Abstract
The objectives of this study were to assess the trends in anxiety and depression levels in older adults nearly 10 months after the outbreak of coronavirus disease 2019 (COVID-19) and explore its determinants. A longitudinal study was performed between October 2019 and December 2020. The Patient Health Questionnaire 9-Item Scale and the Generalized Anxiety Disorder 7-Item Scale were used to assess depression and anxiety. Data were collected before (wave 1), during (wave 2), and 10 months after the COVID-19 outbreak (wave 3). The prevalence of depressive symptoms in the elderly was found to be 18.9%, 28.1%, and 35.9% at wave 1, wave 2, and wave 3 respectively. The prevalence of depressive symptoms at wave 1 was lower than that at wave 2 (χ2 = 15.544, P < 0.001) and wave 3 (χ2= 44.878, P < 0.001). There was no significant change in the prevalence of anxious symptoms (wave 1, 28.5%, wave 2, 30.3%, and wave 3, 30.3%). Older adults who were single/divorced/widowed had higher levels of anxiety compared with those who were married (OR = 2.306 95%CI 1.358–3.914, P = 0.002). The pandemic appeared to be associated with increases in depressive symptoms in older persons. Targeted interventions could be carried out among those with higher risk of maladjustment.
Keywords: COVID-19, Older persons, Depression, Anxiety, Longitudinal
1. Introduction
Coronavirus disease 2019 (COVID-19) was detected in December 2019 and quickly became a global public health problem in early 2020 [1]. The COVID-19 pandemic has been a challenge for public psychological well-being not only by its contagion and severe health consequences, but also the strict control measures applied by the government, such as home quarantine and travel restrictions. Moreover, many people are stressed by the uncertainty about the future of living, working, and studying in the post-pandemic era.
Some studies have reported that the COVID-19 pandemic may have a negative impact on the psychological status of the general population [2,3]. It was found that the majority of the general population experienced negative emotions related to the COVID-19 pandemic [4,5]. And the most significant mental disorders were uncovered as anxiety, depression, and post-traumatic stress disorder (PTSD) symptoms [6], with relatively high rates of 6.33%–50.9%, 14.6%–48.3%, and 7%–53.8%, respectively [7]. Preexisting mood disorders were found to aggravate the health consequences of COVID-19 by the increased risk of hospitalization and even death [8]. What's stunning is the long-term negative impact of the pandemic on mental health. The frequency of depression after 12 weeks of the COVID-19 infection ranged from 11% to 28% [9]. And persistent fatigue and cognitive impairment have been noticed after 12 weeks of infection as well [10]. An interesting finding is that the population's vulnerability to adverse mental consequences differs from country to country. According to a chain mediation model study involving 4612 participants from 3 continents (including America, Asia, and Europe) Poland and the Philippines were the two countries with the highest levels of anxiety, depression, and stress, while Vietnam had the lowest mean scores in these areas [11]. And the need for health information and the perceived impact of the pandemic mediated the impact of individuals' physical symptoms on their mental adaptation [11]. Moreover, a study focusing on the impact of COVID-19 in 7 developing Asian countries reported that individuals' sociodemographic backgrounds, such as age, education, and marital status were associated with their mental outcomes [12]. On top of this, the stringent measures enacted by the government to contain the spread of the pandemic [13], the lockdown policy [14], the social isolation and its related unemployment and economic loss [15], and even the requirement of wearing face masks [16] were reported to be able to influence individuals' mental outcomes.
Some special populations, such as frontline health professionals and patients with mental and chronic illnesses [eg: hypertension, diabetes mellitus, cardiovascular or cerebrovascular disease, respiratory disease and obesity [17]], are inevitably affected by the COVID-19 outbreak and consequently have more severe and complex psychological problems [18,19]. A study explored the perception of the exposure risk to COVID-19 among health professionals found that participants who worked in the emergency or intensive care departments were more likely to report a higher exposure risk [20]. It was disclosed that gender and working departments would affect health professionals’ attitudes and perceived work pressure toward COVID-19 [21]. A study collected data among health workers in five countries in the Asia-Pacific region revealed that non-medically trained personnel, the presence of physical symptoms and the presence of prior medical conditions were independent predictors of adverse psychological outcomes [22]. Interestingly, nurses are more likely to maintain psychologically intact when compared with other healthcare workers according to an Asia-pacific study based on machine learning [23]. Moreover, researchers even postulated that physical symptoms and psychological outcomes were associated bidirectionally in this special population [24].
Older persons were reported to be most susceptible to COVID-19 and have the highest mortality rate compared with the general population. Hence, they were more likely to have psychological problems, such as anxiety, depression, fear, stress, and other negative feelings [25], which could be exacerbated by their existing diseases. Meanwhile, previous studies have demonstrated the negative health impact of anxiety and depression in older persons, including increased risk of stroke, sleep disturbances, cognitive decline, and reduced quality of life [[26], [27], [28]]. However, one systematic review of 25 studies reported that the psychological impact of the COVID-19 lockdowns was small on average and suggested that most people were resilient during the first months of the pandemic [29]. The findings from previous studies are inconsistent. This suggested that the psychological impact of COVID-19 on the population, and especially on the elderly, still needs further research.
In the general population, negative emotions, such as anxiety and depression, were reported to vary across different emotional stages of the pandemic of COVID-19. These stages could be roughly divided into stages of confusion, panic, boredom, and adjustment [30]. Levels of anxiety and depression declined over time. Still, the psychological impact of the epidemic will take a long time to recover from. Most of the published studies on the psychological impacts of COVID-19 in the older populations were cross-sectional, focusing either on the outbreak or period or on the post epidemic period [31,32]. Few studies were found to have focused on the variation tendency of anxiety and depression in the older population corresponding to different stages of the COVID-19 pandemic. Thus, we performed a longitudinal study. The initial goal of this study was to assess the anxious and depressive symptoms among community-dwelling older persons longitudinally, and to establish a mental health database of the elderly in the community to provide data basis for subsequent interventions. There was an outbreak of COVID-19 while the research was going on. By February 9, 2020, a total of 1070 novel coronavirus pneumonia (NCP) patients had been diagnosed in Southwest China, with a cumulative incidence of 0.53/100,000, 565 patients had been cured and discharged from hospital, and 32 patients died. The cumulative number of confirmed cases in southwest China was mainly concentrated in Chongqing municipality (446 cases, 41.68%) and Sichuan Province (386 cases, 36.07%), while Tibet had the lowest number (one case, 0.09%) [33]. As of June 17, 2020, there were 664 cases of COVID-19 in Sichuan Province, with an incidence of 0.80/100,000. Three patients died, with a fatality rate of 0.45%. On January 24, Sichuan province launched a first-level public health emergency response. On 26 February and March 25, 2020, the emergency response was downgraded to level 2 and level 3 respectively. As of November 10, 2020, Sichuan Province had reported a total of 776 NCP patients, including 728 cured and discharged from hospital, with 3 deaths. During the outbreak and epidemic of COVID-19, home isolation and wearing masks were suggested to the elderly to prevent possible COVID-19 infections. Considering the adverse impact on mental health caused by home isolation, we hope to assess the variation trends of anxiety and depression before, during, and nearly 6 months after the COVID-19 pandemic among older persons, and explore its determinants to contribute to the fight against COVID-19.
2. Materials and methods
2.1. Study design
A longitudinal study was conducted in Ya’ an, a city located in southwest China. This study was performed between December 1, 2019, and November 15, 2020, and convenience sampling was used. Participants were recruited offline at activity centers and service centers of communities. Participants recruited into the current study needed to fulfil the following requirements: aged 60 years and above; able to understand or read Chinese; mentally alert to complete the study; live in the urban community. Participants who did not complete the whole study were excluded for data analysis.
2.2. Data collection
Data were collected in three waves: before (2019/12/1–2020/1/10); during ((2020/3/15-4/30), and 6 months after the COVID-19 pandemic ((2020/10/15-11/10). Participants' sociodemographic data were collected by a self-developed data collection form at wave 1. Depressive and anxiety symptoms were assessed in all the 3 waves using the Patient Health Questionnaire 9-Item (PHQ-9) scale and the Generalized Anxiety Disorder 7-Item (GAD-7) scale respectively. Offline data collection was performed by a face-to-face interview method in wave 1, while online data collection by telephone interview was adopted in waves 2 and 3. Investigators read out the items of PHQ-9 and GAD-7 using the telephone and recorded participants’ answers on paper documents with participants' numbers. After that, these answers were entered into the database established for subsequent analysis. Mandarin was used in the investigation process. For participants who did not understand Mandarin, a local dialect of Sichuan was used. The questionnaires took about 10 min to complete. The investigation team consisted of ten nursing students and one registered nurse, and standardized training was performed before the study began. Participants were told the purposes, procedures, and requirements of the study. The questionnaires were reviewed and promptly corrected if there were any problems.
2.3. Instruments
Sociodemographic characteristics included age, gender, marital status, living arrangements, monthly income, chronic disease information and educational level. Age was calculated by subtracting the respondents’ birth year from the survey year. Gender, marital status (married or single/divorced/widow), living status, monthly income and chronic disease were assessed by binary variables. Chronic diseases included hypertension, type 2 diabetes, heart disease, respiratory disease, and cerebrovascular disease. Educational level was assessed by multiple variables.
2.4. Outcomes
Depressive symptom was measured by PHQ-9, which was initially developed by Kroenke [34]. It is a self-rating scale for depression-related symptoms experienced in the past 2 weeks, scoring each item on a four-point Likert scale of 0–3, with 0 indicating “not at all” and 3 indicating “almost every day”. PHQ-9 is widely used in both clinic and community settings. It was introduced to China in 2007 [35]. The reliability and validity of PHQ-9 have been demonstrated in elderly populations in mainland China [36]. The Cronbach's alpha coefficient of PHQ-9 was 0.763 in our study, suggesting good reliability. The confirmatory factor analysis showed that PHQ-9 had good construct validity in this study (CMIN/DF: 1.581; GFI: 0.984; CFI: 0.968; RMSEA: 0.034).
Anxiety was measured by GAD-7. GAD-7 was initially developed by Spitzer [37], and translated and introduced to China by Li [38]. It is a self-rating scale for anxiety-related symptoms experienced in the past 2 weeks, scoring each item on a four-point Likert scale of 0–3, with 0 indicating “not at all” and 3 indicating “almost every day”. The reliability and validity of GAD-7 were confirmed in previous studies [38,39]. The Cronbach's α coefficient of GAD-7 was 0.913 in our study, suggesting good reliability. The confirmatory factor analysis showed GAD-7 had good construct validity in this study (CMIN/DF: 2.368; GFI: 0.987; CFI: 0.980; RMSEA: 0.052).
Ethical statement
This study was approved by the ethical review board of Ya’ an Polytechnic College. Written informed consent was obtained at the beginning of the study. The procedures used in this study adhere to the tenets of the Declaration of Helsinki.
2.5. Data analysis
This study tested the effect of the outbreak of COVID-19 on depressive and anxiety symptoms in the elderly, which were the main data set. Continuous variables such as age, monthly income, PHQ-9 score, and GAD-7 score were described as mean and standard deviation (SD) or median (inter-quartile). Independent t-test, Kruskal-Wallis test and Mann-Whitney U test were used to compare continuous variables. Categorical variables such as gender, marital status, and living arrangements were described as frequency (n) and proportion (%). Chi-square test was used to compare categorical variables. In this study, cut points of 5, 10, and 15 were interpreted as representing mild, moderate, and severe levels of anxiety on the GAD-7 [38], and cut points of 5, 10, 15, and 20 were interpreted as representing mild, moderate, moderate-severe, and severe levels of depression on the PHQ-9 [34].
Generalized Estimating Equations (GEE) models were used to identify factors that contributed to variation in depressive symptoms and anxious symptoms. Therefore, sociodemographic data and time factors were incorporated into the GEE model to explore the independent effects of these variables on PHQ-9 and GAD-7 scores. Autocorrelation working correlation matrix, linear scale response, and main effect model were adopted. GEE can process longitudinal data with missing value. Therefore, we analyzed the data of all samples including the participants exiting at wave 2 and wave 3. Data were analyzed in Statistic Package for Social Science software (SPSS) version 23. A P-value below 0.05 was considered statistically significant. Post hoc analysis was run based on a new P-value (P < 0.017) after Bonferroni adjustment.
3. Results
3.1. Sociodemographic data
A total of 705 participants were recruited and 512 participants completed the whole study (Fig. 1 ). The response rate was 83.69% at wave 2 and 72.62% at wave 3, respectively. The average age of 705 participants was 71.68 ± 7.03 years old. The average monthly income was 1732.8 ± 864.8 yuan (US dollar: 272.9 ± 136.2; Euros: 248.0 ± 123.8). The older persons who completed the whole study were more likely to live alone and had higher level of PHQ-9 score than those who dropped out of the study. The detailed characteristics are shown in Table 1 .
Fig. 1.
The flow chart. COVID-19 = coronavirus disease 2019; PHQ-9 = Patient Health Questionnaire 9-Item; GAD-7 = Generalized Anxiety Disorder 7-Item.
Table 1.
The sociodemographic characteristics of participants.
| Variables | All (n = 705) | Completed group (n = 512) | Drop-out group (n = 193) | χ2/t/Z | P value |
|---|---|---|---|---|---|
| Age (categorical, year) | 3.362 | 0.067 | |||
| 60–74 | 478 (67.8) | 337 (65.8) | 141 (73.1) | ||
| ≥75 | 175 (32.2) | 175 (34.2) | 52 (26.9) | ||
| Gender | 0.030 | 0.862 | |||
| Male | 241 (34.2) | 176 (34.4) | 65 (33.7) | ||
| Female | 464 (65.8) | 336 (65.6) | 128 (66.3) | ||
| Marital status | 2.573 | 0.109 | |||
| Married | 185 (26.2) | 126 (24.6) | 59 (30.6) | ||
| Single/divorced/widowed | 520 (73.8) | 386 (75.4) | 134 (69.4) | ||
| Living status | 64.619 | <0.001 | |||
| Living with family | 352 (49.9) | 240 (46.9) | 112 (58.0) | ||
| Living with caregiver | 183 (26.0) | 113 (22.1) | 70 (36.3) | ||
| Living with common law partner | 37 (5.2) | 26 (5.1) | 11 (5.7) | ||
| Alone | 133 (18.9) | 133 (26.0) | 0 (0) | ||
| Monthly income (yuan) | |||||
| ≤1000 | 179 (25.4) | 135 (26.4) | 44 (22.8) | 0.943 | 0.332 |
| >1000 | 526 (74.6) | 377 (73.6) | 149 (77.2) | ||
| Chronic disease | |||||
| Yes | 367 (52.1) | 238 (46.5) | 100 (51.8) | 1.595 | 0.207 |
| No | 338 (47.9) | 274 (53.5) | 93 (48.2) | ||
| Educational level | 3.610 | 0.307 | |||
| Elementary school and below | 156 (22.1) | 112 (21.9) | 44 (22.8) | ||
| Junior School | 194 (27.5) | 133 (26.0) | 61 (31.6) | ||
| Senior school and vocational | 102 (14.5) | 80 (15.6) | 22 (11.4) | ||
| University degree and above | 253 (35.9) | 187 (36.5) | 66 (34.2) | ||
| Number of diseases | 2 (2) | 2 (2) | 2 (2) | −0.300 | 0.764 |
| PHQ-9 score (wave 1) | 3.50 ± 1.71 | 3.74 ± 1.81 | 2.85 ± 1.19 | 6.374 | <0.001 |
| GAD-7 score (wave 1) | 3.25 ± 2.79 | 3.22 ± 2.80 | 3.32 ± 2.78 | −0.442 | 0.658 |
Note: IQR = interquartile range; 1 yuan = 0.431 Euros = 0.158 US dollars; Number of diseases was described as median (IQR).
3.2. Variation trends of depressive and anxious symptoms over time
The prevalence of depressive symptoms (anxious symptoms) was 18.9% (28.5%), 28.1% (30.3%), and 35.9% (30.3%) before, during, and 6 months after the COVID-19 pandemic respectively (Table 2 ), this indicated an upward trend in prevalence of depressive symptoms in the community-dwelling older person (Fig. 2 ). Ascending trends were also found in the PHQ-9 total scores. The number of participants with moderate to severe depressive symptoms had increased from 5 in wave 1 to 17 in wave 2, and 18 in wave 3. While the number of participants with moderate anxiety symptoms seemed to be decreased slightly, from 2 in wave 1 to 16 in wave 2 and 14 in wave 3, no participants with severe anxiety symptoms were found.
Table 2.
Anxious and depressive symptoms of the participants at wave 1, wave 2 and wave 3.
| Wave 1 (n = 705) | Wave 2 (n = 590) | Wave 3 (n = 512) | χ2/Ζ | P value | |
|---|---|---|---|---|---|
| PHQ-9 score | 3.0 (2.0)a,b | 4.0 (2.0)a,c | 4.0 (2.0)b,c | 50.267 | <0.001 |
| Depressive symptoms | 45.024 | <0.001 | |||
| Yes | 133 (18.9)d,e | 166 (28.1)d,f | 184 (35.9)e,f | ||
| No | 572 (81.1) | 424 (71.9) | 328 (64.1) | ||
| GAD-7 score | 3.0 (4.0) | 3.0 (4.0) | 3.0 (4.0) | 2.952 | 0.229 |
| Anxious symptoms | 0.667 | 0.718 | |||
| Yes | 201 (28.5) | 179 (30.3) | 155 (30.3) | ||
| No | 504 (71.5) | 411 (69.7 | 357 (69.7) |
Note: PHQ-9 = Patient Health Questionnaire 9-Item; GAD-7 = Generalized Anxiety Disorder 7-Item. Chi-square test, Kruskal-Wallis test and Mann-Whitney U test was used. PHQ-9 score and GAD-7 score were described as median and inter-quartile.
Z = −4.665, P<0.001.
Z = −6.815, P<0.001.
Z = −2.364, P=0.018.
χ2 = 15.544, P<0.001.
χ2 = 44.878, P<0.001.
χ2 = 7.699, P=0.006; Bonferroni adjustment was used and a P-value lower than 0.017 was considered statistically significant.
Fig. 2.
Trends in prevalence of depressive symptoms and anxious symptoms. CI = confidence interval.
3.3. Determinants for variation of depressive and anxious symptoms
Generalized estimation equation was used to analyze the influential factors of depressive and anxiety symptoms. Before adjusting for confounding factors including sociodemographic data, time points were the significant predictors for both depressive symptoms and anxious symptoms (Table 3 ).
Table 3.
Generalized estimating equations analysis for time point and PHQ-9 score and GAD-7 score (n = 705).
| PHQ-9 scorea |
GAD-7 scorea |
|||||||
|---|---|---|---|---|---|---|---|---|
| β | Wald χ2 | Exp(β) (95%CI) | P value | β | Wald χ2 | Exp(β) (95%CI) | P value | |
| Time | ||||||||
| Wave 1 | Ref. | Ref. | Ref. | – | Ref. | Ref. | Ref. | – |
| Wave 2 | 0.591 | 95.451 | 1.805 (1.603–2.032) | <0.001 | 0.166 | 8.983 | 1.181 (1.059–1.317) | 0.023 |
| Wave 3 | 0.854 | 165.874 | 2.350 (2.063–2.676) | <0.001 | 0.159 | 5.154 | 1.172 (1.022–1.345) | 0.003 |
Note: PHQ-9 = Patient Health Questionnaire 9-Item; GAD-7 = Generalized Anxiety Disorder 7-Item.
Linear Scale Response GEE model was used.
After incorporating all factors into GEE model, it was found that different time points were independent predictors of depressive symptoms, suggesting that in the initial phase of the pandemic, the severity of depression was higher than before the pandemic. The detailed information is summarized in Table 4 . There was no statistical difference in other variables. Table 5 shows the factors associated with anxiety symptoms. We found that the risk of anxiety symptoms among single/divorced/widowed older adults was 2.306 times higher than that of married older adults (95% CI 1.358–3.914, P = 0.002). Additionally, the results showed that different time points were independent predictors of anxious symptoms. The risk of anxiety symptoms increases over time, suggesting that in the initial phase of the pandemic, the severity of anxiety was higher than before the pandemic.
Table 4.
Generalized estimating equations analysis for all variables and PHQ-9 score (n = 705).
| PHQ-9 scorea |
||||||
|---|---|---|---|---|---|---|
| β | SE | Wald χ2 | Exp(β) (95%CI) | P value | ||
| Age≥75 years old | 0.003 | 0.196 | <0.001 | 1.003 (0.684–1.472) | 0.986 | |
| Gender = female | 0.019 | 0.169 | 0.012 | 1.019 (0.732–1.417) | 0.912 | |
| Marital status = single/divorced/widowed | 0.166 | 0.208 | 0.636 | 1.180 (0.785–1.773) | 0.425 | |
| Living status rowhead | ||||||
| Living with family | Ref. | Ref. | Ref. | Ref. | - | |
| Living with caregivers | 0.151 | 0.238 | 0.403 | 1.163 (0.725–1.852) | 0.525 | |
| Living with law-partner | 0.659 | 0.361 | 3.334 | 1.932 (0.953–3.917) | 0.068 | |
| Alone | 0.148 | 0.239 | 0.381 | 1.159 (0.725–1.852) | 0.537 | |
| Educational background rowhead | ||||||
| Elementary school and below | Ref. | Ref. | Ref. | Ref. | - | |
| Junior school | −0.220 | 0.252 | 0.765 | 0.802 (0.489–1.315) | 0.382 | |
| Senior and vocational school | 0.272 | 0.292 | 0.869 | 1.312 (0.741–2.323) | 0.351 | |
| University degree and above | −0.212 | 0.240 | 0.778 | 0.809 (0.505–1.296) | 0.378 | |
| Monthly income>1000 yuan | −0.251 | 0.200 | 1.571 | 0.778 (0.526–1.152) | 0.210 | |
| Chronic disease = yes | 0.083 | 0.168 | 0.241 | 1.086 (0.781–1.511) | 0.624 | |
| Number of diseases | 0.020 | 0.081 | 0.058 | 1.020 (0.870–1.196) | 0.810 | |
| GAD-7 score | −0.049 | 0.031 | 2.539 | 0.952 (0.897–1.196) | 0.111 | |
| Time rowhead | ||||||
| Wave 1 | Ref. | Ref. | Ref. | Ref. | - | |
| Wave 2 | 0.605 | 0.062 | 95.313 | 1.831 (1.622–2.068) | <0.001 | |
| Wave 3 | 0.847 | 0.067 | 160.142 | 2.332 (2.045–2.659) | <0.001 | |
Note: PHQ-9 = Patient Health Questionnaire 9-Item; GAD-7 = Generalized Anxiety Disorder 7-Item.
Linear Scale Response GEE model was used.
Table 5.
Generalized estimating equations analysis for all variables and GAD-7 score (n = 705).
| GAD-7 scorea |
||||||
|---|---|---|---|---|---|---|
| β | SE | Wald χ2 | Exp(β) (95%CI) | P value | ||
| Age≥75 years old) | 0.351 | 0.235 | 2.233 | 1.420 (0.897–2.249) | 0.135 | |
| Gender = female | −0.212 | 0.228 | 0.860 | 0.809 (0.517–1.266) | 0.354 | |
| Marital status = single/divorced/widowed | 0.835 | 0.270 | 9.567 | 2.306 (1.358–3.914) | 0.002 | |
| Living status rowhead | ||||||
| Living with family | Ref. | Ref. | Ref. | Ref. | - | |
| Living with caregivers | −0.369 | 0.293 | 1.581 | 0.691 (0.389–1.229) | 0.209 | |
| Living with law-partner | −0.782 | 0.481 | 2.644 | 0.458 (0.178–1.174) | 0.104 | |
| Alone | −0.280 | 0.324 | 0.746 | 0.756 (0.400–1.427) | 0.388 | |
| Educational background rowhead | ||||||
| Elementary school and below | Ref. | Ref. | Ref. | Ref. | - | |
| Junior school | 0.209 | 0.308 | 0.462 | 1.232 (0.674–2.252) | 0.497 | |
| Senior and vocational school | −0.531 | 0.323 | 2.696 | 0.588 (0.312–1.108) | 0.101 | |
| University degree and above | −0.080 | 0.281 | 0.083 | 0.923 (0.532–1.600) | 0.775 | |
| Monthly income>1000 yuan | 0.167 | 0.235 | 0.503 | 1.181 (0.745–1.872) | 0.478 | |
| Chronic disease = yes | 0.194 | 0.209 | 0.861 | 1.214 (0.806–1.829) | 0.353 | |
| Kind of diseases | 0.065 | 0.096 | 0.454 | 1.067 (0.884–1.288) | 0.500 | |
| PHQ-9 score | −0.078 | 0.049 | 2.489 | 0.925 (0.840–1.019) | 0.115 | |
| Time rowhead | ||||||
| Wave 1 | Ref. | Ref. | Ref. | Ref. | - | |
| Wave 2 | 0.199 | 0.060 | 10.935 | 1.220 (1.084–1.373) | 0.001 | |
| Wave 3 | 0.217 | 0.077 | 7.942 | 1.242 (1.068–1.445) | 0.005 | |
Note: PHQ-9 = Patient Health Questionnaire 9-Item; GAD-7 = Generalized Anxiety Disorder 7-Item.
Linear Scale Response GEE model was used.
4. Discussion
In this study, we found that the pandemic of COVID-19 increased the prevalence of depressive and anxious symptoms, and had persistent impacts on the psychological health among the elderly. Additionally, we disclosed that the elderly without a partner (single/divorced/widowed) had higher levels of anxiety than those were married. Thus, continuous attention should be paid to the psychological health of the elderly after the outbreak of COVID-19 as well as in the post-pandemic era. The risk factors found in this study can be used to identify high-risk elderly groups and give social and psychological support.
In this study, the prevalence of depressive symptoms among the elderly during and after the outbreak of COVID-19 were 30.3% and 35.9% respectively, which were at a moderate level compared with previous studies [[40], [41], [42]]. Studies reported that the prevalence of depression among people over 60 years of age in mainland China, Hong Kong, American, Canada, Spain, and Japan were 40.77% [43], 39.79% [40], 36.9% [44], 26.4% [45], 25.6% [41], and 11.6% [42], respectively. Moreover, the prevalence of depressive symptoms among our participants (>60 years old) was usually higher than that of the general population (≥18 years old) in China and worldwide, where the prevalence of depression in the general population was 28% [46] and 15.97% respectively [47]. The reason for the high prevalence of depressive symptoms among the elderly may be that they are poor at social adjustment, have multiple morbidities and an inconvenient life at home, and are living alone. Additionally, the prevalence of depressive symptoms among our participants in the late phase of the pandemic was significantly higher than before the outbreak of COVID-19. It suggested that the prevalence of the COVID-19 had a lasting impact on depression in the elderly, which was supported by a recent study [48]. According to our findings, the prevalence of anxious symptoms during and in the late phase of the pandemic were 30.3% and 30.3%, which were higher than some recent studies, in which the prevalence of anxiety among older persons in mainland China, Canada, and Vietnam was 10.10% [49], 23.3% [45] and 13.4% [50] during the pandemic of COVID-19. Anxiety was more likely to be reported among older persons with chronic health problems such as Parkinson's disease [51]. Moreover, the prevalence of anxious symptoms in our study was also slightly higher than that in the general population. Two meta-analyses showed that the prevalence of anxiety in the general population in China and worldwide was 25% [46] and 15.15% [47] respectively. The reason for the high prevalence of anxiety in the elderly may be that they take more stringent measures with social distancing, are more susceptible to COVID-19, and have a higher prevalence of underlying medical health problems. The potential reasons for these discrepancies in the prevalence of anxious symptoms and depressive symptoms reported in our study compared with other studies were as follows: First, different measures used to evaluate anxiety and depression may change the prevalence of anxiety and depression. Second, most previous studies were carried out in the period from January to May 2020 [47,52], when the rapid increase of confirmed COVID-19 cases came out. However, few studies reported the prevalence of anxiety and depression after May 2020, when the post-pandemic era started. Third, the severity of COVID-19 varied in different regions and countries, which synergized with cultural background, resulting in different psychological impacts. Additionally, this was certainly related to the quarantine, treatment, and other policies of COVID-19 implemented locally. Fourth, all participants in this study were over 60 years old and lived in urban communities. They were more susceptible to COVID-19 than the general population and were at high risk of psychological maladjustment.
Indeed, the outbreak of contagion increases the prevalence of psychological health problems. Our study demonstrated the outbreak of COVID-19 increased the prevalence of anxiety and depressive symptoms. Additionally, the pandemic of COVID-19 had a persistent impact on the psychological health of the elderly. In this study, the prevalence of anxious symptoms, depressive symptoms and comorbidities among older adults increased by 1.8%, 17%, and 6.3%, respectively, nearly nine months after the outbreak. It suggested that the impact of the pandemic on anxiety and depression are long-lasting despite the alleviation of the COVID-19 pandemic, and despite strong and active intervention by governments, which was consistent with previous studies [49,53,54]. Wang [49] reported that the prevalence of depression among the elderly in mainland China after the outbreak increased by 5.15% compared to before the outbreak, and another study showed the prevalence of anxiety in Taiwanese people increased by 3.3% three weeks after the outbreak of COVID-19 [55]. The reason for this phenomenon may be due to anti-pandemic measures, social isolation, and even the loss of friends and family. Thus, long-term psychological health tracking and psychological intervention should be given to vulnerable elderly populations. Moreover, a longitudinal study in China among participants aged between 12 and 59 years reported no significant longitudinal changes in anxiety and depression levels [56]. Despite the limited generalization because only 333 participants out of 1210 finally completed the whole study, the findings of the study further highlighted the importance and necessity of effective intervention for the elderly population. Some researchers have made efforts to explore the potential interventions for the mental consequences of COVID-19, and cognitive behaviour therapy (CBT) is the most evidence-based one [57]. Internet cognitive behavioral therapy (I-CBT) is a cost-effective intervention [58], and it has been proven to be able to treat psychiatric symptoms such as insomnia [59]. Thus, the application of I-CBT deserves our expectations, and it might be able to prevent the spread of infection during the pandemic.
This study identified that marital status had the main effect on the anxiety level in the elderly. Older adults who were single/divorced/widowed had higher levels of anxiety than those who were married. This result was supported by many studies [53,60,61]. Zhao [53] reported that divorced/widowed individuals were more likely to suffer from anxiety (OR = 4.170). However, the effect of age, gender, and other variables on the level of anxiety and depression in the elderly was not been found in this study. This was contrary to some studies, which found that age, gender, and some other variables, such as living alone [62], having chronic diseases [63], and relatives/friends/acquaintance suspected of/acquired COVID-19 [64], were risk factors for anxiety and depression among the elderly. These high-risk populations should be given appropriate and targeted psychological interventions. Additionally, non-response analysis of the study suggested that older persons who completed the whole study were more likely to live alone and had high level of depressive symptoms than those who dropped out of study. There were several explanations for this finding: First, older people who live alone are more likely to receive phone calls themselves during COVID-19 isolation because these elderly people are more eager to receive outside information and social support. Second, during the special period of COVID-19 social isolation, older people were more concerned about their own physical and psychological problems. When they have psychological problems such as anxiety and depression, they will be eager to get outside help, because it is difficult for them to get psychological support information or medical help due to the epidemic prevention and control.
This study was the first to report changes in anxiety and depression levels in older adults nearly 10 months after the outbreak of COVID-19 and demonstrated the pandemic had persistent negative effects on depression and anxiety in the elderly. The study lasted nearly 10 months. This study had some limitations. First, the study is based on a convenience sample, and the sample size was relatively small. Second, we did not collect variables related to COVID-19 and thus were unable to identify these high-risk factors. Third, depression and anxiety had negative impacts on somatic status during the pandemic of COVID-19 [65]. However, we did not collect the somatic symptoms of the participants, and the association between anxiety and depression and somatic symptoms was not analyzed. Fourth, the COVID-19 pandemic was found to cause hemodynamic changes in the brain [66] and impairment in olfactory function [67]. This study mainly used self-reported questionnaires to measure psychiatric symptoms and did not make clinical diagnosis. The gold standard for establishing psychiatric diagnosis involved structured clinical interview and functional neuroimaging should be applied in the future face-to-face research after COVID-19 restrictions are removed [[68], [69], [70]]. Fifth, the change in mental health between pre-pandemic to pandemic could be partly due to collection method effects; the first wave was collected with face-to-face interviews and the pandemic-waves with telephone interview questionnaires. It is conceivable that this affects the response to questionnaires about mental health and the observed change is partly an artefact. A further longitudinal follow-up study should be performed to explore it. Last, COVID-19 burnout could not be excluded while assessing mental health, especially in China, the COVID-19 burnout is unique due to the zero policy [71]. And this should be taken into consideration in future studies.
In conclusion, this study demonstrated that the pandemic of COVID-19 increased the levels of depression and anxiety in the elderly, and had persistent psychological impacts in the late phase of the pandemic. Marital status had effects on the anxiety and depression level of the elderly. Targeted interventions could be carried out among those with higher risk of maladjustment. A further longitudinal follow-up study should be performed to explore the effect of anxiety and depression related to COVID-19 on the physical and mental health of the elderly population.
Ethics approval and consent to participate
This study was approved by the ethical review board of Ya’ an Polytechnic College. Written informed consent was obtained at the beginning of the study. The procedures used in this study adhere to the tenets of the Declaration of Helsinki.
Consent for publication
Not applicable.
Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Funding
This study was supported by Sichuan Science and Technology Program, China (grant number: 2022NSFSC1371) and China Postdoctoral Science Foundation, China (grant number: 2021M702322).
Credit author statement
Y.W. contributed to the analysis and interpretation of data, and the drafting of this manuscript. B.R.L. contributed to the interpretation of data and revising the manuscript. J.W. contributed to the analysis and interpretation of data and revising the manuscript. S.J.L. contributed to the acquisition of data, conception and design of the work, and revising of the manuscript. All authors reviewed the manuscript.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
The authors are grateful to the study participants.
Data availability
Data will be made available on request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Data will be made available on request.