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Forecasting drug expenditure with transfer function model

전이함수모형을 이용한 약품비 지출의 예측

  • Park, MiHai (Pharmaceutical Benefit Listing Division, HIRA) ;
  • Lim, Minseong (Department of Statistics, Kyungpook National University) ;
  • Seong, Byeongchan (Department of Applied Statistics, Chung-Ang University)
  • 박미혜 (건강보험심사평가원 약제관리실 약제등재부) ;
  • 임민성 (경북대학교 통계학과) ;
  • 성병찬 (중앙대학교 응용통계학과)
  • Received : 2018.02.20
  • Accepted : 2018.03.18
  • Published : 2018.04.30

Abstract

This study considers time series models to forecast drug expenditures in national health insurance. We adopt autoregressive error model (ARE) and transfer function model (TFM) with segmented level and trends (before and after 2012) in order to reflect drug price reduction in 2012. The ARE has only a segmented deterministic term to increase the forecasting performance, while the TFM explains a causality mechanism of drug expenditure with closely related exogenous variables. The mechanism is developed by cross-correlations of drug expenditures and exogenous variables. In both models, the level change appears significant and the number of drug users and ratio of elderly patients variables are significant in the TFM. The ARE tends to produce relatively low forecasts that have been influenced by a drug price reduction; however, the TFM does relatively high forecasts that have appropriately reflected the effects of exogenous variables. The ARIMA model without the exogenous variables produce the highest forecasts.

본 논문에서는 약품비 지출에 대한 예측을 수행하기 위하여 시계열 모형을 도입한다. 2012년 약가 일괄인하를 반영하기 위하여 구간별 모형을 토대로, 자기회귀오차모형과 전이함수모형을 고려하였다. 자기회귀오차모형에서는 예측의 편리성을 위하여 결정적 추세만을 고려하였으며, 전이함수모형에서는 주요한 외생변수와의 교차상관성을 이용하여 약품비 지출의 인과 메커니즘을 설명하였다. 각 모형에서 약가 일괄인하 이후 수준 변화가 유의하게 나타났으며, 전이함수모형에서는 의약품 사용자 수 및 노인환자 비중 시계열 변수가 유의하게 나타났다. 자기회귀오차모형은 약가 일괄인하로 의한 약품비 수준이동에 좌우되어 비교적 낮은 예측값이 도출되었으며, 전이함수모형은 약품비 지출에 영향을 미치는 외부 설명변수의 증가 추세가 적절히 반영되어 더 높은 예측값을 보였다. 설명변수를 포함하지 않을 경우, 약품비 수준이동만을 고려한 ARIMA 모형은 약품비 지출 추세를 가장 높이 예측하였다.

Keywords

References

  1. Astolfi, R., Lorenzoni, L., and Oderkirk, J. (2012). A comparative analysis of health forecasting methods, OECD Health Working Papers, 59, OECD Publishing, Paris.
  2. Bae, E. Y. (2007). Study on the drug expenditure trend in Korea, The Korean Journal of Health Economics and Policy, 13, 39-54.
  3. Chang, S. M., Park, C. M., Bae, G. L., Lee, H. J., and Kim, H. S. (2010). Analysis of Variable Factors for Drug Expenditure in Health Insurance, Health Insurance Review & Assessment Service.
  4. Cho, S., Son, Y., and Seong, B. (2015). Time Series Analysis Using SAS/ETS, Yulgokbooks, Seoul.
  5. Fan, J. X., Sharpe, D. L., and Hong, G. S. (2003). Health care and prescription drug spending by seniors, Monthly Labor Review, 126, 16-25.
  6. Fogel, R. W. (2009). Forecasting the cost of US health care in 2040, Journal of Policy Modeling, 31, 482-488. https://doi.org/10.1016/j.jpolmod.2009.05.004
  7. Huh, S. I., Jeong, J. C., and Lee, H. Y. (2006). Research on Reasonable Drug Expenditure Plan, National Health Insurance Corporation.
  8. Kim, Y. S. and Kim, S. O. (2009). Analysis and Management Plan for Increase Factors of Drug Expenditure, National Health Insurance Corporation.
  9. Kwon, S. M. and Cho, Y. M. (2015). Population Aging and Health Insurance, Research Center for Market & Government.
  10. Mueller, C., Schur, C., and O'connell, J. (1997). Prescription drug spending: the impact of age and chronic disease status, American Journal of Public Health, 87, 1626-1629. https://doi.org/10.2105/AJPH.87.10.1626
  11. Murthy, V. N. and Okunade, A. A. (2016). Determinants of US health expenditure: Evidence from autoregressive distributed lag (ARDL) approach to cointegration, Economic Modelling, 59, 67-73. https://doi.org/10.1016/j.econmod.2016.07.001
  12. Newhouse, J. P. (1992). Medical care costs: how much welfare loss? The Journal of Economic Perspectives, 6, 3-21.
  13. Sambamoorthi, U., Shea, D., and Crystal, S. (2003). Total and out-of-pocket expenditures for prescription drugs among older persons, The Gerontologist, 43, 345-359. https://doi.org/10.1093/geront/43.3.345
  14. Steinberg, E. P., Gutierrez, B., Momani, A., Boscarino, J. A., Neuman, P., and Deverka, P. (2000). Beyond survey data: a claims-based analysis of drug use and spending by the elderly, Health Affairs, 19, 198-211.
  15. Wolff, J. L., Starfield, B., and Anderson, G. (2002). Prevalence, expenditures, and complications of multiple chronic conditions in the elderly, Archives of Internal Medicine, 162, 2269-2276. https://doi.org/10.1001/archinte.162.20.2269
  16. Won, J., Chang, I., and Seong, B. (2016). An application of ARIMAX for predicting long-term national health insurance expenditure in Korea, The Korean Journal of Health Economics and Policy, 22, 1-27.
  17. Yoon, J. H., Kim, S. W., Chang, Y. H., Cho, H. S., and Song, H. J. (2010). A panel data analysis of the determinants of health care expenditures among older single-person households, Journal of Consumer Studies, 21, 193-218.