Multidimensional profiling of health behaviors in Italian adults (PASSI 2023–2024)

Background

Considering multiple health behaviors jointly is key to informing targeted prevention and health promotion strategies. This study aims to identify multidimensional socio-health profiles and assess their association with four key health-related behavioral outcomes: physical inactivity, smoking, risky alcohol consumption, and low fruit/vegetable intake, using 2023–24 data from the PASSI (Progressi delle Aziende Sanitarie per la Salute in Italia) surveillance system.

Methods

Nineteen categorical variables on sociodemographic characteristics, health status, and risk behaviors were analyzed using Multiple Correspondence Analysis (MCA), with six retained dimensions used for clustering via Kernel Canonical Correlation Analysis (KCCA) and differences assessed through logistic regression. The analysis was based on 62,332 interviews with adults aged 18–69 collected by PASSI dataset.

Results

Four profiles emerged: (A) “At-risk adults” (23.5%), mostly men aged 35–59 with economic difficulties. They report good perceived health but are more exposed to risk behaviors; (B) “Boomers” (20.1%), older men with chronic conditions who still report good health and have a protective behavioral profile; (C) “Protected youth” (37.1%), young adults in good health with a high level of education who often still live in their family of origin. Physically active, but with high alcohol consumption; (D) “Overburdened and vulnerable” (19.4%), mainly women with poor physical and especially psychological health, high sedentary behavior, and multiple risk behaviors. Logistic regression models showed significant behavioral differences across all clusters.

Conclusions

The MCA–KCCA approach identifies multidimensional socio-health profiles that capture how social determinants, health conditions and behaviors interact within the population. This approach can support public health in identifying groups with more targeted, context-sensitive prevention and health promotion interventions.

Key messages

• Multidimensional profiling reveals four distinct socio-health groups with markedly different risk behaviors, highlighting the need for targeted, population-specific prevention strategies.

• Combining MCA and KCCA uncovers how social factors, health status, and behaviors interact, supporting more precise and context-sensitive public health interventions.