Abstract
Objectives
To investigate the prevalence of depression and anxiety symptoms among older adults in an urban district in China, as well as their associated factors.
Design
Cross-sectional study.
Setting
General communities in Shenzhen, Guangdong, China.
Participants
A total of 5372 community-dwelling older adults aged 65 years or older were initially recruited. Ultimately, 5331 participants met the inclusion criteria and were included in this study.
Methods
Participants completed a sociodemographic questionnaire, along with assessments including the Patient Health Questionnaire-9, Generalised Anxiety Scale-7, UCLA Loneliness Simplification Scale, Insomnia Severity Index Scale (ISI), Community Dementia Brief Screening Scale and the 8-item Dementia Screening Questionnaire. Statistical analyses included the Shapiro-Wilk test, independent t-test, Wilcoxon rank test, χ2 test and univariate and multivariate linear regression analysis.
Results
The prevalence of depression and anxiety symptoms among older adults in Shenzhen communities was 10.4% and 11.3%, respectively. In multivariate analysis, age (B=−0.01, p<0.05), relatively poor health status in the past year (B=1.00, p<0.01), poor health status in the past year (B=2.40, p<0.01), ISI score (B=0.21, p<0.01), -item Ascertain Dementia Questionnaire (AD8) score (B=0.22, p<0.01), UCLA Loneliness Scale (ULS) score (B=0.24, p<0.01) were significantly associated with the severity of depression symptom, Compared with their respective reference categories, relatively poor health status in the past year (B=0.50, p<0.01), poor health status in the past year (B=1.32, p<0.01), ISI score (B=0.23, p<0.01), sleep duration (B=0.05, p<0.01), AD8 score (B=0.21, p<0.01), Community Screening Instrument for Dementia score (B=0.13, p<0.01), ULS score (B=0.22, p<0.01) were significantly associated with the severity of anxiety symptom.
Conclusions
We observed a high prevalence of depression and anxiety symptoms among older adults in this study. The existing welfare system and infrastructure should remain and targeted mental health programmes addressing the identified risk factors should be proposed.
Keywords: MENTAL HEALTH, Depression & mood disorders, Anxiety disorders, Old age psychiatry
STRENGTHS AND LIMITATIONS OF THIS STUDY.
This cross-sectional study investigates the prevalence of depression and anxiety symptoms, along with associated factors, in a large sample of community-dwelling elderly individuals in Shenzhen, China.
We reported factors associated with the severity of depressive and anxiety symptoms among Chinese older adults.
Information on both demographic data and health assessment parameters relied on self-report, which introduces the possibility of recall bias.
This study focused specifically on older adults in Shenzhen communities, and caution should be exercised when generalising the findings to other populations or regions.
Introduction
Mental health issues have become increasingly prominent in recent years, and mental disorders represent a growing burden on society. The Global Burden of Diseases Study 2019 confirms that mental disorders continue to rank among the top 10 global burdens.1 In China, anxiety disorders have the highest lifetime prevalence rate, at 7.6%, followed by mood disorders at 7.4%, according to the latest Chinese Mental Health Survey.2 As the elderly population in China continues to grow, studies have reported a high prevalence of mental health issues among this group.3–5 An epidemiological survey of 19 420 Chinese adults aged 60 or older found that 15.94% reported depressive symptoms.6 A systematic review of older adults in care homes showed a prevalence rate of 36.8% for depressive symptoms, and there has been a general increase in the prevalence of depression among older adults in care homes from 1991 to 2019.7 Additionally, a meta-analysis has shown that the overall prevalence of anxiety disorders among ‘empty-nest’ elders in China was as high as 41%.8
Mental disorders in the elderly population are a challenge worldwide, and an increasing number of studies report a high probability of anxiety and depression among this demographic.9–15 In Southern Europe,9 the gender gap in depression prevalence is wider than in Northern Europe, with older men having a 21% lower risk of depression than older women at 38%. This correlation is attributed to psychosocial, socioeconomic and health factors. In Africa,10 depression prevalence among older adults in northwestern Ethiopian communities is 45%, with female sex, widowhood, poor social support and chronic illnesses at higher risk of occurrence. In South Asia,11 a cross-sectional study in Bangladesh found that 55.5% of rural older adults had depression, with gender, age, place of residence, marital status, visual impairment, loneliness, previous falls and fear of falling as important determinants. In a multisite randomised controlled trial of a community-based older adult population in the USA,12 28.1% of 1057 older adults reported mild to severe anxiety symptoms, and 30.1% reported mild to severe depressive symptoms. Studies of mental disorders in elderly populations show that females have a higher risk of developing mental disorders than males.9–11 13–17 This risk is attributed to a combination of biological and social factors, such as greater monthly or lifetime fluctuations in female sex hormones, gender stereotypes and roles, gender inequality, and social stigmatisation.18 19 Gender differences remain a significant factor that cannot be ignored in studies of psychiatric disorders, such as anxiety and depression, in older populations. While gender is often reported as a factor in studies of mental disorders in older Chinese populations,6 20–22 there is less discussion of mental health problems across the genders.
Shenzhen is a pioneering city in the development of its Psychosocial Service System (PSS), with a particular focus on addressing mental health problems among the elderly population. According to the seventh national census conducted in 2020,23 the number of individuals aged 60 and above in Shenzhen was 940 716, accounting for 5.36% of the total population, and 565 217 individuals, or 3.22%, were aged 65 and above. Given the significant attention paid to the mental health of the elderly population in the development of the PSS, this study was conducted as part of a larger survey to investigate the mental health of elderly residents in Shenzhen. The current study aimed to (1) explore the prevalence of depressive and anxiety symptoms among older adults in an urban district in China and (2) identify factors associated with the severity of these symptoms.
Methods
Study population
This study employed a cross-sectional population-based survey research design and used a multistage random whole-group sampling method to select representative samples from 10 districts in Shenzhen. First, we conducted sampling by region and population distribution based on Shenzhen’s 2018 population statistics. One street was randomly selected from each administrative district, and one community was further randomly selected from each street in the first stage. In the second phase, all eligible households were drawn from each of the selected communities. In the final phase, we selected eligible family members from each of the designated families and recruited participants from the selected families from April 2017 to October 2017. To be eligible, participants had to meet the following criteria: (1) aged 65 or above; (2) residing in Shenzhen for at least 6 months and (3) providing informed consent. Participants were excluded if they refused to sign the informed consent form after repeated explanations by the investigator, could not be located on at least three return visits or had a history of a serious medical condition that might prevent them from completing the survey.
From October 2020 to February 2021, a total of 5372 participants were invited to participate in the study and were asked to complete a mental health assessment questionnaire. Of these, 41 participants were excluded due to poor questionnaire completion quality, leaving 5331 participants (99.2%) for data analysis. All eligible participants received mental health education, and those with abnormal assessment results were referred to the nearest mental healthcare facility for counselling and medical care.
Before the formal survey began, the research team recruited investigators who were experienced in mental health work and provided them with structured training sessions. The training covered the purpose of this study, community outreach strategies, proper questionnaire administration and the importance of standardised assessments and research procedures. Investigators used various means such as posters, radio broadcasts and media channels, to promote mental health surveys in the sampled communities and to gain the understanding, attention and cooperation of the community. Investigators then made individual appointments according to the sample list, explained the purpose of the survey, the process and the benefits of participation to the respondents, and obtained consent. A specific time for the face-to-face household survey was then determined. As mental health-related issues are sensitive information, we asked all participants to complete the survey in a private one-on-one setting.
Measurements
A structured questionnaire was used to gather information on the participants’ sociodemographic characteristics and health assessment parameters. The questionnaire required approximately 25 min to complete and consisted of four sections, which included sociodemographic characteristics, lifestyle, physical health and mental health, whether the participants suffer from chronic diseases, is based on the diagnosis of chronic disease by second level and above hospitals. Additionally, a comprehensive self-reported assessment of the participants' health status in the past year was used, with five ratings ranging from good to poor. Drinking behaviour was categorised into three groups: drinkers (one or more drinks per week), previous drinkers who currently abstain from alcohol and non-drinkers. Smoking behaviour was classified into three categories: smokers, individuals with a history of smoking who currently abstain and never smokers.
The Patient Health Questionnaire Depression Scale-9 item (PHQ-9) was used to evaluate the occurrence of depressive symptoms in the participants.24 The PHQ-9 comprises nine items that measure the respondent’s depressive state and severity in the past year, with each item rated on a 4-point scale from 0 (not at all) to 3 (almost every day). The total score ranges from 0 to 27, with higher scores indicating more severe depression. In this study, we used a score of 5 as the cut-off point, with a score greater than or equal to 5 indicating the presence of depressive symptoms and a score less than 5 indicating the absence of depressive symptoms. The Cronbach’s alpha for the scale was 0.805.
The Generalised Anxiety Disorder 7-item scale (GAD-7) was used to assess the occurrence of anxiety symptoms in the participants.25 Respondents recall their anxiety status and severity assessment within the past year, rating each item on a four-point scale from 0 (not at all) to 3 (almost every day), with a total score range of 0–21. Higher scores indicate more severe anxiety in participants. For this study, a score of 5 was used as the threshold, with scores greater than or equal to 5 indicating the presence of anxiety symptoms and scores less than 5 indicating the absence of anxiety symptoms. The Cronbach’s alpha for the scale was 0.911.
A simplified version of the UCLA Loneliness Scale (ULS-6) was used to evaluate the discrepancy between the respondents’ desire for social interaction and their actual level of interaction.26 The ULS-6 was translated and revised in Chinese and comprises six items, each rated on a 4-point scale from 1 (never) to 4 (always). The total score ranges from 6 to 24, with higher scores indicating more severe loneliness. The Cronbach’s alpha for the scale was 0.870.
The Insomnia Severity Index (ISI) was used to evaluate the occurrence and severity of insomnia in the participants, consisting of seven items.27 Respondents are asked to recall their insomnia symptoms in the past month. Each item is rated on a four-point scale ranging from 0 (not at all) to 3 (almost every day). The total score ranges from 0 to 21, with higher scores indicating more severe insomnia symptoms. In this study, a score of 7 was chosen as the threshold, with scores greater than or equal to 7 indicating the presence of insomnia symptoms and scores less than 7 indicating the absence of insomnia symptoms. The Cronbach’s alpha for the scale was 0.929.
The Brief Community Screening Instrument for Dementia (CSI-D) was used to assess the presence of early dementia among the participants.28 The scale includes seven cognitive items, which are ranked in descending order of difficulty as follows: describing the purpose of a hammer, naming the elbow, pointing to the window and then to the door, identifying the location of a nearby shop, identifying the current season, identifying the current week and recalling three words after a delay. The total score on the scale ranges from 0 to 9, with higher scores indicating better cognitive functioning. In this study, a score of 7 was used as the threshold for early dementia, with scores greater than 7 indicating no evidence of early dementia and scores less than or equal to 7 indicating the presence of early dementia. The Cronbach’s alpha for the scale was 0.674.
The eight-item Ascertain Dementia Questionnaire (AD8) was used to assess early mild cognitive impairment (MCI) on an eight-item scale.29 These items include diminished assertiveness, reduced engagement in hobbies, repetition of the same thing, difficulty in learning new things, forgetting the current year, difficulty handling complex financial matters, difficulty recalling appointments with others and problems with memory and thinking. The total score ranges from 0 to 8, with higher scores indicating more severe cognitive impairment. In this study, a score of 2 was used as the nodal point, with scores greater than or equal to 2 indicating possible MCI and scores less than 2 indicating normal cognitive functioning. The Cronbach’s alpha for this scale was 0.796.
Statistical analysis
The statistical analysis was conducted by using R V.4.1.0. Means and SD are used to describe continuous variables, while frequencies and percentages are used for categorical variables. The Shapiro-Wilk test was used to check the normality of continuous variables. The Shapiro-Wilk test was used to check the normality of continuous variables. Student’s independent t-test or Wilcoxon’s rank test was used to compare quantitative variables, depending on the normal distribution test results. The χ2 test was used for categorical variables. The R package ‘compareGroups’ was used for descriptive analysis.30 Dummy variables were created for categorical variables, and the first reference group for each variable was set as the reference group for regression analysis. One-way linear regression was used to identify the factors associated with depressive symptoms and anxiety symptoms. The variables that were statistically significant in the univariate analysis were included in a multifactorial stepwise linear regression model to evaluate the relationship between depressive symptoms and anxiety symptoms. The analyses were performed using the R packages ‘car’ and ‘MASS’.31 32 The test level was set to a two-sided alpha of 0.05, and a p<0.05 was considered statistically significant.
Participant involvement
This study was conducted without patient and public involvement.
Results
Sociodemographic and other characteristics of participants
We present a summary of the sociodemographic and mental health status of all participants in table 1. A total of 5372 elderly community members were surveyed, and 5368 questionnaires were returned, yielding a return rate of 99.9%. After quality screening by the researcher, 37 individuals were excluded, leaving 5331 community elders, of whom 2472 were males and 2859 were females. There were no statistically significant differences in basic characteristics between those with missing data and those without missing data, as shown by the analysis of variables with missing data in the psychological assessment questionnaires for community residents and community elders (p≥0.05).
Table 1.
Basic information of participants
| Variables | Both gender (n=5331) |
Male (n=2472) |
Female (n=2859) |
P value |
| Age (mean, SD) | 71.0 (5.84) | 70.9 (5.69) | 71.0 (5.97) | 0.344 |
| Education (n, %) | <0.001 | |||
| Primary school and below | 2230 (41.8) | 703 (28.4) | 1527 (53.4) | |
| Junior high school | 1386 (26.0) | 749 (30.3) | 637 (22.3) | |
| High school/vocational secondary school | 1031 (19.3) | 571 (23.1) | 460 (16.1) | |
| College | 667 (12.5) | 435 (17.6) | 232 (8.11) | |
| Master and above | 17 (0.32) | 14 (0.57) | 3 (0.10) | |
| Occupation (n, %) | <0.001 | |||
| Civil servant and administrator | 383 (7.18) | 230 (9.30) | 153 (5.35) | |
| Professional technician | 847 (15.9) | 467 (18.9) | 380 (13.3) | |
| Service industry logistician | 449 (8.42) | 212 (8.58) | 237 (8.29) | |
| Farmer | 1837 (34.5) | 681 (27.5) | 1156 (40.4) | |
| Labourer | 898 (16.8) | 439 (17.8) | 459 (16.1) | |
| Military personnel | 63 (1.18) | 57 (2.31) | 6 (0.21) | |
| Others | 854 (16.0) | 386 (15.6) | 468 (16.4) | |
| Marriage (n, %) | <0.001 | |||
| Unmarried/divorced/widowed | 987 (18.5) | 197 (7.97) | 790 (27.6) | |
| Married | 4344 (81.5) | 2275 (92.0) | 2069 (72.4) | |
| Suffering from chronic diseases | <0.001 | |||
| No | 1737 (32.6) | 900 (36.4) | 837 (29.3) | |
| Yes | 3594 (67.4) | 1572 (63.6) | 2022 (70.7) | |
| Monthly personal income (n, %) | <0.001 | |||
| ≤US$216.74 | 2094 (39.3) | 799 (32.3) | 1295 (45.3) | |
| US$216.89–US$433.63 | 1017 (19.1) | 422 (17.1) | 595 (20.8) | |
| US$433.78–US$722.82 | 1205 (22.6) | 638 (25.8) | 567 (19.8) | |
| US$722.96–US$1156.59 | 757 (14.2) | 432 (17.5) | 325 (11.4) | |
| ≥US$1156.74 | 258 (4.84) | 181 (7.32) | 77 (2.69) | |
| Drinking (n, %) | <0.001 | |||
| Non-drinker | 4127 (77.4) | 1483 (60.0) | 2644 (92.5) | |
| Ex-drinker | 444 (8.33) | 364 (14.7) | 80 (2.8) | |
| Current drinker | 760 (14.3) | 625 (25.3) | 135 (4.72) | |
| Smoking (n, %) | <0.001 | |||
| Non-smoker | 4195 (78.7) | 1382 (55.9) | 2813 (98.4) | |
| Ex-smoker | 505 (9.5) | 487 (19.7) | 18 (0.6) | |
| Current smoker | 631 (11.8) | 603 (24.4) | 28 (0.98) | |
| Health status in the past year | <0.001 | |||
| Good | 801 (15.0) | 415 (16.8) | 386 (13.5) | |
| Relatively good | 1739 (32.6) | 874 (35.4) | 865 (30.3) | |
| Ordinary | 2224 (41.7) | 951 (38.5) | 1273 (44.5) | |
| Relatively poor | 470 (8.82) | 190 (7.69) | 280 (9.79) | |
| Poor | 97 (1.82) | 42 (1.70) | 55 (1.92) | |
| Loneliness (ULS-6) (mean, SD) | 7.51 (2.79) | 7.41 (2.68) | 7.60 (2.89) | 0.010 |
| Sleep duration (mean, SD) | 6.93 (1.75) | 7.15 (1.66) | 6.73 (1.80) | <0.001 |
| Insomnia (ISI) (mean, SD) | 3.96 (4.88) | 3.24 (4.35) | 4.58 (5.21) | <0.001 |
| Insomnia | <0.001 | |||
| No | 4272 (80.1) | 2092 (84.6) | 2180 (76.3) | |
| Yes | 1059 (19.9) | 380 (15.4) | 679 (23.7) | |
| Mean score of the PHQ-9 (mean, SD) | 1.19 (3.13) | 1.04 (2.93) | 1.32 (3.28) | 0.001 |
| Depressive symptoms (n, %) | 0.001 | |||
| No | 4777 (89.6) | 2252 (91.1) | 2525 (88.3) | |
| Yes | 554 (10.4) | 220 (8.90) | 334 (11.7) | |
| Mean score of the GAD-7 (mean, SD) | 1.38 (2.78) | 1.16 (2.56) | 1.57 (2.95) | <0.001 |
| Anxiety symptoms (n, %) | <0.001 | |||
| No | 4726 (88.7) | 2249 (91.0) | 2477 (86.6) | |
| Yes | 605 (11.3) | 223 (9.02) | 382 (13.4) | |
| Mean score of the CSID (mean, SD) | 8.23 (1.23) | 8.24 (1.21) | 8.22 (1.24) | 0.572 |
| Mild cognitive impairment | 0.964 | |||
| No | 4302 (80.7) | 1996 (80.7) | 2306 (80.7) | |
| Yes | 1029 (19.3) | 476 (19.3) | 553 (19.3) | |
| Mean score of the AD8 (mean, SD) | 0.98 (1.68) | 0.88 (1.60) | 1.07 (1.74) | <0.001 |
| Early dementia | <0.001 | |||
| No | 4168 (78.2) | 1998 (80.8) | 2170 (75.9) | |
| Yes | 1163 (21.8) | 474 (19.2) | 689 (24.1) |
AD8, 8-item Ascertain Dementia Questionnaire; CSID, Community Screening Instrument for Dementia; GAD-7, Generalised Anxiety Disorder 7-item; ISI, Insomnia Severity Index; PHQ-9, Patient Health Questionnaire Depression Scale-9 item; ULS-6, UCLA Loneliness Scale.
Table 1 summarises the sociodemographic and mental health status of the study participants. The study included 5331 community elders, with an average age of 71.0±5.84 years. The majority of participants had a primary school education or less (41.8%) and were either farmers (34.5%) or labourers (16.8%). Most participants were married (81.5%) and reported having chronic illnesses (67.4%). The monthly income per person of most participants was less than US$216.74, and fewer participants drink alcohol (14.3%) or smoke (11.8%). The average ULS-6 score was 7.51±2.79, and the average sleep duration was 6.93±1.75 hours. The mean ISI score was 3.96±4.88, with 19.9% (1059/5331) of participants meeting the criteria for insomnia. The average PHQ-9 score was 1.19±3.13, with 10.4% (554/5331) of participants exhibiting depressive symptoms. The average GAD-7 score was 1.38±2.78, with 11.3% (605/5331) of participants exhibiting anxiety symptoms. The mean CSI-D score was 8.23±1.23, with 19.3% (1029/5331) of participants exhibiting MCI. The mean AD8 score was 0.98±1.68, with 21.8% (1163/5331) of participants exhibiting early dementia.
We observed significant gender differences in several areas. Among the population surveyed in this study, men were more likely to have a higher level of education, as a larger proportion of men than women had completed primary school or above (71.6% vs 46.6%). In contrast, women were more likely to be single (27.6% vs 7.97%), have a lower monthly income (≤US$216.74: 45.3% vs 32.3%), have a chronic illness (70.7% vs 63.6%) and report fair or poor health (56.2% vs 47.8%). Women had higher mean scores on the PHQ-9 (1.32 vs 1.04) and GAD-7 (2.95 vs 2.56) than men, and women were more likely to have symptoms of depression (11.7% vs 8.90%) and anxiety (13.4% vs 9.02%). Further details are presented in table 1.
Analysis of risk factors for depressive symptoms
The results of the one-way linear regression analysis indicated that several variables were significantly associated with depressive symptoms, including gender, age, having a chronic illness, alcohol consumption, smoking, past year health status, insomnia score, sleep duration, AD8 score, CSID score and ULS score (as shown in table 2). Therefore, these significant variables were included in the multifactor linear regression model, which used stepwise regression to exclude non-significant variables. Those excluded were gender, chronic illness, alcohol consumption, smoking, sleep duration and CSID score. Ultimately, age, the past year’s health status, insomnia score, AD8 score and ULS score were included in the final model (as shown in table 3). The final model demonstrated an AIC of 25 399.01 and a BIC of 25 517.48, with an adjusted R2 of 0.3007. This suggests that the final multiple regression model, comprising all independent variables, accounts for approximately 30% of the variance in depression levels, indicating that the included predictors collectively explain 30% of the variability in the dependent variable.
Table 2.
Results of univariate linear regression analysis for depressive and anxiety symptoms
| Variables | PHQ-9 score | GAD-7 score | ||||
| Estimate | 95% CI | Estimate | 95% CI | |||
| Lower | Upper | Lower | Upper | |||
| Gender | ||||||
| Male | – | – | – | – | – | – |
| Female | 0.29* | 0.12 | 0.45 | 0.41* | 0.26 | 0.56 |
| Age | 0.02† | 0.00 | 0.03 | −0.01 | −0.02 | 0.00 |
| Education | ||||||
| Primary school and below | – | – | – | – | – | – |
| Junior high school | 0.08 | −0.13 | 0.29 | 0.06 | −0.12 | 0.25 |
| High school/vocational secondary school | 0.08 | −0.15 | 0.31 | 0.00 | −0.20 | 0.21 |
| College | 0.17 | −0.1 | 0.44 | 0.00 | −0.24 | 0.24 |
| Master and above | 0.75 | −0.74 | 2.24 | −0.01 | −1.34 | 1.32 |
| Occupation | ||||||
| Civil servant/administrator | – | – | – | – | – | – |
| Professional technician | 0.28 | −0.1 | 0.65 | 0.24 | −0.1 | 0.58 |
| Service industry logistician | −0.14 | −0.57 | 0.28 | 0.3 | −0.08 | 0.68 |
| Farmer | −0.07 | −0.41 | 0.28 | 0.18 | −0.13 | 0.48 |
| Labourer | −0.19 | −0.57 | 0.18 | 0.11 | −0.22 | 0.44 |
| Military personnel | −0.03 | −0.87 | 0.8 | −0.16 | −0.9 | 0.58 |
| Others | 0.04 | −0.33 | 0.42 | 0.07 | −0.26 | 0.41 |
| Marriage | ||||||
| Unmarried/divorced/widowed | – | – | – | – | – | – |
| Married | −0.09 | −0.3 | 0.13 | 0.08 | −0.12 | 0.27 |
| Suffering from chronic diseases | ||||||
| No | – | – | – | – | – | – |
| Yes | 0.98* | 0.8 | 1.16 | 0.85* | 0.69 | 1 |
| Monthly personal income | ||||||
| ≤US$216.74 | – | – | – | – | – | – |
| US$216.89–US$433.63 | −0.06 | −0.29 | 0.18 | −0.1 | −0.31 | 0.11 |
| US$433.78–US$722.82 | −0.01 | −0.23 | 0.21 | −0.08 | −0.28 | 0.12 |
| US$722.96–US$1156.59 | 0.06 | −0.2 | 0.32 | −0.06 | −0.29 | 0.17 |
| ≥US$1156.74 | −0.34 | −0.74 | 0.06 | −0.34 | −0.7 | 0.02 |
| Drinking | ||||||
| Non-drinker | – | – | – | – | – | – |
| Ex-drinker | 0.48* | 0.18 | 0.79 | 0.22 | −0.05 | 0.49 |
| Current drinker | −0.37* | −0.61 | −0.13 | −0.28† | −0.49 | −0.06 |
| Smoking | ||||||
| Non-smoker | – | – | – | – | – | – |
| Ex-smoker | 0.01 | −0.28 | 0.3 | 0.10 | −0.15 | 0.36 |
| Current smoker | −0.27† | −0.53 | −0.01 | −0.34* | −0.57 | −0.11 |
| Health status in the past year | ||||||
| Good | – | – | – | – | – | – |
| Relatively good | 0.27† | 0.02 | 0.51 | 0.31* | 0.09 | 0.54 |
| Ordinary | 1.05* | 0.81 | 1.29 | 1.06* | 0.85 | 1.28 |
| Relatively poor | 2.83* | 2.49 | 3.17 | 2.33* | 2.02 | 2.63 |
| Poor | 5.16* | 4.53 | 5.79 | 3.99* | 3.43 | 4.56 |
| ISI score | 0.29* | 0.28 | 0.31 | 0.29* | 0.28 | 0.3 |
| Sleep duration | −0.32* | −0.37 | −0.27 | −0.30* | −0.35 | −0.26 |
| AD8 score | 0.52* | 0.47 | 0.57 | 0.47* | 0.42 | 0.51 |
| CSID score | −0.28* | −0.35 | −0.21 | −0.14* | −0.2 | −0.08 |
| ULS score | 0.42* | 0.39 | 0.44 | 0.38* | 0.36 | 0.41 |
*Indicates p<0.05.
†Indicates p<0.01.
AD8, 8-item Ascertain Dementia Questionnaire; CSID, Community Screening Instrument for Dementia; GAD-7, Generalised Anxiety Disorder 7-item; ISI, Insomnia Severity Index; PHQ-9, Patient Health Questionnaire Depression Scale-9 item; ULS, UCLA Loneliness Scale.
Table 3.
Results of multivariate linear regression analysis of depression and anxiety symptoms
| Variables | PHQ-9 score | GAD-7 score | ||||
| Estimate | 95% CI | Estimate | 95% CI | |||
| Lower | Upper | Lower | Upper | |||
| Gender | ||||||
| Male | – | – | – | – | – | – |
| Female | −0.13 | −0.30 | 0.04 | 0.08 | −0.07 | 0.22 |
| Age | −0.01* | −0.03 | 0.00 | – | – | – |
| Suffering from chronic diseases | ||||||
| No | – | – | – | – | – | – |
| Yes | 0.15 | −0.01 | 0.31 | 0.07 | −0.07 | 0.21 |
| Drinking | ||||||
| Non-drinker | – | – | – | – | – | – |
| Ex-drinker | 0.28 | 0.00 | 0.56 | 0.07 | −0.18 | 0.31 |
| Current drinker | −0.07 | −0.29 | 0.15 | 0.06 | −0.13 | 0.25 |
| Smoking | ||||||
| Non-smoker | – | – | – | – | – | – |
| Ex-smoker | −0.18 | −0.46 | 0.10 | 0.13 | −0.11 | 0.37 |
| Current smoker | 0.03 | −0.22 | 0.27 | 0.07 | −0.15 | 0.28 |
| Health status in the past year | ||||||
| Good | – | – | – | – | – | – |
| Relatively good | −0.07 | −0.29 | 0.16 | −0.02 | −0.21 | 0.17 |
| Ordinary | 0.08 | −0.15 | 0.30 | 0.10 | −0.10 | 0.29 |
| Relatively poor | 1.00† | 0.67 | 1.32 | 0.50† | 0.22 | 0.78 |
| Poor | 2.40† | 1.82 | 2.98 | 1.32† | 0.82 | 1.82 |
| ISI score | 0.21† | 0.19 | 0.23 | 0.23† | 0.21 | 0.25 |
| Sleep duration | 0.03 | −0.02 | 0.07 | 0.058* | 0.01 | 0.09 |
| AD8 score | 0.22† | 0.18 | 0.27 | 0.21† | 0.17 | 0.25 |
| CSID score | 0.00 | −0.06 | 0.06 | 0.13† | 0.07 | 0.18 |
| ULS score | 0.24† | 0.22 | 0.27 | 0.22† | 0.20 | 0.24 |
| R2 | 0.3028 | 0.3409 | ||||
| Adjusted R2 | 0.3007 | 0.339 | ||||
| F value | 144.2 | 183.2 | ||||
| P value | 0.001 | 0.001 | ||||
| AIC | 25 399.01 | 23 843.92 | ||||
| BIC | 25 517.48 | 23 955.8 | ||||
*Indicates p<0.05.
†Indicates p<0.01.
AD8, 8-item Ascertain Dementia Questionnaire; AIC, Akaike information criterion; BIC, Bayesian information criterion; CSID, Community Screening Instrument for Dementia; GAD-7, Generalised Anxiety Disorder 7-item; ISI, Insomnia Severity Index; PHQ-9, Patient Health Questionnaire Depression Scale-9 item; ULS, UCLA Loneliness Scale.
Table 3 shows that each unit increase in insomnia score, AD8 and ULS was associated with a respective increase of 0.21, 0.22 and 0.24 units in the severity of depressive symptoms (PHQ-9 score). Participants who reported poorer health in the past year had a severity of depressive symptoms 1 unit higher than those who reported good health. Individuals who reported fair or poor health had a severity of depressive symptoms 2.4 units higher than those who reported good health.
Analysis of risk factors for anxiety symptoms
The results of the one-way linear regression analysis for anxiety symptoms showed significant associations with gender, chronic illness, alcohol consumption, smoking, past year health status, insomnia score, sleep duration, AD8 score, CSID score and ULS score (as shown in table 2). When multifactor linear regression analysis was conducted with these significant variables, gender, chronic illness, alcohol consumption and smoking were excluded in the stepwise regression process. The final model included the past year’s health status, insomnia score, sleep duration, total AD8 score, total CSID score and ULS score (as shown in table 3). The final model had an AIC of 23 843.92 and a BIC of 23,955.8, with an adjusted R2 of 0.339. The independent variables entered into the final model explained approximately 33% of the variation in anxiety levels.
Table 3 shows that every unit increase in the insomnia score, AD8 score and ULS score was associated with an increase of 0.23, 0.21 and 0.22 units in the severity of anxiety symptoms (GAD-7 score), respectively. Additionally, for every unit increase in the CSID score, there was an associated 0.13 unit increase in anxiety symptom severity. Participants who reported relatively poor health in the past year had a 0.5-unit higher severity of anxiety symptoms than those who reported good health. Those who reported poor health had a 1.32-unit higher severity of anxiety symptoms than those who reported good health.
Discussion
We identified the following key findings in this sample of 5331 community-dwelling older adults in Shenzhen: (1) The prevalence of depression and anxiety in community-dwelling older adults was 10.4% and 11.3%, respectively, which are higher than the national population average prevalence of anxiety and depression reported in the 2019 Chinese mental health survey. (2) Gender, chronic illness, drinking alcohol, smoking, health status in the past year, insomnia score, sleep duration, CSID score (MCI) and AD8 score (early dementia) were associated with symptoms of anxiety and depression. (3) Significant differences in gender were found in the univariate regressions for depression and anxiety (p<0.01), but these differences were not significant when included in a multifactorial linear model.
Mental disorders and their risk factors are intricately linked, and the findings on them can be influenced by the social context and target population. In Europe, the prevalence of depression in older adults is lower in Northern Europe but higher in Southern Europe, with gender-specific differences in its prevalence.9 In the USA, a multisite randomised controlled trial of community-based older adults reported that 28.1% of participants had mild to severe anxiety symptoms and 30.1% had mild to severe depressive symptoms, with differences in prevalence between ethnic groups.12 The current study found that the prevalence of depression and anxiety in the community elderly population in Shenzhen was 10.4% and 11.3%, respectively, which was higher than the results of the national population-based epidemiological survey on mental disorders conducted by Huang et al,2 but lower than the results of studies on the mental health of the elderly population in other regions of China.6 8 21 Regional differences in study populations greatly influence the findings. Rural empty-nest elders have been reported to have a higher prevalence of depression and anxiety than urban community elders,17 33 and nursing home elders have been reported to have a higher prevalence of depression and anxiety than homebound elders.7 34 Moreover, a positive social environment and social welfare are crucial in promoting the physical and mental health of elderly individuals.35–37 In recent years, the Shenzhen government has actively promoted the construction of an age-friendly city, leading to a significant improvement in the living environment and quality of life of the elders.38–40
In our study, we observed that both the CSID score and the AD8 score contributed significantly to the final depression and anxiety prediction model. These scales play a crucial role in assessing cognitive impairment, which is an important aspect to consider in the elderly population. The presence of early cognitive impairment in the elderly population indicates that higher cognitive functions of the elderly brain have become impaired, and the presence of these as independent dimensions of mental disorders such as depression and anxiety is an extremely important marker in the prevention and early treatment of mental health, and even for the prevention of dementia. An observational study demonstrated that more than 70% of older patients initially diagnosed with pseudodementia progressed to overt dementia over a span of at least 5 years. This finding emphasises that cognitive impairment in older adults with moderate to severe depression serves as a robust predictor of future dementia development.41 A Canadian study indicated that cognitive impairment, rather than dementia itself, was a predictor of long-term use of antianxiety, sedative and hypnotic medications. Furthermore, it increased the likelihood of psychiatric visits. Similarly, in individuals without psychiatric events, symptoms of cognitive impairment, but not dementia, were linked to a higher likelihood of antidepressant usage.42
Recent health status is also significantly associated with the mental health of the elderly population.43–51 The current study revealed that the health status of the elderly population in the past year was a crucial factor contributing to the occurrence of depression and anxiety. A meta-analysis of older Chinese adults with depressive symptoms through 2019 identified poor self-rated health, two or more cardiovascular diseases, and functional impairment as risk factors for depressive symptoms.48 Similarly, a cross-sectional study involving patients with respiratory disease highlighted the significant association between physical activity limitations, sleep disturbances, and depressive and anxiety symptoms.49 A study conducted in the USA investigated the relationship between chronic illness, mobility limitations and depressive symptoms in individuals over 65 years of age. The findings indicated that improving mobility limitations through prevention and treatment strategies played a vital role in reducing depressive symptoms among older adults with chronic illnesses.50 Maintaining a healthy lifestyle has emerged as a critical factor in safeguarding mental health. In our current study, insomnia was identified as one of the influencing factors contributing to the occurrence of depression and anxiety among the elderly population. These findings align with several previously published studies that have reported sleep deprivation as a significant risk factor for depression and anxiety in older adults.34 51 52 The current study revealed statistically significant roles of smoking and drinking in the univariate regression analysis of depression and anxiety symptoms, but these variables did not ultimately enter the multivariate linear model, suggesting that there had no impact in the mental health status of elderly individuals in Shenzhen. This observation contrasts with some domestic and international studies investigating risk factors for mental health in older populations.6 9 14 This may be attributed to the existence of smoking and drinking differences in the factors that eventually entered the model itself, or more likely, to differences in the target population and assessment tools employed in this study.52
Gender has been consistently reported as a significant factor in most studies investigating mental disorders.9 11 13 16 17 53–55 In our study, the univariate regression analysis revealed statistically significant differences in depression and anxiety symptoms based on gender. Women exhibited higher average levels of depression and anxiety, with increases of 0.29 and 0.41 units, respectively, compared with men. However, these gender differences did not enter the multivariate linear model, indicating no disparities in the mental health status of the elderly population in the Shenzhen community based on gender. Gender, as an individual factor, holds importance beyond biological and psychosocial factors, influencing social systems and contributing to gender disparities in mental health among the elderly population. The survey conducted here revealed that older women had lower literacy levels, occupied fewer administrative and technical positions before retirement, had limited upward mobility opportunities in their younger years (approximately 40.4% were farmers) and currently had lower monthly incomes than men (approximately 45.3% earned less than US$216.89 per month). A study investigating the gender prevalence and influencing factors of depression among older adults in rural China also found that women faced disadvantages in terms of education and income levels, which aligns with the results of our survey.16 However, our study conducted among the elderly population in the Shenzhen area did not demonstrate any gender differences. We believe this could be attributed to two factors. First, the progress of society and the advancement of gender equality over the past few decades have granted older women access to the same social resources as men, resulting in a shift from a disadvantaged social role to one of equality. Second, the rapid development of Shenzhen in terms of social welfare, infrastructure and medical resources, exemplified by policy concepts like ‘Elderly Well-being’ and psychological healthcare initiatives like ‘Peace of Mind Action,’ has facilitated greater social participation among the elderly in the community.38 40 56 57 This has led to increased engagement in frequent and diverse social activities, reducing the risk of depression and other mental disorders.58 It is important to acknowledge that variations exist in research studies on mental health conducted in different regions, including differences in research methods and measurement tools. Consequently, no definitive conclusions can be drawn regarding the prevalence rates of depression and anxiety in different populations and regions. Nonetheless, our study suggests that there are no gender differences in the risk factors for depression and anxiety among the elderly population in Shenzhen. This can be attributed to the strides made in terms of gender equality and social development in Shenzhen, as it has emerged as a new first-tier city of China.
This study is a part of the Mental Health Survey of the Elderly Population in Shenzhen, which aims to investigate the current prevalence of common mental disorders among the elderly population in Shenzhen and to specifically examine gender differences. It is important to acknowledge the limitations of this study. First, the data relied on self-reports from the participants, which introduces the possibility of information bias. Second, our study did not gather additional information on chronic illnesses, functional limitations in daily living or physiological indicators such as body mass index. This lack of data hindered our ability to comprehensively address gender differences in demographic characteristics, health factors and psychosocial factors. Consequently, we were unable to accurately identify the mechanisms through which gender characteristics might influence depressive and anxiety disorders. Nevertheless, we encourage and hope that future researchers will incorporate these variables into their studies to gain a better understanding of the relationship between gender and mental health within the elderly population. Third, our study employed a cross-sectional design, which limits our ability to establish causal relationships between depression/anxiety and factors such as cognitive functioning, degree of dementia, self-reported health status and sleep deprivation. Longitudinal studies are required to elucidate and confirm these relationships among older adults in the Shenzhen, China, community.
Conclusion
In conclusion, this cross-sectional study aimed to examine the prevalence of depression and anxiety among older adults in the Shenzhen community of China and analyse the associated factors. Our findings revealed significant associations between depressive and anxiety symptoms and health status, sleep quality and early cognitive impairment. These results have important implications for future policies, suggesting the need to sustain the existing welfare system and infrastructure development while implementing targeted mental health hygiene programmes to address depression and anxiety among older Chinese individuals.
Supplementary Material
Acknowledgments
We sincerely thank all the participants for their collaboration. We sincerely acknowledge all investigators for their rigorous work.
Footnotes
XP and SZ contributed equally.
Contributors: XP and SZ participated in the study design and data analysis, drafted and revised the paper. LY monitored data collection, participated in the data analysis. WH and SJ assisted in data collection. JW initiated the project, designed the study and revised the paper. All authors had full access to all the data in the study and took responsibility for the integrity of the data and the accuracy of the data analysis. All authors read and approved the final manuscript. JH, as the guarantor, is responsible for the overall situation of this study and made the decision on the final publication of the manuscript.
Funding: This study was supported by Shenzhen Science and technology research and Development Fund for Sustainable development project (KCXFZ20201221173613036, Shenzhen Fund for Guangdong Provincial High-level Clinical Key Specialties (No. SZGSP013), and the Shenzhen Key Medical Discipline Construction Fund (No. SZXK072), and JW is the PI of the project.
Competing interests: None declared.
Patient and public involvement: Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Provenance and peer review: Not commissioned; externally peer reviewed.
Data availability statement
Data are available in a public, open access repository. Extra data can be accessed via the Dryad data repository at http://datadryad.org/withthedoi:10.5061/dryad.bnzs7h4j1
Ethics statements
Patient consent for publication
Not applicable.
Ethics approval
This study involves human participants and the Ethics Committee of Shenzhen Kangning Hospital reviewed and approved the protocol, including the informed consent process and approved analysis of deidentified data (KN-2023-06-05-1). Participants gave informed consent to participate in the study before taking part.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Data are available in a public, open access repository. Extra data can be accessed via the Dryad data repository at http://datadryad.org/withthedoi:10.5061/dryad.bnzs7h4j1