Macroeconomic forecasting: a non-standard optimisation approach to the calibration of dynamic factor models.

In this paper, we present a comparison of the forecasting perfomance of selected static and dynamic factor models on two large monthly data panels. The first dataset contains EU variables, whereas the other contains US variables. These data panels are split into two parts: the first subsample (the calibration sample) is used to select the most performing specification for each class of models in a in- sample environment and the second subsample (the proper sample) is used to compare the performances of the selected models in an out-of-sample environment. In the calibration sample, genetic algorithms are employed to achieve an efficient exploration of the parameter space. We find that dynamic factor models are globally the most performing methods on both data panels

The impact of surgical delay on resectability of colorectal cancer: An international prospective cohort study

AIM: The SARS-CoV-2 pandemic has provided a unique opportunity to explore the impact of surgical delays on cancer resectability. This study aimed to compare resectability for colorectal cancer patients undergoing delayed versus non-delayed surgery. METHODS: This was an international prospective cohort study of consecutive colorectal cancer patients with a decision for curative surgery (January-April 2020). Surgical delay was defined as an operation taking place more than 4 weeks after treatment decision, in a patient who did not receive neoadjuvant therapy. A subgroup analysis explored the effects of delay in elective patients only. The impact of longer delays was explored in a sensitivity analysis. The primary outcome was complete resection, defined as curative resection with an R0 margin. RESULTS: Overall, 5453 patients from 304 hospitals in 47 countries were included, of whom 6.6% (358/5453) did not receive their planned operation. Of the 4304 operated patients without neoadjuvant therapy, 40.5% (1744/4304) were delayed beyond 4 weeks. Delayed patients were more likely to be older, men, more comorbid, have higher body mass index and have rectal cancer and early stage disease. Delayed patients had higher unadjusted rates of complete resection (93.7% vs. 91.9%, P = 0.032) and lower rates of emergency surgery (4.5% vs. 22.5%, P < 0.001). After adjustment, delay was not associated with a lower rate of complete resection (OR 1.18, 95% CI 0.90-1.55, P = 0.224), which was consistent in elective patients only (OR 0.94, 95% CI 0.69-1.27, P = 0.672). Longer delays were not associated with poorer outcomes. CONCLUSION: One in 15 colorectal cancer patients did not receive their planned operation during the first wave of COVID-19. Surgical delay did not appear to compromise resectability, raising the hypothesis that any reduction in long-term survival attributable to delays is likely to be due to micro-metastatic disease

Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study

Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74·0%) had emergency surgery and 280 (24·8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26·1%) patients. 30-day mortality was 23·8% (268 of 1128). Pulmonary complications occurred in 577 (51·2%) of 1128 patients; 30-day mortality in these patients was 38·0% (219 of 577), accounting for 81·7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1·75 [95% CI 1·28–2·40], p\textless0·0001), age 70 years or older versus younger than 70 years (2·30 [1·65–3·22], p\textless0·0001), American Society of Anesthesiologists grades 3–5 versus grades 1–2 (2·35 [1·57–3·53], p\textless0·0001), malignant versus benign or obstetric diagnosis (1·55 [1·01–2·39], p=0·046), emergency versus elective surgery (1·67 [1·06–2·63], p=0·026), and major versus minor surgery (1·52 [1·01–2·31], p=0·047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

On the macroeconomic forecasting performance of selected dynamic factor models

In this doctoral thesis, we compare the forecasting performance of three dynamic factor models on macroeconomic and financial datasets. The purpose of the first two chapters is to provide an incremental contribution with respect to the body of literature comparing static versus dynamic factor models. Previous literature compares the forecasting performance of the static factor model SW (see Stock and M. W. Watson, 2002a, Stock and M. W. Watson, 2002b) against those of the dynamic factor model FHLR (see Forni, Hallin, Lippi, and Reichlin, 2000, Forni, Hallin, Lippi, and Reichlin, 2005). This work adds a third dynamic factor model, which is the recently published FHLZ (see Forni, Hallin, Lippi, and Zaffaroni, 2015, Forni, Hallin, Lippi, and Zaffaroni, 2016). In the third chapter, we compare the forecasting performance of two static factor models cast in a state-space form. In the first one, the conditional moments of the factors are estimated under proper hypothesis of linearity and gaussianity of the data. In the second one, the assumptions of linearity and gaussianity are relaxed for the estimation of the conditional moments of the factors. Chapter 1 presents an application of the three factor models (SW, FHLR and FHLZ) for forecasting purposes. It compares the pseudo real-time forecast performances of the three factor models against a benchmark AR(4) (an autoregressive process of order 4) over a dataset of 176 EU macroeconomic and financial time series. In this exercise, FHLZ generally outperforms all methods on the forecasting of the Consumer Price Index (CPI). Instead, no method seems to outperform the others in forecasting the Industrial Production (IP), but all dynamic factor models outperform the benchmark AR(4). Chapter 2 presents two applications on the same topic of the previous chapter. The most innovative part of these applications is that a genetic algorithm is employed to calibrate the three dynamic factor models. The first application exposed in this chapter employs the same dataset of Chapter 1. Instead, in the second application a dataset of 115 US macroeconomic and financial time series is employed. In this chapter, we show that FHLR tends globally to outperform the other methods on the real variables and that FHLZ tends globally to outperform the other methods on the nominal variables. As to EU dataset, in chapter 1 we found similar results for the CPI, but mixed evidences appeared for the IP. As to the US dataset, Forni, Giovannelli, et al., 2016 found similar but less significant results. Chapter 3 extends a previous study from Banbura and Modugno, 2014, by comparing the forecasting performance of a dynamic factor model cast in state-space form in which the conditional moments relative to the factors are estimated by means of the two following techniques: (i) Kalman filter: as in Banbura and Modugno, 2014, the conditional moments relative to the factors are estimated under the hypothesis that the data generating process (DGP) is linear and that the error terms follow a Gaussian distribution; (ii) Paticle Filter: in this case, the conditional moments relative to the factors are estimated in a more general framework, in which the DGP may be affected by sources of nonlinearity and in which the error terms may not follow a Gaussian distribution. Up to our knowledge, the estimation of the conditional moments of the factors by means of the Particle Filter has not been carried out yet. In this application, we employ the same Small dataset of 14 EU/US macroeconomic and financial time series from Banbura and Modugno, 2014. We show that the assumptions of linearity of the DGP and of a gaussian distribution for the error terms seems to hold in this macroeconomic setting. Hence, the estimation of the conditional moments of the factors by means of the Kalman Filter seems to be the more appropriate choice in macroeconomic forecasting. However, it is also possible that the particle filter may outperform in financial forecasting. As can be seen in Habibnia, 2017, it appears that accounting for the sources of nonlinearity in the DGP plays a more relevant role on forecasting financial variables

A Non-Standard Approach to the Calibration of Selected Dynamic Factor Models in Macroeconomic Forecasting

In this paper, we present a comparison of the forecasting performance of selected factor models on two large monthly data panels. The first dataset contains EU variables, whereas the other contains US variables. These data panels are split into two parts: the first subsample is used to select the most performing specification for each class of models in a in-sample environment, and the second subsample is used to compare the performances of the selected models in an out-of-sample environment. In the first subsample, genetic algorithms are employed to achieve an efficient exploration of the parameter space. We find that selected dynamic factor models are globally the most performing methods on the second subsamples of both data panels

Macroeconomic forecasting: a non-standard optimisation approach to the calibration of dynamic factor models.

In this paper, we present a comparison of the forecasting perfomance of selected static and dynamic factor models on two large monthly data panels. The first dataset contains EU variables, whereas the other contains US variables. These data panels are split into two parts: the first subsample (the calibration sample) is used to select the most performing specification for each class of models in a in- sample environment and the second subsample (the proper sample) is used to compare the performances of the selected models in an out-of-sample environment. In the calibration sample, genetic algorithms are employed to achieve an efficient exploration of the parameter space. We find that dynamic factor models are globally the most performing methods on both data panels