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Kusuma D, Atanasova P, Pineda E, Anjana RM, De Silva L, Hanif AAM, Hasan M, Hossain MM, Indrawansa S, Jayamanne D, Jha S, Kasturiratne A, Katulanda P, Khawaja KI, Kumarendran B, Mridha MK, Rajakaruna V, Chambers JC, Frost G, Sassi F, Miraldo M. Food environment and diabetes mellitus in South Asia: A geospatial analysis of health outcome data. PLoS Med 2022; 19:e1003970. [PMID: 35472059 PMCID: PMC9041866 DOI: 10.1371/journal.pmed.1003970] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Accepted: 03/18/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND The global epidemic of type 2 diabetes mellitus (T2DM) renders its prevention a major public health priority. A key risk factor of diabetes is obesity and poor diets. Food environments have been found to influence people's diets and obesity, positing they may play a role in the prevalence of diabetes. Yet, there is scant evidence on the role they may play in the context of low- and middle-income countries (LMICs). We examined the associations of food environments on T2DM among adults and its heterogeneity by income and sex. METHODS AND FINDINGS We linked individual health outcome data of 12,167 individuals from a network of health surveillance sites (the South Asia Biobank) to the density and proximity of food outlets geolocated around their homes from environment mapping survey data collected between 2018 and 2020 in Bangladesh and Sri Lanka. Density was defined as share of food outlets within 300 m from study participant's home, and proximity was defined as having at least 1 outlet within 100 m from home. The outcome variables include fasting blood glucose level, high blood glucose, and self-reported diagnosed diabetes. Control variables included demographics, socioeconomic status (SES), health status, healthcare utilization, and physical activities. Data were analyzed in ArcMap 10.3 and STATA 15.1. A higher share of fast-food restaurants (FFR) was associated with a 9.21 mg/dl blood glucose increase (95% CI: 0.17, 18.24; p < 0.05). Having at least 1 FFR in the proximity was associated with 2.14 mg/dl blood glucose increase (CI: 0.55, 3.72; p < 0.01). A 1% increase in the share of FFR near an individual's home was associated with 8% increase in the probability of being clinically diagnosed as a diabetic (average marginal effects (AMEs): 0.08; CI: 0.02, 0.14; p < 0.05). Having at least 1 FFR near home was associated with 16% (odds ratio [OR]: 1.16; CI: 1.01, 1.33; p < 0.05) and 19% (OR: 1.19; CI: 1.03, 1.38; p < 0.05) increases in the odds of higher blood glucose levels and diagnosed diabetes, respectively. The positive association between FFR density and blood glucose level was stronger among women than men, but the association between FFR proximity and blood glucose level was stronger among men as well as among those with higher incomes. One of the study's key limitations is that we measured exposure to food environments around residency geolocation; however, participants may source their meals elsewhere. CONCLUSIONS Our results suggest that the exposure to fast-food outlets may have a detrimental impact on the risk of T2DM, especially among females and higher-income earners. Policies should target changes in the food environments to promote better diets and prevent T2DM.
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Affiliation(s)
- Dian Kusuma
- Centre for Health Economics Policy Innovation, Imperial College Business School, London, United Kingdom
| | - Petya Atanasova
- Centre for Health Economics Policy Innovation, Imperial College Business School, London, United Kingdom
| | - Elisa Pineda
- Centre for Health Economics Policy Innovation, Imperial College Business School, London, United Kingdom
- School of Public Health, Imperial College London, United Kingdom
| | | | | | - Abu AM Hanif
- Centre for Non-Communicable Diseases and Nutrition, BRAC James P Grant School of Public Health, BRAC University, Dhaka, Bangladesh
| | - Mehedi Hasan
- Centre for Non-Communicable Diseases and Nutrition, BRAC James P Grant School of Public Health, BRAC University, Dhaka, Bangladesh
| | - Md. Mokbul Hossain
- Centre for Non-Communicable Diseases and Nutrition, BRAC James P Grant School of Public Health, BRAC University, Dhaka, Bangladesh
| | | | | | | | | | | | | | | | - Malay K Mridha
- Centre for Non-Communicable Diseases and Nutrition, BRAC James P Grant School of Public Health, BRAC University, Dhaka, Bangladesh
| | | | - John C Chambers
- School of Public Health, Imperial College London, United Kingdom
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Gary Frost
- Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Franco Sassi
- Centre for Health Economics Policy Innovation, Imperial College Business School, London, United Kingdom
- Department of Economics and Public Policy, Imperial College Business School, London, United Kingdom
| | - Marisa Miraldo
- Centre for Health Economics Policy Innovation, Imperial College Business School, London, United Kingdom
- Department of Economics and Public Policy, Imperial College Business School, London, United Kingdom
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Ntarladima AM, Karssenberg D, Poelman M, Grobbee DE, Lu M, Schmitz O, Strak M, Janssen N, Hoek G, Vaartjes I. Associations between the fast-food environment and diabetes prevalence in the Netherlands: a cross-sectional study. Lancet Planet Health 2022; 6:e29-e39. [PMID: 34998457 DOI: 10.1016/s2542-5196(21)00298-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 10/27/2021] [Accepted: 10/27/2021] [Indexed: 05/22/2023]
Abstract
BACKGROUND Diabetes is a major health concern and is influenced by lifestyle, which can be affected by the neighbourhood environment. Specifically, a fast-food environment can influence eating behaviours and thus diabetes prevalence. Therefore, our aim was to assess the relationship between fast-food environment and diabetes prevalence for urban and rural environments in the Netherlands, using multiple indicators and buffer sizes. METHODS In this cross-sectional study, data on a nationwide sample of adults older than 19 years in the Netherlands were taken from the 2012 Dutch national health survey (from Public Health Monitor), in which participants were surveyed on topics related to health and lifestyle behaviour. Fast-food outlet exposures were determined within street-network buffers of 100 m, 400 m, 1000 m, and 1500 m around residential addresses. For each of these buffers, three indicators were calculated: presence (yes or no) of fast-food outlets, fast-food outlet density, and ratio. Logistic regression analyses were carried out to assess associations of these indicators with diabetes, adjusting for potential confounders and stratifying into urban and rural areas. FINDINGS 387 195 adults were surveyed, 284 793 of whom were included in the study. 22 951 (8%) reported having diabetes. Fast-food outlet exposures were positively associated with diabetes prevalence. We did not observe large differences between urban and rural areas. The effect estimates were small for all indicators. For example, in the 400 m buffer in the urban environment, the odds ratio (OR) for having diabetes among people with a fast-food outlet present compared with those without, was 1·006 (95% CI 1·003-1·009) using the presence indicator. The presence indicator showed higher effect estimates and the most consistent results across buffer sizes (ranging from OR 1·005 [95% CI 1·000-1·010] with the 1000 m buffer to 1·016 [1·005-1·028] with the 1500 m buffer in urban areas and from 1·002 [0·998-1·005] with the 1500 m buffer to 1·009 [1·006-1·018] with the 100 m buffer in rural areas) compared with the density and ratio indicators. INTERPRETATION The results confirm the evidence that the fast-food outlet environment is a diabetes risk factor. All data included were at the individual level and the variability was ensured by the spatial distribution and number of participants. In this study, we only accounted for residential exposure because we were unable to account for exposure outside the residential environment. The findings of this study encourage local governments to consider the potential adverse effects of fast-food exposures and aim at minimising unhealthy food access. FUNDING Global Geo Health Data Centre, Utrecht University, Netherlands.
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Affiliation(s)
- Anna-Maria Ntarladima
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands; Department of Physical Geography, Faculty of Geosciences, Utrecht University, Utrecht, Netherlands; Global Geo Health Data Center, Utrecht University, Utrecht, Netherlands; Urban Geographies, Amsterdam Institute for Social Science Research, University of Amsterdam, Amsterdam, Netherlands.
| | - Derek Karssenberg
- Department of Physical Geography, Faculty of Geosciences, Utrecht University, Utrecht, Netherlands; Global Geo Health Data Center, Utrecht University, Utrecht, Netherlands
| | - Maartje Poelman
- Chair group Consumption and Healthy Lifestyles, Wageningen University and Research, Wageningen, Netherlands
| | - Diederick E Grobbee
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands; Global Geo Health Data Center, Utrecht University, Utrecht, Netherlands
| | - Meng Lu
- Department of Physical Geography, Faculty of Geosciences, Utrecht University, Utrecht, Netherlands; Global Geo Health Data Center, Utrecht University, Utrecht, Netherlands
| | - Oliver Schmitz
- Department of Physical Geography, Faculty of Geosciences, Utrecht University, Utrecht, Netherlands; Global Geo Health Data Center, Utrecht University, Utrecht, Netherlands
| | - Maciej Strak
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, Netherlands; National Institute for Public Health and the Environment, Bilthoven, Netherlands
| | - Nicole Janssen
- National Institute for Public Health and the Environment, Bilthoven, Netherlands
| | - Gerard Hoek
- Global Geo Health Data Center, Utrecht University, Utrecht, Netherlands; Institute for Risk Assessment Sciences, Utrecht University, Utrecht, Netherlands
| | - Ilonca Vaartjes
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands; Global Geo Health Data Center, Utrecht University, Utrecht, Netherlands
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Cuadros DF, Li J, Musuka G, Awad SF. Spatial epidemiology of diabetes: Methods and insights. World J Diabetes 2021; 12:1042-1056. [PMID: 34326953 PMCID: PMC8311478 DOI: 10.4239/wjd.v12.i7.1042] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 05/07/2021] [Accepted: 06/04/2021] [Indexed: 02/06/2023] Open
Abstract
Diabetes mellitus (DM) is a growing epidemic with global proportions. It is estimated that in 2019, 463 million adults aged 20-79 years were living with DM. The latest evidence shows that DM continues to be a significant global health challenge and is likely to continue to grow substantially in the next decades, which would have major implications for healthcare expenditures, particularly in developing countries. Hence, new conceptual and methodological approaches to tackle the epidemic are long overdue. Spatial epidemiology has been a successful approach to control infectious disease epidemics like malaria and human immunodeficiency virus. The implementation of this approach has been expanded to include the study of non-communicable diseases like cancer and cardiovascular diseases. In this review, we discussed the implementation and use of spatial epidemiology and Geographic Information Systems to the study of DM. We reviewed several spatial methods used to understand the spatial structure of the disease and identify the potential geographical drivers of the spatial distribution of DM. Finally, we discussed the use of spatial epidemiology on the design and implementation of geographically targeted prevention and treatment interventions against DM.
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Affiliation(s)
- Diego F Cuadros
- Geography and Geographic Information Systems, University of Cincinnati, Cincinnati, OH 45221, United States
| | - Jingjing Li
- Urban Health Collaborative, Drexel University, Philadelphia, PA 19104, United States
| | | | - Susanne F Awad
- Infectious Disease Epidemiology Group, Weill Cornell Medicine – Qatar, Cornell University, Doha 24144, Qatar
- World Health Organization Collaborating Centre for Disease Epidemiology Analytics on HIV/AIDS, Sexually Transmitted Infections, and Viral Hepatitis, Weill Cornell Medicine – Qatar, Cornell University, Doha 24144, Qatar
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY 10044, United States
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Tonaco LAB, Vieira MAS, Gomes CS, Rocha FL, Oliveira-Figueiredo DSTD, Malta DC, Velasquez-Melendez G. Social vulnerability associated with the self-reported diagnosis of type II diabetes: a multilevel analysis. REVISTA BRASILEIRA DE EPIDEMIOLOGIA 2021; 24:e210010. [PMID: 33886883 DOI: 10.1590/1980-549720210010.supl.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 12/13/2020] [Indexed: 11/22/2022] Open
Abstract
OBJECTIVE To analyze the contextual factors associated with type II diabetes mellitus in Belo Horizonte City. METHODS Cross-sectional study with 5,779 adults living in Belo Horizonte City, participating in the Risk and Protection Factors Surveillance System for Chronic Diseases through Telephone Survey (Vigitel), in 2008, 2009, and 2010. Multilevel regression models were used to test the association between contextual indicators of physical and social environments, and self-reported diagnosis of diabetes, adjusted for individual sociodemographic and lifestyle factors. Descriptive analyzes and multilevel logistic regression models were used, considering a 5% significance level. RESULTS The prevalence of diabetes was 6.2% (95%CI 5.54 - 6.92), and 3.1% of the variability of chance of presenting diabetes were explained by contextual characteristics. Living in areas with high density of private places for physical activity and high income was associated with a lower chance of having diabetes. The areas with high level of social vulnerability were strongly associated with the chance of presenting diabetes, adjusted for individual characteristics. CONCLUSION Characteristics of physical and social environments were associated with the chance of diabetes occurrence. Urban centers with opportunities to adopt healthy behaviors can help to reduce the occurrence of diabetes and its complications.
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Quiñones S, Goyal A, Ahmed ZU. Geographically weighted machine learning model for untangling spatial heterogeneity of type 2 diabetes mellitus (T2D) prevalence in the USA. Sci Rep 2021; 11:6955. [PMID: 33772039 PMCID: PMC7997882 DOI: 10.1038/s41598-021-85381-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 03/01/2021] [Indexed: 11/25/2022] Open
Abstract
Type 2 diabetes mellitus (T2D) prevalence in the United States varies substantially across spatial and temporal scales, attributable to variations of socioeconomic and lifestyle risk factors. Understanding these variations in risk factors contributions to T2D would be of great benefit to intervention and treatment approaches to reduce or prevent T2D. Geographically-weighted random forest (GW-RF), a tree-based non-parametric machine learning model, may help explore and visualize the relationships between T2D and risk factors at the county-level. GW-RF outputs are compared to global (RF and OLS) and local (GW-OLS) models between the years of 2013-2017 using low education, poverty, obesity, physical inactivity, access to exercise, and food environment as inputs. Our results indicate that a non-parametric GW-RF model shows a high potential for explaining spatial heterogeneity of, and predicting, T2D prevalence over traditional local and global models when inputting six major risk factors. Some of these predictions, however, are marginal. These findings of spatial heterogeneity using GW-RF demonstrate the need to consider local factors in prevention approaches. Spatial analysis of T2D and associated risk factor prevalence offers useful information for targeting the geographic area for prevention and disease interventions.
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Affiliation(s)
- Sarah Quiñones
- University at Buffalo, State University at New York, Buffalo, USA
| | - Aditya Goyal
- Research and Education in Energy, Environment, and Water (RENEW) Institute, University at Buffalo, State University at New York, 108 Cooke Hall, Buffalo, NY, 14260, USA
| | - Zia U Ahmed
- Research and Education in Energy, Environment, and Water (RENEW) Institute, University at Buffalo, State University at New York, 108 Cooke Hall, Buffalo, NY, 14260, USA.
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Wu J, Wang Y, Xiao X, Shang X, He M, Zhang L. Spatial Analysis of Incidence of Diagnosed Type 2 Diabetes Mellitus and Its Association With Obesity and Physical Inactivity. Front Endocrinol (Lausanne) 2021; 12:755575. [PMID: 34777252 PMCID: PMC8581298 DOI: 10.3389/fendo.2021.755575] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 10/08/2021] [Indexed: 01/03/2023] Open
Abstract
OBJECTIVES To investigate the spatial distribution of 10-year incidence of diagnosed type 2 diabetes mellitus (T2DM) and its association with obesity and physical inactivity at a reginal level breakdown. METHODS Demographic, behavioral, medical and pharmaceutical and diagnosed T2DM incidence data were collected from a cohort of 232,064 participants who were free of diabetes at enrolment in the 45 and Up Study, conducted in the state of New South Wales (NSW), Australia. We examined the geographical trend and correlation between obesity prevalence, physical inactivity rate and age-and-gender-adjusted cumulative incidence of T2DM, aggregated based on geographical regions. RESULT The T2DM incidence, prevalence of obesity and physical inactivity rate at baseline were 6.32%, 20.24%, and 18.7%, respectively. The spatial variation of T2DM incidence was significant (Moran's I=0.52; p<0.01), with the lowest incidence of 2.76% in Richmond Valley-Coastal and the highest of 12.27% in Mount Druitt. T2DM incidence was significantly correlated with the prevalence of obesity (Spearman r=0.62, p<0.001), percentage of participants having five sessions of physical activities or less per week (r=0.79, p<0.001) and percentage of participants walked to work (r=-0.44, p<0.001). The geographical variations in obesity prevalence and physical inactivity rate resembled the geographical variation in the incidence of T2DM. CONCLUSION The spatial distribution of T2DM incidence is significantly associated with the geographical prevalence of obesity and physical inactivity rate. Regional campaigns advocating the importance of physical activities in response to the alarming T2DM epidemic should be promoted.
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Affiliation(s)
- Jinrong Wu
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, VIC, Australia
- Research Centre for Data Analytics and Cognition, La Trobe University, Melbourne, VIC, Australia
| | - Yang Wang
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, VIC, Australia
| | - Xin Xiao
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, VIC, Australia
- Center for Optometry and Visual Science, Department of Optometry, People’s Hospital of Guangxi Zhuang Autonomous Region, Guangxi, China
| | - Xianwen Shang
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, VIC, Australia
| | - Mingguang He
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, VIC, Australia
- Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, VIC, Australia
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
- *Correspondence: Lei Zhang, ; Mingguang He,
| | - Lei Zhang
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, VIC, Australia
- Melbourne Sexual Health Centre, Alfred Health, Melbourne, VIC, Australia
- Central Clinical School, Faculty of Medicine, Monash University, Melbourne, VIC, Australia
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China
- *Correspondence: Lei Zhang, ; Mingguang He,
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Gomez-Peralta F, Abreu C, Benito M, Barranco RJ. Geographical clustering and socioeconomic factors associated with hypoglycemic events requiring emergency assistance in Andalusia (Spain). BMJ Open Diabetes Res Care 2021; 9:9/1/e001731. [PMID: 33397670 PMCID: PMC7783525 DOI: 10.1136/bmjdrc-2020-001731] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 11/26/2020] [Accepted: 12/05/2020] [Indexed: 11/23/2022] Open
Abstract
INTRODUCTION The geographical distribution of hypoglycemic events requiring emergency assistance was explored in Andalusia (Spain), and potentially associated societal factors were determined. RESEARCH DESIGN AND METHODS This was a database analysis of hypoglycemia requiring prehospital emergency assistance from the Public Company for Health Emergencies (Empresa Pública de Emergencias Sanitarias (EPES)) in Andalusia during 2012, which served 8 393 159 people. Databases of the National Statistics Institute, Basic Spatial Data of Andalusia and System of Multiterritorial Information of Andalusia were used to retrieve spatial data and population characteristics. Geographic Information System software (QGIS and GeoDA) was used for analysis and linkage across databases. Spatial analyses of geographical location influence in hypoglycemic events were assessed using Moran's I statistics, and linear regressions were used to determine their association with population characteristics. RESULTS The EPES attended 1 137 738 calls requesting medical assistance, with a mean hypoglycemia incidence of 95.0±61.6 cases per 100 000 inhabitants. There were significant differences in hypoglycemia incidence between basic healthcare zones attributable to their geographical location in the overall population (Moran's I index 0.122, z-score 7.870, p=0.001), women (Moran's I index 0.088, z-score 6.285, p=0.001), men (Moran's I index 0.076, z-score 4.914, p=0.001) and aged >64 years (Moran's I index 0.147, z-score 9.753, p=0.001). Hypoglycemia incidence was higher within unemployed individuals (β=0.003, p=0.001) and unemployed women (β=0.005, p=0.001), while lower within individuals aged <16 years (β=-0.004, p=0.040), higher academic level (secondary studies) (β=-0.003, p=0.004) and women with secondary studies (β=-0.005, p<0.001). In subjects aged >64 years, lower rate of hypoglycemia was associated with more single-person homes (β=-0.008, p=0.022) and sports facilities (β=-0.342, p=0.012). CONCLUSIONS This analysis supports the geographical distribution of hypoglycemia in the overall population, both genders and subjects aged >64 years, which was affected by societal factors such as unemployment, literacy/education, housing and sports facilities. These data can be useful to design specific prevention programs.
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Affiliation(s)
| | - Cristina Abreu
- Endocrinology and Nutrition Unit, Hospital General de Segovia, Segovia, Spain
| | - Manuel Benito
- Department of Urbanism, School of Architecture, Polytechnic University of Madrid, Madrid, Spain
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Hunt KJ, Davis M, Pearce J, Bian J, Guagliardo MF, Moy E, Axon RN, Neelon B. Geographic and Racial/Ethnic Variation in Glycemic Control and Treatment in a National Sample of Veterans With Diabetes. Diabetes Care 2020; 43:2460-2468. [PMID: 32769125 PMCID: PMC7510017 DOI: 10.2337/dc20-0514] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Accepted: 07/09/2020] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Geographic and racial/ethnic disparities related to diabetes control and treatment have not previously been examined at the national level. RESEARCH DESIGN AND METHODS A retrospective cohort study was conducted in a national cohort of 1,140,634 veterans with diabetes, defined as two or more diabetes ICD-9 codes (250.xx) across inpatient and outpatient records. Main exposures of interest included 125 Veterans Administration Medical Center (VAMC) catchment areas as well as racial/ethnic group. The main outcome measure was HbA1c level dichotomized at ≥8.0% (≥64 mmol/mol). RESULTS After adjustment for age, sex, racial/ethnic group, service-connected disability, marital status, and the van Walraven Elixhauser comorbidity score, the prevalence of uncontrolled diabetes varied by VAMC catchment area, with values ranging from 19.1% to 29.2%. Moreover, these differences largely persisted after further adjusting for medication use and adherence as well as utilization and access metrics. Racial/ethnic differences in diabetes control were also noted. In our final models, compared with non-Hispanic Whites, non-Hispanic Blacks (odds ratio 1.11 [95% credible interval 1.09-1.14]) and Hispanics (1.36 [1.09-1.14]) had a higher odds of uncontrolled HBA1c level. CONCLUSIONS In a national cohort of veterans with diabetes, we found geographic as well as racial/ethnic differences in diabetes control rates that were not explained by adjustment for demographics, comorbidity burden, use or type of diabetes medication, health care utilization, access metrics, or medication adherence. Moreover, disparities in suboptimal control appeared consistent across most, but not all, VAMC catchment areas, with non-Hispanic Black and Hispanic veterans having a higher odds of suboptimal diabetes control than non-Hispanic White veterans.
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Affiliation(s)
- Kelly J Hunt
- Charleston Health Equity and Rural Outreach Innovation Center (HEROIC), Ralph H. Johnson VA Medical Center, Charleston, SC
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC
| | - Melanie Davis
- Charleston Health Equity and Rural Outreach Innovation Center (HEROIC), Ralph H. Johnson VA Medical Center, Charleston, SC
| | - John Pearce
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC
| | - John Bian
- Charleston Health Equity and Rural Outreach Innovation Center (HEROIC), Ralph H. Johnson VA Medical Center, Charleston, SC
| | - Mark F Guagliardo
- Data Governance and Analytics, U.S. Department of Veterans Affairs, Washington, DC
| | - Ernest Moy
- Veterans Health Administration Office of Health Equity, Rockville, MD
| | - R Neal Axon
- Charleston Health Equity and Rural Outreach Innovation Center (HEROIC), Ralph H. Johnson VA Medical Center, Charleston, SC
| | - Brian Neelon
- Charleston Health Equity and Rural Outreach Innovation Center (HEROIC), Ralph H. Johnson VA Medical Center, Charleston, SC
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC
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Bagheri N, Konings P, Wangdi K, Parkinson A, Mazumdar S, Sturgiss E, Lal A, Douglas K, Glasgow N. Identifying hotspots of type 2 diabetes risk using general practice data and geospatial analysis: an approach to inform policy and practice. Aust J Prim Health 2019; 26:43-51. [PMID: 31751519 DOI: 10.1071/py19043] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Accepted: 08/23/2019] [Indexed: 01/06/2023]
Abstract
The prevalence of type 2 diabetes (T2D) is increasing worldwide and there is a need to identify communities with a high-risk profile and to develop appropriate primary care interventions. This study aimed to predict future T2D risk and identify community-level geographic variations using general practices data. The Australian T2D risk assessment (AUSDRISK) tool was used to calculate the individual T2D risk scores using 55693 clinical records from 16 general practices in west Adelaide, South Australia, Australia. Spatial clusters and potential 'hotspots' of T2D risk were examined using Local Moran's I and the Getis-Ord Gi* techniques. Further, the correlation between T2D risk and the socioeconomic status of communities were mapped. Individual risk scores were categorised into three groups: low risk (34.0% of participants), moderate risk (35.2% of participants) and high risk (30.8% of participants). Spatial analysis showed heterogeneity in T2D risk across communities, with significant clusters in the central part of the study area. These study results suggest that routinely collected data from general practices offer a rich source of data that may be a useful and efficient approach for identifying T2D hotspots across communities. Mapping aggregated T2D risk offers a novel approach to identifying areas of unmet need.
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Affiliation(s)
- Nasser Bagheri
- Centre for Mental Health Research, Research School of Population Health, Australian National University, 63 Eggleston Road, Acton 2601, Australia; and Corresponding author
| | - Paul Konings
- Department of Health Services Research and Policy, Research School of Population Health, Australian National University, 62 Eggleston Road, Acton, ACT 2601, Australia
| | - Kinley Wangdi
- Department of Global Health, Research School of Population Health, Australian National University, 62 Eggleston Road, Acton, ACT 2601, Australia
| | - Anne Parkinson
- Department of Health Services Research and Policy, Research School of Population Health, Australian National University, 62 Eggleston Road, Acton, ACT 2601, Australia
| | - Soumya Mazumdar
- Healthy People and Place Unit, Population Health, Liverpool Hospital, South West Sydney Local Health District, New South Wales Health, 52 Scrivener Street, Warwick Farm, NSW 2170, Australia
| | - Elizabeth Sturgiss
- Department of General Practice, Monash University, 270 Ferntree Gully Road, Notting Hill, Vic. 3168, Australia
| | - Aparna Lal
- National Centre for Epidemiology and Population Health, Research School of Population Health, Australian National University, 62 Eggleston Road, Acton, ACT 2601, Australia
| | - Kirsty Douglas
- Department of General Practice, Monash University, 270 Ferntree Gully Road, Notting Hill, Vic. 3168, Australia
| | - Nicholas Glasgow
- Department of Health Services Research and Policy, Research School of Population Health, Australian National University, 62 Eggleston Road, Acton, ACT 2601, Australia
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Sarkar C, Webster C, Gallacher J. Are exposures to ready-to-eat food environments associated with type 2 diabetes? A cross-sectional study of 347 551 UK Biobank adult participants. Lancet Planet Health 2018; 2:e438-e450. [PMID: 30318101 DOI: 10.1016/s2542-5196(18)30208-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Revised: 07/23/2018] [Accepted: 09/14/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND Rapid urbanisation and associated socioeconomic transformations have modified current lifestyles, shifting dietary preferences towards ready-to-eat, calorie-dense food of poor nutritional quality. The effect of ready-to-eat food environments that sell food for instant consumption on the risk of type 2 diabetes has received scant attention. We therefore aimed to examine the association between exposure to ready-to-eat food environments and type 2 diabetes in a large and diverse population sample. METHODS We conducted a cross-sectional study of adult male and female participants from the baseline phase of the UK Biobank cohort. Participants in this cohort were aged 37-73 years and resided in one of 21 cities in the UK. Ready-to-eat food environments, which we determined from a modelled and linked built environment database, were objectively measured within 1-km catchment areas of the residential streets of participants and were expressed as metrics of density and proximity to the participants' homes. We used logistic regression models to examine the associations between exposure to ready-to-eat food environments and the odds of type 2 diabetes, adjusting for individual covariates such as physical activity. As sensitivity analyses, we investigated the associations between the street distance to the nearest ready-to-eat food outlet and type 2 diabetes. We also tested post hoc for effect modification by sex, income, body-mass index, and location of the UK Biobank collection centre. FINDINGS Of 502 635 UK Biobank participants enrolled between March 13, 2006, and Oct 1, 2010, the sample analysed included 347 551 (69·1%) participants. The density of ready-to-eat food environments within a 1-km catchment area was associated with higher odds of type 2 diabetes for participants in the groups with highest exposure to restaurants and cafeterias (odss ratio 1·129, 95% CI 1·05-1·21; p=0·0007) and a composite measure of ready-to-eat outlet density (1·112, 1·02-1·21; p=0·0134) compared with those with no exposure. Exposure to hot and cold takeaways was only significantly associated with higher odds of type 2 diabetes at the second highest exposure category that we examined (1·076, 1·01-1·14; p=0·0171), representing a density of 0·75-2·15 units per km2. A protective effect with distance decay was observed: participants in the highest quintile of street distance to nearest ready-to-eat food outlet reported lower odds of type 2 diabetes than those in the lowest quintile (0·842, 0·78-0·91; p<0·0001 for restaurants and cafeterias; and 0·913, 0·85-0·98; p=0·0173 for hot and cold takeaways). These effects were most pronounced in overweight participants (p=0·0329), but there was no evidence of interaction by sex, income, or UK Biobank collection centre. INTERPRETATION Access to ready-to-eat food environments was positively associated with type 2 diabetes. Top-down policies aimed at minimising unhealthy food access could potentially reduce unhealthy consumption and risks of chronic diseases. Further long-term studies are needed to effectively guide such interventions. FUNDING University of Hong Kong, UK Biobank, and UK Economic & Social Research Council.
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Affiliation(s)
- Chinmoy Sarkar
- Healthy High Density Cities Lab, HKUrbanLab, University of Hong Kong, Hong Kong Special Administrative Region, China.
| | - Chris Webster
- Healthy High Density Cities Lab, HKUrbanLab, University of Hong Kong, Hong Kong Special Administrative Region, China
| | - John Gallacher
- Department of Psychiatry, Oxford University, Warneford Hospital, Oxford, UK
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11
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Savoca MR, Ludwig DA, Jones ST, Jason Clodfelter K, Sloop JB, Bollhalter LY, Bertoni AG. Geographic Information Systems to Assess External Validity in Randomized Trials. Am J Prev Med 2017; 53:252-259. [PMID: 28237634 PMCID: PMC5985667 DOI: 10.1016/j.amepre.2017.01.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2016] [Revised: 11/22/2016] [Accepted: 01/05/2017] [Indexed: 01/03/2023]
Abstract
INTRODUCTION To support claims that RCTs can reduce health disparities (i.e., are translational), it is imperative that methodologies exist to evaluate the tenability of external validity in RCTs when probabilistic sampling of participants is not employed. Typically, attempts at establishing post hoc external validity are limited to a few comparisons across convenience variables, which must be available in both sample and population. A Type 2 diabetes RCT was used as an example of a method that uses a geographic information system to assess external validity in the absence of a priori probabilistic community-wide diabetes risk sampling strategy. METHODS A geographic information system, 2009-2013 county death certificate records, and 2013-2014 electronic medical records were used to identify community-wide diabetes prevalence. Color-coded diabetes density maps provided visual representation of these densities. Chi-square goodness of fit statistic/analysis tested the degree to which distribution of RCT participants varied across density classes compared to what would be expected, given simple random sampling of the county population. Analyses were conducted in 2016. RESULTS Diabetes prevalence areas as represented by death certificate and electronic medical records were distributed similarly. The simple random sample model was not a good fit for death certificate record (chi-square, 17.63; p=0.0001) and electronic medical record data (chi-square, 28.92; p<0.0001). Generally, RCT participants were oversampled in high-diabetes density areas. CONCLUSIONS Location is a highly reliable "principal variable" associated with health disparities. It serves as a directly measurable proxy for high-risk underserved communities, thus offering an effective and practical approach for examining external validity of RCTs.
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Affiliation(s)
- Margaret R Savoca
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina.
| | - David A Ludwig
- Division of Pediatric Clinical Research, Department of Pediatrics, and Division of Biostatistics, Public Health Sciences, University of Miami Leonard M. Miller School of Medicine, Miami, Florida
| | - Stedman T Jones
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - K Jason Clodfelter
- MapForsyth|City-County Geographic Information Office, Winston-Salem, North Carolina
| | - Joseph B Sloop
- MapForsyth|City-County Geographic Information Office, Winston-Salem, North Carolina
| | - Linda Y Bollhalter
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Alain G Bertoni
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina; Maya Angelou Center for Health Equity, Wake Forest School of Medicine, Winston-Salem, North Carolina
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