Twitter-derived neighborhood characteristics associated with obesity and diabetes.
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ABSTRACT:
SUBMITTER: Nguyen QC
PROVIDER: S-EPMC5703998 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Characteristics of the neighborhood built environment influence health and health behavior. Google Street View (GSV) images may facilitate measures of the neighborhood environment that are meaningful, practical, and adaptable to any geographic boundary. We used GSV images and computer vision to characterize neighborhood environments (green streets, visible utility wires, and dilapidated buildings) and examined cross-sectional associations with chronic health outcomes among patients from the University of California, San Francisco Health system with outpatient visits from 2015 to 2017. Logistic regression models were adjusted for patient age, sex, marital status, race/ethnicity, insurance status, English as preferred language, assignment of a primary care provider, and neighborhood socioeconomic status of the census tract in which the patient resided. Among 214,163 patients residing in California, those living in communities in the highest tertile of green streets had 16-29% lower prevalence of coronary artery disease, hypertension, and diabetes compared to those living in communities in the lowest tertile. Conversely, a higher presence of visible utility wires overhead was associated with 10-26% more coronary artery disease and hypertension, and a higher presence of dilapidated buildings was associated with 12-20% greater prevalence of coronary artery disease, hypertension, and diabetes. GSV images and computer vision models can be used to understand contextual factors influencing patient health outcomes and inform structural and place-based interventions to promote population health.
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Project description:BackgroundNeighborhood walking connotes physical activity and opportunities for social and cognitive engagement and improved mental health, factors previously associated with outcomes including mortality, cardiovascular disease, and dementia. Few studies have examined correlates of neighborhood-specific walking in older adults.PurposeWe investigated the individual and neighborhood/regional correlates of neighborhood-based walking among US older adults.MethodsWe obtained cross-sectional data on ≥ 65 year olds from the population-based 2017 National Household Travel Survey (n = 73,523). Respondents completed diaries detailing trips during an assigned travel day. Adjusted logistic regression (using survey weights) tested associations between individual, neighborhood, and regional characteristics and ≥ 1 versus no neighborhood walk trips/day (from travel diary).ResultsTwelve percent had ≥ 1 neighborhood walk trip/day and 54% of the neighborhood walkers achieved ≥ 30 min of walking/day. African Americans/Blacks (versus non-Hispanic Whites) and working individuals (versus retired) had lower odds of neighborhood walking. Individuals without cars, bus/train users, and those with higher neighborhood housing density had greater odds of neighborhood walking. Utilitarian walking was less likely among African Americans/Blacks and Hispanics but more likely among Asians (versus non-Hispanic Whites). Social/recreational neighborhood walking was more likely for those without cars, bus/train users, and those with greater neighborhood housing density.ConclusionFew US older adults walked in their neighborhoods, suggesting a potentially fruitful target for health promotion efforts and community interventions to improve health and quality of life in older adults. Future work is needed to determine other neighborhood factors associated with greater neighborhood walking.
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Project description:Socioeconomic status has been associated with cardiovascular disease risk factors. However, few studies have examined this relationship among populations in the US Gulf Coast region. We assessed neighborhood deprivation in relation to obesity and diabetes in 9,626 residents participating in the Gulf Long-Term Follow-Up Study (2011-present) who completed a home visit (2011-2013) with height, weight, waist, and hip measurements. Obesity was categorized as body mass index of at least 30, and diabetes was defined by doctor's diagnosis or prescription medication. Participant home addresses were linked to an established Area Deprivation Index and categorized into 4 levels (1 = least deprived). In adjusted, modified Poisson regression models, participants with greatest deprivation were more likely to have obesity compared with those with least deprivation (adjusted prevalence ratio (aPR) = 1.21, 95% confidence interval (CI): 1.08, 1.35), central obesity (aPR = 1.11, 95% CI: 1.04, 1.19), and diabetes (aPR = 1.49, 95% CI: 1.03, 2.14). Repeated analyses among a subgroup of participants (n = 3,016) whose hemoglobin A1C values were measured 3 years later indicated the association with diabetes (defined as diagnosis, medications, or hemoglobin A1C ≥ 6.5) was similar (aPR = 1.46, 95% CI: 1.14, 1.86). Results suggest neighborhood deprivation is associated with obesity and diabetes in a US region with high baseline prevalence.
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Project description:Type 2 Diabetes (T2D) has reached epidemic levels among the pediatric population. Furthermore, disparities in T2D among youth are distributed in a manner that reflects the social inequality between population sub-groups. Here, we investigated the neighborhood determinants of T2D risk among a sample of Latino adolescents with obesity residing in Phoenix, Arizona (n = 133). In doing so we linked together four separate contextual data sources: the American Community Survey, the United States Department of Agriculture Food Access Research Atlas, the Arizona Healthy Community Map, and the National Neighborhood Data Archive to systematically analyze how and which neighborhood characteristics were associated with T2D risk factors as measured by fasting and 2-h glucose following a 75 g oral glucose tolerance test. Using linear regression models with and without individual/household covariates, we investigated how twenty-two housing and transportation sociodemographic and built and food environment characteristics were independently and jointly associated with T2D risk. The main finding from these analyses was the strong association between the density of fast food restaurants and 2-h glucose values (b = 2.42, p < 0.01). This association was independent of individual, household, and other neighborhood characteristics. Our results contribute to an increasingly robust literature demonstrating the deleterious influence of the neighborhood food environment, especially fast food, for T2D risk among Latino youth.
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Project description:Growing evidence reveals various neighborhood conditions are associated with the risk of developing type 2 diabetes. It is unknown, however, whether the effectiveness of diabetes prevention interventions is also influenced by neighborhood characteristics. The purpose of the current study is to examine the impact of neighborhood characteristics on the outcomes of a lifestyle intervention to prevent diabetes in American Indians and Alaska Natives (AI/ANs). Year 2000 US Census Tract data were linked with those from the Special Diabetes Program for Indians Diabetes Prevention Program (SDPI-DP), an evidence-based lifestyle intervention implemented in 36 AI/AN grantee sites across the US. A total of 3394 participants started the intervention between 01/01/2006 and 07/31/2009 and were followed by 07/31/2016. In 2016-2017, data analyses were conducted to evaluate the relationships of neighborhood characteristics with intervention outcomes, controlling for individual level socioeconomic status. AI/ANs from sites located in neighborhoods with higher median household income had 38% lower risk of developing diabetes than those from sites with lower neighborhood income (adjusted hazard ratio?=?0.65, 95% CI: 0.47-0.90). Further, those from sites with higher neighborhood concentrations of AI/ANs achieved less BMI reduction and physical activity increase. Meanwhile, participants from sites with higher neighborhood level of vehicle occupancy made more improvement in BMI and diet. Lifestyle intervention effectiveness was not optimal when the intervention was implemented at sites with disadvantaged neighborhood characteristics. Meaningful improvements in socioeconomic and other neighborhood disadvantages of vulnerable populations could be important in stemming the global epidemic of diabetes.
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Project description:Using publicly available, geotagged Twitter data, we created neighborhood indicators for happiness, food and physical activity for three large counties: Salt Lake, San Francisco and New York.We utilize 2.8 million tweets collected between February-August 2015 in our analysis. Geo-coordinates of where tweets were sent allow us to spatially join them to 2010 census tract locations. We implemented quality control checks and tested associations between Twitter-derived variables and sociodemographic characteristics.For a random subset of tweets, manually labeled tweets and algorithm labeled tweets had excellent levels of agreement: 73% for happiness; 83% for food, and 85% for physical activity. Happy tweets, healthy food references, and physical activity references were less frequent in census tracts with greater economic disadvantage and higher proportions of racial/ethnic minorities and youths.Social media can be leveraged to provide greater understanding of the well-being and health behaviors of communities-information that has been previously difficult and expensive to obtain consistently across geographies. More open access neighborhood data can enable better design of programs and policies addressing social determinants of health.
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Project description:BackgroundNeighborhood characteristics have been linked to community incidence of COVID-19, but the modifying effect of age has not been examined.ObjectiveWe adapted a neighborhood-wide analysis study (NWAS) design to systematically examine associations between neighborhood characteristics and COVID-19 incidence among different age groups.MethodsThe number of daily cumulative cases of COVID-19 by zip code area in Illinois has been made publicly available by the Illinois Department of Public Health. The number of COVID-19 cases was reported for eight age groups (under 20, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, and 80+). We reviewed this data published from May 23 through June 17, 2020 with complete data for all eight age groups and linked the data to neighborhood characteristics measured by the American Community Survey (ACS). Geographic age-specific cumulative incidence (cases per 1000 people) of COVID-19 was calculated by dividing the number of daily cumulative cases by the population of the same age group at each zip code area. The association between individual characteristics and COVID-19 incidence was examined using Poisson regression models.ResultsAt the zip code level, neighborhood socioeconomic status was a more important risk factor of COVID-19 incidence in children and working-age adults than in seniors. Social demographics and housing conditions were important risk factors of COVID-19 incidence in older age groups. We additionally observed significant associations between transportation-related variables and COVID-19 incidences in multiple age groups.SignificanceWe concluded that age modified the association between neighborhood characteristics and COVID-19 incidence.
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Project description:BackgroundHypertension control and diabetes control are important for reducing cardiovascular disease burden. A growing body of research suggests an association between neighborhood environment and hypertension or diabetes control among patients engaged in clinical care.ObjectiveTo investigate whether neighborhood conditions (i.e., healthy food availability, socioeconomic status (SES), and crime) were associated with hypertension and diabetes control.DesignCross-sectional analyses using electronic medical record (EMR) data, U.S. Census data, and secondary data characterizing neighborhood food environments. Multivariate logistic regression analyses adjusted for potential confounders. Analyses were conducted in 2017.ParticipantsFive thousand nine hundred seventy adults receiving primary care at three Baltimore City clinics in 2010-2011.Main measuresCensus tract-level neighborhood healthy food availability, neighborhood SES, and neighborhood crime. Hypertension control defined as systolic blood pressure < 140 mmHg and diastolic blood pressure < 90 mmHg. Diabetes control defined as HgbA1c < 7.Key resultsAmong patients with hypertension, neighborhood conditions were not associated with lower odds of blood pressure control after accounting for patient and physician characteristics. However, among patients with diabetes, in fully adjusted models accounting for patient and physician characteristics, we found that patients residing in neighborhoods with low and moderate SES had reduced odds of diabetes control (OR = 0.74 (95% CI = 0.57-0.97) and OR = 0.75 (95% CI = 0.57-0.98), respectively) compared to those living in high-SES neighborhoods.ConclusionsNeighborhood disadvantage may contribute to poor diabetes control among patients in clinical care. Community-based chronic disease care management strategies to improve diabetes control may be optimally effective if they also address neighborhood SES among patients engaged in care.
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Project description:The lack of access to sidewalks is a barrier for physical activity (PA) and may be a risk factor for childhood obesity. However, previous studies reported mixed findings and the association between sidewalk accessibility and childhood obesity remains unclear. This study systematically examined the evidence on the association between sidewalk accessibility and childhood obesity. PubMed, Cochrane Library, and Web of Science were searched for relevant articles (published before 1 January 2019) that reported on the association between neighborhood sidewalk access and weight-related behaviors and outcomes in children. Seventeen studies conducted in five countries were included. Ten studies used objective measure of access to sidewalks, seven studies measured children's height and weight, and seven studies objectively measured the PA or sedentary behaviors. Ten studies reported on the association of better access to sidewalks with increased PA (β ranging from 0.032 to 2.159; p < 0.05), reduced sedentary behaviors (β ranging from -0.19 to -0.14; p < 0.05), lower body mass index (BMI) (β ranging from -0.261 to -0.144; p < 0.001), or obesity risks (OR ranging from 1.02 to 1.32; p < 0.05); while the remaining seven studies did not report a desirable obesity-sidewalk association. Our findings support the hypothesis that higher sidewalk accessibility is associated with higher PA levels, lower BMI, and obesity risks. Efforts in building healthy environments, including health-promoting city planning, can help minimize the growing obesity epidemic and promote public health.
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Project description:Central to developing effective control measures for the COVID-19 pandemic is understanding the epidemiology of transmission in the community. Geospatial analysis of neighborhood-level data could provide insight into drivers of infection. In the current analysis of Harris County, Texas, we used custom interpolation tools in GIS to disaggregate COVID-19 incidence estimates from the zip code to census tract estimates-a better representation of neighborhood-level estimates. We assessed the associations between 29 neighborhood-level characteristics and COVID-19 incidence using a series of aspatial and spatial models. The variables that maintained significant and positive associations with COVID-19 incidence in our final aspatial model and later represented in a geographically weighted regression model were the percentage of the Black/African American population, percentage of the foreign-born population, area derivation index (ADI), percentage of households with no vehicle, and percentage of people over 65 years old inside each census tract. Conversely, we observed negative and significant association with the percentage employed in education. Notably, the spatial models indicated that the impact of ADI was homogeneous across the study area, but other risk factors varied by neighborhood. The current findings could enhance decision making by local public health officials in responding to the COVID-19 pandemic. By understanding factors that drive community transmission, we can better target disease control measures.
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