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- Liu, Shengxin, et al.
(author)
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Psychotropic Medication Use in Children and Adolescents With Type 1 Diabetes
- 2023
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In: JAMA Network Open. - : American Medical Association (AMA). - 2574-3805. ; 6:10
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Journal article (peer-reviewed)abstract
- IMPORTANCE: Children and adolescents with type 1 diabetes (T1D) face elevated risks of psychiatric disorders. Despite their nonnegligible adverse effects, psychotropic medications are a common cost-effective approach to alleviating psychiatric symptoms, but evidence regarding their dispensation to children and adolescents with T1D remains lacking. OBJECTIVE: To examine the trends and patterns of psychotropic medication dispensation among children and adolescents with T1D in Sweden between 2006 and 2019.DESIGN, SETTING, AND PARTICIPANTS: This cohort study used data from multiple Swedish registers. The main study cohort included children and adolescents residing in Sweden from 2006 to 2019 and was followed up until the earliest of December 31, 2019, 18th birthday, emigration, or death. Data analyses were conducted from November 1, 2022, to April 30, 2023.EXPOSURES: Type 1 diabetes.MAIN OUTCOMES AND MEASURES: The primary outcomes were trends and patterns of psychotropic medication dispensation (including antipsychotics, antidepressants, anxiolytics, hypnotics, mood stabilizers, and medications for attention-deficit/hyperactivity disorder [ADHD]), psychotropic medication initiation, and history of neurodevelopmental and psychiatric diagnosis. Cumulative incidence curves and Cox proportional hazard models were used to estimate the aggregated incidence and hazard ratios of medication initiation after diabetes onset. RESULTS: Of 3 723 745 children and adolescents (1 896 199 boys [50.9%]), 13 200 (0.4%; 7242 boys [54.9%]) had T1D (median [IQR] age at diagnosis, 11.1 [7.6-14.7] years). Between 2006 and 2019, psychotropic medication dispensation increased from 0.85% (95% CI, 0.65%-1.10%) to 3.84% (3.11%-4.69%) among children and from 2.72% (95% CI, 2.15%-3.39%) to 13.54% (95% CI, 12.88%-14.23%) among adolescents with T1D, consistently higher than their peers without T1D. The most commonly dispensed medications included hypnotics, ADHD medications, anxiolytics, and selective serotonin reuptake inhibitors, and all exhibited increasing trends. For those with T1D, psychiatric care was the primary prescription source, and up to 50.1% of treatments lasted more than 12 months. In addition, children and adolescents with T1D showed higher cumulative incidence and hazard ratios of medication initiation after diabetes onset than their same-age and same-sex counterparts.CONCLUSIONS AND RELEVANCE: This cohort study found an increasing trend in psychotropic medication dispensation among children and adolescents with T1D from 2006 to 2019, persistently higher than those without T1D. These findings call for further in-depth investigations into the benefits and risks of psychotropic medications within this population and highlight the importance of integrating pediatric diabetes care and mental health care for early detection of psychological needs and careful monitoring of medication use.
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| 2. |
- Teixeira, Pedro F., et al.
(author)
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Assisting the implementation of screening for type 1 diabetes by using artificial intelligence on publicly available data
- 2024
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In: DIABETOLOGIA. - : SPRINGER. - 0012-186X .- 1432-0428.
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Journal article (peer-reviewed)abstract
- The type 1 diabetes community is coalescing around the benefits and advantages of early screening for disease risk. To be accepted by healthcare providers, regulatory authorities and payers, screening programmes need to show that the testing variables allow accurate risk prediction and that individualised risk-informed monitoring plans are established, as well as operational feasibility, cost-effectiveness and acceptance at population level. Artificial intelligence (AI) has the potential to contribute to solving these issues, starting with the identification and stratification of at-risk individuals. ASSET (AI for Sustainable Prevention of Autoimmunity in the Society; www.asset.healthcare) is a public/private consortium that was established to contribute to research around screening for type 1 diabetes and particularly to how AI can drive the implementation of a precision medicine approach to disease prevention. ASSET will additionally focus on issues pertaining to operational implementation of screening. The authors of this article, researchers and clinicians active in the field of type 1 diabetes, met in an open forum to independently debate key issues around screening for type 1 diabetes and to advise ASSET. The potential use of AI in the analysis of longitudinal data from observational cohort studies to inform the design of improved, more individualised screening programmes was also discussed. A key issue was whether AI would allow the research community and industry to capitalise on large publicly available data repositories to design screening programmes that allow the early detection of individuals at high risk and enable clinical evaluation of preventive therapies. Overall, AI has the potential to revolutionise type 1 diabetes screening, in particular to help identify individuals who are at increased risk of disease and aid in the design of appropriate follow-up plans. We hope that this initiative will stimulate further research on this very timely topic.
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