant moderate cognitive deficits in executive function, memory, and attention (Cohen d effect sizes ranging from −0.34 to −0.65) [11]. Moreover, depression is a negative factor in cardiovascular disease (CVD) incidence, severity, and outcomes [12]. Rajan et al [13] found that depression was associated with CVD incidence (hazard ratio [HR] 1.14, 95% CI 1.05-1.24) and myocardial infarction (HR 1.14, 95% CI 1.05-1.24). Meng et al [14] also found that depression was associated with a higher risk of CVD mortality (HR 1.22, 95% CI 1.04-1.44). Therefore, the prevention and management of depression is a crucial and urgent public health issue.

A series of social, psychological, and biological factors and their complex interactions can play a role in depression occurrence [15,16]. Some study results posit that lower social support, a lower socioeconomic position, and economic difficulties are associated with a higher risk of depression [17,18], and the association is stronger between financial hardship and depression than other socioeconomic variables [19]. CHE, in theory, may have an impact on the mental health of family members because of reduced necessary expenditures. Nevertheless, current studies mostly focus on the likelihood of CHE events among people with depression, not the impact of CHE on depression occurrence [20,21]. In this study, we used 3 waves (2012, 2016, and 2018) of nationally representative data from the China Family Panel Studies (CFPS) to analyze the association of CHE with the risk of depression.

Study Design and Participants

Data in this study were obtained from CFPS, which is almost a nationally representative longitudinal study covering 25 of 31 provinces/municipalities in Chinese mainland (not including Xinjiang, Tibet, Inner Mongolia, Ningxia autonomous region, and Qinghai and Hainan provinces), representing nearly 94.5% of the total population in the Chinese mainland [22]. The CFPS were implemented by the Institute of Social Science Survey of Peking University to collect individual-, household-, and community-level data every 2 years. A baseline survey was conducted in 2010, and follow-up data from 2012, 2014, 2016, and 2018 were available for download from the official CFPS website [23].

As the information on depression in some of the CFPS waves was deficient, we used data from 2012, 2016, and 2018. The survey in 2012 included 35,720 adults (aged ≥16 years) and 13,315 households with valid interview responses. Individuals who had missing data at baseline (n=505), had depression at baseline (n=448), were lost to follow-up (n=2462), and had missing data of depression at the end of follow-up (n=271) were excluded. Finally, a total of 9629 households (household heads) were included in this study (Figure 1).

Figure 1. Flowchart of participants selected from China Family Panel Studies (CFPS). CHE: catastrophic health expenditure.

Ethics Approval

The CFPS, which involved human participants, were approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052-14010). All written informed consent was provided by participants aged over 15 years or their parents (for those aged 15 years and under). The participants’ personal information and privacy were strictly protected by the CFPS according to the rules set by Peking University’s Biomedical Ethics Review Committee.

Measurements of Catastrophic Health Expenditure

The measurement of catastrophic health expenditure (CHE) was based on households, where a household member was defined by marriage, blood, or adoptive relationship, as well as an ongoing economic tie [24]. The head of the household was identified as the key decision-maker when the household faced important matters and decisions. Household OOP health payments were measured as the medical expenditure of all family members excluding reimbursed expenses in the past 12 months. The yearly household food expenditure was estimated as the monthly meal expenses multiplied by 12, and the total household expenditure in the past 12 months was calculated as the sum of monthly daily expenditures (food, daily used commodities and necessities, transportation, etc) multiplied by 12 plus yearly special expenditures (electricity, medical care, clothing, etc).

Households who experienced CHE were defined as those with OOP medical expenditures exceeding 40% of the household’s capacity to pay (calculated as total household expenditure minus household food expenditure) [25]. CHE prevalence at baseline was measured as the percentage of the number of household heads incurring CHE to total participants. The formula was:

where N is the total number of participants, and CHEi is 1 when the ith household incurred CHE and 0 otherwise.

Measurements of Depression

Depression was measured by the 8-item Center for Epidemiological Studies Depression Scale (CES-D), which is a shortened version of the original 20-item CES-D including 8 depressive symptoms (Table S1 in Multimedia Appendix 1). CES-D was not designed as a diagnostic tool but is widely used to identify individuals at high risk of depression in general populations and various subpopulations [26]. The score of each item in the 20-item CES-D ranges from 0 to 3 according to the frequency response of depressive symptoms in the past week, with 0 indicating rarely or none of the time (<1 day), 1 indicating some or a little of the time (1-2 days), 2 indicating occasionally or a moderate amount of time (3-4 days), and 3 indicating most or all of the time (5-7 days) [26]. The commonly used 8-item CES-D asked respondents whether they experienced any of the 8 depressive symptoms “most of time during the last week” [27], which was scored according to the answer (yes=1; no=0). The 8-item CES-D used in CFPS included the same depressive symptoms but was scored by frequency response. As “most or all of the time” in frequency response is equal to the “yes” answer to “most of time last week,” we transferred the 4-score level of frequency response to the yes/no response [28]. Hence, the score of 8-item CES-D ranged from 0-8, and a total score ≥3 was deemed to indicate depression [28]. As the 8-item CES-D cannot diagnose depressive disorders, people with an 8-item CES-D score ≥3 in our study were identified as having depressive symptoms [29].


Covariates in this study included (1) demographic characteristics: gender (male, female), age group (16-39, 40-49, 50-49, ≥60 years), marital status (married/partnered, other), education (no or some formal education, primary school, middle school, high school and above), and insurance (without any insurance, UEBMI, URBMI, NRCMS, other); (2) health-related characteristics: self-reported health (good, medium, poor), chronic diseases (yes, no), BMI (normal, lower, overweight, obese), outpatient services (yes, no), inpatient services (yes, no), current smoking (yes, no), and drinking (yes, no); and (3) socioeconomic characteristics: residence (urban, rural), family economic level (lowest, lower, higher, highest), family size (1-2, 3-4, ≥5), and socioeconomic development level (lowest, lower, higher, highest ).

The family economic level was classified by the quartiles of household annual income, which was converted into 2012 US dollars based on purchasing power parities published by the World Bank [30] (lowest: <$4775.56, lower: $4775.56 to <$9831.46, higher: $9831.46 to <$17173.60, highest: ≥$17173.60). Numerous studies have reported that nighttime light intensity is highly associated with socioeconomic development [31,32]. Therefore, the socioeconomic development level in this study was identified by the quartiles of provinces’ mean nighttime light intensity in 2012 (lowest: <0.089, lower: 0.089-0.31, higher: 0.32-0.68, highest: ≥0.69), which was obtained from the Harvard Dataverse [33].

Statistical Analyses

Baseline characteristics of the participants were presented as mean (SD) for continuous variables or frequencies and percentages for categorical variables. The Pearson χ2 test was used to compare distributions of CHE according to different baseline characteristics. A multivariate logistical regression model was used to analyze the determinants of CHE.

We calculated the incidence rates (number of events divided by accumulated person-month) and used the univariate and multivariate Cox proportional hazard models to estimate the HRs and 95% CIs of depression among participants with CHE compared with those without CHE. First, the model adjusted demographic characteristics in multivariate model 1, including gender, age group, marital status, education, and insurance. Next, the model adjusted health-related factors in model 2 based on model 1, including self-reported health, chronic diseases, BMI, outpatient services, inpatient services, current smoking, and current drinking. Finally, the model adjusted socioeconomic factors in model 3 based on model 2, including residence, family economic level, family size, and socioeconomic development level.

To examine the robustness of our findings, we conducted 3 sensitivity analyses. First, we defined households with CHE as those having OOP medical expenditure exceeding 25% of the total household expenditure according to the World Bank. [25] Second, we transferred the categorical variables of age group and family economic level into continuous variables and conducted the same analysis in the final model. Third, we changed the variable nighttime light intensity into the gross regional product (GRP, divided into 4 groups based on the quartiles of 2012 per capita GRP [34]) to indicate the socioeconomic development level in the final model.

Finally, the analysis was stratified by gender, age group, insurance, chronic diseases, self-reported health, outpatient services, inpatient services, residence, socioeconomic development level, and family economic level, and a multiplicative interaction term was included in the final model with the stratification variable removed for subgroup analysis.

All data were analyzed in R software (version 4.2.1; R Foundation for Statistical Computing). A 2-sided P value <.05 was considered to be significant.

Baseline Characteristics

A total of 9629 participants were enrolled in this study, of which 6824 (70.9%) were male. The mean age was 50.15 (SD 12.84) years. Among the participants, 89.5% (8622/9629) were married or partnered. Only 987 (10.3%) participants had no medical insurance, and 1341 (13.9%), 603 (6.3%), and 6317 (65.6%) participants had UEBMI, URBMI, and NRCMS, respectively (Table 1).

Table 1. Distribution of CHEa by baseline characteristics.
CharacteristicsTotal (N=9629)Nonexposed groupb (n=8236), n (%)Exposed groupc (n=1393), n (%)Chi-square (df)P value
Demographic characteristics

Gender3.6 (1).06

Male68245867 (86)957 (14)

Female28052369 (84.5)436 (15.5)

Age group (years)346.3 (3)<.001

16-3919311753 (90.8)178 (9.2)

40-4930162750 (91.2)266 (8.8)

50-5923201969 (84.9)351 (15.1)

≥6023621764 (74.7)598 (25.3)

Marital status9.1 (1).003

Married/partnered86227407 (85.9)1215 (14.1)

Other1007829 (82.3)178 (17.7)

Education97.3 (3)<.001

No or some formal education25062005 (80)501 (20)

Primary school23221980 (85.3)342 (14.7)

Middle school27892475 (88.7)314 (11.3)

High school and above20121776 (88.3)236 (11.7)

Insurance5.5 (4).24

None987856 (86.7)131 (13.3)

UEBMId13411169 (87.2)172 (12.8)

URBMIe603511 (84.7)92 (15.3)

NRCMSf63175378 (85.1)939 (14.9)

Other381322 (84.5)59 (15.5)

Health-related characteristics

Self-reported health245.5 (2)<.001

Good58745241 (89.2)633 (10.8)

Medium20011692 (84.6)309 (15.4)

Poor17541303 (74.3)451 (25.7)

Currently smoking12.6 (1)<.001

No53554519 (84.4)836 (15.6)

Yes42743717 (87)557 (13)

Drinking16.4 (1)<.001

No72826168 (84.7)1114 (15.3)

Yes23472068 (88.1)279 (11.9)

Chronic diseases104 (1)<.001

No82687195 (87)1073 (13)

Yes13611041 (76.5)320 (23.5)

BMI25.8 (3)<.001

Normal55964776 (85.3)820 (14.7)

Lower711569 (80)142 (20)

Overweight26582301 (86.6)357 (13.4)

Obese664590 (88.9)74 (11.1)

Outpatient services101.8 (1)<.001

No75776624 (87.4)953 (12.6)

Yes20521612 (78.6)440 (21.4)

Inpatient services164.4 (1)<.001

No87477644 (87.4)1103 (12.6)

Yes882592 (67.1)290 (32.9)

Socioeconomic characteristics

Residence11.7 (1)<.001

Urban43153750 (86.9)565 (13.1)

Rural53144486 (84.4)828 (15.6)

Family economic level55.8 (3)<.001

Lowest29882439 (81.6)549 (18.4)

Lower23362021 (86.5)315 (13.5)

Higher23662084 (88.1)282 (11.9)

Highest19391692 (87.3)247 (12.7)

Family size151.5 (2)<.001

1-221581677 (77.7)481 (22.3)

3-443023833 (89.1)469 (10.9)

≥531692726 (86)443 (14)

Socioeconomic development level6.4 (3).09

Lowest24912158 (86.6)333 (13.4)

Lower14921258 (84.3)234 (15.7)

Higher37263199 (85.9)527 (14.1)

Highest19201621 (84.4)299 (15.6)

aCHE: catastrophic health expenditure.

bPeople without CHE.

cPeople with CHE.

dUEBMI: Urban Employee Basic Medical Insurance.

eURBMI: Urban Resident Basic Medical Insurance.

fNRCMS: New Rural Cooperative Medical Scheme.

CHE Prevalence

At baseline, the prevalence of CHE was 12.9% (1393/9629) among the participants. Except for gender, insurance, and socioeconomic development level, the distribution of baseline characteristics was significantly different between households with CHE and those without CHE (Table 1). The logistical regression analysis revealed that participants with poor (adjusted odds ratio [aOR] 1.64, 95% CI 1.39-1.94) and medium self-reported health (aOR 1.17, 95% CI 1-1.37), chronic diseases (aOR 1.30, 95% CI 1.11-1.52), outpatient services (aOR 1.16, 95% CI 1-1.35) and inpatient services (aOR 2.40, 95% CI 2.03-2.84), rural residence (aOR 1.21, 95% CI 1.05-1.39), family size ≥5 people (aOR 1.22, 95% CI 1.09-1.36), higher family economic level (aOR 1.15, 95% CI 1.02-1.31), and the highest socioeconomic development level (aOR 1.18, 95% CI 1.04-1.34) had a higher prevalence of CHE than reference groups (Table S2 in Multimedia Appendix 1).

Risk of Depression

During a median of 71 (interquartile range: 69-72) person-months of follow-up, 532 (5.53%) of 9629 participants developed depression, of which 403 (75.75%) cases had no CHE and 129 (24.25%) cases had CHE. The incidence rate of depression among participants without and with CHE was 0.73 and 1.41 per 1000 person-months, respectively (Table 2). In the unadjusted analysis, participants who had CHE at baseline were associated with a 99% higher risk of depression (crude HR 1.99, 95% CI 1.63-2.42; Table 2). All multivariable–adjusted analyses showed a significant association of CHE with the risk of depression (Table S3 in Multimedia Appendix 1). In the fully adjusted model, participants with CHE had a 33% increased risk of developing depression compared to those without CHE (adjusted HR [aHR] 1.33, 95% CI 1.08-1.64; Table 2).

In the sensitivity analyses, the association between CHE and risk of depression was stable when we (1) defined CHE as OOP medical expenditure exceeding 25% of the total household expenditure, (2) transferred the categorical variables age group and family economic level to continuous variables, and (3) changed the variable nightlight time intensity into GRP to indicate socioeconomic development level in the final model (Table S4 in Multimedia Appendix 1).

Table 2. Association of CHEa with risk of depression in the univariate and multivariate Cox proportional hazard models.
CharacteristicsOutcome (N=532), n (%)Incidence (per 1000 person-months)Univariate modelMultivariate modelb

cHRc (95% CI)P valueaHRd (95% CI)P value

Without403 (75.8)0.73Reference

With129 (24.2)1.411.99 (1.63-2.42)<.0011.33 (1.08-1.64).008

Male318 (59.8)0.70Reference

Female214 (40.2)1.151.69 (1.42-2.01)<.0011.51 (1.19-1.91).001
Age group (years)

16-3950 (9.4)0.39Reference

40-49144 (27.1)0.712.39 (1.93-2.97)<.0011.61 (1.27-2.06)<.001

50-59154 (28.9)0.990.84 (0.69-1.02).080.78 (0.64-0.96).02

≥60184 (34.6)1.201.03 (0.87-1.22).700.97 (0.82-1.15).74
Marital status

Married/partnered433 (81.4)0.75Reference

Other99 (18.6)1.542.22 (1.78-2.76)<.0011.34 (1.05-1.70).02

No or some formal education255 (47.9)1.56Reference

Primary school121 (22.7)0.780.36 (0.29-0.44)<.0010.63 (0.50-0.80)<.001

Middle school100 (18.8)0.531.27 (1.04-1.54).021.18 (0.96-1.44).12

High school and above56 (10.5)0.420.95 (0.79-1.15).611.01 (0.83-1.22).95

None71 (13.3)1.11Reference

UEBMIe35 (6.6)0.390.35 (0.23-0.52)<.0010.52 (0.34-0.79).002

URBMIf23 (4.3)0.570.51 (0.32-0.81).0040.53 (0.33-0.85).009

NRCMSg394 (74.1)0.930.82 (0.64-1.06).130.68 (0.52-0.89).004

Other9 (1.7)0.350.30 (0.15-0.61).0010.43 (0.21-0.88).02
Self-reported health

Good201 (37.8)0.51Reference

Medium110 (20.7)0.831.61 (1.28-2.03)<.0011.32 (1.04-1.68).02

Poor221 (41.5)1.933.89 (3.21-4.71)<.0012.10 (1.67-2.64)<.001
Currently smoking

No314 (59.0)0.88Reference

Yes218 (41.0)0.770.86 (0.72-1.02).081.14 (0.92-1.42).24

No435 (81.8)0.90Reference

Yes97 (18.2)0.620.67 (0.54-0.84).0010.88 (0.69-1.11).29
Chronic diseases

No419 (78.8)0.76Reference

Yes113 (21.2)1.251.62 (1.31-1.99)<.0011.17 (0.94-1.46).15

Normal311 (58.5)0.84Reference

Lower65 (12.2)1.411.75 (1.34-2.28)<.0011.13 (0.86-1.48).40

Overweight118 (22.2)0.660.79 (0.64-0.98).030.92 (0.74-1.13).42

Obese38 (7.1)0.851.05 (0.75-1.47).781.13 (0.80-1.58).50
Outpatient services

No326 (61.3)0.64Reference

Yes206 (38.7)1.522.36 (1.98-2.81)<.0011.41 (1.16-1.72).001
Inpatient services

No477 (89.7)0.82Reference

Yes55 (10.3)0.951.18 (0.89-1.56).240.70 (0.52-0.94).02

Urban173 (32.5)0.61Reference

Rural359 (67.5)1.011.65 (1.37-1.98)<.0011.23 (1-1.52).06
Family economic level

Lowest240 (45.1)1.21Reference

Lower109 (20.5)0.700.55 (0.46-0.67)<.0010.75 (0.61-0.93).008

Higher116 (21.8)0.741.11 (0.92-1.34).281.03 (0.85-1.24).79

Highest67 (12.6)0.520.79 (0.66-0.96).020.83 (0.69-1).049
Family size

1-2165 (31)1.17Reference

3-4200 (37.6)0.690.72 (0.62-0.84)<.0010.96 (0.81-1.15).69

≥5167 (31.4)0.791.34 (1.16-1.55)<.0011.02 (0.88-1.19).78
Socioeconomic development level

Lowest174 (32.7)1.05Reference

Lower92 (17.3)0.930.69 (0.57-0.83).0011.01 (0.78-1.32).92

Higher186 (35.0)0.750.92 (0.77-1.11).390.86 (0.69-1.06).16

Highest80 (15.0)0.631.01 (0.84-1.20).940.73 (0.55-0.97).03

aCHE: catastrophic health expenditure.

bAdjusted for demographic characteristics (gender, age group, education, marital status, and insurance), health-related characteristics (self-reported health, smoking status, drinking, chronic disease, BMI, and outpatient and inpatient services), and socioeconomic characteristics (residence, family economic level, family size, and socioeconomic development level).

ccHR: crude hazard ratio.

daHR: adjusted hazard ratio.

eUEBMI: Urban Employee Basic Medical Insurance.

fURBMI: Urban Resident Basic Medical Insurance.

gNRCMS: New Rural Cooperative Medical Scheme.

Subgroup Analyses

In the subgroup analyses, the association of CHE with the risk of depression did not appear to be modified by most selected baseline characteristics, such as gender, age group, insurance, or chronic diseases (Figure 2). However, the association between CHE and the risk of depression was found to be somewhat greater among participants without outpatient services (for interaction, P=.048). The risk of depression was higher among participants with CHE who did not have outpatient services (aHR 1.57, 95% CI 1.10-2.25) than those who had outpatient services (aHR 1.06, 95% CI 0.76-1.48).

Figure 2. Association of catastrophic health expenditure (CHE) with risk of depression in univariate and multivariate Cox proportional hazard models. Superscripted a refers to people with CHE. Superscripted b refers to people without CHE. aHR: adjusted hazard ratio; NRCMS: New Rural Cooperative Medical Scheme; UEBMI: Urban Employee Basic Medical Insurance; URBMI: Urban Resident Basic Medical Insurance.

CHE can introduce great financial pressure to a household, as household members may have to borrow money, use savings, and even sell assets [35], which may lead to a longtime negative effect on their mental health [36]. To our knowledge, this is the first cohort study on the association between CHE and the risk of depression. We found that health-related characteristics (chronic diseases, self-reported health, and outpatient and inpatient services) and socioeconomic characteristics (residence, socioeconomic development level, family size, and family economic level) had an impact on CHE. We also found that prior CHE events were strongly associated with depression experienced by household heads and that there was an interaction between CHE and outpatient services for depression.

Our results revealed that people with chronic diseases, medium and poor self-reported health, outpatient services and inpatient services, rural, family size ≥5 people, and the highest socioeconomic development level had a higher prevalence of CHE, and these findings are consistent with those of other studies [7,37]. Poor health directly leads to a higher frequency of accessing health services, further leads to a heavy financial burden of medical expenditure, and finally, leads to CHE. Nighttime light intensity is an indicator of socioeconomic development, and areas with higher economic growth may have access to diagnostic technology for most illnesses. In addition, people in areas undergoing rapid socioeconomic development may pay more attention to the early detection of diseases and other preventive health care services [38]. Consequently, health-related characteristics and socioeconomic characteristics were the main factors behind CHE.

The results of our study revealed that people with CHE had a 33% higher risk of developing depression (aHR 1.33, 95% CI 1.08-1.64) than people without CHE. Socioeconomic variables, especially financial hardship, have a great influence on depression [39]. As a form of financial hardship, CHE plays a role in depression incidence. Once a household incurred CHE, family members had to take various measures to cope with financial loss, which could take a long time to recover [35]. Given this, household heads had a higher risk of developing depression with CHE than those without CHE under financial pressure.

As CHE could trigger depression, which may further lead to adverse outcomes [40,41], it is crucial to eliminate CHE to prevent depression. The first step to eliminating CHE is based on reducing OOP health expenditures. Because poor health is associated with an increased need and use of outpatient and inpatient care, and this negative correlation can be intensified by the number of chronic diseases [37], our results suggest that relevant departments should continue to expand health promotion, especially for chronic diseases. Additionally, governments should provide sufficient and targeted financial protection, including medical insurance and poverty subsidies, to reduce OOP payments and mitigate impoverishment caused by poor health. Most studies on CHE are interested in subpopulations that have specialized diseases like cancers, HIV, and tuberculosis [42-44]. The common features of these diseases are long duration, which may even coexist with people and cause a higher risk of CHE. To decrease individual OOP health expenditure, inclined policies and measures should be introduced for these diseases. For example, to relieve the financial burden faced by people living with chronic diseases, China launched catastrophic medical insurance (critical illness insurance) in 2012 and implemented it nationwide in 2016 after city-based testing, with the aim of reimbursing patients whose OOP health expenditure exceeded a predetermined basic medical insurance level [45]. In addition, for eligible people facing unaffordable medical expenses, China provided medical assistance and even offered treatment for free or at a reduced price for some priority diseases [46]. Of course, these policies are mostly aimed at financially vulnerable people. However, our study found that people with a higher family economic level and the highest socioeconomic development level were more likely to occur CHE, which may be explained by the concentrated distribution and utilization of health services. [47] In view of this, more financial protection policies should be implemented for the whole population regardless of their economic capacity. Additionally, as the current health care delivery in China is still fragmented and treatment-based [48], with increasing incidence of age-related diseases, integrated establishment and improvement of primary health services should be intensified in health-related policies.

To cut off the linkage between CHE and depression, the second point is to halt the progression to depression. As the evidence shows in studies on financial hardship and depression, not all people experiencing financial hardship will develop depression [39]. These differing responses to CHE may be explained by various socioeconomic and psychological variables in the stress process model, such as social support, self-esteem, personal agency, and personal ability to manage difficulties [39,49,50]. Therefore, measures to prevent and regulate depression should be extended widely. Additionally, timely financial assistance should be implemented for vulnerable populations, such as people with chronic diseases and households under the minimum living guarantee.

In this study, we found that the risk of depression was higher among participants with CHE who did not have outpatient services (aHR 1.57, 95% CI 1.10-2.25) than those who had outpatient services (aHR 1.06, 95% CI 0.76-1.48). In CFPS, the variable “outpatient services” was measured by whether participants had used outpatient services in the past 2 weeks when they felt uncomfortable. Those without outpatient services included people who did not feel uncomfortable and who felt uncomfortable but did not use health services. The latter group may have a lower socioeconomic status. Moreover, heads of households with lower social support, a lower socioeconomic position, and economic difficulties were more likely to experience depression due to life pressures [17,18]. Consequently, not accessing outpatient services intensified the association between CHE and the risk of depression.

There are several limitations to our study. First, because the 8-item CES-D is not equal to clinical diagnosis, our study could only estimate the association between CHE and the risk of depressive symptoms, which calls for future research on CHE and depressive disorders. Second, due to the limitation of the original data, households with CHE did not include those facing extreme poverty who could not seek health services and whose household medical expenditure was zero. Third, expenditure data for calculating CHE were mainly based on the participants’ memory, which may be prone to recall bias.

In conclusion, people with poor health and a higher socioeconomic position had a higher prevalence of CHE and CHE was significantly associated with a higher risk of depression. To prevent depression induced by CHE, concentrated work should be made to monitor CHE, and more efforts to ensure financial protection need to be introduced and strengthened, especially for people with higher health care needs.


We appreciate the China Family Panel Studies (CFPS) for conducting the total survey and providing data. We also thank all the interviewers, respondents, and volunteers in the survey. Moreover, we acknowledge the helpful advice of the editors and reviewers. This study was supported by the National Natural Science Foundation of China (72122001, 71934002).

Data Availability

Data from all are available to download from the official China Family Panel Studies (CFPS) website.

Authors' Contributions

JL conceptualized and designed the study. YW carried out the literature search, data analysis, and interpretation; compiled the tables and figures; and drafted the manuscript. WL, ML, and JL wrote, reviewed, and edited the manuscript. All authors read and approved the final manuscript.

Conflicts of Interest

None declared.

Multimedia Appendix 1

Additional tables.

DOCX File , 41 KB

  1. Universal health coverage. World Health Organization. URL: [accessed 2022-08-06]
  2. Tracking universal health coverage: global monitoring report. World Health Organization and World Bank. URL: https:/​/cdn.​​media/​docs/​default-source/​world-health-data-platform/​events/​tracking-universal-health-coverage-2021-global-monitoring-report_uhc-day.​pdf?sfvrsn=fd5c65c6_5&download=true [accessed 2022-08-06]
  3. Universal health coverage (UHC): fact sheets. World Health Organization. URL: [accessed 2022-08-06]
  4. Sun J, Lyu S. The effect of medical insurance on catastrophic health expenditure: evidence from China. Cost Eff Resour Alloc 2020 Feb 27;18:10 [] [CrossRef] [Medline]
  5. Yin W. To accelerate the development of people’s health and promote the innovation of medical insurance system. People's Daily. URL: [accessed 2022-08-06]
  6. Poverty alleviation: China's experience and contribution. The State Council Information Office of the People's Republic of China. URL: [accessed 2022-08-07]
  7. Liu C, Liu Z, Nicholas S, Wang J. Trends and determinants of catastrophic health expenditure in China 2010-2018: a national panel data analysis. BMC Health Serv Res 2021 May 29;21(1):526 [] [CrossRef] [Medline]
  8. GBD 2019 DiseasesInjuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 2020 Oct 17;396(10258):1204-1222 [] [CrossRef] [Medline]
  9. Ruo B, Rumsfeld JS, Hlatky MA, Liu H, Browner WS, Whooley MA. Depressive symptoms and health-related quality of life: the Heart and Soul Study. JAMA 2003 Jul 9;290(2):215-221 [] [CrossRef] [Medline]
  10. Reine G, Lançon C, Di Tucci S, Sapin C, Auquier P. Depression and subjective quality of life in chronic phase schizophrenic patients. Acta Psychiatr Scand 2003 Oct;108(4):297-303 [CrossRef] [Medline]
  11. Rock PL, Roiser JP, Riedel WJ, Blackwell AD. Cognitive impairment in depression: a systematic review and meta-analysis. Psychol Med 2014 Jul;44(10):2029-2040 [CrossRef] [Medline]
  12. Elderon L, Whooley MA. Depression and cardiovascular disease. Prog Cardiovasc Dis 2013 May;55(6):511-523 [CrossRef] [Medline]
  13. Rajan S, McKee M, Rangarajan S, Bangdiwala S, Rosengren A, Gupta R, Prospective Urban Rural Epidemiology (PURE) Study Investigators. Association of symptoms of depression with cardiovascular disease and mortality in low-, middle-, and high-income countries. JAMA Psychiatry 2020 Oct 01;77(10):1052-1063 [] [CrossRef] [Medline]
  14. Meng R, Yu C, Liu N, He M, Lv J, Guo Y, China Kadoorie Biobank Collaborative Group. association of depression with all-cause and cardiovascular disease mortality among adults in China. JAMA Netw Open 2020 Feb 05;3(2):e1921043 [] [CrossRef] [Medline]
  15. Heim C, Binder EB. Current research trends in early life stress and depression: review of human studies on sensitive periods, gene-environment interactions, and epigenetics. Exp Neurol 2012 Jan;233(1):102-111 [CrossRef] [Medline]
  16. Malhi GS, Mann JJ. Depression. Lancet 2018 Nov;392(10161):2299-2312 [CrossRef]
  17. Langer, Crockett MA, Bravo-Contreras M, Carrillo-Naipayan C, Chaura-Marió M, Gómez-Curumilla B, et al. Social and Economic Factors Associated With Subthreshold and Major Depressive Episode in University Students During the COVID-19 Pandemic. Front Public Health 2022;10:893483 [] [CrossRef] [Medline]
  18. Wei D, Au Yeung SL, He J, Xiao W, Lu J, Tu S, et al. The role of social support in family socio-economic disparities in depressive symptoms during early pregnancy: Evidence from a Chinese birth cohort. J Affect Disord 2018 Oct 01;238:418-423 [CrossRef] [Medline]
  19. Butterworth P, Olesen SC, Leach LS. The role of hardship in the association between socio-economic position and depression. Aust N Z J Psychiatry 2012 Apr 05;46(4):364-373 [CrossRef] [Medline]
  20. Hailemichael Y, Hanlon C, Tirfessa K, Docrat S, Alem A, Medhin G, et al. Catastrophic health expenditure and impoverishment in households of persons with depression: a cross-sectional, comparative study in rural Ethiopia. BMC Public Health 2019 Jul 11;19(1):930 [] [CrossRef] [Medline]
  21. Yan R, Li L, Duan X, Zhao J. Association of depressive symptoms with health service use and catastrophic health expenditure among middle-aged and older Chinese adults: analysis of population-based panel data. J Am Med Dir Assoc 2023 May;24(5):664-671.e7 [CrossRef] [Medline]
  22. Xie Y, Lu P. The sampling design of the China Family Panel Studies (CFPS). Chin J Sociol 2015 Dec;1(4):471-484 [] [CrossRef] [Medline]
  23. China Family Panel Studies. URL: [accessed 2022-08-06]
  24. Xie Y, Hu J. An Introduction to the China Family Panel Studies (CFPS). Chinese Sociological Review Sep 2014;47(1):3-29 [CrossRef]
  25. Cylus J, Thomson S, Evetovits T. Catastrophic health spending in Europe: equity and policy implications of different calculation methods. Bull World Health Organ 2018 Sep 01;96(9):599-609 [] [CrossRef] [Medline]
  26. Smarr KL, Keefer AL. Measures of depression and depressive symptoms. Arthritis Care Res (Hoboken) 2020 Oct;72 Suppl 10:608-629 [CrossRef] [Medline]
  27. Turvey CL, Wallace RB, Herzog R. A revised CES-D measure of depressive symptoms and a DSM-based measure of major depressive episodes in the elderly. Int Psychogeriatr 1999 Jun;11(2):139-148 [CrossRef] [Medline]
  28. Wallace R, Herzog A, Ofstedal M, Steffick D, Fonda S, Langa K. Documentation of affective functioning measures in the health and retirement study. HRS Online. URL: [accessed 2022-08-05]
  29. Litwin H. The association between social network relationships and depressive symptoms among older Americans: what matters most? Int Psychogeriatr 2011 Aug;23(6):930-940 [CrossRef] [Medline]
  30. PPP conversion factor, GDP (LCU per international $) - China. World Bank. URL: [accessed 2023-05-20]
  31. Beyer RCM, Franco-Bedoya S, Galdo V. Examining the economic impact of COVID-19 in India through daily electricity consumption and nighttime light intensity. World Dev 2021 Apr;140:105287 [] [CrossRef] [Medline]
  32. Zhang G, Guo X, Li D, Jiang B. Evaluating the potential of LJ1-01 nighttime light data for modeling socio-economic parameters. Sensors (Basel) 2019 Mar 26;19(6) [] [CrossRef] [Medline]
  33. Chen Z, Yu B, Yang C, Zhou Y, Yao S, Qian X. An extended time-series (2000-2018) of global NPP-VIIRS-like nighttime light data. Harvard Dataverse. 2020. URL: [accessed 2022-08-04]
  34. National Bureau of Statistics of the People's Republic of China. China Statistical Yearbook 2012. Beijing, China: China Statistical Publishing House; 2013:57
  35. Kasahun GG, Gebretekle GB, Hailemichael Y, Woldemariam AA, Fenta TG. Catastrophic healthcare expenditure and coping strategies among patients attending cancer treatment services in Addis Ababa, Ethiopia. BMC Public Health 2020 Jun 22;20(1):984 [] [CrossRef] [Medline]
  36. Kiely KM, Leach LS, Olesen SC, Butterworth P. How financial hardship is associated with the onset of mental health problems over time. Soc Psychiatry Psychiatr Epidemiol 2015 Jun 17;50(6):909-918 [CrossRef] [Medline]
  37. Zhao Y, Atun R, Oldenburg B, McPake B, Tang S, Mercer SW, et al. Physical multimorbidity, health service use, and catastrophic health expenditure by socioeconomic groups in China: an analysis of population-based panel data. Lancet Glob Health 2020 Jun;8(6):e840-e849 [CrossRef]
  38. Tang K, Zhang Y, Wang H, Tan SH, Bai L, Liu Y. Regional economic development, household income, gender and hypertension: evidence from half a million Chinese. BMC Public Health 2020 Jun 10;20(1):901 [] [CrossRef] [Medline]
  39. Frankham C, Richardson T, Maguire N. Psychological factors associated with financial hardship and mental health: A systematic review. Clin Psychol Rev 2020 Apr;77:101832 [CrossRef] [Medline]
  40. Yoshikawa H, Aber JL, Beardslee WR. The effects of poverty on the mental, emotional, and behavioral health of children and youth: implications for prevention. Am Psychol 2012;67(4):272-284 [CrossRef] [Medline]
  41. Ridley M, Rao G, Schilbach F, Patel V. Poverty, depression, and anxiety: causal evidence and mechanisms. Science 2020 Dec 11;370(6522) [CrossRef] [Medline]
  42. Sun C, Shi J, Fu W, Zhang X, Liu G, Chen W, et al. Catastrophic health expenditure and its determinants among households with breast cancer patients in China: a multicentre, cross-sectional survey. Front Public Health 2021;9:704700 [] [CrossRef] [Medline]
  43. Jung H, Kwon YD, Noh J. Financial burden of catastrophic health expenditure on households with chronic diseases: financial ratio analysis. BMC Health Serv Res 2022 Apr 27;22(1):568 [] [CrossRef] [Medline]
  44. Assebe LF, Negussie EK, Jbaily A, Tolla MTT, Johansson KA. Financial burden of HIV and TB among patients in Ethiopia: a cross-sectional survey. BMJ Open 2020 Jun 01;10(6):e036892 [] [CrossRef] [Medline]
  45. Li H, Jiang L. Catastrophic medical insurance in China. Lancet 2017 Oct 14;390(10104):1724-1725 [CrossRef] [Medline]
  46. Cao W, Hsieh E, Li T. Optimizing treatment for adults with HIV/AIDS in China: successes over two decades and remaining challenges. Curr HIV/AIDS Rep 2020 Feb;17(1):26-34 [] [CrossRef] [Medline]
  47. Ni X, Li Z, Li X, Zhang X, Bai G, Liu Y, et al. Socioeconomic inequalities in cancer incidence and access to health services among children and adolescents in China: a cross-sectional study. Lancet 2022 Sep 24;400(10357):1020-1032 [CrossRef] [Medline]
  48. Yip W, Fu H, Chen AT, Zhai T, Jian W, Xu R, et al. 10 years of health-care reform in China: progress and gaps in Universal Health Coverage. Lancet 2019 Sep 28;394(10204):1192-1204 [CrossRef] [Medline]
  49. Wickrama KAS, Surjadi FF, Lorenz FO, Conger RD, Walker C. Family economic hardship and progression of poor mental health in middle-aged husbands and wives. Fam Relat 2012 Apr 01;61(2):297-312 [] [CrossRef] [Medline]
  50. Drentea P, Reynolds JR. Where does debt fit in the stress process model? Soc Ment Health 2015 Mar;5(1):16-32 [CrossRef] [Medline]

aHR: adjusted hazard ratio
aOR: adjusted odds ratio
CES-D: Center for Epidemiological Studies Depression Scale
CFPS: China Family Panel Studies
CHE: catastrophic health expenditure
CVD: cardiovascular disease
GRP: gross regional product
HR: hazard ratio
NRCMS: New Rural Cooperative Medical Scheme
OOP: out-of-pocket
UEBMI: Urban Employee Basic Medical Insurance
UHC: universal health coverage
URBMI: Urban Resident Basic Medical Insurance

Edited by A Mavragani, T Sanchez; submitted 05.09.22; peer-reviewed by T Liyuan, J Ma, S Pesälä, J Arias De La Torre; comments to author 25.04.23; revised version received 18.05.23; accepted 26.05.23; published 15.08.23


©Yaping Wang, Wannian Liang, Min Liu, Jue Liu. Originally published in JMIR Public Health and Surveillance (, 15.08.2023.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on, as well as this copyright and license information must be included.