Inability to Work Fulltime and the Association with Paid Employment One Year After the Work Disability Assessment: A Longitudinal Register-Based Cohort Study.

OBJECTIVES: Disability benefit applicants with residual work capacity are often not able to work fulltime. In Dutch work disability benefit assessments, the inability to work fulltime is an important outcome, indicating the number of hours the applicant can sustain working activities per day. This study aims to gain insight into the association between inability to work fulltime and having paid employment 1 year after the assessment. METHODS: The study is a longitudinal register-based cohort study of work disability applicants who were granted a partial disability benefit (n = 8300). Multivariable logistic regression analyses were conducted to study the association between inability to work fulltime and having paid employment 1 year after the assessment, separately for working and non-working applicants. RESULTS: For disability benefit applicants, whether working (31.9%) or not working (68.1%) at the time of the disability assessment, there was generally no association between inability to work fulltime and having paid employment 1 year later. However, for working applicants diagnosed with a musculoskeletal disease or cancer, inability to work fulltime was positively and negatively associated with having paid employment, respectively. For non-working applicants with a respiratory disease or with multimorbidity, inability to work fulltime was negatively associated with paid employment. CONCLUSIONS: Inability to work fulltime has limited association with paid employment 1 year after the disability benefit assessment, regardless of the working status at the time of assessment. However, within certain disease groups, inability to work fulltime can either increase or decrease the odds of having paid employment after the assessment.

Neighborhood socioeconomic differences in BMI: The role of fast-food outlets and physical activity facilities.

OBJECTIVE: The goal of this study was to investigate the association between neighborhood socioeconomic status (NSES) and BMI and to what extent this association is moderated by availability of fast-food (FF) outlets and pay-for-use physical activity (PA) facilities. METHODS: Baseline data of adults in Lifelines (N = 146,629) were linked to Statistics Netherlands and a register using geocoding to compute, respectively, NSES (i.e., low, middle, high) and the number of FF outlets and PA facilities within 1 km of the residential address. Multivariable multilevel linear regression analyses were performed to examine the association between NSES and BMI. Two-way and three-way interaction terms were tested to examine moderation by FF outlets and PA facilities. RESULTS: Participants living in low NSES areas had a higher BMI than participants living in high (B [95% CI]: 0.76 [0.65 to 0.87]) or middle NSES areas (B [95% CI]: 0.40 [0.28 to 0.51]), independent of individual socioeconomic status. Although two- and three-way interactions between NSES, FF outlets, and PA facilities were significant, stratified analyses did not show consistent moderation patterns. CONCLUSIONS: People living in lower NSES areas had a higher BMI, independent of their individual socioeconomic status. The study found no clear moderation of FF outlets and PA facilities. Environmental factors that may mitigate NSES differences in BMI should be the subject of future research.

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.

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.

Vitamin Status and Diet in Elderly with Low and High Socioeconomic Status: The Lifelines-MINUTHE Study.

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.

Personality as a Resource for Labor Market Participation among Individuals with Chronic Health Conditions.

Background: The link between personality traits and employment status in individuals with chronic health conditions (CHCs) is largely unexplored. In this study, we examined this association among 21,173 individuals with CHCs and whether this association differs between individuals suffering from a heart disease, depression, anxiety, cancer, chronic obstructive pulmonary disease, musculoskeletal disease (MSD) and type 2 diabetes mellitus (T2DM). Methods: This study was conducted using baseline data from the Lifelines Cohort Study. Employment status and the presence of CHCs were determined by questionnaire data. The Revised Neuroticism-Extroversion-Openness Personality Inventory (NEO-PI-R) was used to measure eight personality facet traits. We conducted disease-generic and disease-specific logistic regression analyses. Results: Workers with higher scores on self-consciousness (OR: 1.02; 95% CI: 1.01-1.02), impulsivity (1.03; 1.02-1.04), excitement seeking (1.02; 1.01-1.02), competence (1.08; 1.07-1.10) and self-discipline (1.04; 1.03-1.05) were more often employed. Adults with higher scores on anger-hostility (0.97; 0.97-0.98), vulnerability (0.98; 0.97-0.99), and deliberation (0.96; 0.95-0.97) were least often employed. Personality facets were associated strongest with employment status among individuals suffering from MSD and weakest in individuals with T2DM. Conclusions: Personality might be a key resource to continue working despite having a CHC. This may be relevant for the development of targeted personality-focused interventions.

Multimorbidity and the Transition Out of Full-Time Paid Employment: A Longitudinal Analysis of the Health and Retirement Study.

OBJECTIVES: This study aims to examine whether older workers aged 50-64 years with multimorbidity are at increased risk to transition from full-time paid employment to part-time employment, partial retirement, unemployment, disability, economic inactivity, full retirement or die than workers without a chronic health condition and workers with one chronic health condition, and whether socioeconomic position (SEP) modifies these transitions. METHOD: Using data from the Health and Retirement Study (1992-2014; n = 10,719), sub-distribution hazard ratios with 95% confidence intervals were calculated with a time-varying Fine and Gray competing-risks survival regression model to examine exit from full-time paid employment. We investigated the modifying effect of SEP by examining its interaction with multimorbidity. RESULTS: Workers with multimorbidity had a higher risk of transitioning to partial retirement (1.45; 1.22, 1.72), disability (1.84; 1.21, 2.78) and full retirement (1.63; 1.47, 1.81), and they had a higher mortality risk (2.58; 1.71, 3.88) than workers without chronic disorders. Compared to workers with one chronic health condition, workers with multimorbidity had an increased risk for partial (1.19; 1.02, 1.40) and full retirement (1.29; 1.17, 1.42), and mortality (1.49; 1.09, 2.04). Only SEP measured as educational level modified the relationship between multimorbidity and mortality. DISCUSSION: Workers with multimorbidity seem more prone to leave full-time paid employment than workers without or with one a chronic health condition. Personalized work accommodations may be necessary to help workers with multimorbidity prolong their working life.

The Interaction of Genetic Predisposition and Socioeconomic Position With Type 2 Diabetes Mellitus: Cross-Sectional and Longitudinal Analyses From the Lifelines Cohort and Biobank Study.

OBJECTIVE: A strong genetic predisposition for type 2 diabetes mellitus (T2DM) may aggravate the negative effects of low socioeconomic position (SEP) in the etiology of the disorder. This study aimed to examine cross-sectional and longitudinal associations and interactions of a genetic risk score (GRS) and SEP with T2DM and to investigate whether clinical and behavioral risk factors can explain these associations and interactions. METHODS: We used data from 13,027 genotyped participants from the Lifelines study. The GRS was based on single-nucleotide polymorphisms genome-wide associated with T2DM and was categorized into tertiles. SEP was measured as educational level. T2DM was based on biological markers, recorded medication use, and self-reports. Cross-sectional and longitudinal associations and interactions between the GRS and SEP on T2DM were examined. RESULTS: The combination of a high GRS and low SEP had the strongest association with T2DM in cross-sectional (odds ratio = 3.84, 95% confidence interval = 2.28-6.46) and longitudinal analyses (hazard ratio = 2.71, 1.39-5.27), compared with a low GRS and high SEP. Interaction between a high GRS and a low SEP was observed in cross-sectional (relative excess risk due to interaction = 1.85, 0.65-3.05) but not in longitudinal analyses. Clinical and behavioral risk factors mostly explained the observed associations and interactions. CONCLUSIONS: A high GRS combined with a low SEP provides the highest risk for T2DM. These factors also exacerbated each other's impact cross-sectionally but not longitudinally. Preventive measures should target individual and contextual factors of this high-risk group to reduce the risk of T2DM.

SES, Chronic Kidney Disease, and Race in the U.S.: A Systematic Review and Meta-analysis.

CONTEXT: The risk of chronic kidney disease (CKD) in the U.S. is higher in individuals with low SES than in those with high SES. However, differences in these risks between African Americans and whites are unclear. EVIDENCE ACQUISITION: Studies published through August 30, 2016 in Medline and EMBASE were searched. From the seven studies (1,775,267 participants) that met inclusion criteria, association estimates were pooled by race in meta-analysis. The ratio of association estimates and the corresponding 95% CIs for African Americans and whites were also pooled in meta-analysis. Additionally, meta-regression analysis was used to explore whether race is related to the strength of SES-CKD association. The analysis was conducted in September 2016. EVIDENCE SYNTHESIS: The risk of CKD in low-SES people was 58% higher in African Americans (relative risk=1.58, 95% CI=1.33, 1.84) and 91% higher in whites (relative risk=1.91, 95% CI=1.47, 2.35) compared with their high-SES counterparts. The relative risk of CKD in low SES (versus high SES) was lower in African Americans than in whites (relative risk ratio=0.71, 95% CI=0.65, 0.77). Results from meta-regression analyses also indicated that race is potentially related to the strength of the association between low SES and CKD (p for difference between whites and African Americans=0.001). CONCLUSIONS: The risk of CKD in low SES (versus high SES) is higher in whites than in African Americans.

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.

Absolute and Relative Socioeconomic Health Inequalities across Age Groups.

BACKGROUND: The 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. METHODS: The 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. RESULTS: Absolute 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. CONCLUSIONS: Absolute 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.