Abstract
Sağlık sistemi performansı, sağlık sistemi göstergeleri dışında davranışsal ve eğitimsel faktörler gibi birçok faktörden etkilenir. Bu çalışmanın amacı, davranışsal risk faktörleri ve eğitim faktörlerinin Avrupa Birliği üyesi ve aday ülkelerde sağlık sistemi performansı üzerindeki etkilerini değerlendirmektir. Araştırmada kümeleme analizi yöntemi kullanılmıştır. Öncelikle sağlık göstergeleri kullanılarak kümeleme analizi yapılmış, ardından analize davranışsal risk faktörleri ve eğitime ilişkin göstergeler dahil edilmiş ve davranışsal risk faktörleri ve eğitime ilişkin göstergelerin sağlık göstergeleri kullanılarak oluşturulan kümeleri etkileyip etkilemediği araştırılmıştır. Sağlık göstergeleri ile yapılan ilk kümeleme analizinde 4 küme oluşmuş, davranışsal risk faktörlerine ilişkin göstergelerin sağlık göstergelerine eklenmesi ile 5 küme oluşmuş ve son olarak eğitimsel göstergelerin sağlık göstergelerine eklenmesi ile yine 5 küme oluşmuştur. Davranışsal risk faktörlerinin ve eğitim göstergelerinin oluşan kümelerde değişikliklere neden olduğu görülmüştür. Sağlık statüsünü iyileştirmek isteyen ülkeler, davranışsal risk faktörlerinin ve eğitimin sağlık statüsü üzerindeki etkisini dikkate almalıdır.
References
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- Murray C, Evans D. Health Systems Performance Assessment: Debates, Methods and Empiricism. Geneva: World Health Organization; 2013.
- Kaufman L, Peter JR. Finding Groups in Data: An Introduction to Cluster Analysis. USA: John Wiley & Sons; 2009.
- Alpar R. Multivariate Statistical Methods. Ankara: Detay Publishing; 2013.
- Hair JF, Babin BJ, Anderson RE, et al. Multivariate Data Analysis. Essex: Pearson, 2014.
- Clatworthy J, Deanna B, Matthew H, et al. The use and reporting of cluster analysis in health psychology: A review. British Journal of Health Psychology. 2005; 10(3): 329-358.
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Abstract
Health system performance is influenced by many factors such as behavioral and educational factors other than health system indicators. The aim of this study was to evaluate the effects of behavioral risk factors and educational factors on the health system performance in European Union member and candidate countries. Clustering analysis method was employed in the study. Firstly, clustering analysis was performed using health indicators and then, indicators related to behavioral risk factors and education were included in the analysis and it was investigated whether indicators related to behavioral risk factors and education affected the clusters formed by using health indicators. 4 clusters were formed in the clustering analysis using the health indicators, 5 clusters were formed in the clustering analysis with the addition of indicators related to behavioral risk factors to the health indicators and finally 5 clusters were formed in the clustering analysis with the addition of indicators related to education to the health indicators. It was seen that behavioral risk factors and educational indicators caused changes in clusters formed of countries. Countries that want to improve their health status should take into account behavioral risk factors and the impact of education on health status.
References
- WHO. The World Health Report 2000. Health Systems: Improving Performance. Geneva: World Health Organization; 2000.
- Noto G, Corazza I, Kļaviņa K, et al. Health system performance assessment in small countries: The case study of Latvia. Int J Health Plann Mgmt. 2019;1–15.
- Smith PC, Mossialos E, Papanicolas I, et al. Performance Measurement for Health System Improvement. New York: Cambridge University Press; 2009.
- Anderson G, Hussey PS. Comparing health system performance in OECD countries. Health Affairs. 2001; 20(3): 219-232.
- Lalonde M. A New Perspective on the Health of Canadians. Ottawa: Government of Canada; 1974.
- Samut PK, Cafri R. Analysis of the efficiency determinants of health systems in OECD countries by DEA and Panel Tobit. Social Indicators Research. 2016; 129:113–132.
- Çetin VR, Bahçe S. Measuring the efficiency of health systems of OECD countries by data envelopment analysis. Applied Economics. 2016; 48(37): 3497-3507.
- Liu Y, Rao K, Wu J, et al. China's health system performance. The Lancet. 2008; 372(9653): 1914-1923.
- McIntyre D, Rogers L, Heier EJ. Overview, history, and objectives of performance measurement. Health Care Financing Review. 2001; 22(3): 7–43.
- Loeb JM. The current state of performance measurement in healthcare. International Journal for Quality in Health Care. 2004; 16(Suppl. 1): 5–9.
- Tandon A, Murray CJ, Lauer JA, et al. Measuring Overall Health System Performance for 191 Countries. Geneva: World Health Organization; 2000.
- Power M. The Audit Society: Rituals of Verification. Oxford: Oxford University Press; 1999.
- Smith PC. Performance measurement in health care: history, challenges and prospects. Public Money & Management. 2005; 25(4): 213–220.
- Boerma T, Chopra M, Evans D. Health systems performance assessment in the bulletin. Bulletin of the World Health Organization. 2009; 87(1): 2–2A.
- Ozcan YA. Healthcare Benchmarking and Performance Evaluation. 2nd edn New York: Springer; 2014.
- Wendt C. Mapping European healthcare systems: a comparative analysis of financing, service provision and access to healthcare. Journal of European Social Policy. 2009; 19(5): 432-445.
- Klomp J, Jakob de H. Measuring health: a multivariate approach. Social Indicators Research. 2010; 96(3): 433-457.
- Muntaner C, Haejoo C, Joan B, et al. Hierarchical cluster analysis of labour market regulations and population health: a taxonomy of low-and middle-income countries. BMC Public Health. 2012; 12(1): 1-15.
- Miszczyńska K. Multivariate analysıs of healthcare systems in selected European Unıon countrıes: Cluster analysis. Quantitative Methods in Economics. 2013;14(2): 99-107.
- Tanaka OY, Drumond JM, Cristo EB, et al. Cluster analysis as a tool for management improvement in the SUS. Saúde e Sociedade. 2015; 24(1): 34-45.
- Sonğur C, Top M. Regional clustering of medical imaging technologies. Computers in Human Behavior. 2016; 61: 333-343.
- Teleş M, Konca M, Çakmak C. Comparison of Health Systems Performance of Countries in the European Union Cycle. Manisa Celal Bayar University Journal of Management and Economıcs. 2018; 25(3): 811-835.
- Proksch D, Busch-Casler J, Haberstroh MM, et al. National health innovation systems: Clustering the OECD countries by innovative output in healthcare using a multi indicator approach. Research Policy. 2019; 48(1): 169-179.
- Lopez AD, Mathers CD, Ezzati M, et al. Global Burden of Disease and Risk Factors. USA: The World Bank; 2006.
- Pronk NP, Anderson LH, Crain AL, et al. Meeting recommendations for multiple healthy lifestyle factors: prevalence, clustering, and predictors among adolescent, adult, and senior health plan members. American Journal of Preventive Medicine. 2004; 27(2): 25-33.
- Todaro MP, Smith SC. Economic Development. Boston: Pearson; 2015.
- Murray C, Evans D. Health Systems Performance Assessment: Debates, Methods and Empiricism. Geneva: World Health Organization; 2013.
- Kaufman L, Peter JR. Finding Groups in Data: An Introduction to Cluster Analysis. USA: John Wiley & Sons; 2009.
- Alpar R. Multivariate Statistical Methods. Ankara: Detay Publishing; 2013.
- Hair JF, Babin BJ, Anderson RE, et al. Multivariate Data Analysis. Essex: Pearson, 2014.
- Clatworthy J, Deanna B, Matthew H, et al. The use and reporting of cluster analysis in health psychology: A review. British Journal of Health Psychology. 2005; 10(3): 329-358.
- Punj G, Stewart DW. Cluster analysis in marketing research: Review and suggestions for application. Journal of Marketing Research. 1983; 20(2): 134-148.
Details
| Primary Language | English |
|---|---|
| Subjects | Health Policy |
| Journal Section | Research Articles |
| Authors | |
| Publication Date | July 1, 2021 |
| Submission Date | November 5, 2020 |
| Published in Issue | Year 2021Volume: 12 Issue: 3 |
