Christine A March1, Lindsay Leikam2, Linda M Siminerio3, Elizabeth Miller4, Ingrid M Libman5. 1. Division of Pediatric Endocrinology and Diabetes, UPMC Children's Hospital of Pittsburgh, University of Pittsburgh School of Medicine, Pittsburgh, PA. Electronic address: Christine.eklund@chp.edu. 2. Department of Pediatrics, UPMC Children's Hospital of Pittsburgh, University of Pittsburgh School of Medicine, Pittsburgh, PA. 3. Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA. 4. Division of Adolescent and Young Adult Medicine, UPMC Children's Hospital of Pittsburgh, University of Pittsburgh School of Medicine, Pittsburgh, PA. 5. Division of Pediatric Endocrinology and Diabetes, UPMC Children's Hospital of Pittsburgh, University of Pittsburgh School of Medicine, Pittsburgh, PA.
Abstract
OBJECTIVE: To explore the health characteristics of youth with diabetes in cyber school compared with peers with diabetes in traditional brick-and-mortar schools. STUDY DESIGN: This was a single-center cross-sectional study of youth with type 1 or type 2 diabetes in K-12 education during academic year 2017-2018. Youth enrolled in cyber school were matched with traditional school peers by age, sex, race, diagnosis, and diabetes duration. Comparisons included insurance status, hemoglobin A1c, treatment, coexisting conditions, screening, and healthcare use. RESULTS: Of 1694 participants, 5% (n = 87) were enrolled in cyber school. Youth enrolled in cyber school were predominantly white (89%), female (60%), adolescents (median 15.2 years) with type 1 diabetes (91%). Youth with type 2 diabetes were excluded from analyses owing to the small sample (n = 7). Public insurance was more common among youth enrolled in cyber school (P = .005). Youth in cyber school had higher mean hemoglobin A1c, 9.1 ± 1.8% (76 ± 20 mmol/mol) vs 8.3 ± 1.2% (67 ± 13 mmol/mol) (P = .003), lower insulin pump use (OR, 0.36; 95% CI, 0.18-0.73), and more mental health conditions (OR, 4.48; 95% CI, 1.94-10.35) compared with peers in traditional schools. Youth in cyber school were less likely to have recommended vision (OR, 0.34; 95% CI, 0.15-0.75) and dental (OR, 0.33; 95% CI, 0.15-0.75) evaluations. The relationship between hemoglobin A1c and cyber school persisted after adjusting for insurance status, pump use, and mental health conditions (P = .02). Similar trends were observed for participants with type 2 diabetes. CONCLUSIONS: Youth with diabetes in cyber school may be a high-risk population. Understanding the potential impact of cyber school-related factors on health may encourage additional provider/system/school supports for these patients.
OBJECTIVE: To explore the health characteristics of youth with diabetes in cyber school compared with peers with diabetes in traditional brick-and-mortar schools. STUDY DESIGN: This was a single-center cross-sectional study of youth with type 1 or type 2 diabetes in K-12 education during academic year 2017-2018. Youth enrolled in cyber school were matched with traditional school peers by age, sex, race, diagnosis, and diabetes duration. Comparisons included insurance status, hemoglobin A1c, treatment, coexisting conditions, screening, and healthcare use. RESULTS: Of 1694 participants, 5% (n = 87) were enrolled in cyber school. Youth enrolled in cyber school were predominantly white (89%), female (60%), adolescents (median 15.2 years) with type 1 diabetes (91%). Youth with type 2 diabetes were excluded from analyses owing to the small sample (n = 7). Public insurance was more common among youth enrolled in cyber school (P = .005). Youth in cyber school had higher mean hemoglobin A1c, 9.1 ± 1.8% (76 ± 20 mmol/mol) vs 8.3 ± 1.2% (67 ± 13 mmol/mol) (P = .003), lower insulin pump use (OR, 0.36; 95% CI, 0.18-0.73), and more mental health conditions (OR, 4.48; 95% CI, 1.94-10.35) compared with peers in traditional schools. Youth in cyber school were less likely to have recommended vision (OR, 0.34; 95% CI, 0.15-0.75) and dental (OR, 0.33; 95% CI, 0.15-0.75) evaluations. The relationship between hemoglobin A1c and cyber school persisted after adjusting for insurance status, pump use, and mental health conditions (P = .02). Similar trends were observed for participants with type 2 diabetes. CONCLUSIONS: Youth with diabetes in cyber school may be a high-risk population. Understanding the potential impact of cyber school-related factors on health may encourage additional provider/system/school supports for these patients.
Authors: L Fisher; D M Hessler; W H Polonsky; U Masharani; A L Peters; I Blumer; L A Strycker Journal: Diabet Med Date: 2016-01-05 Impact factor: 4.359
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