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1. Cognitive Symptoms of Post–COVID-19 Condition and Daily Functioning

   https://doi.org/10.1001/jamanetworkopen.2023.56098  

   Jaywant, Abhishek ; Gunning, Faith M. ; Oberlin, Lauren E. ; Santillana, Mauricio ; Ognyanova, Katherine ; Druckman, James N. ; Baum, Matthew A. ; Lazer, David ; Perlis, Roy H. ( February 2024 , JAMA Network Open)

   The frequent occurrence of cognitive symptoms in post–COVID-19 condition has been described, but the nature of these symptoms and their demographic and functional factors are not well characterized in generalizable populations.

   To investigate the prevalence of self-reported cognitive symptoms in post–COVID-19 condition, in comparison with individuals with prior acute SARS-CoV-2 infection who did not develop post–COVID-19 condition, and their association with other individual features, including depressive symptoms and functional status.

   Two waves of a 50-state nonprobability population-based internet survey conducted between December 22, 2022, and May 5, 2023. Participants included survey respondents aged 18 years and older.

   Post–COVID-19 condition, defined as self-report of symptoms attributed to COVID-19 beyond 2 months after the initial month of illness.

   Seven items from the Neuro-QoL cognition battery assessing the frequency of cognitive symptoms in the past week and patient Health Questionnaire-9.

   The 14 767 individuals reporting test-confirmed COVID-19 illness at least 2 months before the survey had a mean (SD) age of 44.6 (16.3) years; 568 (3.8%) were Asian, 1484 (10.0%) were Black, 1408 (9.5%) were Hispanic, and 10 811 (73.2%) were White. A total of 10 037 respondents (68.0%) were women and 4730 (32.0%) were men. Of the 1683 individuals reporting post–COVID-19 condition, 955 (56.7%) reported at least 1 cognitive symptom experienced daily, compared with 3552 of 13 084 (27.1%) of those who did not report post–COVID-19 condition. More daily cognitive symptoms were associated with a greater likelihood of reporting at least moderate interference with functioning (unadjusted odds ratio [OR], 1.31 [95% CI, 1.25-1.36]; adjusted [AOR], 1.30 [95% CI, 1.25-1.36]), lesser likelihood of full-time employment (unadjusted OR, 0.95 [95% CI, 0.91-0.99]; AOR, 0.92 [95% CI, 0.88-0.96]) and greater severity of depressive symptoms (unadjusted coefficient, 1.40 [95% CI, 1.29-1.51]; adjusted coefficient 1.27 [95% CI, 1.17-1.38). After including depressive symptoms in regression models, associations were also found between cognitive symptoms and at least moderate interference with everyday functioning (AOR, 1.27 [95% CI, 1.21-1.33]) and between cognitive symptoms and lower odds of full-time employment (AOR, 0.92 [95% CI, 0.88-0.97]).

   The findings of this survey study of US adults suggest that cognitive symptoms are common among individuals with post–COVID-19 condition and associated with greater self-reported functional impairment, lesser likelihood of full-time employment, and greater depressive symptom severity. Screening for and addressing cognitive symptoms is an important component of the public health response to post–COVID-19 condition.

    

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2. Prevalence and Correlates of Long COVID Symptoms Among US Adults

   https://doi.org/10.1001/jamanetworkopen.2022.38804  

   Perlis, Roy H. ; Santillana, Mauricio ; Ognyanova, Katherine ; Safarpour, Alauna ; Lunz Trujillo, Kristin ; Simonson, Matthew D. ; Green, Jon ; Quintana, Alexi ; Druckman, James ; Baum, Matthew A. ; et al ( October 2022 , JAMA Network Open)

   Persistence of COVID-19 symptoms beyond 2 months, or long COVID, is increasingly recognized as a common sequela of acute infection.

   To estimate the prevalence of and sociodemographic factors associated with long COVID and to identify whether the predominant variant at the time of infection and prior vaccination status are associated with differential risk.

   This cross-sectional study comprised 8 waves of a nonprobability internet survey conducted between February 5, 2021, and July 6, 2022, among individuals aged 18 years or older, inclusive of all 50 states and the District of Columbia.

   Long COVID, defined as reporting continued COVID-19 symptoms beyond 2 months after the initial month of symptoms, among individuals with self-reported positive results of a polymerase chain reaction test or antigen test.

   The 16 091 survey respondents reporting test-confirmed COVID-19 illness at least 2 months prior had a mean age of 40.5 (15.2) years; 10 075 (62.6%) were women, and 6016 (37.4%) were men; 817 (5.1%) were Asian, 1826 (11.3%) were Black, 1546 (9.6%) were Hispanic, and 11 425 (71.0%) were White. From this cohort, 2359 individuals (14.7%) reported continued COVID-19 symptoms more than 2 months after acute illness. Reweighted to reflect national sociodemographic distributions, these individuals represented 13.9% of those who had tested positive for COVID-19, or 1.7% of US adults. In logistic regression models, older age per decade above 40 years (adjusted odds ratio [OR], 1.15; 95% CI, 1.12-1.19) and female gender (adjusted OR, 1.91; 95% CI, 1.73-2.13) were associated with greater risk of persistence of long COVID; individuals with a graduate education vs high school or less (adjusted OR, 0.67; 95% CI, 0.56-0.79) and urban vs rural residence (adjusted OR, 0.74; 95% CI, 0.64-0.86) were less likely to report persistence of long COVID. Compared with ancestral COVID-19, infection during periods when the Epsilon variant (OR, 0.81; 95% CI, 0.69-0.95) or the Omicron variant (OR, 0.77; 95% CI, 0.64-0.92) predominated in the US was associated with diminished likelihood of long COVID. Completion of the primary vaccine series prior to acute illness was associated with diminished risk for long COVID (OR, 0.72; 95% CI, 0.60-0.86).

   This study suggests that long COVID is prevalent and associated with female gender and older age, while risk may be diminished by completion of primary vaccination series prior to infection.

    

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3. Association of Post–COVID-19 Condition Symptoms and Employment Status

   https://doi.org/10.1001/jamanetworkopen.2022.56152  

   Perlis, Roy H. ; Lunz Trujillo, Kristin ; Safarpour, Alauna ; Santillana, Mauricio ; Ognyanova, Katherine ; Druckman, James ; Lazer, David ( February 2023 , JAMA Network Open)

   Little is known about the functional correlates of post–COVID-19 condition (PCC), also known as long COVID, particularly the relevance of neurocognitive symptoms.

   To characterize prevalence of unemployment among individuals who did, or did not, develop PCC after acute infection.

   This survey study used data from 8 waves of a 50-state US nonprobability internet population-based survey of respondents aged 18 to 69 years conducted between February 2021 and July 2022.

   The primary outcomes were self-reported current employment status and the presence of PCC, defined as report of continued symptoms at least 2 months beyond initial month of symptoms confirmed by a positive COVID-19 test.

   The cohort included 15 308 survey respondents with test-confirmed COVID-19 at least 2 months prior, of whom 2236 (14.6%) reported PCC symptoms, including 1027 of 2236 (45.9%) reporting either brain fog or impaired memory. The mean (SD) age was 38.8 (13.5) years; 9679 respondents (63.2%) identified as women and 10 720 (70.0%) were White. Overall, 1418 of 15 308 respondents (9.3%) reported being unemployed, including 276 of 2236 (12.3%) of those with PCC and 1142 of 13 071 (8.7%) of those without PCC; 8229 respondents (53.8%) worked full-time, including 1017 (45.5%) of those with PCC and 7212 (55.2%) without PCC. In survey-weighted regression models excluding retired respondents, the presence of PCC was associated with a lower likelihood of working full-time (odds ratio [OR], 0.71 [95% CI, 0.63-0.80]; adjusted OR, 0.84 [95% CI, 0.74-0.96]) and with a higher likelihood of being unemployed (OR, 1.45 [95% CI, 1.22-1.73]; adjusted OR, 1.23 [95% CI, 1.02-1.48]). The presence of any cognitive symptom was associated with lower likelihood of working full time (OR, 0.70 [95% CI, 0.56-0.88]; adjusted OR, 0.75 [95% CI, 0.59-0.84]).

   PCC was associated with a greater likelihood of unemployment and lesser likelihood of working full time in adjusted models. The presence of cognitive symptoms was associated with diminished likelihood of working full time. These results underscore the importance of developing strategies to treat and manage PCC symptoms.

    

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4. Assessing eligibility for lung cancer screening using parsimonious ensemble machine learning models: A development and validation study

   https://doi.org/10.1371/journal.pmed.1004287  

   Callender, Thomas ; Imrie, Fergus ; Cebere, Bogdan ; Pashayan, Nora ; Navani, Neal ; van der Schaar, Mihaela ; Janes, Sam M. ( October 2023 , PLOS Medicine)

   Risk-based screening for lung cancer is currently being considered in several countries; however, the optimal approach to determine eligibility remains unclear. Ensemble machine learning could support the development of highly parsimonious prediction models that maintain the performance of more complex models while maximising simplicity and generalisability, supporting the widespread adoption of personalised screening. In this work, we aimed to develop and validate ensemble machine learning models to determine eligibility for risk-based lung cancer screening.

   For model development, we used data from 216,714 ever-smokers recruited between 2006 and 2010 to the UK Biobank prospective cohort and 26,616 high-risk ever-smokers recruited between 2002 and 2004 to the control arm of the US National Lung Screening (NLST) randomised controlled trial. The NLST trial randomised high-risk smokers from 33 US centres with at least a 30 pack-year smoking history and fewer than 15 quit-years to annual CT or chest radiography screening for lung cancer. We externally validated our models among 49,593 participants in the chest radiography arm and all 80,659 ever-smoking participants in the US Prostate, Lung, Colorectal and Ovarian (PLCO) Screening Trial. The PLCO trial, recruiting from 1993 to 2001, analysed the impact of chest radiography or no chest radiography for lung cancer screening. We primarily validated in the PLCO chest radiography arm such that we could benchmark against comparator models developed within the PLCO control arm. Models were developed to predict the risk of 2 outcomes within 5 years from baseline: diagnosis of lung cancer and death from lung cancer. We assessed model discrimination (area under the receiver operating curve, AUC), calibration (calibration curves and expected/observed ratio), overall performance (Brier scores), and net benefit with decision curve analysis.

   Models predicting lung cancer death (UCL-D) and incidence (UCL-I) using 3 variables—age, smoking duration, and pack-years—achieved or exceeded parity in discrimination, overall performance, and net benefit with comparators currently in use, despite requiring only one-quarter of the predictors. In external validation in the PLCO trial, UCL-D had an AUC of 0.803 (95% CI: 0.783, 0.824) and was well calibrated with an expected/observed (E/O) ratio of 1.05 (95% CI: 0.95, 1.19). UCL-I had an AUC of 0.787 (95% CI: 0.771, 0.802), an E/O ratio of 1.0 (95% CI: 0.92, 1.07). The sensitivity of UCL-D was 85.5% and UCL-I was 83.9%, at 5-year risk thresholds of 0.68% and 1.17%, respectively, 7.9% and 6.2% higher than the USPSTF-2021 criteria at the same specificity. The main limitation of this study is that the models have not been validated outside of UK and US cohorts.

   We present parsimonious ensemble machine learning models to predict the risk of lung cancer in ever-smokers, demonstrating a novel approach that could simplify the implementation of risk-based lung cancer screening in multiple settings.

    

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5. Misinformation, Trust, and Use of Ivermectin and Hydroxychloroquine for COVID-19

   https://doi.org/10.1001/jamahealthforum.2023.3257  

   Perlis, Roy H. ; Lunz Trujillo, Kristin ; Green, Jon ; Safarpour, Alauna ; Druckman, James N. ; Santillana, Mauricio ; Ognyanova, Katherine ; Lazer, David ( September 2023 , JAMA Health Forum)

   The COVID-19 pandemic has been notable for the widespread dissemination of misinformation regarding the virus and appropriate treatment.

   To quantify the prevalence of non–evidence-based treatment for COVID-19 in the US and the association between such treatment and endorsement of misinformation as well as lack of trust in physicians and scientists.

   This single-wave, population-based, nonprobability internet survey study was conducted between December 22, 2022, and January 16, 2023, in US residents 18 years or older who reported prior COVID-19 infection.

   Self-reported use of ivermectin or hydroxychloroquine, endorsing false statements related to COVID-19 vaccination, self-reported trust in various institutions, conspiratorial thinking measured by the American Conspiracy Thinking Scale, and news sources.

   A total of 13 438 individuals (mean [SD] age, 42.7 [16.1] years; 9150 [68.1%] female and 4288 [31.9%] male) who reported prior COVID-19 infection were included in this study. In this cohort, 799 (5.9%) reported prior use of hydroxychloroquine (527 [3.9%]) or ivermectin (440 [3.3%]). In regression models including sociodemographic features as well as political affiliation, those who endorsed at least 1 item of COVID-19 vaccine misinformation were more likely to receive non–evidence-based medication (adjusted odds ratio [OR], 2.86; 95% CI, 2.28-3.58). Those reporting trust in physicians and hospitals (adjusted OR, 0.74; 95% CI, 0.56-0.98) and in scientists (adjusted OR, 0.63; 95% CI, 0.51-0.79) were less likely to receive non–evidence-based medication. Respondents reporting trust in social media (adjusted OR, 2.39; 95% CI, 2.00-2.87) and in Donald Trump (adjusted OR, 2.97; 95% CI, 2.34-3.78) were more likely to have taken non–evidence-based medication. Individuals with greater scores on the American Conspiracy Thinking Scale were more likely to have received non–evidence-based medications (unadjusted OR, 1.09; 95% CI, 1.06-1.11; adjusted OR, 1.10; 95% CI, 1.07-1.13).

   In this survey study of US adults, endorsement of misinformation about the COVID-19 pandemic, lack of trust in physicians or scientists, conspiracy-mindedness, and the nature of news sources were associated with receiving non–evidence-based treatment for COVID-19. These results suggest that the potential harms of misinformation may extend to the use of ineffective and potentially toxic treatments in addition to avoidance of health-promoting behaviors.

    

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