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INTRODUCTIONOlder adults hospitalized with COVID-19 are susceptible to neurological complications, particularly encephalopathy, which may reflect age-related neurodegenerative processes. METHODSSerum total tau, ptau-181, GFAP, NFL, UCHL1, and amyloid-beta(A{beta}-40,42) were measured in hospitalized COVID-19 patients without a history of dementia, and compared among patients with or without encephalopathy, in-hospital death versus survival, and discharge home versus other dispositions using multivariable Cox proportional hazards regression analyses. RESULTSAmong 251 patients, admission serum ptau-181 and UCHL1 were significantly elevated in patients with encephalopathy (both P<0.05) and total tau, GFAP, and NFL were significantly lower in those discharged home(all P<0.05). These markers correlated significantly with severity of COVID illness. NFL, GFAP and UCH-L1 were significantly higher in hospitalized COVID patients than in non-COVID controls with mild cognitive impairment or Alzheimers disease(AD). DISCUSSIONAge-related neurodegenerative biomarkers were elevated to levels observed in AD and associated with encephalopathy and worse outcomes among hospitalized COVID-19 patients.
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As the COVID-19 pandemic spreads across the world, it is important to understand its features and responses to public health interventions in real-time. The field of infectious diseases epidemiology has highly advanced modeling strategies that yield relevant estimates. These include the doubling time of the epidemic and various other representations of the numbers of cases identified over time. Crude estimates of these quantities suffer from dependence on the underlying testing strategies within communities. We clarify the functional relationship between testing and the epidemic parameters, and thereby derive sensitivity analyses that explore the range of possible truths under various testing dynamics. We derive the required adjustment to the estimates of interest for New York City. We demonstrate that crude estimates that assume stable testing or complete testing can be biased.