This study examines intra-urban socio-economic inequalities in Palermo, analysing the degree of variation in household consumption expenditure across three geographical units: census tracts, First-Level Unit, and neighbourhoods. For each geographical unit, we present the distinct patterns of intra-urban inequalities identified and discuss them in the light of the socio-urban evolution of the city of Palermo. The analysis relies on the integration of two different data sources, the 2011 Census microdata and the 2019 Household Budget Survey, through a statistical matching technique. In this way, a synthetic dataset was obtained that includes information on household expenditure and their area of residence. A multilevel modelling approach is therefore used to exploit the hierarchical structure of our data, where households are grouped in nested territorial units. The results show that, even if most of the variation in consumption expenditure is due to household characteristics, significant territorial differences persist. The greatest between-area variation emerges at the census tract and neighbourhood levels, revealing patterns of macro- and micro-segregation.

Abstract Influenza remains a significant and recurrent public health burden in temperate regions. Meteorological factors such as temperature, humidity, and rainfall are recognised as associated with influenza transmission patterns, exhibiting complex, nonlinear, temporally lagged, and spatially heterogeneous effects. This study employed a Spatial Bayesian Distributed Lag Non-Linear Model (SB-DLNM) to investigate the associations between meteorological factors and influenza incidence across 15 Local Health Districts, New South Wales, Australiathe short-term meteorological variables on influenza incidence across multiple Local Health Districts within New South Wales, Australia. The method incorporates (i) cross-basis functions to model delayed and non-linear meteorological impacts; (ii) a comparative analysis of case-crossover and time-series designs to distinguish monthly-lag associations from broader temporal trends; and (iii) spatial partial pooling to enhance the stability of estimates, particularly in data-sparse regions. Temperature demonstrated the strongest associations with influenza risk (Relative Risk (RR) range: 1.16–3.90), with elevated risks observed predominantly at cold temperature extremes. While exposure-response curves suggest minimum risk at moderate temperatures ( $$18-22^{\circ }\hbox {C}$$ ), the available data primarily capture cold-related effects; warm-temperature associations remain uncertain due to limited extreme heat observations. Humidity showed marked spatial heterogeneity with variable effects across districts (RR range: 1.32–5.69), while rainfall demonstrated minimal associations (RR typically 1.03–1.42). Exceedance probabilities for RR>1 were moderate across all variables, ranging from 17.5% to 58%, with no extreme hot spots observed. Partial pooling effectively stabilised estimates in sparse datasets, improving the robustness of spatial risk assessment. These findings underscore the importance of cold temperatures in influenza transmission patterns, providing a robust framework for public health surveillance. Our use of monthly aggregated data captures population-level seasonal associations rather than acute exposure-infection dynamics, which represents an important interpretive constraint.Among the meteorological variables, temperature emerged as the strongest predictor of influenza risk, with peak incidence observed within moderate temperature ranges ( $$20-22^{\circ }\hbox {C}$$ ) Graphical Abstract A schematic overview of the workflow from merging meteorological and influenza data, evaluating four modelling approaches (with Model 3 highlighted as the best), to generating spatial risk maps and relative risk estimates for influenza in NSW.

Italy reports some of the lowest levels of mortality in the developed world. Recent evidence, however, suggests that even in low-mortality countries improvements may be slowing and regional inequalities widening. This study contributes new empirical evidence to the debate by analysing mortality data by single year of age for males and females across 107 provinces in Italy from 2002 to 2019. We extend the widely used Lee-Carter model to include spatially varying age-specific effects, and further specify it to capture space-age-time interactions. The model is estimated in a Bayesian framework using the inlabru package, which builds on INLA (Integrated Nested Laplace Approximation) for non-linear models and facilitates the use of smoothing priors. This approach borrows strength across provinces and years, mitigating random fluctuations in small-area death counts. Results demonstrate the value of such a granular approach, highlighting the existence of an uneven geography of mortality despite overall national improvements. Mortality disadvantage is concentrated in parts of the Centre-South and North-West, while the Centre-North and North-East fare relatively better. These geographical differences have widened since 2010, with clear age- and gender-specific patterns, being more pronounced at younger adult ages for men and at older adult ages for women. Future work may involve refining the analysis to mortality by cause of death or socioeconomic status, informing more targeted public health policies to address mortality disparities across Italy's provinces.

A number of studies have shown that fertility levels differ substantially across settlement types in modern societies. As a rule, as the population density increases, fertility decreases. Such differences can occur due to a combination of three factors: (1) direct contextual influence, (2) differences in population composition between settlement types, and (3) selective migration. In this study, we aim to disentangle these factors with respect to completed fertility. We use linked micro-data from the Estonian population and housing censuses of 2000 and 2021 that we analyse with Poisson regression. The results show large fertility differences between settlement types, with women living in the capital city having the lowest, and women living in the more distant countryside having the highest number of children. Control of individual socio-demographic and housing-related variables (size, type, ownership), as well as migration experience, somewhat reduces the effects of settlement type. The analysis underlines the size of the dwellings to be especially relevant for the contextual effect. Looking at intercensal migration flows in greater detail reveals that the completed fertility of migrants lies mostly between the origin and destination groups.