The tree cover and temperature disparity in US urbanized areas: Quantifying the association with income across 5,723 communities.

Urban tree cover provides benefits to human health and well-being, but previous studies suggest that tree cover is often inequitably distributed. Here, we use National Agriculture Imagery Program digital ortho photographs to survey the tree cover inequality for Census blocks in US large urbanized areas, home to 167 million people across 5,723 municipalities and other Census-designated places. We compared tree cover to summer land surface temperature, as measured using Landsat imagery. In 92% of the urbanized areas surveyed, low-income blocks have less tree cover than high-income blocks. On average, low-income blocks have 15.2% less tree cover and are 1.5°C hotter than high-income blocks. The greatest difference between low- and high-income blocks was found in urbanized areas in the Northeast of the United States, where low-income blocks in some urbanized areas have 30% less tree cover and are 4.0°C hotter. Even after controlling for population density and built-up intensity, the positive association between income and tree cover is significant, as is the positive association between proportion non-Hispanic white and tree cover. We estimate, after controlling for population density, that low-income blocks have 62 million fewer trees than high-income blocks, equal to a compensatory value of $56 billion ($1,349/person). An investment in tree planting and natural regeneration of $17.6 billion would be needed to close the tree cover disparity, benefitting 42 million people in low-income blocks.

"Delivering" on the MDGs?: equity and maternal health in Ghana, Ethiopia and Kenya.

OBJECTIVES: The Millennium Development Goals (MIDGs) have put maternal health in the mainstream, but there is a need to go beyond the MDGs to address equity within countries. We argue that MDG focus on maternal health is necessary but not sufficient. This paper uses Demographic and Health Survey (DHS) data from Kenya, Ethiopia and Ghana to examine a set of maternal health indicators stratified along five different dimensions. The study highlights the interactive and multiple forms of disadvantage and demonstrates that equity monitoring for the MDGs is possible, even given current data limitations. METHODS: We analyse DHS data from Ghana, Kenya and Ethiopia on four indicators: skilled birth attendant, contraceptive prevalence rate, AIDS knowledge and access to a health facility. We define six social strata along five different dimensions: poverty status, education, region, ethnicity and the more traditional wealth quintile. Data are stratified singly (e.g. by region) and then stratified simultaneously (e.g. by region and by education) in order to examine the compounded effect of dual forms of vulnerability. RESULTS: Almost all disparities were found to be significant, although the stratifier with the strongest effect on health outcomes varied by indicator and by country. In some cases, urban-dwelling is a more significant advantage than wealth and in others, educational status trumps poverty status. The nuances of this analysis are important for policymaking processes aimed at reaching the MDGs and incorporating maternal health in national development plans. CONCLUSIONS: The article highlights the following key points about inequities and maternal health: 1) measuring and monitoring inequity in access to maternal health is possible even in low resource settings-using current data 2) statistically significant health gaps exist not just between rich and poor, but across other population groups as well, and multiple forms of disadvantage confer greater risk and 3) policies must be aligned with reducing health gaps in access to key maternal health services.

Setting the stage for equity-sensitive monitoring of the maternal and child health Millennium Development Goals.

OBJECTIVE: This analysis seeks to set the stage for equity-sensitive monitoring of the health-related Millennium Development Goals (MDGs). METHODS: We use data from international household-level surveys (Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS)) to demonstrate that establishing an equity baseline is necessary and feasible, even in low-income and data-poor countries. We assess data from six countries using 11 health indicators and six social stratifiers. Simple bivariate stratification is complemented by simultaneous stratification to expose the compound effect of multiple forms of vulnerability. FINDINGS: The data reveal that inequities are complex and interactive: inferences cannot be drawn about the nature or extent of inequities in health outcomes from a single stratifier or indicator. CONCLUSION: The MDGs and other development initiatives must become more comprehensive and explicit in their analysis and tracking of inequities. The design of policies to narrow health gaps must take into account country-specific inequities.