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. 2020 Apr 8;20(5):642–651. doi: 10.1016/j.acap.2020.03.014

Table 3.

Incident Rate Ratios for Visit Rates by Patient Sociodemographic and Geographic Characteristics, Among Matched Children Cared for by Telemedicine-Using and Telemedicine Nonusing Subspecialists

Subspecialist With
No Telemedicine Use
Subspecialist With
Telemedicine use
IRR 95% CI IRR 95% CI Interaction Term P value
Dyads, N 353,471 17,759
Child sociodemographic characteristics
Child age, y <.001
 <1 1 Ref 1 Ref
 1-5 0.54 0.54-0.55 0.51 0.47-0.54
 6-14 0.48 0.47-0.49 0.48 0.45-0.52
 15-17 0.47 0.47-0.48 0.50 0.47-0.54
Child Gender
 Female 1 Ref 1 Ref
 Male 1.01 1.00-1.01 1.01 0.99-1.04
Child Race/Ethnicity <0.001
 White non-Hispanic 1 Ref 1 Ref
 Black non-Hispanic 0.93 0.92-0.94 0.82 0.79-0.85
 Hispanic or Latino/a/x 0.96 0.95-0.97 0.90 0.86-0.93
 Other, Multiple, or Unknown 0.97 0.97-0.98 0.89 0.86-0.93
Child geographic characteristics
Child residential county <.001
 Large metropolitan 0.78 0.77-0.79 0.66 0.62-0.69
 Small metropolitan 0.86 0.85-0.87 0.89 0.86-0.93
 Large urban 0.85 0.84-0.86 0.79 0.74-0.83
 Small urban 1 Ref 1 Ref
 Rural 0.85 0.82-0.88 0.85 0.73-0.98
Child ZIP median income <.001
 0-138% FPL 0.94 0.93-0.95 1.15 1.08-1.22
 139-200% FPL 0.99 0.98-1.00 0.99 0.94-1.04
 201-300% FPL 1.02 1.01-1.03 0.91 0.87-0.96
 >301% FPL 1 Ref 1 Ref
Child distance to subspecialist <.001
 0-30 miles 1 Ref 1 Ref
 31-60 miles 0.87 0.86-0.88 0.76 0.74-0.79
 61-90 miles 0.84 0.83-0.85 0.72 0.68-0.75
 >90 miles 0.84 0.83-0.85 0.93 0.89-0.97
Child insurance characteristics
Child Medicaid eligibility category <.001
 Financial 1 Ref 1 Ref
 Medical/disability 1.18 1.17-1.19 1.01 0.97-1.04
Child Medicaid plan type .001
 Fee for service 1 Ref 1 Ref
 Managed care organization 1.11 1.10-1.11 1.22 1.18-1.27

IR indicates, incident risk ratio; CI, confidence interval; FPL, federal poverty level.

Incident risk ratios for children cared for by telemedicine-using and non-using subspecialists, determined through negative binomial regression on children matched through coarsened exact matching with child and subspecialist characteristics as independent variables, model offset for the number of months of child enrollment during 2014, and coarsened-exact matching weights with robust standard errors. In addition to listed characteristics, independent variables included subspecialist years in practice, gender, subspecialist type (medical vs surgical), and pediatric training (pediatric vs nonpediatric). In a full model, we tested the significance of all interaction terms together (P < .001) and each interaction term separately (provided in last column). Because all interaction terms together yielded a significant Wald test, final IRRs provided here were estimated through stratified negative binomial models.