Publications on Partially Ordered Set (POSET) Theory
Scientific article
Identification of neighborhood clusters on data balanced by a poset-based approach
Statistical Methods & Applications • 2023 • Vol. 32(4)
Abstract
The identification of territorial clusters where the population suffers from worse health conditions is an important topic in social epidemiology, in order to identify health inequalities in cities and provide health policy interventions. This objective is particularly challenging because of the mechanism of self-selection of individuals into neighborhoods, which causes selection bias. The aim of this paper consists in the identification of neighborhood clusters where elderly people living in Turin, a city in north-western Italy, are exposed to an increased risk of hospitalized fractures. The study is based on administrative data and is a retrospective, observational cohort study. It is composed by a first phase, in which the individual confounding variables are balanced across neighborhoods in order to make them comparable, and a second phase in which the neighborhoods are aggregated into clusters characterized by significantly higher health risk. In the first phase we exploited a balancing technique based on partially ordered sets (poset), called Matching on poset based Average Rank for Multiple Treatments (MARMoT). On the balanced dataset, we used a spatial scan to identify the presence of clusters and we checked whether the risk of fracture is significantly higher in some contiguous areas. The combination of both MARMoT procedure and spatial scan makes it possible to highlight two clusters of neighborhoods in Turin where the risk of incurring hospitalized fractures for elderly people is significantly higher than the mean. These results could have important implications for the implementation of health policies.
Scientific article
Construction of a Frailty Indicator with Partially Ordered Sets: A Multiple-Outcome Proposal Based on Administrative Healthcare Data
Social Indicators Research • 2022 • Vol.160, pp. 989–1017
Abstract
Given the progressive aging of Italian and European populations, the number of cases with chronic diseases is steeply increasing. This calls for new strategies for health resource management and the implementation of prevention policies. Among chronic patients, frail subjects have special and wider care requirements, along with an increased risk of adverse health outcomes. Thus, their identification is an important step for the Italian National Program for Chronic Diseases. This study aims at constructing an indicator that measures the frailty level of individuals in the population aged over 65 y using administrative healthcare data-flows of the Piedmont region. Following the multidimensional nature of frailty, we adopted a multiple-outcome approach in our proposal. This was done by considering the capacity to predict six unfavorable outcomes: death, urgent unplanned hospitalization, access to the emergency room with red code, avoidable hospitalization, hip fracture, and disability. We identified a parsimonious set of seven explanatory variables that can simultaneously predict the six outcomes we considered. We then assembled them into a unique frailty indicator through the use of a partially ordered set (poset) theory. Our indicator performed well with respect to all the outcomes and was able to describe several individual characteristics that are not directly considered in the computation of the indicator. Thanks to its parsimony and to the use of administrative healthcare data, our indicator allows all the stakeholders involved in the healthcare process, such as Italian Local Health Units, general practitioners, and regional managers, to use it to target frail individuals with better comprehensive healthcare actions.
Scientific article
Matching on poset‐based average rank for multiple treatments to compare many unbalanced groups
Statistics in Medicine • 2021 • Vol.40(28), pp. 6443-6458
Abstract
In this article, we propose an original matching procedure for multiple treatment frameworks based on partially ordered set theory (poset). In our proposal, called matching on poset-based average rank for multiple treatments (MARMoT), poset theory is used to summarize individuals' confounders and the relative average rank is used to balance confounders and match individuals in different treatment groups. This approach proves to be particularly useful for balancing confounders when the number of treatments considered is high. We apply our approach to the estimation of neighborhood effect on the fractures among older people in Turin (a city in northern Italy).
Scientific article
Evaluating inverse propensity score weighting in the presence of many treatments. An application to the estimation of the neighbourhood effect
Journal of Statistical Computation and Simulation • 2021 • Vol.91(4), pp. 836-859
Abstract
In this paper we consider the problem of estimating causal effects 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 Generalized Boosted Models. We assess the performance of IPTW under three different 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 effect on the probability of older people experiencing at least one fracture requiring hospitalization during the year 2002 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 the presence of many treatments.
Scientific article
Quantifying Frailty in Older People at an Italian Local Health Unit
Social Indicators Research • 2019 • Vol.146(3), pp. 757-782
Abstract
Population aging, which is common in developed countries, highlights issues related to the health and social assistance of frail individuals. The Italian Chronic Disease Program launched by the Ministry of Health proposes the implementation of tools that stratify the population based on health and care needs and enhances the integration of existing admin- istrative health data flows. This work has its roots in the need to identify frail individuals conveyed by an Italian Local Health Unit (LHU) of the Veneto region and organize an efficient service of care and prevention. We propose an indicator of frailty, computed using the poset approach, comprising only eight variables available in administrative health data at the LHU level. The proposed indicator associates a value of frailty between 0 and 1 to every individual of the population aged 65 years and older, thereby allowing stratification of the population by the risk level. By validating and analyzing the frailty indicator, we show that this approach provides an effective stratification of older people.