Recent Trend in the Prevalence and Correlates of Depression Among Chinese Young Adults from 2010 to 2018

Abstract

As a result of globalization, Chinese young adults who transit to adulthood currently are being exposed to a combination of traditional Chinese values such as collectivism and Confucian philosophy, as well as Western individualism. We analyzed the trends in depression prevalence within an 8-year period using panel data derived from young adults in China. The investigation further delved into the multifaceted correlates of depression, including demographic profiles, health-related parameters, and consequential life events. Data were obtained from five cycles, 2010 to 2018, of the China Family Panel Studies (CFPS). Participants included adults aged 18 to 24 years (total analytic sample N = 15,442). Depression symptoms increased in prevalence in Chinese young adults over the eight years; therefore, depression screening should be regularly conducted in this demographic. This study highlights the issue’s gravity and suggests potential correlations with cultural, regional economic development level, gender, religion, hukou (household registration), lifestyle, chronic health conditions, on-going school attendance, and marital factors.

Keywords

depressive symptoms trends young adults China related factors China family panel studies (CFPS)

Introduction

Emerging adulthood (approximately between the ages of 18 and 24 years) is a unique stage of transition from adolescence to adulthood that features identity exploration, developing worldviews, establishing a career, and pursuing romantic relationships. It is also a period of high incidence of common behavioral deviations and psychological obstacles (Arnett, 2018). A combination of self-perceived adult status and continued development has been found to be a major fact that advances depressive symptoms (Cusack & Merchant, 2013). Depressive symptoms are more likely to occur in the early stages of adulthood than at any time in life (Dominguez, 2022). A national psychological survey in China found that depression and anxiety levels tended to decrease with age in four age groups: 18 to 24, 25 to 34, 35 to 44, and 45+ years old (Fu & Zhang, 2020). In addition, large representative samples of studies from the United States and Brazil have shown varying increases in depression among young adults over the past decade (Lopes et al., 2021; Mojtabai et al., 2016). Depression in young adulthood can have a variety of adverse consequences including adverse relapse of depression; the onset of other mental illnesses; and broader, lasting impairment of interpersonal, social, educational, and occupational functioning (Thapar et al., 2022). Hence, timely identification and management of the predisposing factors, antecedents, and symptoms of depression are crucial for addressing the growing concern of depression among youth (Thapar et al., 2022).

Evidence supported that there may be significant differences in emerging adulthood in a culture that specifies structures roles and responsibilities to others (Arnett, 2000). As a result of globalization, Chinese young adults who transit to adulthood today are being exposed to a combination of traditional Chinese values such as collectivism and Confucian philosophy, as well as Western individualism (Kuang et al., 2023). In addition to independence, China young people generally believe that the benchmarks for adulthood include maintaining stable emotional regulation, going beyond egocentrism, showing more consideration and commitment to others, such as achieving financial independence and supporting parents, and avoiding behaviors that may tarnish the reputation of the family (Badger et al., 2006; Kuang et al., 2023; Nelson, 2004). Key life events such as marriage, childbirth, and education for young adults in China exhibit patterns that are diverse and often delayed, similar to their Western counterparts. However, these patterns are influenced by government policies, such as fertility policies and the household registration system (hukou), which tend to be more prescriptive and passive (Wang & Zhao, 2021). These cultural and structural differences may affect the way depression manifests among young adults, and until recently, the majority of studies on depressive symptoms in emerging adulthood and young adults have been conducted in Western individualistic cultures. In China, several cross-sectional studies have described the current prevalence of depressive symptoms in emerging adults attending college in certain geographic areas (Gao et al., 2016; Liu et al., 2019). However, a gap exists in analyzing nationwide trends in depressive symptoms among emerging adults. Moreover, policy shifts in fertility, education, and employment could affect depression’s prevalence and associated risk factors in young adults. Previous inquiries have predominantly fixated on demographic characteristics, student lifestyles, and social support, frequently sidestepping broader dimensions such as cohabitation arrangements with parents, marital status, parenthood, educational pursuits, and employment status (Liu et al., 2020; Mojtabai et al., 2016; Ranjit et al., 2019). These dimensions hold integral importance in the developmental tasks of early adulthood and can wield substantial influence over the encounter with depression. Deviating from the conventional approach of cross-sectional data analysis, the utilization of panel data embraces inter-individual disparities and intra-individual fluctuations, consequently enhancing the precision of parameter inference (Hsiao, 2007). To validate the insights gleaned from cross-sectional analyses, this study has employed a longitudinal panel data methodology. In addition to incorporating demographic and health-related variables, this research has extended its scope to encompass significant life events. The central aim of this investigation was to elucidate the longitudinal trajectory of depression rates over an eight-year duration. Concurrently, it aimed to assess the influence of demographic and health-related variables, along with pivotal life events, on the onset of depression among young Chinese adults.

Methods

Design and Sample

The data utilized for this study were sourced from the China Family Panel Studies (CFPS), a comprehensive nationwide endeavor that employed a multistage area probability sampling methodology to procure a sample that is nationally representative across China. Encompassing 25 provinces, municipalities, and autonomous regions within mainland China, this survey excluded Xinjiang, Tibet, Qinghai, Inner Mongolia, Ningxia, and Hainan. The CFPS employed an implicit stratification approach, utilizing a multi-stage probability sampling design. Each sub-sample was drawn through a three-stage process involving the administrative district/county (first stage), administrative village/neighborhood committee (second stage), and family household (third stage) levels. The initial two stages employed official administrative division data for the sampling process, while the third stage employed the map address method to construct the final sampling frame. Subsequent to this, the selection of sample households employed cyclic equidistant sampling with randomized starting points (Xie & Lu, 2015). The themes covered by the CFPS include the economic and non-economic welfare of Chinese residents, such as economic activities, educational achievements, family relations and dynamics, population migration, and health. The CFPS was initially administered in 2010, surveying 28,000 individuals across 16,000 households and achieving coverage of 94.5% of China’s total population. Additional information on data collection can be found in other literature (Xie & Lu, 2015). This study was conducted over five biennial cycles (2010–2018). The data analyzed here were restricted to participants aged 18 to 24 years who responded to questions about depression at the time of the interview. Excluding invalid responses and incomplete data, a total of 15,442 participants were included in the analysis: 3,327 individuals in 2010; 3,352 in 2012; 3,053 in 2014; 3,085 in 2016, and 2,625 in 2018. We used the weights constructed by the CFPS team to adjust the prevalence analysis results in this article. As the 8-year study period exceeded the interval of 18 to 24 years, some participants joined or dropped out during this period. This study applied unbalanced panel data. The advantage of unbalanced panel data is the inclusion of the maximum amount of data for analysis, thus increasing efficiency.

Measures

The depression measurements in the CFPS series of surveys varied across rounds. In the 2010 and 2014 rounds, the Kessler 6 Rating Scale (K6) was used. In the 2012, 2016, and 2018 rounds, the Center for Epidemiologic Studies Depression Scale (CESD) was used. The K6 scale contains six items related to frequency of experiencing negative emotions (i.e., depression, nervousness, agitation, hopelessness, psychological fatigue, and worthlessness) over the past 30 days using a 5-point Likert scale: 0 (none of the time), 1 (a little of the time), 2 (some of the time), 3 (most of the time), or 4 (almost every day). The total scale score is the sum of the six items, ranging from 0 to 24 (Bessaha, 2017). The CESD scale consists of 20 items. Sixteen items measure negative and four measured positive feelings. These items use a 4-point Likert scale to ask respondents how often they have experienced both types of feelings in the past week: 0(less than 1 day), 1((1-2 days),2((3-4 days), 3(5-7 days). The total score of the scale is the sum of the scores of the 20 items, between 0 and 60 (Radloff, 1977). In the 2012 round, the CFPS used the original CESD (later referred to as CESD-20). As a short scale reduces the burden on respondents and lowers the refusal rate, the CFPS attempted to simplify the scale by extracting eight items for use in the 2016 and 2018 rounds. Specifically, in 2016, four-fifths of the participants were randomly selected to answer the CESD-8 scale, with the remainder still answering the CESD-20 scale. In 2018, all participants responded to the CESD-8 scale. To strengthen continuity of the analysis, the CFPS team applied the equipercentile method to convert the CESD-8 scores to align with those of the CESD-20. A score of 5, 7, and 16 or above on the K6 scale, CESD-8 scale, and CESD-20 scale, respectively, indicate that the respondent is at risk of depression (Luo & Zhao, 2021; Radloff, 1991) . The available evidence suggests that K6 and CESD-20, as well as CESD-8 and CESD-20, exhibit comparable validity and reliability for measuring depression (Bi et al., 2023; Otowa et al., 2016; Sakurai et al., 2011). Luo and Zhao (2021) employed data from the CFPS spanning the years 2010 to 2018 to investigate the temporal trends of depressive symptoms in Chinese adults. Therefore, we used five rounds of data from the CFPS survey for an 8-year trend analysis.

Sociodemographic, Health-Related Risk Factors, and Key Life Events

Sociodemographic Factors

We assessed gender (male/female), age (18–21, 22–24 years old), religious beliefs (yes/no), geographical region (east, central, western, and northeastern areas), highest level of completed education (primary school and below, junior high school/high school, college and above). Furthermore, we have incorporated the hukou variable, categorized into agricultural and non-agricultural statuses. The hukou system constitutes a multifaceted political, economic, and legal framework centered around household registration management. It serves as not only a mechanism for establishing personal identity but also as a facilitator of resource allocation and wealth distribution (Lu, 2002). Within this system, two distinct classifications exist: agricultural and non-agricultural. The specific classification and geographic placement of an individual’s hukou (whether categorized as agricultural or non-agricultural) significantly impacts their access to essential social resources within rural or urban contexts (Cheng & Selden, 1994). These resources encompass a wide array of advantages, spanning from healthcare, housing, and transportation to social security, preferential employment opportunities, and educational systems for children.

Health-Related Risk Factors

We classified those who did not smoke in the past month in the “non-smokers” group, and those who smoked occasionally or daily in the “smokers” group. Alcohol consumption frequency was classified into two categories. Those consuming >3 times a week were classified as “high frequency consumption” and those consuming ≤3 times a week were classified as “low frequency consumption.” “Frequency of physical activity was categorized as “never,” “sometimes” (1–3 times), and “often” (>4 times). Body mass index (BMI) was categorized into “underweight (<18.5 kg/m²),” “normal weight (18.5 to 24.9 kg/m²),” and “overweight (⩾25 kg/m²).” We defined “underweight” and “overweight” as abnormal weight. Those who were not diagnosed with chronic diseases in the past six months were characterized as “no chronic diseases,” and those who were diagnosed with chronic diseases were characterized as “having chronic diseases.”

Key Life Events

Significant life events encompassed within the study were indicated by variables such as current spouse status ( have spouse or have no spouse), parenthood (yes/no), on-going school attendance (yes/no), and residence arrangement (living in parents’ home or not).

Statistical Analysis

Frequencies and percentages were used to describe sample characteristics. We utilized the Cochran-Armitage trend test to evaluate the prevalence trends of depression over time in the overall population between 2010 and 2018. Since the dependent variable (i.e., depression) was a dichotomous dummy variable, a panel binary choice model was selected to analyze factors influencing changes in depressive symptoms among young adults in the data. In the panel model, the F test was employed to test the null hypothesis of no individual effect. The resulting p-value of .000 indicated rejection of the null hypothesis and led to the selection of the fixed effect model or random effect model. Based on the results of a Hausman test, the null hypothesis that the error term was not correlated with the explanatory variables was tested. The test yielded a p-value of .188 at a significance level of .05, which led to the acceptance of the null hypothesis and the selection of the random effect model. Therefore, we conducted a random effect model analysis of panel data using STATA 16.0 (STATA Corporation, College Station, TX, USA). The statistical significance level was set at p < .05 (two-sided).

Results

Trends of Depression From 2010 to 2018

Table 1 displays the general sample characteristics. Of the respondents, a total of 746, 865, 702, 739, and 788 were positive for depression in 2010, 2012, 2014, 2016, and 2018, respectively. These corresponded to weighted prevalence estimates of 22.0%, 25.6%, 23.2%, 21.1%, and 28.4% in 2010, 2012, 2014, 2016, and 2018, respectively. Despite fluctuations, the prevalence of depression increased significantly from 2010 to 2018 (Z value = 7071.4, time trend p < .001).

Table 1.

Respondents’ Characteristics.

	2010 (n=3,327)	2012 (n=3,352)	2014 (n= 3,053)	2016 (n=3,085)	2018 (n=2,625)	Total (N=15,442)
Age
18–21 years	1832 (55.1%)	1630 (48.6%)	1616 (52.9%)	1610 (52.2%)	1770 (67.4%)	8458 (54.8%)
22–24 years	1495 (44.9%)	1722 (51.4%)	1437 (47.1%)	1475 (47.8%)	855 (32.6%)	6984 (45.2%)
Gender
Male	1568 (47.1%)	1555 (46.4%)	1430 (46.8%)	1481 (48.0%)	1303 (49.6%)	7337 (47.5%)
Female	1759 (52.9%)	1797 (53.6%)	1623 (53.2%)	1604 (52.0%)	1322 (50.4%)	8105 (52.5%)
Religious belief
Yes	34 (1.0%)	21 (0.6%)	40 (1.3%)	24 (0.8%)	52 (2.0%)	171 (1.1%)
No	3293 (99.0%)	3331 (99.4%)	3013 (98.7%)	3061 (99.2%)	2573 (98.0%)	15271 (98.9%)
Hukou
Agricultural	2454 (73.8%)	2627 (78.4%)	2416 (79.1%)	2470 (80.1%)	2095 (79.8%)	12062 (78.1%)
Nonagricultural	873 (26.2%)	725 (21.6%)	637 (20.9%)	615 (19.9%)	530 (20.2%)	3380 (21.9%)
Living in parents’ home
Yes	3265 (98.1%)	2916 (87.0%)	2830 (92.7%)	2741 (88.8%)	2201 (83.8%)	13953 (90.4%)
No	62 (1.9%)	436 (13.0%)	223 (7.3%)	344 (11.2%)	424 (16.2%)	1489 (9.6%)
Marriage
Have no spouse	2417 (72.6%)	2281 (68.0%)	2206 (72.3%)	2415 (78.3%)	2173 (82.8%)	11492 (74.4%)
Have spouse	910 (27.4%)	1071 (32.0%)	847 (27.7%)	670 (21.7%)	452 (17.2%)	3950 (25.6%)
Parenthood
Yes	546 (16.4%)	663 (19.8%)	587 (19.2%)	416 (13.5%)	299 (11.4%)	2511 (16.3%)
No	2781 (83.6%)	2689 (80.2%)	2466 (80.8%)	2669 (86.5%)	2326 (88.6%)	12931 (83.7%)
On-going school attendance
No	2380 (71.5%)	2404 (71.7%)	2074 (67.9%)	1967 (63.8%)	1565 (59.6%)	10390 (67.3%)
Yes	947 (28.5%)	948 (28.3%)	979 (32.1%)	1118 (36.2%)	1060 (40.4%)	5052 (32.7%)
Highest level of completed education
Primary school and below	1498 (45.0%)	2557 (76.3%)	2400 (78.6%)	2486 (80.6%)	2196 (83.7%)	11137 (72.1%)
Junior high school/ high school	1725 (51.8%)	688 (20.5%)	526 (17.2%)	454 (14.7%)	231 (8.8%)	3624 (23.5%)
College and above	104 (3.2%)	107 (3.2%)	127 (4.2%)	145 (4.7%)	198 (7.5%)	681 (4.4%)
Region
East area	1161 (34.9%)	1079 (32.2%)	950 (31.1%)	960 (31.1%)	829 (31.6%)	4979 (32.2%)
Central area	855 (25.7%)	937 (28.0%)	837 (27.5%)	778 (25.2%)	571 (21.8%)	3978 (25.8%)
Western area	918 (27.6%)	972 (29.0%)	920 (30.1%)	1045 (33.9%)	973 (37.1%)	4828 (31.3%)
Northeastern area	393 (11.8%)	364 (10.8%)	346 (11.3%)	302 (9.8%)	252 (9.5%)	1657 (10.7%)
Smoking status
Non-smokers	2668 (80.2%)	2683 (80.0%)	2504 (82.0%)	2492 (80.8%_)	2074 (79.0%)	12421 (80.4%_)
Smokers	659 (19.8%)	669 (20.0%)	549 (18.0%)	593 (19.2%)	551 (21.0%)	3021 (19.6%)
Alcohol consumption frequency
Low frequency consumption	3078 (92.5%)	3127 (93.3%)	2807 (91.9%)	2921 (94.7%)	2458 (93.6%)	14391 (93.2%)
High frequency consumption	249 (7.5%)	225 (6.7%)	246 (8.1%)	164 (5.3%)	167 (6.4%)	1051 (6.8%)
Physical activity
Never	2169 (65.2%)	1503 (44.8%)	1678 (55.0%)	1543 (50.0%)	1171 (44.6%)	8064 (52.2%)
Sometimes	667 (20.0%)	1266 (37.8%)	793 (26.0%)	787 (25.5%)	757 (28.8%)	4270 (27.7%)
Often	491 (14.8%)	583 (17.4%)	582 (19.0%)	755 (24.5%)	697 (26.6%)	3108 (20.1%)
Body mass index
Normal weight	2418 (72.7%)	2347 (70.0%)	2157 (70.7%)	2176 (70.5%)	1776 (67.7%)	10874 (70.4%)
Abnormal weight	909 (27.3%)	1005 (30.0%)	896 (29.3%)	909 (29.5%)	849 (32.3%)	4568 (29.6%)
Chronic diseases
No	3197 (96.1%)	3246 (96.8%)	2929 (95.9%)	2965 (96.1%)	2537 (96.6%)	14874 (96.3%)
Yes	130 (3.9%)	106 (3.2%)	124 (4.1%)	120 (3.9%)	88 (3.4%)	568 (3.7%)

Random Effect Panel Model Results

Depression was constructed as a dependent variable in the model. Independent variables included age, gender, religious belief, hukou, school enrollment, region, current spouse status, parenthood, living in parents’ home, highest level of completed education, BMI, exercise frequency, smoking, drinking, and chronic disease. Detailed variable descriptions are shown in Table 2. A random effects regression model with robust standard errors was adopted to account for unobserved time-invariant individual characteristics using region and year effects. Significant risk factors for depression were female sex (OR = 1.56, 1.39–1.75), religious belief (OR = 1.57, 1.66–2.33), Western region (OR = 1.51, 1.34–1.70), attainment of junior high school/high school education as the highest level (OR = 1.34, 1.19–1.51), smoking (OR = 1.20, 1.04–1.38), high frequency alcohol consumption (OR = 1.49, 1.25–1.79), abnormal weight (OR = 1.13, 1.02–1.04), and chronic diseases (OR = 2.39, 1.93–2.97). The protective factors were agriculture hukou (OR = 0.84, 0.75–0.95), older age (OR = 0.90, 0.81–0.99), on-going school attendance (OR = 0.68, 0.61–0.77), having spouse (OR = 0.84, 0.73–0.97), high frequency exercise (OR = 0.88, 0.77–0.99). The variables of living in parents’ home and parenthood were found to be non-significant factors (p > .05) (See Table 3).

Table 2.

Variable Description.

Category	Variable	Measurement
Dependent	Depression	Sum of K6, CESD-20, and CESD-8 item scores
Sociodemographic FactorsKey Life Events	Age (years old)	1 = 18–21; 2 = 22–24
	Gender	0 = male; 1 = female
	Religious belief	0 = no; 1 = yes
	Hukou	0 = nonagriculture; 1 = agriculture
	Highest level of completed education	0 = primary school and below; 1 = junior high school/ high school 2 = college or above
	Region	1 = east area;2 = central area; 3 = western area; 4 = northeastern area
	Living in parents’ home	0 = no;1 = yes
	Parenthood	0 = no;1 = yes
	Marriage status	0 = have no spouse; 1 = have spouse
	On-going school attendance	0 = no; 1 = yes
Health-related risk factors	BMI	0 = normal weight; 1 = abnormal weight;
	Chronic diseases	0 = no; 1 = yes
	Smoking status	0 = non-smokers; 1 = smokers
	Alcohol consumption frequency	0 = low frequency consumption1 = high frequency consumption
	Physical activity	0 = never; 1 = sometimes; 2 = often

Table 3.

Random Logit Model Estimation Results.

Depression	β	SE	z-value	p-Value	OR [95% CI]
Age	−.11	0.05	−2.02	.043	0.90 [0.81, 0.99]
Gender	.45	0.09	7.52	.000	1.56 [1.39, 1.75]
Religious beliefs	.45	0.32	2.25	.025	1.57 [1.66, 2.33]
Hukou	−.17	0.05	−2.79	.005	0.84 [0.75, 0.95]
Highest level of completed education: Primary school and below
Junior high school/ high school	.29	0.08	4.79	.000	1.34 [1.19, 1.51]
College or above	−.18	0.10	−1.53	.126	0.83 [0.66, 1.05]
Living in parents’ home	−.14	0.07	−1.79	.074	0.87 [0.75, 1.01]
On-going school attendance	−.38	0.04	−6.20	.000	0.68 [0.61, 0.77]
Marriage status	−.17	0.06	−2.34	.019	0.84 [0.73, 0.97]
Parenthood	−.03	0.08	−0.41	.683	0.97 [0.83, 1.13]
Physical activity: Never
Sometimes	.09	0.06	1.63	.102	1.10 [0.98, 1.22]
Often	−.13	0.06	−2.06	.039	0.88 [0.77, 0.99]
Smoking status	.18	0.09	2.50	.012	1.20 [1.04, 1.38]
Alcohol consumption frequency	.40	0.14	4.42	.000	1.49 [1.25, 1.79]
Chronic diseases	.87	0.26	7.93	.000	2.39 [1.93, 2.97]
BMI	.12	0.06	2.40	.016	1.13 [1.02, 1.24]
Region: East area
Central area	−.005	0.065	−0.07	.945	1.0 [0.88, 1.13]
Western area	.41	0.09	6.69	.000	1.51 [1.34, 1.70]
Northeastern area	−.16	0.08	−1.85	.064	0.85 [0.71, 1.00]
Year: 2010
2012	.31	0.097	4.36	.000	1.36 [1.19, 1.57]
2014	.14	0.08	1.90	.057	1.15 [1.00, 1.33]
2016	.22	0.093	2.90	.004	1.24 [1.07, 1.44]
2018	.59	0.14	7.54	.000	1.81 [1.55, 2.11]
Constant	−1.69	0.02	−12.90	.000	0.18 [0.14, 0.24]

Discussion

In this population-based study conducted in China, we found that the percentage of young adults reporting depression has risen significantly from 2010 to 2018. Prevalence of depression was high from the start of this 8-year study period, with almost 22% of respondents screening positive in 2010. In subsequent years, the incidence of depression, although fluctuating slightly, showed a clear upward trend in the last survey. This may be related to delayed and complex transition into adulthood across the major domains of their life course, such as education, work, marriage, and parenthood. A general lack of formal identity, a sense of adulthood, and adequate coping mechanisms managing stressors during emerging adulthood, ultimately contributing to depressive symptoms (Cusack & Merchant, 2013). Another possible explanation is that as societal attitudes toward depression become more accepting, young people may feel more comfortable disclosing their symptoms of depression.

In alignment with prior cross-sectional research, specific demographic factors consistently exhibit correlations with an augmented susceptibility to depression. Notably, attributes such as being female, younger age, religious affiliation, attainment of junior high school/high school education as the highest level, and residing in Western regions are notably associated with a heightened likelihood of experiencing depressive symptoms (Chang et al., 2020; Cui et al., 2022; Lew et al., 2021; Salk et al., 2017). These trends emanate from multifaceted determinants. Gender-related educational biases and constrained opportunities for females might contribute to their elevated vulnerability to depression (Kuang et al., 2023; Salk et al., 2017). Younger individuals, potentially devoid of well-established stress-coping mechanisms, could also be more predisposed to depressive symptoms (Chang et al., 2020). Those whose highest level of education is junior high school/high school represent a demographic that has spent a relatively abbreviated duration in higher education or the workforce, which might lead to underdeveloped coping skills and increased susceptibility to depression. China’s predominantly atheistic context, marked by stringent religious regulations, could conceivably impact the mental well-being of individuals with religious affiliations (Lew et al., 2021). Additionally, regional disparities in economic conditions and healthcare policy provisions across different areas of China could exert significant influence on the prevalence of depressive symptoms (Cui et al., 2022).

In accordance with previous cross-sectional research, specific lifestyle factors consistently demonstrate a correlation with an increased susceptibility to depression. Particularly noteworthy are smoking, frequent alcohol consumption, physical inactivity, and abnormal weight levels, all of which have been linked to heightened proneness to depressive symptoms (Cui et al., 2021; Liao et al., 2020; Marsden et al., 2019; Pascoe & Parker, 2019; Patton et al., 2016). This linkage can be attributed to various underlying mechanisms, including the physiological effects of nicotine in cigarettes, the intricate interplay between alcohol consumption and feelings of guilt, the mood-enhancing impact of engaging in physical activity, and the societal pressures surrounding body image (Cui et al., 2021; Knight et al., 2020; Liao et al., 2020; Pascoe & Parker, 2019; Patton et al., 2016). These findings collectively underscore the significant role that lifestyle choices play in influencing the risk of depression. Furthermore, in alignment with preceding cross-sectional research, a discernible connection exists between chronic diseases and an elevated propensity for experiencing depression (Haarasilta et al., 2004; Mullins et al., 2017). This observed association potentially emanates from cognitive appraisals, particularly those concerning the uncertainties and intrusiveness associated with the experience of illness (Mullins et al., 2017).

The hukou status, a distinct product of China’s intricate residence registration system, is believed to have significantly influenced the transition to adulthood among young Chinese individuals (Kuang et al., 2023). Consequently, our focus lay in discerning the disparities in depression incidence between urban and rural emerging adults. Remarkably, our study unveiled a counterintuitive finding: young adults holding agricultural hukou were surprisingly less prone to experiencing depression in comparison to their counterparts possessing non-agricultural hukou. This finding is inconsistent with most previous studies (Guo et al., 2017; Zhang et al., 2022). China’s society exhibits a dual structure comprising of urban and rural areas, resulting in an unequal distribution of resources, including education. This disparity leads to distinct differences in emerging adulthood between the two populations (Chu, 2020). In rural societies, agriculture serves as the primary industry with robust stability, often resulting in a resistance toward modernization and a propensity toward traditional, non-Western values. Consequently, the criteria for attaining adulthood status in rural areas heavily rely on conventional norms, which can lead to a reduced emphasis on education among young adults, a greater likelihood of early marriage and parenthood, and a relatively structured path toward adulthood that follows a predetermined sequence. It is plausible that residing in a rural environment may offer protective factors for mental health, including stronger familial and community ties, lower stress levels, and a heightened sense of purpose and belonging. On the other hand, urban areas expose individuals to diverse cultures and ideas, thereby emphasizing individualism and independence. This emphasis may contribute to a delay in marriage and having children among urban residents. Additionally, owing to the high population density, fierce competition, and availability of numerous educational opportunities, young individuals in urban areas may face greater uncertainty about their future direction (Chu, 2020; Kuang et al., 2023; Wang & Zhao, 2021).

The study has illuminated a significant relationship between several key life events and depression among young adults, particularly in the context of ongoing schooling and marriage. The existing literature on mental health disparities between young adults who attend college and those who do not is inconclusive, as evidenced by studies conducted by Kovess-Masfety et al. (2016), Han et al. (2016), and Blanco et al. (2008). However, this study shed light on the fact that non-students were more likely to report experiencing depression than their student counterparts. The college environment is known to shape the experiences of emerging adult students, influencing their social age and their self-perception of being in a transitional stage between adolescence and adulthood (Zorotovich & Johnson, 2019). In contrast, young adults who choose not to attend college may feel compelled to pursue employment opportunities primarily for economic independence rather than personal and professional growth. Additionally, they may experience parental and cultural expectations that prioritize marriage and parenthood (Zorotovich & Johnson, 2019). These may influence different perceptions regarding feeling in-betweens and the age of possibilities. According to the study conducted by Zorotovich and Johnson (2019), students reported an increased sense of ambiguity regarding their adulthood status, while also maintaining a more positive outlook toward future possibilities, and this was found to have a positive impact on their mental health. However, more research is needed to fully understand the complex relationship between college attendance, mental health, and emerging adulthood.

Having a spouse also had a significant effect in this analysis, which was consistent with prior studies (Fakhari et al., 2020; Uecker, 2012). However, it is worth noting that the low incidence of marriage among Chinese college students aged 18 to 24 years implies that the findings on the association between marriage and depression may be more applicable to young adults outside of the college environment (Kuang et al., 2023). Married young adults are likely to experience emotional and social support due to the heightened social attachment that comes with marriage (Uecker, 2012). Marriage may also provide a sense of certainty, finality, and satisfaction in achieving one of the milestones of adulthood, which many consider the ultimate accomplishment of adulthood (Cherlin, 2004).

The association between living in parents’ home, parenthood, and depression among young adults in China was found to be insignificant. However, establishing an independent residence is considered a crucial milestone in the transition to adulthood, as per Arnett’s (2000) theory. Recent studies suggest that young adults residing with their families are thriving and express high levels of satisfaction with their living arrangements, along with optimism about their future prospects, as reported by Parker (2012). The link between living arrangements and depressive symptoms may be influenced by the reasons for co-residing with parents, as found in a study by Copp et al. (2017). Furthermore, in cases where employment difficulties are present, these reasons worsen the impact of parent residence dynamics on the development of depressive symptoms. Therefore, incorporating the variable of reasons for living with parents in future studies could provide more insights into the impact of living in parents’ home on depression in young adults. Similarly, it was discovered that parenthood did not have a significant impact on depression levels. This finding aligns with a national study that was carried out in the United States, which reported that there was no difference in depression rates between parents and non-parents regardless of gender (Evenson & Simon, 2005). One potential reason for this outcome is that the benefits of parenthood may have been counterbalanced by the accompanying costs and responsibilities, which could result in a reduction of perceived advantages (Evenson & Simon, 2005). Moreover, it is possible that the results obtained were influenced by the current trend of delaying parenthood among young adults. As a result, fewer individuals between the ages of 18 and 24 years are becoming parents (Wang & Zhao, 2021).

This study is accompanied by certain limitations that warrant acknowledgment. Firstly, the data heavily relies on self-reports provided by young adults who have received medical diagnoses of chronic illnesses; however, these reports lack verification through medical records. This potential lack of corroboration could introduce inaccuracies into the reported medical histories spanning the preceding six months. Nevertheless, the survey’s household-based approach likely mitigated reporting bias concerning diagnosed illnesses. Furthermore, participants were required to furnish the specific names of their diagnosed conditions, thereby facilitating the process of verification. To augment the precision of the data, it is suggested that future research endeavors secure consent to access medical records for validation purposes, while upholding privacy standards. In an effort to enhance the veracity of the self-reported data, a promising avenue would involve cross-referencing these self-reports with actual medical records, particularly within a subset of participants. This cross-validation process would serve to assess the accuracy of self-reporting. Another aspect of concern pertains to recall bias. However, given the relatively youthful age of the respondents (18–24 years) and the inherent significance of events such as smoking, drinking, and physical exercise, the impact of recall bias might be somewhat diminished. To mitigate this potential bias, the consideration of longitudinal studies merits attention. Collecting data in closer proximity to the occurrences in question would attenuate the reliance on memory, thereby enhancing data accuracy.

Conclusions

Our findings provide evidence of a significant 8-year increase in depression prevalence among young adults in China. We’ve identified sociodemographic traits, health-related factors, and major life events as clear risk factors for depression symptoms. This study highlights the issue’s gravity and suggests potential correlations with cultural, regional economic development level, gender, religious, hukou (household registration), lifestyle, chronic health conditions, on-going school attendance, and marital factors. In light of these revelatory insights, we advocate for the implementation of tailored periodic monitoring and screening initiatives aimed at young adults. Furthermore, we underscore the importance of focusing on significant life events to curtail associated risks. Future research should investigate how periods around major life events impact depression trajectories in emerging adults, offering deeper insights into the interplay between life events and mental health outcomes.

Supplemental Material

sj-xlsx-1-yas-10.1177_0044118X231205331 – for Recent Trend in the Prevalence and Correlates of Depression Among Chinese Young Adults from 2010 to 2018

sj-xlsx-1-yas-10.1177_0044118X231205331 for Recent Trend in the Prevalence and Correlates of Depression Among Chine se Young Adults from 2010 to 2018 by Ting Li, Lushaobo Shi, Yi Xia, Zengping Shi and Dong Wang in Youth & Society

This article is distributed under the terms of the Creative Commons Attribution 4.0 License (http://www.creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us-sagepub-com-s.web.bisu.edu.cn/en-us/nam/open-access-at-sage).

Footnotes

Acknowledgements

We are particularly grateful to the team of China Family Panel Studies project which provide assistance in data cleaning.

Author Contributions

Ting Li and Dong Wang contributed to the design of this study. Data analysis was conducted by Ting Li and Lushaobo Shi. Yi Xia and Zengping Shi contributed to the data cleaning. The manuscript was written by Ting Li, with some edits from all authors. All authors approved the final manuscript submitted.

Data Availability Statement

Publicly available datasets were analyzed in this study. These data can be found here: [].

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Public Health Policy Research and Evaluation Key Laboratory Project of the Philosophy and Social Sciences of Guangdong College [grant numbers 2015WSYS0010]; the Public Health Service System Construction Research Foundation of Guangzhou, China[grant number 2021-2023].

Ethical Approval

Ethics approval for the CFPS was obtained from the Biomedical Ethics Committee, Peking University (IRB00001052-14010).

ORCID iD

Dong Wang

Author Biographies

Ting Li is a Ph.D. student at Southern Medical University, Guangzhou, China. Her research focuses on delving into the realm of youth mental health and policy interventions.

Lushaobo Shi is a Ph.D. student at Southern Medical University, focusing on adolescent health behavior and health promotion during adolescence, with a special emphasis on understanding developmental changes in the context of emergencies.

Zengping Shi is a Ph.D. student at Southern Medical University, where her research passion lies in adolescent health, with a particular emphasis on molding and understanding adolescent health behaviors.

Yi Xia is a recent graduate of Southern Medical University in Guangzhou, Guangdong, China. Her research interests revolve around the psychological and behavioral health of children and adolescents, with a specific focus on investigating the diverse factors contributing to depressive symptoms.

Dong Wang, Ph.D., is a Professor of Health Management at Southern Medical University in Guangzhou, China. His research interests include public health policy and the measurement of health behavior, with a specific focus on developing health behavior scales and investigating the influencing factors.

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