Site Selection for Heathcare Enrollment Support (Using ACS Data)

While working with a team in a competition held by the University of Pennsylvania's Fels Institute of Public Policy, we wanted to create a tool that would conduct a site selection analysis to identify a focus area for our submission.  Our overall project goal was to develop a strategy that can analyze the healthcare information added recently in the American Community Survey (ACS) and identify a focus area during the following healthcare enrollment period.  A tailored approach would then be selected within the area to increase enrollment rates and provide support in picking the best plan for each individual/family.  The advantage of developing a tool like this is that it would be cheap to create and implement, and could provide analysis for any town/city/area since it uses ACS data which is standard throughout the country.

We identified 4 factors from the ACS to be used for site selection:

1. Highest total number of households whose incomes were between 138-399% of the federal poverty level.

2. Greatest number of persons within the 25-34  age range as identified on the ACS.

3. High levels of persons employed but without healthcare.

4. High totals of persons whose healthcare is purchased through the public exchanges.


The totals  for each factor were divided into thirds and a score of 1 to 3 was assigned to each factor as are illustrated below:

The geographical area used within these factors are the 2010 U.S. Census tracts for Philadelphia.  However this approach can be applied to census tracts in other cities and regions as well.

To pick the best particular sites within high scoring areas, the score for each census tract was combined with the scores of its neighbors.  Then the total score in a tract was divided by the total number of neighboring tracts to create an average. As a result, a large tract such as the one in the center of Philadelphia that contains the large green swath of Fairmount Park, and neighbors about 20 different census tracts is left with average score comparable to a small tract with only 5 neighbors. The end result of this process will identify the census tracts with a high score that are also surrounded by the best group of other tracts that scored highly as well.

Below are the results of the final site selection analysis:

Based on this site selected we have 2 different approaches:

1.  One approach for a small clustered area.

The southern site reflects this type of result. The best approach for signup and advising support in this area may be to open an on-site enrollment station within a library or other public setting.  The dense compact geography of this site could thereby be suitable for one central site that people can walk to for one-on-one support.

2. And a different approach for a dispersed geographic region.

The area to the north is fairly large and spread out over a wide area.  In this case opening one on-site center may not be the most efficient way to reach our target group. Instead we may look to open a call center (or in an area with high internet usage a website tool with live chat), and mailed materials or flyers that communicate the availability and contact information for our virtual support option.

Starting in 2013 the ACS has added additional questions to its survey to collect data on the availability of internet and computing in households.  A selected approach for either dense clustered sites, or more dispersed area could be tailored even further depending on the results of this additional information.


Modeling Assumptions and Groundrules

There were however a few issues and assumptions used with the ACS data.  The first to note is the margin of error.  The ACS is a statistical survey about a region.  In this case the geography is a census tract.  However since the surveys have just started and do not have a complete collection of data, the 5 year ACS, may only have information from these questions from within the past 2-3 years.  So for example, the count of uninsured persons could be listed as 52 in a tract, but the margin of error may be huge, like +/- 80.

However as the data quality increases over the next few years, this analysis process will become more effective.  Using the data today is also still a good exercise for developing a process and illustrating its usefulness. The goal of this strategy is to use common data that is publicly available from the ACS, that allows for this process to be replicated anywhere within the U.S.

Another assumption used is that the scores were not weighted.  The process could be revised for example, to weigh age and income more greatly in the final score than total persons with public healthcare. Instead in this case all of the factors were held equal.

Finally we are assuming that the various pieces of information overlap within the same groups we are attempting to target.  For example, we are assuming that separate information about high levels of younger persons overlap with the data indicating a high number of employed persons without healthcare.

(Side Note:  The color selections for the maps were chosen using colorbrewer2.org. A great resource for color palette recommendations.)