Identificazione dei soggetti fragili nell' Ulss 15: una nuova proposta basata sugli ordinamenti parziali

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Construction of a frailty indicator with partially ordered sets: a multiple outcome proposal based on administrative healthcare data

Given the progressive ageing of Italian and European populations, chronic diseases attributable to ageing are rising steeply, calling for new strategies for health resources management and implementation of prevention policies. Among chronic patients, frail subjects have special and wider care needs, together with an increased risk of adverse health outcomes. Thus, their identification is a fundamental goal, claimed as the first step of the Italian National Program for Chronic Diseases

Social, ethical and behavioural aspects of COVID-19.

Introduction: Vaccines and drugs for the treatment and prevention of COVID-19 require robust evidence generated from clinical trials before they can be used. Decisions on how to apply non-pharmaceutical interventions such as quarantine, self-isolation, social distancing and travel restrictions should also be based on evidence. There are some experiential and mathematical modelling data for these interventions, but there is a lack of data on the social, ethical and behavioural aspects of these interventions in the literature. Therefore, our study aims to produce evidence to inform (non-pharmaceutical) interventions such as communications, quarantine, self-isolation, social distancing, travel restrictions and other public health measures for the COVID-19 pandemic. Methods: The study will be conducted in the United Kingdom, Italy, Malaysia, Slovenia and Thailand. We propose to conduct 600-1000 quantitative surveys and 25-35 qualitative interviews per country. Data collection will follow the following four themes: (1) Quarantine and self-isolation (2) social distancing and travel restrictions (3) wellbeing and mental health (4) information, misinformation and rumours. In light of limitations of travel and holding in-person meetings, we will primarily use online/remote methods for collecting data. Study participants will be adults who have provided informed consent from different demographic, socio-economic and risk groups. Discussion: At the time of the inception of the study, United Kingdom, Italy, Malaysia, Slovenia and Thailand have initiated strict public health measures and varying degrees of "lockdowns" to curb the pandemic. These public health measures will change in the coming weeks and months depending on the number of cases of COVID-19 in the respective countries. The data generated from our study could inform these strategies in real time

Economic and social impacts of COVID-19 and public health measures: results from an anonymous online survey in Thailand, Malaysia, the UK, Italy and Slovenia.

OBJECTIVES: To understand the impact of COVID-19 and public health measures on different social groups, we conducted a mixed-methods study in five countries ('SEBCOV-social, ethical and behavioural aspects of COVID-19'). Here, we report the results of the online survey. STUDY DESIGN AND STATISTICAL ANALYSIS: Overall, 5058 respondents from Thailand, Malaysia, the UK, Italy and Slovenia completed the self-administered survey between May and June 2020. Poststratification weighting was applied, and associations between categorical variables assessed. Frequency counts and percentages were used to summarise categorical data. Associations between categorical variables were assessed using Pearson's χ2 test. Data were analysed in Stata 15.0 RESULTS: Among the five countries, Thai respondents reported having been most, and Slovenian respondents least, affected economically. The following factors were associated with greater negative economic impacts: being 18-24 years or 65 years or older; lower education levels; larger households; having children under 18 in the household and and having flexible/no income. Regarding social impact, respondents expressed most concern about their social life, physical health, mental health and well-being.There were large differences between countries in terms of voluntary behavioural change, and in compliance and agreement with COVID-19 restrictions. Overall, self-reported compliance was higher among respondents who self-reported a high understanding of COVID-19. UK respondents felt able to cope the longest and Thai respondents the shortest with only going out for essential needs or work. Many respondents reported seeing news perceived to be fake, the proportion varying between countries, with education level and self-reported levels of understanding of COVID-19. CONCLUSIONS: Our data showed that COVID-19 and public health measures have uneven economic and social impacts on people from different countries and social groups. Understanding the factors associated with these impacts can help to inform future public health interventions and mitigate their negative consequences. TRIAL REGISTRATION NUMBER: TCTR20200401002

Propensity score techniques in multiple treatments framework: the estimation of neighbourhood effect

Neighbourhood effects have been defined by Oakes (2004) as the independent causal effects of neighbourhood on a given number of health or social outcome(s). The aim of this thesis is to estimate the neighbourhood effect on old population in Turin with a propensity score approach. To achieve this goal, we need to work on adapting propensity score techniques to work well in a framework with many treatments (with ten or more treatments). Data used in the thesis come from the Turin Longitudinal Study (SLT), described in chapter 3. Our main goal is to understand if the observed differences in health outcomes across neighbourhoods can be causally attributed to neighbourhoods' as opposed to their different composition, i.e. to the fact that individuals with different risks factors live in different areas. In order to adjust for confounders and simulate an experimental approach, we focused on propensity score techniques that are briefly described in chapter 2. The first part of the analysis focuses on the performance evaluation of an inverse probability of treatment approach (IPTW) in a 10-treatment framework (chapter 4) and its application on real data on two different health outcomes: hospitalized fractures and mental health (chapter 5). In the second part of the thesis we propose a novel method that consists on a matching based on partially ordered sets (poset) (chapter 6). The Matching on Poset based Average Rank for Multiple Treatment (MARMoT), tested with some simulations, has revealed to be really useful to estimate neighbourhood effect, reducing bias of estimates because of the initial improvement of covariates' balance between groups.L'effetto di vicinato è stato definito da Oakes (2004) come l'effetto causale indipendente di un vicinato su qualsiasi esito sociale o di salute. Lo scopo principale di questo elaborato consiste nello stimare l'effetto di vicinato sullo stato di salute degli anziani residenti a Torino con un approccio basato sull'uso del propensity score. Tuttavia, risulta necessario adattare le tecniche di propensity score, generalmente utilizzate con trattamenti dicotomici, a casi di trattamento multiplo, in cui siano eventualmente coinvolti molti trattamenti (10 o più). I dati utilizzati nella tesi provengono dallo studio longitudinale torinese (SLT), descritto nel capitolo 3. Una delle domande di ricerca principali in questa tesi consiste nello stimare quanto le differenze osservate nello stato di salute degli anziani residenti in diversi vicinati siano dovute al vicinato di residenza e quanto invece siano legate alle diverse caratteristiche degli individui che lo compongono. Per aggiustare per l'effetto dei confondenti e ricostruire un approccio sperimentale, abbiamo preferito adottare tecniche basate sull'uso del propensity score, che sono brevemente descritte nel capitolo 2. Nella prima parte delle analisi viene valutato il funzionamento di un approccio di inverse probability of treatment weighting in uno scenario costituito da 10 trattamenti (capitolo 4). Viene poi applicato su dati reali per stimare l'effetto di vicinato su due esiti di salute: le fratture ospedalizzate e le malattie mentali (capitolo 5). Nella seconda parte della tesi invece viene descritta una proposta originale che consiste in un matching che sfrutta la teoria degli ordinamenti parziali poset). Questo approccio, che abbiamo chiamato Matching on Poset based Average Rank for Multiple Treatment (MARMoT), è stato testato attraverso uno studio di simulazione e si è rivelato essere particolarmente utile per la stima degli effetti di vicinato, riducendo la distorsione delle stime grazie al miglioramento del bilanciamento delle variabili confondenti tra i vicinati

Propensity score techniques in multiple treatments framework: the estimation of neighbourhood effect

Neighbourhood effects have been defined by Oakes (2004) as the independent causal effects of neighbourhood on a given number of health or social outcome(s). The aim of this thesis is to estimate the neighbourhood effect on old population in Turin with a propensity score approach. To achieve this goal, we need to work on adapting propensity score techniques to work well in a framework with many treatments (with ten or more treatments). Data used in the thesis come from the Turin Longitudinal Study (SLT), described in chapter 3. Our main goal is to understand if the observed differences in health outcomes across neighbourhoods can be causally attributed to neighbourhoods' as opposed to their different composition, i.e. to the fact that individuals with different risks factors live in different areas. In order to adjust for confounders and simulate an experimental approach, we focused on propensity score techniques that are briefly described in chapter 2. The first part of the analysis focuses on the performance evaluation of an inverse probability of treatment approach (IPTW) in a 10-treatment framework (chapter 4) and its application on real data on two different health outcomes: hospitalized fractures and mental health (chapter 5). In the second part of the thesis we propose a novel method that consists on a matching based on partially ordered sets (poset) (chapter 6). The Matching on Poset based Average Rank for Multiple Treatment (MARMoT), tested with some simulations, has revealed to be really useful to estimate neighbourhood effect, reducing bias of estimates because of the initial improvement of covariates' balance between groups

Socio-economic and spatial stratification of frailty in the older population

To measure the frailty level of old individuals and identify elderly with peculiar health care needs, the frailty indicator has been proposed. This indicator presents a simple structure that counts only eight variables; in this way, it is easy to replicate and implement. The indicator is based on administrative healthcare data that are available to the entire population. It is useful to predict the seven negative health outcomes related to the frailty condition, following the definition of a frail subject as susceptible to negative outcomes. Moreover, the indicator is useful to stratify the population on the basis of care needs and captures also some socioeconomic dimensions of frailty, although only health variables are used for its construction

Evaluating inverse propensity score weighting in the presence of many treatments. An application to the estimation of the neighbourhood effect

In this paper we consider the problem of estimating causal eects in a framework with many treatments through a simulation study. We engage in Monte Carlo simulations to evaluate the performance of inverse probability of treatment weighting (IPTW) with 10 treatments, estimating the propensity scores using Generalised Boosted Models. We assess the performance of IPTW under three dierent scenarios representing treatment allocations, and compare it with a simple parametric approach, i.e., logistic regression. IPTW's estimates are less biased, even though they exhibit a higher variance than those based on logistic regression. Moreover, we apply IPTW to the estimation of the neighbourhood eect on the probability of older people experiencing hospitalised fractures by comparing 10 neighbourhoods in the city of Turin (Italy). Our paper demonstrates that IPTW can be successfully applied to the estimation of neighbourhood effects, and, more generally, to the estimation of causal effects in presence of many treatments