Patterns of mental health problems before and after easing COVID-19 restrictions: Evidence from a 105248-subject survey in general population in China.

BACKGROUND: The COVID-19 pandemic has alarming implications for individual and population level mental health. Although the future of COVID-19 is unknown at present, more countries or regions start to ease restrictions. The findings from this study have provided the empirical evidence of prevalence and patterns of mental disorders in Chinese general population before and after easing most COVID-19 restrictions, and information of the factors associated with these patterns.
METHODS: A cross-sectional population-based online survey was carried out from February to March 2020 in the general population across all provinces in China. The 12-item General Health Questionnaire (GHQ-12) was incorporated in the survey. Latent class analyses were performed to investigate the patterns of mental disorders and multinomial logistic regressions were used to examine how individual and regional risk factors can predict mental disorder patterns.
RESULTS: Four distinctive patterns of mental health were revealed in the general population. After the ease of most COVID-19 restrictions, the prevalence of high risk of mental disorders decreased from 25.8% to 20.9% and prevalence of being high risk of unhappiness and loss of confidence decreased from 10.1% to 8.1%. However, the prevalence of stressed, social dysfunction and unhappy were consistently high before and after easing restrictions. Several regional factors, such as case mortality rate and healthcare resources, were associated with mental health status. Of note, healthcare workers were less likely to have mental disorders, compared to other professionals and students.
CONCLUSIONS: The dynamic management of mental health and psychosocial well-being is as important as that of physical health both before and after the ease of COVID-19 restrictions. Our findings may help in mental health interventions in other countries and regions while easing COVID-19 restrictions.

Introduction

Since China reported its first cases to the World Health Organization (WHO) in December 2019, over 140 million COVID-19 cases had been reported worldwide, with more than 3 million deaths by late April 2021 [1]. At least 200 countries have implemented varying degrees of restrictions on population movement to contain the spread of novel coronavirus disease 2019 (COVID-19). While these interventions may be critical in mitigating the spread of the disease during the pandemic crisis, they have generated and intensified stress, as well as negatively affected mental health and well-beings of general population [2, 3]. The symptoms of COVID-19-associated mental health problems include a large range of emotional and behavioral reactions, such as social dysfunction, loss of confidence, depression, anxiety, and insomnia [4-7].

Before the outbreak, the prevalence of mental disorders (excluding dementia) in general population in China was 9.3% (95% CI: 5.4–13.3) in a 12-month study (Huang et al., 2019) [8]. About 16% and 13% of general population had a mood disorder and an anxiety disorder, respectively [9]. The pooled prevalence of insomnia in general population was 15.0% (95% CI: 12.1–18.5) in China according to a meta-analysis in 2017 [10]. Two national surveys [7] conducted in China right before and during the COVID-19 outbreak reported that the pandemic led to a 74% drop in overall emotional wellbeing. Several cross-sectional surveys on the public during the early stages of the outbreak found high levels of mental health problems, and increased symptoms of depression, anxiety, and stress related to COVID-19 [4, 5, 11].

There can be various risk factors associated with mental health status [11, 12]. Sociodemographic factors, such as gender, age, marital status, and Grade 12 graduation, have been reported as essential components [11, 12]. Another important factor is the presence of the peak in the epi curve. After the peak, the daily confirmed cases start to level off or decline, which provides great relief for public health. Several regional factors such as the capability of medical supports and resources and the severity level of the COVID-19 were associated with mental health [13, 14]. The COVID-19 pandemic has resulted in an increase in known risk factors for mental health. Frontline healthcare workers working under extreme conditions and at high risk of getting infected have been experiencing more psychological burdens during this pandemic [15-17]. People with underlying medical conditions are under unprecedented pressure and are experiencing severe psychological distress due to the limited resources for testing and treatment, restrictions/lockdowns, and financial losses [14, 18].

Most previous investigations on COVID-19 related mental health focused on specific subgroups of the population and only a few studies focused on the general populations. A systematic review [19] revealed that studies on general population reported quite different prevalence rates of psychological distress because of various measurement scales, different reporting patterns, and possibly international/cultural differences. As of February 25, 2020, the State Council of the People’s Republic of China declared that the disease transmission had been under control and eased most COVID-19 restrictions in many regions (we consider this as an indicator for easing restrictions. After that, the number of new confirmed COVID-19 cases in mainland China, excluding Hubei province (i.e., the province most severely affected by COVID-19 in China), decreased to under ten for the first time. To our knowledge, no studies have been conducted to examine the differences before and after easing restrictions in the general public’s psychological health. The objectives of the study were to provide empirical evidence of the patterns of mental health in general population both before and after easing restrictions and to examine the factors at both individual and regional levels that contributed to and/or mitigated these patterns.

Materials and methods

Design and population

A cross-sectional and large-scale survey on physical and mental health conditions of Chinese general population along with their medical care needs and knowledge about the COVID-19 was carried out from February 18 to March 12, 2020. An online questionnaire was circulated via WeChat, a most popular social media platform, to collect information among participants from mainland China, and other regions/countries. This was an anonymous survey and the confidentiality of data was ensured. This study was approved by the Ethical Committee of Shantou Longhu People’s Hospital, Shantou, Guangdong, China.

Procedures

The link to the questionnaire was posted and re-posted to multiple WeChat groups and WeChat Moments as a snowballing method. The electronic informed consent was obtained prior to starting the questionnaire from each participant, or his/her parent for those who was younger than 18 years. They could choose either to complete the survey or opt out at any time. Each WeChat account owner was limited to submit only one response. The survey data were stored in the server of Wenjuanxing platform and could be accessed only by the authorized researchers from the involved organizations/institutions.

Exclusion criteria

Exclusion criteria were set as follows: a) being aged < 12 years; b) being aged < 20 years and being married, divorced or widowed; c) being aged < 20 years and having a degree of Master or PhD; or d) questionnaires completed in ≤ 50 seconds. Exclusion criteria b) and c) were set because these participants failed to pass the internal consistent checks. Those participants, who were younger than 12 years or completed questionnaires in ≤ 50 seconds, were also excluded for the data quality control reasons.

Measurements

The 12-item General Health Questionnaire (GHQ-12) was incorporated in the survey to evaluate the participants’ mental well-being. The General Health Questionnaire (GHQ) has been extensively used as a psychiatric disorder screening tool. The GHQ-12 is the shortest questionnaire amongst the GHQ series yet that offers comparable screening accuracy [20, 21]. The reliability and validity of GHQ-12 have been examined in many countries, including China, and reported appropriate to use [22, 23]. Determining the cut-off points for the GHQ-12 scores is challenging and varies according to regions, populations, and the time of a study [24, 25]. Instead of using the traditional scoring method of GHQ-12, we used the Latent Class Analysis (LCA) [26] to investigate the patterns and prevalence of mental disorders in general population during the COVID-19 pandemic.

Statistical analysis

The three stages of data analysis can be described as follows. In the first stage, LCA was used to investigate the patterns of mental disorders for the participants before easing restrictions and after easing restrictions separately. LCA was also used to classify them into distinct classes. Individuals classified into the same class are similar to each other and different from those in other classes. In the second stage, the prevalence of each pattern of mental disorders for each sociodemographic and disease group was estimated by using multinomial logistic regressions. In the final stage, the multivariable multinomial logistic regressions were conducted to examine how individual and regional factors predicted mental disorder patterns. All multinomial logistic regressions were conducted on cohorts before and after easing restrictions separately.

Results

Out of 430,152 visits to the questionnaire from early February to mid-March, 2020, 108,218 individuals completed it (response rate: 25.16%). Final samples available for analyses included 46,508 participants before easing restrictions and 58,740 participants after easing restrictions. Of the total sample, 57,262 (54.4%) were male, and the mean (SD) age was 30.0 (9.8) years with a range of 12 to 100 years. 64,030 participants (60.8%) had a college degree or higher, and 57,999 (53.2%) were married. 16,049 (15.5%) participants were healthcare workers including doctors, nursing professionals, midwifery professionals, dentists and pharmacists, and 19,738 (18.7%) were unemployed. 819 (0.8%) participants had at least one respiratory disease including pneumonia, asthma, and COPD, and 3,206 (3.1%) had one or more non-respiratory diseases.

LCA was conducted for the two cohorts (before and after easing restrictions) separately. As in most LCAs, the bimodal GHQ score method (item as 0-1-1-1) was used for each item in this study, which served as indicators for LCA. We successively tested several models in an iterative fashion to determine the model with the optimal number of classes. A four-class solution was chosen for both cohorts based on Bayesian Information Criterion (BIC), Lo Mendell Rubin likelihood ratio test, and entropy value [27].

The four-class solutions also exhibited good clinical interpretability. As presented in Fig 1, the symptom endorsement profiles of participants were highly comparable across the four classes and the risk profiles were quite similar before and after easing restrictions. Participants in the high-risk group (Class One) displayed high probabilities of all 12 mental disorder indicators (0.80–0.95). This high-risk class was estimated to account for 25.8% of participants before easing restrictions and 20.9% of participants after easing restrictions. Class Two, representing 28.4% of the sample before easing restrictions and 32.8% of the sample after easing restrictions, was identified as “stressed, social dysfunction, and unhappy” class. Participants in Class two had higher probabilities of being unhappy, unable to concentrate and under strain (range of probabilities: [0.43–0.63] and elevated risk on other indicators [0.21–0.38]). Class Three, representing 10.1% of the sample before easing restrictions and 8.1% of the sample after easing restrictions, was identified as “unhappy and loss of confidence” class. Participants in Class Three had higher probabilities of being unhappy and loss of confidence (range of probabilities: 0.68–0.91; and intermediate probabilities of other indicators: 0.11–0.51). The largest class (35.7% of the sample before and 38.2% of the sample after easing restrictions), so-called low-risk class, was comprised of participants who had low or zero probabilities for all mental disorder indicators.

Fig 1

Profile of mental health problems.

1. Unable to concentrate; 2. Loss of sleep; 3. Play much less useful part; 4. Unable to make decisions; 5. Under strain; 6. Couldn’t overcome difficulties; 7. Unable to enjoy 8. Unable to face up problems; 9. Unhappy and depressed; 10. Lose confidence; 11. Worthless person; 12. Feel unhappy.

Participants were assigned to a latent class based on their highest estimated posterior class probability. A series of univariate multinomial logistic regression were conducted separately for two cohorts to examine the prevalence of the four risk classes for each sociodemographic and/or disease group. Before easing restrictions, as displayed in Table 1, males had a higher chance for being in “High Risk” (Class One), whereas females had a higher likelihood for belonging to “Stressed, Social Dysfunction and Unhappy” (Class Two). However, after easing restrictions as shown in Table 2, females had a greater chance of being in these two disorder classes. The prevalence of “High Risk” (Class One) was much higher in the divorced or widowed than others, the prevalence rate of “Stressed, Social Dysfunction and Unhappy” (Class Two) was higher for the married, while the single or unmarried had higher probability being “Unhappy and Loss of Confidence” (Class Three). The prevalence of “High Risk” (Class One) was higher among those with high school or lower education levels than those with college or above education levels. The prevalence of Classes One and Two was lower among healthcare workers than people in the other occupations. Participants with respiratory diseases and/or other chronic diseases had a greater chance of being in those three disorder classes (i.e., Classes One, Two and Three) than healthy participants. These patterns were consistent before and after easing restrictions.

Table 1

Prevalence of mental health problems by characteristic—Before easing COVID-19 restrictions (N = 46508).

Characteristic	Groups	N	High Risk	Stressed, Social Dysfunction and Unhappy	Unhappy and Loss of Confidence
			% (95% CI)	% (95% CI)	%(95% CI)
Gender	Female	19188	24.7(24.1–25.3)	31.4(30.7–32.1)	9.8(9.4–10.2)
	Male	27320	26.5(26.0–27.1)	26.2(25.7–26.7)	10.4(10.0–10.7)
Marriage status	Married	23043	23.5(23.0–24.1)	30.2(29.6–30.8)	8.8(6.9–10.7)
	Divorced/Widowed	839	51.6(48.3–55.0)	21.5(28.7–34.3)	8.8(8.4–9.1)
	Single or never married	22626	27.1(26.6–27.7)	26.8(26.2–27.3)	11.6(11.1–12.0)
Education	High school or below	16862	27.5(26.9–28.2)	28.3(37.6–29.0)	10.4(9.9–10.8)
	University/college or above	29646	24.8(24.3–25.3)	28.4(27.9–28.9)	10.0(9.7–10.3)
Occupation	HWsa	7792	21.9(21.0–22.9)	26.1(25.1–27.1)	10.2(9.5–10.9)
	Non-HWs	30916	27.3(26.8–27.8)	27.8(27.3–28.3)	9.8(9.5–10.2)
	Unemployed	7800	23.8(22.8–24.7)	32.7(31.7–33.7)	11.3(10.6–12.0)
Disease status	No	44697	24.7(24.3–25.1)	28.6(28.2–29.0)	10.1(9.8–10.4)
	Respiratoryb	101	54.5(44.7–64.2)	22.8(14.6–31.0)	8.9(3.4–14.5)
	Non-respiratoryc	1319	49.8(47.1–52.5)	24.8(22.5–27.1)	11.1(9.4–12.8)
	Both	391	65.7(61.0–70.4)	16.4(12.7–20.0)	7.7(5.0–10.3)
Perceived needs met	Yes	44459	24.2(23.8–24.6)	28.7(28.2–29.1)	10.1(9.8–10.3)
	No	2049	60.3(58.2–62.4)	21.8(20.0–23.6)	11.7(10.3–13.1)

Note:

a Healthcare workers including doctors, nursing professionals, midwifery professionals, dentists and pharmacists;

b Respiratory diseases including pneumonia, asthma, and COPD;

c Non-respiratory disease including hypertension, diabetes, heart disease, stroke, hepatitis, cancer, or esophagitis, gastritis, or duodenitis or other.

Table 2

Prevalence of mental health problems by characteristic—After easing COVID-19 restrictions (N = 58740).

Characteristic	Groups	N	High Risk	Stressed, Social Dysfunction and Unhappy	Unhappy and Loss of Confidence
			% (95% CI)	% (95% CI)	% (95% CI)
Gender	Female	28798	21.4(20.9–21.8)	34.0(33.4–34.5)	8.0(7.7–8.3)
	Male	29942	20.6(20.1–21.0)	31.7(31.2–32.2)	8.2(7.9–8.5)
Marriage status	Married	34956	20.0(19.6–20.5)	33.3(32.8–33.8)	7.2(6.9–7.5)
	Divorced/Widowed	1266	33.3(30.7–35.9)	32.7(30.1–35.3)	7.3(5.9–8.8)
	Single or never married	22518	21.7(21.2–22.3)	32.0(31.4–32.6)	9.6(9.2–9.9)
Education	High school or below	24356	22.4(21.9–22.9)	32.8(32.2–33.4)	8.0(7.7–8.4)
	University/college or above	34384	19.9(19.5–20.4)	32.8(32.3–33.3)	8.1(7.9–8.4)
Occupation	HWsa	8257	15.3(14.5–16.1)	27.8(26.8–28.8)	8.0(7.4–8.6)
	Non-HWs	38545	22.2(21.8–22.7)	32.9(32.4–33.3)	8.0(7.7–8.2)
	Unemployed	11938	20.8(20.1–21.5)	36.1(35.2–36.9)	8.6(8.1–9.1)
Disease status	No	57163	20.4(20.1–20.8)	32.8(32.4–33.2)	8.0(7.8–8.3)
	Respiratoryb	81	32.1(21.9–42.3)	34.5(24.2–44.9)	12.4(5.2–19.5)
	Non-respiratoryc	1250	39.0(36.3–41.7)	33.2(30.6–35.8)	10.5(8.8–12.2)
	Both	246	50.0(43.7–56.2)	27.7(22.1–33.2)	8.1(4.7–11.6)
Perceived needs met	Yes	56750	20.1(19.8–20.5)	32.7(32.3–33.1)	8.0(7.7–8.2)
	No	1990	44.6(42.4–46.8)	35.8(33.7–37.9)	12.1(10.6–13.5)

Note:

a Healthcare workers including doctors, nursing professionals, midwifery professionals, dentists and pharmacists;

b Respiratory diseases including pneumonia, asthma, and COPD;

c Non-respiratory disease including hypertension, diabetes, heart disease, stroke, hepatitis, cancer, or esophagitis, gastritis, or duodenitis or other.

In the final analysis, the multivariable multinomial logistic regression was conducted separately for two cohorts to examine how individual and regional factors could predict mental disorder patterns. The parameter estimates and adjusted odds ratios for each mental disorder class by each predictor are shows in Tables 3 and 4, respectively. Females had a higher likelihood of belonging to any of the mental disorder risk classes. In particular, females had increased odds of belonging to Class Two (“Stressed, Social Dysfunction and Unhappy”). In addition, the following participants also had increased likelihood of being in any of the mental disorder risk classes (Classes One, Two and Three): seniors, teenagers, single/unmarried or divorced/widowed participants, the participants with diseases or perceived unmet medical care needs, and those from provinces of higher case fatality rates or lower temperature. Low education increased the likelihood of association in the “High Risk” class. In comparison with other professionals or students, the healthcare workers were less likely to belong to any of the mental disorder risk classes, and unemployed participants were more likely to belong to the “Stressed, Social Dysfunction and Unhappy” class (Class Two), but less likely to belong to the “High Risk” class (Class One). The regions with a higher number of hospital beds had a lower prevalence rate of “High Risk”, whereas participants from provinces with a higher number of hospital beds were more likely to belong to Classes Two and Three. The likelihood of being “Stressed, Social Dysfunction and Unhappy” decreased day by day after the peak of cases. The above associations between these risk factors and mental disorders are quite similar before and after easing restrictions.

Table 3

Parameter estimates (standard errors) from fitted multinomial logistic regression predicting mental health profiles.

	Before Easing COVID-19 Restrictions			After Easing COVID-19 Restrictions
	High Risk	Stressed, Social Dysfunction and Unhappy	Unhappy and Loss of Confidence	High Risk	Stressed, Social Dysfunction and Unhappy	Unhappy and Loss of Confidence
Intercept	2.61(0.15)***	0.80(0.15)***	0.19(0.19)	1.74(0.14)***	0.66(0.13)***	-0.05(0.17)
Demographics
Male	-0.08(0.03)**	-0.24(0.02)***	-0.09(0.03)**	-0.15(0.02)**	-0.15(0.02)***	-0.12(0.03)**
Age	-0.12(0.02)***	0.08(0.02)***	-0.04(0.03)	-0.10(0.02)***	0.01(0.02)	-0.04(0.03)
Age*Age	0.06(0.01)***	-0.02(0.01)+	0.02(0.01)*	0.04(0.01)***	0.01(0.01)+	0.02(0.02)
Marriage Status(ref = ‘Married’)
Divorced/Widowed	1.21(0.10)***	0.35(0.11)**	0.64(0.15)***	0.72(0.08)***	0.32(0.08)***	0.35(0.12)**
Single or never married	0.12(0.04)**	0.05(0.03)	0.27(0.05)***	0.05(0.03)	0.02(0.03)	0.26(0.05)***
High school or below	0.07(0.03)**	0.03(0.03)	0.04(0.04)	0.10(0.02)***	0.00(0.02)	0.01(0.03)
Occupation (ref = ‘Non HWs’)
HWs	-0.44(0.03)***	-0.25(0.03)***	-0.17(0.05)***	-0.70(0.04)***	-0.46(0.03)***	-0.32(0.05)***
Unemployed	-0.13(0.04)***	0.17(0.03)***	0.04(0.05)	-0.10(0.03)***	0.09(0.03)***	0.02(0.04)
Days since Peak	-0.008(0.005)+	-0.015(0.004)**	-0.005(0.006)	0.013(0.004)***	-0.014(0.003)***	0.009(0.005)+
Physical Conditions (ref = ‘No Disease’)
Respiratory only	1.53(0.31)***	0.76(0.34)*	0.79(0.43)+	0.74(0.32)*	0.51(0.31)+	0.83(0.40)*
Non-Respiratory only	1.50(0.09)***	0.78(0.09)***	1.02(0.11)***	1.35(0.08)***	0.77(0.09)***	1.03(0.11)***
Both	2.12(0.17)***	0.82(0.20)***	1.10(0.24)***	1.82(0.20)***	0.91(0.21)***	1.10(0.28)***
Perceived needs met	-2.58(0.09)***	-1.49(0.10)***	-1.90(0.11)***	-2.38(0.09)***	-1.72(0.09)***	-2.03(0.11)***
Case fatality rate	-0.02(0.02)	0.06(0.02)**	0.04(0.03)	0.07(0.02)**	0.07(0.02)***	0.05(0.03)+
Number of beds	-0.10(0.02)***	0.10(0.02)***	0.05(0.03)+	-0.01(0.02)	0.18(0.02)***	0.07(0.03)*
Lowest temperature	0.00(0.002)	0.02(0.002)***	0.03(0.003)***	0.008(0.002)***	0.03(0.002)***	0.03(0.003)***

Note:

+ p < .10,

* p < .05,

** p < .01,

*** p < .001.

Reference category is ‘Low Risk’.

Table 4

Odds ratios (95% CI) from fitted multinomial logistic regression predicting mental health profiles.

		Before			After
	High Risk	Stressed, Social Dysfunction and Unhappy	Unhappy and Loss of Confidence	High Risk	Stressed, Social Dysfunction and Unhappy	Unhappy and Loss of Confidence
Demographics
Male	0.92(0.88–0.97)	0.79(0.75–0.83)	0.91(0.85–0.98)	0.86(0.82–0.90)	0.85(0.82–0.89)	0.89(0.83–0.95)
Marriage Status(ref = ‘Married’)
Divorced/Widowed	3.37(2.77–4.10)	1.42(1.14–1.77)	1.89(1.42–2.51)	2.06(1.77–2.40)	1.38(1.19–1.60)	1.43(1.13–1.81)
Single or never married	1.13(1.05–1.21)	1.05(0.98–1.12)	1.31(1.20–1.44)	1.05(0.99–1.12)	1.02(0.97–1.08)	1.29(1.18–1.41)
High school or below	1.07(1.02–1.13)	1.03(0.98–1.09)	1.04(0.97–1.12)	1.11(1.05–1.06)	1.00(0.96–1.04)	1.01(0.94–1.08)
Occupation (ref = ‘Non HWs’)
HWs	0.64(0.60–0.69)	0.78(0.73–0.83)	0.84(0.77–0.92)	0.50(0.46–0.53)	0.63(0.60–0.67)	0.73(0.67–0.80)
Unemployed	0.88(0.82–0.95)	1.18(1.11–1.26)	1.05(0.96–1.15)	0.90(0.85–0.96)	1.09(1.04–1.15)	1.02(0.94–1.11)
Days since Peak	0.99(0.98–1.00)	0.99(0.98–0.99)	1.00(0.98–1.01)	1.01(1.01–1.02)	0.99(0.98–0.99)	1.01(0.99–1.02)
Physical Conditions (ref = ‘No Disease’)
Respiratory only	4.61(2.52–8.46)	2.14(1.10–4.17)	2.20(0.95–5.10)	2.11(1.12–3.97)	1.67(0.91–3.09)	2.30(1.04–5.08)
Non-Respiratory only	4.47(3.77–5.28)	2.17(1.81–2.61)	2.78(2.23–3.46)	3.87(3.27–4.56)	2.17(1.83–2.56)	2.80(2.24–3.49)
Both	8.34(5.93–11.7)	2.27(1.53–3.38)	3.02(1.87–4.86)	6.15(4.18–9.07)	2.49(1.65–3.76)	2.99(1.72–5.21)
Perceived needs met	0.08(0.06–0.09)	0.22(0.18–0.27)	0.15(0.12–0.19)	0.09(0.08–0.11)	0.18(0.15–0.22)	0.13(0.11–0.16)
Case fatality rate	0.98(0.94–1.03)	1.06(1.01–1.10)	1.04(0.98–1.10)	1.07(1.03–1.11)	1.08(1.04–1.12)	1.05(1.00–1.12)
Number of beds	0.91(0.87–0.95)	1.10(1.06–1.15)	1.05(0.99–1.10)	0.99(0.95–1.03)	1.20(1.16–1.24)	1.07(1.02–1.13)
Lowest temperature	1.00(1.00–1.01)	1.03(1.02–1.03)	1.03(1.02–1.03)	1.01(1.00–1.01)	1.03(1.025–1.032)	1.03(1.02–1.04)

Note: Reference category is ‘Low Risk’.

Discussion

The current study has examined the patterns of mental health disorders and associated factors among the general population in China during the COVID-19 pandemic. Based on a survey over 100,000 participants across all provinces/regions in China, results from latent class analysis revealed that more than one-fifth of the general population were at high risk of mental disorders with symptoms as being stressed, being unhappy, loss of confidence, and social dysfunction. Almost one third of the participants were at moderate risk of being unhappy, being stressed, and social dysfunction; one tenth was at moderate to high risk of unhappiness and loss of confidence. The prevalence of mental health symptoms differed significantly by stages of outbreak. After the ease of most COVID-19 restrictions, the prevalence of high risk of mental disorders decreased from 25.8% to 20.9% and the prevalence of being high risk of unhappiness and loss of confidence decreased from 10.1% to 8.1%. However, the prevalence of stressed, social dysfunction and unhappy are consistently high before and after easing restrictions.

The highlight of this study was to explore factors that contributed to, or mitigated these mental problems, as well as to identify the key populations that should be set as a priority for psychological interventions. Our results indicate that the participants living with one or more chronic diseases were three or four times more likely at risk of mental health disorders. Participants with multiple chronic conditions, especially co-occurring respiratory disease(s), were seven or eight times more likely at risk of mental health problems. Therefore, considerations should be made for people with pre-existing chronic diseases whose care might be disrupted during the COVID-19 pandemic. Steps should be taken to ensure that these people have access to medications without interruptions during the pandemic.

We have also found that the divorced, widowed, or single participants tended to have a higher level of mental health problems. Divorced or widowed participants were two or three times more likely to have psychological disorders and those single or unmarried ones had elevated odds of being unhappy or loss of confidence. Having less communications or supports could be a reason behind this. Provision of more and better mental/social supports may promote mental health of the vulnerable during the pandemic while keeping physical distance.

In other studies [20, 28], healthcare workers have reported negative consequences as a result of stress exposure and fear of getting infected or infecting their families and friends. However, our healthcare workers were less likely to be at high risk of mental disorders, compared to other professionals and students. This might be due to their better knowledge of the disease, protective measures, and professional trainings. The survey was conducted in late February when healthcare workers had been provided with personal protective equipment and psychosocial supports. Another possible reason could be that those frontline healthcare workers were too busy to respond to our survey.

Our results have shown that teenagers or young adults and seniors were vulnerable to mental health/emotional problems. The teenagers or young adults might be at particular risk during the pandemic as quarantined children were more likely to develop acute stress disorder, adjusted disorders, and grief [29]. Elderly people were as well at high risk of having severe COVID-19 illness and mental-health-related consequences because they might already have some cognitive decline [30]. Special considerations should be made to ensure that local community health services, such as schools, community centers for the youths and seniors, should be continued to carry out regular services during the pandemic.

The strengths of this study include its huge sample size, extensive geographic coverage across China, the special study period and the use of advanced statistical techniques. The survey covered both period before and after the ease of most COVID-19 restrictions. We also adjusted for individual and regional factors as well as the stage of pandemic.

Several limitations of this study are worth noting. This survey was based on a convenience sampling methods and the sample might not be representative for certain groups such as frontline healthcare workers and non-internet users. Those with serious mental disorders may be less likely to participate in the survey, and those in particular regions may be more or less likely to participate. Future studies should recruit a representative probability sample or use other social media such as Weibo in order to draw more reliable conclusions. Current study with a cross-sectional design could not evaluate long-term consequences of COVID-19 on mental health. The sample sets used in the two stages before and after the easing of restrictions were quite different in demographics (see S1 Table). Therefore, association between the ease of restrictions and mental health patterns cannot necessarily be considered causal relationships. The survey was fielded in February and March 2020 when the situation of the pandemic was dramatically different from the other periods of the year and early 2021. Thus the prevalence and patterns of mental disorder might not apply to other pandemic periods. The longitudinal studies with follow-up assessments at different periods of pandemic are needed to determine the transition of mental health patterns and the long-term mental health outcomes.

In summary, COVID-19 is both magnifying and contributing to the patterns of mental health disorders in general population. As more countries start to ease some COVID-19 restrictions, it is essential to identify the patterns of mental disorders among different populations and different stages. Some groups (e.g., with pre-existing chronic diseases including mental health problems) are at greater risk of developing more severe difficulties. The capacity of seeing a psychiatrist/psychologist/social worker will be critical for them. Understanding and addressing mental health and psychosocial concerns will be one of the key steps to break down disease transmission, to prevent long-term repercussions on the population’s wellbeing, and to improve their ability to cope with adversity and stress. Mental health interventions should be carried out within general health services (including primary health care). Communities and organizations could consider training nontraditional groups to provide psychological first aids, and mental health clinicians should work with these groups to develop standardized, evidence-informed resources. Governments should strengthen legislations to improve workplace mental health and provide incentives to employers for implementing robust mental health strategies. Although the future of COVID-19 is unknown at present, the dynamic management of mental health and psychosocial well-being is as important as that of physical health both before and after the ease of restrictions.

Number of valid sample size by collection date.

(TIF)

Click here for additional data file.

Sample characteristics of survey participants before and after easing restrictions.

(DOCX)

Click here for additional data file.

9 Jun 2021

PONE-D-21-14417

Patterns of Mental Health Problems Before and After Easing COVID-19 Restrictions: Evidence from a 105248-subject Survey in General Population in China

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Reviewer #1: -A single Wenjuanxing platform was used for data collection. A single platform may have a biased sample due to user groups. Therefore, the rationality of using a single Wenjuanxing platform and the potential impact on the sample should be explained.

-The time of data collection needs to be given more precisely. Due to the long collection time, a trend graph of data collection can be drawn.

-Different sample sets were used in the two stages before and after. It may be due to changes in platform distribution methods and other reasons that the sample structure before and after is quite different (such as age, gender, region, etc.), resulting in a lack of comparability in the analysis. Therefore, it is recommended to explain the risk.

-Table 3 does not give regression statistical parameters, such as R2, F value, etc.

Reviewer #2: Review of Manuscript ID: PONE-D-21-14417

Title of manuscript: Patterns of Mental Health Problems Before and After Easing COVID-19 Restrictions: Evidence from a 105248-subject Survey in General Population in China

The overall purposes of this study are to provide empirical evidence of the patterns of mental health in large general population in China both before and after easing restrictions and to examine the factors at both individual and regional levels that contributed to and/or mitigated these patterns. Understanding these patterns is very important for policy makers and health care workers as well as the scholarly community; thus this paper can be very impactful. In addition, the size of the survey data is unusual and appealing in light of the current literature on this topic, which typically relies on the limited number of respondents for empirical analysis. Overall, the paper had a clear message and was very well-written. I think this paper can make a significant contribution to the literature in mental health among general population during pandemic.

Below I have some minor comments that could improve the manuscript.

Abstract:

• Suggest to add brief information about the mental health issues has become a major health concerns during the COVID-19 pandemic.

• Suggestion to change the last sentence in background: The findings from this research study have provided the empirical evidence …………………, and information of the factors associated with these patterns.

Introduction: It is well organized and easy to follow. The authors have provided sufficient up-to-date citations from other studies which support the readers to understand the current research gaps, objectives and the findings from this paper.

• Page 11 ( page #22 in paper), Line 40 : Suggest to change “To our knowledge, no studies have been conducted to examine the …”

• Page 11 (page#22 in the paper), Line 41-43: Suggest to delete commas and change “ The objectives of the study were to provide empirical evidence of the patterns of mental health in general population both before and after easing restrictions and to examine the factors at both individual and regional levels that contributed to and/or mitigated these mental health patterns.

Methods:

• Page 12 ( page #23 in paper), Line 58: Exclusion criteria – the authors may include the reasons why these groups of population (b. being aged < 20 years and being married, divorced or widowed; c. being aged < 20 years and having a degree of Master or PhD) were excluded from this current study.

• Page 13 (page #24 in paper), Line 75: suggest to change “LCA was also used to classify individuals into distinct classes.”

Results:

• Page 14 (page # 25 in paper), Line 116: Suggest to specify which classes. E.g., However, after easing restrictions as shown in Table 2, females had a greater chance of being in both “High Risk (Class one) and Stressed, Social Dysfunction and Unhappy (Class Two)” .

• Page 15: (page #26 in paper), Line 132: Suggest to provide additional information on the age range/group for seniors and teenagers and include them in the methods section.

• Page 17: (page# 28 in paper), Line 178: Suggest to change the word “children” to “teenagers or young adults” because people who are younger than 12 years old were excluded from the study; Several terms were used to represent a “young adults” group, such as children, youths, teenagers, as well as terms for “seniors”, such as elderly people – suggest to keep them consistent throughout this paper.

Discussion:

• Further to the limitations of this current study, it would be helpful to include the future research studies and/or lines of work that should be considered.

Overall: This paper is clear, well founded that provides the essential information on the mental health patterns and associated factors in a large general population both before and after easing COVID-19 restrictions. Given the potential value of the current study, I strongly recommend that the authors will take the above suggestions into consideration and revise the manuscript, in order to improve this interesting research work.

**********

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23 Jun 2021

Reviewer 1:

Issue 1: A single Wenjuanxing platform was used for data collection. A single platform may have a biased sample due to user groups. Therefore, the rationality of using a single Wenjuanxing platform and the potential impact on the sample should be explained.

Response: Thanks for raising this concern. We have acknowledged the limitation of this convenience sampling methods in the Discussion section (Line 262-275, Page 24-25).

Wenjuanxing (Wenjuanxing TechCo. Ltd., Changsha, China) is an authoritative and widely-used online survey platform that was used to develop the questionnaire and manage data in this study [1]–[7]. Supporting by its technology, the survey hosted by Wenjuanxing can be sent out directly through WeChat wallet [3]. Upon completion, the participant could join a lottery (200 prizes of 50-200 RMB (7-28 USD)) and a symbolic ‘CNY lucky money’ of 1 RMB for 80% of the participants that can be transferred to participants’ WeChat wallet. WeChat is one of the most popular social media platforms with above 1.26 billion users out of the 1.44 billion population in China, which shows its important role and potential in facilitating online surveys. The feasibility of this online survey data collection approach using Wenjuanxing and WeChat platforms was elaborated in an article promoting worldwide researchers to conduct online surveys in China [3].

Adopting an internet-based sampling can introduce bias through restricting the respondents to internet-users, their relative and friends [3], [7]. As a consequence, the sample tends to be younger and technologically savvy, in turn, limit its representativeness [3]. One study in Swedish found that the sample recruited through internet survey over-represent the population aged between 25 and 65 and underrepresent younger and older populations [8], [9]. Unfortunately, the representativeness of the sampling through WeChat has not been investigated [3]. Therefore, caution requires when disseminate the results from this study. In the future studies, other social media such as Weibo and QQ can be included when recruiting participants to increase the representativeness of the sample through multiple platforms [10]. We also have added that future directions in the discussion session “Future studies should recruit a representative probability sample or use other social media such as Weibo in order to draw more reliable conclusions” (Line 265-267, Page 24-25).

Reference

[1] T. Zhou, T. V. T. Nguyen, J. Zhong, and J. Liu, “A COVID-19 descriptive study of life after lockdown in Wuhan, China,” R. Soc. Open Sci., vol. 7, no. 9, 2020.

[2] K. Huang et al., “Attitudes of Chinese health sciences postgraduate students’ to the use of information and communication technology in global health research,” BMC Med. Educ., vol. 19, no. 1, pp. 1–10, 2019.

[3] B. Mei and G. T. L. Brown, “Conducting Online Surveys in China,” Soc. Sci. Comput. Rev., vol. 36, no. 6, pp. 721–734, 2018.

[4] H. Liqian, “Study on the Perceived Popularity of Tik Tok,” Bangkok University, 2018.

[5] Y. Wang, F. Guo, J. Wei, Y. Zhang, Z. Liu, and Y. Huang, “Knowledge, attitudes and practices in relation to antimicrobial resistance amongst Chinese public health undergraduates,” J. Glob. Antimicrob. Resist., vol. 23, pp. 9–15, 2020.

[6] J. Gao et al., “Mental health problems and social media exposure during COVID-19 outbreak,” PLoS One, vol. 15, no. 4, pp. 1–10, 2020.

[7] H. Luo, Y. Lie, and F. W. Prinzen, “Surveillance of COVID-19 in the general population using an online questionnaire: Report from 18,161 respondents in China,” JMIR Public Heal. Surveill., vol. 6, no. 2, pp. 1–14, 2020.

[8] M. W. Ross, S. A. Månsson, K. Daneback, A. Cooper, and R. Tikkanen, “Biases in internet sexual health samples: Comparison of an internet sexuality survey and a national sexual health survey in Sweden,” Soc. Sci. Med., vol. 61, no. 1, pp. 245–252, 2005.

[9] R. L. Marquet et al., “Internet-based monitoring of influenza-like illness (ILI) in the general population of the Netherlands during the 2003-2004 influenza season,” BMC Public Health, vol. 6, no. Ili, pp. 1–8, 2006.

[10] V. D. De Rada, L. V. C. Ariño, and M. G. Blasco, “The use of online social networks as a promotional tool for self-administered internet surveys,” Rev. Española Sociol., vol. 25, no. 2, pp. 189–203, 2016.

Issue 2: The time of data collection needs to be given more precisely. Due to the long collection time, a trend graph of data collection can be drawn.

Response: Thanks for the suggestion. We have given the data collection start and end dates in the Design section (Line 94, Page 3). For your information, the following figure shows the number of valid samples collected by date. This figure was added as a supplemental figure in the supporting information.

Issue 3: Different sample sets were used in the two stages before and after. It may be due to changes in platform distribution methods and other reasons that the sample structure before and after is quite different (such as age, gender, region, etc.), resulting in a lack of comparability in the analysis. Therefore, it is recommended to explain the risk.

Response: We agree with the reviewer that the sample structure before and after the ease of restrictions is quite different. We have added a supplemental table to shows the demographics before and after the ease of restrictions. We acknowledged the limitation of this and caution of the interpretation: “The sample sets used in the two stages before and after the easing of restrictions were quite different in demographics (see S1 Table in the supplemental information). Therefore, association between the ease of restrictions and mental health patterns cannot necessarily be considered causal relationships.” (Line 268-271, Page 25).

Issue 4: Table 3 does not give regression statistical parameters, such as R2, F value, etc.

Response: Table 3 shows results from the multinomial logistic regression to examine how individual and regional factors could predict mental disorder patterns. The R2 and F-value are measures of fit for regular regression using OLS (ordinal least square) estimation method. The logistic regression is usually estimated by maximum likelihood method. The measures of fit for logistic regression differ from the measures of fit for regular regression. There are two categories of measure of fit for logistic regression: measures of predictive power (like R2) and goodness of fit tests (like the Person Chi-square). There are many different ways to calculate R2 for logistic regression and, unfortunately, no consensus on which one is best. Mittlbock and Schemper (1996) reviewed 12 different measures [1]. Menard (2000) considered several others [2]. As for goodness of fit, the popular one is Hosmer and Lemeshow (HL) test. Hosmer and Lemeshow (1980) proposed grouping cases together according to their predicted values from the logistic regression model [3]. Specifically, the predicted values are arrayed from lowest to highest, and then separated into several groups of approximately equal size. Ten groups is the standard recommendation. HL test is shown to have some serious problems [4]. The most obvious problem is that results can depend markedly on the number of groups, and there is no theory to guide the choice of that number.

There is rarely a fixed cut-off that distinguishes an acceptable model from one that is not acceptable. This is why that measures of fit were not as frequently being reported in logistic regression as in regular regression. For your information, the R2 for multinomial logistic regression are 2.9% and 2.1% for the sample before and after the ease of restrictions respectively. The H-L test statistics are 125.77 and 141.11 for the two sample sets respectively and both with degree of freedoms of 24.

Reference

[1] Mittlbock, M. and M. Schemper (1996) “Explained variation in logistic regression.” Statistics in Medicine 15: 1987-1997.

[2] Menard, S. (2000) “Coefficients of determination for multiple logistic regression analysis.” The American Statistician 54: 17-24.

[3] Hosmer D.W. and S. Lemeshow (1980) “A goodness-of-fit test for the multiple logistic regression model.” Communications in Statistics A10:1043-1069.

[4] Hosmer, D.W., T. Hosmer, S. Le Cessie and S. Lemeshow (1997). “A comparison of goodness-of-fit tests for the logistic regression model.” Statistics in Medicine 16: 965–980.

Reviewer 2:

1. Abstract:

• Suggest to add brief information about the mental health issues has become a major health concerns during the COVID-19 pandemic.

• Suggestion to change the last sentence in background: The findings from this research study have provided the empirical evidence …………………, and information of the factors associated with these patterns.

Response: We have revised the abstract according to your suggestion.

2. Introduction:

• Page 11 ( page #22 in paper), Line 40 : Suggest to change “To our knowledge, no studies have been conducted to examine the …”

Response: Thanks. Done.

• Page 11 (page#22 in the paper), Line 41-43: Suggest to delete commas and change “ The objectives of the study were to provide empirical evidence of the patterns of mental health in general population both before and after easing restrictions and to examine the factors at both individual and regional levels that contributed to and/or mitigated these mental health patterns.

Response: Thanks. Done.

3. Methods:

• Page 12 (page #23 in paper), Line 58: Exclusion criteria – the authors may include the reasons why these groups of population (b. being aged < 20 years and being married, divorced or widowed; c. being aged < 20 years and having a degree of Master or PhD) were excluded from this current study.

Response: We have added justifications for these exclusion criteria: “Exclusion criteria b) and c) were set because these participants failed to pass the internal consistent checks. Those participants, who were younger than 12 years or completed questionnaires in ≤ 50 seconds, were also excluded because of data quality controls.” (Line 109-111, Page 4).

• Page 13 (page #24 in paper), Line 75: suggest to change “LCA was also used to classify individuals into distinct classes.”

Response: We have made the suggested change (Line 125, Page 5).

4. Results:

• Page 14 (page # 25 in paper), Line 116: Suggest to specify which classes. E.g., However, after easing restrictions as shown in Table 2, females had a greater chance of being in both “High Risk (Class one) and Stressed, Social Dysfunction and Unhappy (Class Two)”.

Response: Thanks. Done.

• Page 15: (page #26 in paper), Line 132: Suggest to provide additional information on the age range/group for seniors and teenagers and include them in the methods section.

Response: In our data analyses, we have not made any cutoffs of age to form age groups. The age is treated as a continuous variable in the regression analyses. Because the estimated coefficient is negative for the linear slope and positive of quadratic slope of age, the relationship between age and likelihood of being any of the mental disorder risk classes are U-shape. Therefore, we concluded that the seniors and teenagers had increased likelihood of being any of the mental disorder risk classes (Classes One, Two and Three).

• Page 17: (page# 28 in paper), Line 178: Suggest to change the word “children” to “teenagers or young adults” because people who are younger than 12 years old were excluded from the study; Several terms were used to represent a “young adults” group, such as children, youths, teenagers, as well as terms for “seniors”, such as elderly people – suggest to keep them consistent throughout this paper.

Response: As you suggested we have changed the word “children” to teenagers or young adults” in the discussion section.

5. Discussion:

• Further to the limitations of this current study, it would be helpful to include the future research studies and/or lines of work that should be considered.

Response: We have added the following future directions: “Future studies should recruit a representative probability sample or use other social media such as Weibo in order to draw more reliable conclusions”; “The longitudinal studies with follow-up assessments at different periods of pandemic are needed to determine the transition of mental health patterns and the long-term mental health outcomes.” (Line 265-275, Page 24-25)

6. Overall:

This paper is clear, well founded that provides the essential information on the mental health patterns and associated factors in a large general population both before and after easing COVID-19 restrictions. Given the potential value of the current study, I strongly recommend that the authors will take the above suggestions into consideration and revise the manuscript, in order to improve this interesting research work.

Response: Thanks.

Submitted filename: Response_Reviewers_PLOS ONE_June 2021.docx

Click here for additional data file.

13 Jul 2021

Patterns of Mental Health Problems Before and After Easing COVID-19 Restrictions: Evidence from a 105248-subject Survey in General Population in China

PONE-D-21-14417R1

Dear Dr. Jiang,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Wen-Jun Tu

Academic Editor

PLOS ONE

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Reviewer #1: All comments have been addressed

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Reviewer #1: Yes

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Reviewer #1: Yes

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Reviewer #1: Yes

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Reviewer #1: Yes

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Reviewer #1: Yes: Bo Chen

21 Jul 2021

PONE-D-21-14417R1

Patterns of mental health problems before and after easing COVID-19 restrictions: Evidence from a 105248-subject survey in general population in China

Dear Dr. Jiang:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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on behalf of

Dr. Wen-Jun Tu

Academic Editor

PLOS ONE