↓ Skip to main content

Poverty, Wealth, and Health Care Utilization: A Geographic Assessment

Overview of attention for article published in Journal of Urban Health, May 2012
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (95th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

2 blogs
6 tweeters
1 Facebook page


38 Dimensions

Readers on

70 Mendeley
Poverty, Wealth, and Health Care Utilization: A Geographic Assessment
Published in
Journal of Urban Health, May 2012
DOI 10.1007/s11524-012-9689-3
Pubmed ID

Richard A. Cooper, Matthew A. Cooper, Emily L. McGinley, Xiaolin Fan, J. Thomas Rosenthal


Geographic variation has been of interest to both health planners and social epidemiologists. However, while the major focus of interest of planners has been on variation in health care spending, social epidemiologists have focused on health; and while social epidemiologists have observed strong associations between poor health and poverty, planners have concluded that income is not an important determinant of variation in spending. These different conclusions stem, at least in part, from differences in approach. Health planners have generally studied variation among large regions, such as states, counties, or hospital referral regions (HRRs), while epidemiologists have tended to study local areas, such as ZIP codes and census tracts. To better understand the basis for geographic variation in hospital utilization, we drew upon both approaches. Counties and HRRs were disaggregated into their constituent ZIP codes and census tracts and examined the interrelationships between income, disability, and hospital utilization that were examined at both the regional and local levels, using statistical and geomapping tools. Our studies centered on the Milwaukee and Los Angeles HRRs, where per capita health care utilization has been greater than elsewhere in their states. We compared Milwaukee to other HRRs in Wisconsin and Los Angeles to the other populous counties of California and to a region in California of comparable size and diversity, stretching from San Francisco to Sacramento (termed "San-Framento"). When studied at the ZIP code level, we found steep, curvilinear relationships between lower income and both increased hospital utilization and increasing percentages of individuals reporting disabilities. These associations were also evident on geomaps. They were strongest among populations of working-age adults but weaker among seniors, for whom income proved to be a poor proxy for poverty and whose residential locations deviated from the major underlying income patterns. Among working-age adults, virtually all of the excess utilization in Milwaukee was attributable to very high utilization in Milwaukee's segregated "poverty corridor." Similarly, the greater rate of hospital use in Los Angeles than in San-Framento could be explained by proportionately more low-income ZIP codes in Los Angeles and fewer in San-Framento. Indeed, when only high-income ZIP codes were assessed, there was little variation in hospital utilization among California's 18 most populous counties. We estimated that had utilization within each region been at the rate of its high-income ZIP codes, overall utilization would have been 35 % less among working-age adults and 20 % less among seniors. These studies reveal the importance of disaggregating large geographic units into their constituent ZIP codes in order to understand variation in health care utilization among them. They demonstrate the strong association between low ZIP code income and both higher percentages of disability and greater hospital utilization. And they suggest that, given the large contribution of the poorest neighborhoods to aggregate utilization, it will be difficult to curb the growth of health care spending without addressing the underlying social determinants of health.

Twitter Demographics

The data shown below were collected from the profiles of 6 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
United Kingdom 1 1%
India 1 1%
Canada 1 1%
Unknown 65 93%

Demographic breakdown

Readers by professional status Count As %
Student > Master 12 17%
Student > Ph. D. Student 11 16%
Student > Doctoral Student 11 16%
Researcher 11 16%
Other 6 9%
Other 19 27%
Readers by discipline Count As %
Medicine and Dentistry 25 36%
Social Sciences 14 20%
Unspecified 7 10%
Nursing and Health Professions 6 9%
Economics, Econometrics and Finance 4 6%
Other 14 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 22. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 January 2015.
All research outputs
of 12,497,322 outputs
Outputs from Journal of Urban Health
of 931 outputs
Outputs of similar age
of 118,937 outputs
Outputs of similar age from Journal of Urban Health
of 28 outputs
Altmetric has tracked 12,497,322 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 931 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.1. This one has done particularly well, scoring higher than 90% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 118,937 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.