Neighborhood socioeconomic change and diabetes risk: findings from the Chicago childhood diabetes registry

Disaggregation of Green Space Access, Walkability, and Behavioral Risk Factor Data for Precise Estimation of Local Population Characteristics

BACKGROUND

Social and Environmental Determinants of Health (SEDH) provide us with a conceptual framework to gain insights into possible associations among different human behaviors and the corresponding health outcomes that take place often in and around complex built environments. Developing better built environments requires an understanding of those aspects of a community that are most likely to have a measurable impact on the target SEDH. Yet data on local characteristics at suitable spatial scales are often unavailable. We aim to address this issue by application of different data disaggregation methods.

METHODS

We applied different approaches to data disaggregation to obtain small area estimates of key behavioral risk factors, as well as geospatial measures of green space access and walkability for each zip code of Allegheny County in southwestern Pennsylvania.

RESULTS

Tables and maps of local characteristics revealed their overall spatial distribution along with disparities therein across the county. While the top ranked zip codes by behavioral estimates generally have higher than the county's median individual income, this does not lead them to have higher than its median green space access or walkability.

CONCLUSION

We demonstrated the utility of data disaggregation for addressing complex questions involving community-specific behavioral attributes and built environments with precision and rigor, which is especially useful for a diverse population. Thus, different types of data, when comparable at a common local scale, can provide key integrative insights for researchers and policymakers.

Neighborhood-Level Risk Factors for Severe Hyperglycemia among Emergency Department Patients without a Prior Diabetes Diagnosis

A person's place of residence is a strong risk factor for important diagnosed chronic diseases such as diabetes. It is unclear whether neighborhood-level risk factors also predict the probability of undiagnosed disease. The objective of this study was to identify neighborhood-level variables associated with severe hyperglycemia among emergency department (ED) patients without a history of diabetes. We analyzed patients without previously diagnosed diabetes for whom a random serum glucose value was obtained in the ED. We defined random glucose values ≥ 200 mg/dL as severe hyperglycemia, indicating probable undiagnosed diabetes. Patient addresses were geocoded and matched with neighborhood-level socioeconomic measures from the American Community Survey and claims-based surveillance estimates of diabetes prevalence. Neighborhood-level exposure variables were standardized based on z-scores, and a series of logistic regression models were used to assess the association of selected exposures and hyperglycemia adjusting for biological and social individual-level risk factors for diabetes. Of 77,882 ED patients without a history of diabetes presenting in 2021, 1,715 (2.2%) had severe hyperglycemia. Many geospatial exposures were associated with uncontrolled hyperglycemia, even after controlling for individual-level risk factors. The most strongly associated neighborhood-level variables included lower markers of educational attainment, higher percentage of households where limited English is spoken, lower rates of white-collar employment, and higher rates of Medicaid insurance. Including these geospatial factors in risk assessment models may help identify important subgroups of patients with undiagnosed disease.

Exposure to neighborhood poverty from adolescence through emerging adulthood and sleep duration in US adults

Does exposure to neighborhood poverty from adolescence to early adulthood have differential influence on sleep duration across racial/ethnic groups? We used data from the National Longitudinal Study of Adolescent to Adult Health that consisted of 6756 Non-Hispanic (NH) White respondents, 2471 NH Black respondents, and 2000 Hispanic respondents and multinomial logistic models to predict respondent reported sleep duration based on exposure to neighborhood poverty during adolescence and adulthood. Results indicated that neighborhood poverty exposure was related to short sleep duration among NH White respondents only. We discuss these results in relation to coping, resilience, and White psychology.

Using Convolutional Neural Networks to Derive Neighborhood Built Environments from Google Street View Images and Examine Their Associations with Health Outcomes

Built environment neighborhood characteristics are difficult to measure and assess on a large scale. Consequently, there is a lack of sufficient data that can help us investigate neighborhood characteristics as structural determinants of health on a national level. The objective of this study is to utilize publicly available Google Street View images as a data source for characterizing built environments and to examine the influence of built environments on chronic diseases and health behaviors in the United States. Data were collected by processing 164 million Google Street View images from November 2019 across the United States. Convolutional Neural Networks, a class of multi-layer deep neural networks, were used to extract features of the built environment. Validation analyses found accuracies of 82% or higher across neighborhood characteristics. In regression analyses controlling for census tract sociodemographics, we find that single-lane roads (an indicator of lower urban development) were linked with chronic conditions and worse mental health. Walkability and urbanicity indicators such as crosswalks, sidewalks, and two or more cars were associated with better health, including reduction in depression, obesity, high blood pressure, and high cholesterol. Street signs and streetlights were also found to be associated with decreased chronic conditions. Chain link fence (physical disorder indicator) was generally associated with poorer mental health. Living in neighborhoods with a built environment that supports social interaction and physical activity can lead to positive health outcomes. Computer vision models using manually annotated Google Street View images as a training dataset were able to accurately identify neighborhood built environment characteristics. These methods increases the feasibility, scale, and efficiency of neighborhood studies on health.

Neighborhood Conditions and Type 2 Diabetes Risk among Latino Adolescents with Obesity in Phoenix

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.

Google Street View-Derived Neighborhood Characteristics in California Associated with Coronary Heart Disease, Hypertension, Diabetes

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.

Effects of family and neighborhood risks on glycemic control among young black adolescents with type 1 diabetes: Findings from a multi-center study

While individual and family risk factors that contribute to health disparities in children with type 1 diabetes have been identified, studies on the effects of neighborhood risk factors on glycemic control are limited, particularly in minority samples. This cross-sectional study tested associations between family conflict, neighborhood adversity and glycemic outcomes (HbA1c) in a sample of urban, young Black adolescents with type 1 diabetes(mean age = 13.4 ± 1.7), as well as whether neighborhood adversity moderated the relationship between family conflict and HbA1c. Participants (N = 128) were recruited from five pediatric diabetes clinics in two major metropolitan US cities. Diabetes-related family conflict was measured via self-report questionnaire (Diabetes Family Conflict Scale; DFCS). Neighborhood adversity was calculated at the census block group level based on US census data. Indictors of adversity were used to calculate a neighborhood adversity index (NAI) for each participant. Median family income was $25,000, suggesting a low SES sample. In multiple regression analyses, DFCS and NAI both had significant, independent effects on glycemic control (β = 0.174, P = 0.034 and β = 0.226 P = 0.013, respectively) after controlling for child age, family socioeconomic status and insulin management regimen. Tests of effects of the NAI and DFCS interaction on HbA1c found no significant moderating effects of neighborhood adversity. Even within contexts of significant socioeconomic disadvantage, variability in degree of neighborhood adversity predicts diabetes-related health outcomes in young Black adolescents with type 1 diabetes. Providers should assess social determinants of health such as neighborhood resources that may impact adolescents' ability to maintain optimal glycemic control.

Racial/Ethnic Disparities in Household Food Insecurity During the COVID-19 Pandemic: a Nationally Representative Study

Previous research has demonstrated that the burden of household food insecurity is disproportionately high among racial/ethnic minority groups, yet no peer-reviewed studies have systematically examined racial/ethnic disparities in household food insecurity in the context of the COVID-19 pandemic. This cross-sectional study on household food insecurity during COVID-19 used data from a nationally representative sample of US households through the 2020 Household Pulse Survey (HPS) (including all 50 states and the District of Columbia, n = 74,413 households). Six generalized estimating equation (GEE) models were estimated, and the results indicated that households headed by Blacks, Asians, Hispanics, or other racial/ethnic minorities were not significantly more food insecure than White households during the pandemic. However, among food-insecure households, Black households were more likely to report that they could not afford to buy more food; Asian and Hispanic households were more likely to be afraid to go out to buy food; Asian households were more likely to face transportation issues when purchasing food; while White households were more likely to report that stores did not have the food they wanted. Moreover, racial/ethnic minorities were significantly less confident about their household food security for the next 4 weeks than Whites. The coronavirus pandemic crisis has exposed and exacerbated the food injustice in American society. Policymakers and local officials should take concerted actions to improve the capacity of food supply and ensure food equality across all racial/ethnic groups.

Racial/Ethnic Disparities in Household Food Insecurity During the COVID-19 Pandemic: a Nationally Representative Study

Previous research has demonstrated that the burden of household food insecurity is disproportionately high among racial/ethnic minority groups, yet no peer-reviewed studies have systematically examined racial/ethnic disparities in household food insecurity in the context of the COVID-19 pandemic. This cross-sectional study on household food insecurity during COVID-19 used data from a nationally representative sample of US households through the 2020 Household Pulse Survey (HPS) (including all 50 states and the District of Columbia, n = 74,413 households). Six generalized estimating equation (GEE) models were estimated, and the results indicated that households headed by Blacks, Asians, Hispanics, or other racial/ethnic minorities were not significantly more food insecure than White households during the pandemic. However, among food-insecure households, Black households were more likely to report that they could not afford to buy more food; Asian and Hispanic households were more likely to be afraid to go out to buy food; Asian households were more likely to face transportation issues when purchasing food; while White households were more likely to report that stores did not have the food they wanted. Moreover, racial/ethnic minorities were significantly less confident about their household food security for the next 4 weeks than Whites. The coronavirus pandemic crisis has exposed and exacerbated the food injustice in American society. Policymakers and local officials should take concerted actions to improve the capacity of food supply and ensure food equality across all racial/ethnic groups.

Analyzing Associations Between Chronic Disease Prevalence and Neighborhood Quality Through Google Street View Images

Deep learning and, specifically, convoltional neural networks (CNN) represent a class of powerful models that facilitate the understanding of many problems in computer vision. When combined with a reasonable amount of data, CNNs can outperform traditional models for many tasks, including image classification. In this work, we utilize these powerful tools with imagery data collected through Google Street View images to perform virtual audits of neighborhood characteristics. We further investigate different architectures for chronic disease prevalence regression through networks that are applied to sets of images rather than single images. We show quantitative results and demonstrate that our proposed architectures outperform the traditional regression approaches.

Correlating the global increase in type 1 diabetes incidence across age groups with national economic prosperity: A systematic review

BACKGROUND

The global epidemiology of type 1 diabetes (T1D) is not yet well known, as no precise data are available from many countries. T1D is, however, characterized by an important variation in incidences among countries and a dramatic increase of these incidences during the last decades, predominantly in younger children. In the United States and Europe, the increase has been associated with the gross domestic product (GDP) per capita. In our previous systematic review, geographical variation of incidence was correlated with socio-economic factors.

AIM

To investigate variation in the incidence of T1D in age categories and search to what extent these variations correlated with the GDP per capita.

METHODS

A systematic review was performed to retrieve information about the global incidence of T1D among those younger than 14 years of age. The study was carried out according to the PRISMA recommendations. For the analysis, the incidence was organized in the periods: 1975-1999 and 2000-2017. We searched the incidence of T1D in the age-groups 0-4, 5-9 and 10-14. We compared the incidences in countries for which information was available for the two periods. We obtained the GDP from the World Bank. We analysed the relationship between the incidence of T1D with the GDP in countries reporting data at the national level.

RESULTS

We retrieved information for 84 out of 194 countries around the world. We found a wide geographic variation in the incidence of T1D and a worldwide increase during the two periods. The largest contribution to this increase was observed in the youngest group of children with T1D, with a relative increase of almost double when comparing the two periods (P value = 2.5 × e^-5). Twenty-six countries had information on the incidence of T1D at the national level for the two periods. There was a positive correlation between GDP and the incidence of T1D in both periods (Spearman correlation = 0.52 from 1975-1999 and Spearman correlation = 0.53 from 2000-2017).

CONCLUSION

The incidence increase was higher in the youngest group (0-4 years of age), and the highest incidences of T1D were found in wealthier countries.

Prevalence of type 1 and type 2 diabetes among US pediatric population in the MarketScan Multi-State Database, 2002 to 2016

OBJECTIVES

To estimate the prevalence of type 1 (T1DM) and type 2 diabetes mellitus (T2DM) among U.S. Medicaid pediatric population aged <18 years 2002 to 2016 by age, sex, and race/ethnicity.

METHODS

Participants aged <18 years old from 2002 to 2016 were identified from the MarketScan Multi-State Medicaid Database. Diabetes was defined as having (a) ≥1 claims for an outpatient or inpatient diabetes diagnosis and ≥2 prescriptions for any anti-diabetes medications or (b) records of ≥2 claims for an outpatient or inpatient diabetes diagnosis that were at least 30 days apart. Annual prevalence of diabetes and 95% confidence intervals (CIs) were calculated. Age-, sex-, and race-stratified prevalence were also assessed.

RESULTS

The annual prevalence of T1DM increased from 1.29 to 2.34/1000 pediatric persons from 2002 to 2016. The prevalence of T2DM rose from 0.70 in 2002 to 2.76/1000 in 2011, but then dropped to 2.12/1000 pediatric persons in 2016 in the Medicaid population. Prevalence of both T1DM and T2DM increased with age. While the prevalence of T1DM was similar in both sexes, and was most prevalent in Whites, prevalence of T2DM was higher in girls and was most prevalent in Blacks.

CONCLUSIONS

While the annual prevalence of T1DM in pediatric persons enrolled in Medicaid increased continuously from 2002 to 2016, the annual prevalence of T2DM increased from 2002 to 2011, with a subsequent decrease in 2016, possibly because of the increase of relatively healthier participants with the expanded eligibility through the ACA between 2011 and 2016.

Neighborhood social and economic change and diabetes incidence: The HeartHealthyHoods study

We studied the association between neighborhood social and economic change and type 2 diabetes incidence in the city of Madrid (Spain). We followed 199,621 individuals living in 393 census tracts for diabetes incidence for 6 years using electronic health records, starting in 2009. We measured neighborhood social and economic change from 2005 to 2009 using a finite mixture model with 16 indicators that resulted in four types of neighborhood change. Adjusted results showed an association between neighborhood change and diabetes incidence: compared to those living in Aging/Stable areas, people living in Declining SES, New Housing and Improving SES areas have an 8% (HR = 0.92, 95% CI 0.87 to 0.99), 9% (HR = 0.91, 95% CI 0.81 to 1.01) and 11% (HR = 0.89, 95% CI 0.81 to 0.98) decrease in diabetes incidence. This evidence can help guide policies for diabetes prevention by focusing efforts on specific urban areas.

Using Google Street View to examine associations between built environment characteristics and U.S. health outcomes

Neighborhood attributes have been shown to influence health, but advances in neighborhood research has been constrained by the lack of neighborhood data for many geographical areas and few neighborhood studies examine features of nonmetropolitan locations. We leveraged a massive source of Google Street View (GSV) images and computer vision to automatically characterize national neighborhood built environments. Using road network data and Google Street View API, from December 15, 2017-May 14, 2018 we retrieved over 16 million GSV images of street intersections across the United States. Computer vision was applied to label each image. We implemented regression models to estimate associations between built environments and county health outcomes, controlling for county-level demographics, economics, and population density. At the county level, greater presence of highways was related to lower chronic diseases and premature mortality. Areas characterized by street view images as 'rural' (having limited infrastructure) had higher obesity, diabetes, fair/poor self-rated health, premature mortality, physical distress, physical inactivity and teen birth rates but lower rates of excessive drinking. Analyses at the census tract level for 500 cities revealed similar adverse associations as was seen at the county level for neighborhood indicators of less urban development. Possible mechanisms include the greater abundance of services and facilities found in more developed areas with roads, enabling access to places and resources for promoting health. GSV images represents an underutilized resource for building national data on neighborhoods and examining the influence of built environments on community health outcomes across the United States.

Health care access and glycemic control in youth and young adults with type 1 and type 2 diabetes in South Carolina

Affordability and geographic accessibility are key health care access characteristics. We used data from 481 youth and young adults (YYA) with diabetes (389 type 1, 92 type 2) to understand the association between health care access and glycemic control as measured by HbA1c values. In multivariate models, YYA with state or federal health insurance had HbA1c percentage values 0.68 higher (P = 0.0025) than the privately insured, and those without insurance 1.34 higher (P < 0.0001). Not having a routine diabetes care provider was associated with a 0.51 higher HbA1c (P = 0.048) compared to having specialist care, but HbA1c did not differ significantly (P = 0.069) between primary vs specialty care. Distance to utilized provider was not associated with HbA1c among YYA with a provider (P = 0.11). These findings underscore the central role of health insurance and indicate a need to better understand the root causes of poorer glycemic control in YYA with state/federal insurance.

Urban foodscape trends: Disparities in healthy food access in Chicago, 2007-2014

We investigated changes in supermarket access in Chicago between 2007 and 2014, spanning The Great Recession, which we hypothesized worsened local food inequity. We mapped the average street network distance to the nearest supermarket across census tracts in 2007, 2011, and 2014, and identified spatial clusters of persistently low, high or changing access over time. Although the total number of supermarkets increased city-wide, extremely low food access areas in segregated, low income regions did not benefit. Among black and socioeconomically disadvantaged residents of Chicago, access to healthy food is persistently poor and worsened in some areas following recent economic shocks.

Neighborhood characteristics, food deserts, rurality, and type 2 diabetes in youth: Findings from a case-control study

Little is known about the influence of neighborhood characteristics on risk of type 2 diabetes (T2D) among youth. We used data from the SEARCH for Diabetes in Youth Case-Control Study to evaluate the association of neighborhood characteristics, including food desert status of the census tract, with T2D in youth. We found a larger proportion of T2D cases in tracts with lower population density, larger minority population, and lower levels of education, household income, housing value, and proportion of the population in a managerial position. However, most associations of T2D with neighborhood socioeconomic characteristics were attributable to differences in individual characteristics. Notably, in multivariate logistic regression models, T2D was associated with living in the least densely populated study areas, and this finding requires further exploration.

Neighbourhood looking glass: 360º automated characterisation of the built environment for neighbourhood effects research

Background

Neighbourhood quality has been connected with an array of health issues, but neighbourhood research has been limited by the lack of methods to characterise large geographical areas. This study uses innovative computer vision methods and a new big data source of street view images to automatically characterise neighbourhood built environments.

Methods

A total of 430 000 images were obtained using Google’s Street View Image API for Salt Lake City, Chicago and Charleston. Convolutional neural networks were used to create indicators of street greenness, crosswalks and building type. We implemented log Poisson regression models to estimate associations between built environment features and individual prevalence of obesity and diabetes in Salt Lake City, controlling for individual-level and zip code-level predisposing characteristics.

Results

Computer vision models had an accuracy of 86%–93% compared with manual annotations. Charleston had the highest percentage of green streets (79%), while Chicago had the highest percentage of crosswalks (23%) and commercial buildings/apartments (59%). Built environment characteristics were categorised into tertiles, with the highest tertile serving as the referent group. Individuals living in zip codes with the most green streets, crosswalks and commercial buildings/apartments had relative obesity prevalences that were 25%–28% lower and relative diabetes prevalences that were 12%–18% lower than individuals living in zip codes with the least abundance of these neighbourhood features.

Conclusion

Neighbourhood conditions may influence chronic disease outcomes. Google Street View images represent an underused data resource for the construction of built environment features.

Exploring the social and neighbourhood predictors of diabetes: a comparison between Toronto and Chicago

OBJECTIVES

This report examined the impact and extent that spatial access to primary care physicians (PCPs) and social neighbourhood-/community-level factors have on diabetes prevalence for Toronto and Chicago.

METHODS

The two-step floating catchment area method was used to compute spatial access scores. Bivariate correlation and multivariate linear regression identified the factors that were associated with, and/or predicted, diabetes prevalence.

RESULTS

Potential spatial access to PCPs had no strong associations with diabetes prevalence. Low socio-economic status factors and certain ethnic groups were strongly associated with diabetes prevalence for both cities. For Toronto, South American place of birth, households below poverty and high school-level education predicted diabetes prevalence. African ethnicity and households below poverty predicted diabetes prevalence for Chicago.

CONCLUSION

Although this report found no strong association between diabetes prevalence and access to PCPs, contextual factors significant in past individual-level diabetes studies were associated with diabetes prevalence at the neighbourhood/community level for Toronto and Chicago.

Perceived Neighborhood Safety Better Predicts Risk of Mortality for Whites than Blacks

AIM

The current study had two aims: (1) to investigate whether single-item measures of subjective evaluation of neighborhood (i.e., perceived neighborhood safety and quality) predict long-term risk of mortality and (2) to test whether these associations depend on race and gender.

METHODS

The data came from the Americans' Changing Lives Study (ACL), 1986-2011, a nationally representative longitudinal cohort of 3361 Black and White adults in the USA. The main predictors of interest were perceived neighborhood safety and perceived neighborhood quality, as measured in 1986 using single items and treated as dichotomous variables. Mortality due to all internal and external causes was the main outcome. Confounders included baseline age, socioeconomic status (education, income), health behaviors (smoking, drinking, and exercise), and health (chronic medical conditions, self-rated health, and depressive symptoms). Race and gender were focal effect modifiers. Cox proportional hazard models were ran in the pooled sample and stratified by race and gender.

RESULTS

In the pooled sample, low perceived neighborhood safety and quality predicted increased risk of mortality due to all causes as well as internal causes, net of all covariates. Significant interaction was found between race and perceived neighborhood safety on all-cause mortality, indicating a stronger association for Whites compared to Blacks. Race did not interact with perceived neighborhood quality on mortality. Gender also did not interact with perceived neighborhood safety or quality on mortality. Perceived neighborhood safety and quality were not associated with mortality due to external causes.

CONCLUSION

Findings suggest that single items are appropriate for the measurement of perceived neighborhood safety and quality. Our results also suggest that perceived neighborhood safety better predicts increased risk of mortality over the course of 25 years among Whites than Blacks.

Neighbourhood Deprivation, Individual-Level and Familial-Level Socio-demographic Factors and Risk of Congenital Heart Disease: A Nationwide Study from Sweden

OBJECTIVES

The purpose of the study is to examine whether there is an association between neighbourhood deprivation and incidence of congenital heart disease (CHD), after accounting for family- and individual-level potential confounders.

METHODS

All children aged 0 to 11 years and living in Sweden (n = 748,951) were followed between January 1, 2000 and December 31, 2010. Data were analysed by multilevel logistic regression, with family- and individual-level characteristics at the first level and level of neighbourhood deprivation at the second level.

RESULTS

During the study period, among a total of 748,951 children, 1499 (0.2%) were hospitalised with CHD. Age-adjusted cumulative hospitalisation rates for CHD increased with increasing level of neighbourhood deprivation. In the study population, 1.8 per 1000 and 2.2 per 1000 children in the least and most deprived neighbourhoods, respectively, were hospitalised with CHD. The incidence of hospitalisation for CHD increased with increasing neighbourhood-level deprivation across all family and individual-level socio-demographic categories. The odds ratio (OR) for hospitalisation for CHD for those living in high-deprivation neighbourhoods versus those living in low-deprivation neighbourhoods was 1.23 (95% confidence interval (CI) = 1.04-1.46). In the full model, which took account for age, paternal and maternal individual-level socio-economic characteristics, comorbidities (e.g. maternal type 2 diabetes, OR = 3.03; maternal hypertension, OR = 2.01), and family history of CHD (OR = 3.27), the odds of CHD were slightly attenuated but did not remain significant in the most deprived neighbourhoods (OR = 1.20, 95% CI = 0.99-1.45, p = 0.057).

CONCLUSIONS

This study is the largest so far on neighbourhood influences on CHD, and the results suggest that deprived neighbourhoods have higher rates of CHD, which represents important clinical knowledge. However, the association does not seem to be independent of individual- and family-level characteristics.

Social Disorder in Adults with Type 2 Diabetes: Building on Race, Place, and Poverty

The recent resurgence of social and civic disquiet in the USA has contributed to increasing recognition that social conditions are meaningfully connected to disease and death. As a "lifestyle disease," control of diabetes requires modifications to daily activities, including healthy dietary practices, regular physical activity, and adherence to treatment regimens. One's ability to develop the healthy practices necessary to prevent or control type 2 diabetes may be influenced by a context of social disorder, the disruptive social and economic conditions that influence daily activity and, consequently, health status. In this paper, we report on our narrative review of the literature that explores the associations between social disorder and diabetes-related health outcomes within vulnerable communities. We also propose a multilevel ecosocial model for conceptualizing social disorder, specifically focusing on its role in racial disparities and its pathways to mediating diabetes outcomes.

Leveraging geotagged Twitter data to examine neighborhood happiness, diet, and physical activity

OBJECTIVES

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Residential Segregation and Diabetes Risk among Latinos

OBJECTIVE

To examine whether residence in ethnically segregated metropolitan areas is associated with increased diabetes risk for Latinos in the United States.

METHODS

Population data from the 2005 Behavioral Risk Factor Surveillance System and the 2005 American Community Survey were used to determine whether higher levels of Latino-White segregation across metropolitan statistical areas (MSAs) in the United States is associated with increased diabetes risk among Latinos (n=7462).

RESULTS

No significant relationship (P<.05) between levels of segregation and diabetes risk was observed.

CONCLUSION

The research literature examining the impact of residential segregation on health outcomes remains equivocal for Latinos.

Diagnosis and classification of autoimmune diabetes mellitus

Diabetes mellitus is increasing in prevalence worldwide. The economic costs and burden of the disease are considerable given the cardiovascular complications and co-morbidities that it may entail. Two major groups of diabetes mellitus have been defined, type 1, or immune-based, and type 2. In recent years, other subgroups have been described in-between these major groups. Correct classification of the disease is crucial in order to ascribe the most efficient preventive, diagnostic and treatment strategies for each patient. In the present review, we discuss the epidemiology, etiopathogenesis, diagnostic criteria and clinical classification of what is currently known as autoimmune diabetes. In addition, the other groups of diabetes mellitus will be regarded in relation to their pathogenesis and potential autoimmunity features.

Neighbourhood deprivation, individual-level familial and socio-demographic factors and diagnosed childhood obesity: a nationwide multilevel study from Sweden

OBJECTIVES

To examine whether there is an association between neighbourhood deprivation and diagnosed childhood obesity, after accounting for family- and individual-level socio-demographic characteristics.

METHODS

An open cohort of all children aged 0-14 years was followed between January 1, 2000 and December 31, 2010. Childhood residential locations were geocoded and classified according to neighbourhood deprivation. Data were analysed by multilevel logistic regression, with family- and individual-level characteristics at the first level and level of neighbourhood deprivation at the second level.

RESULTS

During the study period, among a total of 948,062 children, 10,799 were diagnosed with childhood obesity. Age-adjusted cumulative incidence for diagnosed childhood obesity increased with increasing level of neighbourhood deprivation. Incidence of diagnosed childhood obesity increased with increasing neighbourhood-level deprivation across all family and individual-level socio-demographic categories. The odds ratio (OR) for diagnosed childhood obesity for those living in high-deprivation neighbourhoods versus those living in low-deprivation neighbourhoods was 2.44 (95% confidence interval (CI) = 2.22-2.68). High neighbourhood deprivation remained significantly associated with higher odds of diagnosed childhood obesity after adjustment for family- and individual-level socio-demographic characteristics (OR = 1.70, 95% CI = 1.55-1.89). Age, middle level family income, maternal marital status, low level education, living in large cities, advanced paternal and maternal age, family history of obesity, parental history of diabetes, chronic obstructive pulmonary disease, alcoholism and personal history of diabetes were all associated with higher odds of diagnosed childhood obesity.

CONCLUSIONS

Our results suggest that neighbourhood characteristics affect the odds of diagnosed childhood obesity independently of family- and individual-level socio-demographic characteristics.

Regional and neighborhood disparities in the odds of type 2 diabetes: results from 5 population-based studies in Germany (DIAB-CORE consortium)

The objective of this study was to investigate the association between residential environment and type 2 diabetes. We pooled cross-sectional data from 5 population-based German studies (1997-2006): the Cardiovascular Disease, Living and Ageing in Halle Study, the Dortmund Health Study, the Heinz Nixdorf Recall Study, the Cooperative Health Research in the Region of Augsburg Study, and the Study of Health in Pomerania. The outcome of interest was the presence of self-reported type 2 diabetes. We conducted mixed logistic regression models in a hierarchical data set with 8,879 individuals aged 45-74 years on level 1; 226 neighborhoods on level 2; and 5 study regions on level 3. The analyses were adjusted for age, sex, social class, and employment status. The odds ratio for type 2 diabetes was highest in eastern Germany (odds ratio = 1.98, 95% confidence interval: 1.81, 2.14) and northeastern Germany (odds ratio = 1.58, 95% confidence interval: 1.40, 1.77) and lowest in southern Germany (reference) after adjustment for individual variables. Neighborhood unemployment rates explained a large proportion of regional differences. Individuals residing in neighborhoods with high unemployment rates had elevated odds of type 2 diabetes (odds ratio = 1.62, 95% confidence interval: 1.25, 2.09). The diverging levels of unemployment in neighborhoods and regions are an independent source of disparities in type 2 diabetes.

Early life socioeconomic indicators and risk of type 1 diabetes in children and young adults

OBJECTIVE

To examine the potential role of 2 early-life socioeconomic indicators, parental education, and crowding index, on risk of type 1 diabetes (T1DM) in patients up to age 29 years to test heterogeneity by age at onset according to the hygiene hypothesis.

STUDY DESIGN

The study base was 330 950 individuals born from 1967 to 2006 who resided in the city of Turin at any time between 1984 and 2007. Data on their early life socioeconomic position were derived from the Turin Longitudinal Study; 414 incident cases of T1DM up to age 29 years were derived from the Turin T1DM registry.

RESULTS

Socioeconomic indicators had opposing effects on risk of T1DM in different age at onset subgroups. In a Poisson regression model that included both socioeconomic indicators, there was a 3-fold greater risk of T1DM (relative risk 2.91, 95% CI 0.99-8.56) in children age 0-3 years at diagnosis living in crowded houses. In the 4- to 14-year subgroup, a low parental educational level had a protective effect (relative risk 0.50, 95% CI 0.29-0.84), and the effect of crowding nearly disappeared. In the 15- to 29-year subgroup, neither crowding nor parental educational level was clearly associated with the incidence of T1DM.

CONCLUSIONS

We provide evidence of heterogeneity by age at onset of the association between early-life socioeconomic indicators and the risk of T1DM. This finding is consistent with the hypothesis that infectious agents in the perinatal period may increase the risk, whereas in the following years they may become protective factors (hygiene hypothesis).

Neighborhood level risk factors for type 1 diabetes in youth: the SEARCH case-control study

BACKGROUND

European ecologic studies suggest higher socioeconomic status is associated with higher incidence of type 1 diabetes. Using data from a case-control study of diabetes among racially/ethnically diverse youth in the United States (U.S.), we aimed to evaluate the independent impact of neighborhood characteristics on type 1 diabetes risk. Data were available for 507 youth with type 1 diabetes and 208 healthy controls aged 10-22 years recruited in South Carolina and Colorado in 2003-2006. Home addresses were used to identify Census tracts of residence. Neighborhood-level variables were obtained from 2000 U.S. Census. Multivariate generalized linear mixed models were applied.

RESULTS

Controlling for individual risk factors (age, gender, race/ethnicity, infant feeding, birth weight, maternal age, number of household residents, parental education, income, state), higher neighborhood household income (p = 0.005), proportion of population in managerial jobs (p = 0.02), with at least high school education (p = 0.005), working outside the county (p = 0.04) and vehicle ownership (p = 0.03) were each independently associated with increased odds of type 1 diabetes. Conversely, higher percent minority population (p = 0.0003), income from social security (p = 0.002), proportion of crowded households (0.0497) and poverty (p = 0.008) were associated with a decreased odds.

CONCLUSIONS

Our study suggests that neighborhood characteristics related to greater affluence, occupation, and education are associated with higher type 1 diabetes risk. Further research is needed to understand mechanisms underlying the influence of neighborhood context.

The epidemic of type 1 diabetes: what is it telling us?

PURPOSE OF REVIEW

Type 1 diabetes (T1D) is an autoimmune disorder which affects millions around the world. The incidence of T1D in children is increasing worldwide at a rate that cannot be explained by genetics alone. This review explores the recent research regarding possible causes of this epidemic.

RECENT FINDINGS

Investigation into T1D epidemiology has recently focused on several hypotheses. These theories include the role of infections, early childhood diet, vitamin D exposure, environmental pollutants, increased height velocity, obesity, and insulin resistance in the risk for T1D. Over the past year, the evidence has strengthened for early childhood infections, dietary proteins, and insulin resistance as risk factors for T1D, but not for vitamin D exposure or environmental pollutants.

SUMMARY

Investigation into the source of the current epidemic of T1D has shed light on several possible causes, but has not provided definitive answers, yet.