IJSTR >> Volume 8 - Issue 8, August 2019 Edition

[Full Text]

AUTHOR(S)

Pallavi Rastogi, B.K. Singh

KEYWORDS

Goodness-of-Fit, Hosmer-Lemeshow test, Logistic regression, odds ratio, ROC curve

ABSTRACT

This study is based on the development of multivariate logistic regression model to assess the effect of various risk factors like age, BMI, meal-regularity and fast-food consumption on the prevalence of diabetes spatially in urban and rural areas of India. The existence of non-multicollinearity, non-normality and non-linearity between the variables was studied. The test-of-association showed that age, BMI and fast-food were significantly associated with diabetes in rural(p<0.05). While, age and BMI were found to be associated with diabetes in urban area. The Wald test and Odds ratio (OR>1)showed that age and BMI were significant predictors of diabetes. The intake of fast-food has 4.08 times more effect on diabetes than those who do not take. Similarly, the persons with regular nutritional diet were at low risk of getting diabetic. The area under the ROC curve showed the better performance of the model. Hence, the developed logistic regression model can be a powerful statistical technique for identifying the association of most prominent risk factor to diabetes and thus timely notify to take needful actions to reduce the risk of getting diabetes.

REFERENCES

[1]. S. Mendis, P. Puska, B. Norrving (ed).: World Health Organization (in collaboration with the World Heart Federation and World Stroke Organization), Geneva: Global Atlas on Cardiovascular Disease Prevention and Control, 2011.
[2]. Diabetes is India's fastest growing disease https://www.firstpost.com/india/diabetes-is-indias-fastest-growing-disease-72-million-cases-recorded-in-2017-figure-expected-to-nearly-double-by-2025-4435203.html [last accessed on 12-Apr-2019]
[3]. IDF SEA Members.https://www.idf.org/our-network/regions-members/south-east-asia/members/94-india.html [last accessed on 12-Apr-2019]
[4]. S.A. Kaveeshwar and J. Cornwal, “The Current State of Diabetes Mellitus in India,” Australas. Med. J., vol. 7, no. 1, pp. 45-48, 2014.
[5]. S. Wild, G Roglic, A. Green, R. Sicree and H. King, “Global Prevalence of Diabetes: Estimates For The Year 2000 and Projections For 2030,” Diabetes Care, Vol. 27, no. 5, pp. 1047-1053, 2004.
[6]. P. Rastogi, V. Rastogi and B.K. Singh, “A Statistical Assessment of Effect of Nutritional Habits on Human Health and Weight,” Int. J. Adv. Res. Sci. Eng., Vol. 7, no.1, pp. 281-295, 2018.
[7]. J.M. Tetrault, M. Sauler, C.K. Wells and J. Concato, “Reporting of Multivariable Methods in The Medical Literature,” J. Investig. Me. Vol. 56, no. 7, pp. 954-957, 2008.
[8]. C. Ronald, Log-Linear Models and Logistic Regression. Second edition. Springer, 1990.
[9]. V. Bewick, L. Cheek and J. Ball, “Statistics Review 14: Logistic Regression,” Crit. Care, vol. 9, no. 1, pp. 112-118, 2005.
[10]. V. Bewick, L. Cheek and J. Ball, “Statistics Review 7: Correlation And Regression,” Crit. Care, vol. 7, pp. 451-459, 2003.
[11]. V. Bewick, L. Cheek and J. Ball, “Statistics Review 13: Receiver Operating Characteristic (ROC) Curves,” Crit. Care, Vol. 8, pp. 508-512, 2004.
[12]. V. Bewick, L. Cheek and J. Ball, “Statistics Review 11: Assessing Risk,” Crit. Care, vol. 8, pp. 287-291, 2004.
[13]. D.W Hosmer, S. Lemeshow, Applied Logistic Regression. Second edition. John Wiley & Sons Inc: New York, 2000.
[14]. H. Midi, S.K. Sardar and S. Rana, “Collinearity Diagnostics of Binary Logistic Regression Model,” J. Interdisciplinary Mathematics, vol. 13, no. 3, pp. 253-267, 2010.
[15]. P. Kennedy, A Guide To Econometrics. Oxford : Blackwell, London, 1992.
[16]. Multicollinearity, www.statisticssolutions.com/multicollinearity, [last accessed on 12-Apr-2019]
[17]. M.S. Kirkman, V.J. Briscoe, N. Clark, H. Florez, L.B. Haas, J.B. Halter, E.S. Huang, M.T. Korytkowski, M.N. Munshi, P.S. Odegard, R.E. Pratley and C.S. Swift, “ Diabetes in Older Adults,” Diabetes Care, vol. 35, no. 12, pp. 2650-2664, 2012.
[18]. F. Chentli, S. Azzoug and S. Mahgoun, “Diabetes Mellitus in Elderly,” Indian J. Endocrinol. Metab, vol. 19, no. 6, pp. 744–752, 2015.
[19]. R.H. Eckel, S.E. Kahn, E. Ferrannini, A.B. Goldfine, D.M. Nathan, M.W Schwartz, R.J. Smith and S.R. Smith, “Obesity and Type 2 Diabetes: What Can Be Unified and What Needs To Be Individualized?,” Diabetes Care, vol. 34, pp. 1424-1430 2011.
[20]. J.C. Bournat and C.W. Brown, “Mitochondrial Dysfunction in Obesity,” Curr. Opin. Endocrinol. Diabetes Obes, vol. 17, pp. 446–452, 2010.