how do medicaid, medicare, and commercial insurance vary ......atlas/brookings institute...

How do Medicaid, Medicare, and Commercial Insurance Vary in

Community-Level Performance?

Using Claims Data from the Wisconsin Health Information Organization (WHIO)

to Assess Variation in Population Health Processes

November 2011

UW Population Health Institute 760 WARF Building, 610 Walnut Street

Madison, Wisconsin http://uwphi.pophealth.wisc.edu

Prepared by Donna Friedsam, MPH

Daphne Kuo, PhD Kristen Voskuil, MA

http://uwphi.pophealth.wisc.edu/

of 25 UW Population Health Institute

Background

The Wisconsin Health Information Organization (WHIO) received Medicare data from the Dartmouth Atlas/Brookings Institute collaboration, with support from the Markel Foundation, to support its understanding of variation among various markets in payer performance. The UW Population Health Institute conducted various exploratory analyses to determine the utility of the Medicare data provided in aggregate at the county level. Specifically, the UW Population Health Institute used the data provided by both Dartmouth (Medicare) and Ingenix (Commercial and Medicaid) to assess the payers’ performance along various parameters. 1. By County, along process measures for diabetes quality: HbA1c, LDL annual, annual retinopathy exam. 2. For three service areas participating in pilot projects for the Partnership for Health Care Payment Reform (PHPR): Three process measures and the composite measure, by payer and for all payers. 3. Correlation between various county-level WHIO measures for Health Utilization and the z-score of county in the Institute’s annual County Health Ranking. The measures of health utilization tested are as follows:

Admits per 1,000: Medical, Surgical, ASC, All visits

IP days per 1,000: Medical, Surgical, ASC, all visits

Emergency Department: Avoidable ED per 100, for all diagnoses Avoidable ED per 100, for primary diagnoses ED payment per day, allowed charge ED payment per day, CMS payment # of ED days per 100, no IP admissions days # of ED days per 100, with IP admission days

Sources of WHIO Data

WHIO was formed in 2005 to serve as a data warehouse for resource use information. Most large insurance companies in the state, along with the Wisconsin Medicaid program, now submit de-identified health insurance claims data to WHIO. The WHIO DataMart Version 5, on which this report is based, includes data submitted by the following contributors: WPS Health Insurance Corp., WEA Trust Insurance, Humana, Anthem, United Healthcare of Wisconsin, State of Wisconsin Medicaid (FFS and HMO), Gundersen Lutheran Health Plan, Dean Health Plan, Security Health Plan, MercyCare Health Plan, Group Health Cooperative of South Central Wisconsin, Network Health Plan, and Physicians Plus Insurance Corporation. Combined, this data represents claims from more than 3.7 million Wisconsin residents. Both the WHIO and Medicare datasets include only health insurance claims data. They should generally reflect services delivered to the patient, but are susceptible to any irregularities that may accompany coding and billing process.


1. Diabetes Process Measures by County

Tables 1a below lists, by county the percent of persons in each payer group receiving the recommended tests – HbA1c and LDL tests – and the difference in rates between the payers – Commercial, Medicaid, and Medicare. The percentage differences that have an asterisk indicate a statistically significant difference in performance between the two payers.

For nearly all counties, Medicaid shows significantly lower performance than Commercial and Medicare in these counties.

Medicaid statewide performs approximately 35% lower than Commercial and Medicare on LDL testing.

Medicaid statewide performs approximately 30% lower than Commercial and Medicare on HbA1c testing.

The performance of Commercial and Medicare are statistically similar in most counties, except

In Jefferson, Juneau, Kenosha, Racine and Sauk Counties, Medicare performance for LDL testing significantly outpaces Commercial payer performance.

In Kenosha, Racine, and Sauk Counties, Medicare performance of HbA1c testing significantly outpaces Commercial payer performance.

The Bar Charts 1a-1c illustrate, by payer (COM, MCR, MCD), each county variation from the county mean for HbA1c and LdL testing. Those bars extending beyond the zone of standard deviation indicate performance either significantly above or significantly below the mean for all counties on that performance measure. The maps 1a-1d that follow illustrate the variation by county in performance of these diabetes tests – HbA1c, LDL, and retinopathy – on a composite all-payer basis (Commercial, Medicare, and Medicaid). The all-payer composite performance rates reflected in the maps are weighted to adjust for varying composition of payer groups in each county. That allows the performance rates to compare actual overall delivery system performance rather than reflecting simply the relative predominance of, for example, Medicare (which generally has higher performance). In other words, the weighted rate indicates the performance as if each county had the statewide standard percentage of payers for each process measure (HbA1c, LDL, eye exam), as indicated by Table 1b. Table 1b. Distribution of Payers, Statewide, within each Diabetes Process Measure

Payer HbA1c LDL Eye Exam

Commercial 0.43 0.52 0.36

Medicaid 0.37 0.27 0.36

Medicare 0.20 0.21 0.28

Total 1.00 1.00 1.00


Table 1a: Percent of persons, by county and payer group receiving HbA1c and LDL tests and the difference in

rates between the payers – Commercial, Medicaid, and Medicare


Map 1a:


Map 1b:


Map 1c:


Map 1d:


Variation from the Overall County Mean, by Payer

The Bar Charts 1a-1c illustrate, by payer (COM, MCR, MCD), each county variation from the county mean for

HbA1c and LdL testing. Those bars extending beyond the zone of standard deviation indicate performance

either significantly above or significantly below the mean for all counties on that performance measure.

The Table 1c, below, lists counties performing outside the bounds of one standard deviation above (better) or below (worse) than the overall mean of all counties. Table 1c. County Performance Relative to Overall Mean for All Counties, Medicaid

Counties Performing Above Overall County Mean for Medicaid

HbA1c (County Mean = 59%) LDL (County Mean = 47%)

Door Door Langlade Langlade

Milwaukee Richland Oneida Shawano

Polk Portage

Price Richland

Waushara

Counties Performing Below Overall County Mean for Medicaid

HbA1c (County Mean = 59%) LDL (County Mean = 47%)

Green Green Iowa Green Lake

Kenosha Iowa LaCrosse Jackson Monroe LaCrosse

Sauk Monroe Sheboygan Sauk Trempeleau Trempeleau

Vernon


Bar Charts 1a:


Bar Chart 1b:


Bar Chart 1c:


2. Payment Reform Service Areas

Three service areas are participating in the pilot projects for the Partnership for Health Care Payment Reform

(PHPR). These service areas are defined in two levels: 1) Counties named by the providers participating in the

PHPR, and 2) as derived from the zip codes associated with the Hospital Service Area within the Dartmouth

Atlas. In the former case, while these provides may have a presence in these counties (and thus consider

them part of their service area), they do not have a significant geographic or market share in many of them.

So these providers’ services will not move the population-level (county-level) data for those counties where

they do not care for a significant percentage of the population.

For this reason, the data were also analyzed using the Dartmouth Atlas’ more narrow service area.

The population for HSA zip code, within each county, was aggregated to determine the share of the

population in each county attributed to that HSA. Counties were only included in the service area if a

predominant portion of the overall county population could be assigned to the HSA for that provider.

Table 2a, below, shows the percent of the population that can be attributed to the HSAs for provider systems

HVN and Monroe Clinic. (This analysis was not run for Milwaukee IPN because the specific providers were not

designated. )

Table 2a NEWHVN

County Total in Service Area

Total County Population Service Area % of County

Brown 249,192 249,192 100%

Door 2,656 27,765 10%

Kewaunee 12,829 20,594 62%

Manitowoc 351 81,406 0%

Marinette 3026 41,719 7%

Oconto 32,043 37,723 85%

Outagamie 4,907 177,455 3%

Shawano 920 41,954 2%

Monroe Clinic*

County Total in Service Area Total County Population Service Area % of

County

Green 36,884 36,884 100%

La Fayette 4,246 16,880 25%

* Monroe Clinic’s service of Stephenson County, Illinois is not included because we did not receive Medicare or WHIO

data about that area. Also, Monroe Clinic did not indicate LaFayette County within its service area. However, Dartmouth

Atlas shows 25% of the county is in the Monroe Clinic HSA. We were not able to provide the service data because

LaFayette County was not included in the data we received from Dartmouth or Ingenix (that data only had 58 counties.)


These two methods resulted in the following service areas, with the more narrow HSA-derived service area in

bold:

NEWHVN:

Brown, Calumet, Kewaunee, Oconto, Outagamie, Shawano, Waupaca, Waushara ,Winnebago

Milwaukee IPN: Milwaukee, Kenosha, Ozaukee, Racine, Waukesha

Monroe Clinic: Green County

Tables 2c,d, and e list, by narrow and wide service area, the percent of persons in each payer group –

Commercial, Medicaid, and Medicare-- receiving the recommended tests – HbA1c, LDL, retinopathy tests, and

the composite score for receipt of all three tests.

The final all-payer rates, in Table 2f, are weighted to adjust for varying composition of payer groups in each

service area. That allows the rates to compare actual overall provider performance rather than simply the

relative predominance of, for example, Medicare (which generally has higher performance). In other words,

the weighted rate indicates the performance against a standardized percentage of payers, as indicated by the

Adjustment Factors Table 2b, below.

Table 2b: Adjustment Factors, Percent of Claims by Payer for Each Service, Statewide

% N % N % N % N

All Three All Svcs HBa1c HBa1c LDL LDL Eye Exam Eye Exam

Statewide 216,302 181,391 165,983 133,731

COM 17% 37,401 18% 32,663 18% 30,093 11% 14,701

MCD 15% 32,086 11% 19,497 9% 15,731 11% 14,632

MCR 68% 146,815 71% 129,231 72% 120,159 78% 104,398

100% 216,302 100% 181,391 100% 165,983 100% 133,731

Commercial:

o Both NEWHVN and Monroe exceed the all payer individual test rates and composite rate.

o Milwaukee trails the statewide rate for each individual test rate and for the composite rate.

Medicaid:

o Monroe substantial trails the statewide rate for HbA1c and LDL testing and for the composite

rate

All Payer:

o Only minor variations appear in the comparison of service area all-payer rates to the statewide

rates, particularly once the service area rates are weighted to correct for differing payer

composition among the population.


Tables 2c-f. Summary of Quality Measure Compliance for Selected Diabetes Measures, By County and Product Category, Year Ending 9/30/2009, Patients within last 12 months.

Table 2c: Commercial

HbA1c LDL Eye exam All Tests

Composite

PRODUCT COUNTY DEN (N) NUM Rate NUM Rate NUM Rate Rate COM Oconto 199 181 91% 168 84% 70 35% 70% COM Kewaunee 123 119 97% 106 86% 46 37% 73%

COM NEWHVN

1,454

1,322 91%

1,208

0.83

632 43% 72%

COM Calumet 448 405 90% 379 85% 209 47% 74% COM Outagamie 823 753 91% 713 87% 387 47% 75% COM Shawano 217 201 93% 192 88% 88 41% 74% COM Waupaca 382 358 94% 351 92% 199 52% 79% COM Waushara 140 134 96% 118 84% 57 41% 74% COM Winnebago 638 593 93% 572 90% 293 46% 76%

COM NEWHVN-out

2,648

2,444 92%

2,325

0.88

1,233 47% 76%

COM NEWHVN-TOTAL

4,102

3,766 92%

3,533

0.86

1,865 45% 74%

COM Milwaukee 6,801 5,851 86% 5,387 79% 2,532 37% 67% COM Waukesha 2,839 2,475 87% 2,287 81% 1,161 41% 70%

COM IPN

9,640

8,326 86%

7,674

0.80

3,693 38% 68%

COM Kenosha 866 643 74% 618 71% 236 27% 58% COM Ozaukee 719 622 87% 577 80% 280 39% 69% COM Racine 1,246 912 73% 860 69% 363 29% 57%

COM IPN-out

2,831

2,177 77%

2,055

0.73

879 31% 60%

COM IPN-TOTAL

12,471

10,503 84%

9,729

0.78

4,572 37% 66%

COM Green/Monroe

298

275 92%

258 87%

148 50% 76%

COM Statewide 37,401 32,663 87% 30,093 80% 14,701 39% 69%


Table 2d: Medicaid


Composite

PRODUCT COUNTY DEN (N) NUM Rate NUM Rate NUM Rate Rate

MCD Oconto 184 114 62% 97 53% 76 41% 52%

MCD Kewaunee 80 45 56% 34 43% 33 41% 47%

MCD NEWHVN

1,538

932 61%

797 52%

714

0.46 53%

MCD Calumet 321 175 55% 156 49% 155 48% 50%

MCD Outagamie 514 324 63% 272 53% 221 43% 53%

MCD Shawano 215 142 66% 126 59% 95 44% 56%

MCD Waupaca 270 147 54% 135 50% 132 49% 51%

MCD Waushara 174 121 70% 91 52% 92 53% 58%

MCD Winnebago 631 353 56% 301 48% 295 47% 50%

MCD NEWHVN-out

2,125

1,262 59%

1,081 51%

990

0.47 52%

MCD NEWHVN-TOTAL

3,663

2,194 60%

1,878 51%

1,704

0.47 53%

MCD Milwaukee 10,891 7,332 67% 5,783 53% 4,364 40% 53%

MCD Waukesha 756 423 56% 354 47% 338 45% 49%

MCD IPN

11,647

7,755 67%

6,137 53%

4,702 40% 53%

MCD Kenosha 884 421 48% 389 44% 345 39% 44%

MCD Ozaukee 162 100 62% 80 49% 59 36% 49%

MCD Racine 1,179 654 55% 554 47% 418 35% 46%

MCD IPN-out

2,225

1,175 53%

1,023 46%

822

0.37 45%

MCD IPN-TOTAL

13,872

8,930 64%

7,160 52%

5,524

0.40 52%

MCD Green/Monroe

154

71 46%

61 40%

86 56% 47%

MCD Statewide` 32,086 19,497 61% 15,731 49% 14,632 46% 52%


Table 2e: Medicare


Composite

Product County DEN (N) NUM Rate NUM Rate NUM Rate Rate

MCR Brown

5,410

4,783 88.41

4,388 81.11

4,031 74.52 81%

MCR Oconto 1,350 1,144 84.76 1,051 77.82 915 67.78 77%

MCR Kewaunee 605 515 85.16 501 82.79 374 61.76 77%

MCR NEWHVN

7,365

6,442 87%

5,940 81%

5,320 72% 80%

MCR Calumet 530 458 86.37 450 84.90 382 71.99 81%

MCR Outagamie 3,530 3,209 90.92 3,076 87.14 2,691 76.22 85%

MCR Shawano 1,120 990 88.37 942 84.12 806 71.93 81%

MCR Waupaca 1,770 1,551 87.64 1,580 89.24 1,368 77.27 85%

MCR Waushara 1,225 1,030 84.09 923 75.34 788 64.36 75%

MCR Winnebago 3,270 2,867 87.68 2,725 83.32 2,261 69.13 80%

MCR NEWHVN-out

11,445

10,105 88%

9,695 85%

8,294 72% 82%

MCR NEWHVN-TOTAL

18,810

16,548 88%

15,635 83%

13,614 72% 81%

MCR Milwaukee 26,910 23,064 85.71 21,273 79.05 17,755 65.98 77%

MCR Waukesha 9,270 8,150 87.92 7,784 83.97 6,875 74.16 82%

MCR IPN

36,180

31,214 86%

29,057 80%

24,630 68% 78%

MCR Kenosha 4,660 4,077 87.50 3,962 85.03 2,918 62.61 78%

MCR Ozaukee 2,185 1,910 87.42 1,781 81.49 1,572 71.92 80%

MCR Racine 6,310 5,468 86.65 5,107 80.93 4,088 64.79 77%

MCR IPN-out

13,155

11,455 87%

10,850 82%

8,577 65% 78%

MCR IPN-TOTAL

49,335

42,669 86%

39,907 81%

33,207 67% 78%

MCR Green 1,240 1,131 91.21 1,091 87.95 806 64.97 81%

MCR Statewide 146,815 129,231 88% 120,159 82% 104,398 71% 80%


Table 2f: All Payers Composite

HbA1c LdL Eye exam All Tests

Composite

Co

un

ty

De

n (

N)

Nu

m

Rat

e

Wei

ghte

d

NU

M

Rat

e

Wei

ghte

d

NU

M

Rat

e

Wei

ghte

d

Rat

e

Wei

ghte

d

Brown 7,816 6,578 84% 86% 5,988 77% 78% 5,152 66% 68% 76% 76%

Oconto 1,733 1,439 83% 83% 1,316 76% 76% 1,061 61% 61% 73% 72%

Kewaunee 808 679 84% 84% 641 79% 79% 453 56% 57% 73% 72%

NEWHVN

10,357

8,696 84% 85%

7,945 77% 78%

6,666 64% 66% 75% 75%

Calumet 1,299 1,038 80% 84% 985 76% 81% 746 57% 67% 71% 75%

Outagamie 4,867 4,286 88% 88% 4,061 83% 83% 3,299 68% 69% 80% 78%

Shawano 1,552 1,333 86% 87% 1,260 81% 82% 989 64% 65% 77% 76%

Waupaca 2,422 2,056 85% 85% 2,066 85% 85% 1,699 70% 71% 80% 79%

Waushara 1,539 1,285 84% 85% 1,132 74% 74% 937 61% 60% 73% 72%

Winnebago 4,539 3,813 84% 85% 3,598 79% 80% 2,849 63% 64% 75% 75%

NEWHVN-out

16,218

13,811 85% 86%

13,101 81% 81%

10,517 65% 67% 77% 76%

NEWHVN-TOTAL

26,575

22,508 85% 86%

21,046 79% 80%

17,183 65% 67% 76% 76%

Milwaukee 44,602 36,247 81% 84% 32,443 73% 76% 24,651 55% 60% 70% 72%

Waukesha 12,865 11,048 86% 84% 10,425 81% 79% 8,374 65% 67% 77% 75%

IPN

57,467

47,295 82% 84%

42,868 75% 77%

33,025 57% 62% 71% 73%

Kenosha 6,410 5,141 80% 81% 4,969 78% 78% 3,499 55% 56% 71% 70%

Ozaukee 3,066 2,632 86% 84% 2,438 80% 78% 1,911 62% 64% 76% 74%

Racine 8,735 7,034 81% 81% 6,521 75% 75% 4,869 56% 58% 70% 69%

IPN-out

18,211

14,807 81% 81%

13,928 76% 77%

10,278 56% 58% 71% 70%

IPN-TOTAL

75,678

62,102 82% 84%

56,796 75% 77%

43,303 57% 61% 71% 72%

WI, Green

1,692

1,477 87% 86%

1,410 83% 82%

1,040 61% 62% 77% 75%

Statewide

216,302

181,391 84% 85%

165,983 77% 78%

133,731 62% 65% 74% 74%


3. Correlation between WHIO measures and County Health Ranking

The correction was tested between various county-level WHIO measures for Health Utilization and the z-score of county in the Institute’s annual County Health Ranking. (Table 3 and Figures 3a-h) WHIO measures of health utilization tested are as follows:

Admits per 1,000: Medical, Surgical, ASC, All visits IP days per 1,000: Medical, Surgical, ASC, all visits Outpatient visits per 100: Primary care, Specialist, Total Emergency Department: Avoidable ED per 100, for all diagnoses Avoidable ED per 100, for primary diagnoses ED payment per day, allowed charge ED payment per day, CMS payment # of ED days per 100, no IP admissions days # of ED days per 100, with IP admission days

The CHR provides two sets of rankings: Factors and Outcomes.

Factors z-score rolls up data on health behaviors, clinical care, social and economic, and the physical environment. Outcomes z-score rolls up data on how long people live (mortality) and how healthy people feel (morbidity). These include premature death, birth outcomes, and health-related quality of life indices.

It is important to note that the CHRs use some Dartmouth Atlas elements as part of Factors z-score. The rankings use of the Dartmouth Medicare data, and their percentage contribution to the overall ranking are as follows: HbA1c testing (2.5%) and hospitalizations for Ambulatory Care Sensitive Conditions (5%). With this context, a note: These correlations appear to serve as a validation exercise rather than providing value-added information that can lead to action. There is no genuinely independent variable here: The WHIO/Dartmouth utilization measures are a function of community health factors (many of which are measured with the CHR), while the CHR ranking includes utilization in their scoring algorithm. Consistent and statistically significant correlation was found between the following WHIO quality parameters and the county health ranking (z-score) for both Factors and Outcomes, for the following measures:

Hospital admissions for Ambulatory Care Sensitive Conditions (per 1,000 population)

Inpatient days for Ambulatory Care Sensitive Conditions (per 1,000 population)

ED payment per day – allowed charge

ED payment per day – CMS payment

# of ED days, no IP admission days (per 100 population)

# of ED days with IP admission days (per 100 population) Correlation was also found with the CHR Factors ranking, but not the Outcomes Ranking, for the following:

Avoidable ED visits for primary diagnosis (rate per 100 population)

Outpatient Visits to Specialist (Rate per 100 population)

Medical and all hospital admissions (rate per 1,000 population)


Table 3


Figure 3a

Figure 3b


Figure 3c

Figure 3d


Figure 3e

Figure 3f


Figure 3g

Figure 3h


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