Background: Coronavirus disease (COVID-19) impairs the free movement of human beings. The study aims to determine how the COVID-19 pandemic affected population mobility. Methods: The study obtained Google COVID-19 population mobility report and e Taiwan COVID-19 pandemic information from Our World in Data. Results: During the Alpha wave, transit decreased the most, with an average difference of >50%, followed by parks, workplaces, groceries, and pharmacies. During the Omicron wave, the average population flow in parks and transit decreased by about 20%. During the pre-existing wave, the average population visits of transit decreased by 10% at the most, followed by parks and workplaces. The peak of daily new confirmed cases per million (7-day rolling average) was 25.02, 6.39, and 0.81 for Alpha, Omicron, and the pre-existing wave, respectively. Daily new confirmed cases per million people correlated with the change in population visits of various places (all p < 0.001). The reproduction rate (7-day rolling average) correlated with the change of population visits of most places, except retail and recreation. We conclude the Alpha variant affected more individuals than Omicron and pre-existing type. Furthermore, changes in population visits in transit were most impacted. This change was consistent with daily new confirmed cases per million people and reproduction rate (7-day rolling average). Conclusion: The Alpha variant affected more individuals than the Omicron and pre-existing types. Furthermore, changes in population visits in transit locations were most impacted. This change was consistent with the daily new number of confirmed cases per million people and the 7-day rolling average reproduction rate.
Background and Objectives:The ADO (age, dyspnea, and airflow obstruction) and BODE (body mass index, airflow obstruction, dyspnea, and exercise capacity) indices are often used to evaluate the prognoses for chronic obstructive pulmonary disease(COPD); however, an index suitable for predicting medical costs has yet to be developed. Materials and Methods: We investigated the BODE and ADO indices to predict medical costs and compare their predictive power. A total of 396 patients with COPD were retrospectively enrolled. Results: For hospitalization frequencies, BODE was R2 = 0.093 (p < 0.001), and ADO was R2 = 0.065 (p < 0.001); for hospitalization days, BODE was R2 = 0.128 (p < 0.001), and ADO was R2 = 0.071 (p < 0.001); for hospitalization expenses, BODE was R2 = 0.020 (p = 0.047), and ADO was R2 = 0.012 (p = 0.179). BODE and ADO did not differ significantly in the numbers of outpatient visits (BODE, R2 = 0.012, p = 0.179; ADO, R2 = 0.017, p = 0.082); outpatient medical expenses (BODE, R2 = 0.012, p = 0.208; ADO, R2 = 0.008, p = 0.364); and total medical costs (BODE, R2 = 0.018, p = 0.072; ADO, R2 = 0.016, p = 0.098). In conclusion, BODE and ADO indices were correlated with hospitalization frequency and hospitalization days. However, the BODE index exhibits slightly better predictive accuracy than the ADO index in these items.
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