The interaction of socioeconomic position and type 2 diabetes mellitus family history:A cross-sectional analysis of the Lifelines Cohort and Biobank Study

Background Low socioeconomic position (SEP) and family history of type 2 diabetes mellitus (T2DM) contribute to increased T2DM risk, but it is unclear whether they exacerbate each other's effect. This study examined whether SEP reinforces the association of T2DM family history with T2DM, and whether behavioural and clinical risk factors can explain this reinforcement. Methods We used cross-sectional data on 51 725 participants from Lifelines. SEP was measured as educational level and was self-reported, just as family history of T2DM. T2DM was diagnosed based on measured fasting plasma glucose and glycated haemoglobin, combined with self-reported disease and recorded medication use. We assessed interaction on the additive scale by calculating the relative excess risk due to interaction (RERI). Results ORs of T2DM were highest for males (4.37; 95% CI 3.47 to 5.51) and females (7.77; 5.71 to 10.56) with the combination of low SEP and a family history of T2DM. The RERIs of low SEP and a family history of T2DM were 0.64 (-0.33 to 1.62) for males and 3.07 (1.53 to 4.60) for females. Adjustment for behavioural and clinical risk factors attenuated associations and interactions, but risks remained increased. Conclusion Low SEP and family history of T2DM are associated with T2DM, but they also exacerbate each other's impact in females but not in males. Behavioural and clinical risk factors partly explain these gender differences, as well as the associations underlying the interaction in females. The exacerbation by low SEP of T2DM risks in T2DM families deserves attention in prevention and community care

The association between fast-food outlet proximity and density and Body Mass Index:Findings from 147,027 Lifelines cohort study participants

Unhealthy food environments may contribute to an elevated Body Mass Index (BMI), which is a chronic disease risk factor. We examined the association between residential fast-food outlet exposure, in terms of proximity and density, and BMI in the Dutch adult general population. Additionally, we investigated to what extent this association was modified by urbanisation level. In this cross-sectional study, we linked residential addresses of baseline adult Lifelines cohort participants (N = 147,027) to fast-food outlet locations using geo-coding. We computed residential fast-food outlet proximity, and density within 500 m(m), 1, 3, and 5 km(km). We used stratified (urban versus rural areas) multilevel linear regression models, adjusting for age, sex, partner status, education, employment, neighbourhood deprivation, and address density. The mean BMI of participants was 26.1 (SD 4.3) kg/m2. Participants had a mean (SD) age of 44.9 (13.0), 57.3% was female, and 67.0% lived in a rural area. Having two or more (urban areas) or five or more (rural areas) fast-food outlets within 1 km was associated with a higher BMI (B = 0.32, 95% confidence interval (CI):0.03,0.62; B = 0.23, 95% CI:0.10,0.36, respectively). Participants in urban and rural areas with a fast-food outlet within <250 m had a higher BMI (B = 0.30, 95% CI:0.03,0.57; B = 0.20, 95% CI:0.09,0.31, respectively). In rural areas, participants also had a higher BMI when having at least one fast-food outlet within 500 m (B = 0.10, 95% CI:0.02,0.18). In conclusion, fast-food outlet exposure within 1 km from the residential address was associated with BMI in urban and rural areas. Also, fast-food outlet exposure within 500 m was associated with BMI in rural areas, but not in urban areas. In the future, natural experiments should investigate changes in the fast-food environment over time

The association between the presence of fast-food outlets and BMI:the role of neighbourhood socio-economic status, healthy food outlets, and dietary factors

BACKGROUND: Evidence on the association between the presence of fast-food outlets and Body Mass Index (BMI) is inconsistent. Furthermore, mechanisms underlying the fast-food outlet presence-BMI association are understudied. We investigated the association between the number of fast-food outlets being present and objectively measured BMI. Moreover, we investigated to what extent this association was moderated by neighbourhood socio-economic status (NSES) and healthy food outlets. Additionally, we investigated mediation by frequency of fast-food consumption and amount of fat intake. METHODS: In this cross-sectional study, we used baseline data of adults in Lifelines (N = 149,617). Geo-coded residential addresses were linked to fast-food and healthy food outlet locations. We computed the number of fast-food and healthy food outlets within 1 kilometre (km) of participants' residential addresses (each categorised into null, one, or at least two). Participants underwent objective BMI measurements. We linked data to Statistics Netherlands to compute NSES. Frequency of fast-food consumption and amount of fat intake were measured through questionnaires in Lifelines. Multivariable multilevel linear regression analyses were performed to investigate associations between fast-food outlet presence and BMI, adjusting for individual and environmental potential confounders. When exposure-moderator interactions had p-value < 0.10 or improved model fit (∆AIC ≥ 2), we conducted stratified analyses. We used causal mediation methods to assess mediation. RESULTS: Participants with one fast-food outlet within 1 km had a higher BMI than participants with no fast-food outlet within 1 km (B = 0.11, 95% CI: 0.01, 0.21). Effect sizes for at least two fast-food outlets were larger in low NSES areas (B = 0.29, 95% CI: 0.01, 0.57), and especially in low NSES areas where at least two healthy food outlets within 1 km were available (B = 0.75, 95% CI: 0.19, 1.31). Amount of fat intake, but not frequency of fast-food consumption, explained this association for 3.1%. CONCLUSIONS: Participants living in low SES neighbourhoods with at least two fast-food outlets within 1 km of their residential address had a higher BMI than their peers with no fast-food outlets within 1 km. Among these participants, healthy food outlets did not buffer the potentially unhealthy impact of fast-food outlets. Amount of fat intake partly explained this association. This study highlights neighbourhood socio-economic inequalities regarding fast-food outlets and BMI

Multimorbidity and exit from paid employment:the effect of specific combinations of chronic health conditions

Background This study aimed to assess the association between multimorbidity and exit from paid employment, and which combinations of chronic health conditions (CHCs) have the strongest association with exit from paid employment. Methods Data from 111 208 workers aged 18-64 years from Lifelines were enriched with monthly employment data from Statistics Netherlands. Exit from paid employment during follow-up was defined as a change from paid employment to unemployment, disability benefits, economic inactivity or early retirement. CHCs included cardiovascular diseases (CVD), chronic obstructive pulmonary disease (COPD), rheumatoid arthritis (RA), type 2 diabetes (T2DM) and depression. Cox-proportional hazards models were used to examine the impact of multimorbidity and combinations of CHCs on exit from paid employment. Results Multimorbidity increased the risk of exiting paid employment compared with workers without CHCs (hazard ratio (HR): 1.52; 95% confidence interval (CI): 1.35-1.71) or one CHC (HR: 1.14; 95% CI: 1.01-1.28). The risk for exit from paid employment increased among workers with COPD if they additionally had CVD (HR: 1.39; 95% CI: 1.03-1.88), depression (HR: 1.46; 95% CI: 1.10-1.93) or RA (HR: 1.44; 95% CI: 1.08-1.91), for workers with T2DM if they additionally had CVD (HR: 1.43; 95% CI: 1.07-1.91) or depression (HR: 2.09; 95% CI: 1.51-2.91) and for workers with depression who also had T2DM (HR: 1.68; 95% CI: 1.21-2.32). Conclusion This study showed that workers with multimorbidity, especially having a combination of COPD and depression or T2DM and depression, have a higher risk for early exit from paid employment and, therefore, may need tailored support at the workplace

Does social support at home moderate the association between social support at work and work functioning among cancer patients?

PURPOSE: The aims of this study were to examine (1) the longitudinal associations of supervisor and colleague social support with work functioning in cancer patients who have returned to work and (2) the moderating role of social support at home.METHODS: Data from the longitudinal Work Life after Cancer study were used (n = 384). Work functioning (low versus moderate to high work functioning) was measured with the validated Work Role Functioning Questionnaire 2.0. Social support at work was measured from both supervisor and colleagues with the Copenhagen Psychosocial Questionnaire. Social support at home was measured with the Social Support List-Discrepancies. Logistic generalized estimating equations were used to analyse associations between supervisor and colleague social support and work functioning, and to examine the possible moderating effect of social support at home.RESULTS: More supervisor (OR: 1.21; 95% CI: 1.10, 1.32) and colleague (1.13; 1.03, 1.24) social support were significantly associated with moderate to high work functioning. The association between colleague social support and work functioning was attenuated for those who did not experience enough social support at home but remained almost significant for supervisor social support (1.17; 1.00, 1.37).CONCLUSIONS: Supervisor social support is associated with better work functioning regardless of social support at home, while colleague social support is only associated with better work functioning when cancer patients experience enough social support at home.IMPLICATIONS FOR CANCER SURVIVORS: Occupational physicians may play a key role in creating awareness that social support at work and at home are beneficial for cancer patients' work functioning.</p

Absolute and Relative Socioeconomic Health Inequalities across Age Groups

BackgroundThe magnitude of socioeconomic health inequalities differs across age groups. It is less clear whether socioeconomic health inequalities differ across age groups by other factors that are known to affect the relation between socioeconomic position and health, like the indicator of socioeconomic position, the health outcome, gender, and as to whether socioeconomic health inequalities are measured in absolute or in relative terms. The aim is to investigate whether absolute and relative socioeconomic health inequalities differ across age groups by indicator of socioeconomic position, health outcome and gender.MethodsThe study sample was derived from the baseline measurement of the LifeLines Cohort Study and consisted of 95,432 participants. Socioeconomic position was measured as educational level and household income. Physical and mental health were measured with the RAND-36. Age concerned eleven 5-years age groups. Absolute inequalities were examined by comparing means. Relative inequalities were examined by comparing Gini-coefficients. Analyses were performed for both health outcomes by both educational level and household income. Analyses were performed for all age groups, and stratified by gender.ResultsAbsolute and relative socioeconomic health inequalities differed across age groups by indicator of socioeconomic position, health outcome, and gender. Absolute inequalities were most pronounced for mental health by household income. They were larger in younger than older age groups. Relative inequalities were most pronounced for physical health by educational level. Gini-coefficients were largest in young age groups and smallest in older age groups.ConclusionsAbsolute and relative socioeconomic health inequalities differed cross-sectionally across age groups by indicator of socioeconomic position, health outcome and gender. Researchers should critically consider the implications of choosing a specific age group, in addition to the indicator of socioeconomic position and health outcome, as findings on socioeconomic health inequalities may differ between them.</p

Childhood Socioeconomic Status and Depressive Symptom Trajectories in the Transition to Adulthood in the United States and Canada

Purpose: We examined whether young people in the U.S. and Canada exhibit similar depressive symptom trajectories in the transition to adulthood and compared the effect of childhood socioeconomic status on trajectory membership. Methods: We used the American National Longitudinal Survey of Youth 1979 Child/Young Adult (n = 6,315) and the Canadian National Longitudinal Survey of Children and Youth (n = 3,666). Depressive symptoms were measured using five items from the Center for Epidemiological Studies on Depression scale. Latent trajectories of depressive symptoms from ages 16–25 years were identified using growth mixture models. We estimated the effect of childhood family income, parental education, and parental unemployment on trajectory membership using multivariable Poisson regression models with robust variances. Results: We identified four similar trajectories in the two countries: (1) low stable; (2) mid-peak; (3) increasing; and (4) decreasing. Relatively more Americans were in the low-stable trajectory group than Canadians (77.6% vs. 64.9%), and fewer Americans were in the decreasing group (7.1% vs. 19.1%). In the U.S., childhood family income in the bottom two quartiles was related to higher rates of increasing trajectory membership compared with income in the top quartile (incidence rate ratios: 1.59–1.79, p <.05), but not in Canada. In the U.S., parental education at a high school level was associated with higher rates of decreasing trajectory membership compared with higher education (incidence rate ratio = 1.45, confidence interval: 1.10–1.91; p =.01), but not in Canada. Conclusions: Depressive symptoms may take a similar course in the transition to adulthood within these two countries. Country differences may modify the degree to which childhood socioeconomic status determines trajectory membership

Occupational distribution of metabolic syndrome prevalence and incidence differs by sex and is not explained by age and health behavior:results from 75 000 Dutch workers from 40 occupational groups

INTRODUCTION: This study examines the association between 40 occupational groups and prevalence and incidence of metabolic syndrome (MetS), separately for male and female workers, and whether age and health behaviors can explain the association. RESEARCH DESIGN AND METHODS: Data from 74 857 Lifelines Cohort and Biobank Study participants were used to regress occupational group membership, coded by Statistics Netherlands, on the prevalence and incidence of MetS using logistic and Cox regression analyses. MetS diagnosis was based on physical examinations, blood analysis, and recorded medication use. Information on age, smoking status, physical activity, diet and alcohol consumption was acquired using questionnaires. RESULTS: Baseline MetS prevalence was 17.5% for males and 10.6% for females. During a median 3.8 years of follow-up, MetS incidence was 7.8% for males and 13.2% for females. One occupational group was associated with an increased MetS risk in both sexes. Six additional occupational groups had an increased risk for MetS among men, four among women. Highest risks were found for male 'stationary plant and machine operators' (HR: 1.94; 95% CI 1.26 to 3.00) and female 'food preparation assistants' (HR: 1.80; 95% CI 1.01 to 3.22). CONCLUSIONS: Findings suggest that occupational group matters for men and women in MetS development, and that differences in MetS prevalence across occupations are not merely a reflection of selection of metabolically unhealthy workers into specific occupations. The striking sex differences in the occupational distribution of MetS indicate that preventive measures should, with some exceptions, target men and women separately

Fast-food environments and BMI changes in the Dutch adult general population:the Lifelines cohort

OBJECTIVE: This study investigated cross-sectional and longitudinal associations of fast-food outlet exposure with BMI and BMI change, as well as moderation by age and genetic predisposition.METHODS: This study used Lifelines' baseline (n = 141,973) and 4-year follow-up (n = 103,050) data. Participant residential addresses were linked to a register with fast-food outlet locations (Nationwide Information System of Workplaces [Dutch: Landelijk Informatiesysteem van Arbeidsplaatsen, LISA]) using geocoding, and the number of fast-food outlets within 1 km was computed. BMI was measured objectively. A weighted BMI genetic risk score was computed, representing overall genetic predisposition toward elevated BMI, based on 941 single-nucleotide polymorphisms genome-wide significantly associated with BMI for a subsample with genetic data (BMI: n = 44,996; BMI change: n = 36,684). Multivariable multilevel linear regression analyses and exposure-moderator interactions were tested.RESULTS: Participants with ≥1 fast-food outlet within 1 km had a higher BMI (B [95% CI]: 0.17 [0.09 to 0.25]), and those with ≥2 fast-food outlets within 1 km increased more in BMI (B [95% CI]: 0.06 [0.02 to 0.09]) than participants with no fast-food outlets within 1 km. Effect sizes on baseline BMI were largest among young adults (age 18-29 years; B [95% CI]: 0.35 [0.10 to 0.59]) and especially young adults with a medium (B [95% CI]: 0.57 [-0.02 to 1.16]) or high genetic risk score (B [95% CI]: 0.46 [-0.24 to 1.16]).CONCLUSIONS: Fast-food outlet exposure was identified as a potentially important determinant of BMI and BMI change. Young adults, especially those with a medium or high genetic predisposition, had a higher BMI when exposed to fast-food outlets.</p