Switching Doctors: Predictors of Voluntary Disenrollment from a Primary Physician’s Practice

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Original Research

Switching Doctors: Predictors of Voluntary Disenrollment from a Primary Physician’s Practice

J Fam Pract. 2001 February;50(2):130-135

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Next, we modeled voluntary disenrollment as a function of the 4 relationship-quality scales together and tested for the equality of their effects (odd ratio [OR]) using a chi-square test. We repeated this using the 4 measures of structural features of care. Finally, using factor analysis methods (principal factor), we explored the potential for defining a single factor denoting relationship quality and a single factor denoting structural features of care. The 4 scales denoting structural features of care failed to generate an acceptable factor (range of factor loadings=0.20 [relationship duration] to 0.67 [access to care]), so this factor was dropped. The relationship-quality factor was retained (range of factor loadings=0.84 [knowledge of patient] to 0.92 [communication]) and tested in multiple logistic regression along with each of the 4 structure-of-care measures. A chi-square test was used to test the equivalence of the effects (OR) associated with the relationship quality factor and each of the 4 structure-of-care scales.

All regression models controlled for patients’ baseline sociodemographic profile (age, sex, race, years of education, household income), baseline health status (physical functioning, mental functioning, number of primary care sensitive conditions [PCSC], and number of primary care insensitive conditions [PCIC]), and baseline utilization (number of ambulatory visits in the previous 6 months). Physical and mental functioning were measured with data from the Medical Outcomes Study Short Form-12 (SF-12) Health Survey, which was included in the patient questionnaire. The numbers of primary care sensitive and insensitive conditions were classified using patients’ baseline reports about 20 chronic medical conditions with high prevalence among adults in the United States. The classification of PCSC and PCIC was defined by 9 generalist physicians, blind to the study objectives, who were asked to identify those conditions for which good primary care management could substantially affect outcomes (PCSC) and those for which it could not (PCIC). PCSC included hypertension, recent myocardial infarction, congestive heart failure, diabetes, angina, migraines, seasonal allergies, asthma, ulcers, arthritis, cancer, back pain, weight problem, and depression. PCIC included blindness, deafness, liver disease, insomnia, nonseasonal allergies (eg, dust, food, pets), and limb paralysis or amputation.

We assessed the goodness-of-fit of the final models using the Hosmer and Lemeshow method. For each scale, the P on the chi-square test statistic was greater than .05, indicating that the model fit the data well.

Results

Slightly more than one fourth of the patients in the longitudinal study panel changed physicians during the 3-year follow-up period (n=899), while approximately three fourths remained with their baseline physician throughout the study (n=2383). Of those who changed physicians, most changed voluntarily (n=669), but some changed involuntarily (n=230) because the physician had moved, retired, died, or the patient had moved a substantial distance. Table 1 shows the unadjusted sociodemographic, health, and utilization characteristics of the analytic sample, comparing those who voluntarily changed physicians with those who remained with their baseline physician throughout our study. Voluntary disenrollees were younger and more likely to be women and nonwhite than those who stayed with their baseline physician (P <.01). There were no differences in the baseline health status or outpatient utilization of the 2 groups.

Table 2 presents the results of the regression analyses examining the 8 PCAS scales as individual predictors of voluntary disenrollment (column 1) and the results of a multivariable model, including the composite relationship-quality factor (RQ) and the 4 structure-of-care scales as predictors of voluntary disenrollment (column 2, columns 3-7). When all scales were modeled independently (column 1), each was a significant predictor of voluntary disenrollment (P <.001), with somewhat larger effects associated with the relationship quality scales (OR=1.49-1.56) than the structure-of-care scales (OR=1.29-1.44). Pairwise tests of the ORs associated with each of the 4 relationship quality scales indicated that they were statistically equivalent in their ability to predict voluntary disenrollment. When the 4 indicators of relationship quality were included together in a multiple regression model, a chi-square test of their effects (OR) revealed the 4 to be statistically equivalent predictors of voluntary disenrollment. Similarly, in a model including the 4 structure-of-care scales, chi-square testing showed these 4 variables to have statistically equivalent effects. With the exception of sex, patient characteristics (sociodemographics, health, utilization) did not significantly predict voluntary disenrollment in any of these models. The gender effect had marginal significance in most cases (.05

Table 2 (column 2) shows the results of modeling voluntary disenrollment as a function of both relationship quality and structure-of-care together. In that multivariable model, the composite relationship quality factor (RQ) emerged as the leading predictor of voluntary disenrollment (OR=1.59; P <.001). This OR signifies that a standard deviation (SD) decline in relationship quality was associated with a 59% increase in the odds of voluntary disenrollment. The results indicate that after accounting for patients’ baseline characteristics (sociodemographic, health, and utilization) and the 4 structural features of care, patients with relationship quality scores in the 5th percentile in 1996 were 3 times more likely to voluntarily disenroll from their physician’s practice than those with 95th percentile relationship quality scores (37.8% vs 12.2%). The 2 measures of continuity also significantly predicted disenrollment in the multivariable model (visit-based continuity: OR=1.14, P=.03; relationship duration: OR=1.16, P=.01). Access to care predicted disenrollment with marginal significance (OR=1.14; P=.08), and integration did not significantly predict disenrollment (P=.59) in this model. None of the patient characteristics (sociodemograhics, health, utilization) significantly predicted disenrollment in the presence of these 5 quality-of-care measures.