SOSYAL GÜVENLİK KURUMU SAĞLIK HARCAMALARININ ANALİZİ: ARDL SINIR TESTİ YAKLAŞIMI

SOSYAL GÜVENLİK KURUMU SAĞLIK HARCAMALARININ ANALİZİ: ARDL SINIR TESTİ YAKLAŞIMI Yunus Emre KARATAŞ, Metin DİNÇER Öz Sağlıkta dönüşüm programı ile genel sağlık sigortasına geçildi. Böylece en önemli sağlık hizmeti alıcısı sosyal güvenlik kurumu olmuştur. Hastanelerin sunduğu hizmetler, sosyal güvenlik kurumunun harcamaları içinde önemli bir yer tutmaya başlamıştır. Bu nedenle çalışma, Türkiye'deki sağlık tesislerinin fonksiyonel özelliklerinin Sosyal Güvenlik Kurumu (SGK) sağlık harcamaları üzerindeki etkisini tahmin etmeyi ve modellemeyi amaçlamaktadır. Çalışmada kullanılan veriler toplanırken fonksiyonel özellik olarak hastanenin hizmet düzeyleri dikkate alınmış ve 01/2009 ile 05/2020 tarihleri arasındaki veriler analiz edilmiştir. Değişkenler arasında kısa ve uzun dönemde eşbütünleşme olup olmadığını analiz etmek için Otomatik Regresif Dağıtılmış Gecikme Modeli (ARDL) sınır testi kullanılmıştır. Uzun vadeli tahminler, ikinci basamaktaki devlet hastanelerinin SGK'nın sağlık harcamalarını azaltırken, üçüncü basamaktaki devlet, üniversite ve ikinci basamaktaki özel hastanelerin SGK'nın sağlık harcamalarını artırdığını göstermektedir. Hastanelerin sunduğu hizmetlerin ve hastalara sağladığı faydaların objektif kriterlere göre ölçülmesi sağlık harcamalarının uygunluğunun en önemli göstergesi olacaktır. Anahtar Kelimeler: ARDL Sınır Testi, Geri Ödeme, Sağlık Harcamaları, Sağlık Sigortası, Sosyal Güvenlik Kurumu Jel Kodları: C32, G22, G28, H51, I13

ANALYSIS OF THE SOCIAL SECURITY INSTITUTION’S HEALTH SPENDING: AN ARDL BOUNDS TEST APPROACH

ANALYSIS OF THE SOCIAL SECURITY INSTITUTION’S HEALTH SPENDING: AN ARDL BOUNDS TEST APPROACH Yunus Emre KARATAŞ, Metin DİNÇER With the health transformation program, universal health insurance was introduced. Thus, it became the most significant health service purchaser social security institution. The services provided by hospitals began to occupy an important place in the expenditures of the social security institution. Thus, the study aims to predict and model the effect of functional characteristics of health facilities on Social Security Institution (SSI) health expenditures in Turkey. While collecting the data used in the study, the hospital’s service levels as functional characteristics were considered, and the data between 01/2009 and 05/2020 were analyzed. Auto-Regressive Distributed Lag Model (ARDL) bounds test was used to analyze the presence of cointegration between variables in the short and long run. Long-run predictions show that while the secondary-level state hospitals reduce the health expenditure of the SSI, the tertiary-level state, university, and secondary-level private hospitals increase the SSI health expenditure. Measuring the services provided by hospitals and the benefits they provide to patients according to objective criteria will be the most significant indicator of the appropriateness of health expenditures. Keywords: ARDL Bounds Test, Reimbursement, Health Spending, Health Insurance, Social Security Institution Jel Codes: C32, G22, G28, H51, I13

___

  • Aryeetey G.C., Nonvignon J., Amissah C., Buckle G., et al. (2016). The effect of the National Health Insurance Scheme (NHIS) on health service delivery in mission facilities in Ghana: a retrospective study. Globalization and Health 12:9.
  • Aytekin A.G.Ç., & Aytekin S. (2010). Türkiye'de sağlık hizmetleri ve kamu sağlık harcamalarının finansmanı. Gümüşhane Üniversitesi Sosyal Bilimler Enstitüsü Elektronik Dergisi 1, (2):163-184.
  • Brown R.L., Durbin J. & Evans J.M. (1975). Techniques for testing the constancy of regression relationships over time. Journal of the Royal Statistical Society: Series B (Methodological) 37, (2):149-163.
  • Costa-Font J., Cowell F.A. & de Miera B.S. (2021). Measuring pure health inequality and mobility during a health insurance expansion: Evidence from Mexico. Health Economics 30, (8):1833-1848.
  • Dickey D.A. & Fuller W.A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of The American Statistical Association 74, (366a):427-431.
  • Gómez V. & Maravall A. (1997). Programs tramo (time series regression with arima noise, missing observations, and outliers) and seats (signal extraction in arima time series). Documento de Trabajo 9628:56.
  • Karagöz, K. (2010). Determining Factors of Private Investments: An Empirical Analysis for Turkey. Sosyoekonomi, 11, (11):8-26.
  • Khalid F., Raza W., Hotchkiss D.R., & Soelaeman R.H. (2021). Health services utilization and out-ofpocket (OOP) expenditures in public and private facilities in Pakistan: an empirical analysis of the 2013-14 OOP health expenditure survey (vol 21, 178, 2021). Bmc Health Services Research 21, (1):2.
  • Kim S. & Kwon S. (2014). Has the National Health Insurance improved the inequality in the use of tertiary-care hospitals in Korea?. Health Policy 118, (3):377-385.
  • Krstic K., Janicijevic K., Timofeyev Y., Arsentyev E.V., et al. (2019). Dynamics of Health Care Financing and Spending in Serbia in the XXI Century. Frontiers in Public Health 7, (381).
  • Linna M., Häkkinen U., & Magnussen J. (2006). Comparing hospital cost efficiency between Norway and Finland. Health Policy 77, (3):268-278.
  • McWilliams J.M., Meara E., Zaslavsky A.M., & Ayanian J.Z. (2009). Medicare Spending for Previously Uninsured Adults. Annals of Internal Medicine 151, (11):757-766.
  • Meda I.B., Kouanda S., Dumont A., & Ridde V. (2020). Effect of a prospective payment method for health facilities on direct medical expenditures in a low-resource setting: a paired pre-post study. Health Policy and Planning 35, (7):775-783.
  • Memirie S.T., Metaferia Z.S., Norheim O.F., Levin C.E., et al. (2017). Household expenditures on pneumonia and diarrhoea treatment in Ethiopia: a facility-based study. Bmj Global Health 2, (1):10.
  • Mikkelsen-Lopez I., Tediosi F., Abdallah G., Njozi M., et al. (2013). Beyond antimalarial stock-outs: implications of health provider compliance on out-of-pocket expenditure during care-seeking for fever in South East Tanzania. Bmc Health Services Research 13:9.
  • Narayan P.K., & Narayan S. (2005). Estimating income and price elasticities of imports for Fiji in a cointegration framework. Economic Modelling 22, (3):423-438.
  • Nkoro E., & Uko A.K. (2016). Autoregressive Distributed Lag (ARDL) cointegration technique: application and interpretation. Journal of Statistical and Econometric Methods 5, (4):63-91.
  • Pesaran M.H., Shin Y. & Smith R.J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics 16, (3):289-326.
  • Phillips P.C. & Perron P. (1988). Testing for a unit root in time series regression. Biometrika 75, (2):335-346.
  • Prinja S., Balasubramanian D., Jeet G., Verma R., et al. (2017). Cost of delivering secondary-level health care services through public sector district hospitals in India. The Indian journal of medical research 146, (3):354-361.
  • Saksena P., Xu K., Elovainio R., & Perrot J. (2012). Utilization and expenditure at public and private facilities in 39 low-income countries. Tropical Medicine & International Health 17, (1):23-35.
  • SGK. (2006). 5510 Sayılı Sosyal Sigortalar ve Genel Sağlık Sigortası Kanunu Resmi Gazete Tarih: 16.06.2006, Sayı:26200 (Act Number 5510, Social Security and Universal Health Insurance Act Official Gazette Date: 16.06.2006, Number: 26200).
  • SGK. (2020a). Sosyal Güvenlik Kurumu, Mali İstatistikler, Social Securty Intutions Financial Statistics Access Date 10.22.2020 from http://eski.sgk.gov.tr/wps/portal/sgk/tr/kurumsal/istatistik/aylik_istatistik_bilgileri.
  • SGK. (2020b). Sosyal Güvenlik Kurumu, Sağlık İstatistikler, Social Securty Intutions Health Statistics Access Date 10.22.2020 from http://eski.sgk.gov.tr/wps/portal/sgk/tr/kurumsal/istatistik/aylik_istatistik_bilgileri.
  • Shactman D., Altman S.H., Eilat E., Thorpe K.E., et al. (2003). The outlook for hospital spending. Health Aff (Millwood) 22, (6):12-26.
  • Shami E., Tabrizi J.S., & Nosratnejad S. (2019). The Effect of health insurance on the utilization of health services: A systematic review and meta-analysis. Galen Medical Journal 8:e1411.
  • Sultana M., Alam N.H., Ali N., Faruque A.S.G., et al. (2021). Household economic burden of childhood severe pneumonia in Bangladesh: a cost-of-illness study. Archives of Disease in Childhood 106, (6):539-546.
  • Thanh N.D., Anh B.T.M., Xiem C.H., & Minh H.V. (2019). Out-of-Pocket health expenditures among insured and uninsured patients in Vietnam. Asia-Pacific Journal of Public Health 31, (3):210-218.
  • Vaidya S., & Boes S. (2021). Strategies to mitigate inequity within mandatory health insurance systems: A systematic review. World Medical & Health Policy 13, (2):272-292.
  • Wagstaff A., & Lindelow M. (2008). Can insurance increase financial risk?: The curious case of health insurance in China. Journal of Health Economics 27, (4):990-1005.
  • Wang Q., Shen J., Rice J., & Frakes K. (2018). Social health insurance difference in inpatient expenditure and service category in China. Asia-Pacific Journal of Public Health 30, (1):56-66.
  • WHO (2014). Making fair choices on the path to universal health coverage: Final report of the WHO (2016). Consultative Group on Equity and Universal Health Coverage, World Health Organization.
  • WHO (2018). Noncommunicable diseases country profiles, World Health Organization. 2018.