The short-term impacts of COVID-2019 on depressive symptoms and cognitive decline: A community-based cohort study

Abstract

Background

Neurological and psychological sequelae may persist after the infection of coronavirus disease 2019 (COVID-19). Depression and cognitive decline could increase the risk of Alzheimer's disease.

Objective

To estimate the impacts of COVID-19 on depressive symptoms and cognitive decline.

Methods

The data was from Beijing Research on Ageing and Vessel (BRAVE), which included all residents in the Xishan community. The first wave survey was performed from October to November 2019 (baseline) before the COVID-19 pandemic. The second wave survey was interrupted into two periods due to the introduction of the Ten New Measures, from October to November 2022 (no participants were infected) and from March to April 2023 (most participants were infected), providing an excellent opportunity to investigate the short-term impacts of COVID-19 on depressive symptoms and cognitive function with linear mixed models.

Results

Among a total of 1012 participants, the median (interquartile range, IQR) age at baseline was 60.00 (56.00, 65.00) years, with 374 (36.96%) men and 479 participants COVID-19 infected. Compared with uninfected participants, the infected did not suffer pronounced depressive symptoms (β = −0.047; 95% CI −0.204 to 0.110) and accelerated declines in global cognition (β = 0.116; 95% CI −0.001 to 0.234) from wave 1 to wave 2. Sensitive analyses shared generally consistent findings.

Conclusions

The impacts of COVID-19 infection on depressive symptoms and cognitive decline were not significant among participants in the BRAVE cohort. Further research is needed to investigate the long-term impacts on neurological and psychiatric symptoms.

Keywords

Alzheimer's disease cognition COVID-19 depressive symptoms

Introduction

Coronavirus disease 2019 (COVID-19) pandemic has affected more than 772 million individuals, of whom 6.9 million individuals were dead globally.¹ In addition to acute respiratory and systemic symptoms, there are a series of persistent symptoms after infection, including fatigue, brain fog, memory problems, attention disorders, headache, and mental health symptoms,^2,3 which are named the post-COVID-19 condition (PCC) by the World Health Organization (WHO). As it is reported, 6.2% of survivors experience several persistent symptoms, with 15.1% having symptoms at 12 months after infection onset,⁴ especially on neurological and neuropsychiatric manifestations.⁵ Due to the link between depression and cognition,^6–8 evaluation of potential depressive and cognitive alterations following COVID-19 infection is regarded as a major priority.

Although several studies using longitudinal data reported elevated rates of depressive symptoms following COVID-19 infection,^9,10 others have not found clear evidence of associations.¹¹ This suggests that the increased incidence of depression could not be simply attributed to COVID-19 infection. What's more, depressive symptoms may be triggered by the fear of the pandemic and dramatic changes in social interactions rather than by COVID-19 infection.¹² The stress of uncertainty about the future and social isolation may lead to mental health conditions such as depression.¹³

Regarding cognitive function, a series of studies showed that cognitive decline is common during the acute onset of COVID-19 and persists until 3 months or even 1 year after infection, including the domain of attention and calculation, short-term memory, and written language.^14,15 However, the above findings were derived from a single cohort arm, which lacked comparison with a concurrent control group.

Beijing Research on Ageing and Vessel (BRAVE) is an ongoing prospective cohort study investigating the relationships of vascular structure and function with consequent cognitive function in a community-based population.^16,17 It was established from October to November 2019, before the COVID-19 pandemic. The second wave survey of the BRAVE cohort was interrupted into two periods, before and after the Chinese government issued Ten New Measures against the pandemic for accurate prevention and control on December 7, 2022.¹⁸ In the first period of wave 2 survey from October to November 2022, no participants were infected with COVID-19. In the second period, from March to April 2023, most participants were infected in the last 3 months. Therefore, we have an excellent opportunity to investigate the short-term impacts of COVID-19 infection on depressive symptoms and cognitive decline in our BRAVE cohort.

Method

Study design

Data was obtained from the BRAVE cohort, which is a community-based, prospective, longitudinal study to investigate the contributors of cognitive impairment from the Xishan community, Shijingshan District, Beijing.^16,17 The baseline survey of BRAVE was conducted from October to November 2019 before the COVID-19 pandemic. At baseline (wave 1), all 1789 residents aged 40–80 years were invited, and 1554 participants were enrolled and underwent baseline evaluation. The second wave of BRAVE was conducted from October 2022 to April 2023, and 1012 participants completed this survey. The screening flowchart of participants is presented in Supplemental Figure 1.

Due to introducing the Ten New Measures for COVID-19 on December 7, 2022, the second wave survey was interrupted into two periods, from October to November 2022 (no participants were infected) and from March to April 2023 (most participants were infected). This interruption allowed the BRAVE cohort to unintentionally generate a concurrent control group according to infective status. We classified participants as the infected and uninfected group according to self-reported COVID-19 infection. The timeline of the second wave survey is presented in Figure 1.

The study was approved by the Institutional Review Board of Peking University Health Science Center (IRB0001052-19060), and all participants gave their informed written consent according to the Declaration of Helsinki. The study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cohort studies (Supplemental Table 1).

Ten new measures for the COVID-19 pandemic

As the pathogenicity and virulence of SARS-CoV-2 variants have significantly decreased and the full vaccination rate against COVID-19 has exceeded 90% of the total population in China,¹⁹ the overall health risks posed by the COVID-19 pandemic appear to be easing. In light of this context, the Chinese government has proposed Ten New Measures to combat COVID-19.¹⁸ These include more precise prevention and control, optimizing testing, accounting, and isolation measures, ensuring normal social operations and medical services, and better facilitating the work and life routines of the people.

COVID-19 infection

Considering the COVID-19 pandemic, we added another separate questionnaire to assess participants for COVID-19 infection and symptoms during the second wave survey. The questionnaire included whether infected with COVID-19, the time of first infection, the confirmed route of COVID-19 infection (nucleic acid positive, antigen positive, family concentrated infection, and others), symptoms of COVID-19, and persistent symptoms. Finally, we utilized self-report COVID-19 infection as the exposure factor confirmed by multiple routes.

Depressive and cognitive assessment

Differences in scores of depressive and cognitive assessments between wave 1 and wave 2 were the main outcomes.

Depressive symptoms were assessed by the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10). CESD-10 was widely used in the evaluation of depressive symptoms in residents in the community due to its simplicity and convenience.^20–22 CESD-10 demonstrates robust psychometric properties, with Cronbach's α ranging from 0.78 to 0.79 and divergent validity of r = 0.84) in the Chinese population.²³ CESD-10 indicates depressive symptoms of participants in the past week with scores ranging from 0 to 30. The higher the scores, the more severe the depressive symptoms of respondents.

Cognitive function was assessed by the Chinese version of Montreal Cognitive Assessment Basic (MoCA-BC). MoCA-BC is a standardized and reliable tool for global cognitive screening that has been extensively utilized to evaluate the cognitive function of the middle-aged and older population in China.^24,25 MoCA-BC shows strong psychometric properties with high criterion-related validity against Mini-Mental State Examination (MMSE; r = 0.787) and good internal consistency (Cronbach's α = 0.807).²⁶ It includes executive function, memory, orientation, calculation, conceptual thinking, visual perception, language, concentration, and attention domains. The maximum score of MoCA-BC is 30 points, where lower scores represent worse cognitive function. All examiners had completed the official MoCA training and certification program before administering and scoring MoCA-BC.

Covariates

Baseline demographic characteristics, lifestyle factors, body mass index (BMI), APOE4 (at least one copy of the APOE4 allele), and chronic disease history were considered as covariates. Demographic characteristics included age (years), sex, cohabitation status (living alone or not), educational background (more than high school or not). Lifestyle factors included current smoking (yes or no), drinking (1 day per week or not), and physical exercise participation (engaging in vigorous or moderate activities no less than once weekly). Chronic disease history was obtained by self-report or examination of participants. Hypertension was defined as self-reported or current use of antihypertensive medications or measured SBP ≥140 mm Hg or DBP ≥90 mm Hg. Diabetes was defined as self-reported or current use of antidiabetic medications or regular use of insulin or fasting blood-glucose concentration ≥ 7.00 mmol/L or glycated hemoglobin A_1C≥ 6.5%. Hyperlipidemia was defined as a self-reported condition, use of statin, or total cholesterol ≥ 6.216 mmol/L. Stroke history was defined as a self-reported doctor-diagnosed stroke condition. Coronary heart disease was defined as self-reported angina and myocardial infarction. Cancer was defined as a self-reported history of having a certain type of cancer. Chronic lung disease was defined as self-reported chronic obstructive pulmonary disease and asthma. Depression was defined as self-reported depression.

Statistical analysis

First, the distribution of demographic characteristics, lifestyle factors, and clinical characteristics were described with the mean ± SD or median (interquartile range, IQR) for continuous variables (based on Shapiro-Wilk normality test) and numbers (percentage) for categorical variables. For the comparison between infected and uninfected groups, Student's t-test or Mann-Whitney U test was used for continuous variables according to their distribution, and the χ² test for categorical variables.

Linear mixed model (LMM) with a random intercept for each participant and fixed factors for groups, time, and the interaction between groups and time were used to estimate the short-term impacts of COVID-19 infection on depressive symptoms and global cognitive decline. The details of LMM method were described in the Supplemental Methods. Considering preexisting characteristics differences between the infected and uninfected groups, we assessed propensity score (PS) of each participant by a logistic regression model where the dependent variable was COVID-19 infection in the second wave (yes/no) and aforementioned predictors. The inverse probability weight (IPW) approach was used to calculate the inverse of PS in line with COVID-19 infection or not as individual level weight.²⁷ The final weights were applied to the linear mixed model when examining the relationships of COVID-19 infection with depressive symptoms and cognitive decline. Analyses were conducted using a staged approach to adjustment for potential confounding factors: unadjusted model, adjusted model for age, sex, and education background, and adjusted model for all covariates.

Three sensitivity analyses were performed. Firstly, to evaluate the potential interactions, we conducted subgroup analyses by age (older than 60 years or not), sex, cohabitation status, educational background, physical exercise, and APOE4 (only for cognitive decline). Secondly, to evaluate the impacts of COVID-19 on each item of CESD-10 and each dimension of MoCA-BC, we conducted separate analyses of LMM. Thirdly, to further address the disproportion in the distribution of characteristics between the infected and uninfected group, a propensity score matching (PSM) approach was applied before LMM.²⁸ Infected and uninfected groups were matched according to PS using the greedy nearest neighbor method, which sequentially matches each infected participant with the closest uninfected participant without replacement.²⁹ We applied 1:1 matching with a caliper width of 0.02 to ensure matching quality.

Analyses were performed using SAS version 9.4 (SAS Institute) and R version 4.1.2 (R Foundation for Statistical Computing). Statistical significance was defined as p < 0.05; all tests were 2-tailed.

Results

Baseline characteristics

A total of 1012 participants who completed both wave 1 and wave 2 assessments were included in the analysis for around 3 years following up. The median age at baseline was 60.0 years (IQR: 56.0–65.0), and 37.0% were men. Of them, 479 were infected and 533 were uninfected. Compared with uninfected participants, those infected were relatively younger (Infected versus uninfected = 58.0 versus 61.0 years; p < 0.001), less percentage of men (32.2% versus 41.3%; p = 0.003), less likely to be alone (8.4% versus 14.5%; p = 0.003), less likely to smoke (16.9% versus 23.5%; p = 0.010), more physically active (25.5% versus 17.3%; p = 0.001), and less likely to have diabetes (23.8% versus 32.1%; p = 0.003), with a better cognitive score (25.0 versus 25.0; p = 0.032) in baseline characteristics. The details are shown in Table 1.

Table 1.

Distribution of baseline sample characteristics (n = 1012).

Characteristics	Total (n = 1012)	Uninfected (n = 533)	Infected (n = 479)	p
Age (y)	60.00 (56.00, 65.00)	61.00 (57.00, 66.00)	58.00 (54.00, 63.00)	<0.001
Men (%)	374 (36.96%)	220 (41.28%)	154 (32.15%)	0.003
Living alone (%)	117 (11.56%)	77 (14.45%)	40 (8.35%)	0.003
Education ≥ HS (%)	627 (61.96%)	327 (61.35%)	300 (62.63%)	0.676
Current smoking (%)	206 (20.36%)	125 (23.45%)	81 (16.91%)	0.010
Drinking ≥ 1d per wk (%)	220 (21.74%)	110 (20.64%)	110 (20.96%)	0.370
Physical exercise (%)	214 (21.15%)	92 (17.26%)	122 (25.47%)	0.001
BMI	25.71 (23.73, 27.99)	25.60 (23.60, 27.74)	25.95 (23.92, 28.20)	0.059
Morbidity (%)
Hypertension	601 (59.39%)	331 (62.10%)	270 (56.37%)	0.064
Diabetes	285 (28.16%)	171 (32.08%)	114 (23.80%)	0.003
Hyperlipidemia	469 (46.34%)	238 (44.65%)	231 (48.23%)	0.255
Stroke history	71 (7.02%)	44 (8.26%)	27 (5.64%)	0.104
Coronary heart disease	95 (9.39%)	54 (10.13%)	41 (8.56%)	0.392
Cancer	33 (3.26%)	20 (3.75%)	13 (2.71%)	0.353
Chronic lung disease	52 (5.14%)	31 (5.82%)	21 (4.38%)	0.303
Depression	21 (2.08%)	12 (2.25%)	9 (1.88%)	0.678
APOE4^b (%)	183 (18.08%)	88 (16.51%)	95 (19.83%)	0.313
CESD-10 score	3.00 (1.00, 5.00)	3.00 (1.00, 5.00)	2 (1.00, 5.00)	0.590
MoCA-BC score	25.00 (23.00, 27.00)	25.00 (23.00, 27.00)	25.00 (23.00, 27.00)	0.033

HS: high school; CESD-10: 10-item Center for Epidemiologic Studies Depression Scale; MoCA-BC: Montreal Cognitive Assessment Basic.

Data are presented as mean ± SD, median (IQR) or n (%).

Missing data for 11 participants.

Supplemental Figure 2 shows that there still was sufficient overlap in the distribution of the PS for the infected and uninfected groups though the preexisting differences. IPW approach could balance the covariates between the two groups well.

Association between COVID-19 infection and depressive symptoms

Table 2 presents the linear mixed model results for the relationship between COVID-19 infection and depressive symptoms using the IPW approach. There was no significant difference between the three models. As shown in model 3, CESD-10 scores of the second wave was higher compared to the baseline survey, but did not differ between the infected and uninfected group (β = −0.047; 95% CI −0.204 to 0.110; p = 0.558). Supplemental Table 2 shows the relationship between the individual items of CESD-10 and COVID-19 infection.

Table 2.

Comparison of changes of depressive symptoms between infected and uninfected groups based on linear mixed model.

	Model 1^a		Model 2^b		Model 3^c
	β (95%CI)	p	β (95%CI)	p	β (95%CI)	p
Infect COVID-19	−0.125 (−0.613, 0.363)	0.615	−0.133 (−0.619, 0.352)	0.590	0.038 (−0.507, 0.431)	0.873
time	0.124 (0.012, 0.235)	0.030	0.124 (0.012, 0.235)	0.030	0.124 (0.012, 0.235)	0.030
time*Infect COVID-19	−0.047 (−0.204, 0.110)	0.559	−0.047 (−0.204, 0.110)	0.559	−0.047 (−0.204, 0.110)	0.558

unadjusted model.

adjust for age, sex, and education background.

adjust for age, sex, cohabitation status, education background, current smoking, drinking, physical exercise participation, BMI, and morbidities.

Association between COVID-19 infection and cognitive decline

Table 3 presents the findings of the relationship between COVID-19 infection and cognitive decline. Time and COVID-19 infection were not significantly associated with cognitive decline in any models. The interaction term for time and COVID-19 infection remained not statistically significant even after full adjustment (β = 0.116; 95% CI −0.001 to 0.234; p = 0.052), suggesting COVID-19 infection may not be associated with an increased rate of cognitive decline over three months. Supplemental Table 3 shows the relationship of dimensions of MOCABC and COVID-19 infection.

Table 3.

Comparison of cognitive decline between infected and uninfected groups based on linear mixed model.

	Model 1^a		Model 2^b		Model 3^c
	β (95%CI)	p	β (95%CI)	p	β (95%CI)	p
Infect COVID-19	0.362 (−0.046, 0.770)	0.082	0.061 (−0.325, 0.447)	0.757	0.047 (−0.342, 0.436)	0.806
time	−0.049 (−0.133, 0.034)	0.247	−0.049 (−0.133, 0.034)	0.247	−0.049 (−0.133, 0.034)	0.247
time*Infect COVID-19	0.116 (−0.001, 0.234)	0.053	0.116 (−0.001, 0.234)	0.052	0.116 (−0.001, 0.234)	0.052

unadjusted model.

adjust for age, sex, and education background.

adjust for age, sex, cohabitation status, education background, current smoking, drinking, physical exercise participation, BMI, APOE4, and morbidities.

Sensitivity analyses

Figure 2 describes the potential impacts of COVID-19 infection on depressive symptoms and cognitive decline among different subgroups. As shown in Figure 2, there is no statistically significant effect modification of sex, cohabitation status, educational background, physical exercise, and APOE4 for depressive symptoms and cognitive decline, except for age. In the subgroup older than 60 years, the infection group experienced an improvement of 0.274 (95% CI 0.103 to 0.445) in cognitive function compared to the uninfected group. According to analysis based on the PSM approach, no material changes in primary results were observed (Supplemental Tables 2 and 3).

Discussion

Based on longitudinal data from the BRAVE cohort, we found that infected participants did not suffer pronounced depressive symptoms and an accelerated cognitive decline compared with those uninfected in a short-term period after COVID-19 infection. To our knowledge, this is the first prospective cohort study incorporating pre-pandemic baseline measurements and a parallel control group to evaluate the short-term impacts of the COVID-19 infection on depressive symptoms and cognitive decline.

Our findings demonstrated no significant association between COVID-19 infection and depressive symptoms regarding demographic characteristics. Several previous observational clinical studies have shown that depressive symptom is a typical symptom of sequelae of COVID-19.^30,31 Given the existing evidence, the main source of depressive symptoms is fear of COVID-19 infection, financial restraints, and job pressure caused by the pandemic.^32,33 Another essential factor is that social isolation contributes to insomnia and decreased social interaction, which further accelerates the onset of depressive symptoms.^34,35 We did not find the interaction between cohabitation status and COVID-19 infection. Based on the above, depressive symptoms are at least partially caused by the social environment. During the COVID-19 pandemic, the Chinese authorities promptly reported the progress of the pandemic through official channels, publicized and implemented proactive response measures. In addition, the coverage rate of SARS-CoV-2 vaccination in China has exceeded 90%,¹⁹ which provides a basis to adjust the epidemic prevention and control measures and issue the Ten New Measures on December 7, 2022. As a result, Chinese people were fairly knowledgeable about COVID-19 and held an optimistic attitude to the government's measures.^36,37 Additionally, a mendelian randomization study demonstrated there was no causal effect of COVID-19 infection status on depression, which also supports our findings.³⁸

There is no significant association between COVID-19 infection and cognitive decline according to our findings. Contrary to the findings of this study, several previous studies presented that cognitive decline was an essential post-COVID-19 condition.³⁹ The following points may account for the discrepancies between our findings and the previous studies. Firstly, our study population comprised community-dwelling residents who had experienced and recovered from relatively mild COVID-19 symptoms.⁴⁰ With the spread of COVID-19,the variation of SARS-CoV-2 strains, and vaccination, the symptoms caused by SARS-CoV-2 gradually alleviate.^41,42 In addition, a stratification study based on self-reported recovery status showed that individuals who reported full recovery from COVID-19 infection had no significant cognitive impairment.⁴³ According to the above, the change in cognitive function may be associated with the type of SARS-CoV-2 strains and recovery status. Secondly, the majority of uninfected group in our study was in the stage of social isolation due to lockdown measures before the Ten New Measures for COVID-19, which may be detrimental to cognitive function. Previous studies also indicated negative impacts of isolation on cognitive function.^44,45 Thirdly, the lack of contemporary control groups in these previous studies may have exaggerated the impacts of COVID-19 infection on cognitive decline.^39,43,46 The inclusion of a contemporary control group of uninfected participants sets the stage for more comprehensive findings, which may be closer to the true effect. Interestingly, our subgroup analyses found that participants older than 60 years might have cognitive benefits after release from isolation, which indicates that the impacts of COVID-19 infection on cognitive decline were possibly related to age.

The primary strength of our study is the longitudinal measurement of depressive symptoms and cognitive function with a concurrent control group naturally generated due to changes in COVID-19 quarantine measures. We could infer the potential depressive and cognitive impacts of COVID-19 infection with a stronger capability. Second, our findings were robust, with generally consistent findings broadly observed between the main and sensitivity analyses. Our study has several limitations. First, given that our main exposure variables were based on self-reported COVID-19 infection according to nucleic acid-positive, antigen-positive, and family-concentrated infection, there was no definitive clinical diagnosis of COVID-19 infection. Additionally, the lack of data on participants’ vaccination status limited our ability to control for this potential confounding factor. This means that the findings of this work need interpreting with caution. Second, the baseline demographic characteristics of the infected group and the uninfected group in our cohort study were inevitably unbalanced. To solve this problem, we used a IPW approach and PSM approach to make the two groups more comparable. Third, this is a single center study focused on community participants, which limits the representativeness of this cohort. Future large sample studies are needed to evaluate the long-term health impact of COVID-19 infection in individuals with varying severity.

There are two major implications of our findings. First, according to the findings of our investigation, COVID-19 infection was not associated with depressive symptoms and general cognitive decline. This suggests that the neuropsychiatric burden directly attributable to COVID-19 infection may not be as substantial as previously hypothesized, particularly among community-dwelling residents. Second, older adults who were infected with COVID-19 experienced slight enhancements in cognitive function compared to those uninfected. This implies that age should be considered as a key variable when evaluating cognitive and mental health after COVID-19 infection.

Conclusion

COVID-19 infection in the past 3 months was not associated with cognitive decline or depressive symptoms in the BRAVE cohort. Further research is needed to investigate the long-term impacts of COVID-19 on neurological and psychiatric symptoms.

Supplemental Material

sj-docx-1-alr-10.1177_25424823251328627 - Supplemental material for The short-term impacts of COVID-2019 on depressive symptoms and cognitive decline: A community-based cohort study

Supplemental material, sj-docx-1-alr-10.1177_25424823251328627 for The short-term impacts of COVID-2019 on depressive symptoms and cognitive decline: A community-based cohort study by Mengmeng Ji, Darui Gao, Jie Liang, Yanyu Zhang, Yang Pan, Wenya Zhang, Yanjun Ma, Yongqian Wang, Chenglong Li, Yidan Zhu, Fanfan Zheng and Wuxiang Xie in Journal of Alzheimer's Disease Reports

Footnotes

Acknowledgments

We extend our deepest gratitude to all of the dedicated staff and generous subjects involved in the BRAVE cohort study.

ORCID iDs

Mengmeng Ji

Yanyu Zhang

Wenya Zhang

Yanjun Ma

Chenglong Li

Wuxiang Xie

Ethical considerations

This study was approved by the Institutional Review Board of Peking University Health Science Center (IRB0001052-19060) on June 21, 2019.

Consent to participate

Participants received verbal and written information about the study's purpose, procedures, risks, and benefits. Participants were informed that their involvement was voluntary and they could withdraw at any time without consequences. After reviewing the information and confirming their understanding, participants provided written consent. Each participant received a copy of their signed consent form.

Consent for publication

This research contains no personally identifiable information. Participants were informed that no personal data would be disclosed in any research publications. All data were collected and processed anonymously.

Author contributions

Mengmeng Ji (Investigation; Methodology; Writing – original draft); Darui Gao (Investigation; Methodology; Writing – original draft); Jie Liang (Investigation); Yanyu Zhang (Investigation); Yang Pan (Investigation); Wenya Zhang (Investigation); Yanjun Ma (Investigation); Yongqian Wang (Investigation); Chenglong Li (Investigation); Yidan Zhu (Investigation); Fanfan Zheng (Conceptualization; Funding acquisition; Methodology; Supervision); Wuxiang Xie (Conceptualization; Funding acquisition; Methodology; Supervision; Writing – original draft).

Funding statement

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work is supported by the National Natural Science Foundation of China (82373665 and 81974490), the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences (2021-RC330-001), and the 2022 China Medical Board–Open Competition research grant (22-466).

Declaration of conflicting interests

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

Data availability

The data that support the findings of this study are available from the corresponding author [WX] upon reasonable request.

Supplemental material

Supplemental material for this article is available online.

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