The influence of neighbourhood deprivation on the prevalence of diabetes in 25‐ to 74‐year‐old individuals: first results from the Dortmund Health Study

Mueller, Georgia; Berger, Katherine E

doi:10.1111/j.1464-5491.2011.03526.x

Cited by 9 publications

(4 citation statements)

References 5 publications

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“…Private health care is expensive and the result is a neglect of most medical non-emergencies. The authors of two other studies reach similar conclusions, linking the diabetes prevalence to regional social and economic factors including unemployment [19,20]. They observed higher diabetes prevalence with increasing unemployment rate.…”

Section: Discussionsupporting

confidence: 68%

Higher Prevalence of Diabetes Mellitus and Impaired Glucose Tolerance Among the Rural Population in Bulgaria

et al. 2016

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Background: The socio-economic level is probably among the central risk factors for type 2 diabetes mellitus (T2DM) along with obesity, decreased physical activity and smoking. The International Diabetes Federation notes in particular that the main impact in the prevalence of diabetes is observed in low and middle-income countries, hence the disease might disproportionately affect lower socioeconomic groups. The aim of the study was to determine the prevalence of diabetes and impaired glucose tolerance (IGT) and assess its association with the type of place of residence in Bulgaria -cities, small towns and villages.

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Section: Discussionsupporting

confidence: 68%

Higher Prevalence of Diabetes Mellitus and Impaired Glucose Tolerance Among the Rural Population in Bulgaria

et al. 2016

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“…[35][36][37][38][39][40][41] This reinforces the notion that deprived neighborhoods have distinct effects on health and care delivery and underscores the importance of using geography as a lens through which to view population health. Although imperfect, we provide a reproducible, practical, and feasible method for integrating community data into EHRs.…”

Section: Discussionsupporting

confidence: 50%

Living in “Cold Spot” Communities Is Associated with Poor Health and Health Quality

et al. 2018

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Purpose:Little is known about incorporating community data into clinical care. This study sought to understand the clinical associations of cold spots (census tracts with worse income, education, and composite deprivation).Methods: Across 12 practices, we assessed the relationship between cold spots and clinical outcomes (obesity, uncontrolled diabetes, pneumonia vaccination, cancer screening-colon, cervical, and prostate-and aspirin chemoprophylaxis) for 152,962 patients. We geocoded and linked addresses to census tracts and assessed, at the census tract level, the percentage earning less than 200% of the Federal Poverty Level, without high school diplomas, and the social deprivation index (SDI). We labeled those census tracts in the worst quartiles as cold spots and conducted bivariate and logistic regression.Results: There was a 10-fold difference in the proportion of patients in cold spots between the highest (29.1%) and lowest practices (2.6%). Except for aspirin, all outcomes were influenced by cold spots. Fifteen percent of low-education cold-spot patients had uncontrolled diabetes compared with 13% of noncold-spot patients (P < .05). In regression, those in poverty, low education, and SDI cold spots were less likely to receive colon cancer screening (odds ratio [

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“…The baseline assessment was conducted in 2003/2004, and 2291 participants (response proportion 67%), aged 25 to 74 years and living in one of 62 statistical districts of the city of Dortmund, Germany, either filled out a mailed questionnaire ( N = 979) on socio-economic status and subjective health, or attended the study centre ( N = 1312) to answer these questions in face-to-face interviews and receive a medical examination. For more details see [ 19 , 20 ]…”

Section: Methodsmentioning

confidence: 99%

A method to define the relevant ego-centred spatial scale for the assessment of neighbourhood effects: the example of cardiovascular risk factors

et al. 2021

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Introduction The neighbourhood in which one lives affects health through complex pathways not yet fully understood. A way to move forward in assessing these pathways direction is to explore the spatial structure of health phenomena to generate hypotheses and examine whether the neighbourhood characteristics are able to explain this spatial structure. We compare the spatial structure of two cardiovascular disease risk factors in three European urban areas, thus assessing if a non-measured neighbourhood effect or spatial processes is present by either modelling the correlation structure at individual level or by estimating the intra-class correlation within administrative units. Methods Data from three independent studies (RECORD, DHS and BaBi), covering each a European urban area, are used. The characteristics of the spatial correlation structure of cardiovascular risk factors (BMI and systolic blood pressure) adjusted for age, sex, educational attainment and income are estimated by fitting an exponential model to the semi-variogram based on the geo-coordinates of places of residence. For comparison purposes, a random effect model is also fitted to estimate the intra-class correlation within administrative units. We then discuss the benefits of modelling the correlation structure to evaluate the presence of unmeasured spatial effects on health. Results BMI and blood pressure are consistently found to be spatially structured across the studies, the spatial correlation structures being stronger for BMI. Eight to 22% of the variability in BMI were spatially structured with radii ranging from 100 to 240 m (range). Only a small part of the correlation of residuals was explained by adjusting for the correlation within administrative units (from 0 to 4 percentage points). Discussion The individual spatial correlation approach provides much stronger evidence of spatial effects than the multilevel approach even for small administrative units. Spatial correlation structure offers new possibilities to assess the relevant spatial scale for health. Stronger correlation structure seen for BMI may be due to neighbourhood socioeconomic conditions and processes like social norms at work in the immediate neighbourhood.

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The influence of neighbourhood deprivation on the prevalence of diabetes in 25‐ to 74‐year‐old individuals: first results from the Dortmund Health Study

Abstract: Diabet. Med. 29, 831–833 (2012)

Cited by 9 publications

References 5 publications

Higher Prevalence of Diabetes Mellitus and Impaired Glucose Tolerance Among the Rural Population in Bulgaria

Higher Prevalence of Diabetes Mellitus and Impaired Glucose Tolerance Among the Rural Population in Bulgaria

Living in “Cold Spot” Communities Is Associated with Poor Health and Health Quality

A method to define the relevant ego-centred spatial scale for the assessment of neighbourhood effects: the example of cardiovascular risk factors

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