Strategy to reduce adverse health outcomes in subjects highly vulnerable to COVID-19: results from a population-based study in Northern Italy

Abstract

ObjectivesThis study describes a new strategy to reduce the impact of COVID-19 on the elderly and other clinically vulnerable subjects, where general practitioners (GPs) play an active role in managing high-risk patients, reducing adverse health outcomes.DesignRetrospective cohort study.SettingPopulation-based study including subjects resident in the province of Milan and Lodi.Participants127 735 residents older than 70 years, with specific chronic conditions.InterventionsWe developed a predictive algorithm for overall mortality risk based on demographic and clinical characteristics. All residents older than 70 years were classified as being at low or high risk of death from COVID-19 infection according to the algorithm. The high-risk group was assigned to their GPs for telephone triage and consultation. The high-risk cohort was divided into two groups based on GP intervention: patients who were not contacted and patients who were contacted by their GPs.Outcome measuresOverall mortality, COVID-19 morbidity and hospitalisation.ResultsPatients with increased risk of death from COVID-19 were 127 735; 495 669 patients were not at high risk and were not included in the intervention. Out of the high-risk subjects, 79 110 were included but not contacted by their GPs, while 48 625 high-risk subjects were included and contacted. Overall mortality, morbidity and hospitalisation was higher in high-risk patients compared with low-risk populations. High-risk patients contacted by their GPs had a 50% risk reduction in COVID-19 mortality, and a 70% risk reduction in morbidity and hospitalisation for COVID-19 compared with non-contacted patients.ConclusionsThe study showed that, during the COVID-19 outbreak, involvement of GPs and changes in care management of high-risk groups produced a significant reduction in all adverse health outcomes.

Algorithm for Individual Prediction of COVID-19–Related Hospitalization Based on Symptoms: Development and Implementation Study

“…The main result of our research, performed using a population-based approach, is the development of a simple and robust stratification tool aimed at improving COVID-19 patient management and health care organization. This tool was constructed combining 2 predictive models developed by ATS Milan: the predictive model for hospitalization and the predictive model for overall mortality risk [ 6 ]. Using these predictive models, a stratification tool was easily generated with a close relationship between patient stratification and the health status.…”

“…Beginning on October 25, 2020, with the start of the second wave of COVID-19 in Lombardy, we developed a surveillance and monitoring system for patients with COVID-19. Each patient was stratified as a high-, medium-, or low-risk patient for the combined outcome of hospitalization and death, according to the clinical and demographic characteristics highlighted by 2 predictive models developed by ATS Milan—the aforementioned predictive model for hospitalization and the predictive model for the overall mortality risk [ 6 ].…”

“…Considering potential misclassification, we decided to use 40% as a probability cutoff for prediction, instead of the usual 50% used in logistic models [ 24 ]; this allowed us to include a larger number of patients in the medium-risk category. We thus used the prediction algorithm for overall mortality risk [ 6 ] to define high-risk patients and the aforementioned predictive model for hospitalization for the definition of medium- and low-risk patients. Individual predicted probabilities for hospitalization were calculated for each patient according to the estimated model parameters ( Table 1 ), as well as demographic and clinical characteristics.…”

“…This could potentially be highly useful to inform and guide treatment and vaccination campaigns. In the initial weeks of March, when the COVID-19 epidemic was growing exponentially, a predictive model was developed to stratify patient risk of dying at the individual level, according to age and the presence of comorbidities [ 6 ].…”

Optimal control and cost-effectiveness analysis of a new COVID-19 model for Omicron strain

“…It is a much weaker strain and is a self-limiting disease that is strongly related to the body’s resistance [34] . Among those exposed, the elderly, children and some younger people with diseases such as diabetes or AIDS, who are less resistant, are at greater risk of contracting COVID-19 [35] , [36] . Because of the significant difference in risk between them and normal young adults, we divided the exposed individuals into high-risk and low-risk .…”