- Data, Statistics and AI for Well-Being of People and Organisations
- A cura di Luigi Fabbris, Alberto Franci, Pietro Renzi
Morbidity and mental health: A 30-year analysis of US health surveillance data
- Alberto Arletti
- Lorenzo Schiavon
- Mattia Stival
- Stefano Campostrini
- Gaia Bertarelli
- Michele Marzulli
- © 2026 Author(s) |
- CC BY 4.0
- DOI: 10.36253/979-12-215-1062-1.7
Emerging changes at a global scale are threatening the living standards of wealthy Western societies. A relevant early warning sign of such changes is stagnating morbidity compression. In a well-functioning healthcare system, as expected in prosperous Western nations, the aging population is expected to stay in good health longer as the years progress. At the same time, healthy young, working individuals are expected to be responsible for promoting innovation and sustaining social security costs. A psychologically strained young workforce breeds trouble for the upcoming generations. Through the use of flexible statistical modeling and pseudo-panel analysis, we present relevant - and alarming - results regarding the health of the US population on these key well-being indicators. Using data from the US health surveillance system, the Behavioural Risk Factor Surveillance System (BRFSS), we present the through-the-years (1990-2023) prevalences of a) diabetes, a highly impactful condition that can be debilitating in aging and significantly shorten life expectancy, and b) self-reported stress and emotional strain measure, an indicator of the overall quality of the mental health conditions of the US population. Results indicate that: a) diabetes is on the rise, with more recent cohorts being more likely to develop diabetes, in what in all effects and purposes is a morbidity expansion; b) poor mental health is on the rise, disproportionally among younger sections of the populations, indicating young Americans are taking the brunt of the global mental health crisis. Both observed trends can be associated with deep-seated societal factors. Diabetes can be connected to a malfunctioning healthcare system and shifting cultural norms around a healthy lifestyle through the years. Declining mental health in the youth can be seen as the result of a stagnating and increasingly uncertain economy and a digitally-absorbed disconnected society.
- Keywords:
- ageing,
- morbidity,
- mental health,
- BRFSS,
Ca' Foscari University of Venice, Italy - ORCID: 0009-0009-1776-3163
Ca' Foscari University of Venice, Italy - ORCID: 0000-0002-1590-5326
Ca' Foscari University of Venice, Italy - ORCID: 0000-0003-0657-0349
Ca' Foscari University of Venice, Italy - ORCID: 0000-0002-7113-4458
Ca' Foscari University of Venice, Italy - ORCID: 0000-0002-1099-530X
Ca' Foscari University of Venice, Italy - ORCID: 0000-0002-1264-4001
- Centers for Disease Control and Prevention - CDC (1990). Behavioral Risk Factor Surveillance System Survey Data. U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, Atlanta, Georgia [Data collected from 1990 to 2023].
- Friedman, J. H. (1991). Multivariate adaptive regression splines. The Annals of Statistics, 19(1), pp. 1–67. DOI: 10.1214/aos/1176347973
- Lo, W.-C., Hu, T.-H., Shih, C.-Y., Lin, H.-H., Hwang, J.-S. (2024). Impact of healthy lifestyle factors on life expectancy and lifetime health care expenditure: Nationwide cohort study. JMIR Public Health and Surveillance, 10(1), e57045. DOI: 10.2196/preprints.57045
- McGorry, P., Gunasiri, H., Mei, C., Rice, S., Gao, C.X. (2025). The youth mental health crisis: Analysis and solutions. Frontiers in Psychiatry, 15: 1517533. DOI: 10.3389/fpsyt.2024.1517533
- Menke, A., Orchard, T.J., Imperatore, G., Bullard, K M., Mayer-Davis, E., Cowie, C.C. (2013). The prevalence of type 1 diabetes in the United States. Epidemiology, 24(5), pp. 773–774. DOI: 10.1097/ede.0b013e31829ef01a
- Nelson, D.E., Holtzman, D., Bolen, J., Stanwyck, C.A., Mack, K.A. (2001). Reliability and validity of measures from the behavioral risk factor surveillance system (brfss). Sozial-und Präventivmedizin, 46(Suppl. 1), S3–S42.
- Pastore, A., Tonellato, S. F., Aliverti, E., Campostrini, S. (2023). When does morbidity start? an analysis of changes in morbidity between 2013 and 2019 in Italy. Statistical Methods & Applications, 32(2), pp. 577–591. DOI: 10.1007/s10260-022-00668-9
- Sinclair, A.J., Conroy, S.P., Bayer, A.J. (2008). Impact of diabetes on physical function in older people. Diabetes Care, 31(2), pp. 233–235. DOI: 10.2337/dc07-1784
- Stival, M., Schiavon, L., Campostrini, S. (2024). A Bayesian approach to model local and temporal heterogeneity in repeated cross-sectional health surveys. Journal of the Royal Statistical Society Series A: Statistics in Society, qnae138. DOI: 10.1093/jrsssa/qnae138
Informazioni sul capitolo
Titolo del capitolo
Morbidity and mental health: A 30-year analysis of US health surveillance data
Autori
Alberto Arletti, Lorenzo Schiavon, Mattia Stival, Stefano Campostrini, Gaia Bertarelli, Michele Marzulli
Lingua
Inglese
DOI
10.36253/979-12-215-1062-1.7
Opera sottoposta a peer review
Anno di pubblicazione
2026
Copyright
© 2026 Author(s)
Licenza d'uso
Licenza dei metadati
Informazioni bibliografiche
Titolo del libro
Data, Statistics and AI for Well-Being of People and Organisations
Curatori
Luigi Fabbris, Alberto Franci, Pietro Renzi
Opera sottoposta a peer review
Numero di pagine
420
Anno di pubblicazione
2026
Copyright
© 2026 Author(s)
Licenza d'uso
Licenza dei metadati
Editore
Firenze University Press
DOI
10.36253/979-12-215-1062-1
ISBN Print
979-12-215-1061-4
eISBN (pdf)
979-12-215-1062-1
eISBN (xml)
979-12-215-1063-8
Collana
Applied Statistics Series