U.S. CENSUS MONITORING BOARD

Presidential Members

Census Effects on Access to Health Care

February 2001

Report Series

Report No. 09
February 20, 2001

The Presidential Members of the U.S. Census Monitoring Board present the

research findings of Dr. Darrell Gaskin, ìThe Effects of Using Unadjusted

Data on Measures of Access to Health Care.î

The study finds that the 1990 census undercount overestimates preventable

hospitalizations, a rate used to determine access to health care. Furthermore,

Gaskin notes that systematic undercounting makes minority communities less

attractive for private investment and development, such as where to place new

doctor offices or hospitals.

Sincerely,

Gilbert F. Casellas, Co-Chair

U.S. Census Monitoring

Board

4700 Silver Hill Road

Suite 1250-3

Suitland, MD 20746

PRESIDENTIAL MEMBERS

Phone: (301) 457-9900

Fax: (301) 457-9901

Gilbert F. Casellas

Co-Chair

Cruz M. Bustamante

Everett M. Ehrlich

Lorraine A. Green

Margarita Roque

Executive Director
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The Effects of Using Unadjusted Data on Measures of Access to Health Care

Darrell J. Gaskin, Ph.D.

Research Assistant Professor

Institute for Health Care Research and Policy

Georgetown University Medical Center

Washington, D.C. 20007

Executive Summary

Using of unadjusted census data can distort measures of access to care. On one hand it

exaggerates the problems in access to care and on the other hand it minimizes the problems of

physician and hospital shortages. In particular, unadjusted data inflates incidence rates,

indicators of unmet health care needs, and provider per capita measures, which are all

indicators of provider availability.

Typically, advocates for using adjusted census data argue that the systematic undercounting of

minority populations results in a loss of needed public funds for publicly supported/provided

services that are allocated on a per capita basis. However, there is another part of the problem

created by using unadjusted census figures for minority communities. The systematic

undercounting of minority populations also makes their communities less attractive for private

investment and development. Because of this undercounting, prevalence and incidence rates,

based on unadjusted census figures, will always be overestimates. This overestimation will be

worse in areas with large minority communities. Hence, the unadjusted data serves to inflate

rates of mortality, morbidity, injuries, accidents, crime and other statistics used to gauge the

problems and challenges facing communities. The overall effect is to exaggerate the risks

associated with living in minority communities, making them less attractive relative to other

communities for private enterprise and investors.

In this brief, this point is illustrated using rates of preventable hospitalizations - a measure of

access to primary health care. I calculated county rates of preventable hospitalizations using

both the adjusted and unadjusted census data by race and ethnicity. A comparison of the rates

reveals that the rates calculated using unadjusted data are higher than those calculated with

adjusted data. In comparison to Whites, the overestimation is six times greater for African

Americans and eight times greater for Hispanics. In addition, the overestimation was greater for

rural counties compared to urban counties, especially for African American and Hispanic rural

residents. Rates of preventable hospitalizations, in counties with high poverty rates and with

high percentages of adults with less than a high school education were also inflated by the

unadjusted census data. The implication of this overestimation is to exaggerate the problems and

challenges facing minority, rural, poor and poorly educated communities. Hence, this

discourages individuals, employers, health care providers and health plans from investing in

these communities.

Health care provider availability measures are inflated by the systematic undercounting of

minority populations in the census. Physician per capita and hospital beds per capita are

common ratios used to measure the supply of health care providers in a county. Using the

unadjusted data to calculate these ratios suggest that counties have more physicians and

hospital beds per capita than they actually have. Such information is misleading in determining the

need for additional physicians and hospital beds. Given that undercounting is not random, all

counties are not affected the same way. In particular, rural counties and counties in Texas,

California, Georgia, Colorado, Mississippi and New Mexico are most likely to be affected. Also,

counties with high percentages of minorities and large poverty populations are more likely to be
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misrepresented. These results show that public policy makers and private investors who are

trying to assess the communities’ needs for additional health care providers should be wary of

unadjusted population estimates. While for most counties the difference in provider per capita

data is marginal, a significant number are disproportionately clustered in six states. In particular,

some rural counties, counties with large minority and poor populations will be mis-characterized

to their detriment. Analysts may incorrectly conclude that these areas have sufficient or excess

health care resources when they actually do not.

Introduction and Background

Researchers have measured access to medical care by tracking rates of adverse health

outcomes or the availability of health providers. The Institute of Medicine’s 1993 report, Access

to Health Care in America, recommended the use of preventable hospitalizations as an objective

measure of access to health care. In the health service research literature these conditions are

also referred to as avoidable hospitalizations and ambulatory care sensitive conditions.

Preventable hospitalizations are those that might not have occurred had the patient received

appropriate and timely outpatient care, in the case of acute health problems — as well as

effective, timely, and continuous care for certain chronic disease conditions (Millman 1993).

Examples of preventable hospitalizations are those admissions for a primary diagnosis of:

cellulitus, dehydration, kidney and urinary tract infections, pneumonia, angina, chronic

obstructive pulmonary disease, or congestive heart failure.

Rates of preventable hospitalizations can be used to evaluate the performance of communities’

primary care delivery systems and identify communities with potential problems with access to

ambulatory care. Health services researchers have established the association between

preventable hospitalizations and socioeconomic and demographic factors. Billings and

colleagues (1993 and 1996) report that residents of low income zip codes had higher rates of

preventable hospitalizations than residents of high income zip codes. They report that these

differences existed in urban areas across the nation and persisted over time, despite

expansions in Medicaid and health insurance reforms in the early 1990s. Bindman and

colleagues (1995) report that rates of preventable hospitalizations were inversely related to

community measures of access to care even after controlling for demographic, income and

health differences across populations. Weissman and colleagues (1993) find that Medicaid and

uninsured patients were more likely to be admitted to a hospital for avoidable conditions than

insured patients. Pappas and colleagues (1997) report class and race difference in rates of

preventable hospitalizations. They find that rates were higher for residents of middle and lowincome areas and were higher for African Americans than for Whites. Gaskin and Hoffman

(2000) examined race and ethnic disparities in preventable hospitalizations. They find that

Hispanic children, working-age African Americans, elderly African Americans and elderly

Hispanics were more likely to be hospitalized for an avoidable condition than comparable White

patients.

Most studies on preventable hospitalizations relied on data collected by the Census Bureau to

construct population-based rates of preventable hospitalizations. Most researchers are

interested in the relationship between rates of preventable hospitalizations and demographic and

socioeconomic factors. Systematic bias related to these demographic variables could seriously

affect the results of these analyses. Undercounts of minority populations would bias rates of

preventable hospitalizations upward in communities with large minority populations. This

undercounting can seriously affect estimates of the impact of poverty, educational attainment

and location on preventable hospitalizations. This project determines effects of using

uncorrected data on preventable hospitalization research. I compare rates of preventable
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hospitalizations using corrected and uncorrected data and examine the effects on rural versus

urban areas, high poverty versus low poverty areas, highly educated versus poorly educated

areas.

The numbers of health care providers per capita are commonly used measures of the availability

of health care providers in a geographic area. The Bureau of Health Professions uses the

numbers of primary care physicians, dentists and mental health professionals per capita to

determine whether counties or part of counties should be designated a health professional

shortage area. Similar to rate of preventable hospitalizations, systematic undercounting of

minority population will bias these ratios upward in communities with large minority populations.

This project explores whether the use of unadjusted data would affect the designation of a

county as a health professional shortage area. I also identify which counties’ supply of health

care resources would be distorted the most by using unadjusted population data to calculate

provider per capita measures.

Data and Methodology

To study the effects on rates of preventable hospitalizations, I used primarily 1996 hospital

discharge abstract data from 13 states: Arizona, California, Florida, Illinois, Massachusetts,

Missouri, New Jersey, New York, Pennsylvania, South Carolina, Virginia, Washington and

Wisconsin. The 13 states chosen for this analysis were based upon the availability of either race

or health maintenance organization (HMO) enrollment information in the discharge data. In 1996,

more than 42 percent of the nation’s population resided in these states. Hispanic Americans

were over-represented while African Americans and White Americans were slightly underrepresented in the 13 state sample. In 1996, 60 percent of the nation’s Hispanic population

resided in these ten states due to the presence of California, Florida and New York in the study.

More than 40 percent of African Americans and White Americans resided in these states. The

state inpatient discharge data contain the following information for each patient: age, sex, race,

Hispanic origin, primary and secondary diagnoses, primary and secondary procedures, primary

source of payment, ZIP code and county of residence. The race and Hispanic origin information

was not reported in the Illinois, Washington and Wisconsin data. Therefore, they were excluded

from tabulations by race and Hispanic origin.

For each county in each state, I calculated the number of discharges for avoidable conditions. In

particular, I calculated total preventable hospitalization, preventable hospitalizations for Whites,

African Americans, Hispanics, and other races (i.e., Asians, Pacific Islanders and Native

Americans). To calculate rates of preventable hospitalizations, I used the county-level adjusted

and unadjusted data for total, White, African American, Hispanic and other races. The population

data is from the 1990 Census. To make it comparable to the 1996 discharge data, I multiplied each

population figure by a growth factor equal to the change in the county’s population from 1990 to

1996 as estimated by the Census Bureau and reported in the Bureau of Health Professions’ Area

Resource File.

The other data used in the analysis were: the urban-rural designation, the 1995 poverty rate and

the percentage of persons 25 years and older who do not have a high school education. The

source of this data is the 1999 Area Resource File compiled by the Office of Research and

Planning of the Bureau of Health Care Professions. To analyze the effect of systematic

undercounting of minority populations, I divided the counties by location (urban versus rural),

poverty level (low - less than 9.6 percent versus high - greater than 16.3 percent) and

educational attainment (low - greater than 27.2 percent versus high - less than 27.2 percent). I

then compared the rates of preventable hospitalizations for the entire population and the racial

and ethnic subgroups across the groups of counties. T-tests were used to distinguish

statistically significant differences. The analysis was performed by calculating average rates of
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preventable hospitalizations, un-weighted and weighted, by county population. I report the

findings from the un-weighted analysis. However, the findings did not change when the

averages calculated weighting by county population.

To study the effects on rates of provider per capita, I used adjusted and unadjusted county level

population data from the 1990 Census. This data was linked to the numbers of general hospital

beds and primary care physicians in each county in 1990 from the Area Resource File. I defined

primary care physicians as general and family practitioners, general internists, pediatricians,

obstetricians, and gynecologists. I calculated hospital beds per capita and primary care

physicians per capita using both the adjusted and unadjusted data. I compared the differences

between the adjusted and unadjusted ratios and identified the ten percent of counties with the

largest percentage difference. The counties with the greatest change were examined by

location, percentage of the population that was minority in 1990 and 1989 poverty rate. The

findings are reported below.

Findings

Systematic undercounting of minorities in the 1990 census does result in overestimation of the

rate of preventable hospitalizations for African American and Hispanics. Table 1 reports the raw

rates of preventable hospitalizations using adjusted and unadjusted census data. (The rates are

not adjusted by age.) The percentage difference in the overall rates of preventable

hospitalizations between unadjusted and adjusted data is 1.23. However, the differences for

African Americans and Hispanics were 4.49 and 5.98 respectively. These differences between

the rates calculated using the unadjusted and adjusted data were statistically significant.

Table 1. Rates of Preventable Hospitalizations by Race and Ethnicity Comparing Unadjusted to

Adjusted Census Data.

Unadjusted Data Adjusted Data Percentage Difference Between

Unadjusted and Adjusted Rates

Total 18.083 17.863 1.23

African Americans 21.503 20.578 4.49

Hispanics 13.553 12.788 5.98

Other Races 14.439 14.318 0.84

Whites 18.749 18.618 0.70

Table 2 reports that overestimation of overall rates of preventable hospitalizations was greater in

urban counties relative to rural counties, poor counties relative to wealthy counties and poorly

educated counties relative to well-educated counties. These differences were statistically

significant. The overall urban-rural results were due to changes in the rates for other races.

However, for African Americans, Hispanics and White residents of rural counties the

undercounting resulted in a greater inflation of rates of preventable hospitalizations compared to

urban residents.

Table 2. Rates of Preventable Hospitalizations by Location, Poverty Level and Educational

Attainment, Comparing Unadjusted to Adjusted Census Data.
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Unadjusted Data Adjusted Data Percentage Difference

Between Unadjusted and

Adjusted Rates

Location Rural 19.370 19.171 1.04

Urban 14.062 13.902 1.15

Poverty

Level

High 21.162 20.824 1.62

Low 13.181 13.101 0.61

Educational

Attainment

Low 20.065 19.832 1.17

High 14.259 14.126 0.94

These differences by location, poverty level and educational attainment were mediated by the

racial and ethnic composition of the county. As shown in Table 3, the rate of preventable

hospitalization for African Americans in rural counties was 4.87 percent higher compared to

African Americans in urban areas at 3.87 percent. Similarly, African Americans in high poverty

counties and counties with low educational attainment had their rates of preventable

hospitalizations inflated by 7.42 and 5.1 percent compared to 3.84 and 3.44 percent in urban

areas. A review of table 3 shows a similar pattern of inflation of the rates of preventable

hospitalization for Hispanics. The rates for Whites were similarly inflated but by less than 1

percent in every instance.

Table 3: Percentage Difference between Unadjusted to Adjusted Rates of Preventable

Hospitalizations for African Americans, Hispanics, Whites, Comparison by Location, Poverty Rate

and Educational Attainment

African Americans Hispanics Whites

Location Rural 4.87 6.11 0.81

Urban 3.87 5.42 0.54

Poverty

Level

High 7.42 7.19 0.79

Low 3.84 4.68 0.42

Educational

Attainment

Low 5.10 6.26 0.73

High 3.44 5.52 0.66

For most counties, using the unadjusted data had a relatively small effect on provider per capita

ratios. The average impact was a 1.4 percent change for physicians per capita and a 1.1

percent change for hospital beds per capita. However, there were counties where the change

in provider-to-population ratios was substantial. Ranking the counties by the percentage change

in physician and hospital beds to population ratios shows that changes for the counties in the top

ten percentile ranged from about 2.5 to 28 percent on both measures. Table 4 reveals that rural

counties not adjacent to metropolitan areas were most likely to have the physician-to-population

ratio substantially inflated by the unadjusted population data. The effect on hospital beds per

capita was more evenly distributed by location. Rural counties not adjacent to an MSA and

counties in an MSA were more likely to have inflated hospital beds-to-population ratios. The
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counties in the top tenth percentile were located primarily in six states Texas, California,

Colorado, Georgia, Mississippi and New Mexico. Over 20 percent of these counties are in

Texas. Washington D.C., which is considered to have too many physicians and hospital beds, is

also among this top ten percent.

Table 4: Urban-Rural Distribution of Counties with the Greatest Percentage Change in Primary

Care Physicians and Hospital Beds Per Capita by Designation

Urban - Rural Designation All Counties Ten Percent of Counties

with Greatest Change

in Primary Care

Physicians Per Capita

Ten Percent Counties

with Greatest Change

in Hospital Beds Per

Capita

Metropolitan 9.62 8.44 10.42

Rural Adjacent to MSA 44.51 34.42 43.32

Rural Not Adjacent to MSA 45.87 57.14 46.26

The error in the provider to population ratios was greater for counties with high poverty rates

and high percentages of minority residents. As shown in tables 5 and 6, all of the counties with

changes in their provider-to-population ratios of greater than 10 percent were counties with high

poverty and minority populations.

Table 5: States with the Most Counties with the Greatest Percentage Change in Primary Care

Physicians and Hospital Beds Per Capita by Designation

Poverty Level Percentage Change in Primary

Care Physicians Per Capita

Percentage Change in Hospital

Beds Per Capita

2.7% to

5.3%

5.3% to

10%

greater

than

10%

2.5 % to

4.4%

4.4% to

10%

greater

than

10%

Low Poverty

(less than 11.2%)

7.22 4.17 0.0 7.61 0.0 0.0

Medium Poverty (between

11.2 and 20.4)

37.18 0.0 0.0 39.49 11.54 0.0

High Poverty

(greater than 20.4%)

55.6 95.83 100.0 52.90 88.46 100.0

Table 6: Association between the Percentage of the Non White County Residents and the

Percentage Change in Primary Care Physicians and Hospital Beds Per Capita by Designation

Percent of Non-White

Residents

Percentage Change in Primary

Care Physicians Per Capita

Percentage Change in Hospital

Beds Per Capita

2.7% to

5.3%

5.3% to

10%

greater

than

10%

2.5 % to

4.4%

4.4% to

10%

greater

than

10%

Less than 1.5% 1.81 0.00 0.0 0.36 0.00 0.00
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Between 1.5% and 19.9 35.74 12.50 0.0 36.23 3.85 0.00

Greater than 19.9% 62.45 87.50 100.0 63.41 96.15 100.00

Conclusions and Implications

The systematic inflation of access to care measures are harmful because they inflate the

preventable hospitalization rate and exaggerate per capita hospital and doctor allocation. The

upward bias of preventable hospitalization rates in African American and Hispanic communities

exaggerates the problems with the primary care delivery systems in these communities. This

inflation of the rates of preventable hospitalizations suggests that residents of these communities

have poor care seeking habits, i.e., they tend to delay seeking medical care until hospitalization is

necessary. This inaccurate portrayal of overuse of hospital services will tend to make these

communities less attractive to health plans. This would tend to be reflected in higher insurance

premiums for community rated health plans.

The rate of preventable hospitalizations is only one incidence rate used to evaluate the wellbeing of communities. Other incidence and prevalence rates such as death rates, disease rates,

health care cost and utilization rates, accident statistics and crime rates are susceptible to this

flaw in the Census data. Systematic undercounting of African Americans and Hispanics will

tend to exaggerate the risks associated with living in their communities. These exaggerated risks

tend to discourage potential employers, businesses, insurers, health care providers, investors,

and homeowners from locating and investing in these communities. Hence, the systematic

undercounting serves to undermined minority communities’ efforts to attract private enterprise

and investment, especially those in poor, poorly educated or rural areas.

Furthermore, the inflation of provider-to-population ratio misleads policy makers and private

investors about the adequacy of the health care delivery system in communities. Poor and

minority communities may appear to have sufficient or an excess of physicians and hospital

beds thus discouraging further expansion of their health care delivery systems. Community

planners should use adjusted Census data in assessing the health care needs of a community or

they may unintentionally steer resources away from poor and minority communities.

Marketing organizations that make Census zip code data available to public officials, businesses

and researchers should correct their data for systematic undercounting of minorities. Their

clients will then be able to obtain an accurate picture of challenges and problems facing minority

communities and realize the opportunities for private and public investment in minority

communities.

References

Bindman, AB, K Grumback, D Osmond, M Komaromy, K Vranizan, N Lurie, Billings, and A

Stewart. 1995. Preventable Hospitalizations and Access to Health Care. JAMA 274(4):305-11.

Billings, J, L Zeitel, J Lukomnik, TS Carey, AE Blank, and L Newman. 1993. Impact of

Socioeconomic Status on Hospital Use in New York City. Health Affairs 2:162.

Billings, J, GM Anderson, and LS Newman. 1996. Recent Findings on Preventable

Hospitalizations. Health Affairs 15(3): 239-49.

Gaskin D, C. Hoffman. 2000, Race and Ethnic Differences in Preventable Hospitalization Across
8

Ten States, Medical Care Research and Review 57(Supp. 1);85-107.

Millman, M. 1993. Access to Health Care in America. Institute of Medicine. Washington, DC:

National Academy Press.

Pappas, G, WC Hadden, LJ Kozak, and GF Fisher. 1997. American Journal of Public Health.

87(5):811-16.

Weissman, JS, C Gatsonis, and AM Epstein. 1992. Rates of Avoidable Hospitalization by

Insurance Status in Massachusetts and Maryland. JAMA 268(17):2388-94.