RESUMO
Background: COVID-19 has greatly impacted the U.S. health system. What is not as well-understood is how this has altered specific aspects of lung cancer care. While cancer incidence and screening have been affected, it is not known whether pre-existing racial and socioeconomic disparities worsened or if treatment standards changed. The purpose of this study is to provide a comprehensive analysis of the impact of COVID-19 on lung cancer in the state of Delaware. Methods: Health care claims were analyzed from the Delaware Healthcare Claims Database for the years 2019-2020. Patients with a new lung cancer diagnosis and those who had undergone lung cancer screening were identified. Demographic and socioeconomic variables including gender, age, race, and insurance were studied. Patients were analyzed for type of treatment by CPT code. The intervention of interest in this study was the institution of restrictions at the end of March 2020. An interrupted time series analysis (ITSA) was utilized to evaluate baseline levels and overall trend changes. Results: The incidence of lung cancer diagnoses and lung cancer screenings decreased in the nine-month time period after the initiation of COVID-19 lockdowns. Demographic and socioeconomic variables including gender, race, income, and education level were not affected; however, statistical differences were seen in the most elderly subgroup. Treatment modalities including number of surgeries, chemotherapy, and radiation therapy did not change significantly. Conclusions: COVID-19 has had a significant impact on lung cancer care within the state of Delaware. Lung cancer incidence, screenings, and elderly patients were affected the most.
RESUMO
Objective: Healthcare systems globally were shocked by coronavirus disease 2019 (COVID-19). Policies put in place to curb the tide of the pandemic resulted in a decrease of patient volumes throughout the ambulatory system. The future implications of COVID-19 in healthcare are still unknown, specifically the continued impact on the ambulatory landscape. The primary objective of this study is to accurately forecast the number of COVID-19 and non-COVID-19 weekly visits in primary care practices. Materials and Methods: This retrospective study was conducted in a single health system in Delaware. All patients' records were abstracted from our electronic health records system (EHR) from January 1, 2019 to July 25, 2020. Patient demographics and comorbidities were compared using t-tests, Chi square, and Mann Whitney U analyses as appropriate. ARIMA time series models were developed to provide an 8-week future forecast for two ambulatory practices (AmbP) and compare it to a naïve moving average approach. Results: Among the 271,530 patients considered during this study period, 4,195 patients (1.5%) were identified as COVID-19 patients. The best fitting ARIMA models for the two AmbP are as follows: AmbP1 COVID-19+ ARIMAX(4,0,1), AmbP1 nonCOVID-19 ARIMA(2,0,1), AmbP2 COVID-19+ ARIMAX(1,1,1), and AmbP2 nonCOVID-19 ARIMA(1,0,0). Discussion and Conclusion: Accurately predicting future patient volumes in the ambulatory setting is essential for resource planning and developing safety guidelines. Our findings show that a time series model that accounts for the number of positive COVID-19 patients delivers better performance than a moving average approach for predicting weekly ambulatory patient volumes in a short-term period.
