Document Type: Original Article
Non-communicable Diseases Research Center, Department of Epidemiology, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran
Division of Neuroradiology, The Russell H. Morgan Department of Radiological Sciences, Baltimore, MD, USA
MSc of Epidemiology, Student Research Committee, Larestan University of Medical Sciences, Larestan, Iran
Department of Epidemiology, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran
Objective: Type 2 diabetes is the most prevalent chronic disease in the world. Timely and appropriate control can significantly reduce the burdens and costs of this disease. Although insulin injection is the most efficient method to control type 2 diabetes, patients avoid this method for unknown reasons. The main aim of the present study is to determine the factors influential in non-adherence to insulin using tools and models that have not been applied in this field so far.
Methods: The tendency to insulin injection in 457 patients with type 2 diabetes was investigated in this cross-sectional study using the classic logistic regression and new learning algorithms, including conditional tree, conditional forest, and random forest. Different fits were compared so that the best model can be determined to identify the factors in non-adherence to insulin.
Results: Although random forest had the highest accuracy among the fitted models, all the methods had a relative consensus that having life insurance, academic education, and insulin injection experience in immediate family members increase the tendency to accept insulin therapy. Our results also showed that younger patients and those who were committed to a specific diet better approved insulin therapy.
Conclusions: The reasons for non-adherence to insulin can be summarized in economic and psychological aspects. Therefore, the health system policies are recommended to address economic issues and also raise public awareness about this treatment method.
- SOROUSH M, ABBASZADEH M, SOROOSH S. The Study of Bone Density in Type 2 Diabetic Patients and Comparison with Non-Diabetic Patients Referred to Two Hospitals in Tehran, Iran. Biomed & Pharmacol J. 2014; 7(2):585-9.
- Deepika N, Poonkuzhali S. Design of Hybrid Classifier for Prediction of Diabetes through Feature Relevance Analysis. Int J Innov Res Sci Eng Technol. 2015; 2(10):788-93.
- Larejani B, Zahedi F. Epidemiology of diabetes mellitus in Iran. J Diabetes Metab Disord. 2001; 1(1):1-8.
- Mohamed EI, Linder R, Perriello G, Di ND, Pöppl S, De AL. Predicting Type 2 diabetes using an electronic nose-based artificial neural network analysis. Diabetes Nutr Metab. 2002; 15(4):215-21.
- Farsaei S, Radfar M, Heydari Z, Abbasi F, Qorbani M. Insulin adherence in patients with diabetes: risk factors for injection omission. Prim Care Diabetes. 2014; 8(4):338-45.
- Organization WH. Screening for type 2 diabetes: report of a World Health Organization and International Diabetes Federation meeting. 2003
- Aljumah AA, Ahamad MG, Siddiqui MK. Application of data mining: Diabetes health care in young and old patients. J King Saud Univ Comp & Info Sci. 2013; 25(2):127-36.
- Dall TM, Zhang Y, Chen YJ, Quick WW, Yang WG, Fogli J. The economic burden of diabetes. 2010; Health Aff, 29(2):297-303.
- Meece J. Dispelling myths and removing barriers about insulin in type 2 diabetes. Diabetes Educ. 2006; 32(1):9-18.
- Asche C, Bode B, Busk A, Nair S. The economic and clinical benefits of adequate insulin initiation and intensification in people with type 2 diabetes mellitus. Diabetes Obes Metab. 2012; 14(1):47-57.
- Brod M, Kongsø JH, Lessard S, Christensen TL. Psychological insulin resistance: patient beliefs and implications for diabetes management. Qual Life Res. 2009; 18(1):23-32.
- Morris AD, Boyle DI, McMahon AD, Greene SA, MacDonald TM, Newton RW, et al. Adherence to insulin treatment, glycaemic control, and ketoacidosis in insulin-dependent diabetes mellitus. The Lancet. 1997; 350(9090):1505-10.
- Peyrot M, Rubin RR, Kruger DF, Travis LB. Correlates of insulin injection omission. Diabetes care. 2010; 33(2):240-5.
- Mirahmadizadeh A, Delam H, Seif M, Banihashemi SA, Tabatabaee H. Factors Affecting Insulin Compliance in Patients with Type 2 Diabetes in South Iran, 2017: We Are Faced with Insulin Phobia. Iran J Med Sci. 2018; 44(3):204-14.
- Pinhas‐Hamiel O, Hamiel U, Greenfield Y, Boyko V, Graph‐Barel C, Rachmiel M, et al. Detecting intentional insulin omission for weight loss in girls with type 1 diabetes mellitus. Int J Eat Disord. 2013; 46(8):819-25.
- Huang G-M, Huang K-Y, Lee T-Y, Weng JT-Y. An interpretable rule-based diagnostic classification of diabetic nephropathy among type 2 diabetes patients. BMC bioinformatics, BioMed Central. 2015.
- Leung RK, Wang Y, Ma RC, Luk AO, Lam V, Ng M, et al. Using a multi-staged strategy based on machine learning and mathematical modeling to predict genotype-phenotype risk patterns in diabetic kidney disease: a prospective case–control cohort analysis. BMC Nephrol. 2013; 14(1):162-72.
- Tapak L, Mahjub H, Hamidi O, Poorolajal J. Real-data comparison of data mining methods in prediction of diabetes in iran. Healthc Inform Res. 2013; 19(3):177-85.
- Safe M, Faradmal J, Mahjub H. A Comparison between Cure Model and recursive partitioning: a retrospective cohort study of Iranian female with breast cancer. Comput Math Methods Med, 2016.
- Safe M, Faradmal J, Poorolajal J, Mahjub H. Model-based Recursive Partitioning for Survival of Iranian Female Breast Cancer Patients: Comparing with Parametric Survival Models. Iran J Public Health. 2017; 46(1):35-43.
- Hastie T, Tibshirani R, Friedman J, Franklin J. The elements of statistical learning: data mining, inference and prediction. The Mathematical Intelligencer. 2005; 27(2):83-5.
- Strobl C, Boulesteix A-L, Zeileis A, Hothorn T, editors. Bias in random forest variable importance measures. Workshop on Statistical Modelling of Complex Systems. 2006.
- Hertz RP, Unger AN, Lustik MB. Adherence with pharmacotherapy for type 2 diabetes: a retrospective cohort study of adults with employer-sponsored health insurance. Clin Ther. 2005; 27(7):1064-73.
- Kaplan RC, Bhalodkar NC, Brown EJ, White J, Brown DL. Race, ethnicity, and sociocultural characteristics predict noncompliance with lipid-lowering medications. Prev Med . 2004; 39(6):1249-55.
- 25. Anderson B, Vangsness L, Connell A, Butler D, Goebel‐Fabbri A, Laffel L. Family conflict, adherence, and glycaemic control in youth with short duration type 1 diabetes. Diabet Med. 2002; 19(8):635-42.
- Morris AD, Boyle DI, MacAlpine R, Emslie-Smith A, Jung RT, Newton RW, et al. The diabetes audit and research in Tayside Scotland (DARTS) study: electronic record linkage to create a diabetes register. Bmj. 1997; 315(7107):524-8.
- Strobl C, Kopf J, Zeileis A. Rasch trees: A new method for detecting differential item functioning in the Rasch model. Psychometrika. 2015; 80(2):289-316.
- de Miguel-Yanes JM, Shrader P, Pencina MJ, Fox CS, Manning AK, Grant RW, et al. Genetic risk reclassification for type 2 diabetes by age below or above 50 years using 40 type 2 diabetes risk single nucleotide polymorphisms. Diabetes care. 2011; 34(1):121-5.
- Khattab M, Khader YS, Al-Khawaldeh A, Ajlouni K. Factors associated with poor glycemic control among patients with type 2 diabetes. Journal of diabetes and its complications. 2010; 24(2):84-9.
- Caro JJ, Salas M, Speckman JL, Raggio G, Jackson JD. Persistence with treatment for hypertension in actual practice. CMAJ. 1999; 160(1):31-7.
- Jin J, Sklar GE, Oh VMS, Li SC. Factors affecting therapeutic compliance: A review from the patient’s perspective. Ther Clin Risk Manag. 2008; 4(1):269-86.
- Donnan PT, MacDonald TM, Morris AD. Adherence to prescribed oral hypoglycaemic medication in a population of patients with Type 2 diabetes: a retrospective cohort study. Diabet Med. 2002; 19(4):279-84.
- 33. de Oliveira SMS, Guimarães DB, Reis JS. Illiteracy and diabetes: educational program for people with type 2 diabetes in the public health system. Diabetol Metab Syndr. 2015; 7(1):A177.
- Hopkins D, Lawrence I, Mansell P, Thompson G, Amiel S, Campbell M, et al. Improved biomedical and psychological outcomes 1 year after structured education in flexible insulin therapy for people with type 1 diabetes. Diabetes Care. 2012; 35(8):1638-42.
- Fleiss JL, Levin B, Paik MC. Statistical methods for rates and proportions. 1st ed. John Wiley & Sons. 2013.
- Boulesteix AL, Janitza S, Kruppa J, König IR. Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics. Wiley Interdiscip Rev Data Min Knowl Discov. 2012; 2(6):493-507.
- Onwukwe C, Aki I. On selection of best sensitive logistic estimator in the presence of collinearity. Am J Appl Math Stat. 2015; 3(1):7-11.
- 38. Maroco J, Silva D, Rodrigues A, Guerreiro M, Santana I, de Mendonça A. Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests. BMC Res Notes. 2011; 4(1):299-313.
- 39. Safe M, Mahjub H, Faradmal J. A Comparative Study for Modelling the Survival of Breast Cancer Patients in the West of Iran. Glob J Health Sci. 2016; 9(2):215-22.