Measuring the need for medical care in an ethnically diverse population

Abstract

Objective: To examine measures of need for health care and their relationship to utilization of health services in different racial and ethnic groups in California.

Data source: Telephone interviews obtained by random-digit dialing and conducted between April 1993 and July 1993 in California, with 7,264 adults (ages 18-64): 601 African Americans, 246 Asians, 917 Latinos interviewed in English; 1,045 Latinos interviewed in Spanish; and 4,437 non-Latino whites.

Study design: A cross-sectional survey was conducted from a stratified, probability telephone sample.

Data collection: Interviews collected self-reported indicators of need for health care: self-rated health, activity limitation, major chronic conditions, need for ongoing treatment, bed days, and prescription medication. The outcome was self-reported number of physician visits in the previous three months.

Principal findings: Compared to whites, one or more of the other ethnic groups varied significantly (p < .05) on each of the six need-for-care measures after adjustment for health insurance, age, sex, and income. Latinos interviewed in Spanish reported lower percentages and means on five of the need measures but the highest percentage with fair or poor health (32 percent versus 7 percent in whites). Models regressing each need measure on the number of outpatient visits found significant interactions of ethnic group with need compared to whites. After adjustment for insurance and demographics, the estimated mean number of visits in those with the indicator of need was consistently lower in Latinos interviewed in Spanish, but the differences among the other ethnic groups varied depending on the measure used.

Conclusion: No single valid estimate of the relationship between need for health care and outpatient visits was found for any of the six indicators across ethnic groups. Applying need adjustment to the use of health care services without regard for ethnic variability may lead to biased conclusions about utilization.