This component was designed as our main contributor to the project's first purpose. As noted, the project's central activity was examining ecological relationships between various neighborhood conditions and health outcomes in a comparable manner across the five study sites. Data for the analysis included statistics from the 1990 and 2000 censuses as well as comparably defined health and context variables drawn from the information systems maintained by our selected NNIP partners. The research employed charts, quantitative analysis of relationships (bi-variate and multi-variate), and selective mapping to illustrate some of these relationships.
The work began with a context analysis; a broader examination of conditions and trends in the study sites in the 1990s at the neighborhood, city, and metropolitan levels. It also included the use of data from all five sites to develop a neighborhood health disparity index that can be applied in policy analysis in other cities.
HHS posed four major research questions for the cross-site analysis:
- What are the similarities and differences across the selected NNIP sites with regard to the ecological correlations among the selected health, demographic, and contextual variables in the 1990s?
- How have ecological correlations among selected health, demographic, and contextual variables changed in the 1990s, and what contextual variables might account for these changes?
- Are the 2000 census tract data useful for identifying contiguous or noncontiguous groupings based on their health and demographic characteristics in each NNIP site?(4)
- What implications do the ecological analyses have for community approaches to problem solving in the health area?
To implement the research, we began by formulating a series of working hypotheses to be tested, building off the rapidly evolving literature in this field (see the References section). Consistent with those hypotheses, we attempted to do the work to best take advantage of U.S. Census data and the data sets maintained by the five selected partner organizations. Specifically, analysis relied on the following types of variables at the neighborhood level:
- Health variables derived from vital records maintained by the five NNIP partners. Examples include teen birth rates, percentage of births with early prenatal care, rate of low-birth weight babies, and age-adjusted death rates.
- Demographic variables derived from the 1990 and 2000 censuses, such as age structure, race/ethnicity, poverty rate, number of households by type, and adults by level of education completed.
- Contextual variables referring to the economic, physical, and social environment of the neighborhood (derived from the census and partners' systems), including crime rates and rates of welfare recipiency.
Background information on urban trends is needed to facilitate understanding of the context for the ecological analyses that are the centerpiece of this research. Whereas conditions in America's inner-city neighborhoods generally worsened in the 1980s, census and other data suggest a much more diverse range of trends and outcomes in the 1990s. Accordingly, the first step in our cross-site analysis was to examine general patterns of social, economic, and physical change in that decade in the five NNIP cities and compare their experiences with those in the 100 largest metropolitan areas. Measures are presented in categories related to our study hypotheses (see below).
The literature of the field suggests that neighborhood level health outcomes are influenced by a variety of types of conditions. Our review of the literature led to the development of the specific hypotheses to be tested in this research. The hypotheses fall into five categories defined by types of independent variables involved. These include four defined by neighborhood level measures: (1) socioeconomic conditions; (2) physical stressors; (3) social stressors; and (4) social networks. Hypotheses are primarily defined with respect to ecological relationships at a point in time, but some address changes in those relationships over time. The dependent variables (health indicators) fall into two categories (1) maternal and infant health, and (2) mortality.
The data are used to examine the hypothesized relationships in four ways. First, we present uniform tables, maps and graphics of basic health conditions and trends for all sites. Second, we present bi-variate correlation analysis to express the relationships between all indicators in our hypotheses (health, demographic, and context) that we have constructed by neighborhood in each city.
Third, we have conducted multiple regression analysis to examine relationships of various measures to health outcomes. We do not have the unit-record data on characteristics of individuals and families to perform the sorts of multi-level regressions that could explain influences on change in outcomes more completely. However, regressions with tract-level variables across five different cities offer lessons about concentration that should be valuable for policy.
As implied by the research questions noted earlier, these analyses examine the relationships within sites and then consider similarities and differences in the findings across sites (i.e. the extent to which levels and changes in health conditions found in one city hold up in similar types of neighborhoods in other cities). We also examine how these relationships have changed over time during the 1990s in the five cities.
In this work, we deal explicitly with what is often termed the "rare events" issue. Even when one has complete annual data for a neighborhood (say at the census tract level) over several years, the numbers of health-relevant events (specific types of births, deaths, and incidences of health problems) may be so small that they are subject to random variation (i.e., they may not exhibit a reliable trend). This issue is normally dealt with by aggregating years and/or neighborhoods. In this work, we assess how varying approaches to aggregation affect results.
We believe there is a need for one or more "neighborhood health disparity indexes." In an Annex to the report, we review relevant concepts, present alternative index formulations, show how index values vary across our five cities and over time.
"report.pdf" (pdf, 5.74Mb)
"Annexc1.pdf" (pdf, 36.01Kb)
"Annexc2.pdf" (pdf, 80.73Kb)
"Annexc3.pdf" (pdf, 81.09Kb)
"Annexc4.pdf" (pdf, 80.81Kb)
"Annexc5.pdf" (pdf, 80.84Kb)
"Annexc6.pdf" (pdf, 80.25Kb)
"Annexc7.pdf" (pdf, 44.47Kb)
"Annexc8.pdf" (pdf, 44.54Kb)
"Annexc10.pdf" (pdf, 44.74Kb)
"Annexc11.pdf" (pdf, 42.16Kb)
"Annexc15.pdf" (pdf, 51.62Kb)
"Annexc19.pdf" (pdf, 38.08Kb)
"Annexc23.pdf" (pdf, 42.28Kb)
"Annexc24.pdf" (pdf, 38.21Kb)
"Annexc25.pdf" (pdf, 39.36Kb)
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"Annexc27.pdf" (pdf, 41.26Kb)
"Annexc28.pdf" (pdf, 42.45Kb)
"Annexc29.pdf" (pdf, 41.76Kb)