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Case Study: Improving Health Status of Underserved
Children
After reading Chapter 19 Case Study, in a 2-page Case Summary, with a reference page
discuss different ways to assess the health status of children in an underserved
neighborhood in Houston. Which methods would you recommend and why?
Include at least two APA-formatted citation (in-text, as well as the full reference). The
citation may be from course textbooks, assigned readings, or an outside source.
CHAPTER 19 CASE STUDY: IMPROVING THE HEALTH
STATUS OF UNDERSERVED CHILDREN IN HOUSTON’S
EAST END
Patricia Gail Bray
Introduction
During the past decade, evidence-based medicine has inspired the development of evidencebased management in healthcare, but to date, few healthcare organizations use this technique.
Moreover, there are no reports of health foundations that rely on an EB management approach to
guide improvements in community health status or even their own funding decisions. Although
commentators suggest that charitable foundations are uniquely placed to promote evidence-based
policy, health foundations in Texas typically use the judgments of program officers rather than
the analyses of trained researchers to guide their grant-making decisions.
Houston-based St. Luke’s Episcopal Health System created a grant-making public charity in
1997.1 The foundation’s mission is to enhance community health for the underserved, not only
through its grants, but also through a public health research agenda that includes a
comprehensive assessment and evaluation of the community’s health. In short, St. Luke’s
Episcopal Health Charities was set up to integrate philanthropy with community-based research
through an EB management approach. This approach includes a commitment to adopt a culture
that values management research, trains managers in the use of research for decision making, and
diffuses research techniques and results throughout the organization. This case study will
highlight how The Charities used an EB management approach to conduct community-based
research and fund healthcare interventions in an underserved neighborhood of Houston.
Following a model similar to that advanced by the Center for Health Management Research, the
Charities’s leadership began by working side by side with the University of Texas School of
Public Health. The leaders soon realized that they had a prime opportunity to bridge public
health theory with public health practice for the purpose of advancing community health.
Embracing a public health research orientation meant focusing on improving health status at a
population, rather than an individual, level. The population health model for assessment includes
four steps: analysis of health issues, priority setting, taking action, and evaluating results.
(Although this model was not explicitly connected to evidence-based management at the time,
the Public Health Agency of Canada [2007] now links a population health approach to evidencebased decision making.)
Building on this simple framework, we expanded the model in two ways (see Table 19.1):
TABLE 19.1: Decision-Making Models Contrasted
Model Type
Initial Steps
Research Steps
Action Steps
Population
Health Model
Analyze health
issues
Set priorities
Take action

St. Luke’s
Research Model •
•Site selection
•Data collection,
with community
participation

•Intervention
design
•Build
capacity
Evaluate
results
Evaluate
results
Acquire information • •Present the
Evaluate
evidence
(evidence) and
results
• •Apply it to
assess its
the decision
• •validity
• •quality
• •applicability
•Community involvement. By focusing the work on a particular neighborhood, we hoped to
engage multiple community partners to assist in decision making and priority setting.
•Action component. Our action component included both intervention and capacity building, to
improve prospects for sustainability.
Evidence-Based Frame the question
Management
Model

In collaboration with •
the community:

• •Set strategic
priorities
• •Build consensus
Evaluation
Steps
The Charities’s hybrid model uses mixed research methods, which align remarkably well with
decision-making features of the EB management model developed by John Hsu and colleagues
(2006). Their model was developed as a “toolbox” for healthcare managers and policymakers.
For this chapter, we adopt the six decision-making steps that constitute the toolbox and examine
how a similar model was put into practice at the Charities. In practice, the steps of these models
are not always performed in a linear fashion. Not only is there significant overlap between steps,
but there also can be non-sequential movement between them.
Applying Evidence-Based Management
Step 1: Framing the Research Question
Setting the Context
The first step in the EB management process is to frame the research question in a way that
captures relevant elements of the proposed intervention, desired outcome, setting, time frame,
and population of concern. For the Charities, these elements could be specifically defined.
Because of our mission, the intervention needed to be directed at health, and the leadership team
adopted the World Health Organization’s well-known definition of health as “a state of complete
physical, mental, and social well-being and not merely the absence of disease or infirmity.”
Thus, we included social, cultural, economic, and environmental health indicators in our
research. The desired outcome was to assess community healthcare needs comprehensively, so
that the Charities could fund interventions that would have the best chance of enhancing
residents’ overall health.
The time frame of the proposed intervention was the current fiscal year, since funding allocations
are distributed annually. The population, specified by our mission, included underserved families
and children, primarily those living below the federal poverty level. The board of directors and
the leadership team then narrowed the study population specifically to children—a priority group
for funding. As a result, we framed the following research question: How do we best assess the
health status of children in an underserved neighborhood in Houston?
Since a large city like Houston has many underserved neighborhoods, the leadership team
wanted to select the one with the highest proportion of underserved children. This step generated
the question: How do we identify, prioritize, and select underserved communities? The choice of
the study area could be based on conventional wisdom, since the most underserved
neighborhoods are usually common knowledge. Or a study area could be chosen on the basis of
formal or informal connections to nonprofit organizations or the advice of board members and
staff. Or it could be chosen based on anecdotal knowledge from local foundations working on
similar interventions. However, the Charities’s intention, since its founding, was to fund
interventions in areas of greatest need, as determined by research. Ultimately, both types of
evidence—colloquial and research-based—guided site selection.
In step one of the Charities’s model, the leadership team needed to acquire preliminary evidence
to choose an appropriate neighborhood. This evidence included best-practice knowledge from
the literature, meta-analysis of recent local studies, and a broad review of recent and appropriate
secondary data.
For this study, the individual team members were all trained in public health and had experience
working in underserved communities to improve health status. Due to this unique team
composition, there was a strong academic connection, as well as a community connection.
Pulling together an inventory of recent research regarding Houston neighborhoods and children
was relatively straightforward for this team, and existing relationships with local health
departments and advocacy agencies facilitated our initial research.
The Meta-Analysis
We conducted a meta-analysis of six major needs assessments conducted in or around the
Houston area during the early to mid 1990s. Three of the six were released in 1997, so the team
had current data regarding most traditional maternal and child health indicators, including:
• •Distribution of births and birth rates
• •Births to mothers aged 17 and under
• •Unmarried mothers under age 18
• •Late or no prenatal care
• •Low birth weight
• •Infant deaths
• •Infant mortality rate and
• •Death rate for children aged 1 to 14
Three retrospective studies from the mid-1990s that assessed the quality of life for Houston-area
children enabled the team to document significant overall progress in maternal and child health
over the previous decade, but pockets of concern remained. Specifically, the infant mortality rate
for all ethnicities except African Americans had dropped below the Healthy People 2000
objective of no more than 7 deaths per 1,000 live births. The percentage of mothers seeking
prenatal care in the first trimester had increased overall, but fell well short of the objective
among Hispanic and African-American mothers and especially among teenage mothers of all
ethnicities. The low-birth-weight rate had remained relatively constant since 1990, but rates of
low-birth-weight babies among African American and teenage mothers remained higher than
among other groups.
As of the late 1990s, health problems appeared to have shifted from infants to children and
adolescents. It appeared that, as children grew older, they were increasingly at risk for
preventable problems, such as unintentional injuries, homicide, suicide, substance abuse, child
abuse and neglect, developmental problems, and lead poisoning. (Unintentional injury was the
leading cause of death.) These findings were critical for the team selecting the research site.
Additionally, the data indicated that 21 percent of Houston-area children lived in poverty. This
figure was increasing, along with the proportion of children who were Medicaid eligible. The
high school dropout rate was rising, as was the percentage of teens not in school or working. On
the basis of this evidence, the team determined to place more focus on prevention, ameliorating
problems related to social disadvantage and social problems in general. Therefore, choosing a
neighborhood experiencing these kinds of challenges among youth was critical.
These criteria narrowed the search to several underserved neighborhoods, including Houston’s
East End, a predominantly Hispanic neighborhood where a significant proportion of the
population lived in poverty. The East End contains three distinct neighborhoods and
approximately 50,000 residents. A majority of the area’s 16,000 children are uninsured and live
below 200 percent of the federal poverty level. Since the mission of the Charities is to serve the
underserved, and the funding priorities were to be geared toward children, evidence gathered so
far indicated this geographic area fit our criteria well. Next we had to obtain data that would help
us understand this specific study area better.
Step 2: Acquiring the Relevant Information
Obtaining Secondary Data at the Neighborhood Level
At this point, we refined our research question to: What evidence exists about the current health
status of children in Houston’s East End neighborhood? Sub-county data are usually difficult to
obtain, but essential when assessing how to improve neighborhood health status in a very large
county such as Harris County, which has a population larger than 24 U.S. states. The Charities
employed a combination of epidemiological, statistical, and estimation methods to create a
comprehensive array of data. We collected some primary data, particularly through cluster
sampling, and relevant secondary data from the U.S. Census Bureau and Texas Department of
State Health Services, among others. Demographic statistics were collected and included
race/ethnicity, age distribution, median income, poverty level, education, and number of singleparent households. We looked at traditional maternal and child health indicators, along with
other data available from birth certificates, such as mothers’ education—an important predictor
of child well-being and a good example of how much variance can be found at the sub-county
level. Approximately 36 percent of all mothers in Harris County did not have a high school
education, whereas in Houston’s East End, 63 percent of mothers did not. Data on environmental
conditions, such as air, water, and land quality, were obtained from the Environmental Protection
Agency.
Most of these sub-county data were geo-coded and put on the Charities’s website
(www.slehc.org), using an interactive mapping program. This effort made them available for use
by other foundations, health planners, and community-based organizations.
Obtaining Qualitative Data
One way we involved the community in the project was to engage members in developing
qualitative data through multiple methods, including a community-based participatory research
(CBPR) approach (Agency for Healthcare Research and Quality and W.K. Kellogg Foundation
2001). During the mid-1990s, articles in the health assessment literature began to suggest that
researchers should expand their traditional approach to assessing, funding, and evaluating
healthcare needs and begin to include the voice of the community. The Charities’s leadership
team was similarly interested in understanding local needs from the community’s perspective,
believing that community members’ perceptions mattered just as much as the reality framed by
the researchers. Over time, the core of our neighborhood-level assessment model has evolved to
emphasize the community voice throughout the research process. It helped validate our planning
process and refine research results by identifying areas for intervention and providing a focus for
our research.
Use of participatory, qualitative research techniques, performed in partnership with the
University of Texas, allowed community members to identify and analyze the major issues of
concern, from their point of view. CBPR, which is gaining more and more of a research
following, is a semi-structured process of learning from and with people rapidly and
progressively, face to face, in a relaxed manner and in an informal setting. It encourages selfreflection, analysis, questioning, and learning. This study asked questions of community
participants, such as:
• •Are some age groups of greater concern than others?
• •What are the major issues by age group?
• •What is the relative importance of each issue?
• •What factors have produced these issues in the community?
• •How might these issues be addressed?
• •What community resources might be directed to help?
• •Could resources from outside the community be helpful?
This study helped fill in missing gaps from the earlier evidence. Here we see an example of the
nonlinearity of the Charities’s model: By investigating community needs (and gathering more
evidence) with a qualitative approach, the participants were also able to assess the accuracy of
the earlier evidence. In summary, after all of the evidence was collected, the team was able to
identify the children most at risk in the East End and the children with the severest problems. We
also had a good understanding of what those problems entailed. Without the multimethod
approach, we would not have been alerted, for example, to rising gang membership among
teenage Hispanic girls.
Steps 3 and 4: Assessing and Presenting the Evidence
How can we corroborate what we learned in the previous step? This step is important for anyone
employing an EB management approach, but it is crucial for leaders working in philanthropy and
public health. There is a general tendency to rely on expert evidence in both areas, but the
consequences of doing so can lead to funding interventions that are ineffective and inefficient.
Relying solely on one type of evidence—colloquial, anecdotal, or just secondary data—may
prompt funding of interventions that do not fit the community or are not sustainable. Because the
community’s perceptions of need often differ dramatically from what the experts think, the
evidence should be verified in partnership with community members.
In this case, if we had based our decisions solely on the secondary data, we would have made
key mistakes in funding interventions. For example, we learned that childhood asthma rates were
increasing in Houston, an impression corroborated by data issued by the Centers for Disease
Control and Prevention. Interventions to curb this kind of problem are usually complex, costly,
and long term. However, our leadership team discovered during the qualitative phase of the study
that, in a certain elementary school in the East End, children were being given Benadryl during
the school day to treat their asthma symptoms. Consequently, they were sleepy in class, which
led to other complications. When all of the evidence—colloquial and research based—was
considered, we selected a simple intervention to solve this immediate problem. A mobile unit
delivering primary care would be parked at the school one day per week, providing children
access to asthma care and appropriate medication.
Limitations of the Evidence
There are likely to be limitations to the data that decision makers have, no matter how they were
collected. The following checklist asks key questions for assessing data accuracy.
Quantitative Data Checklist




Qualitative Data Checklist
•Are the research findings valid?

•Does the evidence provide a complete and
balanced viewpoint?

•Is the analysis appropriate?
•Is the source credible?


•Is the context of the study adequately
described?
•Are the sample selection and data collection
processes appropriate?
•Is the analysis appropriate?
•Are the findings valid?
Such questions need to be asked, even when the data come from credible sources such as the
U.S. Census Bureau and the Texas Department of State Health Services. Census data are, after
all, derived from a survey, which means these data suffer from all of the challenges associated
with survey data, such as sampling error and selection bias. Additionally, since the complete
census data set is updated only every decade, it can become outdated and decreasingly useful,
especially at the neighborhood level or in rapidly changing locales. State-collected vital statistics
are subject to inaccuracies in information provided by respondents. For example, mortality
statistics are based on death certificates, which often do not accurately record important
comorbidities for decedents with chronic diseases, such as diabetes. Further, good data on
illnesses and risk factors—including smoking prevalence, drug and alcohol use, overweight and
obesity rates, mental illnesses, and oral health—are difficult to obtain at the sub-county level.
These data also come primarily from surveys and are subject to the limitations noted above.
Yet, we found that many questions about data accuracy could be answered by community
members—that is, residents and community-based organization leaders living and working in the
East End. We could not feasibly ask every individual in the East End to participate, so we
developed a sampling matrix to identify the major sectors of the community that should be
represented in our research. As with any qualitative study, the sample size was limited. To
overcome any bias in the results, we made the sample broad, seeking the perspective of
representatives of all sectors of the community.
Community Sampling Matrix by Sectors
Political
Economic
Health
Police
Communication
Recreational
Other community groups
Individuals
Education
While
Religious
Social welfare
Immigration and refugee support
community me …
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