We included participants ≥65 years old whose REGARDS study data were linked to Medicare claims at baseline. We excluded participants who did not have fee-for-service Medicare, did not have continuous coverage for 12 months following baseline, or died within one year after the baseline visit. We excluded participants who qualified for Medicare on the basis of end-stage renal disease, as utilization patterns for these beneficiaries are substantively different from those of other beneficiaries [32]. We also excluded those who had ≤3 ambulatory visits in the first year of observation, as calculating fragmentation scores with fewer than 4 visits can yield unstable estimates [12]. Finally, we excluded (and then later re-included in a sensitivity analysis) participants who had fragmentation scores that were equal to the ends of the scale (equal to 0.00 or 1.00), as these scores are relatively uncommon and represent ambulatory care patterns that may violate underlying trends [7]. For example, a beneficiary can have a score equal to 1.00 if he or she has 4 visits with 4 different providers, but this is not conceptually “more fragmented” than a beneficiary who has 9 visits with 6 different providers and a fragmentation score of 0.92 [7].

We used descriptive statistics to characterize the final study sample. We compared those included in the study to those who had been excluded on the basis of having ≤3 ambulatory visits, using t-tests, chi-squared tests, and Wilcoxon rank sum tests.

We divided the study sample into quintiles based on their fragmentation scores. We determined the median number of visits, providers, proportions of visits with the most frequently seen provider, and fragmentation scores within each quintile. We calculated p-values for trend across quintiles for each of these measures of ambulatory utilization.

To explore the unadjusted associations of race, income, and education with fragmentation scores, we calculated the percentage of individuals with a given characteristic (black race; annual household income <$35,000; and high school education or less) within each fragmentation quintile. We then calculated p-values for trend across quintiles. To facilitate interpretation, we generated descriptive statistics of ambulatory utilization (visits, providers, percentage of visits with the most frequently seen provider, and fragmentation score) stratified by race, income, and education. We further stratified visits and providers by primary care vs. specialty care. We compared ambulatory utilization patterns across subgroups by race, income, and education, using non-parametric Wilcoxon two-sample tests.

We used Tobit models to determine whether race, income, and education were associated with fragmentation scores. We used Tobit models instead of linear models, because the possible values for fragmentation were bounded. Interpretation of Tobit models is the same as for linear models; coefficients represent the absolute amount of change in the fragmentation score. Because the fragmentation score is on a scale from 0.01 to 0.99, changes in the absolute fragmentation score can be multiplied by 100 to yield an equivalent percent change in the fragmentation score. Unadjusted models considered race, income, and education separately. Model 1 adjusted for race, income and education in the same model. Model 2 added adjustment for age, sex, marital status, geographic region, and rural geography. Model 3 included all variables in Model 2 plus medical conditions and medications. Model 4 included all variables in Model 3 plus health behaviors, psychosocial variables, physiologic variables, and self-rated health.

Analyses were conducted with SAS (version 9.4, Cary, NC). P-values < 0.05 were considered statistically significant.

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