IDENTIFYING NEWLY DIAGNOSED DIABETES MELLITUS RISK FACTORS USING GRAPHICAL NETWORKS WITH EXPERT KNOWLEDGE

Ooi Wei Lim; Sarat C Dass

doi:10.37268/mjphm/vol.22/no.3/art.1434

Ooi Wei Lim Heriot-Watt Global College Malaysia, Heriot-Watt University, Malaysia
Sarat C Dass School of Mathematical and Computer Science, Heriot-Watt University, Malaysia

DOI: https://doi.org/10.37268/mjphm/vol.22/no.3/art.1434

Keywords: adolescents, newly diagnosed diabetes mellitus, behavioral risk factors, sociodemographic risk factors

Abstract

An increasing trend of newly diagnosed diabetes mellitus (DM) among adolescents is occurring worldwide, including Malaysia. This study aims to determine the overall relationships between risk factors on the prevalence of newly diagnosed diabetes mellitus among Malaysian adolescents. Current study uses a cross-sectional study, data from the Fifth National Health and Morbidity Survey 2015 which consists of individuals who ages 18 and above, extraction of 18 and 19 years old from the data set was done.. Bayesian networks modelling was performed by using graphical networks with expert knowledge to identify the risk factors of newly diagnosed diabetes mellitus among adolescents in Malaysia. Education levels, Body Mass Index (BMI), and physical inactivity were identified as the significant predictors of newly diagnosed DM. The highest conditional probability of developing newly diagnosed DM belongs to both obese and underweight respondents given they have no formal education (probability, pr = 0.5000), followed by obese respondents who had unclassified level of education (pr = 0.4709), and obese respondents with primary level of education (pr = 0.4692). The findings of current study provide insights and allows policymakers to plan for future interventions in order to monitor and reduce the high prevalence of newly diagnosed diabetes mellitus among adolescents in Malaysia.

References

Centers of Disease Control and Prevention. Diabetes basics. https://www.cdc.gov/diabetes/basics/diabetes.html. Published 2020. Updated 11 June 2020. Accessed 17 July, 2020.

National Institute of Diabetes and Digestive and Kidney Diseases. Diabetes overview. https://www.niddk.nih.gov/health-information/diabetes/overview. Published 2016. Accessed 17 July, 2020.

World Health Organization. Diabetes. https://www.who.int/news-room/fact-sheets/detail/diabetes. Published 2020. Updated 8 June 2020. Accessed 17 July, 2020.

Institute for Public Health. The National Health and Morbidity Survey 2011. Institute for Public Health, National Institutes of Health, Ministry of Health. http://iku.moh.gov.my/images/IKU/Document/REPORT/NHMS2011-VolumeII.pdf. Published 2011. Accessed 11 August, 2020.

Institute for Public Health. The National Health and Morbidity Survey 2015- Vol. II: Non-Communicable Diseases, Risk Factors & Other Health Problems. Institute for Public Health, National Institutes of Health, Ministry of Health. http://iku.moh.gov.my/images/IKU/Document/REPORT/nhmsreport2015vol2.pdf. Published 2015. Accessed 11 August, 2020.

Azizi E, Borzouei S, Soltanian AR. A Nonparametric Bootstrap Confidence Interval for the Prevalence of Undiagnosed Type 2 Diabetes Mellitus in Hamadan, West of Iran. Journal of research in health sciences. 2018;18(3):e00421.

Mehmood K, Junaid N. Prevalence of undiagnosed type 2 diabetes mellitus in Pakistan: Results of screen-diabetes disease registry. JPMA. 2018;68(1171).

Siu S, Wong K, Lee K, et al. Prevalence of undiagnosed diabetes mellitus and cardiovascular risk factors in Hong Kong professional drivers. Diabetes research and clinical practice. 2012;96(1):60-67.

Bener A, Zirie M, Janahi IM, Al-Hamaq AO, Musallam M, Wareham NJ. Prevalence of diagnosed and undiagnosed diabetes mellitus and its risk factors in a population-based study of Qatar. Diabetes research and clinical practice. 2009;84(1):99-106.

Pramono LA, Setiati S, Soewondo P, et al. Prevalence and predictors of undiagnosed diabetes mellitus in Indonesia. Age. 2010;46(53.6):100.100.

Rathmann W, Haastert B, Icks A, et al. High prevalence of undiagnosed diabetes mellitus in Southern Germany: target populations for efficient screening. The KORA survey 2000. Diabetologia. 2003;46(2):182-189.

Moy FM, Gan CY, Zaleha MKS. Body mass status of school children and adolescents in Kuala Lumpur, Malaysia. Asia Pacific journal of clinical nutrition. 2004;13(4):324-329.

Leahy S, O’Halloran A, O’Leary N, et al. Prevalence and correlates of diagnosed and undiagnosed type 2 diabetes mellitus and pre-diabetes in older adults: Findings from the Irish Longitudinal Study on Ageing (TILDA). Diabetes research and clinical practice. 2015;110(3):241-249.

Cheng YJ, Kanaya AM, Araneta MRG, et al. Prevalence of diabetes by race and ethnicity in the United States, 2011-2016. Jama. 2019;322(24):2389-2398.

Creatore MI, Moineddin R, Booth G, et al. Age-and sex-related prevalence of diabetes mellitus among immigrants to Ontario, Canada. Cmaj. 2010;182(8):781-789.

Noor S, Bushara S, Sulaiman A, Elmadhoun WM, Ahmed M. Undiagnosed diabetes mellitus in rural communities in Sudan: prevalence and risk factors. 2015. Eastern MediterraneanHealth Journal. 2015; 21(3).

Institute for Public Health. The National Health and Morbidity survey 2015 Methodology & General Findings. Institute for Public Health. http://iku.moh.gov.my/images/IKU/Document/REPORT/nhmsreport2015vol1.pdf. Published 2015. Accessed 10 May 2021.

AIMultiple. Data Cleaning: What it is, why it matters, best practices & tools. https://research.aimultiple.com/data-cleaning/#what-are-the-benefits-of-data-cleaning. Published 2020. Updated 10 August 2020. Accessed 12 August, 2020.

Lewandowski P. What is data cleaning and why is it important? https://sunscrapers.com/blog/why-is-clean-data-so-important-for-analytics-and-business-intelligence/. Published 2018. Accessed 6 August, 2020.

Marco Scutari J-BD. Bayesian Networks with Examples in R. Chapman and Hall/CRC; 2014.

Rahim FF, Abdulrahman SA, Kader Maideen SF, Rashid A. Prevalence and factors associated with prediabetes and diabetes in fishing communities in penang, Malaysia: A cross-sectional study. PLOS ONE. 2020;15(2):e0228570.

Tee ES, Yap RWK. Type 2 diabetes mellitus in Malaysia: current trends and risk factors. European Journal of Clinical Nutrition. 2017;71(7):844-849.

Rampal S, Rampal L, Rahmat R, et al. Variation in the Prevalence, Awareness, and Control of Diabetes in a Multiethnic Population: A Nationwide Population Study in Malaysia. Asia Pacific Journal of Public Health. 2009;22(2):194-202.

Schillinger D, Barton LR, Karter AJ, Wang F, Adler N. Does Literacy Mediate the Relationship between Education and Health Outcomes? A Study of a Low-Income Population with Diabetes. Public Health Reports. 2006;121(3):245-254.

Agardh EE, Sidorchuk A, Hallqvist J, et al. Burden of type 2 diabetes attributed to lower educational levels in Sweden. Population Health Metrics. 2011;9(1):60.

Sacerdote C, Ricceri F, Rolandsson O, et al. Lower educational level is a predictor of incident type 2 diabetes in European countries: The EPIC-InterAct study. International Journal of Epidemiology. 2012;41(4):1162-1173.

Jan Mohamed HJB, Yap RWK, Loy SL, Norris SA, Biesma R, Aagaard-Hansen J. Prevalence and Determinants of Overweight, Obesity, and Type 2 Diabetes Mellitus in Adults in Malaysia. Asia Pacific Journal of Public Health. 2014;27(2):123-135.

Dunstan DW, Salmon J, Owen N, et al. Physical Activity and Television Viewing in Relation to Risk of Undiagnosed Abnormal Glucose Metabolism in Adults. Diabetes Care. 2004;27(11):2603.

Morrato EH, Hill JO, Wyatt HR, Ghushchyan V, Sullivan PW. Physical Activity in U.S. Adults With Diabetes and At Risk for Developing Diabetes, 2003. Diabetes Care. 2007;30(2):203.

Hu FB, Li TY, Colditz GA, Willett WC, Manson JE. Television watching and other sedentary behaviors in relation to risk of obesity and type 2 diabetes mellitus in women. Jama. 2003;289(14):1785-1791.

Hu FB. Sedentary lifestyle and risk of obesity and type 2 diabetes. Lipids. 2003;38(2):103-108.