Publications on Disease Clusters
Scientific article
A model-based scan statistic with enhanced specificity for detecting spatial clusters of high mortality risk
Environmental and Ecological Statistics • 2025 • Vol. 32, pp. 1489–1518
Abstract
Detecting geographical areas in a territory with excess mortality is a crucial step to understand health disparities and implement effective public health policies. In practice, this means identifying both individual areas and clusters of neighbouring areas where mortality is higher than in the rest of the territory. Mortality clusters are commonly detected using spatial scan statistics, which are tools that scan the territory with moving windows and test the presence of excess mortality. However, these techniques often detect spurious clusters or encompass areas not at risk into existing clusters, leading to unreliable epidemiological results. Here, we propose a data-driven initialisation of a generalised linear model scan statistic that improves its specificity and reduces its computational cost. Our strategy consists of identifying individual areas with a significant mortality excess through an improved version of the Besag–York–Mollié model, and using them to initialise the clustering procedure. We investigate the properties of our method with a series of simulation experiments, showing that our proposed initialisation increases clustering specificity relative to standard approaches and also prevents the erroneous inclusion of areas not at risk within clusters of elevated mortality. Finally, we demonstrate the usefulness of the proposed tool for healthcare authorities using a case study on mortality data from the Padua province in northeastern Italy.
Scientific article
Bayesian Mapping of Mortality Clusters
Biostatistics • 2025 • Vol. 26(1)
Summary
Disease mapping analyses the distribution of several disease outcomes within a territory. Primary goals include identifying areas with unexpected changes in mortality rates, studying the relation among multiple diseases, and dividing the analysed territory into clusters based on the observed levels of disease incidence or mortality. In this work, we focus on detecting spatial mortality clusters, that occur when neighbouring areas within a territory exhibit similar mortality levels due to one or more diseases. When multiple causes of death are examined together, it is relevant to identify not only the spatial boundaries of the clusters but also the diseases that lead to their formation. However, existing methods in literature struggle to address this dual problem effectively and simultaneously. To overcome these limitations, we introduce perla, a multivariate Bayesian model that clusters areas in a territory according to the observed mortality rates of multiple causes of death, also exploiting the information of external covariates. Our model incorporates the spatial structure of data directly into the clustering probabilities by leveraging the stick-breaking formulation of the multinomial distribution. Additionally, it exploits suitable global-local shrinkage priors to ensure that the detection of clusters depends on diseases showing concrete increases or decreases in mortality levels, while excluding uninformative diseases. We propose a Markov chain Monte Carlo algorithm for posterior inference that consists of closed-form Gibbs sampling moves for nearly every model parameter, without requiring complex tuning operations. This work is primarily motivated by a case study on the territory of a local unit within the Italian public healthcare system, known as ULSS6 Euganea. To demonstrate the flexibility and effectiveness of our methodology, we also validate perla with a series of simulation experiments and an extensive case study on mortality levels in U.S. counties.
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.
Conference Proceedings
Territorial clusters of mortality and role of social and environmental factors: the case of ULSS 6 Euganea (Italy)
2023 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Eindhoven, Netherlands • 2023 • pp. 1-5
Abstract
The progressive ageing of the population, not accompanied by a corresponding increase in healthy life expectancy, brings to the forefront the study of the health of older adults from a holistic perspective. The concept of frailty aligns with this perspective, as it considers a broader condition of vulnerability involving mainly older individuals, which is much more difficult to define and measure. This article aims to provide an overview of the concept of frailty, the various definitional approaches, and, consequently, the measurement methods, while also focussing on the relationship between frailty and the two main concepts that define health: multimorbidity and disability. Following a brief overview of the types of data and their role in the study of frailty, the article concludes with an analytical approach for defining a measure of frailty.
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.