Cost and utilization of healthcare services for persons with diabetes.

AIMS: Describe and compare healthcare costs and utilization for insured persons with type 1 diabetes (T1D), type 2 diabetes (T2D), and without diabetes in the United States. METHODS: Using a nationally representative healthcare claims database, we identified matched persons with T1D, T2D, and without diabetes using a propensity score quasi-randomization technique. In each year between 2009 and 2018, we report costs (total and out-of-pocket) and utilization for all healthcare services and those specific to medications, diabetes-related supplies, visits to providers, hospitalizations, and emergency department visits. RESULTS: In 2018, we found out-of-pocket costs and total costs were highest for persons with T1D (out-of-pocket: $2,037.2, total: $25,652.0), followed by T2D (out-of-pocket: $1,543.3, total: $22,408.1), and without diabetes (out-of-pocket: $1,122.7, total: $14,220.6). From 2009 to 2018, out-of-pocket costs were increasing for persons with T1D(+6.5 %) but decreasing for T2D (-7.5 %) and without diabetes (-2.3 %). Medication costs made up the largest proportion of out-of-pocket costs regardless of diabetes status (T1D: 51.4 %, T2D: 55.4 %,without diabetes: 51.1 %). CONCLUSIONS: Given the substantial out-of-pocket costs for people with diabetes, especially for those with T1D, providers should screen all persons with diabetes for financial toxicity (i.e., wide-ranging problems stemming from healthcare costs). In addition, policies that aim to lower out-of-pocket costs of cost-effective diabetes related healthcare are needed with a particular focus on medications.

Development of clinical prediction models for renal and cardiovascular outcomes and mortality in patients with type 2 diabetes and chronic kidney disease using time-varying predictors.

AIMS: To develop a set of prediction models for end-stage kidney disease (ESKD), cardiovascular outcomes, and mortality in patients with type 2 diabetes (T2D) and chronic kidney disease (CKD) using commonly measured clinical variables. METHODS: We studied 1432 participants with T2D and CKD enrolled in the Chronic Renal Insufficiency Cohort, followed for a median period of 7 years. We used Cox proportional-hazards models to model the six outcomes (ESKD, stroke, myocardial infarction (MI), congestive heart failure (CHF), death before ESKD, and all-cause mortality). We internally evaluated these models using concordance and calibration measures. RESULTS: The newly developed six prediction models included 15 predictors: age at diabetes diagnosis, sex, blood pressure, body mass index, hemoglobin A1c, high density lipoprotein cholesterol, urine protein-to-creatinine ratio, estimated glomerular filtration rate, smoking status, and history of stroke, MI, CHF, ESKD, and amputation. The resulting models demonstrated good/strong discrimination (cross-validation C-index range: 0.70 to 0.90) and calibration. CONCLUSIONS: This study provided an internally validated and useful tool for predicting individual adverse outcomes and mortality in patients with T2D and CKD. These models may inform optimal use of targeted health interventions.

Neuropsychological Outcomes in Individuals With Type 1 and Type 2 Diabetes.

Objective: To determine the prevalence of neuropsychological outcomes in individuals with type 1 diabetes compared to individuals with type 2 diabetes or without diabetes, and to evaluate the association of diabetes status and microvascular/macrovascular complications with neuropsychological outcomes. Patients and Methods: We used a nationally representative healthcare claims database of privately insured individuals (1/1/2001-12/31/2018) to identify individuals with type 1 diabetes. Propensity score matching was used as a quasi-randomization technique to match type 1 diabetes individuals to type 2 diabetes individuals and controls. Diabetes status, microvascular/macrovascular complications (retinopathy, neuropathy, nephropathy, stroke, myocardial infarction, peripheral vascular disease, amputations), and neuropsychological outcomes (mental health, cognitive, chronic pain, addiction, sleep disorders) were defined using ICD-9/10 codes. Logistic regression determined associations between diabetes status, microvascular/macrovascular complications, and neuropsychological outcomes. Results: We identified 184,765 type 1 diabetes individuals matched to 524,602 type 2 diabetes individuals and 522,768 controls. With the exception of cognitive disorders, type 2 diabetes individuals had the highest prevalence of neuropsychological outcomes, followed by type 1 diabetes, and controls. After adjusting for the presence of microvascular/macrovascular complications, type 1 diabetes was not significantly associated with a higher risk of neuropsychological outcomes; however, type 2 diabetes remained associated with mental health, cognitive, and sleep disorders. The presence of microvascular/macrovascular complications was independently associated with each neuropsychological outcome regardless of diabetes status. Conclusion: Microvascular/macrovascular complications are associated with a high risk of neuropsychological outcomes regardless of diabetes status. Therefore, preventing microvascular and macrovascular complications will likely help reduce the likelihood of neuropsychological outcomes either as the result of similar pathophysiologic processes or by preventing the direct and indirect consequences of these complications. For individuals with type 2 diabetes, risk factors beyond complications (such as obesity) likely contribute to neuropsychological outcomes.

The association of psychedelic use and opioid use disorders among illicit users in the United States.

BACKGROUND: Preliminary studies show psychedelic compounds administered with psychotherapy are potentially effective and durable substance misuse interventions. However, little is known about the association between psychedelic use and substance misuse in the general population. This study investigated the association between psychedelic use and past year opioid use disorders within illicit opioid users. METHODS: While controlling for socio-demographic covariates and the use of other substances, the relationship between classic psychedelic use and past year opioid use disorders was analyzed within 44,000 illicit opioid users who completed the National Survey on Drug Use and Health from 2008 to 2013. RESULTS: Among respondents with a history of illicit opioid use, psychedelic drug use is associated with 27% reduced risk of past year opioid dependence (weighted risk ratio = 0.73 (0.60-0.89) p = 0.002) and 40% reduced risk of past year opioid abuse (weighted risk ratio = 0.60 (0.41-0.86) p = 0.006). Other than marijuana use, which was associated with 55% reduced risk of past year opioid abuse (weighted risk ratio = 0.45 (0.30-0.66) p < 0.001), no other illicit drug was associated with reduced risk of past year opioid dependence or abuse. CONCLUSION: Experience with psychedelic drugs is associated with decreased risk of opioid abuse and dependence. Conversely, other illicit drug use history is largely associated with increased risk of opioid abuse and dependence. These findings suggest that psychedelics are associated with positive psychological characteristics and are consistent with prior reports suggesting efficacy in treatment of substance use disorders.