Top

Diabetes Therapy

Published in:

Open Access 01-12-2019 | Diabetes | Original Research

Forecasting the Prevalence of Diabetes Mellitus Using Econometric Models

Authors: Assel Mukasheva, Nurbek Saparkhojayev, Zhanay Akanov, Amy Apon, Sanjay Kalra

Published in: Diabetes Therapy | Issue 6/2019

Abstract

Introduction

The prevalence of diabetes in Kazakhstan has reached epidemic proportions, and this disease is becoming a major financial burden. In this research, regression analysis methods were employed to build models for predicting the number of diabetic patients in Kazakhstan in 2019, as this should aid the costing and policy-making performed by medical institutions and governmental offices regarding diabetes prevention and treatment strategies.

Methods

A brief review of mathematical models that are potentially useful for the task of interest was performed, and the most suitable methods for building predictive models were selected. The chosen models were applied to explore the correlation between population growth and the number of patients with diabetes as well as the correlation between the increase in gross regional product and the growth in the number of patients with diabetes. Moreover, the relationship of population growth and gross domestic product with the growth in the number of patients with diabetes in Kazakhstan was determined. Our research made use of the scikit-learn library for the Python programming language and functions for regression analysis built into the Microsoft Excel software.

Results

The predictive models indicated that the prevalence of diabetes in Kazakhstan will increase in 2019.

Conclusion

Mathematical models were used to find patterns in a comprehensive statistical dataset on registered diabetes patients in Kazakhstan over the last 15 years, and these patterns were then used to build models that can accurately predict the prevalence of diabetes in Kazakhstan.

Gao Y, Wang Y, Zhai X, He Y, Chen R, Zhou J, et al. Publication trends of research on diabetes mellitus and T cells (1997–2016): a 20-year bibliometric study. PLoS ONE. 2017;12(9):e0184869.CrossRef

Abutaleb MH. Diabetes mellitus: an overview. Pharm Pharmacol Int J. 2016;4(5):406–11. https://doi.org/10.15406/ppij.2016.04.00087.CrossRef

World Health Organization. Health topics: diabetes. https://www.who.int/diabetes/en/.

International Diabetes Federation. IDF diabetes atlas. 8th ed. Brussels: International Diabetes Federation; 2017.

Kazakhstan Society for the Study of Diabetes. Official website. https://www.kssd.site/.

World Health Organization. Diabetes profiles in countries, 2016. Weblink: https://www.who.int/diabetes/country-profiles/kaz_ru.pdf?ua=1.

Deepthi B, Sowjanya K, Lidiya B, et al. A modern review of diabetes mellitus: an annihilatory metabolic disorder. J In Silico In Vitro Pharmacol. 2017;3:1.

Kainz A, Hronsky M, Stel VS, Jager KJ, Geroldinger A, Dunkler D, Heinze G, Tripepi G, Oberbauer R. Prediction of prevalence of chronic kidney disease in diabetic patients in countries of the European Union up to 2025. Nephrol Dial Transpl. 2015;30(4):113–8. https://doi.org/10.1093/ndt/gfv073.CrossRef

Phan TP, Alkema L, Tai ES, et al. Forecasting the burden of type 2 diabetes in Singapore using a demographic epidemiological model of Singapore. BMJ Open Diabetes Res Care. 2014;2:e000012. https://doi.org/10.1136/bmjdrc-2013-000012.CrossRefPubMedPubMedCentral

10.

Kalra S, Akanov Z, Pleshkova A. Thoughts, words, action: the alma-ata declaration to diabetes care transformation. Diabetes Ther. 2018;9(3):873–6. https://doi.org/10.1007/s13300-018-0440-2.CrossRef

11.

Wu H, Yang S, Huang Z, He J, Wang X. Type 2 diabetes mellitus prediction model based on data mining. Informatics in medicine unlocked, vol. 10. Amsterdam: Elsevier; 2018. https://doi.org/10.1016/j.imu.2017.12.006.CrossRef

12.

Huang ES, Basu A, O’Grady M, Capretta JC. Projecting the future diabetes population size and related costs for the US. Diabetes Care. 2009;32(12):2225–9. https://doi.org/10.2337/dc09-0459.CrossRef

13.

Kim JY, Ku YS, Kim HJ, Trinh NT, Kim W, Jeong B, Lee EK. Oral diabetes medication and risk of dementia in elderly patients with type 2 diabetes. Diabetes Res Clin Pract. 2019;154:116–23. https://doi.org/10.1016/j.diabres.2019.07.004.CrossRef

14.

Li TC, Li CI, Liu CS, Lin WY, Lin CH, Yang SY, Chiang JH, Lin CC. Development and validation of prediction models for the risks of diabetes-related hospitalization and in hospital mortality in patients with type 2 diabetes. Metabolism. 2018;85:38–47. https://doi.org/10.1016/j.metabol.2018.02.003.CrossRef

15.

Albers DJ, Levine M, Gluckman B, Ginsberg H, Hripcsak G, Mamykina L. Personalized glucose forecasting for type 2 diabetes using data assimilation. PLoS Comput Biol. 2017;13(4):e1005232. https://doi.org/10.1371/journal.pcbi.1005232.CrossRefPubMedPubMedCentral

16.

Mishra V, Samuel C, Sharma SK. System modeling for forecasting of diabetes prevalence. Indian J Public Health Res Dev. 2018;9(7). https://doi.org/10.5958/0976-5506.2018.00628.9.CrossRef

17.

Saparkhojayev N, Mukasheva A (2018) The development of information system of formation and use of information resources for evaluation of parameters and evaluation of recommendations based on big data technology tools: work with Mongo DB. In: International Conference on Cyber Security and Computer Science (ICONCS’18); 2018 Oct 18–20; Safranbolu, Turkey.

18.

Saparkhojayev N, Mukasheva A, Saparkhojayev P (2017) The concept of monetization of IoT-based project: case of medical system in Kazakhstan. In: 15th International Scientific Conference on Information Technologies and Management; 2017 Apr 27–28; ISMA University, Riga, Latvia.

19.

Saparkhojayev N, Mukasheva A (2018) Introduction to BigData technology for diagnosis of diabetes. In: 16th International Scientific Conference on Information Technologies and Management; 2018 Apr 26–27; ISMA University, Riga, Latvia.

20.

Saparkhojayev N, Mukasheva A, Tussupova B, Zimin I. Development of the information system based on BigData technology to support endocrinologist-doctors for diagnosis and treatment of diabetes in Kazakhstan. In: 6th International Smartcity Symposium; 2018 Oct 15–17; Palm Garden Hotel, Putrajaya, Malaysia.

21.

Committee on Statistics, Ministry of National Economy of the Republic of Kazakhstan. GDP data on official website. http://stat.gov.kz/faces/homePage?_adf.ctrl-state=2fn371p1u_4&lang=ru&_afrLoop=8562077751869222.

22.

Bingham NH, Fry JM. Regression. Linear models in statistics. London: Springer; 2010. https://doi.org/10.1007/978-1-84882-969-5(ISBN 978-1-84882-968-8).CrossRef

23.

Gogtay NJ, Thatte UM. Principles of correlation analysis. J Assoc Physicians India. 2017;65:78–81.

24.

Rumyantsev PO, Saenko VA, Rumyantseva UV. Statisticheskie metody analiza v klinicheskoy praktike. Chast` I. Odnomernyy statisticheskiy analiz [Statistical methods of the analysis in clinical practice. Part I. One-dimensional statistical analysis]. Problemy endokrinologii – Endocrinology Problems. 2009;55(5):48–55.

25.

Committee on Statistics, Ministry of National Economy of the Republic of Kazakhstan. GRP data on official website: http://stat.gov.kz/faces/wcnav_externalId/homeNationalAccountIntegrated?lang=ru&_afrLoop=9050851751248091#%40%3F_afrLoop%3D9050851751248091%26lang%3Dru%26_adf.ctrl-state%3Dzudoooxo_4.

26.

Vach W. Regression models as a tool in medical research. Abingdon: Taylor & Francis; 2013 (ISBN-13: 978-1-4665-1749-3).

27.

Goldberg MA, Cho HA. Introduction to regression analysis. Southampton: WIT Press; 2004 (ISBN-13: 978-1853126246).

28.

Yuan M, Boston-Fisher N, Luo Y, Verma A, Buckeridge DL. A systematic review of aberration detection algorithms used in public health surveillance. J Biomed Inform. 2019;94:103181. https://doi.org/10.1016/j.jbi.2019.103181.CrossRef

29.

Makridakis S, Spiliotis E, Assimakopoulos V. Statistical and machine learning forecasting methods: concerns and ways forward. PLoS ONE. 2018;13(3):e0194889. https://doi.org/10.1371/journal.pone.0194889.CrossRefPubMedPubMedCentral

30.

Sindeeva LV, Medvedeva NN, Nikolaev VG, Strelkovich NN, Orlova II (2013) Application of regression analysis methods in the biomedical researches. Bull New Med Technol. 2013;20(2):S216–S219.

31.

Andersen PK, Skovgaard LT. Regression with linear predictors. New York: Springer; 2010. https://doi.org/10.1007/978-1-4419-7170-8(ISBN 978-1-4419-7169-2).CrossRef

32.

Cherkashina YA (2015) Application of regression analysis for solving diagnosis problem of children’s health. Mod Prob Sci Educ. 2015;1(1).

33.

Yan X, Su X. Linear regression analysis: Theory and computing. Singapore: World Scientific; 2009. ISBN-13: 978-981-283-410-2. https://doi.org/10.1142/6986

34.

Montgomery DC, Peck EA, Vining GG. Introduction to linear regression analysis, vol. 5. Hoboken: Wiley; 2012. p. 672 (ISBN: 978-0-470-54281-1).

35.

4analytics. 3 ways to calculate a polynomial in Excel. https://4analytics.ru/trendi/3-sposoba-rascheta-polinoma-v-excel.html.

36.

Excel2. Least square method: exponential dependence in MS Excel. https://excel2.ru/articles/mnk-eksponencialnaya-zavisimost-v-ms-excel.

37.

Python Software Foundation. The official home of the Python programming language. https://www.python.org/.

38.

Scikit-learn Authors. Scikit-learn: Machine learning in Python. https://scikit-learn.org/stable/.

39.

PANDAS Project Core Team. Python Data Analysis Library. https://pandas.pydata.org/.

40.

Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825–30.

Title: Forecasting the Prevalence of Diabetes Mellitus Using Econometric Models
Authors: Assel Mukasheva
Nurbek Saparkhojayev
Zhanay Akanov
Amy Apon
Sanjay Kalra
Publication date: 01-12-2019
Publisher: Springer Healthcare
Keyword: Diabetes
Published in: Diabetes Therapy / Issue 6/2019
Print ISSN: 1869-6953
Electronic ISSN: 1869-6961
DOI: https://doi.org/10.1007/s13300-019-00684-1

ACC 2024 Congress Coverage

Heart Failure Video

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Forecasting the Prevalence of Diabetes Mellitus Using Econometric Models

Abstract

Introduction

Methods

Results

Conclusion

ACC 2024 Congress Coverage

Year in Review: Heart failure and cardiomyopathies

Semaglutide benefits people with type 2 diabetes and obesity-related heart failure

Incentivised to exercise

New intervention for STEMI-related cardiogenic shock

» View more highlights on the ACC 2024 congress hub page

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Introduction

Methods

Results

Conclusion

Please log in to get access to this content

Other articles of this Issue 6/2019

Prevalence and the Association of Body Mass Index and Other Risk Factors with Prediabetes and Type 2 Diabetes Among 50,867 Adults in China and Sweden: A Cross-Sectional Study

Improving Type 2 Diabetes Management in General Practice Using a Second-Generation Basal Insulin Analogue Insulin Glargine 300 U/mL: A Practical Guide

Is There a Role for Informal Caregivers in the Management of Diabetic Foot Ulcers? A Narrative Review

Drug Utilization Patterns in Patients with Diabetes Initiating Sodium Glucose Co-Transporter-2 Inhibitors (SGLT2i) in Japan: A Multi-Database Study (2014–2017)

Correction to: Cardiac Autonomic Neuropathy in Obesity, Metabolic Syndrome and Prediabetes: A Narrative Review

Preference for Subcutaneously Administered Low-Dose Glucagon Versus Orally Administered Glucose for Treatment of Mild Hypoglycemia: A Prospective Survey Study

ACC 2024 Congress Coverage

Year in Review: Heart failure and cardiomyopathies

Semaglutide benefits people with type 2 diabetes and obesity-related heart failure

Incentivised to exercise

New intervention for STEMI-related cardiogenic shock

» View more highlights on the ACC 2024 congress hub page