Forecasting for Healthcare Expenditure of Turkey Covering the Years of 2018-2050

Abdulkadir Atalan

doi:10.37989/gumussagbil.538111

Araştırma Makalesi

Forecasting for Healthcare Expenditure of Turkey Covering the Years of 2018-2050

Yıl 2020, Cilt: 9 Sayı: 1, 8 - 16, 02.04.2020

Abdulkadir Atalan

https://doi.org/10.37989/gumussagbil.538111

Cited By: 7

Öz

The aim of
this study was to measure health expenditure (HE) estimates for Turkey for the
next 32 years. Considering HE data of Turkey for the period from 1975 to 2017
over 42 years, more than one equation was obtained for estimation. Equations
were formed in trendline analysis in order to estimate the HE values in the
long term by considering the reliability levels of the data. The data were used
for HE of Turkey as a share of gross domestic product (GDP), which ranges from
less than 1.49 % to 5.53 % in this research. Estimation of HE of Turkey for the
next 32 years (the period from 2018 to 2050) according to the formulas
developed were considered in this research. For the years to come, the maximum
ratio of HE of 8.56 % was gained by the exponential trend for the year 2050. In
the opposite direction, the minimum HE ratio was expected to be 2.17 % of the 5^th
order equation for 2018. 7.45 % covers the years after 2030 due to the exponential
distribution for the average the values of HE. While the average value obtained
by the 6^th order equation, which has the highest reliability rate is
3.45 %, the difference between the maximum and the minimum was calculated as
3.4479%. For the period of 2018-2050, an average of HE rates of Turkey was 5.07
%, whereas the maximum value was calculated to be 6.68 %. The minimum value of
HE was estimated at 3.58 % of GDP. As a result, Turkey needs to upgrade the
amount of budget allocated for healthcare on the purpose of improving
healthcare infrastructure.

Anahtar Kelimeler

Healthcare Expenditure, Trendline Analysis, Estimations, Equations

Kaynakça

1. Novak, S., & Djordjevic, N. (2019). Information system for evaluation of healthcare expenditure and health monitoring. Physica A: Statistical Mechanics and its Applications, 520, 72–80. doi:https://doi.org/10.1016/j.physa.2019.01.007
2. Özcan, T., & Tüysüz, F. (2018). Healthcare expenditure prediction in Turkey by using genetic algorithm based grey forecasting models. In International Series in Operations Research and Management Science. doi:10.1007/978-3-319-65455-3_7
3. Eriksen, S., & Wiese, R. (2019). Policy induced increases in private healthcare financing provide short-term relief of total healthcare expenditure growth: Evidence from OECD countries. European Journal of Political Economy. doi:https://doi.org/10.1016/j.ejpoleco.2019.02.001
4. Goel, V., Rosella, L. C., Fu, L., & Alberga, A. (2018). The Relationship Between Life Satisfaction and Healthcare Utilization: A Longitudinal Study. American Journal of Preventive Medicine, 55(2), 142–150. doi:https://doi.org/10.1016/j.amepre.2018.04.004
5. Quercioli, C., Nisticò, F., Troiano, G., Maccari, M., Messina, G., Barducci, M., … Nante, N. (2018). Developing a new predictor of health expenditure: preliminary results from a primary healthcare setting. Public Health, 163, 121–127. doi:https://doi.org/10.1016/j.puhe.2018.07.007
6. Xu, K., Saksena, P., & Holly, A. (2011). The Determinants of Health Expenditure : A Country-Level Panel Data Analysis. Working Paper of the Results for Development Institute. doi:BST0391061 [pii]\r10.1042/BST0391061
7. Clemente, J., Lázaro-Alquézar, A., & Montañés, A. (2019). US state health expenditure convergence: A revisited analysis. Economic Modelling. doi:https://doi.org/10.1016/j.econmod.2019.02.011
8. Liang, L.-L., & Tussing, A. D. (2019). The cyclicality of government health expenditure and its effects on population health. Health Policy, 123(1), 96–103. doi:https://doi.org/10.1016/j.healthpol.2018.11.004
9. OECD. (2018). Health expenditure and financing: Health expenditure indicators (Edition 2018). doi:https://doi.org/https://doi.org/10.1787/91188162-en
10. van Beusekom, I., Bakhshi-Raiez, F., de Keizer, N. F., van der Schaaf, M., Busschers, W. B., & Dongelmans, D. A. (2018). Healthcare costs of ICU survivors are higher before and after ICU admission compared to a population based control group: A descriptive study combining healthcare insurance data and data from a Dutch national quality registry. Journal of Critical Care, 44, 345–351. doi:https://doi.org/10.1016/j.jcrc.2017.12.005
11. Toth, F. (2016). Classification of healthcare systems: Can we go further? Health Policy, 120(5), 535–543. doi:https://doi.org/10.1016/j.healthpol.2016.03.011
12. Manning, W. G. (2014). Modeling Cost and Expenditure for Healthcare. In A. J. Culyer (Ed.), Encyclopedia of Health Economics (pp. 299–305). San Diego: Elsevier. doi:https://doi.org/10.1016/B978-0-12-375678-7.00713-6
13. Wörz, S., & Bernhardt, H. (2018). A new forecasting method for univariate time series. Journal of Computational and Applied Mathematics. doi:10.1016/J.CAM.2018.10.051
14. Kotu, V., & Deshpande, B. (2019). Chapter 12 - Time Series Forecasting. In V. Kotu & B. B. T.-D. S. (Second E. Deshpande (Eds.), (pp. 395–445). Morgan Kaufmann. doi:https://doi.org/10.1016/B978-0-12-814761-0.00012-5
15. Folgado, D., Barandas, M., Matias, R., Martins, R., Carvalho, M., & Gamboa, H. (2018). Time Alignment Measurement for Time Series. Pattern Recognition, 81, 268–279. doi:10.1016/J.PATCOG.2018.04.003
16. Mao, Q., Zhang, K., Yan, W., & Cheng, C. (2018). Forecasting the incidence of tuberculosis in China using the seasonal auto-regressive integrated moving average (SARIMA) model. Journal of Infection and Public Health, 11(5), 707–712. doi:https://doi.org/10.1016/j.jiph.2018.04.009
17. Granger, C. W. . (1980). Chapter 2 - Trend-Line Fitting and Forecasting. In C. W. J. Granger (Ed.), Forecasting in Business and Economics (pp. 19–40). Academic Press. doi:https://doi.org/10.1016/B978-0-12-295180-0.50021-5
18. Qin, W., Zhang, Y., Chen, J., Yu, Q., Cheng, S., Li, W., … Tian, H. (2019). Variation, sources and historical trend of black carbon in Beijing, China based on ground observation and MERRA-2 reanalysis data. Environmental Pollution, 245, 853–863. doi:https://doi.org/10.1016/j.envpol.2018.11.063
19. Lessmann, S., & Voß, S. (2017). Car resale price forecasting: The impact of regression method, private information, and heterogeneity on forecast accuracy. International Journal of Forecasting, 33(4), 864–877. doi:https://doi.org/10.1016/j.ijforecast.2017.04.003
20. Ricci, L. (2010). Adjusted R-squared type measure for exponential dispersion models. Statistics & Probability Letters, 80(17–18), 1365–1368. doi:10.1016/J.SPL.2010.04.019

2018-2050 Yıllarını Kapsayan Türkiye'nin Sağlık Harcamalarına İlişkin Tahminler

Yıl 2020, Cilt: 9 Sayı: 1, 8 - 16, 02.04.2020

Abdulkadir Atalan

https://doi.org/10.37989/gumussagbil.538111

Cited By: 7

Öz

Bu çalışmanın amacı
gelecek 32 yıl için Türkiye sağlık harcamaları (SH) tahminlerini ölçmektir. Türkiye'nin
1975-2017 yılları arasında 42 yıl boyunca elde edilen SH verilerine dayanarak
yapılan tahminler için birden fazla denklem elde edilmiştir. Verilerin
güvenirlilik düzeyleri göz önünde bulundurarak uzun vadede SH değerlerini
tahmin etmek için trend eğilim analiz denklemleri oluşturulmuştur. Gayri safi
yurtiçi hasılanın (GSYH’nin) %1,49 ile %5,53 arasında değişen Türkiye SH
verileri kullanılarak gelecek 32 yıl (2018-2050 dönemi) için Türkiye’ye ait SH
değerleri tahmin edilmiştir. Maksimum SH oranı 2050 yılı için üstel eğilim
gösteren denklem uygulanarak yaklaşık %8,56 hesaplanmıştır. Aksi takdirde,
minimum SH oranı 2018 yılı için beşinci dereceden denklem ile %2,17 olarak elde
edilmiştir. Üstel dağılım metoduyla 2030 yılından sonraki yıllar için ortalama
SH değeri %7,45 hesaplanmıştır. En
yüksek güvenirlilik oranına sahip olan altıncı derecedeki denklem tarafından
elde edilen ortalama değer %3,48 iken, maksimum ve minimum SH değerleri
arasındaki farkı %3,45 olarak bulunmuştur. Çalışmanın sonuçlarına göre SH
oranının, yapılan analizlere göre GSYH’den daha hızlı artmadığı
gözlemlenmiştir. 2018 ile 2050 yılları arasında Türkiye’ye ait SH oranın
ortalama olarak %5,07 bulunurken, maksimum değer %6,68 olarak
hesaplanmıştır. Minimum SH değerinin ise
GSYH’nin %3,58'i olarak tekabül edeceği görülmektedir. Sonuç olarak, Türkiye’nin
sağlık alt yapısını geliştirmesi adına sağlık için ayrılan bütçenin yükseltilmesi
gerekmektedir.

Anahtar Kelimeler

Sağlık Harcamaları, Trend Çizgi Analizi, Tahminler, Denklemler

Kaynakça

1. Novak, S., & Djordjevic, N. (2019). Information system for evaluation of healthcare expenditure and health monitoring. Physica A: Statistical Mechanics and its Applications, 520, 72–80. doi:https://doi.org/10.1016/j.physa.2019.01.007
2. Özcan, T., & Tüysüz, F. (2018). Healthcare expenditure prediction in Turkey by using genetic algorithm based grey forecasting models. In International Series in Operations Research and Management Science. doi:10.1007/978-3-319-65455-3_7
3. Eriksen, S., & Wiese, R. (2019). Policy induced increases in private healthcare financing provide short-term relief of total healthcare expenditure growth: Evidence from OECD countries. European Journal of Political Economy. doi:https://doi.org/10.1016/j.ejpoleco.2019.02.001
4. Goel, V., Rosella, L. C., Fu, L., & Alberga, A. (2018). The Relationship Between Life Satisfaction and Healthcare Utilization: A Longitudinal Study. American Journal of Preventive Medicine, 55(2), 142–150. doi:https://doi.org/10.1016/j.amepre.2018.04.004
5. Quercioli, C., Nisticò, F., Troiano, G., Maccari, M., Messina, G., Barducci, M., … Nante, N. (2018). Developing a new predictor of health expenditure: preliminary results from a primary healthcare setting. Public Health, 163, 121–127. doi:https://doi.org/10.1016/j.puhe.2018.07.007
6. Xu, K., Saksena, P., & Holly, A. (2011). The Determinants of Health Expenditure : A Country-Level Panel Data Analysis. Working Paper of the Results for Development Institute. doi:BST0391061 [pii]\r10.1042/BST0391061
7. Clemente, J., Lázaro-Alquézar, A., & Montañés, A. (2019). US state health expenditure convergence: A revisited analysis. Economic Modelling. doi:https://doi.org/10.1016/j.econmod.2019.02.011
8. Liang, L.-L., & Tussing, A. D. (2019). The cyclicality of government health expenditure and its effects on population health. Health Policy, 123(1), 96–103. doi:https://doi.org/10.1016/j.healthpol.2018.11.004
9. OECD. (2018). Health expenditure and financing: Health expenditure indicators (Edition 2018). doi:https://doi.org/https://doi.org/10.1787/91188162-en
10. van Beusekom, I., Bakhshi-Raiez, F., de Keizer, N. F., van der Schaaf, M., Busschers, W. B., & Dongelmans, D. A. (2018). Healthcare costs of ICU survivors are higher before and after ICU admission compared to a population based control group: A descriptive study combining healthcare insurance data and data from a Dutch national quality registry. Journal of Critical Care, 44, 345–351. doi:https://doi.org/10.1016/j.jcrc.2017.12.005
11. Toth, F. (2016). Classification of healthcare systems: Can we go further? Health Policy, 120(5), 535–543. doi:https://doi.org/10.1016/j.healthpol.2016.03.011
12. Manning, W. G. (2014). Modeling Cost and Expenditure for Healthcare. In A. J. Culyer (Ed.), Encyclopedia of Health Economics (pp. 299–305). San Diego: Elsevier. doi:https://doi.org/10.1016/B978-0-12-375678-7.00713-6
13. Wörz, S., & Bernhardt, H. (2018). A new forecasting method for univariate time series. Journal of Computational and Applied Mathematics. doi:10.1016/J.CAM.2018.10.051
14. Kotu, V., & Deshpande, B. (2019). Chapter 12 - Time Series Forecasting. In V. Kotu & B. B. T.-D. S. (Second E. Deshpande (Eds.), (pp. 395–445). Morgan Kaufmann. doi:https://doi.org/10.1016/B978-0-12-814761-0.00012-5
15. Folgado, D., Barandas, M., Matias, R., Martins, R., Carvalho, M., & Gamboa, H. (2018). Time Alignment Measurement for Time Series. Pattern Recognition, 81, 268–279. doi:10.1016/J.PATCOG.2018.04.003
16. Mao, Q., Zhang, K., Yan, W., & Cheng, C. (2018). Forecasting the incidence of tuberculosis in China using the seasonal auto-regressive integrated moving average (SARIMA) model. Journal of Infection and Public Health, 11(5), 707–712. doi:https://doi.org/10.1016/j.jiph.2018.04.009
17. Granger, C. W. . (1980). Chapter 2 - Trend-Line Fitting and Forecasting. In C. W. J. Granger (Ed.), Forecasting in Business and Economics (pp. 19–40). Academic Press. doi:https://doi.org/10.1016/B978-0-12-295180-0.50021-5
18. Qin, W., Zhang, Y., Chen, J., Yu, Q., Cheng, S., Li, W., … Tian, H. (2019). Variation, sources and historical trend of black carbon in Beijing, China based on ground observation and MERRA-2 reanalysis data. Environmental Pollution, 245, 853–863. doi:https://doi.org/10.1016/j.envpol.2018.11.063
19. Lessmann, S., & Voß, S. (2017). Car resale price forecasting: The impact of regression method, private information, and heterogeneity on forecast accuracy. International Journal of Forecasting, 33(4), 864–877. doi:https://doi.org/10.1016/j.ijforecast.2017.04.003
20. Ricci, L. (2010). Adjusted R-squared type measure for exponential dispersion models. Statistics & Probability Letters, 80(17–18), 1365–1368. doi:10.1016/J.SPL.2010.04.019

Toplam 20 adet kaynakça vardır.

Ayrıntılar

Birincil Dil	Türkçe
Konular	Sağlık Kurumları Yönetimi
Bölüm	Araştırma Makaleleri
Yazarlar	Abdulkadir Atalan 0000-0003-0924-3685
Yayımlanma Tarihi	2 Nisan 2020
Yayımlandığı Sayı	Yıl 2020 Cilt: 9 Sayı: 1

Kaynak Göster

APA	Atalan, A. (2020). Forecasting for Healthcare Expenditure of Turkey Covering the Years of 2018-2050. Gümüşhane Üniversitesi Sağlık Bilimleri Dergisi, 9(1), 8-16. https://doi.org/10.37989/gumussagbil.538111
AMA	Atalan A. Forecasting for Healthcare Expenditure of Turkey Covering the Years of 2018-2050. Gümüşhane Sağlık Bilimleri Dergisi. Nisan 2020;9(1):8-16. doi:10.37989/gumussagbil.538111
Chicago	Atalan, Abdulkadir. “Forecasting for Healthcare Expenditure of Turkey Covering the Years of 2018-2050”. Gümüşhane Üniversitesi Sağlık Bilimleri Dergisi 9, sy. 1 (Nisan 2020): 8-16. https://doi.org/10.37989/gumussagbil.538111.
EndNote	Atalan A (01 Nisan 2020) Forecasting for Healthcare Expenditure of Turkey Covering the Years of 2018-2050. Gümüşhane Üniversitesi Sağlık Bilimleri Dergisi 9 1 8–16.
IEEE	A. Atalan, “Forecasting for Healthcare Expenditure of Turkey Covering the Years of 2018-2050”, Gümüşhane Sağlık Bilimleri Dergisi, c. 9, sy. 1, ss. 8–16, 2020, doi: 10.37989/gumussagbil.538111.
ISNAD	Atalan, Abdulkadir. “Forecasting for Healthcare Expenditure of Turkey Covering the Years of 2018-2050”. Gümüşhane Üniversitesi Sağlık Bilimleri Dergisi 9/1 (Nisan 2020), 8-16. https://doi.org/10.37989/gumussagbil.538111.
JAMA	Atalan A. Forecasting for Healthcare Expenditure of Turkey Covering the Years of 2018-2050. Gümüşhane Sağlık Bilimleri Dergisi. 2020;9:8–16.
MLA	Atalan, Abdulkadir. “Forecasting for Healthcare Expenditure of Turkey Covering the Years of 2018-2050”. Gümüşhane Üniversitesi Sağlık Bilimleri Dergisi, c. 9, sy. 1, 2020, ss. 8-16, doi:10.37989/gumussagbil.538111.
Vancouver	Atalan A. Forecasting for Healthcare Expenditure of Turkey Covering the Years of 2018-2050. Gümüşhane Sağlık Bilimleri Dergisi. 2020;9(1):8-16.