SELECT

Unfolding the SEcrets of LongEvity: Current Trends and future prospects.
A path through morbidity, disability and mortality in Italy and Europe. [Bando MIUR PRIN 2017]

The Research Project

The rapid social, economic, and technological transformations characterizing our society in the recent years are producing several effects on many complex and dynamic processes of human health. We focus, in particular, on the recent upward trend in longevity, and on its relation with current and future morbidity and disability patterns. The joint analysis of such processes, which plays a key role in many public health systems, requires novel qualitative and quantitative paradigms. In fact, unfolding the secrets of longevity, learning the dynamic interrelations between morbidity, disability and mortality, identifying future trends, and devising effective interventions can only proceed under a collective effort by Demographers, Epidemiologists, Social and Data Scientists.

Our mission is to explore the factors and mechanisms of longevity evolution in the recent years and link them with morbidity trends to foster healthy longevity. We will address this goal through a multidisciplinary team of Demographers, Epidemiologists, Social and Data Scientists, who will take advantage from the availability of several datasets to merge public health and epidemiological theories with a data-driven approach based on innovative models.

Data

An understanding of the complex relations between morbidity, disability and mortality, requires a preliminary effort towards linking and reconstructing the current sources of information for these processes.

Statistical Models

Morbidity, disability and mortality are complex dynamic processes characterized by several underlying patterns of relations, thus requiring advances in the modeling techniques, beyond the available ones.

Interventions

The rich databases and the new models are expected to shed light on several complex patterns of longevity and disability. This will open new avenues toward improving intervention strategies for healthy aging.

News and Media

A peculiarity of the research group is its multidisciplinarity, which fosters collaboration between units, especially for such cross-cutting themes. This will be further stimulated by organizing regular meetings of the steering committee, workshops and conferences. Moreover, due to the importance of the project, substantial efforts will be devoted at timely sharing the advances with the scientific communities involved and the policy-makers.

14-15 October 2019

Formal kick-off of SELECT project

The formal kick-off of the SELECT project was held on 14-15 October 2019. In this occasion we discussed the initial outputs with a main focus on data preparation.

28 May 2020

Intermediate [online] meeting

The first intermediate meeting of the SELECT project was held on 28 May 2020 [online]. We presented some scientific outputs of the project and discussed the COVID-19 emergency.

22 October 2021

Intermediate workshop

The intermediate workshop was held on 22 October 2021. We discussed some outputs of the project, and provided an opportunity to discuss 3 macro themes (Covid-19, mortality, morbidity).

30 August - 2 September, 2022

"Climbing mortality models"

Climbing mortality models workshop aimed at promoting fruitful collaborations among statisticians and demographers in terms of future research on mortality modeling and forecasting.

13 -14, July 2023

Final workshop

The final workshop was held in San Servolo (Venice) and aimed at presenting the final results of the SELECT project while discussing promising directions of future research.

19 August 2023

Conclusion of SELECT

On August 19, 2023, project SELECT reached its conclusion with more than 30 publications in statistics, demography and epidemiology, and the creation of important research networks.

Publications

The outputs of the project will be published in statistical, demographic and epidemiological journals to motivate an increasing collaboration among such fields. The focus will be on the project topics, but we will maintain also an open mindset to a broad set of methods which may be useful within SELECT.

List of Datasets List of Codes Useful References
Main articles in refereed journals
[link] Pastore, A., Tonellato, S. F., Aliverti, E., and Campostrini, S. (2023). When does morbidity start? An analysis of changes in morbidity between 2013 and 2019 in Italy.
      → Statistical Methods and Applications.
[link] [arXiv] Anceschi, N., Fasano, A., Durante, D. and Zanella, G. (2023). Bayesian conjugacy in probit, tobit, multinomial probit and extensions: A review and new results.
      → Journal of the American Statistical Association.
[link] Simonetti, I., Belloni, M., Farina, E., & Zantomio, F. (2022). Labour market institutions and long term adjustments to health shocks: evidence from Italian administrative records.
      → Labour Economics.
[link] Macchioni Giaquinto, A., Jones, A. M., Rice, N., & Zantomio, F. (2022). Labour supply and informal care responses to health shocks within couples.
      → HEALTH ECONOMICS.
[link] Aliverti E. Mazzuco, S. and Scarpa B. (2022). Dynamic modeling of mortality via mixtures of skewed distribution functions.
      → Journal of the Royal Statistical Society - Series A.
[link] Aliverti, E. and Russo, M. (2022). Dynamic modeling of the Italians’ attitude towards Covid-19.
      → Statistics in Medicine.
[link] Legramanti, S., Rigon, T. and Durante, D. (2022). Bayesian testing for exogenous partition structures in stochastic block models.
      → Sankhya A.
[link] Mazzuco, S., and Campostrini, S. (2022). Life expectancy drop in 2020. Estimates based on Human Mortality Database.
      → PLoS One.
[link] Fasano, A. and Durante, D. (2022). A class of conjugate priors for multinomial probit models which includes the multivariate normal one.
      → Journal of Machine Learning Research.
[link][code] Stefanucci, M. and Mazzuco, S. (2022). Analyzing cause-specific mortality trends using compositional functional data analysis.
      → Journal of the Royal Statistical Society - Series A.
[link] [arXiv] Fasano, A., Durante, D. and Zanella, G. (2022). Scalable and accurate variational Bayes for high-dimensional binary regression models.
      → Biometrika.
[link] Wade, S., Piccarreta, R., Cremaschi, A. and Antoniano, I. (2022). Colombian women’s life patterns: A multivariate density regression approach.
      → Bayesian Analysis.
[link] Trentini, F., Marziano, V., Guzzetta, G., ... Piccarreta, R., ... Melegaro, A., ... and Merler, S. (2022). Pressure on the health-care system and intensive care utilization during the COVID-19 outbreak in the Lombardy region of Italy: A retrospective observational study in 43,538 hospitalized patients.
      → American Journal of Epidemiology.
[link][arXiv] Aliverti, E. and Russo, M. (2022). Stratified stochastic variational inference for high-dimensional network factor model.
      → Journal of Computational and Graphical Statistics.
[link][arXiv] Aliverti, E. and Dunson, D. B. (2022). Composite mixture of log-linear models for categorical data, with applications to psychiatric studies.
      → The Annals of Applied Statistics.
[link] Poletti, P., Tirani, M., Cereda, D., Trentini, F., Guzzetta, G., Sabatino, G., ... Piccarreta, R., ..., Melegaro, A., ... and Force, T. (2021). Association of age with likelihood of developing symptoms and critical disease among close contacts exposed to patients with confirmed SARS-CoV-2 infection in Italy.
      → JAMA Network Open.
[link][code] Mazzuco, S., Suhrcke M., Zanotto L. (2021). How to measure premature mortality? A proposal combining “relative” and “absolute” approaches.
      → Population Health Metrics.
[link] Duch, R., Roope, L. S., Violato, M., Fuentes Becerra, M., Robinson, T. S., Bonnefon, J. F., ... Melegaro, A., ... and Clarke, P. M. (2021). Citizens from 13 countries share similar preferences for COVID-19 vaccine allocation priorities.
      → Proceedings of the National Academy of Sciences.
[link][code] Leger, A.E. and Mazzuco, S. (2021). What can we learn from functional clustering of mortality data? An application to HMD data.
      →European Journal of Population.
[link] Murphy, K., Murphy, T. B., Piccarreta, R., and Gormley, I. C. (2021). Clustering longitudinal life-course sequences using mixtures of exponential-distance models.
      →Journal of the Royal Statistical Society - Series A.
[link] Zanotto, L., Canudas-Romo, V. and Mazzuco, S. (2021). A mixture-function mortality model: illustration of the evolution of premature mortality.
      → European Journal of Population.
[link] Clarke, P. M., Roope, L. S., Loewen, P. J., Bonnefon, J. F., Melegaro, A., Friedman, J., Violato, M., Barnett, A., and Duch, R. (2021). Public opinion on global rollout of COVID-19 vaccines.
      → Nature Medicine.
[link] Bokemper, S. E., Cucciniello, M., Rotesi, T., Pin, P., Malik, A. A., Willebrand, K., ... and Melegaro, A. (2021). Experimental evidence that changing beliefs about mask efficacy and social norms increase mask wearing for COVID-19 risk reduction: Results from the United States and Italy.
      →PloS One.
[link][arXiv] Rigon, T. and Durante, D. (2021). Tractable Bayesian density regression via logit stick-breaking priors.
      → Journal of Statistical Planning and Inference.
[link] Cereda, D., Manica, M., Tirani, M., Rovida, F., Demicheli, V., Ajelli, M., ... Piccarreta, R. ... Melegaro A. and Merler, S. (2021). The early phase of the COVID-19 epidemic in Lombardy, Italy.
      → Epidemics.
[link] Fazle Rabbi, A.M. and Mazzuco, S. (2021). Mortality forecasting with the Lee-Carter method: Adjusting for smoothing and lifespan disparity.
      → European Journal of Population.
[link] Legramanti, S., Durante, D., Dunson, D.B. (2020). Bayesian cumulative shrinkage for infinite factorizations.
      → Biometrika.
[link][code] Canudas-Romo, V., Mazzuco, S. and Aldair, T. (2020). Cause of death decomposition of cohort survival comparisons.
      → International Journal of Epidemiology - Education Corner.
Books and books chapters
[link] Stefanucci M., Mazzuco, S. (2020). Inspecting cause-specific mortality curves by simplicial functional data analysis.
      → In Pollice, A., Salvati, N., and Schirripa Spagnolo, F. [Eds] Book of Short Papers SIS 2020
[link] Mazzuco, S., and Keilman, N. [Eds] (2020). Developments in Demographic Forecasting.
      → Springer Nature.
[link] Mazzuco, S., Keilman, N. (2020). Introduction.
      → In Mazzuco, S. and Keilman, N. [Eds] Developments in Demographic Forecasting.
[link] Aliverti, E., Durante, D. and Scarpa, B. (2020). Projecting proportionate age-specific fertility rates via Bayesian skewed processes.
      → In Mazzuco, S. and Keilman, N. [Eds] Developments in Demographic Forecasting.
Manuscripts
[link] Pavone, F., Legramanti, S. and Durante, D. (2023 +). Learning and forecasting of age–specific period mortality via B–spline processes with locally–adaptive dynamic coefficients. Manuscript.
[link] De Paoli, E.G., Stefanucci, M., and Mazzuco, S. (2023 +). Functional concurrent regression with compositional covariates and its application to the time-varying effect of causes of death on human longevity. Manuscript.

Meetings

[link] Final workshop (July 13 - 14, 2023). San Servolo. Venice, Italy.
[link] "Climbing mortality models" workshop (August 30 - September 2, 2022). Grand Hotel Misurina. Belluno, Italy.
[link] SELECT intermediate workshop (22 October 2021). University Ca' Foscari Venezia. Venezia, Italy.
[link] SELECT online intermediate meeting (28 May 2020). University of Padova. Padova, Italy.
[link] SELECT kick-off meeting (14-15 October 2019). Ca' Foscari University. Venice, Italy.

> 30

Articles

> 10

Talks at Conferences

> 20

People Involved

5

Meetings Organized

Project Coordinators

To provide successful outcomes the project requires deep interaction and constant feedbacks, currently limited, between experts from Epidemiology, Social Science, Demography and Data Science. The research team has been created to accomplish this goal.

Stefano
Campostrini

Project Coordinator
Full Prof. of Social Statistics
Ca' Foscari University

Stefano
Mazzuco

Padova Unit Coordinator
Associate Prof. of Demography
University of Padova

Daniele
Durante

Bocconi Unit Coordinator
Assistant Prof. of Statistics
Bocconi University

Fabrizio
Faggiano

Novara Unit Coordinator
Full Prof. of Hygiene
University of Piemonte Orientale

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