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Medicare insurance claims may provide an efficient means to ascertain follow-up of older participants in clinical research. We sought to determine the accuracy and completeness of claims- versus site-based follow-up with clinical event committee (+CEC) adjudication of cardiovascular outcomes. We performed a retrospective study using linked Medicare and Duke Database of Clinical Trials data. Medicare claims were linked to clinical data from 7 randomized cardiovascular clinical trials. Of 52,476 trial participants, linking resulted in 5,839 (of 10,497 linkage-eligible) Medicare-linked trial participants with fee-for-service A and B coverage. Death, myocardial infarction (MI), stroke, and revascularization incidences were compared using Medicare inpatient claims only, site-reported events (+CEC) only, or a combination of the 2. Randomized treatment effects were compared as a function of whether claims-based, site-based (+CEC), or a combined system was used for event detection. Among the 5,839 study participants, the annual event rates were similar between claims- and site-based (+CEC) follow-up: death (overall rate 5.2% vs 5.2%; adjusted κ 0.99), MI (2.2% vs 2.3%; adjusted κ 0.96), stroke (0.7% vs 0.7%; adjusted κ 0.99), and any revascularization (7.4% vs 7.9%; adjusted κ 0.95). Of events detected by claims yet not reported by CEC, a minority were reported by sites but negatively adjudicated by CEC (39% of MIs and 18% of strokes). Differences in individual case concordance led to higher event rates when claims- and site-based (+CEC) systems were combined. Randomized treatment effects were similar among the 3 approaches for each outcome of interest. Claims- versus site-based (+CEC) follow-up identified similar overall cardiovascular event rates despite meaningful differences in the events detected. Randomized treatment effects were similar using the 2 methods, suggesting claims data could be used to support clinical research leveraging routinely collected data. This approach may lead to more effective evidence generation, synthesis, and appraisal of medical products and inform the strategic approaches toward the National Evaluation System for Health Technology.

With increasing rates of opioid overdoses in the US, a surveillance tool to identify high-risk patients may help facilitate early intervention. To develop an algorithm to predict overdose using routinely-collected healthcare databases. Within a US commercial claims database (2011-2015), patients with ≥1 opioid prescription were identified. Patients were randomly allocated into the training (50%), validation (25%), or test set (25%). For each month of follow-up, pooled logistic regression was used to predict the odds of incident overdose in the next month based on patient history from the preceding 3-6 months (time-updated), using elastic net for variable selection. As secondary analyses, we explored whether using simpler models (few predictors, baseline only) or different analytic methods (random forest, traditional regression) influenced performance. We identified 5,293,880 individuals prescribed opioids; 2,682 patients (0.05%) had an overdose during follow-up (mean: 17.1 months). On average, patients who overdosed were younger and had more diagnoses and prescriptions. The elastic net model achieved good performance (c-statistic 0.887, 95% CI 0.872-0.902; sensitivity 80.2, specificity 80.1, PPV 0.21, NPV 99.9 at optimal cutpoint). It outperformed simpler models based on few predictors (c-statistic 0.825, 95% CI 0.808-0.843) and baseline predictors only (c-statistic 0.806, 95% CI 0.787-0.26). Different analytic techniques did not substantially influence performance. In the final algorithm based on elastic net, the strongest predictors were age 18-25 years (OR: 2.21), prior suicide attempt (OR: 3.68), opioid dependence (OR: 3.14). We demonstrate that sophisticated algorithms using healthcare databases can be predictive of overdose, creating opportunities for active monitoring and early intervention.

Abstract Background Fungal diseases range from relatively-minor superficial and mucosal infections to severe, life-threatening systemic infections. Delayed diagnosis and treatment can lead to poor patient outcomes and high medical costs. The overall burden of fungal diseases in the United States is challenging to quantify, because they are likely substantially underdiagnosed. Methods To estimate the total, national, direct medical costs associated with fungal diseases from a healthcare payer perspective, we used insurance claims data from the Truven Health MarketScan 2014 Research Databases, combined with hospital discharge data from the 2014 Healthcare Cost and Utilization Project National Inpatient Sample and outpatient visit data from the 2005–2014 National Ambulatory Medical Care Survey and the National Hospital Ambulatory Medical Care Survey. All costs were adjusted to 2017 dollars. Results We estimate that fungal diseases cost more than $7.2 billion in 2017, including $4.5 billion from 75055 hospitalizations and $2.6 billion from 8993230 outpatient visits. Hospitalizations for Candida infections (n = 26735, total cost $1.4 billion) and Aspergillus infections (n = 14820, total cost $1.2 billion) accounted for the highest total hospitalization costs of any disease. Over half of outpatient visits were for dermatophyte infections (4981444 visits, total cost $802 million), and 3639037 visits occurred for non-invasive candidiasis (total cost $1.6 billion). Conclusions Fungal diseases impose a considerable economic burden on the healthcare system. Our results likely underestimate their true costs, because they are underdiagnosed. More comprehensive estimates of the public health impact of these diseases are needed to improve their recognition, prevention, diagnosis, and treatment. To provide insight into the burden of fungal diseases in the United States, we used several administrative data sources to estimate their total direct healthcare costs. We estimate that fungal disease healthcare costs exceed $7 billion annually.

BACKGROUND:The most used score to measure comorbidity is the Charlson index. Its application to a health care administrative database including International Classification of Diseases, 10th edition (ICD-10) codes, medical procedures, and medication required studying its properties on survival. Our objectives were to adapt the Charlson comorbidity index to the French National Health Insurance database to predict 1-year mortality of discharged patients and to compare discrimination and calibration of different versions of the Charlson index. METHODS:Our cohort included all adults discharged from a hospital stay in France in 2010 registered in the French National Health Insurance general scheme. The pathologies of the Charlson index were identified through ICD-10 codes of discharge diagnoses and long-term disease, specific medical procedures, and reimbursement of specific medications in the past 12 months before inclusion. RESULTS:We included 6,602,641 subjects at the date of their first discharge from medical, surgical, or obstetrical department in 2010. One-year survival was 94.88%, decreasing from 98.41% for Charlson index of 0–71.64% for Charlson index of ≥5. With a discrimination of 0.91 and an appropriate calibration curve, we retained the crude Cox model including the age-adjusted Charlson index as a 4-level score. CONCLUSIONS:Our study is the first to adapt the Charlson index to a large health care database including >6 million of inpatients. When mortality is the outcome, we recommended using the age-adjusted Charlson index as 4-level score to take into account comorbidities.

Hospitals can leverage their position between the ultimate buyers and sellers of drugs to retain a substantial share of insurer pharmaceutical expenditures. In this study, we used 2020-2021 national Blue Cross Blue Shield claims data regarding patients in the United States who had drug-infusion visits for oncologic conditions, inflammatory conditions, or blood-cell deficiency disorders. Markups of the reimbursement prices were measured in terms of amounts paid by Blue Cross Blue Shield plans to hospitals and physician practices relative to the amounts paid by these providers to drug manufacturers. Acquisition-price reductions in hospital payments to drug manufacturers were measured in terms of discounts under the federal 340B Drug Pricing Program. We estimated the percentage of Blue Cross Blue Shield drug spending that was received by drug manufacturers and the percentage retained by provider organizations. The study included 404,443 patients in the United States who had 4,727,189 drug-infusion visits. The median price markup (defined as the ratio of the reimbursement price to the acquisition price) for hospitals eligible for 340B discounts was 3.08 (interquartile range, 1.87 to 6.38). After adjustment for drug, patient, and geographic factors, price markups at hospitals eligible for 340B discounts were 6.59 times (95% confidence interval [CI], 6.02 to 7.16) as high as those in independent physician practices, and price markups at noneligible hospitals were 4.34 times (95% CI, 3.77 to 4.90) as high as those in physician practices. Hospitals eligible for 340B discounts retained 64.3% of insurer drug expenditures, whereas hospitals not eligible for 340B discounts retained 44.8% and independent physician practices retained 19.1%. This study showed that hospitals imposed large price markups and retained a substantial share of total insurer spending on physician-administered drugs for patients with private insurance. The effects were especially large for hospitals eligible for discounts under the federal 340B Drug Pricing Program on acquisition costs paid to manufacturers. (Funded by Arnold Ventures and the National Institute for Health Care Management.).

Timely postpartum care is associated with lower maternal morbidity and mortality, yet fewer than half of Medicaid beneficiaries attend a postpartum visit. Medicaid enrollees are at higher risk of postpartum disruptions in insurance because pregnancy-related Medicaid eligibility ends sixty days after delivery. We used Medicaid claims data for 2013-15 from Colorado, which expanded Medicaid under the Affordable care Act, and Utah, which did not. We found that after expansion, new mothers in Utah experienced higher rates of Medicaid coverage loss and accessed fewer Medicaid-financed outpatient visits during the six months postpartum, relative to their counterparts in Colorado. The effects of Medicaid expansion on postpartum Medicaid enrollment and outpatient utilization were largest among women who experienced significant maternal morbidity at delivery. These findings provide evidence that expansion may promote the stability of postpartum coverage and increase the use of postpartum outpatient care in the Medicaid program.

Only a small proportion of people with a substance use disorder (SUD) receive treatment. The shortage of SUD treatment providers, particularly in rural areas, is an important driver of this treatment gap. Telemedicine could be a means of expanding access to treatment. However, several key regulatory and reimbursement barriers to greater use of telemedicine for SUD (tele-SUD) exist, and both Congress and the states are considering or have recently passed legislation to address them. To inform these efforts, we describe how tele-SUD is being used. Using claims data for 2010-17 from a large commercial insurer, we identified characteristics of tele-SUD users and examined how tele-SUD is being used in conjunction with in-person SUD care. Despite a rapid increase in tele-SUD over the study period, we found low use rates overall, particularly relative to the growth in telemental health. Tele-SUD is primarily used to complement in-person care and is disproportionately used by those with relatively severe SUD. Given the severity of the opioid epidemic, low rates of tele-SUD use represent a missed opportunity. As tele-SUD becomes more available, it will be important to monitor closely which tele-SUD delivery models are being used and their impact on access and outcomes.

We evaluated MarketScan, a large commercial insurance claims database, for its potential use as a stable and consistent source of information on Lyme disease diagnoses in the United States. The age, sex, and geographic composition of the enrolled population during 2010-2018 remained proportionally stable, despite fluctuations in the number of enrollees. Annual incidence of Lyme disease diagnoses per 100,000 enrollees ranged from 49 to 88, ≈6-8 times higher than that observed for cases reported through notifiable disease surveillance. Age and sex distributions among Lyme disease diagnoses in MarketScan were similar to those of cases reported through surveillance, but proportionally more diagnoses occurred outside of peak summer months, among female enrollees, and outside high-incidence states. Misdiagnoses, particularly in low-incidence states, may account for some of the observed epidemiologic differences. Commercial claims provide a stable data source to monitor trends in Lyme disease diagnoses, but certain important characteristics warrant further investigation.

Objective: To propose a unifying set of definitions for prescription adherence research utilizing electronic health record prescribing databases, prescription dispensing databases, and pharmacy claims databases and to provide a conceptual framework to operationalize these definitions consistently across studies. Methods: We reviewed recent literature to identify definitions in electronic database studies of prescription-filling patterns for chronic oral medications. We then develop a conceptual model and propose standardized terminology and definitions to describe prescription-filling behavior from electronic databases. Results: The conceptual model we propose defines 2 separate constructs: medication adherence and persistence. We define primary and secondary adherence as distinct subtypes of adherence. Metrics for estimating secondary adherence are discussed and critiqued, including a newer metric (New Prescription Medication Gap measure) that enables estimation of both primary and secondary adherence. Discussion: Terminology currently used in prescription adherence research employing electronic databases lacks consistency. We propose a. clear, consistent, broadly applicable conceptual model and terminology for such studies. The model and definitions facilitate research utilizing electronic medication prescribing, dispensing, and/or claims databases and encompasses the entire continuum of prescription-filling behavior. Conclusion: Employing conceptually clear and consistent terminology to define medication adherence and persistence will facilitate future comparative effectiveness research and meta-analytic studies that utilize electronic prescription and dispensing records.

Insurance claim denials are a common source of administrative burden, especially for patients with private health insurance. Contesting denied claims requires considerable investment from physicians and patients or caregivers, including both institutional knowledge of health policies and billing practices and the means to engage in reconciliation. We used a novel national data set comprising remittance data and patient demographics to describe disparities in the rates of seeking and receiving claim denial corrections across demographic and socioeconomic dimensions. We found that patients from historically disadvantaged racial and ethnic groups or with low household incomes experienced the largest burdens from claim denials. Patients with household incomes less than $50,000 annually were least likely to have denied claims contested and, conditionally, have cost-sharing obligations reduced. Racial minority patients were more likely than non-Hispanic White patients to have cost-sharing obligations reduced but achieved lower mean savings per successfully contested denial. Policy makers working to promote equitable health care access should make available more resources for contesting and rectifying administrative errors and enact policies to prevent billing errors and consequent claim denials.