Sign up for a new account.
And get access to
The latest T1D content
Research that matters
Our daily questions
Sign up by entering your info below.
Reset Your Password
Don't worry.
We will email you instructions to reset your
password.
Roberts, A, Corathers, S, Rapaport, R, Rompicherla, S, Majidi, S, Rioles, N, Ebekozien, O, Malik, F
This study used data from the T1D Exchange Quality Improvement Collaborative to compare depression rates in youth with type 1 diabetes before and during the coronavirus disease 2019 (COVID-19) pandemic and identify characteristics of individuals with moderate-to-severe depressive symptoms. Rates of moderate-to-severe depressive symptoms remained stable before and during the pandemic, at 9.6–10.7%. During the pandemic, youth who screened positive for depression were more likely to be female and on public insurance, to have a higher A1C, and to have a history of diabetic ketoacidosis or severe hypoglycemia. They were less likely to identify as non-Hispanic White and more likely to identify as Hispanic.
Click here to view the article!
If you are a member of the T1DX-QI, you can view the full pdf in the portal.
Related Stories
1 Comment
Depression Rates in Youth With Type 1 Diabetes During the COVID-19 Pandemic: Data From the T1D Exchange Quality Improvement Collaborative Cancel reply
You must be logged in to post a comment.

The rise in depression rates among youth with Type 1 diabetes during the COVID-19 pandemic is deeply concerning and highlights the need for comprehensive care that addresses both physical and mental health. Managing a chronic condition like T1D is challenging on its own, and the added stress of a global pandemic only compounds these difficulties. This underscores the importance of accessible tools and resources that support patients in managing their condition effectively.
Similarly, in the tech world, optimizing complex systems requires the right tools to identify and address performance issues. For those building or upgrading PCs, using a reliable CPU and GPU checker can help detect hardware bottlenecks that may be limiting system performance, ensuring smoother and more efficient operation.