References
- Agardh, A., Allebeck, P., Hallqvist, J., Moradi, T., & Sidorchuk, A. (2011). Type 2 diabetes incidence and socio-economic position: A systematic review and meta-analysis. International Journal of Epidemiology, 40(3), 804–818. https://doi.org/10.1093/ije/dyr029
- Battey, H. S., Cox, D. R., & Jackson, M. V. (2019). On the linear in probability model for binary data. Royal Society Open Science, 6, 190067. https://doi.org/10.1098/rsos.19006
- Beltran-Sánchez, H., & Andrade, F. C. D. (2016). Time trends in adult chronic disease inequalities by education in Brazil. International Journal for Equity in Health, 15, 139. https://doi.org/10.1186/s12939-016-0426-5
- Castro, M. C., Massuda, A., & Almeida, G. (2019). Brazil’s unified health system: The first 39 years and prospects for the future. The Lancet, 394(10195), 345–356. https://doi.org/10.1016/S0140-6736(19)31243-7
- Cohen, A. K., Rai, M., Rehkopf, D. H., & Abrams, B. (2013). Educational attainment and obesity: A systematic review. Obesity Reviews, 14(12), 989–1005. https://doi.org/10.1111/obr.12062
- Dagenais, G. R., Gerstein, H. C., Zhang, H., McQueen, M., & Lear, S. (2016). Variations in diabetes prevalence in low-, middle-, and high-income countries: Results from the prospective urban and rural epidemiological study. Diabetes Care, 39(5), 780–787. https://doi.org/10.2337/dc15-2338
- Diderichsen, F., & Andersen, I. (2019). The syndemics of diabetes and depression in Brazil – An epidemiological analysis. SSM Population Health, 7, 002–002. https://doi.org/10.1016/j.ssmph.2018.11.002
- Diderichsen, F., Hallqvist, J., & Whitehead, M. (2019). Differential vulnerability and susceptibility: How to make use of recent development in our understanding of mediation and interaction to tackle health inequalities. International Journal of Epidemiology, 48(1), 268–274. https://doi.org/10.1093/ije/dyy167
- Espelt, A., Kunst, A. E., & Palencia, L. (2012). Twenty years of socio-economic inequalities in type 2 diabetes mellitus prevalence in Spain, 1987–2006. European Journal of Public Health, 22(6), 765–771. https://doi.org/10.1093/eurpub/ckr158
- Fleischer, N. L., Diez Roux, A. V., & Alazraqui, M. (2011). Socioeconomic gradients in chronic disease risk factors in middle-income countries: Evidence of effect modification by urbanicity in Argentina. American Journal of Public Health, 101(2), 294–301. https://doi.org/10.2105/AJPH.2009.190165
- Fleischer, N. L., Henderson, A. K., & Yun-Hsuan, W. (2016). Disparities in diabetes by education and race/ethnicity in the U.S., 1973–2012. American Journal of Preventive Medicine, 51(6), 947–957. https://doi.org/10.1016/j.amepre.2016.06.019
- Ford, N. D., Patel, S. A., & Narayan, K. M. V. (2017). Obesity in low- and middle-income countries: Burden, drivers, and emerging challenges. Annual Review of Public Health, 38, 145–164. https://doi.org/10.1146/annurev-publhealth-031816-044604
- Goryakin, Y., & Suhrcke, M. (2014). Economic development, urbanization, technological change and overweight: What do we learn from 244 demographic and health surveys? Economics and Human Biology, 14, 109–127. https://doi.org/10.1016/j.ehb.2013.11.003
- Harper, S., King, N. B., Meersman, S. C., Reichman, M. E., Breen, N., & Lynch, J. (2010). Implicit value judgments in the measurement of health inequalities. The Milbank Quarterly, 88(1), 4–29. https://doi.org/10.1111/j.1468-0009.2010.00587.x
- Heraclides, A. M., Chandola, T., & Witte, D. R. (2011). Work stress, obesity and the risk of type 2 diabetes: Gender-specific bidirectional effect in the Whitehall II study. Obesity, 20(2), 428–433. https://doi.org/10.1038/oby.2011.95
- Holm, A., Ejrnæs, M., & Karlson, K. (2015). Comparing linear probability model coefficients across groups. Quality & Quantity, 49, 1823–1834. https://doi.org/10.1007/s11135-014-0057-0
- Horrace, W. C., & Oaxaca, R. L. (2006). Results on the bias and inconsistency of ordinary least squares for the linear probability model. Economics Letters, 90(3), 321–327. https://doi.org/10.1016/j.econlet.2005.08.024
- Houle, J., Beaulieu, M. D., Meunier, S., Coulombe, S., & Coté, J. (2016). Socioeconomic status and glycemic control in adult patients with type 2 diabetes: A mediation analysis. BMJ Open Diabetes Research Care, 4, e000184. https://doi.org/10.1136/bmjdrc-2015-000184
- Hurrelmann, K., Rathmann, K., & Richter, M. (2011). Welche Wohlfahrtspolitik fördert die Gesundheit? Der ungeklärte Zusammenhang von ökonomischer und gesundheitlicher Ungleichheit. Gesundheitswesen, 73(6), 335–343. https://doi.org/10.1055/s-0030-1265195
- IBGE. (2015). Índice de Gini da renda domiciliar per capita segundo Região, Unidade da Federação e Região Metropolitana. http://tabnet.datasus.gov.br/cgi/ibge/censo/cnv/giniuf.de.
- IBGE. (2017). Síntese de indicadores sociais: uma análise das condições de vida da população brasileira 2017. IBGE. https://www.ibge.gov.br/estatisticas/sociais/populacao/9221-sintese-de-indicadores-sociais.html?edicao=18830&t=publicacoes
- Imkampe, A. K., & Gulliford, M. C. (2011). Increasing socio-economic inequality in type 2 diabetes prevalence–repeated cross-sectional surveys in England 1994–2006. European Journal of Public Health, 21, 484–490. https://doi.org/10.1093/eurpub/ckq106
- Jones-Smith, J., Gordon-Larsen, P., Siddiqi, A., & Popkin, B. M. (2011). Cross-national comparisons of time trends in overweight inequality by socioeconomic status among women using repeated cross-sectional surveys from 37 developing countries, 1989–2007. American Journal of Epidemiology, 173(6), 667–675. https://doi.org/10.1093/aje/kwq428
- Kelly, S. J., & Ismail, M. (2015). Stress and type 2 diabetes: A review of how stress contributes to the development of type 2 diabetes. Annual Review of Public Health, 36, 441–462. https://doi.org/10.1146/annurev-publhealth-031914-12292
- Kim, G. R., & Nam, C. M. (2017). Temporal trends in educational inequalities in non-communicable diseases in Korea, 2007–2015. PLoS One, 12(12), e0190143. https://doi.org/10.1371/journal.pone.0190143
- Lundberg, M., Hallqvist, J., & Diderichsen, F. (1999). Exposure-dependent misclassification of exposure in interaction analyses. Epidemiology, 10(5), 545–549.
- Mackenbach, J. P., & Kunst, A. E. (1997). Measuring the magnitude of socio-economic inequalities in health: An overview of available measures illustrated with two examples from Europe. Social Science & Medicine, 44(6), 757–771. https://doi.org/10.1016/S0277-9536(96)00073-1
- Malta, D. C., Bernal, R. T. I., Souza, M. F. M., Szwarcwald, C. L., & Lima, M. H. (2016). Social inequalities in the prevalence of self-reported chronic non-communicable diseases in Brazil: National health survey 2013. International Journal for Equity in Health, 15, 153. https://doi.org/10.1186/s12939-016-0427-4
- Malta, D. C., Duncan, B. B., Schmidt, M. I., Machado, I. E., & da Silva, A. G. (2019). Prevalence of diabetes mellitus as determined by glycated hemoglobin in the Brazilian adult population, National Health Survey. Revista Brasiliera de Epidemiologia, 22(Suppl. 2), E190006. https://doi.org/10.1590/1980-549720190006.supl.2
- Mendoza-Romo, M. A., Zavala-Cruz, G. G., Padron-Salas, A., Ortiz-Nesme, F. J., & Ramírez-Arriola, M. C. (2017). Asociacion del índice de desarrollo humano y diabetes mellitus tipo 2 en unidades de medicina familiar del estado San Luis Potosi. Atención Familiar, 24(4), 156–159. https://doi.org/10.1016/j.af.2017.08.001
- Milaniak, I., & Jaffee, S. R. (2019). Childhood socioeconomic status and inflammation: A systematic review and meta-analysis. Brain and Behaviour Immunity, 78, 161–176. https://doi.org/10.1016/j.bbi.2019.01.018
- NCD RisC. (2016a). Worldwide trends in diabetes since 1980: A pooled analysis of 751 population-based studies with 4.4 million participants. The Lancet, 387, 1513–1530. https://doi.org/10.1016/S0140-6736(16)00618-8
- NCD RisC. (2016b). Trends in adult body-mass index in 200 countries from 1975 to 2014: A pooled analysis of 1698 population-based measurement studies with 19·2 million participants. The Lancet, 387, 1377–1396. https://doi.org/10.1016/S0140-6736(16)30054-X
- Ng, S. W., & Popkin, B. M. (2012). Time use and physical activity: A shift away from movement across the globe. Obesity Review, 13(8), 659–680. https://doi.org/10.1111/j.1467-789X.2011.00982.x.
- PNUD. (2013). Índice de Desenvolvimento Humano Municipal Brasileiro. PNUD, IPEA e FJP / Brasília, http://ipea.gov.br/portal/index.php?option=com_content&id=19153
- Popkin, B. M., Adair, L. S., & Ng, S. W. (2012). Now and then: The global nutrition transition: The pandemic of obesity in developing countries. Nutrition Reviews, 70(1), 3–21. https://doi.org/10.1111/j.1753-4887.2011.00456.x
- Popkin, B. M., & Reardon, T. (2018). Obesity and the food system transformation in Latin America. Obesity Review, 19(8), 1028–1064. https://doi.org/10.1111/obr.12694
- Rodrigues, P. R. M., Gonçalves-Silva, R. M. V., Ferreira, M. G., & Pereira, R. A. (2017). Viabilidade do uso de pergunta simplificada na avaliação da qualidade da dieta de adolescentes. Ciência & Saúde Coletiva, 22(5), 1565–1578. https://doi.org/10.1590/1413-81232017225.14102015
- Sacerdote, C., Ricceri, F., Rolandsson, O., Baldi, I., Chirlaque, M.-D., & Feskens, E. (2012). Lower educational level is a predictor of incident type 2 diabetes in European countries: The EPIC-InterAct study. International Journal of Epidemiology, 41(4), 1162–1173. https://doi.org/10.1093/ije/dys091
- Smith, P. M., Smith, B. T., Mustard, C. A., Lu, H., & Glazier, R. H. (2013). Estimating the direct and indirect pathways between education and diabetes incidence among Canadian men and women: A mediation analysis. Annals of Epidemiology, 23(3), 143–149. https://doi.org/10.1016/j.annepidem.2012.12.012
- Steele, C. J., Schöttker, B., Marshall, A. H., Kouvonen, A., & O’Doherty, M. G. (2017). Education achievement and type 2 diabetes – What mediates the relationship in older adults? Data from the ESTHER study: A population-based cohort study. BMJ Open, 7, e013569. https://doi.org/10.1136/bmjopen-2016-013569
- Szwarcwald, C. L., Malta, D. C., Pereira, C. A., Vieira, M. L. F. P., & Conde, W. L. (2014). Pesquisa Nacional de Saúde no Brasil: Concepção e metodologia de aplicação. Ciencia & Saude Coletiva, 19(2), 333–342. https://doi.org/10.1590/1413-81232014192.14072012
- Volaco, A., Cavalcanti, A. M., Filho, R. P., & Précoma, D. B. (2018). Socioeconomic status: The missing link between obesity and diabetes mellitus? Current Diabetes Review, 14(4), 321–326. https://doi.org/10.2174/1573399813666170621123227
- Wang, A., Stronks, K., & Arah, O. A. (2014). Global educational disparities in the associations between body mass index and diabetes mellitus in 49 low-income and middle-income countries. Journal of Epidemiology and Common Health, 68(8), 705–711. https://doi.org/10.1136/jech-2013-203200
- Wu, H., Meng, X., Wild, S. H., Gasevic, D., & Jackson, C. A. (2017). Socioeconomic status and prevalence of type 2 diabetes in mainland China, Hong Kong and Taiwan: A systematic review. Journal of Global Health, 7(1), 011103. https://doi.org/10.7189/jogh.07.011103