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Cumulative COVID-19 incidence, mortality and prognosis in cancer survivors: A population-based study in Reggio Emilia, Northern Italy.

ABSTRACT: The aim of this population-based study was to evaluate the impact of being a cancer survivor (CS) on COVID-19 risk and prognosis during the first wave of the pandemic (27 February 2020 to 13 May 2020) in Reggio Emilia Province. Prevalent cancer cases diagnosed between 1996 and 2019 were linked with the provincial COVID-19 surveillance system. We compared CS' cumulative incidence of being tested, testing positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), being hospitalized and dying of COVID-19 with that of the general population; we compared COVID-19 prognosis in CS and in patients without cancer. During the study period, 15 391 people (1527 CS) underwent real-time polymerase chain reaction for SARS-CoV-2, of whom 4541 (447 CS) tested positive; 541 (113 CS) died of COVID-19. CS had higher age- and sex-adjusted incidence rate ratios (IRR) of testing (1.28 [95% confidence interval, CI = 1.21-1.35]), of positive test (IRR 1.06 [95% CI = 0.96-1.18]) and of hospitalization and death (IRR 1.27 [95% CI = 1.09-1.48] and 1.39 [95%CI = 1.12-1.71], respectively). CS had worse prognosis when diagnosed with COVID-19, particularly those below age 70 (adjusted odds ratio [OR] of death 5.03; [95% CI = 2.59-9.75]), while the OR decreased after age 70. The OR of death was higher for CS with a recent diagnosis, that is, <2 years (OR = 2.92; 95% CI = 1.64-5.21), or metastases (OR = 2.09; 95% CI = 0.88-4.93). CS showed the same probability of being infected, despite a slightly higher probability of being tested than the general population. Nevertheless, CS were at higher risk of death once infected.

SUBMITTER: Mangone L

PROVIDER: S-EPMC8250826 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundSince February 2020, the COVID-19 epidemic has rapidly spread throughout Italy. Some studies showed an association of environmental factors, such as PM10, PM2.5, NO2, temperature, relative humidity, wind speed, solar radiation and mobility with the spread of the epidemic. In this work, we aimed to predict via Deep Learning the real-time transmission of SARS-CoV-2 in the province of Reggio Emilia, Northern Italy, in a grid with a small resolution (12 km × 12 km), including satellite information.MethodsWe focused on the Province of Reggio Emilia, which was severely hit by the first wave of the epidemic. The outcomes included new SARS-CoV-2 infections and COVID-19 hospital admissions. Pollution, meteorological and mobility data were analyzed. The spatial simulation domain included the Province of Reggio Emilia in a grid of 40 cells of (12 km)2. We implemented a ConvLSTM, which is a spatio-temporal deep learning approach, to perform a 7-day moving average to forecast the 7th day after. We used as training and validation set the new daily infections and hospital admissions from August 2020 to March 2021. Finally, we assessed the models in terms of Mean Absolute Error (MAE) compared with Mean Observed Value (MOV) and Root Mean Squared Error (RMSE) on data from April to September 2021. We tested the performance of different combinations of input variables to find the best forecast model.FindingsDaily new cases of infection, mobility and wind speed resulted in being strongly predictive of new COVID-19 hospital admissions (MAE = 2.72 in the Province of Reggio Emilia; MAE = 0.62 in Reggio Emilia city), whereas daily new cases, mobility, solar radiation and PM2.5 turned out to be the best predictors to forecast new infections, with appropriate time lags.InterpretationConvLSTM achieved good performances in forecasting new SARS-CoV-2 infections and new COVID-19 hospital admissions. The spatio-temporal representation allows borrowing strength from data neighboring to forecast at the level of the square cell (12 km)2, getting accurate predictions also at the county level, which is paramount to help optimise the real-time allocation of health care resources during an epidemic emergency.

Project description:BackgroundInappropriate prescribing of diagnostic procedures leads to overdiagnosis, overtreatment and resource waste in healthcare systems. Effective strategies to measure and to overcome inappropriateness are essential to increasing the value and sustainability of care. We aimed to describe the determinants of inappropriate reporting of the clinical question and of inappropriate imaging and endoscopy referrals through an analysis of general practitioners' (GP) referral forms in the province of Reggio Emilia, Italy.MethodsA clinical audit was conducted on routinely collected referral forms of all GPs of Reggio Emilia province. All prescriptions for gastroscopy, colonoscopy, neurological and musculoskeletal computerised tomography (CT) and magnetic resonance imaging (MRI) from 2012 to 2017 were included. The appropriateness of referral forms was assessed using Clinika VAP software, which combines semantic analysis of clinical questions and available metadata. Local protocols agreed on by all physicians defined criteria of appropriateness. Two multilevel logistic models were used to identify multiple predictors of inappropriateness of referral forms and to analyse variability among GPs, primary care subdistricts and healthcare districts.ResultsOverall, 37% of referral forms were classified as inappropriate, gastroscopy and CT showed higher proportions of inappropriate referrals compared to colonoscopy and MRI. Inappropriateness increased with patient age for CT and MRI; for gastroscopy, it was lower for patients aged 65-84 compared to those younger, and for colonoscopy, it was higher for older patients. Fee exemptions were associated with inappropriateness in MRI referral forms. The effect of GPs' practice organization was consistent across all tests, showing higher inappropriateness for primary care medical networks than in primary care medical groups. Male GPs were associated with inappropriateness in endoscopy, and older GPs were associated with inappropriateness in musculoskeletal CT. While there was moderate variability in the inappropriate prescribing among GPs, there was not among the healthcare districts or primary care subdistricts.ConclusionsRoutinely collected data and IT tools can be useful to identify and monitor diagnostic procedures at high risk of inappropriate prescribing. Assessing determinants of inappropriate referral makes it possible to tailor educational and organizational interventions to those who need them.

Project description:OBJECTIVES:Develop predictive models for a paediatric population that provide information for paediatricians and health authorities to identify children at risk of hospitalisation for conditions that may be impacted through improved patient care. DESIGN:Retrospective healthcare utilisation analysis with multivariable logistic regression models. DATA:Demographic information linked with utilisation of health services in the years 2006-2014 was used to predict risk of hospitalisation or death in 2015 using a longitudinal administrative database of 527?458 children aged 1-13 years residing in the Regione Emilia-Romagna (RER), Italy, in 2014. OUTCOME MEASURES:Models designed to predict risk of hospitalisation or death in 2015 for problems that are potentially avoidable were developed and evaluated using the C-statistic, for calibration to assess performance across levels of predicted risk, and in terms of their sensitivity, specificity and positive predictive value. RESULTS:Of the 527?458 children residing in RER in 2014, 6391 children (1.21%) were hospitalised for selected conditions or died in 2015. 49?486 children (9.4%) of the population were classified in the 'At Higher Risk' group using a threshold of predicted risk >2.5%. The observed risk of hospitalisation (5%) for the 'At Higher Risk' group was more than four times higher than the overall population. We observed a C-statistic of 0.78 indicating good model performance. The model was well calibrated across categories of predicted risk. CONCLUSIONS:It is feasible to develop a population-based model using a longitudinal administrative database that identifies the risk of hospitalisation for a paediatric population. The results of this model, along with profiles of children identified as high risk, are being provided to the paediatricians and other healthcare professionals providing care to this population to aid in planning for care management and interventions that may reduce their patients' likelihood of a preventable, high-cost hospitalisation.

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Cumulative COVID-19 incidence, mortality and prognosis in cancer survivors: A population-based study in Reggio Emilia, Northern Italy.

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