# Presentation: 340044

## Measuring Disparitiesin Health at the State Level

**Jay S. Buechner, Ph.D.**

**APHA Annual Meeting, November 11, 2002**

**National Center for Health Statistics**

**Rhode Island Department of Health**

**Elsie R. Pamuk, Ph.D.**

**Centers for Disease Control & Prevention**

**Notes:**
So far you have heard about the conceptual issues in defining an index of health disparity and a discussion of various possible indices of disparity from the viewpoint of applying them at the national level.  In this presentation I will discuss some issues that arise when we measure health disparities at the state (and local) levels and how those issues may affect the choice of an index that is appropriate for use by states and localities.  

But first, why is this a special concern?  What is different about measuring disparity at the state level?  There are two differences that stand out, even to the point of being obvious.  One is that states are smaller and the composition of their populations differs from that of the nation and from each other.  The second is that the states and localities are where most public health programs are located and where decisions based on data may have real and immediate consequences.

## Healthy People 2010 Goal:

_**Eliminate Health Disparities**_

**How do we measure progress?**

**Notes:**
A prime example is Healthy People 2010.  At the national level, Healthy People 2010 has chosen the goal of eliminating health disparities.  Most states have adopted or will adopt their own Healthy People 2010 objectives and will want to work to eliminate health disparities in their populations.  Some key questions for states are how should we identify and prioritize the disparities we will work on and how will we know whether we are succeeding?

## Conceptual Challenges

**Operational Challenges**

**Notes:**
So far in this session, the speakers have been concerned with the conceptual challenges in developing an index of disparity.  These include –
	What statistic to use in the calculation
	How to combine disparities across multiple groups
	What to use as a reference for comparisons

I will focus more on the operational challenges facing states, i.e., what barriers will states have to overcome in order to use an index of disparity at the state level. 

## Operational Challenges

**  ****Small Numbers**
**  ****Comparability**
**  ****Data Quality  **

**Notes:**
There are three challenges I think every state will face in some degree when trying to measure disparities:

Every state will have a problem with small numbers of cases for at least some of its population groups in some of its health data sources.

Every state will want to compare its data over time and/or with other geographic groups, such as the nation as a whole or its immediate neighbors.

Every state will need to be concerned about the underlying data collection processes, specifically the accuracy of the data that defines who is in what group when we are determining the level of disparity.

## Operational Challenges

**  *****Small Numbers***
**  ****Comparability**
**  ****Data Quality  **

**Notes:**
Looking first at the small numbers issue –

Whether we are measuring disparities by race and ethnicity, disability, income, education, age, or gender, we will be measuring health status, health risk behaviors, access to care, etc., in groups that are less than the full population.  Usually at least some of these sub-groups will be small enough to present difficulties in measurement with any degree of confidence.

As we drill down to more finely disaggregated groups to isolate disparities in particular health measures, we will be stressing the “data supply” even further.

## HP 2010 LHI Data Sources

**  ****Data from sample surveys****	****	****	****[e.g., BRFSS, YRBS] ****	****	****	****	****=> Small **_**percentages**_
**  ****Data from “registries” ****	****	****	****	****[e.g., births, deaths] ****	****	****	****	****	****=> Small **_**numbers**_

**Notes:**
For an example, let’s return to Healthy People 2010.  There are ten Leading Health Indicators, such as Tobacco Use, Physical Activity, Access to Health Care, etc.  Associated with these Leading Health Indicators are approximately two dozen specific measures with targets for the year 2010.

Of these measures, about two-thirds can be measured from survey data sources, for which data are collected from a representative sample of the population.  (At the state level, these include primarily the Behavioral Risk Factor Surveillance System, BRFSS, and the Youth Risk Behavior Survey, YRBS.)  Groups that represent small percentages of the total population, no matter what their numbers, will generally have few respondents in such surveys.

Most of the remaining one-third of these measures are derived from “registry” data sources, where every event of a particular kind is recorded.  The primary “registries” involved in the measures associated with the Leading Health Indicators are birth and death certificate files.  In these files, groups that include small numbers of persons, no matter what percentage of the population they represent, will generally have few events recorded in such registries.

Both situations present measurement challenges, albeit of different types.

## Minority Populations, Rhode Island, 2000

***Group***

***Number***

***Percent***

**Notes:**
To be even more concrete, I will show you findings on the” measurability” of the LHI data items in my state, Rhode Island.  Here, the focus is on disparity by race and ethnicity, which follows the examples in the previous talks.  

It also recognizes the primacy of race and ethnicity as an issue in health disparity.  As evidence of this primacy, refer back to Dr. Sondik’s remarks this morning. He presented a list of “Challenges for the Statistical System.”  First on his list was “Support state and local data needs,” followed immediately by “Measure and evaluate racial and ethnic health disparities.”

Returning to my example, in Rhode Island we recently performed a baseline measurement of the Leading Health Iindicator data items by race and ethnicity.  As background, here is the racial and ethnic distribution of the state’s population, from the 2000 Census.  Note the range in size of the population groups we included in the study (the italicized groups were excluded).  The existence of a number of such small population groups within a state is more typical than atypical among the 50 states.

## HP2010 Leading Health Indicators by Race and Ethnicity, Rhode Island, 2002

**Notes:**
We included 24 data items related to the Leading Health Indicators in our study.  Here is how many we could measure for each group.

Note that for four data items we have no current data source, and for two we have no data collected by race and ethnicity.

Among the population groups included, there is a wide range in the numbers of items that are measurable, and the number of measurable items closely parallels the size of the population in my state.  The largest number of measurable items is for non-Hispanic Whites, our largest population group; the smallest number is for American Indians, the smallest population group included in the study.

## HP2010 Leading Health Indicators by Race and Ethnicity, Rhode Island

**Notes:**
Here the same findings are organized differently in an attempt to answer the question: Do we have sufficient data to compute an index of disparity for each of the LHI data items?

For only 2 measures do we have data for all 5 race and ethnicity groups in our study.  For only 4 more do we have data for 4 of the 5 groups.

The number of “cells” for which we have inadequate data represents a major impediment to computing any index of disparity in our state.

## State Level Mortality by Race and Ethnicity, 1997-99

|  | Number of States with reliable rates |  |
| --- | --- | --- |
|  | Adult mortality | Infant mortality |
| Total population | 51 | 51 |
| White, non-Hispanic | 51 | 50 |
| Black, non-Hispanic | 47 | 39 |
| American Indian or Alaska Native | 15 | 14 |
| Asian / Pacific Islander | 45 | 25 |
| Hispanic | 45 | 37 |

## Operational Challenges

**  ****Small Numbers**
**  *****Comparability***
**  ****Data Quality  **

**Notes:**
The next challenge is the comparability of the data over time and/or among geographic areas.  For this issue, I have no specific example, just a list of concerns.

## Comparability Issues

**  ****Demographic differences ****	****	****among areas**
**  ****Demographic changes ****	****	****	****over time**
**  ****Differences in targets ****	****	****	****across areas **
**  ****Changes in measurability ****	****	****of indicators  **

**Notes:**
Most states will want to compare their level of disparity, preferably as measured by an index of disparity, with other geographic areas –
	With the nation
	With other states, notably neighboring states
	Between localities within their states

One issue is that important demographic characteristics other than the one for which disparity is being measured may differ across areas.  For example, the age distributions may be different within different racial and ethnic groups within a state, or disability rates may differ.  Both of these will affect the health measures for population groups if they are not adjusted for.

A second issue is that sometimes these characteristics change over time within a population, affecting comparability with other populations not experiencing such change or changing in different ways.

Third, states and localities choose different target numbers for otherwise similar Healthy People 2010 measures.  This would impact any index of disparity that is calculated using target rates.

Finally, we may find that our ability to measure some of these data items in small groups may improve over time, complicating the process of monitoring changes in an index of disparity over time.

All of the above lead to difficulties in interpretation and application of indices.

## State Level Mortality by Race and Ethnicity, 1997-99

***  States for which a comparable Index of Disparity can be computed.**

| Number of states with reliable rates | Number of States |  |
| --- | --- | --- |
|  | Adult mortality | Infant mortality |
| Five* | 11 | 8 |
| Four | 32 | 15 |
| Three | 5 | 9 |
| Two | 2 | 14 |
| One | 1 | 5 |

## Percent of women with no prenatal care in the first trimester, 1997-1999

## Operational Challenges

**  ****Small Numbers**
**  ****Comparability**
**  *****Data Quality*****  **

**Notes:**
The final issue I will address is the quality of the underlying data:  Are they complete and accurate as collected in the states?

In the following example, I again will focus on issues of race and ethnicity.

## Data Quality Issues

**  ****Sample surveys****	****	****	****	****	****	****Self-reported****	****	****	****	****	****	****	****Non-standard responses****	****	****	****	****Refusals****	****	****	****	****	****	****	****	****Bias**
**  “****Registries”****	****	****	****	****	****	****	****Self-reported?****	****	****	****	****	****	****Missing data****	****	****	****	****	****	****	****Under-reporting**

**Notes:**
For race and ethnicity data collected in population surveys, e.g., the BRFSS and the YRBS, the good news is that race and ethnicity are usually self-reported, which is the preferred collection methodology for these items.  The bad news is that sometimes the responses don’t fit our prescribed categories.  Also, there may be a proportion of respondents who do not respond to this item.  There may as well be bias inherent in the survey design, e.g., the use of telephone numbers as the sampling frame, leaving out households without telephone coverage.

For race and ethnicity data collected in “registry” databases, race and ethnicity may often be reported by a health care provider or a member of the provider’s staff based on observation of the person.  Sometimes the data may be missing in the source record, such as a hospital medical record, leading to under-reporting of some race and ethnicity categories.

The last possibility is the subject of the example to follow.

## Hispanic Surname Study, RI Cancer Rates

**  ****From Bureau of Census:****	****	****	****	****	****List of 639 “heavily Hispanic” surnames**** ****	****	****	**** **
**  ****Apply to:****	****	****	****	****	****	**** ****	****	****	****RI Cancer Incidence (Males)****	****	****	****	****	****RI Cancer Deaths (Males, Females)**

**Notes:**
We recently performed a study of cancer rates among Hispanics in Rhode Island.  Over many years, we have refrained from publishing death rates for Hispanics and some other racial and ethnic minority populations, as they look suspiciously “immortal,” even after adjusting for their generally young age distribution.

Our cancer registrar applied a methodology developed by the Bureau of the Census to our cancer registry data, and later expanded the study to include death certificates.

He compared males’ surnames and females’ fathers’ surnames to a list of 639 surnames that the Bureau of the Census has designated “heavily Hispanic,” i.e., 75% or more of people with each surname had self-identified as Hispanic in the Census data.

## Hispanic Male Cancer Cases, Rhode Island, 1989-1998

**0**

**10**

**20**

**30**

**40**

**50**

**60**

**70**

**80**

**1989**

**1990**

**1991**

**1992**

**1993**

**1994**

**1995**

**1996**

**1997**

**1998**

**Year of Diagnosis**

**Number of Cases**

**Combined**

**Surname**

**Reported**

**Method**

**Hispanic Male Cancer Cases, Rhode Island, 1989-1998**

**Notes:**
This chart shows a typical result, in this case cancer incidence for males based on cancer registry data.  The bottom trend line shows the number of cases reported as Hispanic to the registry.  The middle line shows the number of registry cases with “heavily Hispanic” surnames.  The topmost line shows the number of registry cases identified as Hispanic through either method (some were identified by both methods).

Our main conclusion: Hispanic origin is under-reported by 50% or more in our cancer registry and death certificate files.

## Recommendations/Suggestions

**  ****Oversample/stratify in sample****	****	****	****	****	****surveys**
**  ****Choose an index that accommodates ****	****	****	****variety among states and localities**
**  ****Establish and support improved data ****	****	****	****collection  **

**Notes:**
In conclusion, there are ways to address these challenges at the state and local levels.  We can –
1. Oversample small population groups in our surveys.
2. Choose an index that remains valid when applied to heterogeneous populations.
3. Monitor and improve our data collection processes.

These are all doable with the right technical skills and adequate resources.  But that leaves a final question- how many states have the skills and resources they need to support accurate and appropriate measurement among different race and ethnicity groups, not to mention among groups defined by income, education, disability status, etc?

Thank you.