The standardized precipitation index (SPI) is used for characterizing and predicting meteorological droughts on a range of time scales. However, in forecasting applications, when SPI is computed on the entire available dataset, prior to model-validation, significant biases are introduced, especially under changing climatic conditions. In this paper, we investigate the theoretical and numerical implications that arise when SPI is computed under stationary and non-stationary probability distributions. We demonstrate that both the stationary SPI and non-stationary SPI (NSPI) lead to increased information leakage to the training set with increased scales, which significantly affects the characterization of drought severity. The analysis is performed across about 36,500 basins in Sweden, and indicates that the stationary SPI is unable to capture the increased rainfall trend during the last decades and leads to systematic underestimation of wet events in the training set, affecting up to 22% of the drought events. NSPI captures the non-stationary characteristics of accumulated rainfall; however, it introduces biases to the training data affecting 19% of the drought events. The variability of NSPI bias has also been observed along the country’s climatic gradient with regions in snow climates strongly being affected. The findings propose that drought assessments under changing climatic conditions can be significantly influenced by the potential misuse of both SPI and NSPI, inducing bias in the characterization of drought events in the training data.

The Standardized Precipitation Index (SPI) is one of the most popular indices for characterizing the meteorological drought on a range of time scales.  To date, SPI has been thoroughly used to monitor and predict drought in the precipitation signal and to further support early warning and climate services. While many studies focus on the performance improvement of drought models, there is to our knowledge no reference around the correct computation of SPI in a drought forecasting setting. As SPI is typically computed on the entire data set, prior to model-validation, bias is introduced to both the training and validation sets. This stems from the fact that the distribution parameters of the index are estimated using observations from the validation and test sets leading to information leakage. Here, we propose a modified calculation of SPI oriented for forecasting applications by measuring the bias introduced to the SPI values in the training set. Moreover, we propose the best practice for calculating the SPI during model-validation and encapsulate these in a drought forecasting framework. The proposed framework is further demonstrated using a 50-year data set from Sweden. Our findings suggest that the amount of bias introduced to the training sets increases with increased SPI scale, significantly affecting in some cases more than 80% of the available basins.

Background France has significant clinical research and development potential, however, struggles in comparison to neighbouring countries. A significant reason is the difficulty to recruit patients, thus causing delays in the availability of new therapies to market. IQVIA uses Health Insurance Claims Data among other data assets, to better locate patients for trials based on the potential of hospitals. Objective The aim of the study was to monitor whether an increased number of patients enrolled in clinical trials in France was observed when PMSI data supported patient recruitment, as well as describing clinical trial landscape worldwide and in Europe. Methods We used data from ClinicalTrials.gov and Citeline to describe the clinical trial landscape in Europe between 2010 and 2019. We also looked at the IQVIA internal clinical trial tracker, Clinical Trial Management System (CTMS) to describe IQVIA-run trials and their performance after matching trials supported with PMSI data in France. We compared the average number of enrolled patients per site in PMSI and non-PMSI supported trials according to the study phase, using a Student t-test. Results Results suggest that the support of PMSI on the average number of enrolled patients per site, when comparing at similar trial phase level, shows a positive trend especially for phase 4 studies (11.0 with PMSI vs 9.3 without PMSI, p=0.67), and for phases 3b, 3 and 1, when compared to non-PMSI supported studies. Conclusions The findings of this study suggest that PMSI use has the potential to increase patient recruitment into clinical trials run in France, rendering France more attractive in its exploitation of the clinical research potential. Optimising patient recruitment has a direct impact on the availability and timeliness of innovative therapies to market for French patients.