Abstracts

Background

Diabetes is one of the main public health concerns globally and in 2016, it accounted for nearly 1.6 million deaths worldwide ¹1. World Health Organization. World health statistics 2018: monitoring health for the SDGs, Sustainable Development Goals. Geneva: World Health Organization; 2018.. An estimated 8.8% of people have diabetes (425 million individuals), with this prevalence expected to increase to 9.9% by 2045 (629 million individuals). Brazil ranks fourth in the ranking of countries with the highest number of individuals with diabetes, 12.5 million people, with the possibility of reaching 20.3 million people in 2045 ¹1. World Health Organization. World health statistics 2018: monitoring health for the SDGs, Sustainable Development Goals. Geneva: World Health Organization; 2018.. Type 2 diabetes mellitus is associated with a higher risk of cardiovascular diseases ²2. Danaei G, Lawes CM, Vander HS, Murray CJL, Ezzati M. Global and regional mortality from ischaemic heart disease and stroke attributable to higher than optimum blood glucose concentration: comparative risk assessment. Lancet 2006; 368:1651-9.^,33. Sarwar N, Gao P, Seshasai SR, Gobin R, Kaptoge S, Di Angelantonio E, et al. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative metaanalysis of 102 prospective studies. Lancet 2010; 375:2215-22., chronic kidney disease ⁴4. DeFronzo RA, Ferrannini E, Zimmet P, Alberti G. International textbook of diabetes mellitus, 2 volume set. 4th Ed. Hoboken: Wiley-Blackwell; 2015., and poor clinical and functional outcomes ⁵5. Bourne RR, Stevens GA, White RA, Smith JL, Flaxman SR, Price H, et al. Causes of vision loss worldwide, 1990-2010: a systematic analysis. Lancet Glob Health 2013; 1:e339-49.^,66. Boulton AJ, Vileikyte L, Ragnarson-Tennvall G, Apelqvist J. The global burden of diabetic foot disease. Lancet 2005; 366:1719-24., as well as higher healthcare costs ⁷7. International Diabetes Federation. Diabetes atlas. 10th Ed. Brussels: International Diabetes Federation; 2021.. Thus, type 2 diabetes mellitus also creates a great economic and social burden for countries ⁸8. Chan JCN, Lim LL, Wareham NJ, Shaw JE, Orchard TJ, Zhang P, et al. The Lancet Commission on diabetes: using data to transform diabetes care and patient lives. Lancet 2021; 396:2019-82..

Due to its magnitude, clinical repercussions, and economic and social impact, type 2 diabetes mellitus is an important contemporary public health problem. Therefore, understanding the spatial distribution of diabetes could be an important tool to guide public managers to design programs and rationally allocate resources for its prevention, control, and treatment ⁹9. Lord J, Roberson S, Odoi A. Investigation of geographic disparities of pre-diabetes and diabetes in Florida. BMC Public Health 2020; 20:1226.. Scientific evidence shows disparities in the geographic distribution of type 2 diabetes mellitus within communities, with areas of higher or lower frequencies of the disease identified in investigations conducted in Florida, Unietd States ⁹9. Lord J, Roberson S, Odoi A. Investigation of geographic disparities of pre-diabetes and diabetes in Florida. BMC Public Health 2020; 20:1226.; Kerala, India ¹⁰10. Valson JS, Kutty VR, Soman B, Jissa VT. Spatial clusters of diabetes and physical inactivity: do neighborhood characteristics in high and low clusters differ? Asia Pac J Public Health 2019; 31:612-21.; and Adelaide, Australia ¹¹11. Smurthwaite K, Bagheri N. Using geographical convergence of obesity, cardiovascular disease, and type 2 diabetes at the neighborhood level to inform policy and practice. Prev Chronic Dis 2017; 14:E91..

Built environment and socioeconomic characteristics differ significantly between areas with high or low frequencies of diseases, and this relationship seems to vary among populations. A study in Kerala found spatial clusters of diabetes and physical inactivity and, after comparing sociodemographic aspects between clusters, built environment characteristics proved to be relevant in Kerala’s urban and rural areas ¹⁰10. Valson JS, Kutty VR, Soman B, Jissa VT. Spatial clusters of diabetes and physical inactivity: do neighborhood characteristics in high and low clusters differ? Asia Pac J Public Health 2019; 31:612-21.. In contrast, in an Australian study, an inverse relationship was observed between higher type 2 diabetes mellitus clusters and the socioeconomic status of the areas of spatial clusters ¹¹11. Smurthwaite K, Bagheri N. Using geographical convergence of obesity, cardiovascular disease, and type 2 diabetes at the neighborhood level to inform policy and practice. Prev Chronic Dis 2017; 14:E91.. However, the associations between the structural and socioeconomic conditions of the type 2 diabetes mellitus cluster areas observed in both studies were not independent and may be influenced by potential individual and environmental confounding factors ¹²12. Bravo MA, Anthopolos R, Miranda ML. Characteristics of the built environment and spatial patterning of type 2 diabetes in the urban core of Durham, North Carolina. J Epidemiol Community Health 2019; 73:303-10..

Furthermore, it is important to highlight that the urban environment and the population’s health conditions are interdependent and socially determined ¹³13. Dahlgren G, Whitehead M. Policies and strategies to promote social equity in health. Stockholm: Institute for Futures Studies; 1991.. Therefore, these aspects need to be better explored in epidemiological studies.

Hence, studies that investigate spatial variability of the type 2 diabetes mellitus distribution must also analyze the individual characteristics of the residents of the areas with the highest and lowest chances of occurrence of disease, along with their structural and socioeconomic conditions ⁹9. Lord J, Roberson S, Odoi A. Investigation of geographic disparities of pre-diabetes and diabetes in Florida. BMC Public Health 2020; 20:1226.. For instance, previous findings of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) showed that individuals living in economically segregated neighborhoods were more likely to have type 2 diabetes mellitus regardless of the income, schooling level, and other sociodemographic factors ¹⁴14. Barber S, Diez Roux AV, Cardoso L, Santos S, Toste V, James S, et al. At the intersection of place, race, and health in Brazil: residential segregation and cardio-metabolic risk factors in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Soc Sci Med 2018; 199:67-76. suggesting inequalities when one considers the spatial distribution of type 2 diabetes mellitus. Furthermore, results from a study in the United States indicated that individual characteristics, such as being male, older age, being married, having a low income, insufficient practice of physical activity, overweight/obesity, as well as hypertension, hypercholesterolemia, or arthritis were independently associated with high cluster areas of type 2 diabetes mellitus ⁹9. Lord J, Roberson S, Odoi A. Investigation of geographic disparities of pre-diabetes and diabetes in Florida. BMC Public Health 2020; 20:1226..

This study aimed to identify spatial clusters of type 2 diabetes mellitus among participants of ELSA-Brasil residing in two cities and verify individual and neighborhood socioeconomic environmental characteristics associated with spatial clusters of type 2 diabetes mellitus.

Methods

This is a cross-section analysis of the ELSA-Brasil baseline (2008-2010), a multicenter, prospective cohort study consisting of 15,105 active and retired civil servants, aged from 35 to 74 years at baseline (2008-2010), from universities and research institutions. Its primary objective was to identify risk factors and natural history of diabetes and cardiovascular diseases. Data collection included face-to-face interviews, clinical examinations, and laboratory tests conducted by trained, certified professionals using standardized tools. The ELSA-Brasil study was approved by the Ethics Research Committees of all institutions involved [Minas Gerais Federal University (CAAE: 186/06); Bahia Federal University (CAAE: 0017.1.069.000-06027/06); São Paulo University (CAAE: 0016.1.198.000-06669/06); Rio Grande do Sul Federal University (CAAE: 194/061); Oswaldo Cruz Foundation (CAAE: 0058.0.011.000-07343/06); Espírito Santo Federal University (CAAE: 0058.0.011.000-07343/06), and all participants signed an informed consent form. Details of the study design and the cohort profile have been previously described ¹⁴14. Barber S, Diez Roux AV, Cardoso L, Santos S, Toste V, James S, et al. At the intersection of place, race, and health in Brazil: residential segregation and cardio-metabolic risk factors in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Soc Sci Med 2018; 199:67-76.^,1515. Aquino EM, Barreto SM, Bensenor IM, Carvalho MS, Chor D, Duncan BB, et al. Brazilian Longitudinal Study of Adult Health (ELSA-Brasil): objectives and design. Am J Epidemiol 2012; 175:315-24..

This study was developed with 3,115 participants living in the city of Belo Horizonte (Minas Gerais State) and 2,029 participants living in the city of Salvador (Bahia State) at baseline. Among them, those with missing geographic coordinates (n = 59), those with geographic coordinates out of the municipalities of Belo Horizonte and Salvador (n = 570 and n = 84, respectively), and participants who self-reported as Indigenous (n = 69), due to the very few observations, were excluded. The final sample size was 2,486 individuals for Belo Horizonte and 1,849 for Salvador.

Belo Horizonte is the state capital of Minas Gerais, with Human Development Index (HDI) of 0.810 ¹⁶16. Programa das Nações Unidas para o Desenvolvimento. Atlas do desenvolvimento humano nas regiões metropolitanas brasileiras. Brasília: Instituto de Pesquisa Econômica Aplicada/Fundação João Pinheiro/Programa das Nações Unidas para o Desenvolvimento; 2014. and an estimated population of 2,375,151 inhabitants, being the sixth most populous municipality in Brazil ¹⁷17. Instituto Brasileiro de Geografia e Estatística. Censo demográfico 2010. Características da população e dos domicílios: resultados do universo. Rio de Janeiro: Instituto Brasileiro de Geografia e Estatística; 2011.. Salvador is the state capital of Bahia. with a 0.759 HDI ¹⁶16. Programa das Nações Unidas para o Desenvolvimento. Atlas do desenvolvimento humano nas regiões metropolitanas brasileiras. Brasília: Instituto de Pesquisa Econômica Aplicada/Fundação João Pinheiro/Programa das Nações Unidas para o Desenvolvimento; 2014. and an estimated population of 2,675,656 inhabitants, being the third most populous municipality in Brazil ¹⁷17. Instituto Brasileiro de Geografia e Estatística. Censo demográfico 2010. Características da população e dos domicílios: resultados do universo. Rio de Janeiro: Instituto Brasileiro de Geografia e Estatística; 2011..

The data collection team at the ELSA-Brasil Research Centers was trained and certified for the collection of anthropometric measurements, blood samples, and application of questionnaires. Collection in all centers was standardly conducted according to the procedures described in the specific Operation Manuals for interviews and exams.

The study outcome was to live inside or outside a detected prevalence cluster of type 2 diabetes mellitus. This variable was fitted in two steps.

Participants were considered as having type 2 diabetes mellitus when they presented the following parameters: fasting blood glucose ≥ 126mg/dL (7.0mmol/L), oral glucose tolerance test (OGTT) ≥ 200mg/dL (11.1mmol/L), glycated hemoglobin (HbA1c) ≥ 6.5% (48mmol/L), or by antidiabetic drug use; or by the self-reported medical diagnosis of diabetes. Blood samples were collected after a 10 to 14-hour fasting, stored in a freezer at -80°C and sent to the certified central laboratory in São Paulo (Brazil). An OGTT was administered to all participants without a known diabetes diagnosis. Blood glucose level was measured using the enzymatic colorimetric method (ADVIA 1200, https://www.diamonddiagnostics.com), and HbA1c was measured using high-pressure chromatography (HPLC, https://www.bio-rad.com).

Initially, the database from ELSA-Brasil was obtained in georeferenced formats from the residential address informed at baseline. The census sectors were identified from the geographic coordinates (X and Y coordinates of the study participants) of each point of residence of the individuals in the study who were georeferenced in the census tract.

The first step to remove out-of-region points is defining the shape of each capital city, which in this case was defined as the shape obtained from the geobr R package (http://www.r-project.org) for each municipality, constituted of all the shapes of its census tracts.

After data preparation, a spatial data analysis was conducted with the SaTScan (http://www.satscan.org) method of cluster detection of punctual data, which tests for the existence of an area with multiple clusters of any size up to 20% of the total population for the outcome. The SaTScan detects a spatial cluster on different maps and computes its significance based on Monte Carlo simulations. The Bernoulli spatial scan statistic ¹⁸18. Kulldorff M. A spatial scan statistic. Commun Stat Theory Methods 1997; 26:1481-96. was used to detect spatial clustering of tye 2 diabetes mellitus among the participants of ELSA-Brasil. Analyses were performed at the individual level to maximize spatial heterogeneity ¹⁹19. Tamura K, Duncan DT, Athens JK, Bragg MA, Rienti Jr. M, Aldstadt J, et al. Geospatial clustering in sugar-sweetened beverage consumption among Boston youth. Int J Food Sci Nutr 2017; 68:719-25.. For statistical inference the null hypothesis of complete spatial randomness was rejected at a p-value ≤ 0.05.

The covariates of this study were divided into: individual characteristics of the participants and neighborhood characteristics of the area where the participants lived.

Individual characteristic variables included sex; age; race/skin color; schooling level; nature of occupation. This study also included tobacco use; alcohol consumption measured by the sum of doses of each type of alcoholic beverage consumed turned into grams, with excessive consumption being ≥ 210g/week of alcohol for men and ≥ 140g/week for women; leisure physical activity was assessed using the long-modified version of the International Physical Activity Questionnaire (IPAQ) and categorized according to time spent at different intensities of physical activity (light: < 600 MET-min/week; moderate: 600-3,000 MET-min/week; or vigorous: ≥ 3,000 MET-min/week) ²⁰20. Sjöström M, Ainsworth B, Bauman A, Bull F, Hamilton-Craig C, Sallis J. Guidelines for data processing analysis of the International Physical Activity Questionnaire (IPAQ) - short and long forms. http://www.ipaq.ki.se/scoring.pdf (accessed on 14/Jan/2022).
http://www.ipaq.ki.se/scoring.pdf... ; waist circumference (WC) measured at the midpoint between the last rib and the iliac crest, using a non-elastic anthropometric tape (Mabis, https://www.livehealthsmart.com) with 0.1cm precision; and abdominal obesity, which was classified according to IDF cut-off points: WC ≥ 90cm for men and WC ≥ 80cm for women ²¹21. Alberti KG, Zimmet P, Shaw J. Metabolic syndrome: a new worldwide definition. A consensus statement from the International Diabetes Federation. Diabet Med 2006; 23:469-80..

The neighborhood socioeconomic environmental variables were obtained from the 2010 Brazilian Demographic Census¹⁷17. Instituto Brasileiro de Geografia e Estatística. Censo demográfico 2010. Características da população e dos domicílios: resultados do universo. Rio de Janeiro: Instituto Brasileiro de Geografia e Estatística; 2011.. The unit of analysis to build up neighborhood variables was the census tracts. This study included: (1) neighborhood household income per capita (in Brazilian Reais - BRL) stratified in tertiles, the highest area income was used as the reference in data analysis; (2) percentage of neighborhoods with adequate housing (dwellers living in properly identified adequate houses/dwellers in owned homes); (3) percentage of neighborhoods with litter (dwellers with an accumulation of litter on the public roads/dwellers in owned homes); (4) percentage of neighborhoods with sidewalks (dwellers that have sidewalks/dwellers in owned homes); due to the asymmetric distribution, these last three variables were divided according to the best possible statistical participation using two categories: low and high (cut-off points = 67th percentile for percentage of neighborhoods with adequate housing, 85th percentile for percentage of neighborhoods with littering, 66th percentile for percentage of neighborhoods with sidewalks in the city of Belo Horizonte). For the city of Salvador, the only variable with two categories was the percentage of neighborhoods with exposed trash (cut-off points = 80th percentile), with the others divided by tertiles.

Then, a frequency distribution of individuals and socioeconomic environmental characteristics was performed (Tables 1 and 2), fitting the logistic regression models to estimate the magnitude of the associations of the individuals and neighborhood characteristics between participants living inside and those living outside the clusters. The models were adjusted for sex, age, race/skin-color, schooling level, smoking, alcohol consumption, nature of occupation, leisure physical activity, waist circumference, and neighborhood socioeconomic environmental variables.

All analyses and plots presented and discussed in this study were produced using the R programming language version 3.5.1 with the RStudio IDE v1.3.125 and the SaTScan program version 9.6.0.

Results

Tables 1 and 2 describe individual and neighborhood socioeconomic characteristics. The characteristics that predominated in the study population were female sex, aged 41 to 60 years, self-reported race/skin color as white (Belo Horizonte) or mixed-race/black (Salvador), higher education, non-manual work, never smoked, moderate alcohol consumption, and light leisure physical activity. Prevalence of type 2 diabetes mellitus was higher among women, participants with manual jobs, and those with abdominal adiposity (Table 1). In Belo Horizonte, the prevalence of type 2 diabetes mellitus was higher in neighborhoods with low-income household and in neighborhoods with a high percentage of littering. In Salvador, the prevalence of type 2 diabetes mellitus was higher in neighborhoods with the poorest environmental socioeconomic characteristics for all indicators included in this study (Table 2).

Figure 1 shows the cluster area with the highest prevalence of type 2 diabetes mellitus in Belo Horizonte (circle; n = 132). This area was in the northeastern region of the city, with a 4.8km radius. The prevalence ratios of type 2 diabetes mellitus were 28.8% and 13.5%, respectively, among those who lived inside and outside the cluster area.

Figure 2 shows the cluster area with the highest prevalence of type 2 diabetes mellitus in Salvador (n = 355). This area was in the northern region of the city, with a 8.5km radius. The prevalence of type 2 diabetes mellitus was 27.6% and 18.5%, respectively, among those who lived inside and outside the cluster area. The Figure 2 also shows the cluster area with a low prevalence of type 2 diabetes mellitus (n = 71). This area was in the southern region of the city, with a 0.92km radius. The prevalence of type 2 diabetes mellitus was 1.4%.

Tables 3, 4, and 5 present the comparison of the individual characteristic of the participants who lived inside and outside the cluster area of type 2 diabetes mellitus.

After adjusting for covariables, living inside the cluster area with a high prevalence of type 2 diabetes mellitus was associated with increased odds of self-reporting mixed-race and, primarily, black race/skin color, a lower education level, and manual work occupation than those living outside the cluster (Tables 3 and 4).

Table 6 shows the comparison of the socioeconomic neighborhood environmental characteristics, after adjustment for all covariables, from the area inside and outside of the type 2 diabetes mellitus cluster area.

In Belo Horizonte, 90.1% of the area within the cluster presented low-income neighborhoods. Thus, this location is characterized as an impoverished area, which is the only socioeconomic environmental factor independently related to the cluster type 2 diabetes mellitus area. In Salvador, the cluster with the highest prevalence of type 2 diabetes mellitus had a substantial and high variation in odds ratio (OR) estimated for each covariable (area income, neighborhoods with adequate housing, neighborhoods with litter, and neighborhoods with sidewalks). In the same municipality, participants living inside the cluster areas with a low prevalence of type 2 diabetes mellitus had lower odds of reporting mixed-race or black race/skin color and a lower schooling level, and higher odds of excessive alcohol consumption than participants living inside, after adjusting for covariables (Table 5). When we analyzed the OR, the cluster with a low prevalence of type 2 diabetes mellitus in Salvador exhibited geographical areas with higher incomes.

Discussion

This study used spatial scan methods to identify clusters of type 2 diabetes mellitus and verified individual and neighborhood characteristics associated with spatial clusters of type 2 diabetes mellitus in ELSA-Brasil participants living in Belo Horizonte and Salvador. One spatial cluster of type 2 diabetes mellitus was found in Belo Horizonte (higher prevalence of type 2 diabetes mellitus), whereas two spatial clusters of type 2 diabetes mellitus were found in Salvador (higher and lower prevalence of type 2 diabetes mellitus). At the individual level, black and mixed-race individuals and lower schooling levels were associated with greater chances of belonging to the cluster area with a high prevalence of type 2 diabetes mellitus in Belo Horizonte and Salvador, even after adjustments for the individual covariables. In the cluster identified with a low prevalence of type 2 diabetes mellitus in Salvador, this study found a minimal chance of black and low schooling level participants living in this area. In both cities, the low levels of neighborhood income per capita were related to high spatial clusters of type 2 diabetes mellitus. In Salvador, the best level of these variables (neighborhood income per capita and pertcentage of neighborhoods with adequate housing) were associated with the low spatial cluster of type 2 diabetes mellitus.

Our findings indicate a high prevalence of type 2 diabetes mellitus in participants of ELSA-Brasil from Belo Horizonte (13.5%) and Salvador (18.5%). In Belo Horizonte and Salvador, the occurrence of type 2 diabetes mellitus was greater in the cluster areas located, respectively, in the northeastern and northern regions compared to other areas. These cluster areas were characterized as being impoverished locations, with lower neighborhood income and higher proportions of mixed-race/black people, lower schooling levels, and manual work occupations. By contrast, the southern region of Salvador was a cluster area with a lower chance of type 2 diabetes mellitus. This area was characterized as a wealthier location, with a higher neighborhood income, and with a high schooling level and low proportion of black people . Finally, in both cities, we identified that, regardless of the individual characteristics, poor socioeconomic status of neighborhoods increased the chance of type 2 diabetes mellitus (low income, low percentage of adequate housing, high percentage of litter, and low percentage of sidewalks).

In summary, our results showed a relationship between poverty and social inequality and type 2 diabetes mellitus. Poorer people are more exposed to illness from NCDs, including type 2 diabetes mellitus, and to the worsening of their clinical conditions, due to the difficulty in meeting their health demands ²²22. 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; 104:2147-55.. Furthermore, low schooling levels make it difficult to understand health promotion, disease prevention, and treatment actions, limiting the subject’s empowerment regarding self-care ²²22. 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; 104:2147-55.^,2323. Tirapani LDS, Fernandes NMDS. A narrative review of the impacts of income, education, and ethnicity on arterial hypertension, diabetes mellitus, and chronic kidney disease in the world. Saudi J Kidney Dis Transpl 2019; 30:1084-96..

Regarding skin color, some studies have suggested that the racial and ethnic disparities observed in type 2 diabetes mellitus may reflect differences in socioeconomic factors associated with skin color rather than genetic issues ²⁴24. Link CL, McKinlay JB. Disparities in the prevalence of diabetes: is it race/ethnicity or socioeconomic status? Results from the Boston Area Community Health (BACH) survey. Ethn Dis 2009; 19:288-92.^,2525. Signorello LB, Schlundt DG, Cohen SS, Steinwandel MD, Buchowski MS, McLaughlin JK, et al. Comparing diabetes prevalence between African Americans and whites of similar socioeconomic status. Am J Public Health 2007; 97:2260-7..

Furthermore, our results indicated that the areas with the highest and lowest chance of the occurrence of type 2 diabetes mellitus had territorial characteristics that highlighted social inequities. The areas with the highest concentration of people with type 2 diabetes mellitus had higher percentages of low-income families and neighborhoods with litter, and lower percentages of neighborhoods with adequate housing and neighborhoods with sidewalks. This profile was the opposite in the area with the lowest concentration of people with type 2 diabetes mellitus. Previous studies corroborate our findings, as they also showed that the areas with the greatest chance of the occurrence of type 2 diabetes mellitus were characterized as areas with worse socioeconomic conditions ⁹9. Lord J, Roberson S, Odoi A. Investigation of geographic disparities of pre-diabetes and diabetes in Florida. BMC Public Health 2020; 20:1226.^,1111. Smurthwaite K, Bagheri N. Using geographical convergence of obesity, cardiovascular disease, and type 2 diabetes at the neighborhood level to inform policy and practice. Prev Chronic Dis 2017; 14:E91.. A survey conducted in Argentina found that the diabetes mortality rate was higher in provinces with low socioeconomic development. Such results could be a consequence of structural problems and health service coverage ²⁶26. Leveau CM, Marro MJ, Alonso V, Lawrynowicz AE. Does geographic context matter in diabetes-related mortality? Spatial and time trends in Argentina, 1990-2012. Cad Saúde Pública 2017; 33:e00169615..

The distribution of population and economic activities in the urban space of cities is not homogeneous, but rather very uneven in several aspects. This article exemplifies this spatial inequality, associated with the prevalence of type 2 diabetes mellitus. The literature on regional and urban economics indicates two major forces that act on this distribution of economic activities ²⁷27. Weber A. Theory of the location of industries. Chicago: Chicago University Press; 1969.. The first is a specific hierarchy in the complexity of products and services offered in urban environments.

The effect on land rent, or rents, in this spatial distribution of economic activities on urban space will also affect the spatial location of the residences or dwellings of a city’s population. Homes with more space available and more equipment associated with quality of life and leisure will be attracted to the proximity to these centers and the “concentric rings” formed in their surroundings. This trend will be reproduced by real estate developers who seek to maximize the income earned from the land they develop. Thus, the supply of high-standard housing is usually not homogeneous across space, but similar to economic activities concentrated in neighborhoods close to urban centers and constituents of so-called noble areas ²⁸28. Jacobs J. Morte e vida de grandes cidades. 3rd Ed. São Paulo: Martins Fontes; 2014..

The constitution of this “urban fabric” produces a socioeconomic segregation not only by income level but is also reproduced in the urban space. The families with the highest income are in the same neighborhoods and regions, which, due to the logic of distribution of economic activities, are the neighborhoods closest to complex and diversified urban services. This dynamic creates a differentiation in the quality of life of these populations compared to those who live in peripheral areas ²⁸28. Jacobs J. Morte e vida de grandes cidades. 3rd Ed. São Paulo: Martins Fontes; 2014.^,2929. Schootman M, Andresen EM, Wolinsky FD, Malmstrom TK, Miller JP, Yan Y, et al. The effect of adverse housing and neighborhood conditions on the development of diabetes mellitus among middle aged African Americans. Am J Epidemiol 2007; 166:379-87..

Contemplating policies that do not consider this socioeconomic segregation indicated by the spatial distribution reinforce the tendency of increasing spatial heterogeneity associated with the occurrence of chronic diseases ³⁰30. Crespo R, Alvarez C, Hernandez I, García C. A spatially explicit analysis of chronic diseases in small areas: a case study of diabetes in Santiago, Chile. Int J Health Geogr 2020; 19:24..

This study used the circular scan statistic of Kulldorff ¹⁸18. Kulldorff M. A spatial scan statistic. Commun Stat Theory Methods 1997; 26:1481-96. to indicate the spatial structure of the point process associated with type 2 diabetes mellitus. This is a well-known tool in spatial analysis, able to handle the major problems that have been faced by scan-type statistics in general: multiple hypothesis tests associated with a scanning window that moves through many regions of varying shapes and sizes, and the presence of non-negligible spatial correlations in the underlying point process. In fact, statistical inference under these circumstances tends to be problematic: the correlations that arise between the different tests due to factors such as proximity and adjacency invalidate standard procedures such as Bonferroni-type corrections for p-values. The method proposed by Kulldorff ¹⁸18. Kulldorff M. A spatial scan statistic. Commun Stat Theory Methods 1997; 26:1481-96. solves both problems by considering a maximum likelihood ratio test based on a single p-value associated with the most likely cluster area. From an inferential point of view, this is an important advantage of this approach and one of the main reasons for it becoming a standard in investigations such as the one we presented here.

The cross-sectional design of this study limits the possibility of establishing temporal relation between variables. The association observed between the neighborhood and individual characteristics with type 2 diabetes mellitus inside and outside the clusters may be subject to unmeasured sources of confounding factors. However, generalization in epidemiological studies may also be based on the plausibility of associations ³¹31. Rothman KJ, Gallacher JEJ, Hatch EE. Why representativeness should be avoided. Int J Epidemiol 2013; 42:1012-4. to guide, for example, prevention and control programs to the different areas identified.

Conclusion

In conclusion, individual and neighborhood socioeconomic characteristics influenced the geographic distribution of participants with type 2 diabetes mellitus, leading to cluster areas with higher and lower chances of the occurrence of diabetes, characterized as poor and wealthy areas, respectively.

Acknowledgments

The authors thank the staff and participants of the ELSA-Brasil study for their important contributions. Brazilian Ministry of Health; Brazilian Funding Authority for Studies and Projects (FINEP); Brazilian National Research Council (CNPq) and Brazilian Coordination for the Improvement of Higher Education Personnel (CAPES).

References

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