OBJECTIVE: Continuous glucose monitoring (CGM) is increasingly used in type 1 diabetes management; however, funding models vary. This study determined the uptake rate and glycemic outcomes following a change in national health policy to introduce universal subsidized CGM funding for people with type 1 diabetes aged <21 years. RESEARCH DESIGN AND METHODS: Longitudinal data from 12 months before the subsidy until 24 months after were analyzed. Measures and outcomes included age, diabetes duration, HbA1c, episodes of diabetic ketoacidosis and severe hypoglycemia, insulin regimen, CGM uptake, and percentage CGM use. Two data sources were used: the Australasian Diabetes Database Network (ADDN) registry (a prospective diabetes database) and the National Diabetes Service Scheme (NDSS) registry that includes almost all individuals with type 1 diabetes nationally. RESULTS: CGM uptake increased from 5% presubsidy to 79% after 2 years. After CGM introduction, the odds ratio (OR) of achieving the HbA1c target of <7.0% improved at 12 months (OR 2.5, P < 0.001) and was maintained at 24 months (OR 2.3, P < 0.001). The OR for suboptimal glycemic control (HbA1c ≥9.0%) decreased to 0.34 (P < 0.001) at 24 months. Of CGM users, 65% used CGM >75% of time, and had a lower HbA1c at 24 months compared with those with usage <25% (7.8 ± 1.3% vs. 8.6 ± 1.8%, respectively, P < 0.001). Diabetic ketoacidosis was also reduced in this group (incidence rate ratio 0.49, 95% CI 0.33-0.74, P < 0.001). CONCLUSIONS: Following the national subsidy, CGM use was high and associated with sustained improvement in glycemic control. This information will inform economic analyses and future policy and serve as a model of evaluation diabetes technologies.
OBJECTIVE: Continuous glucose monitoring (CGM) is increasingly used in type 1 diabetes management; however, funding models vary. This study determined the uptake rate and glycemic outcomes following a change in national health policy to introduce universal subsidized CGM funding for people with type 1 diabetes aged <21 years. RESEARCH DESIGN AND METHODS: Longitudinal data from 12 months before the subsidy until 24 months after were analyzed. Measures and outcomes included age, diabetes duration, HbA1c, episodes of diabetic ketoacidosis and severe hypoglycemia, insulin regimen, CGM uptake, and percentage CGM use. Two data sources were used: the Australasian Diabetes Database Network (ADDN) registry (a prospective diabetes database) and the National Diabetes Service Scheme (NDSS) registry that includes almost all individuals with type 1 diabetes nationally. RESULTS: CGM uptake increased from 5% presubsidy to 79% after 2 years. After CGM introduction, the odds ratio (OR) of achieving the HbA1c target of <7.0% improved at 12 months (OR 2.5, P < 0.001) and was maintained at 24 months (OR 2.3, P < 0.001). The OR for suboptimal glycemic control (HbA1c ≥9.0%) decreased to 0.34 (P < 0.001) at 24 months. Of CGM users, 65% used CGM >75% of time, and had a lower HbA1c at 24 months compared with those with usage <25% (7.8 ± 1.3% vs. 8.6 ± 1.8%, respectively, P < 0.001). Diabetic ketoacidosis was also reduced in this group (incidence rate ratio 0.49, 95% CI 0.33-0.74, P < 0.001). CONCLUSIONS: Following the national subsidy, CGM use was high and associated with sustained improvement in glycemic control. This information will inform economic analyses and future policy and serve as a model of evaluation diabetes technologies.
Authors: Roy W Beck; Tonya D Riddlesworth; Katrina J Ruedy; Craig Kollman; Andrew J Ahmann; Richard M Bergenstal; Anuj Bhargava; Bruce W Bode; Stacie Haller; Davida F Kruger; Janet B McGill; William Polonsky; David Price; Elena Toschi Journal: Lancet Diabetes Endocrinol Date: 2017-07-12 Impact factor: 32.069
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Authors: Marcus Lind; William Polonsky; Irl B Hirsch; Tim Heise; Jan Bolinder; Sofia Dahlqvist; Erik Schwarz; Arndís Finna Ólafsdóttir; Anders Frid; Hans Wedel; Elsa Ahlén; Thomas Nyström; Jarl Hellman Journal: JAMA Date: 2017-01-24 Impact factor: 56.272
Authors: Marie-Anne Burckhardt; Mary B Abraham; Jennifer Mountain; Daina Coenen; Jaimee Paniora; Helen Clapin; Timothy W Jones; Elizabeth A Davis Journal: Diabetes Technol Ther Date: 2019-08-22 Impact factor: 6.118
Authors: Helen Phelan; Helen Clapin; Loren Bruns; Fergus J Cameron; Andrew M Cotterill; Jennifer J Couper; Elizabeth A Davis; Kim C Donaghue; Craig A Jefferies; Bruce R King; Richard O Sinnott; Elaine B Tham; Jerry K Wales; Timothy W Jones; Maria E Craig Journal: Med J Aust Date: 2017-02-20 Impact factor: 7.738
Authors: Lori M Laffel; Lauren G Kanapka; Roy W Beck; Katherine Bergamo; Mark A Clements; Amy Criego; Daniel J DeSalvo; Robin Goland; Korey Hood; David Liljenquist; Laurel H Messer; Roshanak Monzavi; Thomas J Mouse; Priya Prahalad; Jennifer Sherr; Jill H Simmons; R Paul Wadwa; Ruth S Weinstock; Steven M Willi; Kellee M Miller Journal: JAMA Date: 2020-06-16 Impact factor: 56.272
Authors: Arndís F Ólafsdóttir; William Polonsky; Jan Bolinder; Irl B Hirsch; Sofia Dahlqvist; Hans Wedel; Thomas Nyström; Magnus Wijkman; Erik Schwarcz; Jarl Hellman; Tim Heise; Marcus Lind Journal: Diabetes Technol Ther Date: 2018-04-02 Impact factor: 6.118