Melanoma, one of the serious forms of skin cancer, is the fifth most common cancer in the United States.¹ In 2018, more than 91 000 individuals were newly diagnosed with melanoma.² Most early-stage melanomas (stages I and II) are curable, with a 5-year survival rate of 98.4%.¹ However, advanced melanoma (stages III and IV) with dacarbazine chemotherapy had a low 3-year survival rate of 12.2%.³ In recent years, immunotherapy with novel immune checkpoint inhibitors (ICIs) has revolutionized treatment approaches for advanced melanoma. Ipilimumab, the first ICI for treatment of advanced melanoma, was introduced to the US market in 2011. Since then, 2 additional ICIs (pembrolizumab and nivolumab) have been approved by the US Food and Drug Administration as a first-line treatment for advanced melanoma.

Little is known about whether machine-learning algorithms developed to predict opioid overdose using earlier years and from a single state will perform as well when applied to other populations. We aimed to develop a machine-learning algorithm to predict 3-month risk of opioid overdose using Pennsylvania Medicaid data and externally validated it in two data sources (ie, later years of Pennsylvania Medicaid data and data from a different state). This prognostic modelling study developed and validated a machine-learning algorithm to predict overdose in Medicaid beneficiaries with one or more opioid prescription in Pennsylvania and Arizona, USA. To predict risk of hospital or emergency department visits for overdose in the subsequent 3 months, we measured 284 potential predictors from pharmaceutical and health-care encounter claims data in 3-month periods, starting 3 months before the first opioid prescription and continuing until loss to follow-up or study end. We developed and internally validated a gradient-boosting machine algorithm to predict overdose using 2013–16 Pennsylvania Medicaid data (n=639 693). We externally validated the model using (1) 2017–18 Pennsylvania Medicaid data (n=318 585) and (2) 2015–17 Arizona Medicaid data (n=391 959). We reported several prediction performance metrics (eg, C-statistic, positive predictive value). Beneficiaries were stratified into risk-score subgroups to support clinical use. A total of 8641 (1·35%) 2013–16 Pennsylvania Medicaid beneficiaries, 2705 (0·85%) 2017–18 Pennsylvania Medicaid beneficiaries, and 2410 (0·61%) 2015–17 Arizona beneficiaries had one or more overdose during the study period. C-statistics for the algorithm predicting 3-month overdoses developed from the 2013–16 Pennsylvania training dataset and validated on the 2013–16 Pennsylvania internal validation dataset, 2017–18 Pennsylvania external validation dataset, and 2015–17 Arizona external validation dataset were 0·841 (95% CI 0·835–0·847), 0·828 (0·822–0·834), and 0·817 (0·807–0·826), respectively. In external validation datasets, 71 361 (22·4%) of 318 585 2017–18 Pennsylvania beneficiaries were in high-risk subgroups (positive predictive value of 0·38–4·08%; capturing 73% of overdoses in the subsequent 3 months) and 40 041 (10%) of 391 959 2015–17 Arizona beneficiaries were in high-risk subgroups (positive predictive value of 0·19–1·97%; capturing 55% of overdoses). Lower risk subgroups in both validation datasets had few individuals (≤0·2%) with an overdose. A machine-learning algorithm predicting opioid overdose derived from Pennsylvania Medicaid data performed well in external validation with more recent Pennsylvania data and with Arizona Medicaid data. The algorithm might be valuable for overdose risk prediction and stratification in Medicaid beneficiaries. National Institute of Health, National Institute on Drug Abuse, National Institute on Aging.

Background: Little is known about economic and productivity loss by severity of asthma. We investigate health-care utilization, direct medical costs, and indirect costs due to productivity loss from asthma by severity. Methods: We conducted a cross-sectional analysis of the Medical Expenditure Panel Survey database (2010– 2017) of patients with asthma aged ≥ 12 years and categorized them into mild, moderate, and severe asthma groups based on symptom control medications. Study outcomes included health-care utilization, direct medical costs, and indirect costs of asthma-related absenteeism. We used zero-inflated Poisson regression models to estimate incremental health-care utilization and generalized linear models to estimate incremental annual direct medical costs compared to patients without asthma. Results: An estimated 139 million persons had an asthma diagnosis. Of patients with asthma, 77.1%, 22.2%, and 0.7% had mild, moderate, and severe asthma, respectively. Compared to patients without asthma, patients with asthma had incremental mean differences of 4.16 outpatient visits, 0.18 emergency department visits, and 0.07 hospitalizations per year. Annual direct medical costs were significantly associated with asthma severity ($3305 in mild, $7250 in moderate, and $9175 in severe asthma) (P < 0.05). Patients with mild, moderate, and severe asthma reported 0.76, 2.31, and 7.19 missed work or school days, resulting in $106, $321, and $1000 indirect costs per person per year, respectively. Conclusion: Asthma-related direct and indirect costs are significantly associated with asthma severity, with severe asthma medical costs being about three times higher than mild. Controlling asthma symptoms is important to reduce the economic and social burden of asthma.

Increasing gabapentinoid use has raised concerns of misuse and abuse in the United States (US). Little is known about the characteristics of gabapentinoid use in general clinical practice over time. This cross-sectional study used data from the National Ambulatory Medical Care Survey. We examined the trends of patient and prescriber characteristics and the diagnoses associated with US ambulatory care visits involving gabapentinoids for adult visits from 2003 to 2016. Using multivariable logistic regression, we estimated the adjusted proportion of gabapentinoid-involved visits among all visits and tested for trend significance. Among the weighted estimate of 260.1 million gabapentinoid-involved visits (aged 18–64 years: 61.8%; female: 61.9%; white: 85.5%), the adjusted annual proportion of gabapentinoid-involved visits nearly quadrupled from 2003 to 2016 (9.1 to 34.9 per 1000 visits; P_trend < 0.0001), driven mainly by gabapentin. Nearly half had concurrent use with opioids (32.9%) or benzodiazepines (15.3%). Primary care physicians (45.8%), neurologists (8.2%), surgeons (6.2%), and psychiatrists (4.8%) prescribed two-thirds of the gabapentinoids. Most (96.6%) of the gabapentinoid visits did not have an approved indication for gabapentinoids among the first three diagnoses. Among US ambulatory care visits from 2003 to 2016, gabapentinoid use increased substantially, commonly prescribed by primary care physicians.