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Background: The COVID-19 pandemic has shown how intraurban inequalities are likely to reinforce health and social inequalities. Studies at small area level help to visualize social inequialities hidden in large areas as cities or regions., Aim: To describe the spatial patterning of COVID-19 death rates in neighborhoods of the medium-sized city of Bariloche, Argentina, and to explore its relationship with the socioeconomic characteristics of neighborhoods., Methods: We conducted an ecological study in Bariloche, Argentina. The outcome was counts of COVID-19 deaths between June 2020 and May 2022 obtained from the surveillance system and georeferenced to neighborhoods. We estimated crude- and age-adjusted death rates by neighborhood using a Bayesian approach through a Poisson regression that accounts for spatial-autocorrelation via Conditional Autoregressive (CAR) structure. We also analyzed associations of age-adjusted death rates with area-level socioeconomic indicators., Results: Median COVID-19 death rate across neighborhoods was 17.9 (10th/90th percentile of 6.3/35.2) per 10,000 inhabitants. We found lower age-adjusted rates in the city core and western part of the city. The age-adjusted death rate in the most deprived areas was almost double than in the least deprived areas, with an education-related relative index of inequality (RII) of 2.14 (95% CI 1.55 to 2.96)., Conclusion: We found spatial heterogeneity and intraurban variability in age-adjusted COVID-19 death rates, with a clear social gradient, and a higher burden in already deprived areas. This highlights the importance of studying inequalities in health outcomes across small areas to inform placed-based interventions., (© 2023. The Author(s).)

Objectives: To evaluate variability in life expectancy at birth in small areas, describe the spatial pattern of life expectancy, and examine associations between small-area socioeconomic characteristics and life expectancy in a mid-sized city of a middle-income country., Design: Cross-sectional, using data from death registries (2015-2018) and socioeconomic characteristics data from the 2010 national population census., Participants/setting: 40 898 death records in 99 small areas of the city of Córdoba, Argentina. We summarised variability in life expectancy at birth by using the difference between the 90th and 10th percentile of the distribution of life expectancy across small areas (P90-P10 gap) and evaluated associations with small-area socioeconomic characteristics by calculating a Slope Index of Inequality in linear regression., Primary Outcome: Life expectancy at birth., Results: The median life expectancy at birth was 80.3 years in women (P90-P10 gap=3.2 years) and 75.1 years in men (P90-P10 gap=4.6 years). We found higher life expectancies in the core and northwest parts of the city, especially among women. We found positive associations between life expectancy and better small-area socioeconomic characteristics, especially among men. Mean differences in life expectancy between the highest versus the lowest decile of area characteristics in men (women) were 3.03 (2.58), 3.52 (2.56) and 2.97 (2.31) years for % adults with high school education or above, % persons aged 15-17 attending school, and % households with water inside the dwelling, respectively. Lower values of % overcrowded households and unemployment rate were associated with longer life expectancy: mean differences comparing the lowest versus the highest decile were 3.03 and 2.73 in men and 2.57 and 2.34 years in women, respectively., Conclusion: Life expectancy is substantially heterogeneous and patterned by socioeconomic characteristics in a mid-sized city of a middle-income country, suggesting that small-area inequities in life expectancy are not limited to large cities or high-income countries., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.)

We examined associations of individual-, neighborhood- and city-level education -as proxies of SES at different levels-, with diabetes, hypertension, obesity, smoking and binge drinking (non-communicable disease risk factors -NCD/RF) among Argentinian adults. We estimated mixed models based on 21,415 individuals from the 2013 National Survey of Risk Factors, living in 2,698 neighborhoods and 33 cities. Gradients by individual-level education differed by gender and NCD/RF, and some were modified by city education. In addition, we identified contextual effects of neighborhood and city education on some NCD/RF. Urban efforts to tackle NCD/RF in Argentina should be context- and gender-sensitive, and mainly focused on socially disadvantaged groups., Competing Interests: Declaration of competing interest None., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: We investigated a) whether urbanicity is associated with individual-level non-communicable diseases (NCD) risk factors and whether urbanicity modifies trends over time in risk factors; and (b) whether educational inequalities in NCD risk factors change over time or are modified by province urbanicity., Methods: We used data from three large national surveys on NCD risk factors (Encuesta Nacional de Factores de Riesgo; ENFR 2005-2009-2013 ) conducted in urban areas of Argentina (n = 108,489). We used gender-stratified logistic random-intercept models (individuals nested within provinces) to determine adjusted associations of self-reported individual NCD risk factors (hypertension, diabetes, obesity, and current smoking) with education and urbanicity., Results: In both men and women, the prevalence of obesity and diabetes increased over time but smoking decreased. Hypertension prevalence increased over time in men. Higher urbanicity was associated with higher odds of smoking and lower odds of hypertension in women but was not associated with NCD risk factors in men. Obesity increased more over time in more compared to less urbanized provinces (in men) while smoking decreased more over time in less urbanized provinces. All risk factors had a higher prevalence in persons with lower education (stronger in women than in men), except for diabetes in men and smoking in women. Educational inequalities in obesity (in men) and hypertension (in men and women) became stronger over time, while an initial inverse social gradient in smoking for women reverted and became similar to other risk factors over time. In general, the inverse associations of education with the risk factors became stronger with increasing levels of province urbanicity., Conclusion: Increasing prevalence of diabetes and obesity over time and growing inequities by education highlight the need for policies aimed at reducing NCD risk factors among lower socioeconomic populations in urban environments in Argentina., (© 2021. The Author(s).)

Rapid urbanization in low- and middle-income countries (LMIC) is associated with increasing population living in informal settlements. Inadequate infrastructure and disenfranchisement in settlements can create environments hazardous to health. Placed-based physical environment upgrading interventions have potential to improve environmental and economic conditions linked to health outcomes. Summarizing and assessing evidence of the impact of prior interventions is critical to motivating and selecting the most effective upgrading strategies moving forward. Scientific and grey literature were systematically reviewed to identify evaluations of physical environment slum upgrading interventions in LMICs published between 2012 and 2018. Thirteen evaluations that fulfilled inclusion criteria were reviewed. Quality of evaluations was assessed using an adapted Effective Public Health Practice Project Quality Assessment Tool for Quantitative Studies. Findings were then pooled with those published prior to 2012. Narrative analysis was performed. Of thirteen evaluations, eight used a longitudinal study design ("primary evaluations"). All primary evaluations were based in Latin America and included two housing, two transportation, and four comprehensive intervention evaluations. Three supporting evaluations assessed housing interventions in Argentina and South Africa; two assessed a comprehensive intervention in India. Effects by intervention-type included improvements in quality of life and communicable diseases after housing interventions, possible improvements in safety after transportation and comprehensive interventions, and possible non-statistically significant effects on social capital after comprehensive interventions. Effects due to interventions may vary by regional context and intervention scope. Limited strong evidence and the diffuse nature of comprehensive interventions suggests a need for attention to measurement of intervention exposure and analytic approaches to account for confounding and selection bias in evaluation. In addition to health improvements, evaluators should consider unintended health consequences and environmental impact. Understanding and isolating the effects of place-based interventions can inform necessary policy decisions to address inadequate living conditions as rapid urban growth continues across the globe., (Copyright © 2020 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: Latin America is one of the most unequal regions in the world, but evidence is lacking on the magnitude of health inequalities in urban areas of the region. Our objective was to examine inequalities in life expectancy in six large Latin American cities and its association with a measure of area-level socioeconomic status., Methods: In this ecological analysis, we used data from the Salud Urbana en America Latina (SALURBAL) study on six large cities in Latin America (Buenos Aires, Argentina; Belo Horizonte, Brazil; Santiago, Chile; San José, Costa Rica; Mexico City, Mexico; and Panama City, Panama), comprising 266 subcity units, for the period 2011-15 (expect for Panama city, which was for 2012-16). We calculated average life expectancy at birth by sex and subcity unit with life tables using age-specific mortality rates estimated from a Bayesian model, and calculated the difference between the ninth and first decile of life expectancy at birth (P90-P10 gap) across subcity units in cities. We also analysed the association between life expectancy at birth and socioeconomic status at the subcity-unit level, using education as a proxy for socioeconomic status, and whether any geographical patterns existed in cities between subcity units., Findings: We found large spatial differences in average life expectancy at birth in Latin American cities, with the largest P90-P10 gaps observed in Panama City (15·0 years for men and 14·7 years for women), Santiago (8·9 years for men and 17·7 years for women), and Mexico City (10·9 years for men and 9·4 years for women), and the narrowest in Buenos Aires (4·4 years for men and 5·8 years for women), Belo Horizonte (4·0 years for men and 6·5 years for women), and San José (3·9 years for men and 3·0 years for women). Higher area-level socioeconomic status was associated with higher life expectancy, especially in Santiago (change in life expectancy per P90-P10 change unit-level of educational attainment 8·0 years [95% CI 5·8-10·3] for men and 11·8 years [7·1-16·4] for women) and Panama City (7·3 years [2·6-12·1] for men and 9·0 years [2·4-15·5] for women). We saw an increase in life expectancy at birth from east to west in Panama City and from north to south in core Mexico City, and a core-periphery divide in Buenos Aires and Santiago. Whereas for San José the central part of the city had the lowest life expectancy and in Belo Horizonte the central part of the city had the highest life expectancy., Interpretation: Large spatial differences in life expectancy in Latin American cities and their association with social factors highlight the importance of area-based approaches and policies that address social inequalities in improving health in cities of the region., Funding: Wellcome Trust., (Copyright © 2019 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Objective: We investigated temporal trends in BMI, and assessed hypothesized predictors of trends including socio-economic position (SEP) and province-level economic development, in Argentina., Design: Using multivariable linear regression, we evaluated cross-sectional patterning and temporal trends in BMI and examined heterogeneity in these associations by SEP and province-level economic development with nationally representative samples from Argentina in 2005 and 2009. We calculated mean annual changes in BMI for men and women to assess secular trends., Results: Women, but not men, exhibited a strong cross-sectional inverse association between SEP and BMI, with the lowest-SEP women having an average BMI 2.55 kg/m(2) greater than the highest-SEP women. Analysis of trends revealed a mean annual increase in BMI of 0.19 kg/m(2) and 0.15 kg/m(2) for women and men, respectively, with slightly greater increases occurring in provinces with greater economic growth. No significant heterogeneity in trends existed by individual SEP., Conclusions: BMI is increasing rapidly over time in Argentina irrespective of various sociodemographic characteristics. Higher BMI remains more common in women of lower SEP compared with those of higher SEP.

The influence of variables at different levels of organization and the effect of time on the occurrence of firearm-related homicides (FRH) in Argentina between 1991 and 2006 was analyzed using multilevel analysis. A three-level Poisson regression model was used. The first level corresponded to the distribution of the number of FRH by sex and age group for each administrative region and (four-year) period; the second corresponded to the variation over time in the interior of each administrative region; the third modeled the variation between administrative regions in accordance with the Level of Urbanization, Percentage of Homes with Unsatisfied Basic Needs and the Percentage of Working Adults. There were 15,067 FRH in persons aged 14 and over between 1991 and 2006 in the 493 administrative regions. The risk of death was higher in males and persons of 15 to 29 years of age; ages above that were associated with a lower risk. The influence of age was greater in central-urban zones and between 1999 and 2002 than during other periods. The level of urbanization was the socioeconomic variable most strongly associated with FRH risk. The risk of death from FRH was 1.6 times higher in central-urban zones compared with non-central zones. In both zones, the risk was highest between 1999 and 2002.

Introduction: Globally, tobacco is the number one preventable cause of death, killing 1 in 10 adults. By 2030, 80% of all tobacco deaths will occur in developing countries. Social factors drive the adoption and cessation of smoking in high-income countries, but few studies have examined the socioeconomic patterning of smoking in developing countries., Methods: Using data from the 2005 National Survey of Risk Factors for Non-communicable Diseases in Argentina, we investigated gender-specific socioeconomic patterning of current, former, and never-smoking status; the intensity of smoking; and smokers' readiness for cessation using multinomial logistic and ordinary least squares regression. We also investigated heterogeneity in the patterning by age., Results: Higher socioeconomic position (SEP) was associated with less smoking for men in all age groups, although the results were most pronounced for men at younger ages (odds ratio [OR] of current vs. never smoking = 0.57, 95% CI 0.51-0.63 for higher vs. lower education at ages 18-24 years). For women, higher SEP was associated with more smoking in older age groups but less smoking in younger age groups (OR = 1.51, CI 1.41-1.62 and OR = 0.86, CI 0.78-0.96 for higher vs. lower education at ages 50-64 and 18-24 years, respectively). Higher SEP was also associated with higher odds of recently quitting compared to not considering quitting for men regardless of age group but for women only in younger age groups., Discussion: The higher burden of smoking among those of lower SEP, especially in younger age groups, is a troubling pattern that is certain to impact future disparities in chronic disease outcomes unless interventions are undertaken.

Objectives: We investigated associations of socioeconomic position (SEP) with chronic disease risk factors, and heterogeneity in this patterning by provincial-level urbanicity in Argentina., Methods: We used generalized estimating equations to determine the relationship between SEP and body mass index, high blood pressure, diabetes, low physical activity, and eating fruit and vegetables, and examined heterogeneity by urbanicity with nationally representative, cross-sectional survey data from 2005. All estimates were age adjusted and gender stratified., Results: Among men living in less urban areas, higher education was either not associated with the risk factors or associated adversely. In more urban areas, higher education was associated with better risk factor profiles (P < .05 for 4 of 5 risk factors). Among women, higher education was associated with better risk factor profiles in all areas and more strongly in more urban than in less urban areas (P < 0.05 for 3 risk factors). Diet (in men) and physical activity (in men and women) were exceptions to this trend., Conclusions: These results provide evidence for the increased burden of chronic disease risk among those of lower SEP, especially in urban areas.

Self-rated health is a quality-of-life indicator. This study investigates the impact of individual-level and neighborhood-level socioeconomic characteristics, considered simultaneously, on the state of self-rated health at the individual level in Buenos Aires, Argentina. The study employs a two-level (individual and neighborhood) multilevel analysis, and the data sources were the 2005 Argentina National Risk Factor Survey (multistage probabilistic sample) and the 2001 Population Census. Linear regression shows that higher schooling and income, as well as occupational category, are related to better self-rated health, and increasing age with worse health. In the multilevel analysis, an increase in the proportion (per census tract) of individuals with less schooling was associated with an increase in the proportion of individuals with worse self-rated health. Improving the general health of the population requires strategies and action that reduce the levels of social inequalities in their multiple dimensions, including the individual and neighborhood levels.

Most studies of socioeconomic status (SES) and chronic disease risk factors have been conducted in high-income countries, and most show inverse social gradients. Few studies examine these patterns in lower- or middle-income countries. Using cross-sectional data from a 2005 national risk factor survey in Argentina (a middle-income country), we investigated the associations of individual- and area-level SES with chronic disease risk factors (body mass index [BMI], hypertension, and diabetes) among residents of Buenos Aires. Associations of risk factors with income and education were estimated after adjusting for age, sex (except in sex-stratified models), and the other socioeconomic indicators. BMI and obesity were inversely associated with education and income for women, but not for men (e.g., mean differences in BMI for lowest versus highest education level were 1.55 kg/m2, 95%CI = 0.72-2.37 in women and 0.17 kg/m2, 95%CI = -0.72-1.06 in men). Low education and income were also associated with increased odds of hypertension diagnosis in all adults (adjusted odds ratio [AOR] = 1.48, 95%CI = 0.99-2.20 and AOR = 1.50, 95%CI = 0.99-2.26 for the lowest compared to the highest education and income categories, respectively). Lower education was strongly associated with increased odds of diabetes diagnosis (AOR = 4.12, 95%CI = 1.85-9.18 and AOR = 2.43, 95%CI = 1.14-5.20 for the lowest and middle education categories compared to highest, respectively). Area-level education also showed an inverse relationship with BMI and obesity; these results did not vary by sex as they did at the individual level. This cross-sectional study of a major urban area provides some insight into the global transition with a trend toward concentrations of risk factors in poorer populations.

Urbanization is high and growing in low- and middle-income countries, but intraurban variations in adult health have been infrequently examined. We used spatial analysis methods to investigate spatial variation in total, cardiovascular disease, respiratory disease, and neoplasm adult mortality in Buenos Aires, Argentina, a large city within a middle-income country in Latin America. Conditional autoregressive models were used to examine the contribution of socioeconomic inequalities to the spatial patterning observed. Spatial autocorrelation was present in both men and women for total deaths, cardiovascular deaths, and other causes of death (Moran's Is ranging from 0.15 to 0.37). There was some spatial autocorrelation for respiratory deaths, which was stronger in men than in women. Neoplasm deaths were not spatially patterned. Socioeconomic disadvantage explained some of this spatial patterning and was strongly associated with death from all causes except respiratory deaths in women and neoplasms in men and women [relative rates (RR) for 90th vs 10th percentile of percent of adults with incomplete high school and 95% confidence intervals: 1.23 and 1.09-1.39 vs 1.24 and 1.08-1.42 for total deaths in men and women, respectively; 1.36 and 1.15-1.60 vs 1.22 and 1.01-1.47 for cardiovascular deaths; 1.21 and 0.97-1.52 vs 1.07 and 0.85-1.34 for respiratory deaths; 0.94 and 0.85-1.04 vs 1.03 and 0.87-1.22 for neoplasms; and 1.49 and 1.20-1.85 vs 1.63 and 1.31-2.03 for other deaths]. There is substantial intraurban variation in risk of death within cities. This spatial variability was present for multiple causes of death and is partly explained by the spatial patterning of socioeconomic disadvantage. Our results highlight the pervasive role of space and social inequalities in shaping life and death within large cities.