Social impact analysis of the effects of a telemedicine intervention to improve diabetes outcomes in an ethnically diverse, medically underserved population: findings from the IDEATel Study

Abstract

Objectives: We examined the social impact of the telemedicine intervention effects in lower- and higher-socioeconomic status (SES) participants in the Informatics for Diabetes Education and Telemedicine (IDEATel) study.

Methods: We conducted a randomized controlled trial comparing telemedicine case management with usual care, with blinded outcome evaluation, in 1665 Medicare recipients with diabetes, aged 55 years or older, residing in federally designated medically underserved areas of New York State. The primary trial endpoints were hemoglobin A1c (HbA1c), low-density lipoprotein cholesterol, and systolic blood pressure levels.

Results: HbA1c was higher in lower-income participants at the baseline examination. However, we found no evidence that the intervention increased disparities. A significant moderator effect was seen for HbA1c (P = .004) and systolic blood pressure (P = .023), with the lowest-income group showing greater intervention effects.

Conclusions: Lower-SES participants in the IDEATel study benefited at least as much as higher-SES participants from telemedicine nurse case management for diabetes. Tailoring the intensity of the intervention based on clinical need may have led to greater improvements among those not at goal for diabetes control, a group that also had lower income, thereby avoiding the potential for an innovative intervention to widen socioeconomic disparities.

Trial registration: ClinicalTrials.gov NCT00271739.

Figures

FIGURE 1—

Treatment effect and change over time in (a) mean hemoglobin A1c, (b) mean low-density lipoprotein, and (c) systolic blood pressure: Informatics for Diabetes Education and Telemedicine (IDEATel) Study, NY, 2002–2007. Note. Adjusted means were based on a nonlinear model for HbA1c and LDL and a linear model for SBP, and the best fitting covariance structure was used for each. Income group (IG) 3 (>$20 000) was the reference group for income (IG1 < $10 000; IG2 = $10 000–$20 000). Time was measured in months from baseline, but the figure shows annual points. To enhance readability, the plot symbols and error bars for the telemedicine group have been offset. Error bars represent 95% confidence intervals. For more information, see the Appendix (available as a supplement to the online version of this article at http://www.ajph.org). aContrast estimates for usual care vs telemedicine IG1 quadratic term, P = .003; exponential term, P = .001; IG2 quadratic term, P = .884; exponential term, P = .976; IG3 quadratic term, P = .156; exponential term, P = .523. bContrast estimates for usual care vs telemedicine IG1 exponential term. P = .035; IG2 exponential term, P = .369; IG3 exponential term, P = .155. cContrast estimates for usual care vs telemedicine term IG1, P = .019; IG2, P = .543; IG3, P = .686.