You are presented with a paragraph from a COVID-19 literature. Classify the topics of this piece of text by choosing from the following categories:

- Epidemic Forecasting: The content relates to predictions about the spread of COVID-19.
- Treatment: The paragraph discusses therapeutic methods or medication for COVID-19.
- Prevention: The content covers measures to prevent the spread of COVID-19.
- Mechanism: The paragraph explains the biological mechanisms of the virus.
- Case Report: The content is a report of a COVID-19 case.
- Transmission: The paragraph discusses how COVID-19 is transmitted.
- Diagnosis: The content is about diagnosing COVID-19 in individuals.

Your classification should be provided as an element within a JSON array. The array should be formatted with topics enclosed in double quotes and separated by commas, following the JSON format, for example: ["Topic1", "Topic2", ...]. Include all applicable topics within the array. Your output should be a valid JSON array, and no additional text should be included in your response.

Article:

OBJECTIVE: In the wake of the COVID-19 pandemic, telemedicine has become rapidly adopted by the neurosurgical community; however, few studies have examined predictors of telemedicine utilization. Here, we analyze patient variables associated with the acceptance of a telemedicine encounter by a pediatric neurosurgical population during the early phases of the COVID-19 pandemic. METHODS: All patients seen in a single institution's outpatient pediatric neurosurgery clinic between April 1, 2020 and July 31, 2020 were retrospectively reviewed. Demographic variables were collected for each patient's first completed encounter. Patients participating in telemedicine were compared with those seen in person. Univariate analysis was performed using the Wilcoxon rank sum test for continuous variables and Fischer exact test for categorical variables. A logistic regression multivariable analysis was then performed. RESULTS: We included 682 patients (374 telemedicine and 308 in person). Univariate analysis demonstrated that telemedicine visits were more likely to occur at earlier study dates (P < 0.001) and that patients participating in telemedicine visits were more likely to be established rather than new patients (P < 0.001), White or Caucasian (P < 0.001), not Hispanic or Latino (P < 0.001), English-speaking (P < 0.001), non-Medicare/Medicaid recipients (P < 0.001), have lower no-show rates (P = 0.006), and live farther from the hospital (P = 0.005). Multivariable analysis demonstrated older age (P = 0.031), earlier appointment date (P < 0.01), established patient status (P < 0.001), English-speaking (P < 0.02), and non-Medicare/Medicaid insurance (P < 0.05) were significant predictors of telemedicine utilization. CONCLUSIONS: Significant demographic differences exist among pediatric patients who participated in telemedicine versus those who requested an in-person visit at our institution. Addressing barriers to access will be crucial for promoting health equity in continued utilization of telemedicine.
["Prevention"]