Abstract
Purpose of Review
The objective of this review is to highlight the evidence on the association between contextual characteristics of residential environments and type 2 diabetes, to provide an overview of the methodological challenges and to outline potential topics for future research in this field.
Recent Findings
The link between neighborhood socioeconomic status or deprivation and diabetes prevalence, incidence, and control is robust and has been replicated in numerous settings, including in experimental and quasi-experimental studies. The association between characteristics of the built environment that affect physical activity, other aspects of the built environment, and diabetes risk is robust. There is also evidence for an association between food environments and diabetes risk, but some conflicting results have emerged in this area.
Summary
While the evidence base on the association of neighborhood socioeconomic status and built and physical environments and diabetes is large and robust, challenges remain related to confounding due to neighborhood selection. Moreover, we also outline five paths forward for future research on the role of neighborhood environments on diabetes.
Similar content being viewed by others
Avoid common mistakes on your manuscript.
Introduction
Diabetes trends in the last 30 years are worrisome [1,2,3]. Estimates vary by method and definition of diabetes, but national data from the USA show an increase from a prevalence of diagnosed and undiagnosed diabetes from 5.5% in the period 1998–1994 (9.7 million adults) to 10.8% in the period 2011–2014 (25.5 million adults) [4]. Worldwide, prevalence is around 8 to 9% in adults, and has increased twofold over the last three decades [5].
There are large disparities in diabetes burden by social factors [6]. In particular, individuals of lower socioeconomic status have higher rates of diabetes and lower rates of diabetes control [7, 8]. Moreover, there are large disparities in diabetes prevalence by country [7], across regions within countries [7, 9], and by neighborhood [6]. Cities and neighborhoods have many opportunities for the prevention of cardiovascular diseases and diabetes [10]. Urban environments are uniquely dense, involving a large number of social interactions and human-made characteristics. Thus, policy changes at this level are important and can affect substantial numbers of people [10].
Historically, and until relatively recently, most epidemiologic studies of diabetes have focused on individual-level factors as predictors of diabetes prevalence, incidence, or control. However there has been growing interest in identifying how various features of the contexts in which individuals live and work affect health [11]. Neighborhoods have emerged as important contexts because they have features that may influence the distribution of individual-level risk factors (like diet or physical activity) or may interact with individual level factors [12]. Given strong residential segregation by race and socioeconomic status, neighborhood factors may also contribute to inequities in disease risk by these factors.
This review focuses on diabetes prevalence, incidence, and control and the influence of two sets of environmental factors: neighborhood socioeconomic status and the built and physical environment. We focus on neighborhood socioeconomic status as a marker of general living conditions in neighborhoods. In particular, neighborhood socioeconomic status is usually measured using markers of the socioeconomic features of the residents in the area, aggregated into deprivation indices. Establishing the link between neighborhood socioeconomic status with health outcomes is important in that such associations can point to areas of increased disease risk, and highlight the social gradient that exists for many health outcomes [12].
The actionability of the findings related to neighborhood socioeconomic status and diabetes outcomes may be lower than for links to specific contextual characteristics that are more amenable to change [12]. For this reason, we also focus on the built and physical environments as potential pathways through which the living conditions proxied by neighborhood socioeconomic status affect diabetes risk. The built environment is defined as the set of human-made physical surroundings of a neighborhood. To better conceptualize the built environment and its association with diabetes risk, we look at characteristics of the built environment that may have an effect on diet (food environment), physical activity (physical activity environment), smoking and drinking (tobacco and alcohol environments), and stress and inflammation (noise, pollution, traffic, and other exposures).
Figure 1 shows the conceptual framework for this review. In summary, a series of built and physical and social environment factors influences the natural history of diabetes. These environmental domains are, in turn, influenced by area-level socioeconomic status. The natural history of diabetes is greatly simplified, summarized as a condition caused by a caloric intake/expenditure imbalance, leading to obesity and subsequent insulin resistance, that leads to diabetes incidence and lack of control, with subsequent complications. In this review, we focus on diabetes risk and lack of control.
Conceptual framework
The objective of this narrative review is to provide an overview of the evidence base regarding the association between contextual characteristics of urban environments and type 2 diabetes risk and control. This review is structured in four parts. First, we provide a conceptual overview of the overall association between neighborhood socioeconomic status and the diabetes risk and control. Second, we review domains of the built and physical environment associated with these same diabetes outcomes. Third, we summarize methodological challenges in the study of this association. Last, we outline paths forward for future research in this area.
Neighborhood Socioeconomic Status
The link between neighborhood socioeconomic status and diabetes prevalence, incidence, or control is robust. Table 1 summarizes the results of 24 studies across several settings, published since 2010. Most studies used four or more neighborhood socioeconomic status-related indicators (education, income, employment, etc.) to create a composite index. The advantage of doing this is that multiple items will provide a multidimensional and more robust measure of the construct than any single item. Neighborhood deprivation has been associated with increased diabetes prevalence and incidence and worse rates of control. A series of studies using electronic health records (EHR) from half a million people in urban areas of Sweden have looked at the association of neighborhood deprivation, as measured using a principal component analysis of four variables, and diabetes incidence, finding consistently that increased neighborhood deprivation was associated with increased incidence after adjusting for covariates, including individual-level socioeconomic status (SES) [19, 20]. Other studies in the USA, Germany, and Australia have also found increased diabetes prevalence with increased unemployment and lower income [28, 29], increased poverty concentration [23], and multi-indicator measures of deprivation [26, 27, 30]. Last, other studies have looked at the association of deprivation and rates of control, finding that people with diabetes living in areas with higher deprivation are less likely to achieve diabetes control, as measured by HbA1c levels [17, 18, 30]. Other studies have also found associations between lower neighborhood socioeconomic status (or higher neighborhood deprivation) and complications among persons with diabetes, such as cardiovascular disease [13, 14], diabetes-related eye disease [15], and foot ulcer [16].
This association between neighborhood socioeconomic status and diabetes has also been found in experimental studies. In an analysis of the Moving To Opportunity study, where families were randomized to either receive a housing voucher to move to a low poverty area, a housing voucher to move freely, or no voucher, diabetes prevalence was lower in people who were assigned to the voucher to move to a low poverty area [24]. In an analysis of a natural experiment in Sweden, investigators followed refugees that were relocated to different areas of the country, finding that those that were relocated to areas with higher neighborhood deprivation had a higher incidence of diabetes [21]. Given that refugees had no control over where they were being relocated, this analysis is similar to a randomized experiment (see the “Methodological Challenges” section below).
Built and Physical Environment
Food Environment
The association between healthier food environments and diabetes risk and control has been examined using numerous measures and study designs (Table 2). Food environments have been typically operationalized as favorable for diet/health (e.g., access to healthier foods at farmers markets and supermarkets) or unfavorable for diet/health (access and density of fast food and convenience stores). Favorable or unfavorable has been operationalized either as single items or combined into a composite index. Results in this area have been somewhat mixed. A recent meta-analysis that included studies up to June 2017 concluded that there was weak to null evidence that a greater availability of grocery stores or supermarkets was associated with lower diabetes risk [59].
A few studies published since then reported on change in environment and change in diabetes risk or control have also found mixed results. Two studies looking at change in the presence of supermarkets found lower diabetes prevalence [32] and marginally improved diabetes control [31] in people living in areas where new supermarkets had opened. However, these changes were small in magnitude. Other studies have found no changes in diabetes incidence in people living in areas with a higher density of healthier food establishments; however, incidence was lower in people living in areas with higher perceived healthy food availability [37].
Studies of the unfavorable food environment and diabetes have been somewhat more promising. Diabetes incidence and prevalence have been found to be higher in people who stay or move into areas with more health-harming food outlets (convenience stores and fast-food outlets) [35], or who live in areas with more fast-food outlets [33, 34]. However, a study conducted in the USA found that diabetes incidence was also higher in adults living both in areas with greater density of unfavorable stores (defined as convenience and fast food outlets) and in areas with higher density of favorable (“healthy”) food stores (although confidence intervals included the null) [36].
Physical Activity Environment
Physical activity environments appear to be somewhat more consistently associated with diabetes risk (Table 2) [59]. Walkability has been measured and used in several studies of the built environment and diabetes. Walkability is determined by higher population density, greater mix of land use (commercial, residential, parks), and greater street connectivity. In several studies, walkability of the environment has been associated with lower diabetes prevalence [42] and incidence [39,40,41]. Recent studies have found that the incidence of diabetes is lower in more walkable areas [39], and that this association was stronger in immigrants and people living in lower neighborhood socioeconomic status areas [40], but this association may be partially explained by individual-level factors [41]. Green and open spaces have also been associated with a lower diabetes prevalence and incidence. Two studies in the UK and two in Australia have found associations between higher availability of green or open spaces and lower diabetes prevalence [45, 46] and incidence [44, 47].
Other Physical Environment Characteristics
Other built environment factors also seem to be associated with diabetes risk (Table 2). Higher exposure to particulate matter, nitrous oxide, and traffic intensity have been associated with higher diabetes prevalence [50,51,52, 54] and incidence [48, 52], poorer diabetes control [51], and higher diabetes-related mortality [49, 53], though the magnitude of these associations has typically been small. A hypothesized general pathway in these studies is air pollution’s effects on inflammation and oxidative stress and subsequent increased insulin resistance [60].
Recent studies have also found an association between diabetes markers and novel built environment measures such as traffic-related noise [55], the burden of abandoned coal mine lands [58], and even the average slope (steepness of the streets in the neighborhood) of neighborhoods [57]. Some of these factors may operate directly (by increasing inflammation, as with air pollution), or through more indirect mechanisms (such as effects on physical activity levels).
Methodological Challenges
The literature studying the link between residential environments and diabetes is subject to similar methodological challenges as other epidemiologic studies of neighborhoods and neighborhood-level exposures. One of the main challenges is the limited ability to address confounding by neighborhood selection. The issue of neighborhood selection arises when individual characteristics determine residential location, through either segregation or individual preferences [11]. If the same characteristics that lead to segregation are also associated with diabetes risk (for example, individual socioeconomic status), the association between residential environments and diabetes may be confounded and be partially or fully explained by other factors. There are two main ways of correcting for this type of bias. One approach is via randomization or pseudo-randomization. These methods have been employed by studies such as the Moving To Opportunity study [24] and natural experiments [21]. The key idea in these settings is that individuals have no control regarding their neighborhood assignment, thus removing the effect of potential confounders that would be related to neighborhood selection. However, randomization may not be able to properly account for exposures occurring before randomization that may carry over to the new neighborhoods [61]. Alternatively, there are standard statistical methods to adjust for or match based on factors leading to neighborhood selection. However, these types of studies can be challenging if important factors are unknown or unmeasured, or if there is no overlap between the residential environments where individuals of, lower and higher SES live [11]. Alternatively, the use of within-person effects (exploiting changes to exposures due to mobility to a new area) can help control for this type of confounding. Some studies have found that a large portion of the association between environmental factors and cardiometabolic factors may be due to neighborhood selection [62, 63]. However, as with randomized experiments of mobility, some exposures may have long-term effects that are not washed out with mobility [61].
Future Research
Here, we outline five potential paths forward in the study of the links between residential environments and diabetes risk: (1) increased attention to the issue of scale, (2) consideration of the consequences of neighborhood dynamics, (3) possible use of neighborhood variables to improve risk prediction or treatment decisions, (4) new research to understand the contributions of neighborhoods to the emergence of health inequalities, and (5) increased leveraging of natural experiments and evaluation of policy interventions.
The Issue of Scale
The first path forward for future research is to gain an understanding of whether neighborhoods are the most appropriate or relevant spatial context for explaining diabetes risk. For example, previous research has shown that dietary patterns may not vary as much between neighborhoods as they do between nations [64]. It is likely that we should consider multiple levels of analysis simultaneously (individual, neighborhood, city, region, or country).
Recommendation
Adequate theorization about the construct of interest (e.g., the food system/environment) should guide data collection. If the neighborhood construct of interest has a strong city- or nation-wide component, studies in multiple cities or countries would be warranted. Cross-national comparisons of neighborhood or city characteristics and health are becoming more common and should be encouraged.
Neighborhood Dynamics and Change
A second path forward for future research is to conceptualize and measure neighborhoods as dynamic environments that change over time [65]. A static characterization of neighborhoods, where the exposure is assessed only once, limits inferences on the dynamic links between the environment and diabetes. First, it ignores the changes that may occur from the measurement point to when diabetes occurs, potentially resulting in misclassification of the exposure. Second, studies of neighborhood characteristics measured at a single time point are especially subject to the issue of neighborhood selection: are the observed health patterns because of contextual characteristics or are they just the reflection of differential neighborhood selection patterns?
Further, care must be paid to avoid the assumption that neighborhood mobility is akin to neighborhood change. Many studies of neighborhood change operationalize the change in the exposure as neighborhood mobility. While this is a useful device to be able to study longitudinal changes in the exposure side, it comes with its own challenges derived from (a) who moves and why, in the case of non-randomized studies, and (b) whether neighborhood mobility can be translated into feasible or helpful policy intervention. Some re-analyses of the Moving To Opportunity study have showed how, while the intervention may have been beneficial for diabetes and obesity [24], it may have been harmful for other health outcomes [66]. This may reflect unintended consequences and/or neighborhood stickiness (carry-over effects) [61], as the effects of the study intervention on some outcomes were differential by age (stronger and more positive effects in people that moved earlier in life [67]).
Recommendation
Future studies of neighborhood characteristics and diabetes should explicitly measure and evaluate changes in neighborhood characteristics. Longitudinal data sources for neighborhood characteristics should be leveraged to understand the consequences of neighborhood change, not just residential mobility. In the case of residential mobility studies, care must be paid to the issue of stickiness and whether neighborhood exposures before a certain age may have long-term consequences.
Neighborhood-Level Factors to Improve Risk Prediction and Clinical Practice
A third path forward for future research is to evaluate the utility of neighborhood measures in diabetes risk prediction or clinical practice. Recent research has shown that risk prediction models for diabetes underestimate risk for people living in lower neighborhood socioeconomic status areas and overestimated risk for people living in higher neighborhood socioeconomic status areas [68], while adding SES or neighborhood socioeconomic status to risk prediction models did not significantly improve prediction [68]. However, a systematic examination of diabetes control in electronic health records can help identify neighborhoods where community-level interventions for improved diabetes treatment are in need [69], or be used as a systematic tool for surveillance [70]. Moreover, electronic health records can be a powerful tool to improve diabetes control and quality of care [71].
Recommendation
Future studies should assess whether diabetes control is improved by incorporating measures of the environment into electronic health records to better guide treatments. An Institute of Medicine report from 2014 [72] recommends collecting neighborhood-level median household income and geocoded residential address, that can then be used to derive measures of the built environment [72]. These measures can then help in identifying access to resources and potential hazards, leading to improved quality of care and subsequent improved diabetes control.
Other techniques, such as asset mapping, allow for patients to develop a systematic exploration of resources available in their community [73]. These techniques, if implemented in health systems, could help patients understand the resources available to improve diabetes prevention and/or control [73].
Contribution of Neighborhoods to Health Inequalities
A fourth path forward for future research is to better understand the interactions between individual characteristics and contextual characteristics, and how neighborhoods contribute to inequalities by individual social factors such as race or SES. Recent research has shown that part of the inequality in cardiovascular disease by SES is explained by differential exposure to lower neighborhood socioeconomic status neighborhoods and increased prevalence of diabetes and hypertension in lower SES individuals [74]. Moreover, research in Canada has shown that the association between walkability and diabetes is stronger in new residents (immigrants) as compared to long-term residents, and that this association was even stronger in recent immigrants living in high poverty areas [40]. A potential mechanism mediating these disparities may be inflammation, that has been found to account for at least a third of the increased risk of diabetes in people of lower SES [75].
Recommendation
Future studies should try to decompose the inequalities in diabetes by social factors (including SES and race) and understand the contribution of neighborhood characteristics to the widening of these inequalities. Understanding the contribution of neighborhood characteristics to inequalities in diabetes can also provide policy-actionable results.
Leveraging Natural Experiments and Evaluating Policy
A final potential path forward for future research is for the field to better leverage natural experiments and policy evaluation. As mentioned before in this review, the Moving To Opportunity study evaluated the effects of moving to a lower poverty neighborhood, finding lower HbA1c in people (with and without diabetes) that were randomized to this intervention [24], while refugee relocation in Sweden to wealthier areas reduced the incidence of diabetes [21]. Other natural experiments at higher scales than the neighborhood have found decreased rates of diabetes incidence and mortality after the Special Period of Cuba [76], and increased rates of diabetes in those exposed in-utero to the Ukrainian famine in the 1930s [77] and other famines [78]. While the intensity of neighborhood exposures tends to be of lower magnitude than the upstream political changes described above, some actually represent large-scale relocation events. For example, the relocation of hurricane Katrina survivors to areas with higher sprawl was associated with body weight gain [79].
Recommendation
Surveillance and formal evaluation of policy changes [80] and other type of macrosocial exposures [81] can offer opportunities to study the effects of neighborhoods on health. Examples on the impact of policies on the built environment and obesity-related outcomes are available [82], but they should be expanded to diabetes risk and control.
Limitations
This review is narrative in nature, and therefore there exists the possibility that we have missed some relevant studies (see the study by den Braver et al. for a comprehensive review that also includes county-level studies [59]). We could not find any studies of the association between local alcohol and tobacco availability and diabetes risk and control. We also did not review the association between social environment characteristics and diabetes, as these have been recently reviewed [6, 83,84,85]. It is important to note that distinction between the social and the built environment can be fuzzy, as both influence each other. A good example of a factor that may be simultaneously considered a feature of the built and social environments is foreclosure, a topic that has received recent attention in relation to diabetes [86, 87]. We have also not reviewed neighborhood determinants of obesity, the main risk factor for diabetes, as this literature has been reviewed elsewhere [84, 88, 89].
Conclusion
In this review, we summarized the evidence regarding the overall association between neighborhood socioeconomic status and diabetes risk and control, as well as the specific associations between built environment characteristics and diabetes risk and control. To address the methodological shortcomings of previous studies, future research should also consider looking at more upstream levels of inference, understanding the role of neighborhood change in generating diabetes risk, further explore the potential for the use of neighborhood characteristics in diabetes risk prediction and treatment decisions, examine the contributions of neighborhood factors to health inequalities, and leverage natural experiments and policy evaluation to better understand how neighborhoods contribute to diabetes risk and poor control.
References
Selvin E, Parrinello CM, Sacks DB, Coresh J. TRends in prevalence and control of diabetes in the United States, 1988–1994 and 1999–2010. Ann Intern Med. 2014;160(8):517–25.
Menke A, Rust KF, Fradkin J, Cheng YJ, Cowie CC. Associations between trends in race/ethnicity, aging, and body mass index with diabetes prevalence in the United States: a series of cross-sectional studies. Ann Intern Med. 2014;161(5):328–35.
Menke A, Casagrande S, Geiss L, Cowie CC. Prevalence of and trends in diabetes among adults in the United States, 1988-2012. JAMA. 2015;314(10):1021–9.
Selvin E, Wang D, Lee AK, Bergenstal RM, Coresh J. Identifying trends in undiagnosed diabetes in US. adults by using a confirmatory definition: a cross-sectional study. Ann Intern Med. 2017;167(11):769–76.
NCD-RisC. Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4.4 million participants. Lancet. 2016;387(10027):1513–30.
Gary-Webb TL, Suglia SF, Tehranifar P. Social epidemiology of diabetes and associated conditions. Curr Diab Rep. 2013;13(6):850–9.
Espelt A, Borrell C, Roskam AJ, Rodríguez-Sanz M, Stirbu I, Dalmau-Bueno A, et al. Socioeconomic inequalities in diabetes mellitus across Europe at the beginning of the 21st century. Diabetologia. 2008;51(11):1971–9.
Agardh E, Allebeck P, Hallqvist J, Moradi T, Sidorchuk A. Type 2 diabetes incidence and socio-economic position: a systematic review and meta-analysis. Int J Epidemiol. 2011;40(3):804–18.
Dandona L, Dandona R, Kumar GA, Shukla D, Paul VK, Balakrishnan K, et al. Nations within a nation: variations in epidemiological transition across the states of India, 1990–2016 in the global burden of disease study. Lancet. 2017;390(10111):2437–60.
Franco M, Bilal U, Diez-Roux AV. Preventing non-communicable diseases through structural changes in urban environments. J Epidemiol Community Health. 2015;69(6):509–11.
Oakes JM, Andrade KE, Biyoow IM, Cowan LT. Twenty years of neighborhood effect research: an assessment. Curr Epide Rep. 2015;2(1):80–7.
Diez-Roux AV. Neighborhoods and health: where are we and were do we go from here?: environnement résidentiel et santé: état de la question et perspectives pour le futur. Rev Epidemiol Sante Publique. 2007;55(1):13–21.
Chaikiat Å, Li X, Bennet L, Sundquist K. Neighborhood deprivation and inequities in coronary heart disease among patients with diabetes mellitus: a multilevel study of 334,000 patients. Health Place. 2012;18(4):877–82.
Booth GL, Bishara P, Lipscombe LL, Shah BR, Feig DS, Bhattacharyya O, et al. Universal drug coverage and socioeconomic disparities in major diabetes outcomes. Diabetes Care. 2012;35(11):2257–64.
Hamano T, Li X, Tanito M, Nabika T, Shiwaku K, Sundquist J, et al. Neighborhood deprivation and risk of age-related eye diseases: a follow-up study in Sweden. Ophthalmic Epidemiol. 2015;22(5):308–20.
Leese GP, Feng Z, Leese RM, Dibben C, Emslie-Smith A. Impact of health-care accessibility and social deprivation on diabetes related foot disease. Diabet Med. 2013;30(4):484–90.
Wilf-Miron R, Peled R, Yaari E, Shem-Tov O, Weinner VA, Porath A, et al. Disparities in diabetes care: role of the patient's socio-demographic characteristics. BMC Public Health. 2010;10:729.
James GD, Baker P, Badrick E, Mathur R, Hull S, Robson J. Ethnic and social disparity in glycaemic control in type 2 diabetes; cohort study in general practice 2004-9. J R Soc Med. 2012;105(7):300–8.
Mezuk B, Chaikiat Å, Li X, Sundquist J, Kendler KS, Sundquist K. Depression, neighborhood deprivation and risk of type 2 diabetes. Health Place. 2013;23:63–9.
Sundquist K, Eriksson U, Mezuk B, Ohlsson H. Neighborhood walkability, deprivation and incidence of type 2 diabetes: a population-based study on 512,061 Swedish adults. Health Place. 2015;31:24–30.
White JS, Hamad R, Li X, Basu S, Ohlsson H, Sundquist J, et al. Long-term effects of neighbourhood deprivation on diabetes risk: quasi-experimental evidence from a refugee dispersal policy in Sweden. Lancet Diabetes Endocrinol. 2016;4(6):517–24.
Müller G, Wellmann J, Hartwig S, Greiser KH, Moebus S, Jöckel KH, et al. Association of neighbourhood unemployment rate with incident type 2 diabetes mellitus in five German regions. Diabet Med. 2015;32(8):1017–22.
Gaskin DJ, Thorpe RJ, McGinty EE, Bower K, Rohde C, Young JH, et al. Disparities in diabetes: the nexus of race, poverty, and place. Am J Public Health. 2014;104(11):2147–55.
Ludwig J, Sanbonmatsu L, Gennetian L, Adam E, Duncan GJ, Katz LF, et al. Neighborhoods, obesity, and diabetes—a randomized social experiment. N Engl J Med. 2011;365(16):1509–19.
Mirowsky JE, Devlin RB, Diaz-Sanchez D, Cascio W, Grabich SC, Haynes C, et al. A novel approach for measuring residential socioeconomic factors associated with cardiovascular and metabolic health. J Expo Sci Environ Epidemiol. 2017;27(3):281–9.
Rachele JN, Giles-Corti B, Turrell G. Neighbourhood disadvantage and self-reported type 2 diabetes, heart disease and comorbidity: a cross-sectional multilevel study. Ann Epidemiol. 2016;26(2):146–50.
Garcia L, Lee A, Zeki Al Hazzouri A, Neuhaus J, Epstein M, Haan M. The impact of neighborhood socioeconomic position on prevalence of diabetes and prediabetes in older Latinos: the Sacramento area Latino study on aging. Hisp Health Care Int. 2015;13(2):77–85.
Müller G, Kluttig A, Greiser KH, Moebus S, Slomiany U, Schipf S, et al. Regional and neighborhood disparities in the odds of type 2 diabetes: results from 5 population-based studies in Germany (DIAB-CORE consortium). Am J Epidemiol. 2013;178(2):221–30.
Mueller G, Berger K. The influence of neighbourhood deprivation on the prevalence of diabetes in 25- to 74-year-old individuals: first results from the Dortmund health study. Diabet Med. 2012;29(6):831–3.
Corriere MD, Yao W, Xue QL, Cappola AR, Fried LP, Thorpe RJ, et al. The association of neighborhood characteristics with obesity and metabolic conditions in older women. J Nutr Health Aging. 2014;18(9):792–8.
Zhang YT, Mujahid MS, Laraia BA, Warton EM, Blanchard SD, Moffet HH, et al. Association between neighborhood supermarket presence and glycated hemoglobin levels among patients with type 2 diabetes mellitus. Am J Epidemiol. 2017;185(12):1297–303.
Richardson AS, Ghosh-Dastidar M, Beckman R, Flórez KR, DeSantis A, Collins RL, et al. Can the introduction of a full-service supermarket in a food desert improve residents' economic status and health? Ann Epidemiol. 2017;27(12):771–6.
Polsky JY, Moineddin R, Glazier RH, Dunn JR, Booth GL. Relative and absolute availability of fast-food restaurants in relation to the development of diabetes: a population-based cohort study. Can J Public Health. 2016;107(Suppl 1):5312.
Bodicoat DH, Carter P, Comber A, Edwardson C, Gray LJ, Hill S, et al. Is the number of fast-food outlets in the neighbourhood related to screen-detected type 2 diabetes mellitus and associated risk factors? Public Health Nutr. 2015;18(9):1698–705.
Mezuk B, Li X, Cederin K, Rice K, Sundquist J, Sundquist K. Beyond access: characteristics of the food environment and risk of diabetes. Am J Epidemiol. 2016;183(12):1129–37.
Gebreab SY, Hickson DA, Sims M, Wyatt SB, Davis SK, Correa A, et al. Neighborhood social and physical environments and type 2 diabetes mellitus in African Americans: the Jackson heart study. Health Place. 2017;43:128–37.
Christine PJ, Auchincloss AH, Bertoni AG, Carnethon MR, Sanchez BN, Moore K, et al. Longitudinal associations between neighborhood physical and social environments and incident type 2 diabetes mellitus: the multi-ethnic study of atherosclerosis (MESA). JAMA Intern Med. 2015;175(8):1311–20.
Tabaei BP, Rundle AG, Wu WY, Horowitz CR, Mayer V, Sheehan DM, et al. Associations of residential socioeconomic, food, and built environments with glycemic control in persons with diabetes in new York City from 2007–2013. Am J Epidemiol. 2018;187(4):736–45.
Creatore MI, Glazier RH, Moineddin R, Fazli GS, Johns A, Gozdyra P, et al. Association of neighborhood walkability with change in overweight, obesity, and diabetes. JAMA. 2016;315(20):2211–20.
Booth GL, Creatore MI, Moineddin R, Gozdyra P, Weyman JT, Matheson FI, et al. Unwalkable neighborhoods, poverty, and the risk of diabetes among recent immigrants to Canada compared with long-term residents. Diabetes Care. 2013;36(2):302–8.
Sundquist K, Eriksson U, Mezuk B, Ohlsson H. Neighborhood walkability, deprivation and incidence of type 2 diabetes: a population-based study on 512,061 Swedish adults. Health Place. 2015;31:24–30.
Loo CK, Greiver M, Aliarzadeh B, Lewis D. Association between neighbourhood walkability and metabolic risk factors influenced by physical activity: a cross-sectional study of adults in Toronto, Canada. BMJ Open. 2017;7(4):e013889.
Müller-Riemenschneider F, Pereira G, Villanueva K, Christian H, Knuiman M, Giles-Corti B, et al. Neighborhood walkability and cardiometabolic risk factors in Australian adults: an observational study. BMC Public Health. 2013;13(1):755.
Paquet C, Coffee NT, Haren MT, Howard NJ, Adams RJ, Taylor AW, et al. Food environment, walkability, and public open spaces are associated with incident development of cardio-metabolic risk factors in a biomedical cohort. Health & Place. 2014;28:173–6.
Astell-Burt T, Feng X, Kolt GS. Is neighborhood green space associated with a lower risk of type 2 diabetes? Evidence from 267,072 Australians. Diabetes Care. 2014;37(1):197–201.
Bodicoat DH, O'Donovan G, Dalton AM, Gray LJ, Yates T, Edwardson C, et al. The association between neighbourhood greenspace and type 2 diabetes in a large cross-sectional study. BMJ Open. 2014;4(12):e006076.
Dalton AM, Jones AP, Sharp SJ, Cooper AJ, Griffin S, Wareham NJ. Residential neighbourhood greenspace is associated with reduced risk of incident diabetes in older people: a prospective cohort study. BMC Public Health. 2016;16(1):1171.
Weinmayr G, Hennig F, Fuks K, Nonnemacher M, Jakobs H, Mohlenkamp S, et al. Long-term exposure to fine particulate matter and incidence of type 2 diabetes mellitus in a cohort study: effects of total and traffic-specific air pollution. Environ Health. 2015;14:53.
Brook RD, Cakmak S, Turner MC, Brook JR, Crouse DL, Peters PA, et al. Long-term fine particulate matter exposure and mortality from diabetes in Canada. Diabetes Care. 2013;36(10):3313–20.
Strak M, Janssen N, Beelen R, Schmitz O, Vaartjes I, Karssenberg D, et al. Long-term exposure to particulate matter, NO2 and the oxidative potential of particulates and diabetes prevalence in a large national health survey. Environ Int. 2017;108:228–36.
Honda T, Pun VC, Manjourides J, Suh H. Associations between long-term exposure to air pollution, glycosylated hemoglobin and diabetes. Int J Hyg Environ Health. 2017;220(7):1124–32.
Qiu H, Schooling CM, Sun S, Tsang H, Yang Y, Lee RS, et al. Long-term exposure to fine particulate matter air pollution and type 2 diabetes mellitus in elderly: a cohort study in Hong Kong. Environ Int. 2018;113:350–6.
Raaschou-Nielsen O, Sorensen M, Ketzel M, Hertel O, Loft S, Tjonneland A, et al. Long-term exposure to traffic-related air pollution and diabetes-associated mortality: a cohort study. Diabetologia. 2013;56(1):36–46.
Heidemann C, Niemann H, Paprott R, Du Y, Rathmann W, Scheidt-Nave C. Residential traffic and incidence of type 2 diabetes: the German health interview and examination surveys. Diabet Med. 2014;31(10):1269–76.
Clark C, Sbihi H, Tamburic L, Brauer M, Frank LD, Davies HW. Association of long-term exposure to transportation noise and traffic-related air pollution with the incidence of diabetes: a prospective cohort study. Environ Health Perspect. 2017;125(8):087025.
Sorensen M, Andersen ZJ, Nordsborg RB, Becker T, Tjonneland A, Overvad K, et al. Long-term exposure to road traffic noise and incident diabetes: a cohort study. Environ Health Perspect. 2013;121(2):217–22.
Fujiwara T, Takamoto I, Amemiya A, Hanazato M, Suzuki N, Nagamine Y, et al. Is a hilly neighborhood environment associated with diabetes mellitus among older people? Results from the JAGES 2010 study. Soc Sci Med. 2017;182:45–51.
Liu AY, Curriero FC, Glass TA, Stewart WF, Schwartz BS. The contextual influence of coal abandoned mine lands in communities and type 2 diabetes in Pennsylvania. Health Place. 2013;22:115–22.
den Braver NR, Lakerveld J, Rutters F, Schoonmade LJ, Brug J, Beulens JWJ. Built environmental characteristics and diabetes: a systematic review and meta-analysis. BMC Med. 2018;16(1):12.
Brook RD, Rajagopalan S, Pope CA, Brook JR, Bhatnagar A, Diez-Roux AV, et al. Particulate matter air pollution and cardiovascular disease. An update to the scientific statement from the American Heart Association. Circulation. 2010;121(21):2331–78.
Glass TA, Bilal U. Are neighborhoods causal? Complications arising from the 'stickiness' of ZNA. Soc Sci Med. 2016;166:244–53.
Braun LM, Rodriguez DA, Song Y, Meyer KA, Lewis CE, Reis JP, et al. Changes in walking, body mass index, and cardiometabolic risk factors following residential relocation: longitudinal results from the CARDIA study. J Transp Health. 2016;3(4):426–39.
Braun LM, Rodríguez DA, Evenson KR, Hirsch JA, Moore KA, Diez Roux AV. Walkability and cardiometabolic risk factors: cross-sectional and longitudinal associations from the multi-ethnic study of atherosclerosis. Health Place. 2016;39:9–17.
Cummins S, Macintyre S. Food environments and obesity - neighborhood or nation? Int J Epidemiol. 2006;25.
van Ham M, Manley D, Bailey N, Simpson L, Maclennan D. Understanding neighbourhood dynamics: new insights for neighbourhood effects research. Netherlands: Springer; 2012.
Osypuk TL, Tchetgen E, Acevedo-Garcia D, et al. Differential mental health effects of neighborhood relocation among youth in vulnerable families: results from a randomized trial. Arch Gen Psychiatry. 2012;69(12):1284–94.
Chetty R, Hendren N, Katz LF. The effects of exposure to better neighborhoods on children: new evidence from the moving to opportunity experiment. Am Econ Rev. 2016;106(4):855–902.
Christine PJ, Young R, Adar SD, Bertoni AG, Heisler M, Carnethon MR, et al. Individual- and area-level SES in diabetes risk prediction: the multi-ethnic study of atherosclerosis. Am J Prev Med. 2017;53(2):201–9.
Gabert R, Thomson B, Gakidou E, Roth G. Identifying high-risk neighborhoods using electronic medical records: a population-based approach for targeting diabetes prevention and treatment interventions. PLoS One. 2016;11(7):e0159227.
Perlman SE, McVeigh KH, Thorpe LE, Jacobson L, Greene CM, Gwynn RC. Innovations in population health surveillance: using electronic health records for chronic disease surveillance. Am J Public Health. 2017;107(6):853–7.
Cebul RD, Love TE, Jain AK, Hebert CJ. Electronic health records and quality of diabetes care. N Engl J Med. 2011;365(9):825–33.
Io M. Capturing social and behavioral domains and measures in electronic health records: phase 2. Washington, DC: The National Academies Press; 2014.
Florian J, Roy NM, Quintiliani LM, Truong V, Feng Y, Bloch PP, et al. Using Photovoice and asset mapping to inform a community-based diabetes intervention, Boston, Massachusetts, 2015. Prev Chronic Dis. 2016;13:E107.
Hussein M, Diez Roux AV, Mujahid MS, Hastert TA, Kershaw KN, Bertoni AG, Baylin A Unequal exposure or unequal vulnerability? Contributions of neighborhood conditions and cardiovascular risk factors to socioeconomic inequality in incident cardiovascular disease in the multi-ethnic study of atherosclerosis. Am J Epidemiol. 2017.
Stringhini S, Batty GD, Bovet P, Shipley MJ, Marmot MG, Kumari M, et al. Association of lifecourse socioeconomic status with chronic inflammation and type 2 diabetes risk: the Whitehall II prospective cohort study. PLoS Med. 2013;10(7):e1001479.
Franco M, Bilal U, Ordunez P, Benet M, Morejon A, Caballero B, et al. Population-wide weight loss and regain in relation to diabetes burden and cardiovascular mortality in Cuba 1980-2010: repeated cross sectional surveys and ecological comparison of secular trends. BMJ. 2013;346:f1515.
Lumey LH, Khalangot MD, Vaiserman AM. Association between type 2 diabetes and prenatal exposure to the Ukraine famine of 1932-33: a retrospective cohort study. Lancet Diabetes Endocrinol. 2015;3(10):787–94.
de Rooij SR, Roseboom TJ, Painter RC. Famines in the last 100 years: implications for diabetes. Curr Diab Rep. 2014;14(10):536.
Arcaya M, James P, Rhodes JE, Waters MC, Subramanian SV. Urban sprawl and body mass index among displaced hurricane Katrina survivors. Prev Med. 2014;65:40–6.
Basu S, Meghani A, Siddiqi A. Evaluating the health impact of large-scale public policy changes: classical and novel approaches. Annu Rev Public Health. 2017;38:351–70.
Craig P, Katikireddi SV, Leyland A, Popham F. Natural experiments: an overview of methods, approaches, and contributions to public health intervention research. Annu Rev Public Health. 2017;38:39–56.
Mayne S, Auchincloss A, Michael Y. Impact of policy and built environment changes on obesity-related outcomes: a systematic review of naturally occurring experiments. Obes Rev. 2015;16(5):362–75.
Steve SL, Tung EL, Schlichtman JJ, Peek ME. Social disorder in adults with type 2 diabetes: building on race, place, and poverty. Curr Diab Rep. 2016;16(8):72.
Kershaw KN, Pender AE. Racial/ethnic residential segregation, obesity, and diabetes mellitus. Curr Diab Rep. 2016;16(11):108.
Flor CR, Baldoni NR, Aquino JA, Baldoni AO, Fabbro ALD, Figueiredo RC et al. What is the association between social capital and diabetes mellitus? A systematic review. Diabetes Metab Syndr. 2018.
Downing J, Laraia B, Rodriguez H, Dow WH, Adler N, Schillinger D, et al. Beyond the great recession: was the foreclosure crisis harmful to the health of individuals with diabetes? Am J Epidemiol. 2017;185(6):429–35.
Christine PJ, Moore K, Crawford ND, Barrientos-Gutierrez T, Sánchez BN, Seeman T, et al. Exposure to neighborhood foreclosures and changes in cardiometabolic health: results from MESA. Am J Epidemiol. 2017;185(2):106–14.
Noriea AH, Patel FN, Werner DA, Peek ME. A narrative review of physician perspectives regarding the social and environmental determinants of obesity. Curr Diab Rep. 2018;18(5):24.
Krueger PM, Reither EN. Mind the gap: race/ethnic and socioeconomic disparities in obesity. Curr Diab Rep. 2015;15(11):95.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
Usama Bilal, Amy H. Auchincloss, and Ana V. Diez-Roux declare that they have no conflict of interest.
Human and Animal Rights and Informed Consent
This article does not contain any studies with human or animal subjects performed by any of the authors.
Additional information
This article is part of the Topical Collection on Diabetes Epidemiology
Rights and permissions
About this article
Cite this article
Bilal, U., Auchincloss, A.H. & Diez-Roux, A.V. Neighborhood Environments and Diabetes Risk and Control. Curr Diab Rep 18, 62 (2018). https://doi.org/10.1007/s11892-018-1032-2
Published:
DOI: https://doi.org/10.1007/s11892-018-1032-2