# Presentation: 439243

## USING GIS TO ELIMINATE DISPARITIES IN HEALTH CARE

- Kevin Fiscella, MD, MPH
- Primary Care Institute   University of Rochester

**Notes:**
Good Afternoon. I’m Kevin Fiscella and I am honored to have the opportunity to present to you on Using GIS to eliminate disparities in Health Care.

## The Elimination of Racial and Ethnic Disparities in Health by 2010

**Notes:**
As you know, a key, overarching goal of Health People 2010 is the...

## Socioeconomic position and race/ethnicity are risk factors for disparities in the process and outcome of care.

**Notes:**
As most of you know, both socioeconomic position and minority race or ethnicity  are risk factors for suboptimal health care.

## The Elimination of Socioeconomic, Racial, and Ethnic Disparities in Health Care by 2010

**Notes:**
So, a key step toward the elimination of disparities in health is the elimination of disparities in health care.

## Linking disparities to quality

**Concepts are incompatible**
**Disparities are a quality issue**
**QI should focus on the elimination of disparities**
**Existing measures can be adapted to monitor disparities**

**Notes:**
Before I describe the role of GIS in this process, I will first present a conceptual framework that links monitoring and addressing disparities in health care to quality. Then I will outline the role of GIS in this framework, and given sufficient time, I will discuss important research questions related to the use of  GIS  in this regard.

The framework that I will outline, links  quality and disparities. Disparities in  health care are not compatible with quality. As such, disparities represent a quality issue. Thus, disparities represent a focus for quality improvement. Disparities can be identified and monitored by adapting existing measures.

## I. Disparities in the process and    outcome of care are incompatible with quality

**Notes:**
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## The notion of quality implies not simply the overall attainment of a defined standard (benchmark), but uniformity and consistency in process and outcome.

**Notes:**
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## If an HMO that achieves an overall 90% immunization rate by immunizing 95% of low risk patients, but only a 60% of high risk patients (who comprise only 15% of the population),  is the HMO providing quality care?

**Notes:**
To illustrate this point, consider the example of an HMO immunization program. 

READ SLIDE.

I submit they are not for two reasons:
First, the immunization rate is not consistent across all enrollees.
Second, the program is engaging in reverse targeting. Those who would benefit the most from immunization have the lowest rates. 

## Quality implies that care will be allocated according to need; priority will be given to those who will derive the most benefit. 

**Notes:**
This illustrates a fundamental aspect of quality when applied to health care. READ SLIDE.

## II. Disparities are a quality issue and represent a key component of performance assessment.

**Notes:**
The second principle follows from the first. READ SLIDE

## If the goal is the elimination of disparities in care, then we must begin to routinely monitor the care provided to groups at risk for suboptimal care.

**Notes:**
Therefore  READ SLIDE

## If you don’t count it, it doesn’t count.

**Notes:**
In other words, READ SLIDE.

## III. A central role of quality improvement should be the elimination of disparities in health care.

**Notes:**
The third principle continues in this vane.

## Disparities in health care are not immutable

**High performing HMOs in NYS show little gap in HEDIS scores between Medicaid and commercial plans.**
**There are no appreciable racial disparities in access to cardiovascular procedures in NYC.**
** ****Racial disparities in mammography and breast cancer survival have been eliminated through targeted interventions.**

**Notes:**
I want to emphasize at this point that socioeconomic and racial/ethnic disparities in health care are not a fact of life. They are not immutable as shown by the following.

## IV. Existing performance measures such as HEDIS can be adapted to monitor care provided to groups at risk for suboptimal care and/or worse outcomes.

**Notes:**
The last principle is that we do not need to re-invent the wheel to monitor many of the disparities in care. We can begin the process by adapting existing measures such as HEDIS for this purpose.

## Absence of reliable data is a major barrier to monitoring socioeconomic, racial, and ethnic disparities in care.

**Notes:**
The biggest obstacle to linking disparities to quality is the absence of reliable data by socioeconomic position or race.

## HMOs and Hospitals do not collect socioeconomic data on their patients.

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## Hospitals do not uniformly collect race/ethnicity data or do so reliably.

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## HMOs do not collect race/ethnicity data.

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## GIS technology can be used to overcome this barrier

**Patient addresses can be geocoded to census blocks or tracts.**
**Census data can used to approximate socioeconomic position.**
**A similar approach could be used for race/ethnicity.**

**Notes:**
As most have guessed by now, this is where GIS comes into play. READ SLIDE

## This GIS derived measures can then be used to report performance for at-risk groups.

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## Advantages to using GIS to monitor disparities in health care 

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##  Relies on existing data - no additional data collection needed

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##  Avoids asking sensitive questions

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## Avoids non-response or response bias to questions

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## Avoids misclassification associated with individual measures

- Estimating parental or spousal SES
- Age-education confound
- Non equivalence of  schools
- Income vs wealth

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## Captures neighborhood contextual data

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## Performance can be mapped  to neighborhoods providing the opportunity for targeted interventions

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## Enrollment/disenrollment patterns by neighborhood or SES can be monitored over time

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## Data can be used to adjust payments to HMOs based on the socioeconomic position of plan members (in addition to adjusting for case mix).

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## What are the limitations of  using GIS to monitor care to vulnerable groups?

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## Relies on census data collected every ten years.

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## Misclassification of  SES or race based on residence

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## May require larger sample sizes(Due to increased measurement error) 

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## Potential for misuse by health care organizations

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## Research questions

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## To what extent can socioeconomic measures be used to monitor care provided to racial/ethnic minority groups?

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## How do GIS derived measures compare to individual measures of socioeconomic position (or race/ethnicity) for monitoring disparities in health care?

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## Project Design

- _Sample_: Data from AHCPR Medical Expenditure Panel Survey (MEPS)
- _Independent measures_: GIS derived SES measures and individual SES measures  (education, income, occupation)
- _Dependent measures:_ HEDIS type measures of quality of care.
- _Analyses:_  Will compare the effect of  GIS with individual measures on quality measures. Will also explore the effect of  race/ethnicity on quality after complete adjustment for SES.

**Notes:**
In this project, we will use data from AHCPRs Medical Expenditure Panel Survey to compare the effect on Quality measures of individual measures of SEP such as family income, years of schooling, and occupational rank with GIS derived measures of SEP. This will allow us to define more precisely the advantages and limitations of this approach.

## What is the impact on physician report cards of adjusting for patients socioeconomic position using GIS?

**Notes:**
Another project addresses a related but perhaps more controversial question. READ SLIDE.

## Project Design

- _Sample_: Claims data from an HMO in Rochester.
- _Independent Measure:_ GIS derived patient SES.
- _Covariates_: Casemix, age and sex.
- _Dependent measures_: Visits, preventive care, diabetic care, avoidable  hospitalizations, total expenditures.
- _Physician Performance measures_: Impact of SES adjustment on change in physician performance rank

**Notes:**
In this project, we will link GIS measures of SEP to actual HMO claims data. We will begin by ranking physicians based on various measures after adjusting for age, sex, and case-mix. We will then compare these rankings after additional adjustment for GIS.

## Effect of GIS-SES on Change in Physician Rank by Mean Patient Physical Health  According to Mean Practice Education Level

- <date/time>

**Mean practice patient education level (years)**

**Change**** in rank**

**Effect of GIS-SES on Change in Physician Rank by Mean Patient Physical Health  According to Mean Practice Education Level**

**Notes:**
Our preliminary data show that GIS measures exert an appreciable effect on physician rankings based on patient reported health status.

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- <date/time>

**Notes:**
As you can see, adjusting for GIS measures exerts a very similar effect on rankings as does adjusting for patient education.

In closing, I want to emphasize that disparities are a quality issue. If we are goal is eliminate disparities we must begin measuring disparities. GIS provides us the tools to begin doing so. Thank you for your attention.