Social and spatial disparities in heat-related mortality in Italy: a nationwide small-area study

Technical Summary: Social and Spatial Disparities in Heat-Related Mortality in Italy

Problem Statement
While exposure to extreme ambient heat is a known driver of excess mortality, particularly among older adults, the fine-scale spatial variation in heat-related mortality remains poorly understood. Previous studies have documented geographical heterogeneity in temperature-mortality relationships across Italy and Europe, yet the specific environmental, demographic, health, and socio-economic factors driving these disparities at the municipal level are not fully characterized. This lack of granular understanding limits the development of targeted adaptation strategies for vulnerable communities.

Methodology
This nationwide small-area study analyzed data from 7,895 Italian municipalities over the summer months (June–August) from 2011 to 2023. The study focused on individuals aged 65 years and older.

• Data Sources: Daily all-cause mortality and population data were obtained from the Italian National Institute of Statistics (ISTAT). Daily mean temperature data (9 km × 9 km grid) were derived from the ERA5-Land reanalysis dataset.
• Exposure Definition: Heat exposure was defined as the average temperature on the day of death and the three preceding days (lag 0–3).
• Statistical Framework: The authors employed a two-stage Bayesian hierarchical model:
  • Stage 1: A Poisson regression model with municipality-specific non-linear associations between temperature and mortality (using natural cubic splines) was fitted to estimate municipality-specific relative risks (RR), attributable fractions (AF), and excess deaths (ER). The model adjusted for seasonality, day of the week, holidays, year, and relative humidity.
  • Stage 2: A second-stage analysis assessed the contribution of community factors to the spatial variability observed in Stage 1. Three models were fitted: a univariable model, a multivariable model, and a spatial model incorporating an intrinsic conditional auto-regressive (ICAR) prior to account for residual spatial autocorrelation.
• Covariates: The study evaluated environmental conditions (e.g., green space via NDVI, average temperature), demographic characteristics (e.g., population density, proportion of residents ≥85 years), health indicators (e.g., obesity prevalence, hospital beds), and socio-economic deprivation using the 2018 Municipal Fragility Index and its 12 component indicators.
• Sensitivity Analyses: Analyses were repeated allowing seasonality to vary by municipality, excluding the post-pandemic period (2020–2023), and testing different temperature percentiles (95th and 99th) to ensure robustness.

Key Results

• Geographical Disparities: Heat-related mortality exhibited marked geographical variation. The highest rates were observed in southern and southeastern Italy. The relative risk at the 90th temperature percentile (relative to the minimum mortality temperature) ranged from 1.06 to 1.33 across municipalities.
• Attributable Burden: The heat-attributable fraction exceeded 6% in several southern municipalities. Excess mortality surpassed 8 deaths per 1,000 inhabitants in parts of the Po Valley, Tuscany, Apulia, and Sicily. National heat-attributable mortality peaked in 2022 with an estimated 17,828 deaths (95% CrI: 17,339–18,285) among older adults.
• Determinants of Vulnerability:
  • Environmental: Municipalities with higher average temperatures and less green space had significantly higher heat-related mortality. Each standard deviation increase in green space was associated with approximately 4.4 fewer heat-related deaths per 10,000 population.
  • Socio-Economic: Socio-economic deprivation, measured by the Fragility Index, showed a clear dose-response relationship with heat-related mortality. In the spatial model, the highest fragility level (Level 5) was associated with an increase of 3.10 heat-related deaths per 10,000 population compared to Level 1.
  • Specific Drivers: The association with the Fragility Index was primarily driven by socio-economic indicators, including low educational attainment, lower employment rates, a higher proportion of workers in low-productivity sectors, and transport-related emissions.
  • Demographic/Health: Higher prevalence of obesity and a larger proportion of residents aged ≥85 years were associated with increased heat-related mortality, though evidence for urbanicity and population density was less conclusive after adjustment.

Significance and Claims
The paper claims to provide the first comprehensive assessment of municipal heat-related mortality disparities in Italy at a fine spatial scale, linking risk variation to specific community characteristics. The authors assert that their findings highlight substantial small-area differences in vulnerability, identifying socio-economic deprivation as a key determinant.

The study concludes that heat adaptation strategies cannot rely on uniform national approaches. Instead, the authors argue for geographically targeted actions that address local social and environmental vulnerabilities. They emphasize that extreme heat disproportionately affects disadvantaged communities, reinforcing existing inequalities, and that climate policy must integrate these social and environmental factors to mitigate unequal health impacts. The study does not propose new experimental interventions but calls for the application of these findings to inform targeted public health and adaptation policies.