Antoñanzas Villar Fernando, Rodríguez-Ibeas Roberto, Juárez-Castelló Carmelo A, Lorente Antoñanzas Ma Reyes
Rev Esp Salud Publica. 2014 Mar-Apr;88(2):233-49. doi: 10.4321/S1135-57272014000200006.
this research aims to understand if the consequences on drug expenditures and number of prescriptions of Royal Decree-Law 16/2012 as estimated by the Ministry of Health, Social Services and Equality (MHSSE) are similar to those found by using common statistical approaches. In addition, several models have been built to forecast the evolution of both variables for the period September 2013-December 2014.
the Box-Jenkins methodology and the Box-Tiao intervention analysis were applied to data of the period 2003-13 to forecast the monthly values of the number of prescriptions and pharmaceutical expenditures. Forecasts were used in a counter-factual analysis to be compared to the actual values of prescriptions and drug expenditures. Moreover, forecasts for the period September 2013 to December 2014 were obtained to observe the impact of the policy in the future.
the counterfactual analysis estimated a decrease in the number of prescriptions of 12.18% and 12.83% in the pharmaceutical expenditure; these figures were 12,75% and 14,03% respectively, when the intervention analysis was used.
the estimated reduction in the number of prescriptions for the period June 2012-August 2013 was similar to the figure offered by the MHSSE, while the reduction in the drug expenditure series was smaller. The Box-Jenkins methodology generated low forecast errors (less than 3%) what makes this procedure useful to reliably anticipate future consumptions.
本研究旨在了解卫生、社会服务和平等部（MHSSE）估计的2012年第16号皇家法令对药品支出和处方数量的影响是否与使用常规统计方法得出的结果相似。此外，还建立了几个模型来预测2013年9月至2014年12月期间这两个变量的演变。
将Box-Jenkins方法和Box-Tiao干预分析应用于2003 - 2013年期间的数据，以预测处方数量和药品支出的月度值。预测结果用于反事实分析，与处方和药品支出的实际值进行比较。此外，还获得了2013年9月至2014年12月期间的预测结果，以观察该政策在未来的影响。
反事实分析估计处方数量减少了12.18%，药品支出减少了12.83%；使用干预分析时，这些数字分别为12.75%和14.03%。
2012年6月至2013年8月期间处方数量的估计减少幅度与MHSSE提供的数字相似，而药品支出系列的减少幅度较小。Box-Jenkins方法产生的预测误差较低（小于3%），这使得该方法有助于可靠地预测未来的消费量。
