COVID-19 rural health inequities: insights from a real-world scenario. (Special Issue: COVID-19 and health inequities: insights from communication research and practice.)

Background: During the pandemic, Mount Pleasant, Texas became a hotspot for Covid-19 cases in the Hispanic community employed by a local meat packing plant and many other industries. An important consideration for rural communities is the language barrier and lack of easily accessible Spanish information explaining Covid-19. In addition, rapidly changing discoveries about the virus and subsequent vaccines creates a sense of confusion within this population already burdened with difficulty understanding health information leading to even more confusion about prevention, treatment and vaccine acceptance.

Pilot sleep during Brazil-to-China COVID-19 humanitarian mission flights compared to biomathematical predictions of sleep

Introduction: n response to the COVID-19 pandemic, Azul Airlines organized and conducted five separate humanitarian missions to China between May and July, 2020. Each mission consisted of 4 flight legs between 11-15 hours long crewed by a team of 8 pilots. Each pilot was given a 9-hour sleep opportunity during the flight period. Prior to conducting the missions, a sleep-prediction algorithm (AutoSleep) within the Sleep, Activity, Fatigue, and Task Effectiveness (SAFTE) model Fatigue Avoidance Scheduling Tool (FAST) was used to predict in-flight time in bed (TIB) and total sleep time (TST). During missions, pilots wore a wrist actigraph and completed a sleep diary. These analyses compare the accuracy of SAFTE-FAST AutoSleep predictions against pilots' sleep diary and actigraphy from Azul's COVID-19 humanitarian missions. Methods: Pilots wore a sleep-tracking actigraphy device (Zulu Watch, Institutes for Behavior Resources), and reported the TIB and sleep quality of their in-flight rest periods using a sleep diary. Diary TST was estimated from TIB and sleep quality. AutoSleep, diary, and actigraphy measures were compared using paired samples t-tests. Agreement was compared using intraclass correlation coefficients (ICC). Results: Twenty (n=20) pilots flying across 5 humanitarian missions provided sleep diary and actigraphy data. AutoSleep predictions of TIB (235±20 minutes) and TST (193±16 minutes) were significantly lower than diary (TIB: 330±123, t=6.80, p≤0.001;TST: 262±108, t=5.60, p≤0.001) and comparable to actigraphy (TIB: 246±127, t=0.78, p=0.43;TST: 212±113, t=1.59, p=0.12). ICC values were >0.90, indicating excellent agreement, for TIB (0.94) and TST (0.91). Conclusion: Biomathematical predictions of in-flight sleep during unprecedented humanitarian missions were in agreement with actual sleep patterns during flights. These findings indicate that biomathematical models may retain accuracy even under extreme circumstances like the COVID-19 pandemic. Pilots may overestimate the amount of sleep that they receive during extreme flights-duty periods, which could constitute a fatigue risk.