Culture and the social context of health inequalities

There has been a great deal of interest and debate recently concerning the linkages between inequality and health cross-nationally. Exposures to social and health inequalities likely vary as a consequence of different cultural contexts. It is important to guide research by a theoretical perspective that includes cultural and social contexts cross-nationally. If inequality affects health only under specific cultural conditions, this could explain why some of the literature that compares different societies finds no evidence of a relationship between inequality and health in certain countries. A theoretical framework is presented that combines sociological theory with constructs from cultural psychology in order to identify pathways that might lead from cultural dimensions to health inequalities. Three analyses are carried out. The first analysis explores whether there is a relationship between cultural dimensions at the societal level and self-rated health at the individual level. The findings suggest that different cultural norms at the societal level can produce both social and health inequalities, but the effects on health may differ depending on the socio-cultural context. The second analysis tests the hypothesis that health is affected by the density of social networks in a society, levels of societal trust, and inequality. The results suggest that commonly used measures of social cohesion and inequality may have both contextual and compositional effects on health in a large number of countries, and that societal measures of social cohesion and inequality interact with individual measures of social participation, trust, and income, moderating their effects on health. The third analysis explores whether value systems associated with vertical individualist societies may lead to health disparities because of their stigmatizing effects. I test the hypothesis that, within vertical individualist societies, subjective well-being will be affected by a social context where competition and the Protestant work ethic are valued, mediated by inequality. The hypothesis was not supported by the available cross-national data, most likely because of inadequate measures, missing data, and the small sample of vertical individualist countries. The overall findings demonstrate that cultural differences are important contextual factors that should not be overlooked when examining the causes of health inequalities. ^

Correlation of tuberculosis and human immunodeficiency virus in Harris County, Texas from 2009 through 2010 using Geographic Information Systems

A population based ecological study was conducted to identify areas with a high number of TB and HIV new diagnoses in Harris County, Texas from 2009 through 2010 by applying Geographic Information Systems to determine whether distinguished spatial patterns exist at the census tract level through the use of exploratory mapping. As of 2010, Texas has the fourth highest occurrence of new diagnoses of HIV/AIDS and TB.[31] The Texas Department of State Health Services (DSHS) has identified HIV infected persons as a high risk population for TB in Harris County.[29] In order to explore this relationship further, GIS was utilized to identify spatial trends. ^ The specific aims were to map TB and HIV new diagnoses rates and spatially identify hotspots and high value clusters at the census tract level. The potential association between HIV and TB was analyzed using spatial autocorrelation and linear regression analysis. The spatial statistics used were ArcGIS 9.3 Hotspot Analysis and Cluster and Outlier Analysis. Spatial autocorrelation was determined through Global Moran's I and linear regression analysis. ^ Hotspots and clusters of TB and HIV are located within the same spatial areas of Harris County. The areas with high value clusters and hotspots for each infection are located within the central downtown area of the city of Houston. There is an additional hotspot area of TB located directly north of I-10 and a hotspot area of HIV northeast of Interstate 610. ^ The Moran's I Index of 0.17 (Z score = 3.6 standard deviations, p-value = 0.01) suggests that TB is statistically clustered with a less than 1% chance that this pattern is due to random chance. However, there were a high number of features with no neighbors which may invalidate the statistical properties of the test. Linear regression analysis indicated that HIV new diagnoses rates (β=−0.006, SE=0.147, p=0.970) and census tracts (β=0.000, SE=0.000, p=0.866) were not significant predictors of TB new diagnoses rates. ^ Mapping products indicate that census tracts with overlapping hotspots and high value clusters of TB and HIV should be a targeted focus for prevention efforts, most particularly within central Harris County. While the statistical association was not confirmed, evidence suggests that there is a relationship between HIV and TB within this two year period.^