The Affect of Behavioral Risk Factors on Healthcare System Performance

Year 2021, , 1 - 14, 30.06.2021

Mehmet Emin Kurt , Cuma Çakmak , Murat Konca , İsmail Biçer

https://doi.org/10.25229/beta.831174

Cited By: 1

Abstract

Behavioural risk factors are known to have an impact on countries' health system performance. Behavioral risk factors include habits such as alcohol consumption, smoking, and patterns of food consumption which might lead to different types of obesity among different age groups in every community. In the context of OECD countries, this study aims at investigating whether behavioral risk factors have an impact on healthcare system performance or not. Data Envelopment Analysis (DEA) and then Ordinary Least Squares Regression (OLS) was utilized to bring into the open the factors that affect health performance scores of OECD countries. In OLS, the obtained health performance score was utilized as a dependent variable and alcohol and tobacco consumption and obesity rate were utilized as independent variables. According to the OLS results, The only variable that has a statistically significant effect on the health performance scores of OECD countries is the alcohol consumption rate. To reduce health expenditures and improve health system performance, OECD countries need to develop more effective, macro and micro, level policies to eliminate the negative effects of behavioral risk factors. Such policies might include health awareness campaigns and more strict taxing policies upon the risk factor products, in addition to increasing community-based healthcare services.

Keywords

Behavioral Risks Factors, Healthcare System Performance, Efficiency of Healthcare Systems, Data Envelopment Analysis, Ordinary Least Squares Regression

References

• World Health Organization (WHO) (200). World health report: Health systems improving performance. Chapter One, Why Do Health Systems Matter? 5-6.
• Kruk ME, Freedman LP. (2008). Assessing health system performance in developing countries: A review of the literature. Health policy. 2008; 85: 263-276.
• Caballer-Tarazona M, Moya-Clemente I, Vivas-Consuelo D, Barrachina-Martínez I. (2010). A model to measure the efficiency of hospital performance. Math Comp Model, 52: 1095-1102.
• Tyagi A, Singh P. (2017). Hospital performance management: A multi-criteria decision-making approach. International Journal of Healthcare Management, doi: 10.1080/20479700.2017.1337606
• Giannini M. (2015). Performance and quality improvement in healthcare organizations. International Journal of Healthcare Management, 8(3): 173-179. doi: 10.1179/2047971915Y.0000000002.
• Joachim A, Adeyemi KS. (2017). Performance role models among public health facilities: An application of data envelopment analysis. International Journal of Healthcare Management. doi: 10.1080/20479700.2017.1397379.
• Roberts M, Hsiao W, Berman P, Reich M. (2008). Getting health reform right: A guide to improving performance and equity. Oxford University Press. Oxford
• Papanicolas I, Smith P. (2013). Health system performance comparison: an agenda for policy, information and research. McGraw-Hill. United Kingdom.
• Brown S, Birtwistle J, Roe L, Thompson C. (1999). The unhealthy lifestyle of people with schizophrenia. Psychol. Med, 29: 697-701.
• WHO. (2017). Noncommunicable diseases: progress monitor 2017. Geneva.
• Nuti S. (2008). La valutazione della performance in sanità. Il Mulino. Italia.
• Anderson G, Hussey PS. (2001). Comparing health system performance in OECD countries. Health Aff, 20: 219-232.
• Charnes A, Cooper W, Rhodes, E. (1978). Measuring the efficiency of decision making units. Eur J Oper Res. 1978; 2: 429-444.
• Banker RD, Charnes A, Cooper WW. (1984). Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Manag Sci. 1984; 30: 1078-1092.
• Narcı HÖ. (2012). “Efficiency measurement and methods in health institutions”, In: Operations management in health ınstitutions, (Editors) Şahin İ. and Narcı HÖ. Anatolian University Publishing. pp.112-139. Eskişehir.
• Charnes A, Cooper W, Rhodes E. (1981). Evaluating program and managerial efficiency: An application of data envelopment analysis to program follow through. Manag Sci. 1981; 27: 668-697.
• Sherman H, Zhu J. (2006). Service productivity management: Improving service performance using data envelopment analysis (DEA). Springer, USA.
• Özcan Y. (2014). Healthcare benchmarking and performance evaluation. 2nd edition. Springer, 2014. USA.
• Chern JY, Wan TT. (2000). The Impact of the prospective payment system on the technical efficiency of hospitals. J Med Syst. 2000; 24: 159–172.
• Stöckl D, Dewitte K, Thienpont LM. (1998). Validity of linear regression in method comparison studies: Is it limited by the statistical model or the quality of the analytical input data? Clin Chem. 1998; 44:2340-6.
• Rimer BK, Glanz K. (2015). Health behavior: Theory, research, and practice. 5th edition. John Wiley & Sons, USA.
• Sturm R. (2002). The effects of obesity, smoking, and drinking on medical problems and costs. Health Aff. 2002; 21: 245-253.
• Sacks JJ, Gonzales KR, Bouchery EE, Tomedi LE, Brewer RD. (2015). 2010 national and state costs of excessive alcohol consumption. Am J Prev Med. 49(5): e73-e79.
• WHO (2018). Global status report on alcohol and health 2018. Geneva.
• Rabiee R, Agardh E, Coates MM, Allebeck P, Danielsson AK. (2017).Alcohol–attributed disease burden and alcohol policies in the BRICS–countries during the years 1990–2013. Journal of Global Health. 2017; 7:1-8.
• Johnston MC, Ludbrook A, Jaffray MA. (2012). Inequalities in the distribution of the costs of alcohol misuse in Scotland: A cost of illness study. Alcohol and Alcoholism, 47(6): 725-731.
• Stewart D, Han L, Doran T, McCambridge J. (2017). Alcohol consumption and all-cause mortality: An analysis of general practice database records for patients with long-term conditions. J Epidemiol Community Health, 71(8):729-735.
• Xi B, Veeranki SP, Zhao M, Ma C, Yan Y, Mi J. (2017). Relationship of alcohol consumption to all-cause, cardiovascular, and cancer-related mortality in US adults. J Am Coll Cardiol., 70(8): 913-922.