Using Ecological and Twitter-Based Assessments to Examine Impacts in Temporal and Community Context

Abstract In March 2020, Bronx County (NY) saw one of the first U.S. COVID-19 outbreaks. This outbreak coincided with the ongoing Einstein Aging Study (EAS), which involved older adults living in Bronx County completing annual two-week intensive data collection “bursts.” Thus, it serves as a natural experiment to study pre-COVID to early pandemic-related changes in the daily well-being of participants who were at risk both due to their age and their location. We examined within-person change in self-reported negative thoughts, affect, stress, and loneliness from a subsample of 78 EAS participants. Participants’ data from a two-week “burst” of momentary surveys during 2019 were compared with their data from the corresponding timeframe during the early COVID-19 period (February-June 2020). Personality and mild cognitive impairment were examined as predictors of change. Average momentary loneliness significantly increased from 2019 to 2020. Participants with greater neuroticism increased more in thought unpleasantness and depressed feelings. To understand the community context, community distress markers were analyzed using Artificial Intelligence (AI)-based assessments of public Twitter posts from Bronx County during the same periods. These Twitter posts also showed a surge of COVID-related topics at the onset of the Bronx outbreak. Language analysis showed a 2019-2020 increase in Bronx community markers of anxiety, depressivity, and negatively-valenced affect extracted from Twitter. We observed 2019-2020 change in both individuals’ well-being (via intensive reports) and in their communities (via Twitter). Contextualizing these with the increased COVID-19 discussion online suggests that these may reflect common pandemic effects.

States, 2. The University of Texas at Austin, The University of Texas at Austin,Texas,United States,3

. The University of Texas at Austin, Menard, Texas, United States
Since December 2019, COVID-19 has spurred rapid and extensive research, but this research has focused on some perspectives with others understudied. In particular, studies have not yet explored the complexities of community-dwelling older adults' lived experiences during the pandemic. This study aimed to address this gap. Community-dwelling older adults living in Central Texas (N = 200; age, 65-92 years, M = 73.6± 6.33) responded to open-and closed-ended questions over the telephone during June-August 2020. Data were analyzed using inductive thematic analysis. We identified three key themes. (1) Positive experiences, with 4 subthemes: perception that the pandemic has not changed one's lifestyle; adjusting well-particularly with the aid of technology; being positive in perspective; and a "loner advantage" (being a "loner" pre-pandemic was advantageous during the pandemic).
(2) Mixed experiences, with 4 subthemes: doing okay but unhappy about changing lifestyle routines; doing okay but unhappy about loss of in-person interactions with family and friends; doing okay but frustrated by witnessing absence of social distancing or facemask use by others; and maintaining physical health with fluctuating symptoms of depression or anxiety.
(3) Negative experiences, with 3 subthemes: bitter about others/society/government not caring for older adults; feeling isolated, bored, and powerless; and worsening as time goes by. A thematic map was subsequently developed. These findings reveal the complexities of community-dwelling older adults' lived experiences, illustrating effective coping and resilience during the pandemic and dissatisfaction owing to the pandemic's effects on their lives and to their observations of others' behaviors.
increased more in thought unpleasantness and depressed feelings. To understand the community context, community distress markers were analyzed using Artificial Intelligence (AI)-based assessments of public Twitter posts from Bronx County during the same periods. These Twitter posts also showed a surge of COVID-related topics at the onset of the Bronx outbreak. Language analysis showed a 2019-2020 increase in Bronx community markers of anxiety, depressivity, and negatively-valenced affect extracted from Twitter. We observed 2019-2020 change in both individuals' well-being (via intensive reports) and in their communities (via Twitter). Contextualizing these with the increased COVID-19 discussion online suggests that these may reflect common pandemic effects.

AGE DIFFERENCES IN BECOMING COVID LONG-HAULERS AND IN POST-ACUTE SEQUELAE OF SARS-COV-2 Qiao Wu, and Eileen Crimmins, University of Southern California, Los Angeles, California, United States
People who have had COVID-19 can suffer from the continuation of Post-Acute Sequelae of SARS-CoV-2 (PASC), also known as "long COVID", for months after infection. Understanding PASC is important for treatment, care, and projecting future health of the population. Since older adults are at higher risk of severe illness and consequences from COVID, we hypothesize that they are more likely to become COVID long-haulers and report more symptoms at the time of diagnosis and three months after. We use a nationally representative sample of adults from the Understanding America Study COVID-19 Survey, from March to December 2020, to estimate the prevalence of long COVID and identify the most common long-term symptoms and how they vary by age. We use multilevel models to examine the determinants of symptom count and change over time. Among the 608 people with a COVID diagnosis, 83 (13.7%) aged over 65; almost half (47.9%) reported symptoms three months after diagnosis; the proportion did not differ across age groups. The most common symptoms were fatigue (25.0%), runny/ stuffy nose (18.9%), body aches (16.4%), sneezing (15.1%), and headache (13.6%). These symptoms were consistent across age groups, while people aged 65 and older reported significantly less cough (χ2=3.96; P=0.05) and headache (χ2=4.24; P=0.04) compared to their younger counterparts. Neither the mean at the time of the diagnosis nor the rate of change of the symptom count varied across age groups. Our analyses suggest that age is not a significant determinant of PASC symptom count or becoming a COVID long-hauler.

BEYOND CHRONOLOGICAL AGE: FRAILTY AND MULTIMORBIDITY PREDICT IN-HOSPITAL MORTALITY IN PATIENTS WITH COVID-19
Alberto Zucchelli, 1 Alessandra Marengoni, 2 Davide Vetrano, 3 Luigi Ferrucci, 4 Laura Fratiglioni, 5 Roberto Bernabei, 6 Graziano Onder, 7 and Giuseppe Romanelli, 8 1. Università degli Studi di Brescia, Karolinska Institutet, Stockholms Lan, Sweden, 2. University of Brescia, Brescia, Lombardia, Italy, 3. Karolinska Institutet, Solna, Stockholms Lan, Sweden, 4. National Institute on Aging, Baltimore, Maryland, United States, 5. Karolinska Institutet 17175, Stockholms Lan, Sweden, 6. Catholic University of Rome, Rome, Lazio, Italy, 7. Italian National Institute of Health, Rome, Lazio, Italy, 8. Università degli Studi di Brescia, Università degli Studi di Brescia, Lombardia, Italy Background: We evaluated whether frailty and multimorbidity predict in-hospital mortality in patients with COVID-19 beyond chronological age. Methods: 165 patients admitted from March 8th to April 17th, 2020, with COVID-19 in an acute geriatric ward in Italy were included. Pre-disease frailty was assessed with the Clinical Frailty Scale (CFS). Multimorbidity was defined as the co-occurrence of ≥2 of these in the same patient. The hazard (HR) of in-hospital mortality as a function of CFS score and number of chronic diseases in the whole population and in those aged 70+ years were calculated. Results Among the 165 patients, 112 were discharged, 11 were transferred to intensive care units and 42 died. Patients who died were older (81.0 vs. 65.2 years, p<0.001), more frequently multimorbid (97.6 vs. 52.8%; p<0.001) and more likely frail (37.5 vs. 4.1%; p<0.001). Less than 2.0% of patients without multimorbidity and frailty, 28% of those with multimorbidity only and 75% of those with both multimorbidity and frailty died. Each unitary increment in the CFS was associated with a higher risk of in-hospital death in the whole sample (HR=1.3; 95%CI=1.05-1.62) and in patients aged 70+ years (HR=1.29;95%CI=1.04-1.62), whereas the number of chronic diseases was not significantly associated with higher risk of death. The CFS addition to age and sex increased mortality prediction by 9.4% in those aged 70+ years. Conclusions Frailty identifies patients with COVID-19 at risk of in-hospital death independently of age. Multimorbidity contributes to prognosis because of the very low probability of death in its absence.