Background and Significance
Ohio ranked as the eighth-worst state in the United States for infant mortality in
2017, with 7.2 infant deaths per 1,000 births.[1 ]
[2 ] There is a large disparity between white and black infants; the infant mortality
rate was almost three times as high among black infants (15.6 per 1,000 births for
black infants vs. 5.3 for white infants in 2017).[2 ] Although a few causes of infant mortality are primarily due to genetic factors,
there are medical, behavioral, and environmental risk factors that also increase the
risk of infant mortality.[3 ]
[4 ] Medical and behavioral risks including hypertension; diabetes; unplanned pregnancy;
drug, alcohol, or tobacco use; poor nutrition; late or inadequate prenatal care; and
stress can interact with environmental risks such as poverty, poor social support,
transportation barriers, and low food availability.[5 ]
[6 ]
[7 ]
[8 ]
[9 ]
[10 ]
[11 ] Women who live in low-income or highly segregated neighborhoods may have multiple
risk factors that are exacerbated by their environment.[12 ]
Taken together, these risk factors disproportionately affect women in black communities
and urban communities. For instance, preterm birth is among the leading risk factors
for infant mortality; the proportion of preterm birth-related deaths was 41% for infants
of black mothers in comparison to 34% for the general population.[13 ] Moreover, black mothers are more likely to experience the risk factors of late or
inadequate prenatal care, unintended pregnancy, stress, poverty, anemia, hypertension,
and obesity more than other races.[7 ]
[12 ] Despite reductions in the disparities between black–white infant mortality rates
in some states, significantly higher infant mortality rates among black infants persist
in certain geographic areas that public health programs need to address.[14 ]
[15 ] Areas with large disparities in infant mortality experience additional health disparities
related to low socioeconomic status and poor health equity, encouraging interventions
in these high-risk communities.[16 ]
[17 ]
Effective interventions or policies targeting infant mortality must account for multifaceted
deprivations existing across communities.[18 ] Such deprivations have been measured by composite measures that are known as area
deprivation indices (ADIs). ADIs are geographically based measures of socioeconomic
status that take into account for factors that may include income, employment, transportation,
crime, health, education, and housing quality.[19 ] There is evidence supporting the use of a geographic approach to map deprivation
across multiple indices, which can inform policy makers about the areas with the highest
concentration of at-risk individuals with the highest levels of need.[20 ]
[21 ]
[22 ] Measures of ADI have been associated with higher risk of and increased adverse birth
outcomes.[23 ]
[24 ]
Insights using ADI can inform the enactment of reliable and effective interventions
or policies targeting women in high-risk communities, including women in Ohio. There
are multiple efforts in the United States to visually map disparities in opportunity
that inspired this work.[25 ]
[26 ]
[27 ]
[28 ] The need for an Ohio-specific deprivation index was recognized among Ohio policy
makers and researchers. This concern led to the development of the Ohio Opportunity
Index (OOI). The OOI is a collaboration between the Ohio Department of Medicaid and
researchers at The Ohio State University. To create the OOI, first, census data and
other population-level data were aggregated across the seven domains of transportation,
education, employment, housing, health, access to services, and crime. Within each
domain, variables measuring different aspects of the domain were assessed and scored,
such that each domain score is a combination of three to seven variables. These domains
are weighted and then summed to yield an overall score. This overall score describes
the relative deprivation/opportunity of different census tracts across the state.
Once the OOI was created, additional collaboration was necessary to develop ideas
for how this data will be communicated to stakeholders and the public in an effective
manner.
A clear and concise approach to communicating ADI information is through the visualization
of deprivations across geospatial boundaries to help identify clusters of risk factors.
Data visualization can be broadly defined as a purposeful use of any physical object
(digital or analog) for some form of analysis of data.[29 ] It is the graphical representation of data to enhance cognition that, when made
interactive through a dashboard, also provides users with the ability to carry out
multiple tasks to achieve their intended goals.[30 ]
[31 ]
[32 ] Interactively visualizing an ADI associated with infant mortality on a dashboard
can present health care providers with valuable insights about their patients' social
determinants of health and provide policy makers with information to make better decisions
to alleviate infant mortality across the state. The dashboard we have developed represents
one of the first of its kind for Ohio and will be a template for similar dashboards
that can be used to effectively communicate information to stakeholders for other
health care priorities. The priorities may include health inequities, chronic diseases,
childhood development, and mental and behavioral health.
Methods
Our dashboard represents a real use-case representing how design, development, and
implementation occurred based on important relationships and needs that existed among
stakeholders.
Setting
Our project is an extension of the Infant Mortality Research Project to improve birth
outcomes. Our project consists of multiple stakeholders working together to examine
the social, behavioral, and health risk factors that contribute to infant mortality
and affect birth outcomes in Ohio. The project is sponsored by the Ohio Departments
of Higher Education and Medicaid and funded in part by the Ohio Medicaid Technical
Assistance and Policy Program. The project sponsors helped guide the research team
by helping them collaborate to create a dashboard that met the sponsors' goals. Researchers
at the Ohio State University and at the Ohio State University Center for Urban and
Regional Analysis compiled the pertinent data sources and calculated the standardized
measures, domain scores, and index score. The development of the dashboard provides
an opportunity to understand and identify the social determinants of health experienced
in Ohio census tracts that undermine health for individuals. The structure of the
data and its presentation in the article are based on this project.
Our research team consisted of a biomedical informatician, an infant mortality subject
matter expert, an expert in geographic information systems, a computer programmer,
a project manager, and a scientific editor.
The Ohio Opportunity Index Dashboard
The Opportunity Index Dashboard (OID) utilizes the OOI, a measure of opportunities
available to people in a given census tract. The index is constructed at the census
tract level. Higher scores indicate relatively greater opportunity or lower deprivation;
lower scores denote lesser opportunity or higher deprivation. The index is composed
of 34 measures organized in seven domains. The primary end-users are subject matter
experts; other users could include health care providers and the public. The current
implementation reflects a partial rollout of the dashboard. The OID is on a password-protected
online server accessible by the research team and project sponsors. The stakeholders
are collaborating to provide a public version online in the future.
Data Structure and Development of Metrics
The index used for the OID is a relative measure that is a “composite of different
dimensions or domains of deprivation”[22 ] and focuses on a specific area level. To limit cancellation effects of one domain
over another, the index is weighted. In many ways, the OOI is similar to the deprivation
indices used in England, New Zealand, and Scotland, where they have been used for
targeted policy interventions and initiatives to alleviate social and economic problems
such as poverty.[20 ]
[21 ]
[22 ]
The OOI is a measure comprising 34 variables that capture the social and economic
opportunities present across Ohio. [Table 1 ] provides an overview of variables included in the OOI and the approach used to combine
the variables. Seven domains were identified as being associated with health and well-being:
crime, education, employment, health, housing, transportation, and access to services.
Several variables linked to each domain were identified through an iterative process
(see [Fig. 1 ] for variables associated with each domain). The primary sources for the variables
used are: The American Community Survey, Ohio state databases (e.g., Housing Finance
Agency, Department of Health, Department of Education, and Department of Medicaid
data sets), Longitudinal Employer-Household Dynamics, and Infogroup business data.
The data from these sources were extracted for the years 2012 to 2016.
Table 1
Ohio Opportunity Index domains and variables associated with the domains
Domain
Variables
Transportation
• Public transit access[a ]
• Average commute time to employment[a ]
• Households without access to a vehicle[a ]
Education
• Population with an associate's degree or higher[a ]
• Average school performance[b ]
• Average free and reduced lunch rate[b ]
• High school dropout rate[b ]
Employment
• Low-wage job access by educational attainment[c ]
• Access to workforce or job training sites[d ]
• Unemployment rate[a ]
Housing
• Median rent[a ]
• Median home value[a ]
• Concentration of existing low-income housing tax credit units[e ]
• Population living with overcrowding[a ]
• Population that moved 3+ times in the last year[a ]
Health
• Poverty rate[a ]
• Preterm-birth rates[f ]
• Age-adjusted mortality rate[g ]
• Preventable emergency department admissions/visits[h ]
• Cardiovascular disease deaths/admits[h ]
• Diabetes admits/diagnoses[h ]
• Diagnosis with drug addiction or Medication Assisted Treatment[h ]
Access
• Access to healthy food options[d ]
• Distance to nearest primary care physician[d ]
• Distance to nearest primary/secondary school[d ]
• Distance to nearest post office[d ]
• Availability of internet connection[i ]
Crime
• Homicide, aggravated/sexual assault[j ]
• Robbery[j ]
• Burglary, larceny, motor vehicle theft[j ]
• Public drunkenness and driving under the influence[j ]
• Drug-related crime[j ]
a American Community Survey.
b Ohio Department of Education.
c Longitudinal Employer Household Dynamics.
d Infogroup business data.
e Ohio Housing Finance Agency.
f Ohio Department of Health.
g Department of Vital Statistics, Ohio Department of Health.
h Ohio Department of Medicaid.
i Federal Communication Commission.
j Office of Criminal Justice Services, Ohio Department of Public Safety.
Fig. 1 Key milestones and project timeline for the Ohio Opportunity Index Dashboard.
Data for each variable was first obtained for 2,948 of the 2,952 census tracts in
Ohio. The census tract level of aggregation provides a standard geographical area
that can help generate statistically robust estimates and minimizes the potential
for small area shrinkage estimations that may be prevalent at more granular levels.
Following Townsend et al's[33 ] and Noble et al's[22 ] approach, we computed the OOI based on the following procedures:
(1) Standardizing and summing: Each variable is converted to a z -score (some are inverted to harmonize the direction of the values and make them comparable
across variables). These z -scores are subsequently summed within each domain.
(2) Ranking: The summed z -scores for a domain are ranked and scaled to a range between 0 and 1 (with the least
deprived tract having a 1/number of tracts score).
(3) Exponential distributing: An exponential distribution, according to Noble et al,[22 ] helps each domain have a common distribution, the same range, and identical maximum
and minimum values. (This helps isolate the impact of domain weights when the domains
are weighted and combined into a single index.) The distribution also helps to buffer
the effect of population size of the census tract, creates a “tail” that spreads out
the most deprived census tracts in each domain, and regularizes the cancellation property
used in the creation of the OOI. To achieve this, the ranks are transformed using
the exponential distribution, making each domain's value range from 0 to 100. The
transformed domain would be given by[22 ]:
X = –23ln {1–R [1–exp – (100/23)]}
Following Noble et al we retain the constant,[22 ] which determines that roughly 10% of census tracts have a score higher than 50.
This skewness ensures that the combined domains do not cancel each other out, wherein
the low opportunity in one domain can be cancelled out by a high opportunity in another.
(4) Factor analyzing: A factor analysis approach was used to attach weights to each
domain to gauge the different levels of contribution in opportunity toward the OOI.
This approach allows us to extract a “latent factor” called overall OOI with standardized
coefficients that represent the specific contributions of each domain toward this
factor.
The data file used for our dashboard contains one row per census tract. For each tract,
the data comprises variables that report the county, the OOI score, and the seven
constituent domains. The current data file contains information for only one period,
with the goal of incorporating additional period and a longitudinal element to the
dashboard once additional data for the domain variables become available.
Dashboard Structure
The original request from project sponsors divided the dashboard into three components:
(1) visualizing OOI scores for a specific census tract on the Ohio map displayed with
the help of a choropleth map; (2) visualizing plots of OOI or domain scores between
tracts to compare relative positions for tracts; and (3) visualizing sortable scores
in a table for a specific census tract. The project sponsors requested an interactive
display that allows the user to select specific parameters that in turn would update
the display. Other sponsor requirements included using Tableau as the visualization
software, deploying the dashboard to a secure Tableau Server environment, creation
of a training manual, and conducting feedback and usability sessions to solicit input
on the OID from end-users.
Tableau Data Visualization
As noted by Wahi and Dukach,[34 ] statistical software packages such as SAS, STATA, and SPSS have been traditionally
used for health data analytics, but these products are limited in regard to visual
capabilities and require knowledge of programming languages. Tableau uses VizQL, a
visual query language that can convert drag-and-drop actions into data queries.[35 ] Tableau Desktop allows the visualization team to first connect to a data set (stored
in files, warehouses, and online clouds), and subsequently use a front-end interface
to concomitantly query the data and view the results in different graphical forms
(e.g., charts, graphs, and maps). Independent worksheets containing specific visuals
can then be arranged together on dashboards that can communicate key insights. These
visuals can also be linked together by the creation of filters, parameters, and actions
to make the dashboard react to user actions that direct the visuals to display a specific
type of information (e.g., highlighting or subsetting a specific census tract) across
one or more visuals.
For geospatial visualizations, Tableau can automatically recognize several geographical
fields and generate respective latitude and longitude coordinates. These include state,
county, metropolitan statistical area, and ZIP code. However, as our geographical
focus is on census tracts, we imported an Esri[36 ] shapefile of Ohio with vector data that included the latitudes and longitude coordinates
for the state's census tracts. We linked our data set file with this shapefile in
Tableau Desktop using the Federal Information Processing Standards code as the primary
key.
Iterative Testing of Prototype and Usability Evaluation
Our approach to developing, testing, and deploying the OID followed a user-centered
design approach. This approach involved constant engagement with various stakeholders
(representatives and end-users from project sponsors and subject matter experts that
formed an external advisory board), who provided our team with feedback at predefined
milestones on the progress of the project. These key milestones are presented in the
project timeline (see [Fig. 1 ]).
Additionally, our team conducted a usability study with the end-users from project
sponsors after a production-ready version of the OID, which incorporated feedback
from prior versions, was deployed on the Tableau Server.
The usability study involved an evaluation of the OID that focused on the effectiveness
of the dashboard and satisfaction from its use, these are primary outcomes that have
been similarly employed by other dashboard evaluations.[37 ]
[38 ]
[39 ] Wu et al define effectiveness as the accuracy and completeness of achieving goals
and satisfaction as subjective opinions of use. Given the use of dashboards to discover
insights through exploration, we recognize the challenges with operationalizing an
efficiency metric on dashboard use and chose to informally track this metric.[40 ] Six potential end-users from our state agency participated in our usability evaluation
session. Prior research has shown that a single iteration of usability testing with
at least five participants uncovers 85% of usability problems.[41 ] Of the six participants, two participants had analyst roles and four participants
had administrative roles with varying levels of seniority. Our study and all instruments
were reviewed by our institutional review board and deemed exempt (see page 522 of
the Appendix A for the instruments).
For this usability study, we operationalized effectiveness in two ways: (1) administer
a survey and focus group to inquire about participants' expectations for the OID,
challenges with using the dashboard, and potential improvements to the tool; and (2)
cognitive tests to assess their ability to successfully accomplish seven tasks that
reflect potential uses of the dashboard. Results from the survey and focus group were
recorded and summarized. We scored successful completion of tasks with the number
1 and a 0 otherwise. In regard to efficiency, we operationalized this by benchmarking
the time the participants needed to complete tasks with a preestablished time to completion
threshold (i.e., 15 minutes for all tasks) that was deemed reasonable based on a priori
tests completed by three members of our research team.
Satisfaction was operationalized through the 10-item System Usability Scale (SUS)
and administered at the end of the usability session.[42 ] The SUS is a flexible questionnaire designed to assess any technology, and is relatively
quick and easy to complete. It consists of 10 statements that are scored on a 5-point
scale of strength of agreement. These scores are first transformed, where individual
SUS scores are converted to a consistent, positive score range from 1 to 10 by either
taking the raw score and subtracting by 1 and multiplying by 2.5 (for positive questions)
or taking the raw score and subtracting from 1 and multiplying by 2.5 (for negative
questions). These scores are subsequently totaled for each responded for a score range
of 0 to 100. A higher score indicates better usability. As a general rule, a lower
score means that the system needs continued improvement.[42 ]
Data Analysis
We used descriptive statistics to analyze task completion. Descriptive statistics
were also used to summarize the SUS scores across all evaluators of the dashboard,
and mean scores for each SUS question are graphically displayed. Analysis of the usability
metrics was performed using Microsoft Excel.
Results
The dashboard team initially created a mockup of the OID in Adobe InDesign (see [Fig. 2 ]). This document contained the critical elements of the dashboard as defined by the
project sponsors and was annotated to briefly describe key aspects of the dashboard.
This document was then presented at an external advisory board meeting, where the
subject matter experts and representatives from the project sponsors provided initial
feedback.
Fig. 2 Adobe InDesign mockup of the Opportunity Index Dashboard (OID). Comments at the top
right of each component consisted of a brief description of that component.
[Table 2 ] lists the initial feedback factored into the development of the prototype dashboard.
An initial prototype of the OID was created and presented to the stakeholders along
with a brief functional test with project sponsor end-users. Together, several improvements
to the dashboard were suggested, and we summarize these in [Table 3 ]. For our summaries, we generally categorize feedback into one of three groups: (1)
function, issues that are related to thoughtful navigation of the dashboard; (2) content,
problems with information provided in the dashboard that complicate or lead to misinterpretation
of data; and (3) aesthetics, concerns that impede the dashboard from having a minimalist
design that effectively communicates information.
Table 2
Feedback and rationale from dashboard mockup
Problem
Rationale
Content
• Reverse OOI scores from least opportunity having higher OOI scores to having lower
scores
As the OOI score was to demonstrate opportunity and not deprivation, reversing the
values from the original score allowed for easier interpretation
• Switch from quantile to septile groups of the OOI score to use in the state-level
heat map
Using quantiles did not provide adequate contrasts between OOI scores, and it was
decided that the septile distribution was easier for interpretation
• Remove temporal trend function in the score plot
The data used for calculating the OOI score is currently static and this functionality
would only be required for a future iteration when additional years of OOI data are
available
Aesthetics
• Increase the size of the map and allow the map to zoom in to a specific county
Provide end-users with a convenient map that was easy to read and help them focus
on census tracts within a county of interest for the end-user
Abbreviation: OOI, Ohio Opportunity Index.
Table 3
Feedback and rationale from initial prototypes
Problem
Rationale
Function
• Turn off hovering and switch to selection feature
Rapid changes while hovering made it distracting for the end-user to navigate the
dashboard
• Selection of county results by filtering dashboard content to only the census tracts
within that county
This helped the end-users specifically assess the census tracts within a county of
interest
• Provide an icon by each component to help the end-user understand it
Provide end-users with a conveniently located icon to quickly understand what the
information a specific component can provide them
• Display street and highway patterns
Allow end-users to get a better sense of the communities present within a census tract
by locating them using streets and highways
• Improve dashboard performance and load time
Improving response time of the dashboard components would ensure that end-users continued
to use the dashboard over time
Content
• Include a breakdown of domain scores in the table and provide standard deviations
from the mean of a census tract for each domain variable in the score plot
As the raw values were highly skewed for domain variables, use of standard deviations
provided for a simple and quick means for end-users to gauge whether a census tract's
score is better or worse than its standardized mean for a specific variable
• Switch distribution plot from using rank on the Y -axis to actual OOI score in the Y -axis
The linear ramp was misleading because it led to misinterpretation of the OOI score
and did not let the user quickly identify the distribution of the OOI scores across
all census tracts
Aesthetics
• Use a divergent color scheme for the heat map
The initial green-gold color scheme made it difficult to identify census tracts that
were in the middle septile groups on the heat map.
• Allow each domain variable to cluster together when multiple census tracts are selected
Provide end-users with a convenient means through which they can directly compare
how two or more census tracts are performing for a specific domain or domain variable
• Switch the score plot from line plots to bar graphs
The connecting of dots across domains provided no statistical or theoretical significance;
bar graphs were more effective at communicating scores across domains or domain variables
Abbreviation: OOI, Ohio Opportunity Index.
These improvements were all incorporated in a production version of the dashboard
and reflect comments that were made over the course of several months and multiple
in-person/virtual meetings.
Usability Evaluation: Effectiveness and Efficiency
In regard to expectations for use, it was noted that end-users might use it to identify
health disparities occurring among health beneficiaries with specific health conditions.
The median success rate in regard to task completion rate for the seven tasks by our
participants was 83%, with participants finding three tasks particularly challenging.
Focus group feedback indicated that misinterpretation of metrics and incorrect use
of dashboard components were the primary causes for failure to complete the tasks.
Several participants noted that prior knowledge about the dashboard software could
have significantly helped in the use of the OID. For example, it could have helped
to know how to quickly reset views, resize the map, and use keyboard shortcuts. The
approximate time range to complete tasks ranged between 20 and 30 minutes.
Final Usability Evaluation: Satisfaction
[Fig. 3 ] illustrates the average scores for each of the SUS domains. The dashboard median
SUS score of 68 (interquartile range 1.9) indicated good usability. A majority of
participants noted that the dashboard was not unnecessarily complex and was an approachable
tool. There was, however, some ambivalence about ease of use and challenges to quickly
learn to use the dashboard.
Fig. 3 Summary of System Usability Scores (SUS) by individuals' components. Bars in black
are positively worded and those in gray are negatively worded. Negatively worded responses
are transformed in order for lower scores to indicate more favorable responses.
Based on the feedback from our final usability session, we improved an existing training
manual we had developed for the OID, which would aid end-users. This manual was developed
to familiarize the end-user with the components present in the OID. Each section of
this guide explained the capabilities of a different component (available by authors
upon request). We also used the feedback to improve the visual elements on the OID
to make sure the information presented was better able to aid the end-user to use
the dashboard. [Fig. 4 ] provides an overall snapshot of the current OID. [Fig. 5 ] illustrates how the map displays census tracts that belong to a specific septile
group based on the end-user's selection on the OI septile groups legend. [Fig. 6 ] is a snapshot of the dashboard when one county, Harrison, is selected. [Fig. 7 ] is a snapshot of the dashboard when three census tracts within Harrison County are
selected and compared for the Health domain. In the figure, the table provides the
summary score for the Health domain, and hovering over each census tract in the table
provides its ranking for that domain across all the census tracts in the state. The
distribution plot highlights where each tract falls in regard to the score distribution
across all the census tracts. The score plot provides a comparison of the census tracts
for a specific measure used to compute the Health domain, and each score represents
a standard deviation from the mean. Deviations greater than plus or minus 1 indicate
the census tract has a subcategory score greater than 95% of the scores from other
census tracts. Deviation greater than plus or minus 2 are greater than 99.7% of scores.
Fig. 4 Overall snapshot of the Opportunity Index Dashboard (OID). The OID offers four major
components: a map, a table, a distribution plot, and a score plot. The score plot,
located in the bottom right, activates only when the end-user selects an appropriate
subset of census tracts.
Fig. 5 Snapshot of how the map displays census tracts that belong to a specific septile
group based on the end-user's selection on the Ohio Opportunity Index (OOI) septile
groups legend. By selecting a septile in the map legend, the corresponding census
tracts are highlighted on the map to show their geographic distribution.
Fig. 6 Snapshot of the dashboard when one county is selected. When a county is selected,
the score plot will populate to show the domain scores for each census tract within
that county to allow for comparisons by the end-user. The map also zooms in on the
selected county.
Fig. 7 Snapshot of the dashboard when three census tracts within a county are selected and
compared for health. By selecting individual census tracts, the map will further highlight
them. In addition, in this view, a single domain has been selected, and the score
plot has adjusted to display the applicable domain scores as a standard deviation
from the mean.
Discussion
Environmental disparities are associated with poor health and both infant and adult
mortality, encouraging geographical research to describe, visualize, and imagine solutions
for these disparities. The OID was created to visualize 7 key domains made up of 34
variables that vary geographically to show the areas with the highest and lowest levels
of opportunity. The census-tract level map of Ohio shows scores that further describe
the specific disparities experienced. These results suggest that the health care delivery
system could be more responsive to the needs of patients with more complex underlying
social determinants of health, particularly for women of reproductive age. It could
help health care providers deliver better health care as they are cognizant of the
social determinants of health. Both health care providers and public health programs
can focus efforts that provide multidisciplinary services that combine health and
social care in lower OOI census tracts. In addition, this information can be used
to prioritize getting people likely to experience multiple risk factors into health
care and preventative care before, during, and after pregnancy. It should be noted
that the deprivation index used for our study is positively framed as an “opportunity”
because there is the potential to learn from communities that are successful or performing
well over time.
Despite this being the first large-scale opportunity index developed for the state
of Ohio, there are similar efforts to map deprivation or opportunity in other parts
of the United States. There is a national OID that maps by state and county on four
domains, education, economy, health, and community, and with an overall score.[27 ] The major difference is that the OID maps by census tract and has additional variables
and seven distinct domains. The national dashboard includes some of the variables
in the OID housing and employment domain under “economy” and includes similar variables
to the OID access to services and crime domains in the “community” domain, but it
does not include any variables related to transportation and has fewer health, housing,
crime, and access variables throughout. One feature the national index has is an average
score that each county is compared with, while the OID compares census tracts to each
other. An average reference score may be a potential improvement for the OID to implement,
however, the statistical implications of rolling up scores need further consideration.
There is also a national Children's Opportunity Index that uses data for the 100 most
populated metro areas in the United States, however, individual domains are not shown.[25 ]
One statewide effort is the Virginia Health Opportunity Index, which shows four separate
domains and one overall score that are each visually mapped.[26 ] This is the closest visualization to the OID, and differs mainly on the availability
of within-domain variable scores. Another statewide dashboard is the Regional Opportunity
Index in California, and this has multiple layers to view six domains or an overall
score, with comparisons for each census tract to the mean.[28 ] This dashboard differs from the OID because it does not rank census tracts compared
with each other outside of the mapped colors. The dashboard, however, does have very
extensive displays for variables within each census tract. Both the OID and these
other efforts can be further improved by identifying strengths and weaknesses of other
dashboards to inform future developments.
In keeping with the user-centered design approach, the most updated version of the
dashboard has incorporated the feedback from the usability evaluation. It is noteworthy
that respondents did not find the dashboard unnecessarily complex based on the results
of the system usability survey. However, the findings from the cognitive tests and
focus group revealed a need for technical assistance to better understand the metrics
used in the creation of the OOI and some basic functions within Tableau. These findings
have implications for the further refinement of the dashboard; future strategies may
involve the creation of instructional videos on certain features of Tableau allowing
users to reset views, resize the map, and use keyboard shortcuts, as well as videos
explaining the metrics used to create the OOI. Research has shown that print and video
as instruction media are used differently by users and each medium has its benefits
and shortcomings[43 ]; the development of instructional videos will complement the training manual that
has already been created in response to the evaluation feedback.
There are some potential concerns to consider when disseminating this tool to a wider
audience. The OOI is a measure of a neighborhood rather than the individuals residing
in it, and there may be interpersonal variation in opportunity; allocating funding
to the people in the most need within a district therefore require more individualized
data. Researchers and policy makers using the index need to exercise caution when
presenting and sharing this tool and take care to avoid attaching any derogatory connotations
of low opportunity areas with the people residing within them. Caution should be used
when using this measure for purposes outside of policy making and research, as certain
industries may try to use this tool to take advantage of communities at either end
of the opportunity scale. Finally, OOI domains impacted rural and urban areas in different
ways, and policy makers should take the individual variables into account when developing
interventions instead of attempting a one-size-fits-all approach for low domain scores.
The index may be further revised in the future. Upcoming iterations could involve
data that deconstruct variations that may exist between racial groups or socioeconomic
factors. Additional years of data would allow for learning about temporal trends in
opportunity data. This will specially enable end-users to view changes in census tracts
over time for both overall and individual domain scores. Updates for different time
periods will enable studies of effectiveness of interventions and funding, along with
displaying social change as populations move to different areas. From a policy perspective,
considerations for linking OOI scores to locations of service provision and specific
health outcomes (such as infant mortality) across the census tracts could greatly
inform decisions on targeting areas in need of resources. The demand for geospatial
tools to display risk will continue to grow as improved data visualization tools proliferate
and changing community dynamics lead to more individualized areas of risk.
