ObjectivesStudies 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 measuresParticipants 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.ResultsThere 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.ConclusionOur findings suggest that the low SES group was the most vulnerable population to self-reported and tested COVID-19 status in the general population.

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.

Socioeconomic health inequalities are an important global public health problem. However, it is not well known to what extent socioeconomic inequalities culminate in impaired vitamin status and whether this is mediated by diet. We, therefore, aimed to assess vitamin status in a population already at increased risk of micronutrient deficiency, i.e., elderly with high and low socioeconomic status (SES), and to investigate whether potential differences therein were mediated by diet quality. Vitamin status in 1605 individuals (60–75 years) from the Lifelines- Micronutrients and Health inequalities in Elderly (MINUTHE) Study was assessed by measuring folic acid and the vitamins B6, B12, D, A, E, and K. Multinomial logistic and linear regression analyses were applied to test the associations between SES and vitamin status. Mediation analysis was used to explore the interrelationship between SES, diet quality, and vitamin status. Low SES was associated with poorer status of vitamin B6, vitamin B12, and, notably, folic acid. Moreover, multivitamin deficiencies were more prevalent in the low SES group. Diet quality was found to mediate the associations of SES with folic acid (for 39.1%), vitamin B6 (for 37.1%), and vitamin B12 (for 37.2%). We conclude that low SES is a risk factor for a spectrum of vitamin deficiencies. Diet quality can partially explain the socioeconomic differences in vitamin status, suggesting that policymakers can mitigate socioeconomic inequality in nutritional status through improving diet quality.

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