Abstract
Background
Numerous studies have suggested that catastrophic health expenditure (CHE) is associated with depressive mood. However, most published studies have examined the relationship between CHE and depressive mood only among middle-aged and older people who are already susceptible to depressive mood. The objective of our analysis was to determine the associations between household CHE and depressive mood among adults and children/adolescents.
Methods
Our study population consisted of Chinese residents who participated in the 2016 CFPS, 2018 CFPS, and 2020 CFPS. Our analytical sample was restricted to children/adolescents aged 10–17 years and adults aged 18 years and older. We utilized multilevel random effects multivariate logistic regression models to investigate the associations between CHE and depressive mood among both adults and children/adolescents.
Results
Our study revealed that 15% of adults and 12.61% of children/adolescents had experienced CHE and that CHE was positively associated with depressive mood among adults (OR = 1.34, 95% CI: 1.21, 1.50) and among children/adolescents (OR = 1.48, 95% CI: 1.12, 1.96) after adjustment for potential confounding factors. This positive association persisted in different subgroup analyses. In addition, we found that being insured with either urban or rural health insurance was associated with decreased odds of depressive mood.
Conclusion
Our study indicated that CHE is common in Chinese families and may increase the risk of depressive mood for both adults and children/adolescents. These findings emphasize the need to focus on expanding health insurance coverage, as well as implementing family-based mental health resources and financial literacy programs to reduce the psychological impact of CHE across all age groups.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12888-025-06788-2.
Keywords: Catastrophic health expenditure, Depressive mood, Panel data, China, Multilevel model
Background
Concerned with safeguarding individuals from overwhelming health care expenses, governments worldwide have prioritized the prevention of catastrophic health expenditure (CHE). This term refers to health care costs that surpass a predetermined percentage of households’ financial capacity to cover medical expenses [1, 2]. Despite a decrease in out-of-pocket (OOP) payments for health care, the proportion of OOP payments in relation to income has remained unchanged. In 2010, it was estimated that a staggering 808 million people globally experienced CHE [3]. This figure increased to 996 million people in 2019, and most of these individuals were concentrated in low- and middle-income countries (LMICs) [4]. Previous research has indicated that CHE may significantly impact household quality of life by disrupting financial stability, hindering access to health care, and contributing to or exacerbating poverty [5–8].
The issue of CHE and its impacts on mental health has received increasing attention from researchers in recent decades. Many studies have suggested that CHE is associated with depressive mood [9–11]. One of the main reasons for this is the psychological stress caused by financial burdens and uncertainty about future health care needs. Financial stress from CHE can exacerbate feelings of helplessness, anxiety, and stress, all of which are known contributors to depressive symptoms. Research has demonstrated that financial insecurity, especially in the context of poor health, can directly impact mental health and lead to increased levels of depressive symptoms [12, 13]. To date, most published studies have examined the impact of CHE on depressive mood only among middle-aged and older adults who are already susceptible to depressive mood. A previous population-based study investigated the relationship between CHE and depressive mood among people older than 45 years in China [11]. Furthermore, studies highlight that lower social support, lower socioeconomic status, and financial hardship are linked to increased risks of depressive symptoms in adults [14]. The link between financial hardship and depressive symptoms is particularly strong, often outweighing the influence of other socioeconomic factors [15]. As a form of financial hardship, CHE forces families to reduce basic expenditures, increasing the vulnerability of depressive mood in adults. Notably, a previous study focused on the likelihood of CHE events among people with depressive moods and did not assess the longitudinal association between CHE and depressive mood occurrence [16, 17]. Numerous studies indicate that family socioeconomic challenges play a considerable role in various aspects related to the mental health of children/adolescents, including symptoms of anxiety and depression [18, 19]. However, little is known about how household CHE impacts younger individuals, especially children/adolescents.
In 2012, China introduced a form of insurance known as catastrophic medical insurance or critical illness insurance. Following successful testing in various cities, this insurance was rolled out nationwide in 2016. The primary objective of this insurance is to provide reimbursement to individuals who surpass a predefined threshold of OOP payments, as established by their basic medical insurance coverage [20]. Previous studies in China and other countries have demonstrated that financial stress is a significant problem for people with depressive moods and that health insurance can be instrumental in alleviating this burden [21]. Additionally, findings from earlier studies in China on CHE and depression among individuals aged 45 years and older suggest that, to eliminate the link between CHE and depression, comprehensive financial protection, including medical insurance, should be broadened and strengthened [13]. However, the impact of health insurance on depressive moods across all age groups, including adults and children/adolescents, has not been extensively explored. Therefore, in this study, we included health insurance coverage as a covariate to examine its influence on the link between CHE and depressive moods across a range of ages, including both adults and children/adolescents.
We utilized data from three waves (2016, 2018, and 2020) of the China Family Panel Studies (CFPS). The CFPS collects information on socioeconomic status and health conditions from a nationally representative sample of Chinese communities, families, and individuals. The objective of our analysis was to explore the associations between household CHE and depressive mood among adults and children/adolescents.
Methods
Study design and population
In this study, we used panel data from three waves of the CFPS. The participants in our study were Chinese residents who took part in the CFPS in 2016, 2018, and 2020. The respondents within our study are not fully independent of each other, as individuals from the same household may share common environmental, socioeconomic, and genetic factors, potentially leading to clustering effects at the household level. The CFPS has been described in greater detail elsewhere [22]. Briefly, the CFPS project did not have the resources to conduct longitudinal surveys in remote minority regions, especially where traveling would be very difficult and non-Han languages would likely be spoken. Therefore, the CFPS sample is considered “nearly nationally representative” or, for convenience, simply “nationally representative.” The CFPS covers 25 of 31 provinces/municipalities in the Chinese mainland (not including Xinjiang, Tibet, Inner Mongolia, Ningxia Autonomous Region, and Qinghai and Hainan Provinces), representing approximately 94.5% of the total population in the Chinese mainland [23]. Data were gathered from a diverse range of variables in various fields, such as health, education, and sociology. This study included information from 33,600 adults and 8,990 children, representing 14,960 families and 634 communities in China. The CFPS collected data from individuals of all age groups, but depressive mood was only assessed for people aged 10 years and above. Therefore, our analytical sample was restricted to children/adolescents aged 10–17 years and adults aged 18 years and above. CFPS data collection was reviewed and approved by the Biomedical Ethics Committee of Peking University (IRB00001052-14010). All participants signed informed consent forms.
Measures
CHE
In our study, we identified the head of the household as the primary respondent. To assess the occurrence of CHE within a household, we employed a widely used definition. According to this definition, a household was deemed to have incurred CHE when the OOP health care expenses reached or surpassed 40% of the household’s capacity to pay [24]. To determine the capacity to pay of a household, we computed it by subtracting the food-based household spending from the total consumption expenditure. The expenditure level, denoting the total consumption expenditure of the household minus the food-based household spending, served as the denominator in the formula. The numerator was obtained by summing the OOP spending of the participant and their spouse for both outpatient and inpatient care over the previous year. Introducing a binary variable indicating whether the participant’s household had experienced CHE, we created a clear indicator of the occurrence of CHE within the household.
Depressive mood
Depressive mood was evaluated via the Center for Epidemiologic Studies Depression Scale-8 (CES-D8) scale [25], which is a simplified version of the CES-D20 scale. To assess depressive mood, participants were presented with a series of situations experienced over the past week. These situations included feelings of depression, lack of motivation, insomnia or hypersomnia symptoms, unpleasantness, loneliness, dissatisfaction with life, sadness, and a sense of inability to continue with life. The participants evaluated their experience with each situation using a 4-point Likert scale, where they could choose from options ranging from 0 to 3. These options corresponded to the number of days each situation was encountered: 0 indicated less than 1 day, 1 represented 1 to 2 days, 2 accounted for 3 to 4 days, and 3 denoted 5 to 7 days. The total CES-D8 score can range from 0 to 8, and scores of 3 or higher suggest a clinical diagnosis of depression. The reliability and validity of the CES-D8 have been well described in previous studies [26]. The Cronbach’s alpha coefficient of the CES-D8 in this study was 0.782.
Covariates
The participants provided sociodemographic data, including age, sex, marital status, and education level. Age was categorized into three groups: 18–45, 46–64, and 65 years and older. Sex was recorded as female or male. Ethnicity was classified as Han and non-Han ethnicities. Residence was self-reported as either urban or rural neighborhood. Marital status options included unmarried, married, and divorced or widowed. The participants’ education levels were categorized as follows: middle school and below, high school, and college and above. Employment status was assessed with the following question: “Did you work for at least 1 hour in the past week?“. The participants could respond with ‘yes’ or ‘no’. The participants were asked if they were covered by health insurance, which was then recoded as no insurance, urban health insurance or rural health insurance. Urban health insurance comprises urban employee basic medical insurance [UEBMI] and urban resident basic medical insurance [URBMI]. Rural health insurance includes the new rural cooperative medical scheme [NRCMS]. Household income consists of various sources, such as salary, business earnings, transfers, and other family members’ income. After removing outliers, household income was divided into five tiers: 20000 yuan or less, > 20000 but ≤ 30000 yuan, > 30000 but ≤ 50000 yuan, > 50000 but ≤ 70000 yuan, and above 70000 yuan.
We also included a series of health and behavioral factors. The participants were asked to indicate and list any chronic diseases in the past 6 months. Physical activity was determined by the following question: “What was the frequency of your exercise during the previous week?” The responses were categorized as ‘≤3 times’ or ‘> 3 times’ for this study. Tobacco usage was assessed through the following question: “Did you smoke in the last month?” with ‘yes’ or ‘no’ options. Similarly, alcohol consumption was gauged by asking, “Did you consume alcohol more than 3 times a week in the last month?” with ‘yes’ or ‘no’ response choices. The survey inquired about the amount of sleep participants had on both work/school days and weekends. To calculate sleep duration, the total hours of sleep on work/school days were multiplied by 5, and the total hours on weekends were multiplied by 2. The combined sum was then divided by 7. Previous studies have defined 6 hours or less as short sleep duration or sleep deprivation, with less than 6 hours of sleep per night being a significant risk factor for the onset and recurrence of depressive symptoms [27, 28]. In this study, sleep duration was categorized into two groups: 6 hours or less and > 6 hours.
Statistical analysis
Descriptive analyses were conducted for the overall sample and were stratified by adults and children/adolescents. The baseline characteristics of the participants were summarized using either the means ± standard deviations (SDs) for continuous variables or frequencies and percentages for categorical variables. To compare the distributions of various baseline characteristics, the researchers used the Pearson 𝜒2 test or Fisher’s exact test, depending on the appropriateness of each method.
To address the panel design of the CFPS, we organized repeated observations (Level 1) for individual CFPS respondents (Level 2) and applied clustering by household. We employed multilevel random effects multivariate logistic regression to explore the relationship between CHE and depressive mood for both adults and children/adolescents. This approach ensured the robustness of our findings by obtaining accurate standard errors for all our analyses, which is consistent with a previous study [29]. Our analysis systematically adjusted for various factors that could influence depressive mood reported in previous studies [13], with covariates introduced sequentially across two models. Model 1 was adjusted for sociodemographic factors, whereas Model 2 was further adjusted for behavioral and health-related characteristics. The models were developed separately for adults and children/adolescents to ensure age-appropriate covariate inclusion. For adults, Model 1 included sex, age, marital status, education level, employment status, household income, health insurance, and residence, whereas Model 2 added adjustments for the presence of chronic disease, exercise frequency, smoking, drinking, and sleep time. For children/adolescents, Model 1 included sex, age, health insurance, and residence, and Model 2 further included exercise frequency and sleep time. This structured approach allowed us to capture the distinct sociodemographic and health-related factors affecting depressive mood in adults and children/adolescents.
We conducted tests to assess potential collinearity issues and found that all variables in this study had acceptable variance inflation factors and tolerance. Then, to test the generalization ability and potential variations in different subgroups, we repeated all analyses stratified by age (18–45, 46–64, and 65 + years), sex, employment status, residence, health insurance, presence of chronic diseases, exercise frequency, and sleep time for adults. Among children/adolescents, we repeated the analyses stratified by age (10–13 and 14–17 years), sex, residence, health insurance, exercise frequency, and sleep time to test the consistency of findings across different groups. Significance levels (p values) were calculated through two-tailed tests and were considered statistically significant when p was < 0.05. Analyses were conducted via R 4.2.1 (R Core Team, Vienna, Austria).
Results
In this study, we included 37,521 participants from the 2016 CFPS, 35,135 participants from the 2018 CFPS, and 28,590 participants from the 2020 CFPS, yielding a total of 21,884 participants from the 3 waves of the CFPS. A total of 2051 participants were excluded because of incomplete data to determine CHE, and 972 individuals were excluded because they had no assessment data for depressive mood. Finally, a total of 18,911 participants, including 17,199 adults (≥ 18 years old) and 1712 children/adolescents (aged 10–17 years old), who had information from the 2016, 2018, and 2020 surveys, were included in this study (Fig. 1).
Fig. 1.
Flowchart of the study participants. Abbreviation: CHE, Catastrophic health expenditure
Table 1 presents the distributions of the study variables for all participants in the 2016 2016 CFPS and by sex. The study population comprised 17,199 adults (90.95%) and 1,712 children/adolescents (9.05%). For adults, the average age of the respondents was 46.8 ± 16.1 years, and approximately 15% of participants had experienced CHE. The depressive mood rate was 22.65% among males and 30.91% among females. Most of the participants were of Han ethnicity; lived in rural areas; had a middle school education or above; and were married, employed, and insured. Over half of the participants belonged to families with household incomes less than 50,000 yuan per year (approximately 7000 US dollars) and with family sizes less than 4 people. A total of 14.1% of the males and 18.02% of the females had at least 1 chronic disease. A total of 54.38% and 26.62% of males reported smoking and drinking, respectively, which were significantly higher than the 2.54% and 2.54% reported among females. For both males and females, the majority of individuals (over 70%) exercised three times or fewer per week. Additionally, most participants reported sleeping more than 6 h per day, including 94.51% of males and 88.85% of females. For children/adolescents, the average age of the respondents was 13.3 ± 2.3 years, and approximately 12% of participants had experienced CHE. The depressive mood rate was 17.8% among males and 18.7% among females. Most of the children/adolescents were of Han ethnicity, lived in rural areas, had a middle school education or above, and were insured. Over half of the children/adolescents belonged to families with household incomes less than 50,000 yuan per year (approximately 7000 US dollars) and with family sizes comprising less than 4 people. Male children/adolescents were more likely to report physical exercise than female children/adolescents were (p < 0.01). Additionally, 98.53% of male and 96.12% of female children/adolescents reported sleeping more than 6 h per day. Supplementary Fig. 1 presents the percentages of CHE and depressive mood across different age groups in 2016, showing a tendency for higher CHE proportions to be associated with a greater prevalence of depressive mood.
Table 1.
Baseline characteristics of 18,911 China family panel studies participants in 2016 by Adults, Children/Adolescents, and gender
| Adults (n = 17199,90.95%) |
Children/adolescents (n = 1712, 9.05%) |
|||||||
|---|---|---|---|---|---|---|---|---|
|
Male (n = 8505, 49.45%) |
Female (n = 8694, 50.55%) |
P-value |
Male (n = 887, 51.81%) |
Female (n = 825, 48.19%) |
P- value |
|||
| CHE | 0.70 | 0.68 | ||||||
| Yes | 1258(14.79) | 1304(15.00) | 106(11.95) | 104(12.61) | ||||
| No | 7247(85.21) | 7390(85.00) | 781(88.05) | 721(87.39) | ||||
| Depressive mood | < 0.001 | 0.76 | ||||||
| Yes | 1927 (22.65) | 2692 (30.91) | 158 (17.8) | 154 (18.7) | ||||
| No | 6578 (77.35) | 6002 (69.09) | 729 (82.2) | 671 (81.3) | ||||
| Ethnicity | 0.03 | 0.17 | ||||||
| Han | 8222(96.67) | 8349(96.03) | 829(93.46) | 775(93.94) | ||||
| non-Han | 283(3.33) | 345(3.97) | 58(6.54) | 50(6.06) | ||||
| Marital status | < 0.001 | |||||||
| Unmarried | 1186(13.94) | 767(8.82) | / | / | ||||
| Married | 6901(81.14) | 7151(82.25) | / | / | ||||
| Divorced or widowed | 418(4.91) | 776(8.93) | / | / | ||||
| Education level | < 0.001 | 0.17 | ||||||
| Middle school and below | 6097(71.69) | 6711(77.19) | 883(99.55) | 816(98.91) | ||||
| High school | 1448(17.03) | 1135(13.05) | 4(0.45) | 9(1.09) | ||||
| College and above | 960(11.29) | 848(9.75) | / | / | ||||
| Employment status | 0.57 | |||||||
| Employed | 6618(77.81) | 6734(77.46) | / | / | ||||
|
Adults (n = 17199,90.95%) |
Children/adolescents (n = 1712, 9.05%) |
|||||||
|
Male (n = 8505, 49.45%) |
Female (n = 8694, 50.55%) |
P-value |
Male (n = 887, 51.81%) |
Female (n = 825, 48.19%) |
P- value |
|||
| Unemployed | 1887(22.18) | 1960(22.54) | / | / | ||||
| Health insurance | < 0.001 | 0.71 | ||||||
| None | 621(7.30) | 678(7.80) | 116(13.08) | 97(11.76) | ||||
| Urban health insurance | 2058(14.21) | 1867(21.48) | 111(12.52) | 105(12.72) | ||||
| Rural health insurance | 5826(68.50) | 6149(70.73) | 660(74.41) | 623(75.52) | ||||
| Residence | 0.05 | 0.63 | ||||||
| Rural | 4623(54.36) | 4596(52.86) | 538(60.65) | 491(59.52) | ||||
| Urban | 3882(45.64) | 4098(47.14) | 349(39.35) | 334(40.48) | ||||
| Presence of Chronic Disease | < 0.001 | |||||||
| No | 7306(85.90) | 7127(81.98) | / | / | ||||
| Yes | 1199(14.10) | 1567(18.02) | / | / | ||||
| Exercise frequency (per week) | 0.03 | < 0.001 | ||||||
| ≤ 3 | 6185(72.72) | 6446(74.14) | 531(59.86) | 600(72.73) | ||||
| > 3 | 2320(27.28) | 2248(25.86) | 356(40.14) | 225(27.27) | ||||
| Smoking | < 0.001 | |||||||
| No | 3880(45.62) | 8473(97.46) | / | / | ||||
| Yes | 4625(54.38) | 221(2.54) | / | / | ||||
| Drinking | < 0.001 | |||||||
| No | 6241(73.38) | 8473(97.46) | / | / | ||||
| Yes | 2264(26.62) | 221(2.54) | / | / | ||||
|
Adults (n = 17199,90.95%) |
Children/adolescents (n = 1712, 9.05%) |
|||||||
|
Male (n = 8505, 49.45%) |
Female (n = 8694, 50.55%) |
P-value |
Male (n = 887, 51.81%) |
Female (n = 825, 48.19%) |
P-value | |||
| Sleep time (hours per day) | < 0.001 | 0.002 | ||||||
| ≤ 6 | 467(5.49) | 969(11.15) | 13(1.47) | 32(3.88) | ||||
| > 6 | 8038(94.51) | 7725(88.85) | 874(98.53) | 793(96.12) | ||||
| Household income | 0.38 | 0.38 | ||||||
| Q1 (≤ 20000) | 2323(27.31) | 2356(27.10) | 257(28.97) | 263(31.88) | ||||
| Q2 (> 20000 & <=30000) | 1139(13.39) | 1138(13.09) | 116(13.08) | 120(14.55) | ||||
| Q3 (> 30000 & <=50000) | 1882(22.13) | 1844(21.21) | 193(21.76) | 156(18.91) | ||||
| Q4 (> 50000 & <=70000) | 1040(12.23) | 1112(12.79) | 116(13.08) | 97(11.76) | ||||
| Q5 (> 70000) | 2121(24.94) | 2488(28.62) | 205(23.11) | 204(24.73) | ||||
| Family size | 0.51 | 0.46 | ||||||
| ≤ 4 | 4894(57.54) | 5046(58.04) | 538(60.65) | 486(58.91) | ||||
| > 4 | 3611(42.46) | 3648(41.96) | 349(39.35) | 339(41.09) | ||||
Abbreviation: CHE, Catastrophic Health Expenditure
Table 2 presents the outcomes of random-effects logistic regression models that investigated factors associated with depressive mood among adults. Model 1 was adjusted for sociodemographic background, indicating that CHE was associated with an elevated risk of depressive mood (OR = 1.34, 95% CI: 1.21, 1.50). In addition, we found that female sex (OR = 1.53, 95% CI: 1.42, 1.66) and being divorced or widowed (OR = 1.89, 95% CI: 1.53, 2.34) increased the odds of depressive mood among adults. However, being insured decreased the odds of having a depressive mood (OR = 0.75, 95% CI: 0.63, 0.90; OR = 0.84, 95% CI: 0.72, 0.99). In Model 2, we further adjusted for behavioral and health factors, and CHE still increased the odds of depressive mood (OR = 1.24, 95% CI: 1.12, 1.39). In addition, we found that having at least one health condition (OR = 2.06, 95% CI: 1.86,2,29), smoking (OR = 1.24, 95% CI: 1.11,1.39), and sleeping 6 h or less (OR = 2.04, 95% CI: 1.74,2.40) increased the odds of depressive mood.
Table 2.
Odds of depressive mood from random effect logistic regression models for adults in China family panel studies the 2016–2020
| Model 1 OR (95% CI) |
P-value | Model 2 OR (95% CI) |
P-value | ||||||
|---|---|---|---|---|---|---|---|---|---|
| CHE | |||||||||
| No | Reference | Reference | |||||||
| Yes | 1.34 | (1.21, 1.50) | < 0.001 | 1.24 | (1.12, 1.39) | < 0.001 | |||
| Sociodemographic | |||||||||
| Gender | |||||||||
| Male | Reference | Reference | |||||||
| Female | 1.53 | (1.42, 1.66) | < 0.001 | 1.58 | (1.43, 1.76) | < 0.001 | |||
| Age group (years) | |||||||||
| 18 ~ 45 | Reference | Reference | |||||||
| 46 ~ 64 | 1.18 | (1.07, 1.29) | < 0.001 | 1.02 | (0.93, 1.12) | 0.68 | |||
| 65+ | 1.26 | (1.11, 1.44) | < 0.001 | 0.96 | (0.84, 1.11) | 0.61 | |||
| Marital status | |||||||||
| Unmarried | Reference | Reference | |||||||
| Married | 1.00 | (0.86, 1.17) | 0.97 | 1.00 | (0.86, 1.17) | 0.98 | |||
| Divorced or widowed | 1.89 | (1.53, 2.34) | < 0.001 | 1.79 | (1.44, 2.22) | < 0.001 | |||
| Education level | |||||||||
| Middle school and below | Reference | Reference | |||||||
| High school | 0.82 | (0.72, 0.93) | 0.002 | 0.82 | (0.72, 0.93) | 0.002 | |||
| College and above | 1.07 | (0.92, 1.25) | 0.35 | 1.15 | (0.98, 1.34) | 0.08 | |||
| Employment status | |||||||||
| Employed | Reference | Reference | |||||||
| Unemployed | 0.93 | (0.85, 1.02) | 0.12 | 0.93 | (0.85, 1.02) | 0.13 | |||
| Household income | |||||||||
| Q1 (≤ 20000) | Reference | Reference | |||||||
| Q2 (> 20000 & <=30000) | 0.93 | (0.81, 1.07) | 0.30 | 0.93 | (0.81, 1.07) | 0.32 | |||
| Q3 (> 30000 & <=50000) | 1.04 | (0.92, 1.17) | 0.55 | 1.03 | (0.92, 1.16) | 0.58 | |||
| Q4 (> 50000 & <=70000) | 0.93 | (0.81, 1.05) | 0.25 | 0.92 | (0.81, 1.05) | 0.23 | |||
| Q5 (> 70000) | 0.99 | (0.89, 1.11) | 0.90 | 1.00 | (0.90, 1.12) | 0.96 | |||
| Health insurance | |||||||||
| None | Reference | Reference | |||||||
| Urban health insurance | 0.75 | (0.63, 0.90) | 0.001 | 0.71 | (0.60, 0.85) | < 0.001 | |||
| Rural health insurance | 0.84 | (0.72, 0.99) | 0.03 | 0.83 | (0.71, 0.97) | 0.02 | |||
| Residence | |||||||||
| Rural | Reference | Reference | |||||||
| Urban | 0.94 | (0.82, 1.08) | 0.38 | 0.87 | (0.76, 1.00) | 0.05 | |||
| Health and Behavioral factors | |||||||||
| Presence of chronic disease | |||||||||
| No | Reference | Reference | |||||||
| Yes | / | 2.06 | (1.86, 2.29) | < 0.001 | |||||
|
Model 1 OR (95% CI) |
P-value |
Model 2 OR (95% CI) |
P-value | ||||||
| Exercise frequency (per week) | |||||||||
| ≤ 3 | Reference | Reference | |||||||
| > 3 | / | 1.02 | (0.93, 1.12) | 0.66 | |||||
| Smoking | |||||||||
| No | Reference | Reference | |||||||
| Yes | / | 1.24 | (1.11, 1.39) | < 0.001 | |||||
| Drinking | |||||||||
| No | Reference | Reference | |||||||
| Yes | / | 0.89 | (0.79, 1.01) | 0.08 | |||||
| Sleep time (hours per day) | |||||||||
| ≤ 6 | Reference | Reference | |||||||
| > 6 | / | 2.04 | (1.74, 2.40) | < 0.001 | |||||
Abbreviations: CHE, Catastrophic Health Expenditure; CI, Confidence Interval; OR, Odds Ratio. Note: Model 1 is adjusted for sociodemographic characteristics; Model 2 adjusts for sociodemographic characteristics, health factors, and behavioral factors
Table 3 presents the outcomes of random-effects logistic regression models that investigated factors associated with depressive mood among children/adolescents. After we adjusted for sociodemographic variables, we found that CHE was significantly associated with depressive mood among children/adolescents (OR = 1.48, 95% CI: 1.12, 1.96). Female sex was only marginally significantly associated with increased odds of depressive mood (OR = 1.21, 95% CI: 0.97, 1.51). Being insured with either urban health insurance (OR = 0.80, 95% CI: 0.73,0.88) or with rural health insurance (OR = 0.87, 95% CI: 0.77,0.98) was associated with decreased odds of having a depressive mood. In Model 2, we further adjusted for behavioral and health factors, and CHE still increased the odds of depressive mood (OR = 1.23, 95% CI: 1.07, 1.41). In addition, we found that sleeping 6 h or less (OR = 1.80, 95% CI: 1.05, 3.10) increased the odds of depressive mood among children/adolescents.
Table 3.
Odds of depressive mood from random effect logistic regression models for children/adolescents in China family panel studies the 2016–2020
| Model 1 OR (95% CI) |
P-value | Model 2 OR (95% CI) |
P-value | |||||
|---|---|---|---|---|---|---|---|---|
| CHE | ||||||||
| No | Reference | Reference | ||||||
| Yes | 1.48 | (1.12, 1.96) | 0.01 | 1.23 | (1.07, 1.41) | 0.003 | ||
| Sociodemographic | ||||||||
| Gender | ||||||||
| Male | Reference | Reference | ||||||
| Female | 1.21 | (0.97, 1.51) | 0.09 | 1.24 | (0.99, 1.55) | 0.06 | ||
| Age group (years) | ||||||||
| 10 ~ 13 | Reference | Reference | ||||||
| 14 ~ 17 | 0.99 | (0.81, 1.23) | 0.93 | 0.95 | (0.77, 1.18) | 0.64 | ||
| Residence | ||||||||
| Rural | Reference | Reference | ||||||
| Urban | 0.89 | (0.76, 1.04) | 0.14 | 0.80 | (0.76, 8.40) | 0.85 | ||
| Health insurance | ||||||||
| None | Reference | Reference | ||||||
| Urban health insurance | 0.80 | (0.73, 0.88) | < 0.001 | 0.77 | (0.65, 0.91) | 0.002 | ||
| Rural health insurance | 0.87 | (0.77, 0.98) | 0.02 | 0.76 | (0.64, 0.90) | 0.001 | ||
| Health and Behavioral factors | ||||||||
| Exercise frequency (per week) | ||||||||
| ≤ 3 | Reference | Reference | ||||||
| > 3 | / | 0.96 | (0.77, 1.19) | 0.71 | ||||
| Sleep time (hours per day) | ||||||||
| ≤ 6 | Reference | Reference | ||||||
| > 6 | / | 1.80 | (1.05, 3.10) | 0.03 | ||||
Abbreviations: CHE, Catastrophic Health Expenditure; CI, Confidence Interval; OR, Odds Ratio. Note: Model 1 is adjusted for sociodemographic characteristics; Model 2 adjusts for sociodemographic characteristics, health factors, and behavioral factors
Figure 2 presents the stratified analyses of the associations between CHE and depressive mood for both adults and children/adolescents. For adults, consistent ORs ranging from 1.13 to 1.37 were obtained in different subgroup analyses. However, in the subgroups of age (18–45: OR = 1.13, 95% CI: 0.95, 1.34; 65+: OR = 1.24, 95%: 0.97, 1.58) and health insurance (none: OR = 1.34, 95% CI: 0.86, 2.07; urban health insurance: OR = 1.15, 95% CI: 0.87, 1.53), we observed statistically nonsignificant associations between CHE and depressive mood. There is no statistically significant interaction effect between the variables in the relationship between CHE and depressive mood among adults. Because of the relatively small sample size, more statistically nonsignificant associations between CHE and depressive mood were observed in the subgroup analysis of children/adolescents. Exercise frequency was identified as the only statistically significant interaction factor influencing the relationship between CHE and depressive mood among children/adolescents.
Fig. 2.
Subgroup analysis of the association between catastrophic health expenditure and the odds of depressive mood. A: Adults; B: Children/adolescents, Abbreviation: CI, Confidence Interval; OR, Odds Ratio
Discussion
To our knowledge, this study is the first panel data analysis of a nationally representative longitudinal survey of the general Chinese population to examine the effect of CHE on the depressive mood of adults as well as children/adolescents. A 2015 study in Myanmar reported a CHE incidence of 6% under the 40% threshold, whereas the incidence was 3.25% in Iran (2015) and 4.52% in Kenya (2013) [30–32]. These data indicate that the CHE burden is more pronounced in China than in several other low- and middle-income countries. We found that CHE is common in Chinese families and may increase the risk of depressive mood for both adults and children/adolescents. This finding also enhances the current body of knowledge by documenting that CHE not only increases the risk of depressive mood among adults but also may impact the children/adolescents within the affected family. In addition, we found that being insured with either urban or rural health insurance may lower the risk of depressive mood among both adults and children/adolescents. This finding has profound implications for health care policy and practice. By bolstering insurance coverage, we may fortify individuals and families against the psychological toll that often accompanies CHE.
Our study suggested a positive association between CHE and depressive mood, which was consistent with the findings of previous studies. Wang et al. revealed that individuals aged 45 years and older who experienced CHE had a 13% greater likelihood of developing depressive mood than those who did not face CHE (aHR = 1.13, 95% CI: 1.02–1.26) [11]. However, middle-aged and elderly people usually have a high prevalence of chronic diseases, which may substantially confound the associations between CHE and depressive mood. In contrast, our study is among the first to explore the association between CHE and depressive mood in the general population. Our findings may further underpin the unconfounded association between CHE and depressive mood. CHE may lead to depressive mood through a multifaceted interplay of financial strain, psychological distress, and reduced well-being. CHE represents a dual challenge encompassing both substantial financial strain and a psychosocial ordeal. Many previous studies have suggested associations between CHE and financial stress, which subsequently results in depression and anxiety [33, 34]. In addition, CHE compels families to reduce essential spending, increasing the susceptibility of affected individuals to depression [35]. Moreover, the burden of coping with substantial medical bills and the fear of not being able to afford essential treatments can lead to stress, anxiety, and depression [36].
In addition to its impact on adults, our study is the first to examine the relationship between CHE and depressive mood in children/adolescents. Although previous studies have focused primarily on this association in adult populations [13, 17], the mental health effects of CHE on younger age groups have largely been overlooked. By addressing this gap, our study demonstrates that CHE is also associated with an increased likelihood of depressive mood in children/adolescents. The financial burden caused by CHE can considerably increase stress levels within the family [37]. This heightened stress can have adverse effects on family dynamics and the mental health of family members, including children and young adults. The chronic stress and anxiety experienced by offspring due to their family’s financial struggles may contribute to the development or exacerbation of depression. In addition, families experiencing financial hardship due to health care expenses may need to make major lifestyle changes or reduce their social activities [38]. Such changes can impact the social support system of children or young adults, potentially leading to feelings of isolation and exacerbating depression [39]. When a family faces CHE, they may encounter difficulties meeting essential requirements such as nourishment, shelter, and education. A lack of essential resources and instability in living conditions can negatively affect a child’s mental well-being and increase the risk of depression [40]. CHE may also cause changes in interpersonal relationships. Financial difficulties within the family can occasionally strain relationships between family members, causing conflict and emotional distress [41]. Discord within the family environment can contribute to feelings of sadness and hopelessness in offspring, increasing the risk of depression.
Gender differences in health behaviors and socioeconomic factors appear to influence depressive moods in both adults and youth. Among adults, females presented a greater prevalence of chronic disease, shorter sleep duration, and lower smoking and alcohol use, alongside slightly lower educational attainment, than males did. These disparities in health conditions and lifestyle factors may serve as potential moderating factors in the relationship between CHE and depressive moods. Among children and adolescents, female youth had a lower exercise frequency and shorter sleep duration. These differences in physical activity and sleep could play a role in mental health outcomes, as both factors are known to impact mood and emotional well-being. Given these significant gender-based variations, targeted strategies to address depressive moods may be more effective if tailored to address these specific needs and behaviors across genders in both adults and youth.
The subgroup analysis results indicated that the association between CHE and depressive mood generally remained consistent across demographic and socioeconomic groups, as suggested by the lack of significant interactions for most variables. However, some notable trends and variations were observed within the subgroups. For children/adolescents, the analysis of exercise frequency revealed a significant interaction effect between CHE and depressive mood. Although exercise is typically associated with mental health benefits, the mental health benefits of exercise can be moderated by external stressors, such as peer interactions or school-related pressures [42], which are more prominent among physically active children and adolescents. Future research should further explore these dynamics to develop age-specific strategies that balance the benefits of physical activity with the unique challenges faced by this population.
The subgroup analysis for adults found nonsignificant associations in certain groups, such as younger (18–45 years) and older (65 + years) age groups, as well as those without health insurance or covered by urban health insurance. These nonsignificant findings may reflect the fact that younger and older adults potentially experience differing financial and psychological responses to CHE. The lack of significant associations among the uninsured and urban insurance subgroups emphasizes the need for broader or more comprehensive insurance options. For children and adolescents, more nonsignificant associations were observed in the subgroup analysis, likely due to the smaller sample size in these groups. Despite this limitation, we observed a stronger association in older adolescents, indicating that this age group may be more sensitive to the financial strain that CHE imposes on households. Overall, these subgroup findings suggest that the impact of CHE on mental health varies by age, insurance type, and potentially other demographic factors. To mitigate the mental health impacts of CHE, policy interventions should consider these age-specific effects and insurance type effects. For middle-aged adults, enhancing financial protection through expanded health insurance coverage may help alleviate financial stress. For adolescents, family-based mental health resources and educational programs addressing financial stress may provide crucial support. By targeting these unique vulnerabilities, policies can more effectively promote mental health outcomes across age groups.
One of the core goals of the health care system is to provide extensive financial safeguards, such as medical insurance, to the public to shield them from the financial burdens associated with sickness and medical expenses [43]. Being insured was a protective factor against depressive mood for both adults and children/adolescents in this study. Among individuals facing CHE, those who have health insurance are less concerned about the possibility of their health care costs becoming unmanageable or experiencing recurring CHE in the future, which leads to a reduction in the deterioration of their mental well-being compared with those without health insurance [12]. This finding suggests a rationale for adopting a more substantial health care funding system. Although our research is rooted in Chinese data, its implications are important for other low- and middle-income countries (LMICs). Introducing comprehensive financial safeguards, such as medical insurance, could serve as a fundamental step for governments in LMICs to explore. This approach not only eases the OOP load for beneficiaries but also holds considerable potential for positively influencing the mental well-being of affected families.
Limitations
Several limitations should be acknowledged before the results of this study can be interpreted. First, the use of self-reported measures of depressive mood potentially underestimated or overestimated the prevalence of depressive mood, particularly among those from different socioeconomic and educational backgrounds. Second, although longitudinal or panel surveys that gather repeated measures for the same individuals over time may limit the effects of bias and improve causal estimation [44], causality could not be determined because of the observational study design. Finally, the impact of confounding by unmeasured, residual or unknown confounding factors in this study could not be fully excluded. Further studies with comprehensive measures of covariates are still warranted.
Conclusion
Our study highlights that CHE is prevalent in Chinese families and is associated with an increased risk of depressive mood in both adults and children/adolescents. Specifically, we found that the mental health impacts of CHE are more pronounced among certain demographic groups, with older adolescents and middle-aged adults showing heightened vulnerability. However, individuals with urban or rural health insurance coverage appear to have a reduced risk of developing depressive mood. Our findings recommend increasing health insurance coverage, providing more comprehensive financial protection, especially for middle-aged adults, and implementing family-based mental health resources and financial literacy programs to support adolescents. These interventions could help reduce CHE-related financial stress and its psychological impact across age groups.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Acknowledgements
We highly appreciate the financial support from the National Natural Science Foundation of China, Zhejiang University K.P. Chao’s High Technology Development Foundation, the Mega-Project of National Science and Technology for the 13th Five-Year Plan of China, the Fundamental Research Funds for the Central Universities and Zhejiang Province Pharmaceutical and Health Innovation Talents.
Abbreviations
- CHE
Catastrophic health expenditure
- OR
Odds ratio
- CI
Confidence interval
Author contributions
JW designed the study. SWL, KLF, and YZ accessed and verified all the data. SWL, KLF, YZ, and LYZ analyzed the data and interpreted the results. SWL, YZ, KLF, YSL and JW wrote the manuscript. All authors revised the manuscript from the preliminary draft to submission. JW supervised the whole study. JW is responsible for the decision to submit the manuscript.
Funding
This study was funded by the National Natural Science Foundation of China (72374179, 71904170), Zhejiang University K. P. Chao’s High Technology Development Foundation (2022RC017), the Mega-Project of National Science and Technology for the 13th Five-Year Plan of China (2018ZX10721102-003-006, 2018ZX10715013-003-003), the Fundamental Research Funds for the Central Universities (2022ZFJH003, K20210205), and Zhejiang Province Pharmaceutical and Health Innovation Talents.
Data availability
This study made use of publicly available datasets, with all the data pertinent to our research accessible via the official China Family Panel Studies website: https://www.isss.pku.edu.cn/cfps/.
Declarations
Ethics approval and consent to participate
CFPS data collection was reviewed and approved by the Biomedical Ethics Committee of Peking University (IRB00001052-14010), and all participants signed informed consent forms. This study was conducted in accordance with the principles of the Declaration of Helsinki.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
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Supplementary Materials
Data Availability Statement
This study made use of publicly available datasets, with all the data pertinent to our research accessible via the official China Family Panel Studies website: https://www.isss.pku.edu.cn/cfps/.