Full List of Publications
Conference Proceedings
Evaluating Frailty Among Older Adults Using a Principal Components-Based Indicator
Statistical Science: From Theory to Applied Research II – SIS2026 • 2026 • pp. 356-362
Abstract
Population ageing is an expanding phenomenon, driven by increased life expectancy and declining fertility, leading to higher prevalence of chronic-degenerative conditions and growing pressure on care services. Therefore, early identification of frail subjects is essential to protect older adults. This study proposes a new frailty indicator developed using administrative health data from the ULSS6 Euganea(ULSS6 Euganea is a health authority that manages the health care of the population of the 101 municipalities in the province of Padua (located in north-east of Italy), which has approximately 936000 inhabitants.) and including all individuals aged at least 65 years, observed between 2016 and 2017. Six distinct outcomes were chosen to represent frailty, each associated with a specific group of previously identified determinants. For each outcome, a logistic regression was estimated and a principal component analysis was applied to the values predicted by the models, identifying the indicator in the first component. Different frailty stratification methods were compared, including quantiles, the F1-score, and the k-means method. The indicator was validated using two different cohorts, which also made it possible to evaluate its temporal evolution.
Scientific article
Redefining cognitive testing: the impact of cognitive reserve and sex from early to late adulthood
Frontiers in Psychology • 2026 • Vol. 17
Abstract
Objectives: Italy, one of the world’s super-aged societies, faces profound demographic transformations amid relevant regional disparities in sociodemographic trends, institutional structures, and economic conditions. These features make it an ideal laboratory to study both the challenges and opportunities of population aging. This article introduces Age-It, a Research Program designed to leverage Italy’s position at the forefront of global aging to advance transdisciplinary research and inform evidence-based policies and practices on aging.
Methods: Age-It adopts a life course perspective encompassing individual, family, and societal levels. It conceptualizes “aging well” as the outcome of multi-agent, multi-context processes unfolding from early life through old age. Furthermore, Age-It moves beyond a multidisciplinary approach by fostering true cross-fertilization between biomedical, sociodemographic, and technological sciences. Structured as an umbrella initiative, the program brings together multiple interlinked projects that address diverse dimensions of aging through transdisciplinary and collaborative research.
Results: The program addresses key limitations in Italy’s current aging research and policy landscape: fragmented data, disciplinary silos, and weak connections between research and policymaking. By integrating biomedical, technological, and socioeconomic perspectives into structured, theory-driven research centers (Spokes), Age-It provides a coordinated and innovative platform for studying aging.
Discussion: Leveraging Italy’s unique demographic profile and internal heterogeneity, Age-It promotes sustainable aging by harnessing the opportunities embedded in demographic change. The program ranges from the biology of aging to mental and physical health prevention, long-term care, labor market dynamics, and social participation—ultimately aiming to reshape how aging is perceived and managed in aging societies.
Scientific article
Mapping social health and dementia risk: A register-based study of older adults in Finland
Social Science & Medicine • 2026 • Vol. 398, Article 119154
Abstract
This study investigates the role of individual and area-level social health factors in shaping geographic variation in dementia incidence among older adults in Finland. Using nationwide register data on all individuals born between 1935 and 1939 and residing in Finland in 2015 (N = 185,712), we estimate cumulative dementia incidence over a four-year follow-up period (2016-2019). To reduce compositional bias in geographical comparison, we applied Matching on poset-based Average Rank for Multiple Treatments (MARMoT), a non-parametric matching approach that balances observed individual level characteristics across municipalities. Spatial scan statistics were then used to identify geographic clusters of excess dementia incidence considering municipality-level contextual measures after adjustment for individual-level characteristics. Before MARMoT adjustment, several contiguous clusters of elevated dementia incidence were identified, particularly in eastern and southern Finland, with the highest risk cluster exhibiting a 36% higher incidence than the rest of the country. After balancing individual-level characteristics, some clusters attenuated, whereas others persisted or newly emerged, suggesting a confounding role of individual characteristics in the relationship between dementia incidence and place of residence. Excess incidence remained in parts of eastern Finland (21% - 51% excess risk) and emerged in west-central municipalities (27% excess risk). The inclusion of municipality-level indicators did not substantially alter these patterns. These findings underscore the importance of accounting for social health and socio-demographic composition in spatial analysis of dementia and demonstrate the value of integrating matching-based and spatial methods to distinguish compositional from contextual disparities in ageing societies.
Scientific article
Aging well in an aging society: Italy at the forefront of global aging and the Age-It Research Program
The Journals of Gerontology: Series B • 2025 • Vol. 80, pp. S99–S109
Abstract
Objectives: Italy, one of the world’s super-aged societies, faces profound demographic transformations amid relevant regional disparities in sociodemographic trends, institutional structures, and economic conditions. These features make it an ideal laboratory to study both the challenges and opportunities of population aging. This article introduces Age-It, a Research Program designed to leverage Italy’s position at the forefront of global aging to advance transdisciplinary research and inform evidence-based policies and practices on aging.
Methods: Age-It adopts a life course perspective encompassing individual, family, and societal levels. It conceptualizes “aging well” as the outcome of multi-agent, multi-context processes unfolding from early life through old age. Furthermore, Age-It moves beyond a multidisciplinary approach by fostering true cross-fertilization between biomedical, sociodemographic, and technological sciences. Structured as an umbrella initiative, the program brings together multiple interlinked projects that address diverse dimensions of aging through transdisciplinary and collaborative research.
Results: The program addresses key limitations in Italy’s current aging research and policy landscape: fragmented data, disciplinary silos, and weak connections between research and policymaking. By integrating biomedical, technological, and socioeconomic perspectives into structured, theory-driven research centers (Spokes), Age-It provides a coordinated and innovative platform for studying aging.
Discussion: Leveraging Italy’s unique demographic profile and internal heterogeneity, Age-It promotes sustainable aging by harnessing the opportunities embedded in demographic change. The program ranges from the biology of aging to mental and physical health prevention, long-term care, labor market dynamics, and social participation—ultimately aiming to reshape how aging is perceived and managed in aging societies.
Scientific article
Multidimensional determinants of active and healthy aging trajectories: a position paper from the Age-It Research Program
The Journals of Gerontology: Series B • 2025 • Vol. 80, pp. S145-S157
Abstract
Objectives: This position paper presents perspectives from Spoke 4 (Trajectories for Active and Healthy Aging) of the Age-It Research Program, which adopts a One Health perspective to examine the interplay of cognitive, behavioral, nutritional, social, and environmental determinants of aging. Addressing these multidimensional factors is crucial to promoting health, independence, and well-being across the life course.
Methods: We reviewed evidence on lifelong determinants of aging, including physical activity, nutrition, mental engagement, and social participation, alongside emerging digital health solutions. The One Health framework guided our analysis, emphasizing the interconnectedness of individual, societal, and environmental influences. Spoke 4 integrates multidisciplinary expertise to translate scientific knowledge into practical tools for communities, healthcare providers, and policymakers.
Results: Evidence shows that sustained engagement in physical activity, cognitively stimulating activities, and strong social networks supports resilience, reduces frailty, and preserves independence. Tailored nutritional strategies further enhance functional capacity. Digital technologies—such as mobile apps, wearable devices, and online platforms—demonstrate potential to improve disease prevention and health monitoring. However, disparities in digital literacy and access remain significant barriers, particularly for older adults.
Discussion: Spoke 4 of Age-It highlights the need for multidimensional, One Health–based strategies that integrate traditional health determinants with digital innovations. By combining evidence-based interventions with user-centered e-health platforms, scalable and inclusive solutions can be developed to support healthy aging. These efforts provide policymakers and healthcare systems with tools to foster resilience, mitigate frailty, and enhance quality of life in aging populations.
Scientific article
Identifying core adverse health outcomes for frailty assessment in older adults using administrative data
Frontiers in medicine • 2025 • Vol. 12
Abstract
Objectives: Measurement of frailty can be based on the ability to predict adverse health outcomes. Although frailty research is progressing rapidly, a unique work that analyzes together outcomes related to frailty condition is still lacking in literature. This article aims to fill this gap, selecting a parsimonious set of outcomes relevant in frailty studies that exploit administrative healthcare data.
Methods: Starting with an extensive literature review, we identified several health outcomes that can be measured with administrative healthcare databases. We computed the prevalence and correlation of these outcomes in a local health unit in North-East Italy. We performed a factor analysis and estimated a graphical model to examine the conditional independence relationships between the outcomes.
Results: Our analysis revealed two primary outcome groups: adverse events (characterized by various forms of hospital use) and adverse conditions (such as dementia and disability). Femur fracture emerged as a distinct outcome, while death showed positive associations with all other outcomes. Considering overlaps and relationships, we selected a core set of six representative outcomes: death, high priority access to the emergency room, femur fracture, hospitalization, disability, and dementia.
Conclusion: This study identified six central and non-redundant adverse health outcomes related to frailty that can be easily derived from routinely available administrative healthcare data. These findings provide a methodologically grounded selection of outcomes that are clinically meaningful and feasible, offering a solid foundation for developing population-based frailty indices.
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.
Book chapter
Beyond longevity. Measuring frailty to inform health policy and practice
Associazione Neodemos • 2025 • pp. 79-86
Introduction
The increase in life expectancy is one of the greatest achievements of modern societies. However, the so-called ‘longevity paradox’, described by Fries in 1980 and, more recently, by Garmany and colleagues, shows that the additional years are not always lived in good health. In Italy, life expectancy at birth rose from 79.6 years in 2000 to 83.4 years in 2019. Naghavi and the Global Burden of Disease group have calculated that healthy life expectancy (HALE) increased from 68.5 years (95% CI: 65.271.3) in 2000 to 70.9 years (67.4–73.8) in 2021, confirming that longer lives do not necessarily translate into years lived in full independence. The issue is not purely medical: it concerns health systems, families, and the sustainability of social services. Over the last two decades, the concept of ‘frailty’ has become central to explaining why some older people, even without serious disease, are more vulnerable to adverse events such as falls, hospital admissions, loss of autonomy, and mortality. Recognising and measuring frailty is crucial for public health and for the planning of preventive interventions.
Scientific article
Protective and risk factors for cognitive decline: The role of sex and occupational status
Social science & medicine (1982) • 2025 • Vol. 389
Summary
Objective:
With ageing cognitive functions become less efficient affecting daily tasks and well-being. We investigate protective factors for decline focusing on the role of sex and occupation.
Method: the Mini Mental State Examination (Folstein et al., 1975) and the Prose Memory Test (Mondini et al., 2003) were administered to 3081 older adults with the purpose of understanding the role of Age, Sex, Education, Occupation, and Comorbidities.
Results: A negative effect of Age and a positive effect of Education with an interaction between Education and Sex were found: men performed better than women when Education was low, but women performed better than men when Education was high. A protective effect of Occupation was also found: “Manager” showed a better performance than other types of occupations, while manual workers had the worst outcomes.
Discussion: The protective role of higher professional positions and the risks of manual and agricultural occupations are underlined. Our study gives new insights into the limited research on the effects of occupation on cognition in ageing.
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.
Book chapter
La Fragilità negli anziani: comprenderla e misurarla per orientare le politiche sanitarie
Associazione Neodemos • 2025 • pp. 84-92
Introduction
L’aumento della longevità è uno dei più grandi successi delle società moderne. Tuttavia, il cosiddetto “paradosso della longevità”, ben descritto, fra gli altri, da Fries nel 1980 e da Garmany e colleghi in anni più recenti, mostra che gli anni guadagnati non sempre sono vissuti in buona salute. In Italia, la speranza di vita alla nascita è aumentata da 79,6 anni nel 2000 a 83,4 anni nel 2019. Naghavi e il gruppo Global Burden of Disease hanno calcolato che la speranza di vita in buona salute (HALE) è aumentata da 68,5 anni (IC 95%: 65,2–71,3) nel 2000 a 70,9 anni (67,4–73,8) nel 2021, confermando che l’allungamento della vita non si traduce in pari misura in anni vissuti in piena autonomia. Il problema non è solo medico: riguarda i sistemi sanitari, le famiglie, la sostenibilità dei servizi sociali. Negli ultimi vent’anni il concetto di “fragilità” (frailty) è diventato fondamentale per spiegare perché alcune persone anziane, anche senza malattie gravi, siano più vulnerabili a eventi avversi come cadute, ricoveri, perdita di autonomia e morte. Riconoscere e misurare la fragilità è cruciale per la sanità pubblica e per la programmazione di interventi di prevenzione.
Book of short papers
IES 2025 - Innovation & Society: Statistics and Data Science for Evaluation and Quality. Book of Short Papers
Cleup sc “Coop. Libraria Editrice Università di Padova” • 2025
Preface
Statistical thinking and data-driven methodologies are essential to understanding contemporary challenges and driving innovation in several sectors. Since 2009, the Innovation and Society (IES) biennial conference, organized by the Group of Statistics for the Evaluation and Quality of Services (SVQS) of the Italian Statistical Society (SIS), has served as a key venue for advancing research in statistical methods applied to evaluation and quality. The 12th edition, titled “Innovation & Society: Statistics and Data Science for Evaluation and Quality” took place from June 25 to 27, 2025, at the Bressanone-Brixen campus of the University of Padova. This year’s conference reinforced the role of statistics and data science as key tools for evaluating complex phenomena and ensuring quality in a variety of fields, including education, healthcare, environment, public administration, finance, tourism, sports, and beyond. The scientific program was particularly rich, featuring 3 plenary lectures, 44 invited sessions, 6 contributed sessions, one special plenary lecture and a round table co-organized by The Italian (ISTAT) and Albanian (INSTAT) National Statistical Institutes. In total, 199 contributions were presented within invited and contributed sessions. The event also saw the highest international participation in the history of IES, with contributions from the Austrian and Luxembourg Statistical Societies, among others. Three distinguished keynote speakers enriched the program with plenary lectures: Fabrizio Ruggeri, Christophe Ley, Werner Müller. In addition, a special plenary session featured two prominent invited talks by Fortunato Pesarin and Narayanaswamy Balakrishnan, further enhancing the depth and breadth of the scientific discussions. The conference also hosted the Young Statisticians Award, which attracted 36 high-quality submissions, showcasing the energy and creativity of early-career researchers working at the intersection of statistics and real-world evaluation problems. The short papers in this volume highlight the wide spectrum of topics discussed at IES 2025, and testify to the evolving role of statistics and data science in producing knowledge and supporting decision-making in complex systems. We are sincerely grateful to all authors, session chairs and discussants, session organizers, reviewers, and participants. We also warmly thank our institutional partners for their support: Italian Statistical Society (SIS), SVQS Group, DMS StatLab of the University of Brescia, Departments of Statistical Sciences and of Managements and Engineering of the University of Padova, Österreichische Statistische Gesellschaft (Austrian Statistical Society), International Society for Business and Industrial Statistics (ISBIS), European Network for Business and Industrial Statistics (ENBIS). We hope this volume will serve as a source of inspiration and a valuable reference for re- searchers working at the interface of statistics, data science, and the evaluation of quality in contemporary society.
Conference Proceedings
Exploring Ontology-Based Mining of ADRs
Statistics for Innovation IV – SIS2025 • 2025 • pp. 386-392
Abstract
Pharmacovigilance is essential for protecting public health as it identifies and evaluates adverse events (AEs) associated with the use of pharmaceuticals and vaccines. This study explores a novel approach for detecting adverse drug reactions which integrates AE ontology into a zero-inflated negative binomial model. Correlated AEs are more effectively disentangled by taking account of their similarities while accounting for excess of zero counts. The aim of this contribution is to numerically assess the model on Italian pharmacovigilance data.
Conference Proceedings
Old Age Informal Caring and Impact on Health Among Adults in Italy
Statistics for Innovation I – SIS2025 • 2025 • pp. 379-383
Abstract
This study examines the relationship between informal intergenerational caregiving and self-rated health (SRH) among Italian adults, with a focus on gender and employment status. Using data from the 2016 Italian Family, Social Subjects, and Life Cycle (FSS) ISTAT survey, we analyse a sample of 8,610 individuals aged 35–64 with at least one living parent. Caregivers are identified by weekly informal caregiving, co-residence with the cared recipient, or assistance provided outside the household. Logistic regression models are used to estimate the association between informal caregiving and health outcomes, while Inverse Probability of Treatment Weighting (IPTW) is applied to infer causal effects of providing care. Results indicate that caregivers are less likely to report good or very good SRH than non-caregivers, though gender-specific effects are not statistically significant. Among employed or employable individuals, caregiving has the strongest negative impact, particularly for women, while among inactive individuals, no significant association is observed. This suggests that role conflict may intensify stress from caregiving responsibilities. Future research will further investigate care intensity and multiple caregiving roles using advanced IPTW methods to refine causal estimates.
Conference Proceedings
Multimorbidity as a Network: Connections Between Diseases and Adverse Health Outcomes
Statistics for Innovation I – SIS2025 • 2025 • pp. 366-371
Abstract
Multimorbidity, the coexistence of multiple chronic diseases, is increasingly prevalent in aging populations, complicating disease management and clinical decision-making. This study examines the multimorbidity network and its association with adverse health outcomes over one year, as well as its role in the process leading to death. Using a network-based approach, we analyze data from individuals aged 65 or older in the province of Padova (Italy). Chain graphical models were used to estimate the network structure and capture both direct and indirect pathways linking diseases to negative outcomes. We assess disease proximity to critical health events and their centrality within the network. Our findings highlight hospitalization and disability as key factors in health trajectories, with hospitalization emerging as a central node due to its strong associations with most diseases and outcomes.
Book
Misurare la fragilità negli anziani. Metodi e strumenti a supporto delle politiche e della ricerca
Franco Angeli • 2025
In this volume:
- G. Boccuzzo, A. Donno. Prefazione, pp. 9–14
- A. Donno, M. Nicolaio, M. Silan, G. Boccuzzo. Cos’è la fragilità dell’anziano e come può essere identificata. pp. 15–34
- E. Bovo, G. Boccuzzo. Misure di fragilità sulla base di indagini di popolazione. pp. 35–86
- M. Nicolaio, A. Donno, G. Boccuzzo. Indicatori di fragilità sulla base di dati amministrativi. pp. 87–170
- A. Ghirardo, G. Boccuzzo. La fragilità sociale. pp. 194–231
- E. Bovo, A. Donno. La fragilità in Italia. pp. 232–268
Scientific article
Understanding multimorbidity: insights with graphical models
BMC Medical Research Methodology • 2025 • Vol. 25, Article 84
Abstract
Background: The use of graphical models in the multimorbidity context is increasing in popularity due to their intuitive visualization of the results. A comprehensive understanding of the model itself is essential for its effective utilization and optimal application. This article is a practical guide on the use of graphical models to better understand multimorbidity. It provides a tutorial with a focus on the interpretation of the model structure and of the parameter values. In this study, we analyze data related to a cohort of 214,401 individuals, who were assisted by the Local Health Unit of the province of Padova (north-eastern Italy), collecting information from hospital discharge forms.
Methods: We explain some fundamental concepts, with special attention to the difference between marginal and conditional associations. We emphasize the importance of considering multimorbidity as a network, where the variables involved are part of an interconnected system of interactions, to correct for spurious effects in the analysis. We show how to analyze the network structure learned from the data by introducing and explaining some centrality measures. Finally, we compare the model obtained by adjusting for population characteristics with the results of a stratified analysis.
Results: Using examples from the estimated model, we demonstrate the key differences between marginal and conditional associations. Specifically, we show that, marginally, all variables appear associated, while this is not the case when considering conditional associations, where many variables appear to be conditionally independent given the others. We present the results from the analysis of centrality indices, revealing that cardiovascular diseases occupy a central position in the network, unlike more peripheral conditions such as sensory organ diseases. Finally, we illustrate the differences between networks estimated in subpopulations, highlighting how disease associations vary across different groups.
Conclusion: Graphical models are a versatile tool for analyzing multimorbidity, offering insights into disease associations while controlling for the effects of other variables. This paper provides an overview of graphical models without focusing on detailed methodology, highlighting their utility in understanding network structures and potential subgroup differences, such as gender-related variations in multimorbidity patterns.
Scientific article
Assessing Frailty in Older Adults: Strategies and Tools for Effective Policy and Research
Rivista Italiana di Economia Demografia e Statistica • 2024 • Vol. 78(3)
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
Spouses’ Health: What Happens Beyond the Widowhood Effect?
Journal of Family Issues • 2024 • Vol. 46(1), pp. 46-63
Abstract
Objectives. We focus on married couples, and we analyse how the susceptibility and survival of individuals can be influenced by the illnesses and death experienced by their spouses.
Methods. We perform a cohort study following married couples (age 65–75 years) from 2001 to 2013. We monitor individual’s susceptibility status and three spouses’ illnesses (i.e. diabetes, cancer, and mental diseases). The methodology used is the Cox regression.
Results. The initial cohort is composed of 22,639 couples. During the follow-up, 24% of the individuals dies, 91% experiences at least one susceptibility increase and 43% experiences one spouse’s illness. Results from the Cox regressions report a change in the individual health that is specifically related to the occurrence of the spouse’s diseases and death. Moreover, the three diseases hit individuals differently.
Discussion. What emerges from this work is the importance of considering the mechanism of the widowhood effect with an extensive approach.
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
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.