Regional Variation in Health Care:

Primary Care, Health Outcomes, and Demographics

Susie Judd7/26/2011

CRP 551

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Purpose

Regional variation is a hot topic in the health care industry right now. Provider payment and utilization of services can vary widely, even within areas that are near to each other geographically and which have been adjusted for the relative age and health of the populations. There are many groups of people studying this phenomenon, and one aspect that is intriguing to me is the idea that better access to primary care can lower health care costs and improve health outcomes. I am using this project to study whether this might hold true in Minnesota.

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DataThis project relies on data from three sources:1.The Dartmouth Institute for Health Policy and Clinical Practice provided shapefiles for Primary Care Service Areas (PCSAs), and also a statistic of what percent of the population within each PCSA has poor access to primary care. This data is the foundation for the project. (http://www.dartmouthatlas.org/)2.The US Census. I got 2009 county shapefiles and 2010 population by ethnicity from the Census website. (http://www.census.gov/)3.County Health Rankings. Various measures of health risks, utilization, and demographics by county are available on this site. (http://www.countyhealthrankings.org/)

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Projections

One of the biggest initial problems I had was finding a projection that allowed Minnesota to look right. After trying several projections with no luck, I realized it is the census file of MN counties that was making the Northeast counties look funny, not the projection.

Example 1 Example 2

NAD_1983_CORS96_UTM_Zone_15N NAD_1983_StatePlane_Minnesota_Central_FIPS_2202_Feet

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Projections

The Dartmouth Institute’s Primary Care Service Area shapefile with Minnesota’s UTM projection looked correct. Since having counties represented by the actual areas is not important for this analysis, I used the PCSA shapefile to clip the county file and finally had a shape that looked like Minnesota.

All maps are on NAD_1983_CORS96_UTM_Zone_15N 5

2. Clipped PCSA by MN Counties

1. PCSA file is country-wide, without State in Attribute Table

3. Clipped MN Counties by PCSA

County

PCSA

Data on Health Care

Finding health outcomes data was a challenge for a couple reasons:•Since most health care is paid by private insurance and subject to strict privacy regulations, it is very difficult to get data in the first place•Many sites do not provide sufficient background information on the numbers to understand what exactly they represent•I wanted data that was broken down into a finer level of detail than county because utilization can change so much, even within a county, and I was afraid larger areas would mask the patterns

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County Health Rankings

The County Health Rankings website provided sufficient detail for me to feel comfortable using the data, plus they had a variety of measures from outcomes to demographics to health risks. Unfortunately, the data on this site is only by county.

This data was available in excel and was easy to format and import into my geodatabase, using ArcCatalog. Then I joined it to the county shapefile in ArcMap.

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Census Data

The only measure I wanted but was unable to get from County Health Rankings was the mix of ethnicity by county. This is available from the 2010 Census .

I used the following process to import the data:•Download the Census file with race information into Excel•Format for use in ArcMap (field names and field types must be appropriate)•Added to my geodatabase•In ArcMap, add a column for % Minority (1-% White)•Join table to county shapefile

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Create Basemap

Create map showing distribution of primary care access• Use Primary Care Service Areas• Percent within each area with poor access to primary care • Darker areas represent less access to primary care• Then add counties for reference• This becomes base map for all remaining analysis

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Add Data

Add health care data to the basemap to see if patterns emerge•Outcomes data (premature death, avoidable hospital stays)•Demographic data (% minority, high school dropout rate, children living in poverty)•Need to add one attribute at a time to see patterns•Need to remove county labels to see data clearly

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Premature Death Rate Harder to see with labels

Two County Comparison

I wanted to compare two counties next to each other but with different health care access and outcomes, to see more detail about how the demographics of the counties differ. I chose two counties in the center of MN: Todd and Stearns county.

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MeasureStearns County

Todd County MN Total

Premature Death Rate 4,706 6,629 5,301

Avoidable Hospital Stays 70 95 62

Percent Children in Poverty 11 20 12

Percent Minority (Non-White) 8 5 15

Percent Graduate High School 84 91 86

Percent with Some College 23 13 31

Percent Adult Smokers 15 20 19

Percent Adult Obesity 27 28 26

Percent Binge Drinking 21 23 19

Percent Adults Uninsured 12 12 10

Percent Unemployed 6 7 5

Final Result

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What Didn’t Make ItOn the Todd and Stearns County detail, I was hoping to calculate the percent of the population with poor access to primary care by each county. Unfortunately there were some PCSAs that crossed the county boundary and I had no statistic other than area to apportion them on, so I chose not to include this. However I did dissolve within each of the choropleth bands:

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What Didn’t Make It

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It appeared at first glance that the areas with better access to primary care were nearer large cities. I created a buffer of 50 miles around cities over 50,000 in population. It does not completely explain the variation in access, though it be a part of the story.

What I Would Do Differently

Most of my trouble had to do with finding the right data. I would have spent more time up front looking for the appropriate data:•The analysis that I wanted to do required a finer level of detail than by county •Other pieces of data I wanted was utilization for semi-electable procedures such as knee replacement or back surgery

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