Genetic Risk for Smoking: Disentangling Interplay Between Genes and Socioeconomic Status

This study aims to disentangle the contribution of genetic liability, educational attainment (EA), and their overlap and interaction in lifetime smoking. We conducted genome-wide association studies (GWASs) in UK Biobank (N = 394,718) to (i) capture variants for lifetime smoking, (ii) variants for EA, and (iii) variants that contribute to lifetime smoking independently from EA (‘smoking-without-EA’). Based on the GWASs, three polygenic scores (PGSs) were created for individuals from the Netherlands Twin Register (NTR, N = 17,805) and the Netherlands Mental Health Survey and Incidence Study-2 (NEMESIS-2, N = 3090). We tested gene–environment (G × E) interactions between each PGS, neighborhood socioeconomic status (SES) and EA on lifetime smoking. To assess if the PGS effects were specific to smoking or had broader implications, we repeated the analyses with measures of mental health. After subtracting EA effects from the smoking GWAS, the SNP-based heritability decreased from 9.2 to 7.2%. The genetic correlation between smoking and SES characteristics was reduced, whereas overlap with smoking traits was less affected by subtracting EA. The PGSs for smoking, EA, and smoking-without-EA all predicted smoking. For mental health, only the PGS for EA was a reliable predictor. There were suggestions for G × E for some relationships, but there were no clear patterns per PGS type. This study showed that the genetic architecture of smoking has an EA component in addition to other, possibly more direct components. PGSs based on EA and smoking-without-EA had distinct predictive profiles. This study shows how disentangling different models of genetic liability and interplay can contribute to our understanding of the etiology of smoking.

What Determines Vulnerability to Neighborhood Walkability in Older Adults?

Environmental influences are recognized as important predictors of walking behaviors in older adults. However, individuals may differ in vulnerability to low environmental walkability. We determined associations of a walkability index (factor analysis of 16 variables; range -1.65 to 2.23) from audits of online images with self-reported walking behaviors in 406 adults mean age=82 (44% male, 39% Black). Effect modification by 12 variables representing sociodemographics, physical and mental health, and neighborhood characteristics was tested in general linear models. Effect modification was evident for knee pain, marital status, and neighborhood socioeconomic status (nSES) (all p-interaction<0.05); associations were present only in those with knee pain, those who were unmarried, and those in the highest race-specific tertile of nSES. For example, a 1 point higher walkability score was associated with 1.06 (CI: 0.78, 1.44) higher odds of walking in those without knee pain compared to 1.91 (CI: 1.25, 2.90) in those with knee pain.

Interpreting a standardized and normalized measure of neighborhood socioeconomic status for a better understanding of health differences

Background Standardization and normalization of continuous covariates are used to ease the interpretation of regression coefficients. Although these scaling techniques serve different purposes, they are sometimes used interchangeably or confused for one another. Therefore, the objective of this study is to demonstrate how these scaling techniques lead to different interpretations of the regression coefficient in multilevel logistic regression analyses. Methods Area-based socioeconomic data at the census tract level were obtained from the 2015–2019 American Community Survey for creating two measures of neighborhood socioeconomic status (SES), and a hypothetical data on health condition (favorable versus unfavorable) was constructed to represent 3000 individuals living across 300 census tracts (i.e., neighborhoods). Two measures of neighborhood SES were standardized by subtracting its mean and dividing by its standard deviation (SD) or by dividing by its interquartile range (IQR), and were normalized into a range between 0 and 1. Then, four separate multilevel logistic regression analyses were conducted to assess the association between neighborhood SES and health condition. Results Based on standardized measures, the odds of having unfavorable health condition was roughly 1.34 times higher for a one-SD change or a one-IQR change in neighborhood SES; these reflect a health difference of individuals living in relatively high SES (relatively affluent) neighborhoods and those living in relatively low SES (relatively deprived) neighborhoods. On the other hand, when these standardized measures were replaced by its respective normalized measures, the odds of having unfavorable health condition was roughly 3.48 times higher for a full unit change in neighborhood SES; these reflect a health difference of individuals living in highest SES (most affluent) neighborhoods and those living in lowest SES (most deprived) neighborhoods. Conclusion Multilevel logistic regression analyses using standardized and normalized measures of neighborhood SES lead to different interpretations of the effect of neighborhood SES on health. Since both measures are valuable in their own right, interpreting a standardized and normalized measure of neighborhood SES will allow us to gain a more rounded view of the health differences of individuals along the gradient of neighborhood SES in a certain geographic location as well as across different geographic locations.

Medical and Social Factors Associated with Referral for Elder Abuse Services in a National Healthcare System

Background Elder abuse (EA) is common and has devastating health consequences yet is not systematically assessed or documented in most health systems, limiting efforts to target healthcare-based interventions. Our objective was to examine sociodemographic and medical characteristics associated with documented referrals for EA assessment or services in a national US healthcare system. Methods We conducted a national case-control study in US Veterans Health Administration facilities of primary care (PC)-engaged Veterans age ≥60 years who were evaluated by social work (SW) for EA-related concerns between 2010-18. Cases were matched 1:5 to controls with a PC visit within 60 days of the matched case SW encounter. We examined the association of patient sociodemographic and health factors with receipt of EA services in unadjusted and adjusted models. Results Of 5,567,664 Veterans meeting eligibility criteria during the study period, 15,752 (0.3%) received services for EA (cases). Cases were mean age 74, and 54% unmarried. In adjusted logistic regression models (aOR; 95%CI), age ≥85 (3.56 v. age 60-64; 3.24-3.91), female sex (1.96; 1.76-2.21), child as next-of-kin (1.70 v. spouse; 1.57-1.85), lower neighborhood socioeconomic status (1.18 per higher quartile; 1.15-1.21), dementia diagnosis (3.01; 2.77-3.28) and receiving a VA pension (1.34; 1.23-1.46) were associated with receiving EA services. Conclusion In the largest cohort of patients receiving EA-related healthcare services studied to date, this study identified novel factors associated with clinical suspicion of EA that can be used to inform improvements in healthcare-based EA surveillance and detection.

Effects of Buffer Size and Shape on the Association of Neighborhood SES and Adult Fruit and Vegetable Consumption

Introduction: Neighborhood environment factors are relevant for dietary behaviors, but associations between home neighborhood context and disease prevention behaviors vary depending on the definition of neighborhood. The present study uses a publicly available dataset to examine whether associations between neighborhood socioeconomic status (NSES) and fruit/vegetable (FV) consumption vary when NSES is defined by different neighborhood sizes and shapes.Methods: We analyzed data from 1,736 adults with data in GeoFLASHE, a geospatial extension of the National Cancer Institute's Family Life, Activity, Sun, Health, and Eating Study (FLASHE). We examined correlations of NSES values across neighborhood buffer shapes (circular or street network) and sizes (ranging from 400 to 1,200 m) and ran weighted simple and multivariable regressions modeling frequency of FV consumption by NSES for each neighborhood definition. Regressions were also stratified by gender.Results: NSES measures were highly correlated across various neighborhood buffer definitions. In models adjusted for socio-demographics, circular buffers of all sizes and street buffers 750 m and larger were significantly associated with FV consumption frequency for women only.Conclusion: NSES may be particularly relevant for women's FV consumption, and further research can examine whether these associations are explained by access to food stores, food shopping behavior, and/or psychosocial variables. Although different NSES buffers are highly correlated, researchers should conceptually determine spatial areas a priori.