Differential effects of COVID-19 and containment measures on mental health: Evidence from ITA.LI—Italian Lives, the Italian household panel

Mario Lucchini; Tiziano Gerosa; Marta Pancheva; Maurizio Pisati; Chiara Respi; Egidio Riva

doi:10.1371/journal.pone.0259989

. 2021 Nov 16;16(11):e0259989. doi: 10.1371/journal.pone.0259989

Differential effects of COVID-19 and containment measures on mental health: Evidence from ITA.LI—Italian Lives, the Italian household panel

Mario Lucchini ¹, Tiziano Gerosa ¹, Marta Pancheva ², Maurizio Pisati ¹, Chiara Respi ¹, Egidio Riva ^1,^*

Editor: Stéphanie Baggio³

PMCID: PMC8594801 PMID: 34784397

Abstract

This study used a subsample of a household panel study in Italy to track changes in mental health before the onset of COVID-19 and into the first lockdown period, from late April to early September 2020. The results of the random-effects regression analyses fitted on a sample of respondents aged 16 years and older (N = 897) proved that there was a substantial and statistically significant short-term deterioration in mental health (from 78,5 to 67,9; β = -10.5, p < .001; Cohen’s d -.445), as measured by a composite index derived from the mental component of the 12-item Short-Form Health Survey (SF-12). The findings also showed heterogeneity in the COVID-related effects. On the one hand, evidence has emerged that the pandemic acted as a great leveller of pre-existing differences in mental health across people of different ages: the decrease was most pronounced among those aged 16–34 (from 84,2 to 66,5; β = -17.7, p < .001; Cohen’s d -.744); however, the magnitude of change reduced as age increased and turned to be non-significant among individuals aged 70 and over. On the other hand, the COVID-19 emergency widened the mental health gender gap and created new inequalities, based on the age of the youngest child being taken care of within the household.

Introduction

The ongoing SARS-CoV-2 (hereafter COVID-19) pandemic is unprecedented when compared to earlier periods of adversity. Leading to the most severe economic shock the world has experienced in decades, it has triggered a global emergency, the course of which has brought about rapid changes to people’s everyday lives, and it is likely to have both short and long-lasting consequences. A mounting body of evidence produced internationally indicates that mental health has significantly declined since the COVID-19 outbreak. Indeed, increased levels of psychological distress—in the form of anxiety, depressive symptoms, stress, fear of death, and insomnia—have been registered in both cross-sectional [1–14] and longitudinal studies [15–21]. Socio-demographic characteristics, household structure and composition, employment status and financial strain, underlying health conditions, and the living space available or people’s satisfaction with their housing are among the factors that can play a crucial role in increasing or mitigating the COVID-related consequences for mental health. For instance, recent research suggests that women and young adults have been negatively affected [3–6, 10–17]. Individuals living with children or in an overcrowded house, as well as those living alone, have reported worse mental health conditions [2, 15, 16]. Unemployment or disrupted working activity due to lockdown or containment measures [6, 11, 15], as well as a lower position in income distribution [9, 15], have also had a deteriorating psychological effect. Finally, direct or indirect exposure to COVID-19 has been proven to be a predictor of a higher impact of the outbreak on depression and anxiety [3–5, 16, 17].

In countries such as Italy, the nature and magnitude of the possible psychosocial consequences of COVID-19 are difficult to assess. The empirical evidence available is anecdotal or cross-sectional [e.g. 4–6]. Hence, longitudinal studies are needed to address the individual unobserved heterogeneity problem and the problem of reversed causality, while shedding light on the cumulative nature of life courses—that is on how the previous life experiences are linked to subsequent experiences—during the pandemic and beyond. Otherwise, it remains unclear whether and, if so, to what extent the COVID-19 outbreak has been worsening or reducing existing inequalities or even creating new ones in relation to mental health [22].

Against this background, this paper investigates the short-term COVID-related consequences on mental health—as assessed by a composite index derived from the mental component of the 12-item Short-Form Health Survey (SF-12) [23]—on a longitudinal sample (N = 897) of individuals (aged 16+) in Italy. Drawing on available evidence on COVID-19 [24] and previous health or economic crises [25–27], we expect to find a generalised deterioration of mental health (Hypothesis 1). In addition, building on longitudinal studies conducted at times of COVID-19 in countries such as the UK and Switzerland [15–17, 28], we may anticipate heterogeneity in the pandemic-related effects on mental health. Specifically, following Kuhn and colleagues [29] we hypothesised a stronger deterioration of mental health among specific subgroups such as a) those more exposed to the detrimental effects of social isolation, such as young adults, individuals living without a partner, and people belonging to a COVID-19 risk group (i.e. those tested because had symptoms or were potentially exposed to the virus and people living in municipalities with high rates of new coronavirus infections) (Hypothesis 2); b) individuals with a heavier workload, such as women and people with preschool-age children in the household (Hypothesis 3); and c) individuals with fewer socioeconomic resources, such as the lower educated, the unemployed, and people with poorer housing conditions (Hypothesis 4). Indeed, there is reason to believe that the COVID-19 emergency may expose the more vulnerable groups and exacerbate mental health inequalities; however, it may also act as a great leveller and reduce gaps in mental health existing before the epidemic, to the extent that all people, not just the more disadvantaged, struggle to cope with the new circumstances [15].

Data and methods

Data source and sample

Our analyses draw on data obtained from an ad-hoc survey on the impact of COVID-19 on individuals’ everyday lives in Italy (ITA.LI COVID-19), which was conducted on a sample of respondents to ITA.LI—Italian Lives (ITA.LI). ITA.LI is a newly established longitudinal study on a probability sample of 4,900 households and 8,967 individuals (aged 16+) living in 280 municipalities in Italy. The first wave of data collection started in June 2019 and finished in December 2020, gathering information on a broad range of topics, such as education, employment and working conditions, family life and caring, wealth, health, well-being, and housing and residential mobility. From 20 April 2020, all panel members who had already taken part in ITA.LI wave 1 were invited, using SMS or email as contact modes, to answer the ITA.LI COVID-19 survey using computer-aided web interviewing (CAWI) and computer-assisted telephone interviewing (CATI) methods [30]. The questionnaire collected information on the consequences of the pandemic—mostly through measures that were already used in ITA.LI wave 1—on the quality of life, health and well-being, employment status and working conditions, family and social relationships, children, and distance education. Moreover, the ITA.LI COVID-19 survey included specific questions on health issues such as perception of the risk of infection, preventive behaviours related to the pandemic, testing for coronavirus, and self-isolation. Overall, 950 of 2,415 eligible people (i.e. respondents who took part in ITA.LI wave 1 and for whom an email and/or a phone number was/were available and valid) participated in the ITA.LI COVID-19 survey [30], which ended on 2 September 2020. The final response rate was 39.3% (AAPOR response rate 1). When merging data collected from the records of the respondents to ITA.LI wave 1, 53 participants were excluded because they were unmatched to previous interviews or because they completed the questionnaire after 9 March 2020. The final sample (Table 1) comprised 897 respondents. Observations containing missing values on any of the variables included in the models were omitted from analysis.

Table 1. Sample.

	ITA.LI wave 1 (Pre-lockdown)				ITA.LI COVID-19 (Post-lockdown)
	M	SD	N	(%)	M	SD	N	(%)
Mental health	78.5	17.7	875	97.6	67.9	22.2	880	98.1
Missing			22	2.4			17	1.9
Age
16–34							149	16.6
35–44							142	15.8
45–54							203	22.6
55–69							268	29.9
70 or more							135	15.1
Living with a partner
No							384	42.8
Yes							513	57.2
Testing for COVID–19
No							803	89.6
Yes							93	10.4
Missing							1
Increase in mortality rate at the municipal level
Up to 10%							482	53.7
11–50%							316	35.2
51% or more							99	11.1
Sex
Male							357	39.8
Female							540	60.2
Age of the youngest child
No child 0–14 years							741	82.6
0–6 years							74	8.3
7–14 years							82	9.1
Education^°
Up to lower secondary			277	30.9
Upper secondary			459	51.2
Tertiary			161	18.0
Employment status^°
Employed			452	50.4
Unemployed			66	7.4
Economically inactive			158	17.6
Retired			221	24.6
Shortage of living space^°
No			792	89.9
Yes			89	10.1
Missing			16

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° Variables measured in ITA.LI wave 1.

Variables

Dependent variable: Mental health

A summary measure of mental health was constructed using the following six items, which are generally employed to assess the mental component of the SF-12 [18]: ‘During the past 4 weeks…’ 1) ‘have you accomplished less than you would have liked?’; 2) ‘did you fail to do work or other activities as carefully as usual?’; 3) ‘have you felt calm and peaceful?’; 4) ‘did you have a lot of energy?’; 5) ‘have you felt downhearted and low?’; and 6) ‘has your health limited your social activities?’. Following SF-12 guidelines, items were recoded where necessary so that higher scores indicated better mental health. Specifically, items 1 and 2 were dummy-coded (yes or no), while items 3 and 4 were coded into six categories ranging from 0 (none of the time) to 5 (all of the time). Item 5 was coded into six categories ranging from 0 (all of the time) to 5 (none of the time). Item 6 was coded into five categories ranging from 0 (all of the time) to 4 (none of the time). The dimensionality of the single-item summary measure of mental health was assessed over time using pooled data, that is, data from the ITA.LI wave 1 and ITA.LI COVID-19 survey [21]. The suitability of the data was first assessed by analysing the determinant of the correlation matrix (det = .112), Kaiser-Meyer-Olkin measure of sampling adequacy (KMO = .784), and Bartlett’s test of sphericity (χ2 = 3838.8, df = 15, p < .001). Subsequently, principal component analysis was performed. The first component or factor, which had the largest eigenvalue (3.190) and explained 53% of the total variance, was retained. Factor loadings ranged from .697 for Item 5 to .753 for Item 6 (Table 2). On the basis of the Eigenvalues-greater-than-one rule, only the first component or factor appeared to be meaningful; thus, the unidimensionality of the item response data could be detected. This assessment was confirmed by Horn parallel analysis with 5,000 iterations, which indicated that one factor was retained, with an adjusted eigenvalue of 3.121. The internal consistency of the scale formed from the six items was tested and assessed by computing Cronbach’s alpha (.778). The mental health factor was scored on the entire sample for both measurement occasions using the regression method, and the values were normalised within a 0–100 range (Fig 1). Finally, the convergent validity of the resulting measure of mental health was assessed by estimating its correlation with the mental component of the SF-12, which was computed from ITA.LI wave 1 data using the standard US algorithm. The Pearson product-moment correlation coefficient (.958, p< .001) indicated that, although measured in different ways, the mental health factor and the mental component of the SF-12 were strongly correlated.

Table 2. Weights (principal components loadings) and internal reliability of the mental health summary measure.

Item	Standardized factor loadings	Scale reliability coefficient
I1—Accomplished less	0.750
I2—Do activities less carefully	0.743
I3—Feeling calm and peaceful^*	0.701
I4—Having a lot of energy^*	0.731
I5—Feeling downhearted and low	0.697
I6—Limited social activities	0.751
Total scale		0.778

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* Reverse-coded survey items.

Independent variables

Building on recent research, as well as on studies on earlier pandemics and economic crises [24, 31–33], a set of independent variables were included in the models. To test the social isolation hypothesis, we included age (recoded into five categories: 16–34, 35–44, 45–54, 55–69, and 70+), an item assessing whether the respondent lived with a partner (yes or no), and two items measuring respondents’ direct and indirect exposure to COVID-19. The first item measured whether the respondent had taken either a serological or nasopharyngeal swab test (yes or no). The second item assessed the mortality risk of COVID-19 at the local level. More specifically, we extracted mortality statistics, namely, monthly death registration data, at the municipal level for six years (2015 to 2020), and calculated changes in monthly mortality rates by comparing the 2020 average (from January to the month of the interview) with the five-year (2015 to 2019) average (from January to the month of the interview). The resulting variable, which measured changes or differences in mortality rates at the municipal level, was coded into three categories (less than 10% increase, 11% to 50% increase, more than 50% increase) and merged with data on respondents based on their municipality of residence. For the workload hypothesis, we inserted respondents’ sex into the model and the age of the youngest child living in the same household and taken care of by the respondent (no child aged 0–14 years; 0–6 years; 7–14 years). Socioeconomic variables encompassed employment status (coded into four categories: employed, unemployed, economically inactive—i.e. persons of working-age outside the labour force, such as students and housewives—and retired), education (coded into three categories: up to lower secondary, upper secondary, and tertiary), and the respondent’s perception of the shortage of living space (yes or no). Values of the socioeconomic variables were extracted from ITA.LI wave 1.

Analytic strategy

The impact of COVID-19 on mental health was investigated using a one-group pre-test–post-test design. The random-effects (RE) estimator was preferred, as more appropriate, over the fixed-effects (FE) estimator of the model parameters on the basis of the results of the Hausman Test (chi2 = 24.35; prob>chi2 = 0.14). Following recent studies on the consequences of the pandemic [e.g. 15], we first estimated a RE model with pre- and post-lockdown period indicator as the only predictor of variation in mental health. Subsequently, the interactions between the pre- and post-lockdown period indicator and the entire set of explanatory variables were included. The parameter estimates of this multiple-interaction model are interpretable as the change in mental health scores within a specific subgroup. For each of the two models we computed both the unadjusted p-values of the estimates and Cohen’s d; the latter is an effect size measure for single group pre-post study designs, which assesses the magnitude of changes in mental health scores overtime calculating the difference between the post- and the pre-test means and dividing such difference by the standard deviations of the differences. In addition, we ran the Wald test to perform some joint tests of group comparisons, that is, to estimate differences in the COVID-related effects on mental health across the levels of independent variables. The Bonferroni method of correcting p-values was used in Model 2 to counteract the problem of multiple testing. Given that ITA.LI wave 1 data used for this study were collected from June 2019 to March 2020, whereas the ITA.LI COVID-19 survey was conducted over four months in 2020, we carried out a sensitivity analysis to check for potential seasonality effects. To do so, we replicated the RE regression models on the subsample of respondents (N = 257) who took part in ITA.LI wave 1 over the same four months. The results of this additional set of analyses are reported in Table 1 in S1 File. Analyses were performed using STATA 17.

Findings

Table 3 displays the results of the RE regression models that were fitted to estimate both changes in mental health following the extension of emergency measures nationwide and the differential effects of containment measures across groups of respondents. Before 9 March 2020, the mental health score, which was 78.5 (95% CI 77.3–79.6) in the entire sample, varied significantly across gender and age groups, with females (77.3, 95% CI 75.8–78.8) and older individuals, namely, those aged 55+ (74.7, 95% CI 72.4–77.0 for 55–69 years old; 74.9, 95% CI 71.5–78.4 for 70+ years old), reporting the lowest scores. Regarding household structure and composition, respondents cohabiting with a partner had a comparatively higher mental health score (81.2, 95% CI 79.9–82.6). Turning to socioeconomic variables, individuals who were employed had higher scores (80.1, 95% CI 78.5–81.7) than those who were unemployed (72.9, 95% CI 61.2–66.4).

Table 3. Random-effects regression analysis showing the change in mental health scores associated with the implementation of COVID-19 lockdown measures.

	Pre-lockdown (95% CI)		Post-lockdown (95% CI)		Pre-Post average change (95% CI)		Cohen’s d (Effect size)	p-value	Wald test p-value
Total sample	78.5	(77.3; 79.6)	67.9	(66.5; 69.4)	-10.5	(-12.1; -9.0)	-.445	< .001
Age
16–34	84.2	(81.3; 87.1)	66.5	(62.7; 70.3)	-17.7^*	(-21.8; -13.6)	-.744	< .001	< .001
35–44	81.6	(78.7; 84.6)	67.6	(63.8; 71.4)	-14.1^*	(-18.1; -10.0)	-.621	< .001
45–54	80.2	(77.7; 82.7)	68.4	(65.0; 71.7)	-11.8^*	(-15.5; -8.2)	-.502	< .001
55–69	74.7	(72.4; 77.0)	67.3	(64.4; 70.1)	-7.4^*	(-10.4; -4.5)	-.316	< .001
70 or more	74.9	(71.5; 78.4)	71.9	(67.8; 76.1)	-3.0	(-7.5; 1.5)	-.136	.186
Living with a partner
No	75.2	(73.2; 77.2)	66.4	(64.0; 68.8)	-8.8^*	(-11.4; -6.2)	-.358	< .001	.093
Yes	81.2	(79.9; 82.6)	69.4	(67.5; 71.4)	-11.8^*	(-13.8; -9.7)	-.515	< .001
Testing for COVID-19
No	78.6	(77.4; 79.8)	67.9	(66.4; 69.4)	-10.7^*	(-12.3; -9.1)	-.461	< .001	.544
Yes	78.8	(75.9; 81.7)	69.9	(65.0; 74.8)	-8.9^*	(-14.4; -3.5)	-.331	.001
Increase in mortality rate at the municipal level
Up to 10%	77.1	(75.4; 78.7)	67.5	(65.5; 69.5)	-9.6^*	(-11.7; -7.4)	-.402	< .001	.473
11–50%	79.6	(77.8; 81.4)	68.0	(65.6; 70.4)	-11.6^*	(-14.2; -8.9)	-.481	< .001
51% or more	83.2	(80.3; 86.2)	71.7	(67.2; 76.3)	-11.5^*	(-15.9; -7.0)	-.543	< .001
Sex
Male	80.6	(78.8; 82.4)	72.5	(70.3; 74.8)	-8.1^*	(-10.6; -5.6)	-.354	< .001	.018
Female	77.3	(75.8; 78.8)	65.3	(63.3; 67.2)	-12.1^*	(-14.1; -10.0)	-.504	< .001
Age of the youngest child
No child aged 0–14	78.9	(77.7; 80.2)	68.5	(66.9; 70.1)	-10.4^*	(-12.2; -8.7)	-.439	< .001	.027
0–6 years	78.9	(74.9; 82.9)	62.8	(57.0; 68.5)	-16.2^*	(-22.2; -10.2)	-.678	< .001
7–14 years	75.8	(71.4; 80.2)	70.0	(64.9; 75.1)	-5.8	(-11.0; -0.6)	-.271	< .030
Education^°
Up to lower secondary	78.2	(75.9; 80.4)	68.6	(66.0; 71.3)	-9.5^*	(-12.6; -6.5)	-0.383	<0.001	0.236
Upper secondary	79.4	(77.9; 90.0)	69.4	(67.3; 71.5)	-10.1^*	(-12.2; -7.9)	-0.437	<0.001
Tertiary	77.1	(74.3; 80.0)	64.0	(60.3; 67.3)	-13.4^*	(-17.0; -9.8)	-0.596	<0.001
Employment status^°
Employed	80.1	(78.5; 81.7)	69.7	(67.5; 71.9)	-10.4^*	(-12.8; -8.1)	-0.466	<0.001	0.652
Unemployed	72.9	(67.1; 78.7)	61.6	(55.8; 67.4)	-11.3^*	(-17.3; -5.3)	-0.461	<0.001
Economically inactive	77.1	(74.2; 80.1)	68.8	(65.1; 72.4)	-8.4^*	(-12.6; -4.1)	-0.343	<0.001
Retired	78.3	(75.2; 80.5)	66.5	(62.8; 70.1)	-11.9^*	(-15.9; -7.9)	-0.474	<0.001
Shortage of living space^°
No	79.3	(78.1; 80.4)	68.8	(67.3; 70.3)	-10.5^*	(-12.1; -8.9)	-0.451	<0.001	0.921
Yes	73.2	(68.4; 78.0)	62.4	(57.8; 67.0)	-10.8^*	(-16.6; -5.0)	-0.399	<0.001

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° Variables measured in ITA.LI wave 1.

* Statistically significant at the 5% level after the Bonferroni correction for multiple testing in Model 2.

Between April and September 2020 (i.e. at the time of the ITA.LI COVID-19 interviews), the mental health score was 67.9 (66.5–69.4) for the entire sample, which indicated a significant deterioration (β = -10.5). Cohen’s d (-.445) suggested that the effect size was medium. Hence, Hypothesis 1 is supported.

The findings of the multiple-interaction model revealed that the mental health of the younger age groups was the most severely affected. For those aged 16–34 years, the estimated mental health score was -17.7 points (p < .001, Cohen’s d = -.744) lower than the pre-lockdown baseline measurement. The mental health score was also -14.1 points lower in people aged 35–44 years (p < .001, Cohen’s d = -.621); however, those aged 55–69 years experienced a relatively small reduction of -7.4 points (p < .001, Cohen’s d = -.316). For individuals aged 70+ no statistically significant change in mental health scores was recorded. For individuals living without a partner, those tested because had symptoms or were potentially exposed to the virus, and people living in municipalities with high rates of new coronavirus infections the COVID-19 crisis failed to bring about statistically significant worsening mental health. Hence, the social isolation hypothesis (Hypothesis 2) was confirmed only with respect to age.

Furthermore, comparisons of estimated marginal means showed that downward trends in mental health scores were significantly more pronounced for women (β = -12.1, p < .001, Cohen’s d = -.504) than for men (β = -8.1, p < .001, Cohen’s d = -.354), and for those cohabiting with and taking care of children aged 0–6 years (β = -16.2, p < .001, Cohen’s d = -.678) than for individuals in households with no children below the age of 14 (β = -10.4, p < .001, Cohen’s d = -.439). Accordingly, the heavier workload hypothesis (Hypothesis 3) was fully sustained.

For any of the remaining subgroups—that is the lower educated, the unemployed, and people with poorer housing conditions—pre- and post-lockdown estimated mental health scores were not significantly different from each other at a 5% significance level. Therefore, the socioeconomic resource hypothesis (Hypothesis 4) was rejected.

Seasonal sensitivity analyses on the heterogeneity of COVID-related effects provided similar results (see Table 1 in S1 File).

Discussion and conclusion

To the best of our knowledge, this study is the first to use a subsample of a household panel study to track changes in mental health in Italy before the onset of the COVID-19 pandemic and into the first lockdown period, from late April to early September 2020. The results of RE regression analyses fitted on a sample (N = 897) of respondents aged 16 years and older provided support for our main research hypothesis and indicated that there was a substantial and statistically significant short-term deterioration in mental health, as measured by a composite index derived from the mental component of the SF-12.

In addition, we found evidence of significant heterogeneity in the COVID-related effects on mental health. In particular, parameter estimates proved that negative changes in mental health were unevenly distributed across the sample by age. Indeed, findings confirm previous research [3, 15, 17] and suggest that, at least during the first wave of the pandemic, while older age groups were the most infected and faced the greatest risk of severe illness and death—the younger age group (namely, those aged 16–34 years) was the most affected in terms of mental distress. Moreover, this study provides further support for previous studies that pointed to both the gender-specific effects of COVID-19, with women suffering more than men the mental health consequences of the outbreak in the short-term, and to a steeper decline in mental health from pre-pandemic baseline levels for individuals with pre-schoolers, who were exposed to more stressful childcare dynamics [3, 10, 15, 16].

There are several possible explanations for these results. The RE models showed that differences in employment or marital status, direct or indirect exposure to infection and perception of a shortage of living space did not impact the summary measure of mental health. Hence a plausible explanation for the worst effects on mental health found in younger age groups is linked to the COVID-induced reduction in social relations. In other words, the social distancing requirements and policy-induced variation in early life caused by responses to the COVID-19 outbreak produced harsher consequences for those aged 16–34 years, who, before the pandemic had enjoyed more frequent and intense social relationships outside of the home—that is, at school, at work, at the neighbourhood level, and so on. Another possible explanation is that young people were the most deeply concerned about their future—because of crushed employment opportunities, possible financial hardship, or fears over not being able to have children and form stable families—or were the most exposed to the fear of or to the actual course of illness and death of close family members or friends [34]. These were all variables that could not be controlled for in the models. When assessing the short-term differential effects of the COVID-19 pandemic on mental health, we also found that women and individuals living with and taking care of preschool-age children were disproportionately affected, consistent with the workload research hypothesis. It seems possible that these results were due to the over-representation of female employment in jobs and sectors that have been at the forefront of the COVID-19 response, such as health care, but also teaching and retailing, which were profoundly impacted by the physical environment, work intensity, and working time quality [35–37]. It worth mentioning that women have been the main component of the workforce in the hardest-hit sectors, such as accommodation and food service activities, which may have resulted in higher perceived job insecurity or even greater job and income losses, which may have brought about increased mental distress [35]. That said, in Italy, gross imbalances in the household distribution of unpaid care work remain; thus, the closure of childcare services put an additional burden and strain on women, especially on those who working remotely [38, 39]. Multiple sources reveal an intensified risk of gender-based domestic violence, harassment, and abuse in times of lockdown and quarantine [40], possibly resulting in substantial mental health consequences.

In sum, we may argue that the COVID-19 outbreak has, on the one hand, been levelling the social gradient in mental health, as long as it has translated into disadvantages for young people, who had previously enjoyed greater psychological well-being but, on the other hand, the pandemic has widened the mental health gender gap and also created new inequalities, based on the age of the youngest child being taken care of within the household. As stated before, these findings may be interpreted as immediate and unintended short-term consequences of containment and mitigation policies [31]. However, these effects may persist in the longer run, as the direct and indirect social and economic consequences of COVID-19 gradually unfold [41], and the available research suggests that the levels of mental distress are higher than expected even once the lockdown has eased and mitigation policies are adapted to new circumstances [16]. Therefore, it is important that future research tracks and models change in mental health in the population, as well as in specific subgroups, relative to baseline levels measured before and during the COVID-19 pandemic. Furthermore, future research and policymaking on containment and mitigation measures should consider the differential effects found in this study and should shed light on the indirect impact of the disruption of normal daily activities and not just on the net impact of government lockdown measures in terms of economic and employment contraction. COVID-related consequences, and the effects pertinent to COVID-induced mitigation policies, are likely to include more than just financial strain and labour market vulnerability. The unintended and possible outcomes deserve specific attention. For instance, isolation and loneliness for younger people have been found to result in subsequent health risk behaviours [42] and adverse effects in terms of sense of purpose, ability to make decisions, and feeling of having a meaningful and useful role to play in life [17]. In this regard, in Italy, which is among the most affected countries worldwide, the immediate worries about the COVID-19 emergency and economic crisis have added to the fears and concerns that the young adults have been suffering the most (i.e. unemployment, precarious work, life uncertainty, etc.). This may result in harsh, cumulative, and longer-term troubles in young people’s lives: facing the pandemic at a crucial stage of the life course—in which professional careers and social identities are shaped and decisions on forming a new household and starting a family are taken—may further delay the transition to adulthood, as life plans and priorities have been severely hit [22]. The same holds true for women and individuals with pre-school age children, whose overall well-being and life chances have already been undermined by the gender-biased and familistic nature of the Mediterranean model of the welfare state [43].

As previously discussed, there was no significant evidence confirming previous research that pointed to the differential effects across socioeconomic groups [3, 10, 15, 16]. Indeed, the magnitude of the COVID-related outcomes associated with differences in employment status, educational attainment, and housing conditions was not statistically significant. These unexpected results may be related to the limited sample size, which may hamper the possibility of detecting changes in mental health for specific subgroups; additionally, they may be related to the fact that only past values of socioeconomic variables were used as explanatory variables in the models, which did not allow amounts of recent past to be brought into the prediction. A further limitation, which concerns period and maturation threats to the validity of the one-group pre-test–post-test design used in this study [44], needs to be acknowledged. Specifically, we cannot exclude that mental changes occurred within the respondents following regular seasonal patterns [e.g., 45, 46], which could account for the results. However, sensitivity analysis confirmed the patterns of the results (see Table 1 in S1 File). Nonetheless, we could not account for other potential trends in mental distress that had already occurred, regardless of the pandemic. In this regard, the lack of multiple pre-pandemic measurements did not allow us to conduct further analysis and forced us to assume a priori the absence of maturation threats to the internal validity of our one-group pre-test–post-test design. However, recent longitudinal studies that were able to draw on several waves of data collection before and during the pandemic detected higher-than-expected reductions in mental health scores; therefore, the trajectories of change in mental health scores at times of COVID-19 were different from previous trends [15, 17]. Finally, there might have been a differential non-response to the ITA.LI COVID-19 survey, which could lead to biased parameter estimates, namely to an overestimation of possible negative COVID-related effects to mental health.

Supporting information

S1 File

(DOCX)

Click here for additional data file.^{(92.8KB, docx)}

Data Availability

In accordance with EU General Data Protection Regulation (EU 2016/79), University of Milano-Bicocca internal regulations (D.R. 6256/2018, prot. 90980/18) ITA.LI – Italian Lives data are fully encrypted, stored anonymously in the cloud, and protected against unauthorized access, disclosure, modification, or destruction. ITA.LI – Italian Lives data (specifically ITA.LI – Italian Lives wave 1 and ITA.LI COVID-19 survey data, which have been used for this study) are currently available only to researchers working at the ITA.LI – Italian Lives project who completed a registration process at the personal data protection office, in accordance with the above-mentioned regulations. As already stated at the time of first submission, the same data will be publicly available to researchers from University of Milano-Bicocca and through Cross National Equivalent File (https://www.cnefdata.org/) in due time. However, the data release policy (time of public release, details of how to apply for and access the datasets, end user licence, etc.) has not been formally defined yet. Due to this lack of formal policies and procedures, all data underlying the findings may be currently accessed, only for the purpose of reproducing the analyses, through the corresponding author (or any of the remaining authors) and the personal data protection officer at University of Milano-Bicocca. The personal data protection officer can be contacted (at rpd@unimib.it or certified email rpd@pec.unimib.it) for all queries concerning personal data processing and the exercise of any rights deriving from General Data Protection Regulation (EU 2016/79). ITA.LI – Italian Lives Data Controller is the University of Milano-Bicocca, represented by its legal representative, the Rector Giovanna Iannantuoni (rettorato@unimib.it or or certified email ateneo.bicocca@pec.unimib.it). All relevant materials that may be reasonably requested by others to reproduce the results will made available upon the publication of the study.

Funding Statement

ITA.LI – Italian Lives project is funded by the Italian Ministry of Education, Universities and Research under the “Departments of Excellence 2018-2022” initiative (Italian Law 232 of 11 December 2016) (https://www.miur.gov.it/dipartimenti-di-eccellenza). Internal grant number at the Department of Sociology and Social Research of the University of Milan-Bicocca is 2018-NAZ-0116. The award was received by the Department of Sociology and Social Research of the University of Milan-Bicocca. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS One. doi: 10.1371/journal.pone.0259989.r001

Decision Letter 0

Stéphanie Baggio

21 Jul 2021

PONE-D-21-13037

The differential effect of Covid-19 pandemic on the mental health of young and healthy persons: Evidence from ITA.LI - Italian Lives, the Italian household panel

PLOS ONE

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- add the baseline response rate and retention rate and, if relevant, comparisons for baseline variables between responders and non-responders at follow-up

- remove formula from the subsection Statistical analyses (not needed for the understanding of our readers)

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Reviewer #1: The authors here present a study investigating self-perceived mental health in a group of Italian people before and after the Covid-19 lockdown. They only include data on self-perceived mental health from 904 participants who answered the SF-12 questionnaire both prior to March 9 2020 and again during lockdown between April and September 2020. With their data they highlight a general increase in mental distress, which is found to impact the younger population and those without prior health conditions more.

With their longitudinal approach, and data dating back prior to the Covid-19 pandemic, the data is generally quite interesting; however, I do have some major concerns regarding the analyses and reporting of the data. Unfortunately, the manuscript is not very well written and it would be recommended to make the language clearer and more understandable. It is often difficult to follow the points that the authors are trying to make, and the sentences are often very long. The section on statistical analyses needs to be revised in order for the readers to more easily follow the methods behind the findings.

Specific comments:

- Abstract: needs revision, particularly regarding presenting the results but also for concluding remarks

- Abstract, line 30: Both in the abstract and in the discussion the authors state that they have included 906 participants; however, throughout the manuscript it is clear that the actual number is 904.

- line 31: “indicate that there was a significant”??? is it significant or not? And provide the estimates

- line 33-37: estimates are lacking and are the effects small or large?

- Introduction, line 69: Please insert relevant references when stating that you ”draw on available evidence from previous pandemics”.

- Introduction, line 70-78: Please specify what is your primary and secondary hypotheses and outcomes

- There are many tests – the secondary outcomes should preferable undergo multiple testing

- Table 1: Some variables herein are not easy to understand and needs some rephrasing; Please change the word gender to sex (relevant throughout the manuscript), change ”testing of symptoms” to ”testing for Covid-19”?, note that mortality rates are for the municipalities of the individuals, and either remove the asterix* after Employment status or explain the asterix below the table.

- Table 1: It is not clear what the “mean score” is off and this is not defined in the table. There should also be p-values to see if the difference is significant and preferable relative risk estimates with CI also to evaluate the magnitude of the differences

- Table 2: why is the results for the total sample not adjusted?

- Variables, line 133: Please specify what is meant by ”economically inactive”.

- Variables, line 135: I would prefer a rephrasing of ”shortage of space in the dwelling” to something along the lines of ”shortage of living space”, which would make it easier to understand.

- Variables, line 135-136: Please in your manuscript specify how data on previous health conditions were obtained, and optimally also which conditions are included herein.

- Findings: I would find it highly relevant to be able to read somewhere the p-values for the different observations of differences in mental health between subgroups as reported on in the first part of the findings sections. This could perhaps be added to Table 1.

- Findings, line 199: The sentence ”which was indicative of a possible overall increase in mental stress” does not correspond with the observation of a significant overall decrease in mental health scores with a p-value below 0.001 Please rephrase.

- Regarding analyses performed in the manuscript I find it highly relevant to investigate the following; 1) can the difference in self-perceived mental health between males and females be explained by age? And the same question applies for educational level, previous health conditions and taking care of children between 0-14 years. All of these variables are most likely highly influenced by age. 2) Can you stratify previous health conditions in to physical and mental health conditions and analyze them separately?

- Table 2: Please make sure that you use the same labels for the variables in both Table 1 and Table 2.

- Table 2, legend: Are you here mentioning that data on employment status and education level is obtained only post-lockdown? This needs to be highlighted previously in the manuscript, and should also be discussed as a limitation. In particular employment status might have changed with the lockdown, and so your analyses of mental health in subgroups of employment status prior to the lockdown renders less useful.

- Findings: You do not mention the findings of your sensitivity analyses anywhere in your findings section.

Reviewer #2: Dear Authors

The paper is overall well written. However, some details need to be clarified, and I have some questions regarding multiple points.

1.Title: “differential” effect really means “higher” effect here and could maybe be written as such.

2.Abstract: Please mention the data collection period of the pre covid wave in the abstract

3.Line 86: This is confusing: the data collection period stopped in March 2020 for the purpose of this study as later respondents were not invited. The next date mentioned is April 2020, which is the start of data collection. The date of 9 March 2020 should be mentioned earlier and clearer as the end of the data collection period of wave 1.

4.Please clarify why no ethic statement is required. In my opinion, this type of study requires an ethical statement, including the name of the ethical review board and a mention of how participants consent was obtained. Collection of health related data with the possibility to link records to earlier data collection waves and to e-mail addresses/phone number, i.e. non-anonymous data collection clearly requires ethical approval from an ethical review board/commission at least in my country (Switzerland). I was under the impression that this is very similar in the European Union. If you did not obtain ethical approval and/or, please provide evidence that this was not needed under Italian law. Please also mention which identifier was used to link the data (line 99).

5.Line 105: Please specify which translation from the SF-12 was used. It would also appear that no reference refers specifically to the SF-12, the only reference refers to an evaluation paper for the SF-36.

6.The coding instructions for the SF-12 https://www.researchgate.net/publication/242636950_SF-12_How_to_Score_the_SF-12_Physical_and_Mental_Health_Summary_Scales are to use factor score weights normed to the population for the mental and physical health scores, with all 12 items loading on both factors. You used a simple sum score measure of only the 6 items for mental health, respectively your two factor model (but based on only 6 items). Please explain why you did not use the scoring method as described by the authors. There may be good reason for that (different population etc), but this should be clarified.

7.Line 115: I do not understand what the two factors were? Just reading like this it would seem that there was a factor for wave 1 and one for wave 2, while the point of such an analysis is usually to measure the same factor across time. Please reformulate and clarify this.

8.Line 125: in the title of S1 Fig, an “of” is missing after “distribution”.

9.Line 124: Please cite a reference for the MAP method. It is very unclear where the summary measure mentioned in line 106 were used and were the factor scores.

10.Line 125: How were the scores normalized? With respect to the sample or with respect to the reference values of the SF-12 coding instructions (https://www.researchgate.net/publication/242636950_SF-12_How_to_Score_the_SF-12_Physical_and_Mental_Health_Summary_Scales)

11.Please mention the software used in your analysis

12.Line 281: There are multiple points to consider regarding the findings about vulnerable groups. First, what is the role of floor effects? Can those with already low mental health even decrease or are they at the bottom floor and can only increase? And could regression to the mean explain why those with better mental health decrease more (towards the mean) than those with lower mental health (who could rather be expected to increase towards the mean)? If I understand that right, you only tested for overall heterogeneity of mean differences across a predictor (i.e. age) and did not contrast changes between groups and adjust for individual pre-crisis mental health, therefore your analysis did likely not account for these effects. Thus, it is possible that the between group differences are entirely due to regression to the mean (essentially measurement error) rather than the effect of the crisis. So for example in line 226, it follows from your analysis that those with no underlying health condition decreased in mental health, while those with a mental health condition did not decrease. However, I see no analysis showing that the effect of the crisis is greater in those without underlying health condition, compared to those with an underlying health condition, when taking baseline levels into account.

13.Related to the underlying health condition group, unless only chronic health problems were measured, most health related problems tend to improve over a period of several month, what is often accompanied with an improvement in mental health, whether there was a crisis in between or not. This alternative explanation for the increasing score / not decreasing score may be worth considering.

14.As regards the adjusted analysis, I do not see the rationale for basing the discussion exclusively on the adjusted results. Lets take employment status as an example: Those unemployed would appear to decrease more in mental health than those retired. If you adjust this for age (which probably had the most impact because of the large differences according to age), there is naturally no more difference between the groups, because those retired are older than the unemployed. Does this mean that there is no effect of unemployment or retirement on changes in mental health? In my opinion, no. There may be an effect of unemployment or retirement, which is however logically confounded with age. Similarly, having children between 0-6 years is naturally unlikely in those 16 years old and in those older than 50, and will be most frequent at the age between 25 and 40. What is the rationale of adjusting such a variable for age? That those with children between 0-6 years showed a higher decrease in mental health would be of some interest, and the fact that they were of a certain age does not invalidate that result. In my opinion, most of the discussion about the crisis as “leveller of mental health” is based on a questionable adjustment strategy. Judging from the raw scores it clearly looks like the mental health of those unemployed (before the crisis) deteriorated more compared to those employed, although this was not tested statistically in your paper.

15.Again regarding vulnerable groups, I wonder why on of the most vulnerable group, those with low mental health before the crisis (i.e. those with a low SF-12 score), were not considered and investigated.

16.Line 287: should distress be “decrease” ?

17.Line 257: I see no evidence that the sharper decrease in young people is due to social restrictions. It may as well have been due to fear of for example financial hardship, fear of losing once older relatives, etc, variables you did not adjust for.

18.Line 307: If, the levelling only concerned the gradient in mental health, not health in general.

**********

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PLoS One. 2021 Nov 16;16(11):e0259989. doi: 10.1371/journal.pone.0259989.r002

Author response to Decision Letter 0

4 Sep 2021

Dear editor and reviewers,

we would like to thank you for your comments and advice, which we believe helped us a lot in improving the overall clarity, soundness, and readability of the paper. The revised version of our study is attached. We made several major changes to the original version, as it is also highlighted in the marked-up copy of the manuscript, which is attached. Responses to each point raised during the review process are listed below. Briefly, based on your comments, suggestions, and concerns, which have all been addressed, we:

- better framed the main argument and spelt out more clearly our research hypotheses

- performed more appropriately and rigorously the statistical analyses

- presented and discussed more in depth the results of the study.

Please note that the English has been thoroughly and professionally reviewed and meets the required standards for publication, as well as PLOS ONE’s style requirements, including those for file naming.

Thank you.

Reviewer 1

Reviewer: The authors here present a study investigating self-perceived mental health in a group of Italian people before and after the Covid-19 lockdown. They only include data on self-perceived mental health from 904 participants who answered the SF-12 questionnaire both prior to March 9, 2020, and again during lockdown between April and September 2020. With their data they highlight a general increase in mental distress, which is found to impact the younger population and those without prior health conditions more. With their longitudinal approach, and data dating back prior to the Covid-19 pandemic, the data is generally quite interesting; however, I do have some major concerns regarding the analyses and reporting of the data. Unfortunately, the manuscript is not very well written and it would be recommended to make the language clearer and more understandable. It is often difficult to follow the points that the authors are trying to make, and the sentences are often very long. The section on statistical analyses needs to be revised in order for the readers to more easily follow the methods behind the findings.

Reviewer: Abstract: needs revision, particularly regarding presenting the results but also for concluding remarks

The abstract was revised accordingly

- line 30: Both in the abstract and in the discussion the authors state that they have included 906 participants; however, throughout the manuscript it is clear that the actual number is 904.

Thank you. That was a typo. Following your request, we better controlled and adjusted the size of the final sample used in our analysis (N = 897). A detailed description of the sample selection process is provided in the data section.

- line 31: “indicate that there was a significant”??? is it significant or not? And provide the estimates

- line 33-37: estimates are lacking and are the effects small or large?

We revised that sentence and provided parameter estimates and added the effect sizes (Cohen’s d)

- Introduction, line 69: Please insert relevant references when stating that you draw on available evidence from previous pandemic.

We revised that sentence and provided additional references, as requested

- Introduction, line 70-78: Please specify what is your primary and secondary hypotheses and outcomes

We believe we better framed the study and spell out clearly our main research hypotheses

- There are many tests – the secondary outcomes should preferable undergo multiple testing

We would like to thank you for advice. We used the Bonferroni correction for multiple testing in Model 2 (i.e. the model estimating multiple-interactions between time predictor and covariates). Furthermore, we added the unadjusted p-values for each parameter estimate.

We revised the text accordingly.

We opted to report mean pre-post values of the dependent variable and its average variation over time in Table 2 (Results of the RE regression models). In this table, as you requested, we added the p-values for all the estimates. Moreover, to evaluate the magnitude of the variations over time, we computed Cohen’s d effect size for single-group pre-post design with continuous outcomes.

- Table 2: why is the results for the total sample not adjusted?

Please note that, based on the suggestion and advice that the second reviewer provided, we changed the analytical strategy. As documented in the paper, first we estimated a random effect model with pre- and post-lockdown period indicators as the only predictors of variation in mental health. Subsequently, the interactions between the pre- and post-Covid lockdown period indicators and the entire set of explanatory variables were included. We abandoned the two-stage strategy based on testing single interaction (labelled “unadjusted” in the previous version of the paper) models before the multiple-interaction one (labelled “adjusted”). Consequently, the issue of “not adjusted” and “adjusted” models no longer stands.

- Variables, line 133: Please specify what is meant by ”economically inactive”.

We revised the text accordingly.

- Variables, line 135: I would prefer a rephrasing of ”shortage of space in the dwelling” to something along the lines of ”shortage of living space”, which would make it easier to understand.

We revised the text accordingly.

- Variables, line 135-136: Please in your manuscript specify how data on previous health conditions were obtained, and optimally also which conditions are included herein.

Based on your advice, we reflected on the specificity of this predictor and concluded that it was too vague (as for the definition of the type and chronicity of the underlying health condition). Moreover, it could semantically overlay with the outcome. For these reasons, we decided to remove it from the analyses.

We revised the text accordingly. As for p-values please see what we have already indicated in previous response to comments.

It was a typo. We meant increase in mental distress, instead. We revised the text accordingly.

We would like to thank you for your advice. As for the point n.1, please note that in our study – both in the original version and in the revised version – we focussed on general research hypotheses on the Covid-related effects, and building on available research, we selected a few independent variables (listed in the introduction). Testing for gender-specific differences related to age, as you suggest, is no doubt a relevant research proposition, which we believe is suitable for a different paper. However, due to the small size of the sample, we believe that introducing in the model multi-way interactions across levels of covariates and the time predictor could result in a concrete risk of over-parameterization. Turning to issue raised in point 2, we are sorry but, unfortunately, we cannot do what you invite us to do. We have at our disposal only measures of mental health. However, following your advice, we additionally checked the multi-group stability of the pre-post variations in mental health over the distribution of the outcome with a quantile regression approach, and the results show a high degree of stability.

- Table 2: Please make sure that you use the same labels for the variables in both Table 1 and Table 2.

The table was revised accordingly.

Employment status was included in the models as a lagged variable in ITA.LI wave 1. That was discussed as a possible limitation of the study in the revised version of the paper.

- Findings: You do not mention the findings of your sensitivity analyses anywhere in your findings section.

Findings of sensitivity analyses are now mentioned in the findings section and also reported in the Annex

Reviewer 2:

Dear Authors

This paper investigates changes in mental health between the period before COVID-19 and the COVID-19 period and finds that there was an overall decrease in mental health, which was most marked in young people and those without prior health conditions. The paper is overall well written. However, some details need to be clarified, and I have some questions regarding multiple points.

1.Title: “differential” effect really means “higher” effect here and could maybe be written as such.

2.Abstract: Please mention the data collection period of the pre covid wave in the abstract

The title of the paper and the abstract were revised based on both your suggestions and the results of the analyses, which were run differently following the points raised by reviewer #1.

We are sorry for confusion. We now made it clear that data collection for ITA.LI wave 1 started in June 2019 and ended in December 2020, while the ITA.LI COVID-19 survey was conducted from 20 April to 2 September 2020. We also clearly stated that the final sample for this study did not include respondents who completed ITA.LI wave 1after 9 March 2020.

ITA.LI – Italian Lives data collection protocols were written in accordance with data protection law (Regulation EU 2016/679, Italian Legislative Decree 196/2003) and were approved by the Ethics Committee of the University of Milano-Bicocca (protocol number 0042665/19). Therefore, data collection obtained an ethical approval and observed specific laws and rules. As for sample participant consent, at the time of the first meeting with the interviewer, potential respondents were given the information sheet (with detailed info about personal data treatment) and the consent form. If the respondents agreed to take part in research, they had to fill in the research consent form with their personal details. Concerning the identifier, respondents were assigned a unique random code, which was used to merge data from the ITA.LI wave 1 and ITA.LI COVID-19 survey

Thank you for your advice. We provided additional reference for the Italian translation of the SF-12. It is a study that cross-validate it in 9 countries, including Italy. See Gandek et al. [18]

Mental health was measured in ITA.LI wave 1 (i.e. pre-lockdown) using the 12 items generally used in the Short-Form of the Health Survey (SF-12), which was adapted to the Italian population. As you suggested, scores from the 12 items are computed using standardized scoring algorithms to construct physical and mental component summary measures (PCS-12 and MCS-12). However, the ITA.LI COVID-19 survey questionnaire (i.e. post-lockdown), due to time/budget constraints, could collect only 6 items of the SF-12; specifically those assessing the mental component of the SF-12 scale. Hence, for the scope of this study we could only use the 6 items assessing mental health, which were asked twice (i.e. before and after the introduction of lockdown measures) to the respondents. Accordingly, we validated a different summary measure of mental health, based on the analysis of pooled data and principal components analysis. This approach, which could also address the points that you raised in the revision process, produced a composite measure of mental health that – as results of convergent validity analysis suggest – was strongly correlated with the mental component of SF-12 computed using the standard US algorithm. More details are provided in the revised version of the paper.

8.Line 125: in the title of S1 Fig, an “of” is missing after “distribution”.

That was a typo. We revised the text accordingly.

9.Line 124: Please cite a reference for the MAP method. It is very unclear where the summary measure mentioned in line 106 were used and were the factor scores.

As just explained, in the revised version of the paper we used a different scoring method based on a factor weights and regression approach.

We normalized the scores with respect to the sample, since we validated a new version of the scale (6 items) which could not be directly compared with the mental health component of the SF-12 (12 items).

11.Please mention the software used in your analysis

We used STATA 17. It is now mentioned at the end of the “Analytic strategy” section

We had already discussed the possibility of a “floor-effect” that when writing the original version of the paper. To address this point, in the revised version of the study we additionally checked the multi-group stability of the pre-post variations in mental health over the distribution of the outcome with a quantile regression approach. Results show a high degree of stability; thus, we believe there should not be such a risk.

As already indicated (see comments to reviewer #1), we considered the specificity of the items assessing underlying health conditions. After careful consideration, we believe that its formulation was too vague to understand what the nature and actual extent of underlying health conditions were. Accordingly, following the advice of reviewer #1, we decided not to include this item in the models.

We decided to abandon the two-stage strategy based on the adjustment approach. In the revised version of the paper, we first estimated a random-effects model (RE) with pre- and post-lockdown period indicator as the only predictor of change in mental health scores. In a second RE model the interactions between the pre- and post-lockdown period indicator and the entire set of explanatory variables were included. Based on the results of the Hausman test, we rejected the hypothesis that the individual-level effects could be more adequately modelled by a fixed-effects model.

To check the stability of mental health score variations overtime conditional to its distribution before the lockdown, we ran a quantile regression. Analyses confirmed the stability of the coefficients across quantiles.

16.Line 287: should distress be “decrease” ?

It was a typo.

We revised the discussion and conclusion accordingly.

18.Line 307: If, the levelling only concerned the gradient in mental health, not health in general.

It was a typo.

Additional Editor Comments

In addition to the relevant comments of the reviewers that should be addressed, please:

- add the baseline response rate and retention rate and, if relevant, comparisons for baseline variables between responders and non-responders at follow-up

As indicated in the revised version of the paper, 950 of 2,415 eligible people (i.e. respondents who took part in ITA.LI wave 1 and for whom an email and/or a phone number was/were available and valid) participated in the ITA.LI COVID-19 survey [30]. Hence, the final response rate of the ITA.LI COVID-19 survey was 39.3% (AAPOR response rate 1). The final response rate of the ITA.LI wave 1 survey was 37.5% (AAPOR response rate 1). Retention rates for ITA.LI survey will be calculated over wave 2, which is projected to start in fall 2021.

We believe that providing descriptive statistics, for all the variables included in the models, for both respondents and non-respondents is not relevant. Please note that, in the revised version of this study, after careful consideration we decided not to use inverse probability weights, which adjusted for differential non response to the ITA.LI COVID-19 survey. This is due to the following reasons: i) results from the subsample of ITA.LI COVID-19 survey could not be generalized to the all sample of ITA.LI wave 1 respondents; and ii) results of weighted regression analysis were pretty similar to those of unadjusted regression analysis

- remove formula from the subsection Statistical analyses (not needed for the understanding of our readers)

We removed the formula from the paper.

Attachment

Submitted filename: Response to reviewers.docx

Click here for additional data file.^{(30.1KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0259989.r003

Decision Letter 1

Stéphanie Baggio

18 Oct 2021

PONE-D-21-13037R1Differential effects of COVID-19 and containment measures on mental health: Evidence from ITA.LI - Italian Lives, the Italian household panelPLOS ONE

Dear Dr. Riva,

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Partly

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: No

Reviewer #2: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: - Abstract: The relative and absolute differences are still not clear – it is important to the readers that it is clear if it is a small but significant effect

- It is also important to mention in the abstract that there was no significant changes for the group >70 years and that it was most pronounced among the youngest group 16-34 years of age were there were a 21% absolute decline on the score

- It should be highlighted that there might be a bias of the individuals attending here for the second time, which was under the lockdown, and they might be biased towards more likelihood of people responding that felt affected by the pandemic. So this should also be clear with how many in total took part of the first investigation, and how many of these also responded here the second time, also in the abstract. As the attendance seemed to be low for this follow-up investigation, which could be a considerable bias

- And good that the authors sorted out the number of participants, which ended up to be different than the two numbers initially reported

- I don’t really see how the authors see that hypothesis 2 was really fulfilled, as there are many outcomes in hust this hypothesis, where most where not significant. So if their hypothesis 2 were just if there were any differences in any subgroups, these should also be adjusted for multiple testing regarding all the subgroups included in this

- How was hypothesis 3 sustained?

- It might be easier to read if they have subheadings in the result section for each of the hypothesis investigated

- Overall, I'm also concerned how much their used questions actually represent the mental health. Results for specific questions are not presented, and the overall score seem to also have questions as "accomplished less" and "limited social activities", which a lockdown will induce for most, but it doesn't answer how their mental health is affected, and you can score significantly lower on these items for instance, but still have the same overall mental health. Like the question "feeling downhearted and low" is a bit closer to what the article tries to answer, so how is the difference on this question for instance?

Reviewer #2: Dear Authors,

Thank you for your careful consideration of my comments. I am overall happy with your replies to my comments. I have some very minor comments:

-The abbreviation RE and FE are not introduced in the paper.

-In table 3 in the last column, it should be mentioned that this is the p-value of the Wald test

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

PLoS One. 2021 Nov 16;16(11):e0259989. doi: 10.1371/journal.pone.0259989.r004

Author response to Decision Letter 1

22 Oct 2021

Rebuttal letter

Dear editor and reviewers, please find listed below the responses to each point raised during the review process. Responses are in italics.

Reviewer 1

- Abstract: The relative and absolute differences are still not clear – it is important to the readers that it is clear if it is a small but significant effect

We revised the abstract accordingly

We clearly acknowledged this limitation in the final section of the paper. Please note that due to word limit count we could not add all this information in the abstract

- And good that the authors sorted out the number of participants, which ended up to be different than the two numbers initially reported

We would like to thank you for your comments and notes to previous round of reviews, which helped in sorting this issue out.

We revised the discussion of the results accordingly. In particular, building on Kuhn and colleagues we hypothesized (Hypothesis 2) a stronger deterioration of mental health among those more exposed to the detrimental effects of social isolation, such as young adults, individuals living without a partner, and people belonging to a COVID-19 risk group (i.e. those tested because had symptoms or were potentially exposed to the virus and people living in municipalities with high rates of new coronavirus infections) (Hypothesis 2). In the revised version of the study we clearly stated, in a separate paragraph (to make it clearer), as follows:

“The findings of the multiple-interaction model revealed that the mental health of the younger age groups was the most severely affected. For those aged 16–34 years, the estimated mental health score was -17.7 points (p < .001, Cohen’s d = -.744) lower than the pre-lockdown baseline measurement. The mental health score was also -14.1 points lower in people aged 35–44 years (p < .001, Cohen’s d = -.621); however, those aged 55–69 years experienced a relatively small reduction of -7.4 points (p < .001, Cohen’s d = -.316). For individuals aged 70+ no statistically significant change in mental health scores was recorded. For individuals living without a partner, those tested because had symptoms or were potentially exposed to the virus, and people living in municipalities with high rates of new coronavirus infections the COVID-19 crisis failed to bring about statistically significant worsening mental health. Hence, the social isolation hypothesis (Hypothesis 2) was confirmed only with respect to age.”

Please note that in the previous version of this study we already stated that Hypothesis 2 was only partially confirmed, just with respect to age. Besides, we had already run multiple testing analyses in previous round of review, too.

- How was hypothesis 3 sustained?

We revised the discussion of the results accordingly. In particular, again, building on Kuhn and colleagues, as well as on previous research conducted during the pandemic, we hypothesized (Hypothesis 3) a stronger deterioration of mental health among individuals with a heavier workload, such as women and people with preschool-age children in the household (Hypothesis 3). In the revised version of the paper, we discussed the results as follows:

“Furthermore, comparisons of estimated marginal means showed that downward trends in mental health scores were significantly more pronounced for women (β = -12.1, p < .001, Cohen’s d = -.504) than for men (β = -8.1, p < .001, Cohen’s d = -.354), and for those cohabiting with and taking care of children aged 0–6 years (β = -16.2, p < .001, Cohen’s d = -.678) than for individuals in households with no children below the age of 14 (β = -10.4, p < .001, Cohen’s d = -.439). Accordingly, the heavier workload hypothesis (Hypothesis 3) was fully sustained.”

- It might be easier to read if they have subheadings in the result section for each of the hypothesis investigated

In third-round review, we tested the hypotheses and presented the findings in separate paragraphs, which we believe improved the clarity and readability of the paper.

The 12-item Short Form Survey (SF-12) is a general health questionnaire that was first published in the mid-90s as part of the Medical Outcomes Study (MOS). It is one of the most widely used measure of mental health. See, for instance: Ware J, Kosinski M, Keller SD. A 12-item short-form health survey: construction of scales and preliminary tests of reliability and validity. Med Care. 1996; 34:220–233. doi: 10.1097/00005650-199603000-00003. Hence, it has been extensively studied and employed over time. In this study we had already provided additional evidence of the reliability and robustness of the mental health-related scale that includes the selected items. Furthermore, in response to your concerns, please find below some additional analyses, which further support the scale.

Variable Frequencies (%) p-value

During the past 4 weeks, have you had any of the following problems with your work or other regular daily activities as a result of any emotional problems (such as feeling depressed or anxious)?

Have you accomplished less than you would like?

Yes No

Pre 93 (11) 765 (89) - - - - <0.001*

Post 250 (29) 608 (71) - - - -

Didn't do work or other activities as carefully as usual?

Yes No

Pre 86 (10) 772 (90) - - - - <0.001*

Post 246 (29) 612 (71) - - - -

These questions are about how you feel and how things have been with you during the past 4 weeks. For each question, please give the answer that comes closest to the way you have been feeling. How much of the time during the past 4 weeks…

Have you felt downhearted and low?

All of the time Most of the time A good bit of the time Some of the time A little of the time None of the time

Pre 7 (1) 29 (3) 29 (3) 163 (19) 422 (49) 208 (24) <0.001¥

Post 18 (2) 54 (6) 49 (6) 261 (30) 321 (37) 155 (18)

Did you have a lot of energy?

None of the time A little of the time Some of the time A good bit of the time Most of the time All of the time

Pre 9 (1) 48 (6) 218 (25) 186 (22) 280 (33) 117 (14) 0.006¥

Post 12 (1) 89 (10) 262 (31) 90 (11) 304 (35) 101 (12)

Have you felt calm and peaceful?

None of the time A little of the time Some of the time A good bit of the time Most of the time All of the time

Pre 8 (1) 36 (4) 162 (19) 183 (21) 329 (38) 140 (16) 0.001¥

Post 10 (1) 63 (7) 227 (27) 101 (12) 344 (40) 113 (13)

During the past 4 weeks, how much of the time has your physical health or emotional problems interfered with your social activities?

All of the time Most of the time Some of the time A little of the time None of the time

Pre 6 (1) 29 (3) 100 (12) 202 (24) 521 (61) - <0.001¥

Post 17 (2) 60 (7) 215 (25) 311 (36) 255 (29) -

* p-values derived from the Mc Nemar’s test for dichotomous variables

¥ p-values derived from the Wilcoxon signed rank sum test for ordered categorical variables

Reviewer #2: Dear Authors, Thank you for your careful consideration of my comments. I am overall happy with your replies to my comments. I have some very minor comments:

-The abbreviation RE and FE are not introduced in the paper

We revised the text accordingly

-In table 3 in the last column, it should be mentioned that this is the p-value of the Wald test

We revised the text accordingly

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PLoS One. doi: 10.1371/journal.pone.0259989.r005

Decision Letter 2

Stéphanie Baggio

2 Nov 2021

Differential effects of COVID-19 and containment measures on mental health: Evidence from ITA.LI - Italian Lives, the Italian household panel

PONE-D-21-13037R2

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PLoS One. doi: 10.1371/journal.pone.0259989.r006

Acceptance letter

Stéphanie Baggio

5 Nov 2021

PONE-D-21-13037R2

Differential effects of COVID-19 and containment measures on mental health: Evidence from ITA.LI - Italian Lives, the Italian household panel

Dear Dr. Riva:

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Data Availability Statement

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PERMALINK

Differential effects of COVID-19 and containment measures on mental health: Evidence from ITA.LI—Italian Lives, the Italian household panel

Mario Lucchini

Tiziano Gerosa

Marta Pancheva

Maurizio Pisati

Chiara Respi

Egidio Riva

Roles

Abstract

Introduction

Data and methods

Data source and sample

Table 1. Sample.

Variables

Dependent variable: Mental health

Table 2. Weights (principal components loadings) and internal reliability of the mental health summary measure.

Fig 1. Kernel distribution of the pre- and post-Covid mental health latent-trait scores.

Independent variables

Analytic strategy

Findings

Table 3. Random-effects regression analysis showing the change in mental health scores associated with the implementation of COVID-19 lockdown measures.

Discussion and conclusion

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Stéphanie Baggio

Roles

Author response to Decision Letter 0

Decision Letter 1

Stéphanie Baggio

Roles

Author response to Decision Letter 1

Decision Letter 2

Stéphanie Baggio

Roles

Acceptance letter

Stéphanie Baggio

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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