Direct health care costs associated with COVID-19 in the United States.

BACKGROUND: Data on the real-world health care burden of COVID-19 in the United States are limited. OBJECTIVE: To compare health care resource use (HRU), direct health care costs, and long-term COVID-19-related complications between patients with vs patients without COVID-19 diagnoses. METHODS: Using IBM MarketScan Commercial Claims and Encounters and Medicare Supplemental and Coordination of Benefits administrative claims databases (January 1, 2018, to March 1, 2021), this retrospective, matched cohort study compared patients with a recorded COVID-19 diagnosis to control subjects with no recorded diagnosis for COVID-19, personal history of COVID-19, or pneumonia due to COVID-19. To capture typical health care utilization, the control group was analyzed in 2019 (prepandemic); their index date was assigned as 1 year before the index date (first observed COVID-19 diagnosis) of their matched COVID-19 patient. All patients had continuous health plan coverage for at least 6 months pre-index (baseline) and at least 6 months post-index (allowing censoring during month 6). Separately for commercial and Medicare cohorts, COVID-19 and control patients were matched 1:1 using propensity scores, number of followup months, and indicator of age 18 years or older. During each month of the 6-month follow-up, all-cause HRU, health care costs, and COVID-19-related complications were compared between patients with COVID-19 and controls. RESULTS: After matching COVID-19 and control patients 1:1, a total of 150,731 commercial matched pairs and 1,862 Medicare matched pairs were retained; baseline characteristics were similar between patients with COVID-19 and controls. Patients with COVID-19 and controls had mean ages of 38.9 and 39.7 years in the commercial cohort and 74.3 and 75.3 years in the Medicare cohort, respectively. In month 1 of follow-up, patients with COVID-19 relative to controls were significantly more likely to have at least 1 inpatient admission (commercial: 6.9% vs 0.5%; Medicare: 29.1% vs 1.3%; both P < 0.001) and at least 1 emergency department visit (commercial: 37.3% vs 3.4%; Medicare: 26.2% vs 4.1%; both P < 0.001). Total health care costs in month 1 were significantly higher among patients with COVID-19 than controls (mean differences: $3,706 for commercial; $10,595 for Medicare; both P < 0.001), driven by inpatient costs. Though the incremental HRU and cost burden of COVID-19 decreased over time, patients with COVID-19 continued to have significantly higher total costs through month 5 (all P < 0.001 for both commercial and Medicare). During follow-up, patients with COVID-19 had significantly higher rates of complications than controls (commercial: 52.8% vs 29.0% with any; Medicare: 74.5% vs 47.9% with any; both P < 0.001), most commonly cough, dyspnea, and fatigue. CONCLUSIONS: COVID-19 was associated with significant economic and clinical burden, both in the short-term and over 6 months following diagnosis. DISCLOSURES: Jessica K DeMartino is an employee of Janssen Scientific Affairs, LLC. Elyse Swallow, Debbie Goldschmidt, Karen Yang, Marta Viola, Tyler Radtke, and Noam Kirson are employees of Analysis Group, Inc., which has received consulting fees from Janssen Scientific Affairs, LLC. This study was funded by Janssen Scientific Affairs, LLC. The sponsor was involved in the study design, interpretation of the results, manuscript review, and the decision to publish the article.

Development of a specialty intensity score to estimate a patient's need for care coordination across physician specialties.

BACKGROUNDS AND AIMS: This article develops a Specialty Intensity Score, which uses patient diagnosis codes to estimate the number of specialist physicians a patient will need to access. Conceptually, the score can serve as a proxy for a patient's need for care coordination across doctors. Such a measure may be valuable to researchers studying care coordination practices for complex patients. In contrast with previous comorbidity scores, which focus primarily on mortality and utilization, this comorbidity score approximates the complexity of a patient's the interaction with the health care system. METHODS: We use 2015 inpatient claims data from the Centers for Medicare and Medicaid Services to model the relationship between a patient's diagnoses and physician specialty usage. We estimate usage of specialist doctors by using a least absolute shrinkage and selection operator Poisson model. The Specialty Intensity Score is then constructed using this predicted specialty usage. To validate our score, we test its power to predict the occurrence of patient safety incidents and compare that with the predictive power of the Charlson comorbidity index. RESULTS: Our model uses 127 of the 279 International Classification of Disease, 10th Revision, Clinical Modification (ICD-10-CM) diagnosis subchapters to predict specialty usage, thus creating the Specialty Intensity Score. This score has significantly greater power in predicting patient safety complications than the widely used Charlson comorbidity index. CONCLUSION: The Specialty Intensity Score developed in this article can be used by health services researchers and administrators to approximate a patient's need for care coordination across multiple specialist doctors. It, therefore, can help with evaluation of care coordination practices by allowing researchers to restrict their analysis of outcomes to the patients most impacted by those practices.

Preoperative predictive risk to cancer quality in robotic rectal cancer surgery.

BACKGROUND: Circumferential resection margin (CRM) involvement is widely considered the strongest predictor of local recurrence after TME. This study aimed to determine preoperative factors associated with a higher risk of pathological CRM involvement in robotic rectal cancer surgery. METHODS: This was a retrospective review of a prospectively maintained database of consecutive adult patients who underwent elective, curative robotic low anterior or abdominoperineal resection with curative intent for primary rectal adenocarcinoma in a tertiary referral cancer center from March 2012 to September 2019. Pretreatment magnetic resonance imaging (MRI) reports were reviewed for all the patients. Risk factors for pathological CRM involvement were investigated using Firth's logistic regression and a predictive model based on preoperative radiological features was formulated. RESULTS: A total of 305 patients were included, and 14 (4.6%) had CRM involvement. Multivariable logistic regression found both T3 >5 mm (OR 6.12, CI 1.35-36.44) and threatened or involved mesorectal fascia (OR 4.54, CI 1.33-17.55) on baseline MRI to be preoperative predictors of pathologic CRM positivity, while anterior location (OR 3.44, CI 0.72-33.13) was significant only on univariate analysis. The predictive model showed good discrimination (area under the receiver-operating characteristic curve >0.80) and predicted a 32% risk of positive CRM if all risk factors were present. CONCLUSION: Patients with pre-operatively assessed threatened radiological margin, T3 tumors with greater than 5 mm extension and anterior location are at risk for a positive CRM. The predictive model can preoperatively estimate the CRM positivity risk for each patient, allowing surgeons to tailor management to improve oncological outcomes.