Mental health indicators in Sweden over a 12-month period during the COVID-19 pandemic – Baseline data of the Omtanke2020 Study

Funding Information: This study was funded with grants from NordForsk (CovidMent, 105668 ), Horizon 2020 (CoMorMent, 847776 ), and the Karolinska Institutet . Funding Information: The Omtanke2020 study is supported by NordForsk (project No. 105668 ) and Karolinska Institute (Strategic Research Area in Epidemiology and Senior Researcher Award). We acknowledge The Swedish Twin Registry for access to contact information to participating twins. The Swedish Twin Registry is managed by Karolinska Institutet and receives funding through the Swedish Research Council under the grant no 2017-00641. The Funding Sources had no direct or indirect impact on the analysis and interpretation of the results. Publisher Copyright: © 2022 The AuthorsBackground: The ongoing COVID-19 pandemic has had an unprecedented impact on the lives of people globally and is expected to have profound effects on mental health. Here we aim to describe the mental health burden experienced in Sweden using baseline data of the Omtanke2020 Study. Method: We analysed self-reported, cross-sectional baseline data collected over a 12-month period (June 9, 2020–June 8, 2021) from the Omtanke2020 Study including 27,950 adults in Sweden. Participants were volunteers or actively recruited through existing cohorts and, after providing informed consent, responded to online questionnaires on socio-demographics, mental and physical health, as well as COVID-19 infection and impact. Poisson regression was fitted to assess the relative risk of demonstrating high level symptoms of depression, anxiety, and COVID-19 related distress. Result: The proportion of persons with high level of symptoms was 15.6 %, 9.5 % and 24.5 % for depression, anxiety, and COVID-19 specific post-traumatic stress disorder (PTSD), respectively. Overall, 43.4 % of the participants had significant, clinically relevant symptoms for at least one of the three mental health outcomes and 7.3 % had significant symptoms for all three outcomes. We also observed differences in the prevalence of these outcomes across strata of sex, age, recruitment type, COVID-19 status, region, and seasonality. Conclusion: While the proportion of persons with high mental health burden remains higher than the ones reported in pre-pandemic publications, our estimates are lower than previously reported levels of depression, anxiety, and PTSD during the pandemic in Sweden and elsewhere.Peer reviewe

Acute COVID-19 severity and mental health morbidity trajectories in patient populations of six nations: an observational study

Background Long-term mental and physical health consequences of COVID-19 (long COVID) are a persistent public health concern. Little is still known about the long-term mental health of non-hospitalised patients with COVID-19 with varying illness severities. Our aim was to assess the prevalence of adverse mental health symptoms among individuals diagnosed with COVID-19 in the general population by acute infection severity up to 16 months after diagnosis. Methods This observational follow-up study included seven prospectively planned cohorts across six countries (Denmark, Estonia, Iceland, Norway, Sweden, and the UK). Participants were recruited from March 27, 2020, to Aug 13, 2021. Individuals aged 18 years or older were eligible to participate. In a cross-sectional analysis, we contrasted symptom prevalence of depression, anxiety, COVID-19-related distress, and poor sleep quality (screened with validated mental health instruments) among individuals with and without a diagnosis of COVID-19 at entry, 0–16 months from diagnosis. In a cohort analysis, we further used repeated measures to estimate the change in mental health symptoms before and after COVID-19 diagnosis. Findings The analytical cohort consisted of 247 249 individuals, 9979 (4·0%) of whom were diagnosed with COVID-19 during the study period. Mean follow-up was 5·65 months (SD 4·26). Participants diagnosed with COVID-19 presented overall with a higher prevalence of symptoms of depression (prevalence ratio [PR] 1·18 [95% CI 1·03–1·36]) and poorer sleep quality (1·13 [1·03–1·24]) but not symptoms of anxiety (0·97 [0·91–1·03]) or COVID-19-related distress (1·05 [0·93–1·20]) compared with individuals without a COVID-19 diagnosis. Although the prevalence of depression and COVID-19-related distress attenuated with time, individuals diagnosed with COVID-19 but never bedridden due to their illness were consistently at lower risk of depression (PR 0·83 [95% CI 0·75–0·91]) and anxiety (0·77 [0·63–0·94]) than those not diagnosed with COVID-19, whereas patients who were bedridden for more than 7 days were persistently at higher risk of symptoms of depression (PR 1·61 [95% CI 1·27–2·05]) and anxiety (1·43 [1·26–1·63]) than those not diagnosed throughout the study period. Interpretation Severe acute COVID-19 illness—indicated by extended time bedridden—is associated with long-term mental morbidity among recovering individuals in the general population. These findings call for increased vigilance of adverse mental health development among patients with a severe acute disease phase of COVID-19.Funding Nordforsk, Horizon2020, Wellcome Trust, and Estonian Research Council

Latent Variable Models for Clustered and Incomplete Observational Data

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BFI-10

Over the years personality tests got shorter and shorter to meet the expectations of the test users while also aiming to uphold to the methodological standards. Here we would like to present some of the psychometric properties of the ten-item version of the Big Five Inventory (Rammstedt & John, 2007) as a subset of the original Big Five Inventory which contained 44 items. The data come from the Divorce in Flanders study where the validated Dutch language version of the BFI (Denissen et al., 2008) was used. The sample consists of a full sample of 7533 not-unrelated respondents coming from 4460 families and a subset of 4457 uncorrelated adult respondents. Principal component analysis shows the presence of the Big Five factor structure with very high primary loadings for most items. However, one of the Agreeableness items loads exclusively on the Extraversion factor and other measures are also not entirely convincing. Despite this, the BFI-10 correlates well with the BFI-44. Therefore, more research is needed before the reliability of the test can be definitively concluded.status: publishe

Evaluation of a very short test to measure the Big Five personality factors on a Flemish sample

In this paper, we study the psychometric properties of the ten-item version of the Big Five Inventory as a subset of the original BFI in a Flemish sample. The data come from the Divorce in Flanders study and consist of a full sample of 7533 individuals from 4460 families. Factor analysis shows the presence of the Big Five factor structure with very high primary loadings for most items. However, one of the Agreeableness items loads exclusively on the Extraversion factor and within-factor correlations are also low. Despite this, the BFI-10 correlates well with the BFI. Therefore, while more research is needed before validity and reliability of the Dutch-language version of the test can be concluded, it is clear that the BFI-10 may prove very effective in the assessment of the Big Five factors in the Flemish and Dutch cultures when assessment with longer questionnaires is not feasible.status: publishe

On using multiple imputation for exploratory factor analysis of incomplete data

A simple multiple imputation-based method is proposed to deal with missing data in exploratory factor analysis. Confidence intervals are obtained for the proportion of explained variance. Simulations and real data analysis are used to investigate and illustrate the use and performance of our proposal.status: publishe

Combining factors from different factor analyses

While factor analysis is one of the most often used techniques in psychometrics, comparing or combining solutions from different factor analyses can be cumbersome even though combining factors is necessary in several situations. For example, when applying multiple imputation (to account for incompleteness) or in case of multilevel data (where a simple solution is the application of multiple outputation) often tens or hundreds of results have to be combined into one nal solution. While dierent solutions have been in use, we propose a simple and easy to implement solution to match factors from different analyses based on factor congruence. A modified Tucker's congruence coefficient is used to match factors. Advantages, cut-off values for accepting factor equivalence and limitations of this method will be presented based on simulations and a real life example.status: publishe

Combining factors from different factor analyses

While factor analysis is one of the most often used techniques in psychometrics, comparing or combining solutions from different factor analyses can be cumbersome even though combining factors is necessary in several situations. For example, when applying multiple imputation (to account for incompleteness) or in case of multilevel data (where a simple solution is the application of multiple outputation) often tens or hundreds of results have to be combined into one final solution. While different solutions have been in use, we propose a simple and easy to implement solution to match factors from different analyses based on factor congruence. A modified Tucker's congruence coefficient is used to match factors. To demonstrate this method, the Big Five Inventory data collected under the auspices of the Divorce in Flanders study was analysed combining multiple imputation with multiple outputation and factor analysis. The data were collected in 2008 and the validated Dutch language version of the BFI was administered among a battery of test with the aim to study the phenomenon of divorce in families. This multilevel sample consists of 7533 individuals coming from 4460 families with about 10% of incomplete observations.status: publishe

A Modified Tucker’s Congruence Coefficient for Factor Matching

Since factor analysis is one of the most often used techniques in psychometrics, comparing or combining solutions from different factor analyses is often needed. Several measures to compare factors exist, one of the best known is Tucker’s congruence coefficient, which is enjoying newly found popularity thanks to the recent work of Lorenzo-Seva and ten Berge (2006), who established cut-off values for factor congruence. While this coefficient is in most cases very good in comparing factors in general, it also has some disadvantages, which can cause trouble when one needs to compare or combine many analyses. In this paper, we propose a modified Tucker’s congruence coefficient to address these issues