Health Information Systems: Systems Closed to Social Citizens. One Challenge in Reducing Inequalities in Local Management.

Objective: To reflect on the use of health information systems (SIS in Spanish and Portuguese) and their impact on reducing inequalities in health care from the local administration

[Self-rated health and social inequalities, Buenos Aires, Argentina, 2005]

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.http://deepblue.lib.umich.edu/bitstream/2027.42/78519/1/AlazraquiDiezRoux2009_CadSaudePublica.pd

Identifying Unique Neighborhood Characteristics to Guide Health Planning for Stroke and Heart Attack: Fuzzy Cluster and Discriminant Analyses Approaches

Socioeconomic, demographic, and geographic factors are known determinants of stroke and myocardial infarction (MI) risk. Clustering of these factors in neighborhoods needs to be taken into consideration during planning, prioritization and implementation of health programs intended to reduce disparities. Given the complex and multidimensional nature of these factors, multivariate methods are needed to identify neighborhood clusters of these determinants so as to better understand the unique neighborhood profiles. This information is critical for evidence-based health planning and service provision. Therefore, this study used a robust multivariate approach to classify neighborhoods and identify their socio-demographic characteristics so as to provide information for evidence-based neighborhood health planning for stroke and MI.The study was performed in East Tennessee Appalachia, an area with one of the highest stroke and MI risks in USA. Robust principal component analysis was performed on neighborhood (census tract) socioeconomic and demographic characteristics, obtained from the US Census, to reduce the dimensionality and influence of outliers in the data. Fuzzy cluster analysis was used to classify neighborhoods into Peer Neighborhoods (PNs) based on their socioeconomic and demographic characteristics. Nearest neighbor discriminant analysis and decision trees were used to validate PNs and determine the characteristics important for discrimination. Stroke and MI mortality risks were compared across PNs. Four distinct PNs were identified and their unique characteristics and potential health needs described. The highest risk of stroke and MI mortality tended to occur in less affluent PNs located in urban areas, while the suburban most affluent PNs had the lowest risk.Implementation of this multivariate strategy provides health planners useful information to better understand and effectively plan for the unique neighborhood health needs and is important in guiding resource allocation, service provision, and policy decisions to address neighborhood health disparities and improve population health

Intraurban variations in adult mortality in a large Latin American City

Abstract 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.http://deepblue.lib.umich.edu/bitstream/2027.42/57778/1/Intraurban variations in Adult Mortality in a large Latin American City.pd