Dataset Information

Exploring the factors contributing to sibling correlations in BMI: a study using the Panel Study of Income Dynamics.

ABSTRACT: Understanding the mechanisms contributing to correlated BMI outcomes in a social network such as siblings will help policy makers reduce the burden of disease associated with obesity. There are two potential mechanisms explaining correlated BMI outcomes in a biologically related social network: (i) time constant factors such as genetic heritability and habits formed during childhood and (ii) factors that change over time some of which are dependent on the frequency of interactions between the social network, for example, social norms shaped by the social network's shifting attitudes towards weight and behaviors related to weight, or environmental factors like opportunities for exercise. This study aims to distinguish between time constant factors from factors that are likely to change over time to gain a better understanding of the mechanisms explaining the correlation in sibling BMI. We exploit data from the Panel Study of Income Dynamics (PSID) over 1999-2007 estimating the correlation in BMI for adult siblings who currently live in separate households but grew-up in the same household and adolescent siblings currently living in the same household to isolate the influence of factors that change over time. The findings indicate that time constant factors explain some of the overall correlation in sibling BMI for both cohorts of siblings. Factors that change over time only significantly impact on the overall correlation in BMI for adolescent siblings suggesting if there is a social network influence on correlations in BMI this is facilitated by sharing the same household.

SUBMITTER: Brown HW

PROVIDER: S-EPMC3346928 | biostudies-other | 2012 May

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:Background and Purpose- Cross sectional analyses have found large race differences in poststroke disability, yet these analyses do not account for prestroke disability, hospitalization factors, postacute care, transitions, or mortality. In this context, we explore mortality, nursing home placement, and disability in a longitudinal analysis of older stroke survivors who survived at least 90 days poststroke. Methods- A prospective cohort of black or white stroke survivors from the National Health and Aging Trends Study (2009-2016) linked to Medicare were used. Disability was assessed during in-person interviews with validated scales (0-7). We used cox proportional hazards models to separately assess mortality and nursing home admission adjusting for age, sex, sociodemographics (marital status, education, income, insurance status, social network size), comorbidities, hospitalization factors, postacute care, and 90-day readmissions. To estimate racial differences in disability, we used a multilevel linear regression model initially adjusting for age and sex and then compared with a model adjusted for sociodemographics, comorbidities, hospitalization factors, postacute care, and 90-day readmissions. Results- There were 282 stroke survivors, of which 76 (12.6%) were black. There were no race differences in long-term mortality (hazard ratio for black, 1.2 [95% CI, 0.7-2.2]; P=0.5) or nursing home placement (hazard ratio for black, 0.7 [95% CI, 0.2-2.4]; P=0.5). The largest race differences in disability were observed immediately prestroke, estimated age- and sex-adjusted activity limitations were (2.6 [2.0-3.2] in blacks versus 1.4 [1.0-1.8] in whites, mean difference, 1.2 [0.5-1.9], P<0.001) and immediately poststroke (2.6 [2.0-3.3] in blacks versus 1.7 [1.2-2.1] in whites, mean difference, 1.0 [0.2-1.7], P<0.01). Full adjustment did not substantially change the associations between race and disability. Conclusions- Race differences in nursing home placement, long-term mortality, sociodemographics, comorbidities, hospitalization factors, postacute care, and readmissions are unlikely to be large contributors to race differences in poststroke disability. Further research is needed to understand the drivers of race differences in poststroke disability.

Project description:BACKGROUND:Despite compelling evidence demonstrating the negative impact of poverty and income disparity on children's psychological well-being, there has been a lack of qualitative information which addresses its contributing factors. This study aimed to shed light on this area by comparing the experiences toward daily life between children living in low- and high-income families. METHODS:A qualitative study using a phenomenological approach was conducted from May 2012 to January 2013. A random sample of 42 children aged 10-13, with 25 from low- and 17 from high-income families were asked to voluntarily response to a demographic sheet and undergo individual semi-structured interviews which lasted about 25-30 min. Content analysis was used to analyze the data. Approval for the study was obtained from the Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West Cluster (reference UW 12-237). RESULTS:The findings of this study revealed that the living environment, physical health, social life and ability to function at school of children from low-income families are severely impaired. CONCLUSIONS:It fills a gap in the literature by showing how poverty and income disparity affect the daily lives of children from low-income families on different levels. Also, adopting a sedentary lifestyle and unhealthy eating habits are possible factors mediating the effects of poverty and income disparity on the psychological well-being of children from low-income families. It is vital for healthcare professionals going beyond their normal roles to give advice on healthy lifestyles and behaviors by building multidisciplinary partnerships with schools and the community. Additionally, healthcare professionals should also target on these two possible factors to develop and implement appropriate interventions for promoting the psychological well-being among children living in poverty. Trial registration NCT02877719. 19 August 2016 retrospectively registered.

Project description:Large changes have taken place in smallholder farming systems in South Asia's coastal areas in recent decades, particularly related to cropping intensity, input availability, climate risks, and off-farm activities. However, few studies have investigated the extent to which these changes have impacted farm-level crop productivity, which is a key driver of food security and poverty in rainfed, low-input, rice-based systems. The objective of this study was to conduct an integrated assessment of variables related to socioeconomic status, farm characteristics, and crop management practices to understand the major factors influencing crop productivity and identify promising leverage points for sustainable development in coastal Bangladesh. Using a panel survey dataset of 32 variables from 502 farm households located within polder (coastal embankment) and outside polder systems during 2005-2015, we employed statistical factor analysis to characterize five independent latent factors named here as Farming Challenges, Economic Status, Crop Management Practices, Asset Endowment, and Farm Characteristics. The factor Farming Challenges explained the most variation among households (31%), with decreases observed over time, specifically households located outside polders. Individual variables contributing to this factor included perceived cyclone severity, household distance to main roads and input-output markets, cropping intensity, and access to extension services. The most important factors for increasing crop productivity on a household and per unit area basis were Asset Endowment and Crop Management Practices, respectively. The former highlights the need for increasing gross cropped area, which can be achieved through greater cropping intensity, while the latter was associated with increased fertilizer, labor, and pesticide input use. Despite the importance of these factors, household poverty trajectory maps showed that changes in off-farm income had played the strongest role in improving livelihoods in this coastal area. This study can help inform development efforts and policies for boosting farm-level crop productivity, specifically through agricultural intensification (higher cropping intensity combined with appropriate and efficient use of inputs) and expanding opportunities for off-farm income as key pathways to bring smallholder households out of poverty.

Dataset Information

Exploring the factors contributing to sibling correlations in BMI: a study using the Panel Study of Income Dynamics.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure