OBJECTIVES: Assess the determinants of nonresponse to a consumer health care survey. METHODS: The first (1997; CAHPS 1.0) and third (1999; CAHPS 2.0) Medicare managed care (MMC) CAHPS surveys collected data on 215 and 365 health plan reporting units, respectively. Data indicated which beneficiaries responded by mail, responded by phone, could not be located, and did not respond. InterStudy data described plan characteristics. chi2 tests and logistic regression models, adjusted for clustering by plan, were used to test associations of individual and plan characteristics with availability of good contact information and response given good contact information. RESULTS: Response rates in the 1997 and 1999 surveys were 75% and 80%, respectively. Older and disabled beneficiaries, women, nonwhite beneficiaries, and persons living in areas with more residents who were nonwhite, on public assistance, and less educated had lower response rates. These associations were partly explained by the distribution of bad contact information, but even among beneficiaries who could be located plan response rates varied greatly. For-profit plans are significantly more likely to have high rates of bad contact information and lower response rates. Telephone follow-up improved the sociodemographic representativeness of the sample, for both high and low response rate plans. CONCLUSION: CAHPS-MMC survey procedures, in particular telephone follow-up, have resulted in high response rates, and current case-mix strategies compensate for some of the remaining effects of differing response rates on comparisons among plans. Further efforts to explore the determinants of response rates are warranted.
OBJECTIVES: Assess the determinants of nonresponse to a consumer health care survey. METHODS: The first (1997; CAHPS 1.0) and third (1999; CAHPS 2.0) Medicare managed care (MMC) CAHPS surveys collected data on 215 and 365 health plan reporting units, respectively. Data indicated which beneficiaries responded by mail, responded by phone, could not be located, and did not respond. InterStudy data described plan characteristics. chi2 tests and logistic regression models, adjusted for clustering by plan, were used to test associations of individual and plan characteristics with availability of good contact information and response given good contact information. RESULTS: Response rates in the 1997 and 1999 surveys were 75% and 80%, respectively. Older and disabled beneficiaries, women, nonwhite beneficiaries, and persons living in areas with more residents who were nonwhite, on public assistance, and less educated had lower response rates. These associations were partly explained by the distribution of bad contact information, but even among beneficiaries who could be located plan response rates varied greatly. For-profit plans are significantly more likely to have high rates of bad contact information and lower response rates. Telephone follow-up improved the sociodemographic representativeness of the sample, for both high and low response rate plans. CONCLUSION: CAHPS-MMC survey procedures, in particular telephone follow-up, have resulted in high response rates, and current case-mix strategies compensate for some of the remaining effects of differing response rates on comparisons among plans. Further efforts to explore the determinants of response rates are warranted.
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