Sarcopenia augments the risk of excess weight on COVID-19 hospitalization: A prospective study using the Lifelines COVID-19 cohort.

OBJECTIVE: We investigated the associations of sarcopenia alone, overweight or obesity, and sarcopenic overweight or obesity with COVID-19 hospitalization. METHODS: Participants from the Lifelines COVID-19 cohort who were infected with COVID-19 were included in this study. Sarcopenia was defined as a relative deviation of muscle mass of ≤ -1.0 SD from the sex-specific mean 24-h urinary creatinine excretion. Overweight or obesity was defined as a body mass index ≥ 25 kg/m2. Sarcopenic overweight or obesity was defined as the presence of overweight or obesity and low muscle mass. COVID-19 hospitalization was self-reported. Logistic regression models were used to analyze the associations of sarcopenia alone, overweight or obesity, and sarcopenic overweight or obesity with COVID-19 hospitalization. RESULTS: Of the 3594 participants infected with COVID-19 and recruited in this study, 173 had been admitted to the hospital. Compared with the reference group, individuals with overweight or obesity and sarcopenic overweight or obesity were 1.78-times and 2.09-times more likely to have been hospitalized for COVID-19, respectively, whereas sarcopenia alone did not increase the risk of COVID-19 hospitalization. CONCLUSIONS: In this middle-aged population, sarcopenic overweight or obesity elevated the risk of hospitalization for COVID-19 in those infected with COVID-19 more than overweight or obesity alone. These data support the relevance of sarcopenic overweight or obesity as a risk factor beyond the geriatric setting and should be considered in risk stratification in future public health and vaccination campaigns.

Using data on biomarkers and siblings to study early-life economic determinants of type-2 diabetes.

We study the effect of economic conditions early in life on the occurrence of type-2 diabetes in adulthood using contextual economic indicators and within-sibling pair variation. We use data from Lifelines: a longitudinal cohort study and biobank including 51,270 siblings born in the Netherlands from 1950 onward. Sibling fixed-effects account for selective fertility. To identify type-2 diabetes we use biomarkers on the hemoglobin A1c concentration and fasting glucose in the blood. We find that adverse economic conditions around birth increase the probability of type-2 diabetes later in life both in males and in females. Inference based on self-reported diabetes leads to biased results, incorrectly suggesting the absence of an effect. The same applies to inference that does not account for selective fertility.

Types of fish consumption differ across socioeconomic strata and impact differently on plasma fish-based omega-3 fatty acids: a cross-sectional study.

PURPOSE: We investigated the associations of socioeconomic position (SEP) with total and type of fish intake in a large general population and validated whether types of fish intake were differently associated with plasma EPA and DHA in a subset of the population. METHODS: From the Lifelines Cohort Study, 94,246 participants aged 44 ± 13 years old were included to test the association of two SEP indicators, i.e., education level and household income level, with dietary intakes of total, oily, lean, fried, and other types of fish. In a subset of 575 participants (mean age: 50 ± 13 years), EPA and DHA levels were measured in plasma phospholipids and triglycerides. Dietary fish intake was assessed using Food Frequency Questionnaire. Linear regressions were applied and adjusted for relevant covariates. RESULTS: Compared to the high education level, lower education levels were negatively associated with total, oily, lean, and other fish intake (p < 0.001 for all), and positively associated with fried fish intake (ß (SE): 0.04 (0.04), p < 0.001 for middle education; 0.07 (0.04), p < 0.001 for low education), independently of relevant covariates. Similar results were observed for income levels. In the subset population, total and oily fish intakes were positively associated with plasma EPA and DHA (p < 0.02 for all). Lean and other fish intakes were positively associated with only DHA (p < 0.008 for all), but not EPA, while fried fish was not associated with either EPA or DHA in plasma (p > 0.1 for all). CONCLUSION: Lower SEP was associated with a lower total intake of fish, and of oily and lean fish, but with higher intake of fried fish. Fried fish was not associated with the fish-based EPA and DHA in plasma. Hence, SEP-related differences in fish consumption are both quantitative and qualitative.

Do differences in sport participation contribute to socioeconomic health inequalities? Evidence from the Lifelines cohort study on all-cause mortality, diabetes and obesity.

Little is known about the role of sport participation in socioeconomic health inequalities. We studied the association between different aspects of sport participation with all-cause mortality, type 2 diabetes mellitus (T2DM) and obesity, including inequalities between socioeconomic subpopulations. Using the Dutch Lifelines cohort study (n = 84,230), we assessed the associations of sport participation, as well as the amount, intensity, type and number of sports, with all-cause mortality, T2DM and obesity in individuals. We studied the effect of sport participation on health outcomes within and between educational categories. Outcomes were compared with moderate to vigorous physical activity (MVPA). Sport participation was significantly associated with lower mortality (HR = 0.81), T2DM (HR = 0.70), and obesity (HR = 0.77). No significant additional effects of the amount or intensity of sport participation were found, while participating in teams sport was associated with significantly lower mortality (HR = 0.53) compared with other types of sport. These effects were similar among educational categories. Sport participation explained between 11% (T2DM and obesity) and 22% (mortality) of health inequalities between educational categories. This was more than twice the effect size of MVPA. The sensitivity analysis with net income as the socioeconomic indicator showed similar results. Our results suggest that to reduce socioeconomic differences in health, public health policies should focus on increasing sport participation in groups with a low socioeconomic status, rather than increasing the amount or intensity of sport participation, or MVPA in general.

Life years lost for users of specialized mental healthcare.

BACKGROUND: Mental disorders are burdensome and are associated with increased mortality. Mortality has been researched for various mental disorders, especially in countries with national registries, including the Nordic countries. Yet, knowledge gaps exist around national differences, while also relatively less studies compare mortality of those seeking help for mental disorders in specialized mental healthcare (SMH) by diagnosis. Additional insight into such mortality distributions for SMH users would be beneficial for both policy and research purposes. We aim to describe and compare the mortality in a population of SMH users with the mortality of the general population. Additionally, we aim to investigate mortality differences between sexes and major diagnosis categories: anxiety, depression, schizophrenia spectrum and other psychotic disorders, and bipolar disorder. METHODS: Mortality and basic demographics were available for a population of N = 10,914 SMH users in the north of The Netherlands from 2010 until 2017. To estimate mortality over the adult lifespan, parametric Gompertz distributions were fitted on observed mortality using interval regression. Life years lost were computed by calculating the difference between integrals of the survival functions for the general population and the study sample, thus correcting for age. Survival for the general population was obtained from Statistics Netherlands (CBS). RESULTS: SMH users were estimated to lose 9.5 life years (95% CI: 9.4-9.6). Every major diagnosis category was associated with a significant loss of life years, ranging from 7.2 (95% CI: 6.4-7.9) years for anxiety patients to 11.7 (95% CI: 11.0-12.5) years for bipolar disorder patients. Significant differences in mortality were observed between male SMH users and female SMH users, with men losing relatively more life years: 11.0 (95% CI: 10.9-11.2) versus 8.3 (95% CI: 8.2-8.4) respectively. This difference was also observed between sexes within every diagnosis, although the difference was insignificant for bipolar disorder. CONCLUSION: There were significant differences in mortality between SMH users and the general population. Substantial differences were observed between sexes and between diagnoses. Additional attention is required, and possibly specific interventions are needed to reduce the amount of life years lost by SMH users.

The Effect of Periodic Email Prompts on Participant Engagement With a Behavior Change mHealth App: Longitudinal Study.

BACKGROUND: Following the need for the prevention of noncommunicable diseases, mobile health (mHealth) apps are increasingly used for promoting lifestyle behavior changes. Although mHealth apps have the potential to reach all population segments, providing accessible and personalized services, their effectiveness is often limited by low participant engagement and high attrition rates. OBJECTIVE: This study concerns a large-scale, open-access mHealth app, based in the Netherlands, focused on improving the lifestyle behaviors of its participants. The study examines whether periodic email prompts increased participant engagement with the mHealth app and how this effect evolved over time. Points gained from the activities in the app were used as an objective measure of participant engagement with the program. The activities considered were physical workouts tracked through the mHealth app and interactions with the web-based coach. METHODS: The data analyzed covered 22,797 unique participants over a period of 78 weeks. A hidden Markov model (HMM) was used for disentangling the overtime effects of periodic email prompts on participant engagement with the mHealth app. The HMM accounted for transitions between latent activity states, which generated the observed measure of points received in a week. RESULTS: The HMM indicated that, on average, 70% (15,958/22,797) of the participants were in the inactivity state, gaining 0 points in total per week; 18% (4103/22,797) of the participants were in the average activity state, gaining 27 points per week; and 12% (2736/22,797) of the participants were in the high activity state, gaining 182 points per week. Receiving and opening a generic email was associated with a 3 percentage point increase in the likelihood of becoming active in that week, compared with the weeks when no email was received. Examining detailed email categories revealed that the participants were more likely to increase their activity level following emails that were in line with the program's goal, such as emails regarding health campaigns, while being resistant to emails that deviated from the program's goal, such as emails regarding special deals. CONCLUSIONS: Participant engagement with a behavior change mHealth app can be positively influenced by email prompts, albeit to a limited extent. Given the relatively low costs associated with emails and the high population reach that mHealth apps can achieve, such instruments can be a cost-effective means of increasing participant engagement in the stride toward improving program effectiveness.

[The contours of the new health system: competition is over.] / Contouren van een nieuw gezondheidsstelsel.

Increasing waiting lists and a structural staff shortage are putting pressure on the health system. Because care production is lower than care demand, there is no longer competition. Competition is over and we are beginning to see the contours of the new health system. The new system takes health instead of care as its starting point by legally embedding health goals in addition to the duty of care. The new system is based on health regions, but does not require a regional health authority. It is based on health manifestos that include agreements about cooperation in good and bad times.

Mental health over the life course: Evidence for a U-shape?

Mental health problems impose substantial individual and societal costs over the life-cycle. The age-profile of mental health problems is, however, not well understood. Hence, we study the age-profile of mental health while introducing minimal bias to reach identification. Using mental health data from the United States Panel Study of Income Dynamics we apply first difference estimation to derive an unbiased estimate of the second derivative of the age effect as well as an estimate up to a linear period trend of the first derivative. Next, we use a battery of estimators with varying restrictions to approximate the first derivative. Our results suggest that the age profile of mental health in the US is not U-shaped and we find tentative evidence that the age-profile could follow an inverse U-shape where individuals experience a mental health high during their life course. Further analyses, using German and Dutch data, confirm that these results do not only apply to the US, but also to Germany and the Netherlands.

Socio-economic gradients in diagnosed and undiagnosed Type 2 diabetes and its related health complications.

BACKGROUND AND AIMS: Diagnosed and undiagnosed Type 2 Diabetes (T2D) remains a challenge in high-income countries. In addition, the presence of T2D can cause further disease burden because of its high susceptibility to complications. Nevertheless, there is limited evidence of socio-economic gradients in undiagnosed T2D and its complications in a large population cohort. We investigated this using the Dutch Lifelines Cohort Study (Lifelines). METHODS AND RESULTS: Within Lifelines, baseline data of 102 163 adults aged 30 and above were collected from 2007 to 2013. The associations of Socio-Economic Status (SES), indicated by monthly household income, with the prevalence of T2D status and the number of T2D complications were assessed using multinomial Poisson and linear regressions with adjustments for age and sex. The prevalence of diagnosed and undiagnosed T2D was, respectively, 3.0% and 3.0% in the low SES group compared to 1.1% and 1.8% in the high SES group. Individuals with lower SES were at higher risk of having undiagnosed T2D (relative risk ratio (rrr) [95% CI]: 1.63 [1.47-1.81] for low SES and 1.16 [1.05-1.29] for middle SES) and diagnosed T2D, compared with those with high SES. Lower SES was positively associated with the number of T2D complications (low SES vs. high SES (ref); B [95% CI]: 0.15 [0.13-0.16]). CONCLUSION: Complementing the known socio-economic gradients in diagnosed T2D, we document socio-economic gradients in undiagnosed T2D and T2D complications in a single, large general representative population. Furthermore, individuals with low SES with diagnosed or undiagnosed T2D were more susceptible to T2D complications.

Frailty and risk of hospitalization from COVID-19 infection among older adults: evidence from the Dutch Lifelines COVID-19 Cohort study.

BACKGROUND: Frailty is associated with COVID-19 severity in clinical settings. No general population-based studies on the association between actual frailty status and COVID-19 hospitalization are available. AIMS: To investigate the association between frailty and the risk of COVID-19 hospitalization once infected. METHODS: 440 older adults who participated in the Lifelines COVID-19 Cohort study in the Northern Netherlands and reported positive COVID-19 testing results (54.2% women, age 70 ± 4 years in 2021) were included in the analyses. COVID-19 hospitalization status was self-reported. The Groningen Frailty Indicator (GFI) was derived from 15 self-reported questionnaire items related to daily activities, health problems, and psychosocial functioning, with a score ≥ 4 indicating frailty. Both frailty and COVID-19 hospitalization were assessed in the same period. Poisson regression models with robust standard errors were used to analyze the associations between frailty and COVID-19 hospitalization. RESULTS: Of 440 older adults included, 42 were hospitalized because of COVID-19 infection. After adjusting for sociodemographic and lifestyle factors, a higher risk of COVID-19 hospitalization was observed for frail individuals (risk ratio (RR) [95% CI] 1.97 [1.06-3.67]) compared to those classified as non-frail. DISCUSSION: Frailty was positively associated with COVID-19 hospitalization once infected, independent of sociodemographic and lifestyle factors. Future research on frailty and COVID-19 should consider biomarkers of aging and frailty to understand the pathophysiological mechanisms and manifestations between frailty and COVID-19 outcomes. CONCLUSIONS: Frailty was positively associated with the risk of hospitalization among older adults that were infected with COVID-19. Public health strategies for frailty prevention in older adults need to be advocated, as it is helpful to reduce the burden of the healthcare system, particularly during a pandemic like COVID-19.

Effects of Education and Income on Incident Type 2 Diabetes and Cardiovascular Diseases: a Dutch Prospective Study.

BACKGROUND: Education and income, as two primary socioeconomic indicators, are often used interchangeably in health research. However, there is a lack of clear distinction between these two indicators concerning their associations with health. OBJECTIVE: This study aimed to investigate the separate and combined effects of education and income in relation to incident type 2 diabetes and cardiovascular diseases in the general population. DESIGN AND PARTICIPANTS: Participants aged between 30 and 65 years from the prospective Dutch Lifelines cohort study were included. Two sub-cohorts were subsequently created, including 83,759 and 91,083 participants for a type 2 diabetes cohort and a cardiovascular diseases cohort, respectively. MAIN MEASURES: Education and income level were assessed by self-report questionnaires. The outcomes were incident type 2 diabetes and cardiovascular diseases (defined as the earliest non-fatal cardiovascular event). KEY RESULTS: A total of 1228 new cases of type 2 diabetes (incidence 1.5%) and 3286 (incidence 3.6%) new cases of cardiovascular diseases were identified, after a median follow-up of 43 and 44 months, respectively. Low education and low income (<1000 euro/month) were both positively associated with a higher risk of incident type 2 diabetes (OR 1.24 [95%CI 1.04-1.48] and OR 1.71 [95%CI 1.30-2.26], respectively); and with a higher risk of incident cardiovascular diseases (OR 1.15 [95%CI 1.04-1.28] and OR 1.24 [95%CI 1.02-1.52], respectively); independent of age, sex, lifestyle factors, BMI, clinical biomarkers, comorbid conditions at baseline, and each other. Results from the combined associations of education and income showed that within each education group, a higher income was associated with better health; and similarly, a higher education was associated with better health within each income group, except for the low-income group. CONCLUSIONS: Education and income were both independently associated with incident type 2 diabetes and cardiovascular diseases. The combined associations of these two socioeconomic indicators revealed that within each education or income level, substantial health disparities existed across strata of the other socioeconomic indicator. Education and income are two equally important socioeconomic indicators in health, and should be considered simultaneously in health research and policymaking.

Predictors of adherence to public health behaviors for fighting COVID-19 derived from longitudinal data.

The present paper examines longitudinally how subjective perceptions about COVID-19, one's community, and the government predict adherence to public health measures to reduce the spread of the virus. Using an international survey (N = 3040), we test how infection risk perception, trust in the governmental response and communications about COVID-19, conspiracy beliefs, social norms on distancing, tightness of culture, and community punishment predict various containment-related attitudes and behavior. Autoregressive analyses indicate that, at the personal level, personal hygiene behavior was predicted by personal infection risk perception. At social level, social distancing behaviors such as abstaining from face-to-face contact were predicted by perceived social norms. Support for behavioral mandates was predicted by confidence in the government and cultural tightness, whereas support for anti-lockdown protests was predicted by (lower) perceived clarity of communication about the virus. Results are discussed in light of policy implications and creating effective interventions.

Adoption of the Website and Mobile App of a Preventive Health Program Across Neighborhoods With Different Socioeconomic Conditions in the Netherlands: Longitudinal Study.

BACKGROUND: Socioeconomic disparities in the adoption of preventive health programs represent a well-known challenge, with programs delivered via the web serving as a potential solution. The preventive health program examined in this study is a large-scale, open-access web-based platform operating in the Netherlands, which aims to improve the health behaviors and wellness of its participants. OBJECTIVE: This study aims to examine the differences in the adoption of the website and mobile app of a web-based preventive health program across socioeconomic groups. METHODS: The 83,466 participants in this longitudinal, nonexperimental study were individuals who had signed up for the health program between July 2012 and September 2019. The rate of program adoption per delivery means was estimated using the Prentice, Williams, and Peterson Gap-Time model, with the measure of neighborhood socioeconomic status (NSES) used to distinguish between population segments with different socioeconomic characteristics. Registration to the health program was voluntary and free, and not within a controlled study setting, allowing the observation of the true rate of adoption. RESULTS: The estimation results indicate that program adoption across socioeconomic groups varies depending on the program's delivery means. For the website, higher NSES groups have a higher likelihood of program adoption compared with the lowest NSES group (hazard ratio 1.03, 95% CI 1.01-1.05). For the mobile app, the opposite holds: higher NSES groups have a lower likelihood of program adoption compared with the lowest NSES group (hazard ratio 0.94, 95% CI 0.91-0.97). CONCLUSIONS: Promoting preventive health programs using mobile apps can help to increase program adoption among the lowest socioeconomic segments. Given the increasing use of mobile phones among disadvantaged population groups, structuring future health interventions to include mobile apps as means of delivery can support the stride toward diminishing health disparities.

Separate and combined effects of individual and neighbourhood socio-economic disadvantage on health-related lifestyle risk factors: a multilevel analysis.

BACKGROUND: Socio-economic disadvantage at both individual and neighbourhood levels has been found to be associated with single lifestyle risk factors. However, it is unknown to what extent their combined effects contribute to a broad lifestyle profile. We aimed to (i) investigate the associations of individual socio-economic disadvantage (ISED) and neighbourhood socio-economic disadvantage (NSED) in relation to an extended score of health-related lifestyle risk factors (lifestyle risk index); and to (ii) investigate whether NSED modified the association between ISED and the lifestyle risk index. METHODS: Of 77 244 participants [median age (IQR): 46 (40-53) years] from the Lifelines cohort study in the northern Netherlands, we calculated a lifestyle risk index by scoring the lifestyle risk factors including smoking status, alcohol consumption, diet quality, physical activity, TV-watching time and sleep time. A higher lifestyle risk index was indicative of an unhealthier lifestyle. Composite scores of ISED and NSED based on a variety of socio-economic indicators were calculated separately. Linear mixed-effect models were used to examine the association of ISED and NSED with the lifestyle risk index and to investigate whether NSED modified the association between ISED and the lifestyle risk index by including an interaction term between ISED and NSED. RESULTS: Both ISED and NSED were associated with an unhealthier lifestyle, because ISED and NSED were both positively associated with the lifestyle risk index {highest quartile [Q4] ISED beta-coefficient [95% confidence interval (CI)]: 0.64 [0.62-0.66], P < 0.001; highest quintile [Q5] NSED beta-coefficient [95% CI]: 0.17 [0.14-0.21], P < 0.001} after adjustment for age, sex and body mass index. In addition, a positive interaction was found between NSED and ISED on the lifestyle risk index (beta-coefficient 0.016, 95% CI: 0.011-0.021, Pinteraction < 0.001), which indicated that NSED modified the association between ISED and the lifestyle risk index; i.e. the gradient of the associations across all ISED quartiles (Q4 vs Q1) was steeper among participants residing in the most disadvantaged neighbourhoods compared with those who resided in the less disadvantaged neighbourhoods. CONCLUSIONS: Our findings suggest that public health initiatives addressing lifestyle-related socio-economic health differences should not only target individuals, but also consider neighbourhood factors.

The impact of the Covid-19 crisis on socioeconomic differences in physical activity behavior: Evidence from the Lifelines COVID-19 cohort study.

Covid-19 and measures to contain spreading the disease have led to changed physical activity behavior. This study aims to investigate the relationship between socioeconomic status (SES) and changes in the amount of moderate to vigorous physical activity (MVPA) during the Covid-19 crisis. Using the Dutch Lifelines Covid-19 cohort study (n = 17,749), the amount of MVPA was measured at 15 time-points between March and December 2020, and compared with the amount before the Covid19 pandemic. For SES, the population was stratified in three education and income levels. Logistic regression models were used to estimate the odds ratio (OR) and confidence interval (CI) of altered MVPA for low and high SES groups, with the middle SES category as the reference group. A clear socioeconomic gradient in changes in MVPA behavior was observed. Low educated individuals had significantly higher odds (OR = 1.14; CI: 1.03-1.27) of decreasing MVPA, while the high educated had significantly lower odds of decreased MVPA (OR = 0.84, CI: 0.79-0.90). Both low education (OR = 0.87; CI: 0.77-0.98) and low income (OR = 0.85; CI 0.78-0.92) had significantly lower odds to increase MVPA, while high education (OR = 1.21, CI: 1.12-1.30) and high income (OR = 1.17; CI: 1.07-1.28) had significantly higher odds to increase MVPA. Most findings were consistent over the full research period. Socioeconomic inequalities in MVPA have increased during the Covid-19 pandemic, even when Covid-19 containment measures were relaxed. Our findings suggest that future public health policies need to increase efforts to improve physical activity behavior with an even larger focus on low SES groups.

Comparing the COVID-19 pandemic in space and over time in Europe, using numbers of deaths, crude rates and adjusted mortality trend ratios.

Comparison of COVID-19 trends in space and over time is essential to monitor the pandemic and to indirectly evaluate non-pharmacological policies aimed at reducing the burden of disease. Given the specific age- and sex- distribution of COVID-19 mortality, the underlying sex- and age-distribution of populations need to be accounted for. The aim of this paper is to present a method for monitoring trends of COVID-19 using adjusted mortality trend ratios (AMTRs). Age- and sex-mortality distribution of a reference European population (N = 14,086) was used to calculate age- and sex-specific mortality rates. These were applied to each country to calculate the expected deaths. Adjusted Mortality Trend Ratios (AMTRs) with 95% confidence intervals (C.I.) were calculated for selected European countries on a daily basis from 17th March 2020 to 29th April 2021 by dividing observed cumulative mortality, by expected mortality, times the crude mortality of the reference population. These estimated the sex- and age-adjusted mortality for COVID-19 per million population in each country. United Kingdom experienced the highest number of COVID-19 related death in Europe. Crude mortality rates were highest Hungary, Czech Republic, and Luxembourg. Accounting for the age-and sex-distribution of the underlying populations with AMTRs for each European country, four different patterns were identified: countries which experienced a two-wave pandemic, countries with almost undetectable first wave, but with either a fast or a slow increase of mortality during the second wave; countries with consistently low rates throughout the period. AMTRs were highest in Eastern European countries (Hungary, Czech Republic, Slovakia, and Poland). Our methods allow a fair comparison of mortality in space and over time. These might be of use to indirectly estimating the efficacy of non-pharmacological health policies. The authors urge the World Health Organisation, given the absence of age and sex-specific mortality data for direct standardisation, to adopt this method to estimate the comparative mortality from COVID-19 pandemic worldwide.

Lives versus Livelihoods? Perceived economic risk has a stronger association with support for COVID-19 preventive measures than perceived health risk.

This paper examines whether compliance with COVID-19 mitigation measures is motivated by wanting to save lives or save the economy (or both), and which implications this carries to fight the pandemic. National representative samples were collected from 24 countries (N = 25,435). The main predictors were (1) perceived risk to contract coronavirus, (2) perceived risk to suffer economic losses due to coronavirus, and (3) their interaction effect. Individual and country-level variables were added as covariates in multilevel regression models. We examined compliance with various preventive health behaviors and support for strict containment policies. Results show that perceived economic risk consistently predicted mitigation behavior and policy support-and its effects were positive. Perceived health risk had mixed effects. Only two significant interactions between health and economic risk were identified-both positive.

Association between socioeconomic status and self-reported, tested and diagnosed COVID-19 status during the first wave in the Northern Netherlands: a general population-based cohort from 49 474 adults.

OBJECTIVES: Studies in clinical settings showed a potential relationship between socioeconomic status (SES) and lifestyle factors with COVID-19, but it is still unknown whether this holds in the general population. In this study, we investigated the associations of SES with self-reported, tested and diagnosed COVID-19 status in the general population. DESIGN, SETTING, PARTICIPANTS AND OUTCOME MEASURES: Participants were 49 474 men and women (46±12 years) residing in the Northern Netherlands from the Lifelines cohort study. SES indicators and lifestyle factors (i.e., smoking status, physical activity, alcohol intake, diet quality, sleep time and TV watching time) were assessed by questionnaire from the Lifelines Biobank. Self-reported, tested and diagnosed COVID-19 status was obtained from the Lifelines COVID-19 questionnaire. RESULTS: There were 4711 participants who self-reported having had a COVID-19 infection, 2883 participants tested for COVID-19, and 123 positive cases were diagnosed in this study population. After adjustment for age, sex, lifestyle factors, body mass index and ethnicity, we found that participants with low education or low income were less likely to self-report a COVID-19 infection (OR [95% CI]: low education 0.78 [0.71 to 0.86]; low income 0.86 [0.79 to 0.93]) and be tested for COVID-19 (OR [95% CI]: low education 0.58 [0.52 to 0.66]; low income 0.86 [0.78 to 0.95]) compared with high education or high income groups, respectively. CONCLUSION: Our findings suggest that the low SES group was the most vulnerable population to self-reported and tested COVID-19 status in the general population.

Lifelines COVID-19 cohort: investigating COVID-19 infection and its health and societal impacts in a Dutch population-based cohort.

PURPOSE: The Lifelines COVID-19 cohort was set up to assess the psychological and societal impacts of the COVID-19 pandemic and investigate potential risk factors for COVID-19 within the Lifelines prospective population cohort. PARTICIPANTS: Participants were recruited from the 140 000 eligible participants of Lifelines and the Lifelines NEXT birth cohort, who are all residents of the three northern provinces of the Netherlands. Participants filled out detailed questionnaires about their physical and mental health and experiences on a weekly basis starting in late March 2020, and the cohort consists of everyone who filled in at least one questionnaire in the first 8 weeks of the project. FINDINGS TO DATE: >71 000 unique participants responded to the questionnaires at least once during the first 8 weeks, with >22 000 participants responding to seven questionnaires. Compiled questionnaire results are continuously updated and shared with the public through the Corona Barometer website. Early results included a clear signal that younger people living alone were experiencing greater levels of loneliness due to lockdown, and subsequent results showed the easing of anxiety as lockdown was eased in June 2020. FUTURE PLANS: Questionnaires were sent on a (bi)weekly basis starting in March 2020 and on a monthly basis starting July 2020, with plans for new questionnaire rounds to continue through 2020 and early 2021. Questionnaire frequency can be increased again for subsequent waves of infections. Cohort data will be used to address how the COVID-19 pandemic developed in the northern provinces of the Netherlands, which environmental and genetic risk factors predict disease susceptibility and severity and the psychological and societal impacts of the crisis. Cohort data are linked to the extensive health, lifestyle and sociodemographic data held for these participants by Lifelines, a 30-year project that started in 2006, and to data about participants held in national databases.