Arima Model to Predict the Prevalence of Diabetes Type 1 and Type 2 Patients: A Case Study of Jos University Teaching Hospital
Termen Nanfwang Yunana *
Department of Mathematical Sciences, Abubakar Tafawa Balewa University, P.M.B 0248 Bauchi, Nigeria.
K. E. Lasisi
Department of Mathematical Sciences, Abubakar Tafawa Balewa University, P.M.B 0248 Bauchi, Nigeria.
A. M. kwami
Department of Mathematical Sciences, Abubakar Tafawa Balewa University, P.M.B 0248 Bauchi, Nigeria.
Douglas Jah Pam
Department of Mathematical Sciences, Abubakar Tafawa Balewa University, P.M.B 0248 Bauchi, Nigeria.
Sheyi Mafolasire
Federal College of Forestry, Jos, Nigeria.
Chibuike John Echebiri
Department of Mathematical Sciences, Abubakar Tafawa Balewa University, P.M.B 0248 Bauchi, Nigeria.
Friday Ezekiel Danung
Department of Mathematical Sciences, Abubakar Tafawa Balewa University, P.M.B 0248 Bauchi, Nigeria.
Salihu Gambo
Department of Statistics School of Technology, Kano State Polytechnic, Nigeria.
*Author to whom correspondence should be addressed.
Abstract
Diabetes Mellitus is a huge burden for human health, increasing number of patient is likely to result in rising demand for the medical emergencies. Due to limited number of hospitals with standard laboratory test kits to differentiate between type 1 and type 2 diabetes it is important to forecast the future incidences and prepare with proper resource planning. The monthly number of Diabetes patients obtained from Jos University Teaching Hospital is fitted by autoregressive integrated moving average (ARIMA) model. Dataset starting from January, 2010 to December,2020. Using ARIMA, several models were evaluated based on the Bayesian Information Criterion (BIC) and Ljung-Box Q statistics. ARIMA(3, 1, 1) is found to be better and used to describe and predict the future trends of Diabetes type 1 and ARIMA(1,1,1) is a better model to predict the future prevalence of diabetes type 2. Therefore, the proposed model will help in the appropriate planning and allocation of resources for emergencies.
Keywords: Diabetes, mellitus, hospital, ARIMA, statistics, forecasting, BIC
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References
Diabetes Care. 2006;29:S43–S48. Available:Carediabetesjournals.org
Nathan DM, Davidson MB, DeFronzo RA, Heine RJ, Henry RR, Pratley R, Zinman B. Impaired fasting glucose and impaired glucose tolerance: Implications for care. Diabetes Care. 2007;30:753–759.
Ronald G, Zubin P. Definition, classification and diagnosis of diabetes, pre-diabetes and metabolic syndrome. CDA Clinical Practice Guidelines Expert Committee. 2013;37:S8–S11.
World Health Organization, WHO; 2019.
Deshpande AD, Harris-Hayes M, Schootman M. Epidemiology of diabetes and Diabetes-Related complications.Diabetes Special Issue. Physical Therapy. 2008;88(11):1255.
Naemiratch B, Manderson L. Lay Explanations of Type 2 Diabetes in Bangkok, Thailand. Anthropology & Medicine. 2007;14(1):83.
International Diabetes Federation, IDF; 2007.
Akinkugbe OO. (Ed.). Final report of national survey on non-communicable diseases in Nigeria series 1. Lagos: Federal Ministry of Health and Social Services; 1997.
Wild S, Roglic G, Green A, Sicree R, King H. Global prevalence of diabetes: Estimates for the year 2000 and projections for 2030. Diabetes Care. 2004;27:1047–1053.
Zimmet P. The burden of type 2 diabetes: Are we doing enough? Diabetes & Metabolism. 2003;29:659–681.
Chris EE, Akpan UP, John OI, Daniel EN. Gender and age specific prevalence and associated risk factors of Type 2 Diabetes Mellitus in Uyo metropolis, South Eastern Nigeria. Diabeto logical Croatica. 2012;41:17–28.
Olatunbosun ST, Ojo PO, Fineberg NS, Bella AF. Prevalence of diabetes mellitus and impaired glucose tolerance in a group of urban adults in Nigeria. Journal of the National Medical Association. 1998;90:293–301.
Owoaje EE, Rotimi CN, Kaufman JS, Tracy J, Cooper RS. Prevalence of adult diabetes in Ibadan, Nigeria. East African Medical Journal. 1997;74:299–302.
Nyenwe EA, Odia OJ, Ihekwaba AE, Ojule A, Babatunde S. Type 2 diabetes in adult Nigerians: A study of its prevalence and risk factors in Port Harcourt, Nigeria. Diabetes Research and Clinical Practice. 2003;62:177– 185.
Lucia YO, Prisca OA. Type 2 diabetes mellitus and impaired fasting plasma glucose in Urban South Western Nigeria. International Journal of Diabetes and Metabolism. 2012;21:9–12.
Unwin N, Sobugwi E, Alberti KGMM. Type 2 diabetes: The challenge of preventing a global epidemic. Diabetes International. 2001;11:4–8.
Zeng Q, et al. Time series analysis of temporal trends in the pertussis incidence in Mainland China from 2005 to 2016. Scientific Reports. 2016;6:32367.
Wang K, et al. The use of an autoregressive integrated moving average model for prediction of the incidence of dysentery in Jiangsu, China. Asia-Pacific Journal of Public Health. 2016;28:336–346.
Xu Q, et al. Forecasting the incidence of mumps in Zibo city based on a SARIMA model. International Journal of Environmental Research and Public Health. 2017;14:925.
Zhang X, Zhang T, Young AA, Li X. Applications and Comparisons of Four Time Series Models in Epidemiological Surveillance Data. PLoS ONE. 2014;9(2):e88075.
DOI: 10.1371/journal.pone.0088075
PMID: 24505382.
Zhang X, Liu Y, Yang M, Zhang T, Young AA, Li X. Comparative Study of Four Time Series Methods in Forecasting Typhoid Fever Incidence in China. PLoS ONE. 2013;8(5):e63116. DOI: 10.1371/journal.pone.0063116 PMID: 23650546.
Rios M, Garcia JM, Sanchez JA, Perez D. A statistical analysis of the seasonality in pulmonary tuberculosis. Eur J Epidemiol. 2000;16(5):483–8. PMID: 10997837.
Dowell D, Tian LH, Stover JA, Donnelly JA, Martins S, Erbelding EJ, et al. Changes in Fluoroquinolone Use for Gonorrhea Following Publication of Revised Treatment Guidelines. Am J Public Health. 2011;102(1):148–55. DOI: 10.2105/ajph.2011.300283 PMID: 22095341.
Ture M, Kurt I. Comparison of four different time series methods to forecast hepatitis A virus infection. Expert Systems with Applications. 2006;31(1):41–6.
Williams K. Investigating Risk Factors Associated with Syphilis Rate in the United States Based on ARIMA and ARCH/GARCH Time Series Models.
Martinez EZ, Silva EA, Fabbro AL. A SARIMA forecasting model to predict the number of cases of dengue in Campinas, State of Sao Paulo. Brazil. Revista da Sociedade Brasileira de Medicina Tropical. 2011;44:436–440.
Zheng YL, et al. Forecast model analysis for the morbidity of tuberculosis in Xinjiang, China. PLoS One. 2015;10:e116832.
Peng Y, et al. Application of seasonal auto-regressive integrated moving average model in forecasting the incidence of hand-foot-mouth disease in Wuhan, China. Journal of Huazhong University of Science and Technology-Medical Sciences. 2017;37:842–848.
Song X, et al. Time series analysis of influenza incidence in Chinese provinces from 2004 to 2011. Medicine (Baltimore). 2016;95:e3929.
Lemieux I. Reversing type 2 diabetes: The time for lifestyle medicine has come! Nutrients. 2020;12(7):1974. Available:https:// doi. org/ 10.3390/ nu120 71974
GBD 2017 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017 a systematic analysis for the global burden of disease study 2017. Lancet. 2018;392(10159):1789–858.
GBD 2017 Causes of Death Collaborators. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: A systematic analysis for the global burden of disease study 2017. Lancet. 2018;392(10159):1736–88.
GBD 2017 DALYs and HALE Collaborators. Global, regional, and national disability-adjusted life-years (DALYs) for 359 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2017: A systematic analysis for the global burden of disease study 2017. Lancet. 2018;392(10159):1859–922.
Harding JL, Pavkov ME, Magliano DJ, et al. Global trends in diabetes complications: A review of current evidence. Diabetologia. 2019;62(1):3–16.
Saeedi P, Petersohn I, Salpea P, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the international diabetes federation diabetes Atlas, 9(th) edition. Diabetes Res Clin Pract. 2019;157:107843.
Wong CKH, Jiao F, Tang EHM, et al. Direct medical costs of diabetes mellitus in the year of mortality and year preceding the year of mortality. Diabetes Obes Metab. 2018;20(6):1470–8.
Sun H, Saeedi P, Karuranga S, et al. IDF diabetes atlas: global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract. 2022;183:109119. Available:https:// doi. org/ 10. 1016/j. diabr es. 2021. 109119
GBD 2019 Dementia Forecasting Collaborators. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the global burden of disease study 2019. Lancet Public Health. 2022;7(2):e105–25.
Li S, Chen H, Man J, et al. Changing trends in the disease burden of esophageal cancer in China from 1990 to 2017 and its predicted level in 25 years. Cancer Med. 2021;10(5):1889–99.
Ji W, Xie N, He D, et al. Age-period-cohort analysis on the time trend of hepatitis B incidence in four prefectures of southern xinjiang, China from 2005 to 2017. Int J Environ Res Public Health. 2019;16(20):3886. Available:https:// doi. org/ 10. 3390/ ijerp h1620 3886
Akita T, Ohisa M, Kimura Y, et al. Validation and limitation of age-period-cohort model in simulating mortality due to hepatocellular carcinoma from 1940 to 2010 in Japan. Hepatol Res. 2014;44(7):713–9.
Zheng Y, Zhang L, Zhu X, et al. A comparative study of two methods to predict the incidence of hepatitis B in guangxi, China. PLoS One. 2020;15(6):e0234660.
Ceylan Z. Estimation of COVID-19 prevalence in Italy, Spain, and France. Sci Total Environ. 2020;729:138817. Available:https:// doi. org/ 10.1016/j. scito tenv. 2020. 138817
Fu Z, Xi X, Zhang B, et al. Establishment and evaluation of a time series model for predicting the seasonality of acute upper gastrointestinal bleeding. Int J Gen Med. 2021;14:2079–86.
Li Y, Guo C, Cao Y. Secular incidence trends and effect of population aging on mortality due to type 1 and type 2 diabetes mellitus in China from 1990 to 2019: findings from the global burden of disease study 2019. BMJ Open Diabetes Res Care. 2021;9(2):e002529. Available:https:// doi. org/ 10. 1136/ bmjdrc- 2021- 002529
Gallagher EJ, LeRoith D. Obesity and diabetes: The increased risk of cancer and cancer-related mortality. Physiol Rev. 2015;95(3):727–48.
Uzuke CA, 0biora-Ilouno HO, Eze FC, Daniel J. Time series Analysis of All Shares Index of Nigerian Stock Exchange: A Box-Jenkin Approach. 2016;6:25-26.
Singye T, Unhapipat S. Time series analysis of diabetes patients: A case study of Jigme Dorji Wangchuk National Referral Hospital in Bhutan. In Journal of Physics: Conference Series. IOP Publishing. 2018 Jun 1;1039(1):012033.
Box GP, Jenkins GM. Time series analysis: Forecasting and control, San Francisco, Calif, Holden Day; 1976.