Dataset Information

Associations between neighbourhood street pattern, neighbourhood socioeconomic status and sleep in adults.

ABSTRACT: Sleep duration is a risk factor for poor health and all-cause mortality. Evidence suggests that neighbourhood characteristics such as built environment and socioeconomic status (SES) may affect sleep duration in adults. This study examined the relationship between neighbourhood built environment (i.e., measured via the street pattern) and SES with sleep duration in adults (n = 797) from 12 neighbourhoods in Calgary (Canada). Covariate adjusted linear and multinomial logistic regression models estimated the associations between street pattern (grid, warped-grid, curvilinear), SES and sleep duration. We also tested if the interaction between street pattern and SES was associated with sleep duration. Although neighbourhood street pattern and neighbourhood SES were not independently associated with sleep, the interaction between street pattern and neighbourhood SES, was associated with mean sleep duration. Individuals living in curvilinear low SES neighbourhoods had the shortest sleep duration (6.93 h per day; 95% CI 6.68, 7.18), while those living in curvilinear high SES neighbourhoods slept the longest (7.43 h per day; 95% CI 7.29, 7.57). Neighbourhood street pattern and SES, as well as their interaction, were not associated with the odds of sleeping shorter or longer than 7 to 8 h per day. Our findings suggest that the combined effect of the neighbourhood built environment and SES is potentially important for influencing sleep duration. More research is needed to understand the complex interrelationships between the built environment, SES, and sleep.

SUBMITTER: Lukic R

PROVIDER: S-EPMC8022243 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:The developmental health of young children is highly influenced by the socioeconomic conditions in which they are raised. How to accurately measure these conditions is a point of debate in the current literature on child development, health, and social determinants. We have evaluated four existing indices of socioeconomic status (SES) to determine the most relevant for the analysis of early childhood development (ECD) in Canada. Following a literature review of published SES indices which used 2006 Canadian Census data, four indices were chosen based on their relevance to ECD and the number of citations in subsequent articles. These were: the Canadian Deprivation Index, the Socioeconomic Factor Index, the Canadian Marginalization Index and an index created by the Early Childhood Mapping Project in Alberta, Canada. The indices were replicated using SES data for 2038 customized geographic neighbourhoods encompassing 99.9% of the Canadian population, and the relationship of the indices to ECD was investigated by linking to aggregated data from the Early Development Instrument (EDI), a teacher-completed questionnaire used to assess kindergarten children's physical, social, emotional, and cognitive development, and communication skills. The derived SES indices were compared based on four criteria: the input variables used, the index structure, the interpretability of the index and the variance they explained (R2) in the different EDI outcome measures. In terms of variance explained, material components of the SES indices (e.g., income, education) consistently showed the strongest association with children's language and cognitive development. The patterns of association for the non-material SES components and the other developmental domains of the EDI were more complex. We discuss the findings in regard to current developments in the field, and the need for refining empirical and theoretical approaches to examine associations between different facets of SES contextual factors and different aspects of ECD outcomes.

Project description:BACKGROUND: Past studies of associations between measures of the built environment, particularly street connectivity, and active transportation (AT) or leisure walking/bicycling have largely failed to account for spatial autocorrelation of connectivity variables and have seldom examined both the propensity for AT and its duration in a coherent fashion. Such efforts could improve our understanding of the spatial and behavioral aspects of AT. We analyzed spatially identified data from Los Angeles and San Diego Counties collected as part of the 2001 California Health Interview Survey. RESULTS: Principal components analysis indicated that ~85% of the variance in nine measures of street connectivity are accounted for by two components representing buffers with short blocks and dense nodes (PRIN1) or buffers with longer blocks that still maintain a grid like structure (PRIN2). PRIN1 and PRIN2 were positively associated with active transportation (AT) after adjustment for diverse demographic and health related variables. Propensity and duration of AT were correlated in both Los Angeles (r = 0.14) and San Diego (r = 0.49) at the zip code level. Multivariate analysis could account for the correlation between the two outcomes.After controlling for demography, measures of the built environment and other factors, no spatial autocorrelation remained for propensity to report AT (i.e., report of AT appeared to be independent among neighborhood residents). However, very localized correlation was evident in duration of AT, particularly in San Diego, where the variance of duration, after accounting for spatial autocorrelation, was 5% smaller within small neighborhoods (approximately 0.01 square latitude/longitude degrees = 0.6 mile diameter) compared to within larger zip code areas. Thus a finer spatial scale of analysis seems to be more appropriate for explaining variation in connectivity and AT. CONCLUSIONS: Joint analysis of the propensity and duration of AT behavior and an explicitly geographic approach can strengthen studies of the built environment and physical activity (PA), specifically AT. More rigorous analytical work on cross-sectional data, such as in the present study, continues to support the need for experimental and longitudinal study designs including the analysis of natural experiments to evaluate the utility of environmental interventions aimed at increasing PA.

Project description:IntroductionTo gain a better understanding of the complex and independent associations between different measures of socioeconomic position (SEP) and smoking in England.Aims and methodsBetween March 2013 and January 2019 data were collected from 120 496 adults aged 16+ in England taking part in the Smoking Toolkit Study. Of these, 18.04% (n = 21 720) were current smokers. Six indicators of SEP were measured: social grade, employment status, educational qualifications, home and car ownership and income. Models were constructed using ridge regression to assess the contribution of each measure of SEP, taking account of high collinearity.ResultsThe strongest predictor of smoking status was housing tenure. Those who did not own their own home had twice the odds of smoking compared with homeowners (odds ratio [OR] = 2.01). Social grade, educational qualification, and income were also good predictors. Those in social grades C1 (OR = 1.04), C2 (OR = 1.29), D (OR = 1.39), and E (OR = 1.78) had higher odds of smoking than those in social grade AB. Similarly, those with A-level/equivalent (OR = 1.15), GCSE/vocational (OR = 1.48), other/still studying (OR = 1.12), and no post-16 qualifications (OR = 1.48) had higher odds of smoking than those with university qualifications, as did those who earned in the lowest (OR = 1.23), third (OR = 1.18), and second quartiles (OR = 1.06) compared with those earning in the highest. Associations between smoking and employment (OR = 1.03) and car ownership (OR = 1.05) were much smaller.ConclusionsOf a variety of socioeconomic measures, housing tenure appears to be the strongest independent predictor of smoking in England, followed by social grade, educational qualifications, and income. Employment status and car ownership have the lowest predictive power.ImplicationsThis study used ridge regression, a technique which takes into account high collinearity between variables, to gain a better understanding of the independent associations between different measures of SEP and smoking in England. The findings provide guidance as to which SEP measures one could use when trying to identifying individuals most at risk from smoking, with housing tenure identified as the strongest independent predictor.

Dataset Information

Associations between neighbourhood street pattern, neighbourhood socioeconomic status and sleep in adults.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets