ABSTRACT
OBJECTIVE: The coronavirus disease-2019 pandemic has affected the entire health system and patients other than coronavirus-infected patients. Hospital admissions of cancer patients decreased during the closure periods due to the pandemic. This study was conducted to determine whether there was an effect on the hospital admissions of newly diagnosed lung cancer patients in Turkey during the corona- virus disease-2019 pandemic. MATERIAL AND METHODS: In this retrospective study, newly diagnosed lung cancer patients were recorded from the Hospital Information Management System between January 1, 2017, and December 31, 2020, at our tertiary hospital. The number of newly diag- nosed lung cancer patients diagnosed in 2020 was compared with each year from 2017 to 2019. RESULTS: Between 2017 and 2020, 15 150 newly diagnosed lung cancer cases were analyzed. According to Global Cancer Observatory data, in 2018, 34 703 newly diagnosed lung cancer cases, and in 2020, 41 264 newly diagnosed lung cancer cases were observed in Turkey. Although a decrease was not observed in the number of patients according to Global Cancer Observatory data, both the total number of patients admitted to our hospital and the number of newly diagnosed lung cancer patients decreased in 2020. The number of newly diagnosed lung cancer patients by year was 4030 patients in 2017, 4004 patients in 2018, 4391 patients in 2019, and 2725 in 2020, respectively. In 2020, newly diagnosed lung cancer patients decreased by 38%, 32%, and 32% compared to 2019, 2018, and 2017, respectively. Also, a significant decrease was seen in the number of newly diagnosed lung cancer patients in the months with clo- sure due to the pandemic compared to the months without closure. CONCLUSION: There was a significant decrease in hospital admissions of newly diagnosed lung cancer cases in the coronavirus dis- ease-2019 pandemic in our referral hospital. Precautions should be considered to diagnose and treat lung cancer patients in specialized centers during a pandemic due to epidemic diseases such as coronavirus disease-2019.
ABSTRACT
This study aims to evaluate the monitoring and predictive value of web-based symptoms (fever, cough, dyspnea) searches for COVID-19 spread. Daily search interests from Turkey, Italy, Spain, France, and the United Kingdom were obtained from Google Trends (GT) between January 1, 2020, and August 31, 2020. In addition to conventional correlational models, we studied the time-varying correlation between GT search and new case reports; we used dynamic conditional correlation (DCC) and sliding windows correlation models. We found time-varying correlations between pulmonary symptoms on GT and new cases to be significant. The DCC model proved more powerful than the sliding windows correlation model. This model also provided better at time-varying correlations (r ≥ 0.90) during the first wave of the pandemic. We used a root means square error (RMSE) approach to attain symptom-specific shift days and showed that pulmonary symptom searches on GT should be shifted separately. Web-based search interest for pulmonary symptoms of COVID-19 is a reliable predictor of later reported cases for the first wave of the COVID-19 pandemic. Illness-specific symptom search interest on GT can be used to alert the healthcare system to prepare and allocate resources needed ahead of time.