The Role of Geography in
Health Care Spending and
Monitoring Services Use
A Getis-OrdGi* Statistic Hot Spot
Analysis of SC Medicaid Paid Claims
Per Capita by ZCTA
Ana Lòpez-De Fede, PhD
Kathy Mayfield-Smith, MA, MBA
John Stewart, MA, MPH
Policy and Research Unit on Medicaid and Medicare
Institute for Families in Society
University of South Carolina
Table of Contents
Geographic Variation in Health Care Spending ............................................................................1
Table 1: Research on Geographic Variation in Health Care Spending ...........................................1
Methodology ..............................................................................................................................2
Preliminary Results .....................................................................................................................3
Table 2: South Carolina Medicaid Paid Claims per Capita, FY 08 – 10 ..........................................3
Figure 1: Mean Medicaid Paid Claim Per Capita by ZCTA, FY 2008 to FY 2010 Getis-Ord Gi*
Statistic (Hot Spot Analysis) ......................................................................................................4
Figure 2: Mean Medicaid Paid Claim Per Capita for Adults 19 years and Older by ZCTA, FY 2008
to FY 2010 Getis-Ord Gi* Statistic (Hot Spot Analysis) ..............................................................5
Figure 3: Mean Medicaid Paid Claim Per Capita for Children Less than 19 years by ZCTA, FY 2008
to FY 2010 Getis-Ord Gi* Statistic (Hot Spot Analysis) ..............................................................6
Next Steps ...................................................................................................................................6
Geographic Variation in Health Care Spending
Health policy analysts have long known there is a geographical difference in per capita spending
across the Unites states (Dartmouth Atlas – Fisher and others, 2003 and 2011). Recent studies
suggest that adjusting for demographic characteristics and measures of health reduced the
differences between the highest and lowest Medicare spending quintiles (NEJM refute of
Dartmouth, 2011). However, these adjustments do not explain more than 60 percent of the
geographic variations per capita spending and the studies have been limited to Medicare
recipients (Table 1).
Table 1
Research on Geographic Variation in Health Care Spending
Study
Type of Spending
Explanatory Factors
Welch and
others (1993)
Medicare physician spending
per beneficiary, 1989
Inpatient hospital admission rate, physicians
per capita, proportion of physicians engaged in
primary care
Cutler and
Sheiner (1999)
Medicare spending per
beneficiary, 1995
Health risk behaviors, mortality rates, race,
income, education, HMO market share, supply
of medical providers
Gage, Moon,
and Chi (1999)
Medicare spending per
beneficiary, 1995
Share of beneficiary population under age 65,
share over age 85
Center for the
Evaluative
Clinical Sciences
(1999)
Medicare spending per
beneficiary, various years
Age, sex, race, illness, prices, HMO market
share, supply of medical providers
Fuchs,
McClellan, and
Skinner (2001)
Utilization of Medicare-covered
services per beneficiary, 1989–
1991
Education, income, cigarette sales, obesity, air
pollution, race, region, urbanization
Medicare
Payment
Advisory
Commission
(2003)
Medicare spending per
beneficiary, 2000
Prices, health status, Medicare Part A and Part
B participation rates, special hospital payments
Super (2003)
Medicare spending per
beneficiary, various years
Health status, local practice costs, special
payments to hospitals, managed care
enrollment, intensity of care
Gold (2004)
Medicare spending per
beneficiary, various years
Population characteristics, health care needs,
prices, intensity of care
Hadley and
others (2006)
Medicare spending per
beneficiary,
1992–2002
Age, race, urbanization, health status, "end-oflife expenditure index” from the Dartmouth
Atlas, tobacco use, educational attainment,
income, Medicare payment policy, dualbeneficiary status, percentage of physicians in
primary care
Martin and
others (2007)
Overall health care spending per
capita, Medicare spending per
beneficiary, Medicaid spending
per beneficiary, 2004
Income, availability of physicians and hospitals,
Medicaid eligibility and benefits, age
(descriptive analysis only)
Source: Congressional Budget Office, 2009
To understand geographical differences, an examination of per capita spending might need to
include such factors as the organization of practices, establishment of catchment areas based
on consumer preference patterns, and state policies guiding service delivery (e.g., level of
managed care versus fee-for-service). The national findings cited suggest the role of geography
in per capita spending as an area for examination using the SC Medicaid data aggregated at the
Zip Code level. This is the first examination of this approach as a vehicle for gaining an
understanding of health care spending and recipient access to care in South Carolina.
Methodology
Using data from fiscal year 2007 to fiscal year 2009, total Medicaid paid claims and total
number of Medicaid recipients per SC ZIP code area were aggregated to the SC ZIP Code
Tabulation Area (ZCTA) level. Medicaid paid claims per capita values then were calculated for all
SC ZCTAs. Separately, ZCTA-level Medicaid paid claims per capita for children less than 19
years, and for adults 19 years and older, were calculated.
ESRI’s ArcMap Getis-Ord Gi* Statistic Hot Spot Analysis tool (part of the ArcMap Spatial
Statistics toolbox) was used to measure the clustering of relatively “high” and “low” paid claims
per capita ZCTAs. The Hot Spot Analysis tool evaluates the relative value of an attribute (in this
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case paid claims per capita) in one areal unit (ZCTA) in the context of attribute values in
neighboring areal units (ZCTAs). A “hot spot” occurs where attribute values in one areal unit as
well as neighboring areal units are all “high,” relative to the distribution of attribute values
across all areal units. Conversely, a “cold spot” occurs where attribute values in one areal unit
as well as neighboring areal units are all “low,” relative to the distribution of attribute values
across all areal units. The Hot Spot Analysis tool produces a Gi* Z score for all areal units in a
given study region. An areal unit with a Gi* Z score greater than 1.96 is a statistically significant
“hot spot” at the .05 alpha level; conversely, an areal unit with a Gi* Z score less than -1.96 is a
statistically significant “cold spot” at the .05 alpha level.
The relative nearness of ZCTAs was evaluated using a Fixed Distance Band of 25,000 meters
(approximately 15.5 miles). This distance ensured that all ZCTAs had at least one neighboring
ZCTA; moreover, spatial autocorrelation (Morans l) at this scale of analysis was determined to
be statistically significant, indicating that spatial clusters of “high” and “low” attribute values
might be detected.
Preliminary Results
For the years 2008 to 2010, the mean Medicaid paid claim in South Carolina was $3,932. In all
three years, mean paid claims for adults 19 years and older were higher than mean paid claims
for children. Mean paid claims for adults and children increased between 2008 and 2009, then
decreased in 2010 (Table 2).
Table 2
South Carolina Medicaid Paid Claims per Capita, FY 08 – 10 1
Fiscal Year
Group
FY 2008
All Recipients
Adults 19 and Older
Children < 19 Years
808,863
352,258
456,605
$2,983,285,392
$1,775,765,561
$1,207,519,831
$3,688
$5,041
$2,645
FY 2009
All Recipients
Adults 19 and Older
Children < 19 Years
859,947
367,858
492,089
$3,695,488,746
$2,215,098,032
$1,480,390,714
$4,297
$6,022
$3,008
FY 2010
All Recipients
Adults 19 and Older
Children < 19 Years
901,910
378,561
523,349
$3,428,241,252
$2,088,688,612
$1,339,552,640
$3,801
$5,517
$2,560
1
2
Number of
Recipients
Total Paid
Claims 2
Mean Paid Claim
Per Capita
Fiscal year (FY) paid claims were calculated based on date of service and not processed claims within the FY.
This approach may result in slight variation associated with this analysis.
Total paid claims may vary slightly based n processing time, application of readjustments, and void files provided
for this analysis.
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FY 2008 – 2010
All Recipients
Adults 19 and Older
Children < 19 Years
2,570,720
1,098,677
1,472,043
$10,107,015,390
$6,079,552,205
$4,027,463,185
$3,932
$5,534
$2,736
At the ZCTA level, the mean paid claim (2008-2010) ranged from $1,409 to $14,165 for adults
(mean = $5,200), and from $1,001 to $5,446 for children (mean = $2,606).
The hot spot analysis for this study uses three years of data to “normalize” variability associated
with the use of claims data. Three years of data provides stronger evidence of trends. For the
total Medicaid population, statistically significant clusters of high paid claims per capita ZCTAs
(“hot spots”) were most pronounced in Lexington and Richland Counties, and in portions of
Chesterfield and Marlboro Counties. Conversely, statistically significant clusters of low paid
claims per capita ZCTAs (“cold spots”) existed in parts of Aiken, Chester, Hampton, and
Spartanburg Counties (Figure 1).
Figure 1
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For adult Medicaid recipients, high paid claims per capita ZCTAs (“hot spots”) existed in
Lexington and Richland Counties, Chesterfield and Marlboro Counties, and portions of Beaufort
County. Low paid claims per capita ZCTAs (“cold spots”) were less pronounced for adults, with
statistically significant clusters existing only in parts of Spartanburg, Allendale and Hampton
Counties (Figure 2).
Figure 2
Finally, for Medicaid recipients less than 19 years of age, statistically significant clusters of high
paid claims per capita ZCTAs (“hot spots”) were most pronounced in Lexington and Richland
Counties, the Greenville metropolitan area, and the Charleston metropolitan area (all locations
with children’s hospitals). Low paid claims per capita ZCTAs (“cold spots”) were widespread for
children on Medicaid, existing in all four Medicaid managed care enrollment regions. These
results reveal significant geographic variation in Medicaid paid claims per capita across South
Carolina ZCTAs and indicate the importance of evaluating Medicaid paid claims per capita
clusters separately for children and adults (Figure 3).
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Figure 3
Next Steps
ZCTAs in South Carolina vary substantially in geographic area. In general, rural ZCTAs are larger
in area than ZCTAs in urban centers. Rural ZCTAs therefore tend to have fewer neighboring
ZCTAs relative to urban ZCTAs when the nearness of ZCTAs is measured using a fixed band
width. To better ensure a uniform scale of analysis across both rural and urban areas, our next
set of Hot Spot analyses will use a lattice of identically sized grid cells, rather than ZCTAs, to
evaluate Medicaid paid claims clusters in South Carolina.
Paid claims per capita clusters (“hot” and “cold” spots) may differ for young adults versus adults
aged 65 years and older. Subsequent Hot Spot analyses therefore will evaluate these age
categories independently. Finally, additional analyses will control for the relative severity of
local health conditions, as reflected by mean CRG scores, in evaluating paid claim per capita
clusters. The current analysis suggests that geography is a factor when examining the
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differences in per capita paid claims and provides a useful tool in monitoring changes in
provider and recipient service use.
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