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  • 标题: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
  • 本地全文:下载
  • 作者:Steven Shea ; Dhruva Kothari ; Jeanne A. Teresi
  • 期刊名称:American journal of public health
  • 印刷版ISSN:0090-0036
  • 出版年度:2013
  • 卷号:103
  • 期号:10
  • 页码:1888-1894
  • DOI:10.2105/AJPH.2012.300909
  • 语种:English
  • 出版社:American Public Health Association
  • 摘要: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. We recently conducted a randomized trial, the Informatics for Diabetes Education and Telemedicine (IDEATel) trial, comparing telemedicine-based nurse case management with usual care for Medicare beneficiaries with diabetes living in federally designated medically underserved areas of New York State. 1,2 We found improved levels of the 3 prespecified trial outcomes—namely, hemoglobin A1c (HbA1c), systolic blood pressure, and low-density lipoprotein (LDL) cholesterol—in the intervention group compared with the usual-care group at 1- and 5-year follow-up. 3,4 Targeting underserved patients was a key design feature in the IDEATel trial. Lack of access to care for chronic conditions in general and for diabetes specifically may be an important contributing factor in shortfalls in meeting treatment guideline–defined management goals. 5–7 Thus, an eligibility requirement for randomization in the IDEATel study was residing in a federally defined medically underserved area, and the individual-level socioeconomic status (SES) of the enrolled study participants therefore was generally low. Nonetheless, SES had substantial variability among the randomly assigned participants. Theoretical and empirical studies of the adoption of innovations indicate a general pattern such that earlier adopters tend to have higher income and to be better educated than later adopters. 8 In the context of public health, this phenomenon has the potential to increase social disparities. Other theoretical models also identify socioeconomic factors as important determinants of health services use. 9 Thus, although the IDEATel intervention improved outcomes compared with usual care overall in the randomly assigned groups, the intervention potentially could have improved clinical outcomes to a greater degree in the more socioeconomically advantaged participants than in the less advantaged, thereby widening disparities while improving overall outcome. Few if any randomized trials have analyzed the social effects of complex interventions for chronic disease management. We therefore tested the hypothesis that the IDEATel intervention had differential effects by SES on the primary trial outcomes, with the null hypothesis being no difference.
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