Despite declining mortality rates in Sweden, significant geographic and socioeconomic disparities persist. Previous research has typically employed single socioeconomic indicators, which may not adequately capture the multidimensional nature of deprivation. To address this limitation, this study applied the Index of Multiple Deprivation in Sweden (IMDIS). Using a granular approach based on Demographic Statistical Areas (DeSO), we examined how small-area deprivation, measured through the overall composite IMDIS score and its underlying domains and indicators, relates to all-cause mortality by sex.We conducted an ecological cross-sectional study using Swedish register data between 2015 and 2019, totalling 50,400,585 person-years across 5983 DeSO-areas, including the total resident population across all age groups. We applied Bayesian spatio-temporal models to assess associations between all-cause mortality and IMDIS scores, domains (Education, Income & Capital, Employment, Housing), and 15 individual indicators of deprivation using Mortality Rate Ratios (MRRs). The Relative Index of Inequality (RII) was calculated to quantify the magnitude and direction of the deprivation gradient.A consistent deprivation gradient was observed where, for the overall IMDIS score, the most deprived areas experienced a 43% higher mortality risk (MRR 1.43; 95% CrI: 1.39–1.46) compared to the least deprived. Relative inequalities were more pronounced in men (1.51, 1.47–1.55) than in women (1.34, 1.29–1.38). RII values indicated systematic gradients across nearly all indicators, except for preschool attendance in women, with the highest inequality found when selecting disposable income as a measure of deprivation for men.All-cause mortality in Sweden follows a clear deprivation gradient in both men and women, with higher risks in the most deprived areas. The associations also demonstrate that the composite IMDIS measure is not merely the sum of its individual domains or indicators, but reflects a broader, underlying deprivation construct that captures their combined pattern of association.
Indirect standardization is widely used in disease mapping to control for demographic confounding, implicitly assuming proportional age-specific risks across space and time. When this assumption is violated, aggregated standardized estimates may provide an incomplete description of disease risk. Using data on suicide-related emergency calls in the Valencian Community (Spain), we evaluate the validity of this assumption and propose age-structured hierarchical Bayesian spatio-temporal models as an alternative to indirect standardization. The proposed framework relaxes the proportionality assumption by incorporating space–time, space–age, and time–age interaction effects, allowing age-specific risks to vary across spatial and temporal dimensions. Model comparison based on the Watanabe–Akaike Information Criterion (WAIC) and logarithmic score indicates substantially improved model fit when age-dependent interactions are included. The estimated main effects of the selected model identify spatial variation in relative risk across municipalities, a clear upward temporal trend in suicide-related emergency calls between 2017 and 2022, and a non-linear age pattern. Interaction effects indicate that spatial patterns vary across age strata, reflecting age-specific geographic differences in risk, while temporal increases are more pronounced among younger age groups. To compare the proposed framework with conventional indirect standardization, aggregated and age-specific hotspot classifications were examined. Although aggregated hotspot classifications remained largely consistent, age-specific hotspot maps revealed substantial heterogeneity. These results illustrate departures from the proportionality assumption underlying indirect standardization and demonstrate the value of age-structured spatio-temporal models for capturing complex epidemiological patterns.
Climate change and socioeconomic inequalities influence the spatial patterns of mortality from diseases of the circulatory system. This study analyzed the spatial autocorrelation of mortality rates from circulatory system diseases in Brazilian municipalities over the period from 1999 to 2022. We employed the Global Moran Index (GISA) and the Local Moran Index (LISA) to assess these patterns. In addition, we examined the relationship between mortality and various socioeconomic and environmental variables, including GDP per capita, population density, urbanization, and greenhouse gas (GHG) emissions. The findings reveal distinct spatial patterns over time, with a reduction in spatial autocorrelation and redistribution of cases, as well as marked variations in correlations with socioeconomic factors, especially GDP per capita and educational attainment. Stronger clustering was observed in the North and Northeast, regions with greater socioeconomic vulnerability. This study contributes to the understanding of the complex interplay between factors that drive climate change, in this study we utilized GHG emissions, and socioeconomic factors in public health, emphasizing the need for targeted policies that address regional inequalities and foster sustainable urban planning.
Lumpy Skin Disease (LSD) is an emerging transboundary viral disease of cattle with major economic impacts, particularly in endemic countries. However, limited information exists on how livestock production systems, seasonal variability, temporal trends, and spatial patterns influence disease dynamics. This study investigated the temporal and spatial distribution of LSD across livestock production systems in Tanzania using outbreak data collected between 2017 and 2024. Descriptive analyses were conducted in R, while spatial clustering was assessed using Getis-Ord Gi* statistics. Smallholder dairy systems recorded the highest numbers of outbreaks, cases, and deaths, followed by agropastoral systems, whereas pastoral systems showed the lowest disease burden, despite larger cattle populations. Non-parametric analysis revealed no significant differences in LSD cases among production systems during dry season, but significant differences were observed in wet season, with higher cases in smallholder dairy systems compared to pastoral systems. Temporal analysis showed a marked increase in cases from 2020, peaking in 2021 with resurgence in 2024. Monthly trends indicated higher cases during wet months (February–May), coinciding with the expected peak in vector activity in smallholder dairy and agropastoral systems, while cases peaked in June among pastoral systems. Spatial analysis revealed significant hotspot corridors in agropastoral areas across southern highlands, central, and northern Tanzania, localized hotspots in smallholder dairy systems in southern highlands and central Tanzania, and weak, scattered clustering in pastoral systems. These findings indicate LSD dynamics are strongly influenced by livestock production systems, with seasonal and spatial factors amplifying transmission, highlighting targeted, system-specific control strategies in Tanzania.
Climate change has become a significant factor in the emergence of diseases and must therefore be prioritized when addressing these issues. Changes in air temperature, relative air humidity, and precipitation may be associated with the emergence of zoonoses.The purpose of this study is to evaluate the relationship between climatic parameters and the prevalence of zoonoses in São Paulo State, located in the southeastern region of Brazil. Four zoonotic diseases with distinct modes of transmission and high incidences were selected for the study: leptospirosis, visceral leishmaniasis, spotted fever, and hantavirus infection. The study examined historical data on human cases of these diseases from 2001 to 2020. Regression analysis was used to determine the relationship between climatic and environmental variables, such as air temperature, relative air humidity, precipitation, latitude, elevation, predominant biome, deforestation, and the incidence of the selected zoonoses per year and per 100,000 inhabitants.Climatic and environmental parameters were associated with the incidence of selected zoonoses, primarily favoring an increase in vector-borne zoonoses (visceral leishmaniasis and spotted fever). However, despite the association between hantavirus infection and climatic factors, climate change will probably have little to no influence on its incidence in São Paulo State. Finally, climate change may be associated with a decrease in leptospirosis incidence.Climate change favors vector-borne diseases, requiring transdisciplinary attention and greater involvement from health authorities to allocate resources for strategic disease surveillance, control, and prevention.
