Background To assess the association between walking speed (WS) and its improvement on hospitalization rates and costs in outpatients with cardiovascular disease. Methods Six hundred forty-nine patients participating in an exercise-based secondary prevention program were studied. Patients were divided at baseline into two groups characterized by low and high WS based on the average WS maintained during a moderate 1-km treadmill-walking test. WS and other covariates were grouped into three domains (demographic factors, medical history and risk factors), and used to estimate a propensity score, in order to create homogeneous groups of patients. All-cause hospitalization was assessed 3 years after baseline as a function of WS. Hospitalization and related costs were also assessed during the fourth-to-sixth years after enrollment. To test whether the hospitalization costs were related to changes in WS after 36 months, a multistrata permutation test was performed by combining within strata partial tests. Results The results support the hypothesis that hospitalization costs are significantly reduced in accordance with an improvement in WS. This effect is most evident among older patients, overweight or obese, smokers, and those without a history of coronary artery bypass surgery. Conclusions The present study supports growing evidence of an inverse association between WS, risk of hospitalization and consequent health-care costs. The joint use of propensity score and multistrata permutation approaches represent a flexible and robust testing method which avoids the possible effects of several confounding factors typical of these studies.

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Amongst the several consequences of the COVID-19 pandemic, we should include psychological effects on the population. The mental health consequences of lockdown are affected by several factors. The most important are: the duration of the social isolation period, the characteristics of the living space, the number of online (virtual) and offline (physical) contacts and perceived contacts’ closeness, individual characteristics, and the spread of infection in the geographical area of residence. In this paper, we investigate the possible effects of environmental, social and individual characteristics (predictors) on mental health (response) during the COVID-19 lockdown period. The relationship between mental health and predictors can be studied with a multivariate linear regression model, because “mental health” is a multidimensional concept. This work provides a contribution to the debate about the factors affecting mental health in the period of the COVID-19 lockdown, with the application of an innovative approach based on a multivariate regression analysis and a combined permutation test on data collected in a survey conducted in Italy in 2020.

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