https://doi.org/10.4081/gh.2022.1072
Modelling geographical heterogeneity of diabetes prevalence and socio-economic and built environment determinants in Saudi City - Jeddah
Submitted: 13 January 2022
Accepted: 21 April 2022
Published: 16 May 2022
Accepted: 21 April 2022
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
Type-2 diabetes is a growing lifestyle disease mainly due to increasing physical inactivity but also associated with various other variables. In Saudi Arabia, around 58.5% of the population is deemed to be physically inactive. Against this background, this study attempts explore the spatial heterogeneity of Type-2 diabetes prevalence in Jeddah and to estimate various socio-economic and built environment variables contributing to the prevalence of this disease based on modelling by ordinary least squares (OLS), weighted regression (GWR) and multi-scale geographically weighted (MGWR). Our OLS results suggest that income, population density, commercial land use and Saudi population characteristics are statistically significant for Type-2 diabetes prevalence. However, by the GWR model, income, commercial land use and Saudi population characteristics were significantly positive while population density was significantly negative in this model for 70.6%, 9.1%, 26.6% and 58.7%, respectively, out of 109 districts investigated; by the MGWR model, the corresponding results were 100%, 22%, 100% and 100% of the districts. With the given data, the corrected Akaike information criterion (AICc), the adjusted R2, the log-likelihood and the residual sum of squares (RSS) indices demonstrated that the MGWR model outperformed the GWR and OLS models explaining 29% more variance than the OLS model, and 10.2% more than the GWR model. These results support the development of evidence-based policies for the spatial allocation of health associated resources for the control of Type-2 diabetes in Jeddah and other cities in the Arabian Gulf.
Aguiar EJ, Morgan PJ, Collins CE, Plotnikoff RC, Callister R, 2014. Efficacy of interventions that include diet, aerobic and resistance training components for Type 2 diabetes prevention: a systematic review with meta-analysis. Int J Behav Nutr Phys Act 11:2.
DOI: https://doi.org/10.1186/1479-5868-11-2
Aljoufie M, Tiwari A, 2015a. Climate change adaptions for urban water infrastructure in Jeddah, Kingdom of Saudi Arabia. J Sustain Dev 8:3.
DOI: https://doi.org/10.5539/jsd.v8n3p52
Aljoufie M, Tiwari A, 2015b. Valuing ‘green infrastructure’ in Jeddah: a city lost in ‘grey’ infrastructure. J Archit Urban 39:248-59.
DOI: https://doi.org/10.3846/20297955.2015.1113901
Aljoufie M, Tiwari A, 2020. Exploring housing and transportation affordability in Jeddah. Hous Policy Debate, October 1-27. Available from: https://doi.org/10.1080/10511482.2020.1815070
DOI: https://doi.org/10.1080/10511482.2020.1815070
Aljoufie M, Tiwari A, 2021. Modeling road safety in car-dependent cities: case of Jeddah City, Saudi Arabia. Sustainability 13:1816.
DOI: https://doi.org/10.3390/su13041816
Arnaldo PS, Torres LM, 2005. Spatial distribution and sampling of Thaumetopoea pityocampa (Den. & Schiff.) (Lep. thaumetopoeidea) populations on Pinus pinaster Ait. in Montesinho, N. Portugal. For Ecol Manag 210:1-7.
DOI: https://doi.org/10.1016/j.foreco.2005.02.041
Atkinson MA, Eisenbarth GS, Michels AW, 2014. Type 1 diabetes. Lancet 383:69-82.
DOI: https://doi.org/10.1016/S0140-6736(13)60591-7
Brunsdon C, Fotheringham S, Charlton M, 1998. Geographically weighted regression. J Roy Stat Soc D-Sta J 47:431-43.
DOI: https://doi.org/10.1111/1467-9884.00145
Dutra AC, Silva LL, Pedroso RB, Tchuisseu YP, da Silva MT, Bergamini M, Scheidt JFSC, 2021. The impact of socioeconomic factors, coverage and access to health on heart ischemic disease mortality in a Brazilian Southern state: a geospatial analysis. Global Heart 16:770.
DOI: https://doi.org/10.5334/gh.770
Chan TC, Chiang P, Su M, Wang H, Liu MS, 2014. Geographic disparity in chronic obstructive pulmonary disease (COPD) mortality rates among the Taiwan population. PLoS One 9:e98170.
DOI: https://doi.org/10.1371/journal.pone.0098170
Colagiuri R, 2010. Diabetes: a pandemic, a development issue or both? Expert Rev Cardiovasc Ther 8: 305-9.
DOI: https://doi.org/10.1586/erc.10.12
Dawish MAA, Robert AA, Braham R, Hayek AAA, Saeed AA, Ahmed RA, Sabaan FSA, 2016. Diabetes mellitus in Saudi Arabia: a review of the recent literature. Curr Diabetes Rev 12:359-68.
DOI: https://doi.org/10.2174/1573399811666150724095130
Dieren SV, Beulens JWJ, Schouw YTVD, Grobbee DE, Nealb B, 2010. The global burden of diabetes and its complications: an emerging pandemic. Eur J Prev Cardiol 17:s3-8.
DOI: https://doi.org/10.1097/01.hjr.0000368191.86614.5a
Elhadd TA, Al-Amoudi AA, Alzahrani SA, 2007. Epidemiology, clinical and complications profile of diabetes in Saudi Arabia: a review. Ann Saudi Med 27:241-50.
DOI: https://doi.org/10.5144/0256-4947.2007.241
Fotheringham AS, Yang W, Kang W, 2017. Multiscale geographically weighted regression (MGWR). Ann Assoc Am Geogr 107:1247-65.
DOI: https://doi.org/10.1080/24694452.2017.1352480
Gariepy G, Kaufman JS, Blair A, Kestens Y, Schmitz N, 2015. Place and health in diabetes: the neighbourhood environment and risk of depression in adults with Type 2 diabetes. Diabet Med 32:944–50.
DOI: https://doi.org/10.1111/dme.12650
Gazzaz ZJ, 2020. Knowledge, attitudes, and practices regarding diabetes mellitus among university students in Jeddah, Saudi Arabia. Diabetes Metab Syndr Obes Targets Ther 13:5071-78.
DOI: https://doi.org/10.2147/DMSO.S287459
Gelman A, Goodrich B, Gabry J, Vehtari A, 2019. R-Squared for Bayesian regression models. Am Stat 73:307-9.
DOI: https://doi.org/10.1080/00031305.2018.1549100
Glazier RH, Creatore MI, Weyman JT, Fazli G, Matheson FI, Gozdyra P, Moineddin R, Kaufman-Shriqui V, Booth GL, 2014. Density, destinations or both? a comparison of measures of walkability in relation to transportation behaviors, obesity and diabetes in Toronto, Canada. PLoS One 9:e85295.
DOI: https://doi.org/10.1371/journal.pone.0085295
Goovaerts P, Xiao H, Adunlin G, Ali A, Tan F, Gwede CK, Huang Y, 2015. Geographically-weighted regression analysis of percentage of late-stage prostate cancer diagnosis in Florida. Appl Geogr 62:191-200.
DOI: https://doi.org/10.1016/j.apgeog.2015.04.018
Hipp JA, Chalise N, 2015. Spatial analysis and correlates of county-level diabetes prevalence, 2009-2010. Prev Chronic Dis 12:140404.
DOI: https://doi.org/10.5888/pcd12.140404
Hu FB, Satija A, Manson JE, 2015. Curbing the diabetes pandemic. JAMA 313:2319.
DOI: https://doi.org/10.1001/jama.2015.5287
Hurvich CM , Tsai CL, 1993. A corrected Akaike information criterion for vector autoregressive model selection. J Time Ser Anal 14:271-9.
DOI: https://doi.org/10.1111/j.1467-9892.1993.tb00144.x
Hutcheson GD, 1999. Ordinary least-squares regression. The Multivariate Social Scientist. SAGE Publ., London, UK, pp. 56-113.
DOI: https://doi.org/10.4135/9780857028075.d49
Ishiguro M, Sakamoto Y, Kitagawa G, 1997. Bootstrapping log likelihood and EIC, an extension of AIC. Ann Inst Stat Math 49:411-34.
DOI: https://doi.org/10.1023/A:1003158526504
Jiang J, Luo L, Xu P, Wang P, 2018. How does social development influence life expectancy? a geographically weighted regression analysis in China. Public Health 163:95-104.
DOI: https://doi.org/10.1016/j.puhe.2018.06.003
Jo EK, Seo EW, Lee KS, 2016. Spatial distribution of diabetes prevalence rates and its relationship with the regional characteristics. Health Policy Manag 26:30-8.
DOI: https://doi.org/10.4332/KJHPA.2016.26.1.30
Kohl HW, Craig CL, Lambert EV, Inoue S, Alkandari JR, Leetongin G, Kahlmeier S, 2012. The pandemic of physical inactivity: global action for public health. Lancet 380:294-305.
DOI: https://doi.org/10.1016/S0140-6736(12)60898-8
Lakhotia S, Lassarre S, Rao KR, Tiwari G, 2020. Pedestrian Accessibility and Safety around Bus Stops in Delhi. IATSS Res 44:55-66.
DOI: https://doi.org/10.1016/j.iatssr.2019.07.001
LaMotte LR, 1994. A note on the role of independence in t statistics constructed from linear statistics in regression models. Am Stat 48:238-40.
DOI: https://doi.org/10.1080/00031305.1994.10476065
Lee IM, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT, 2012. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet 380:219-29.
DOI: https://doi.org/10.1016/S0140-6736(12)61031-9
Lewis-Beck M, Bryman AE , Liao TF, 2003. The Sage encyclopaedia of social science research methods. Sage Publications, London, UK.
DOI: https://doi.org/10.4135/9781412950589
Mandic S, Jackson A, Lieswyn J, Mindell JS, Bengoechea EG, Spence JC, Coppell K, Wade-Brown C, Wooliscroft B, Hinckson E, 2020. Development of key policy recommendations for active transport in New Zealand: a multi-sector and multidisciplinary endeavour. J Transp Health 18:100859.
DOI: https://doi.org/10.1016/j.jth.2020.100859
Mangla P, Gupta S, Chopra A, Bhatia V, Vishwakarma R, Asthana P, 2020. Influence of socio-economic and cultural factors on type 1 diabetes management: report from a tertiary care multidisciplinary diabetes management Center in India. Indian J Pediatr 87:520-25.
DOI: https://doi.org/10.1007/s12098-020-03227-w
Mansfield ER, Helms BP, 1982. Detecting multicollinearity. Am Stat 36:158-60.
DOI: https://doi.org/10.1080/00031305.1982.10482818
Martynenko TS, 2021. Lifestyle diseases: health in the contemporary city. Rudn J Sociol 21:18-35.
DOI: https://doi.org/10.22363/2313-2272-2021-21-1-18-35
Miles J, 2005. R squared, adjusted R-squared. In: Encyclopaedia of Statistics in Behavioral Science; John Wiley & Sons, Ltd.: Chichester, UK.
Morgan JA, Tatar JF, 1972. Calculation of the residual sum of squares for all possible regressions. Technometrics 14:317-25.
DOI: https://doi.org/10.1080/00401706.1972.10488918
Ngom R, Gosselin P, Blais C, Rochette L, 2016. Type and proximity of green spaces are important for preventing cardiovascular morbidity and diabetes - a cross-sectional study for Quebec, Canada. Int J Environ Res 13:423.
DOI: https://doi.org/10.3390/ijerph13040423
Oshan TM, Smith JP, Fotheringham AS, 2020. Targeting the spatial context of obesity determinants via multiscale geographically weighted regression. Int J Health Geogr 19:11.
DOI: https://doi.org/10.1186/s12942-020-00204-6
Oshan, T, Li Z, Kang W, Wolf L, Fotheringham A, 2019. Mgwr: a python implementation of multiscale geographically weighted regression for investigating process spatial heterogeneity and scale. ISPRS Int J Geoinf 8:269.
DOI: https://doi.org/10.3390/ijgi8060269
Pasala SK, Rao AA, Sridhar GR, 2010. Built environment and diabetes. Int J Diabetes Dev Ctries 30:63.
DOI: https://doi.org/10.4103/0973-3930.62594
Pinter-Wollman N, Andrea J, Nancy MW, 2018. The impact of the built environment on health behaviours and disease transmission in social systems. Philos Trans R Soc Lond B Biol Sci 373:20170245.
DOI: https://doi.org/10.1098/rstb.2017.0245
Quiñones S, Goyal A, Ahmed ZU, 2021. Geographically weighted machine learning model for untangling spatial heterogeneity of type 2 diabetes mellitus (T2D) prevalence in the USA. Sci Rep 11:6955.
DOI: https://doi.org/10.1038/s41598-021-85381-5
Rogerson PA, 2009. Monitoring changes in spatial patterns. The SAGE handbook of spatial analysis. SAGE, USA, pp.343-354.
DOI: https://doi.org/10.4135/9780857020130.n18
Salmerón R, GarcÃa CB, GarcÃa J, 2018. Variance inflation factor and condition number in multiple linear regression. J Stat Comput Simul 88:2365-84.
DOI: https://doi.org/10.1080/00949655.2018.1463376
Saudi Gazette Report, 2020. Jeddah Boasts of 23 Walkways, Covering 49 km. Saudi Gazette, July 22, 2020. Available from: https://saudigazette.com.sa/article/595843
Tabb LP, Ortiz A, Judd S, Cushman A, McClure LA, 2020. Exploring the spatial patterning in racial differences in cardiovascular health between blacks and whites across the United States: The REGARDS Study. J Am Heart Assoc 9:016556.
DOI: https://doi.org/10.1161/JAHA.120.016556
Temelkova-Kurktschiev T, Stefanov T , 2012. Lifestyle and genetics in obesity and type 2 diabetes. Exp Clin Endocrinol 120:1-6.
DOI: https://doi.org/10.1055/s-0031-1285832
Tiwari A, Aljoufie M, 2021. Modeling spatial distribution and determinant of PM2.5 at micro-level using geographically weighted regression (GWR) to inform sustainable mobility policies in campus based on evidence from King Abdulaziz University, Jeddah, Saudi Arabia. Sustainability 13:12043.
DOI: https://doi.org/10.3390/su132112043
Torun AÖ, GöçerK, Yeşiltepe D, Argın G, 2020. Understanding the role of urban form in explaining transportation and recreational walking among children in a logistic GWR model: a spatial analysis in Istanbul, Turkey. J Transp Geogr 82:102617.
DOI: https://doi.org/10.1016/j.jtrangeo.2019.102617
Tu J, Tu W, 2018. How the relationships between preterm birth and ambient air pollution vary over space: a case study in Georgia, USA using geographically weighted logistic regression. Appl Geogr 92:31-40.
DOI: https://doi.org/10.1016/j.apgeog.2018.01.007
Tu J, Tu W, Tedders SH, 2012. Spatial variations in the associations of birth weight with socioeconomic, environmental, and behavioral factors in Georgia, USA. Appl Geogr 34:331-44.
DOI: https://doi.org/10.1016/j.apgeog.2011.12.009
Wang L, Chen R, Sun W, Yang X, Li X, 2019. Impact of high-density urban built environment on chronic obstructive pulmonary disease: a case study of Jing’an District, Shanghai. Int J Environ Res 17:252.
DOI: https://doi.org/10.3390/ijerph17010252
Wheeler DC, Páez A, 2010. Geographically weighted regression. In: Handbook of Applied Spatial Analysis, 461-86. Springer, Berlin Heidelberg, Germany.
DOI: https://doi.org/10.1007/978-3-642-03647-7_22
WHO, 2016. Diabetes country profiles. World Health Organization, Geneva, Switzerland. Available from: https://www.who.int/diabetes/country-profiles/sau_en.pdf
Wilson N, Brander B, Mansoor OD, Pearson AL, 2014. Building a reliable measure for unobtrusive observations of street-connecting pedestrian walkways. J Urban Health 91:1129-35.
DOI: https://doi.org/10.1007/s11524-014-9891-6
How to Cite
Murad, A., Faruque, F., Naji, A., Tiwari, A., Helmi, M., & Dahlan, A. (2022). Modelling geographical heterogeneity of diabetes prevalence and socio-economic and built environment determinants in Saudi City - Jeddah. Geospatial Health, 17(1). https://doi.org/10.4081/gh.2022.1072
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