Multidimensional Sleep Health Prior to SARS-CoV-2 Infection and Risk of Post-COVID-19 Condition.

Importance: The association of multiple healthy sleep dimensions with post-COVID-19 condition (PCC), also known as long COVID, has not been investigated. Objective: To examine whether multidimensional sleep health before and during the COVID-19 pandemic, prior to SARS-CoV-2 infection, was associated with the risk of PCC. Design, Setting, and Participants: This prospective cohort study (2015-2021) included Nurses' Health Study II participants who reported testing positive (n = 2303) for SARS-CoV-2 infection in a substudy series of COVID-19-related surveys (n = 32 249) between April 2020 and November 2021. After exclusion for incomplete information about sleep health and nonresponse to a question about PCC, 1979 women were included in the analysis. Exposures: Sleep health was measured both before (June 1, 2015, to May 31, 2017) and early (April 1 to August 31, 2020) in the COVID-19 pandemic. Prepandemic sleep score was defined according to 5 dimensions: morning chronotype (assessed in 2015), 7 to 8 hours of sleep per day, low insomnia symptoms, no snoring, and no frequent daytime dysfunction (all assessed in 2017). On the first COVID-19 substudy survey (returned between April and August 2020), average daily sleep duration and sleep quality for the past 7 days were queried. Main Outcomes and Measures: SARS-CoV-2 infection and PCC (≥4 weeks of symptoms) were self-reported during 1 year of follow-up. Comparisons were examined between June 8, 2022, and January 9, 2023, using Poisson regression models. Results: Of the 1979 participants reporting SARS-CoV-2 infection (mean [SD] age, 64.7 [4.6] years; 1979 [100%] female; and 1924 [97.2%] White vs 55 [2.8%] other races and ethnicities), 845 (42.7%) were frontline health care workers, and 870 (44.0%) developed PCC. Compared with women who had a prepandemic sleep score of 0 or 1 (least healthy), those who scored 5 (most healthy) had a 30% lower risk of developing PCC (multivariable-adjusted relative risk, 0.70; 95% CI, 0.52-0.94; P for trend <.001). Associations did not differ by health care worker status. No or little daytime dysfunction prepandemic and good sleep quality during the pandemic were independently associated with a lower risk of PCC (relative risk, 0.83 [95% CI, 0.71-0.98] and 0.82 [95% CI, 0.69-0.99], respectively). Results were similar when PCC was defined as having 8 or more weeks of symptoms or as having ongoing symptoms at the time of PCC assessment. Conclusions and Relevance: The findings indicate that healthy sleep measured prior to SARS-CoV-2 infection, both before and during the COVID-19 pandemic, may be protective against PCC. Future research should investigate whether interventions on sleep health may prevent PCC or improve PCC symptoms.

Pregnancy Intention, Changes in Pregnancy Intention, and Pregnancy Incidence Among Female Nurses in North America.

Importance: Pregnancy intention assessment is a key element of preconception and contraceptive care. The association between a single screening question and the incidence of pregnancy is unknown. Objective: To prospectively evaluate the dynamics of pregnancy intention and pregnancy incidence. Design, Setting, and Participants: This prospective cohort study (the Nurses' Health Study 3) was conducted from June 1, 2010, to April 1, 2022, in 18 376 premenopausal, nonpregnant female nurses aged 19 to 44 years. Main Outcomes and Measures: Pregnancy intention and pregnancy status were assessed at baseline and approximately every 3 to 6 months thereafter. Cox proportional hazards regression models were used to estimate the association between pregnancy intention and pregnancy incidence. Results: A total of 18 376 premenopausal, nonpregnant women (mean [SD] age, 32.4 [6.5] years) participated in the study. At baseline, 1008 women (5.5%) were trying to conceive, 2452 (13.3%) were contemplating pregnancy within 1 year, and the remaining 14 916 (81.2%) were neither trying to conceive nor thought they would be pregnant within 1 year. A total of 1314 pregnancies were documented within 12 months of pregnancy intention assessment. The cumulative incidence of pregnancy was 38.8% in women actively trying to conceive (median [IQR] time to pregnancy, 3.3 [1.5-6.7] months), 27.6% in women contemplating pregnancy (median [IQR] time to pregnancy, 6.7 [4.2-9.3] months), and 1.7% in women neither trying to conceive nor contemplating pregnancy (median [IQR] time to pregnancy, 7.8 [5.2-10.5] months) among those who became pregnant. Women who were actively trying to conceive were 23.1 times (95% CI, 19.5-27.4 times) and women who were contemplating pregnancy were 13.0 times (95% CI, 11.1-15.2 times) more likely to conceive within 12 months than women who were neither attempting nor contemplating pregnancy. Among women contemplating pregnancy at baseline who did not get pregnant during follow up, 18.8% were actively trying and 27.6% were not trying by 12 months. Conversely, only 4.9% of women neither trying to conceive nor contemplating pregnancy within 1 year at baseline changed pregnancy intention during follow up. Conclusions and Relevance: In this cohort study of reproductive-aged nurses in North America, pregnancy intention was highly fluid among women who were contemplating pregnancy but relatively stable among women trying to conceive and women who were neither trying to conceive nor contemplating pregnancy. Pregnancy intention was strongly associated with pregnancy incidence, but the median time to pregnancy points to a relatively short time window to initiate preconception care.

Association of laparoscopically-confirmed endometriosis with long COVID-19: a prospective cohort study.

BACKGROUND: Women are at greater risk than men of developing chronic inflammatory conditions and "long COVID." However, few gynecologic health risk factors for long COVID-19 have been identified. Endometriosis is a common gynecologic disorder associated with chronic inflammation, immune dysregulation, and comorbid presentation with autoimmune and clotting disorders, all of which are pathophysiological mechanisms proposed for long COVID-19. Therefore, we hypothesized that women with a history of endometriosis may be at greater risk of developing long COVID-19. OBJECTIVE: This study aimed to investigate the association between history of endometriosis before SARS-CoV-2 infection and risk of long COVID-19. STUDY DESIGN: We followed 46,579 women from 2 ongoing prospective cohort studies-the Nurses' Health Study II and the Nurses' Health Study 3-who participated in a series of COVID-19-related surveys administered from April 2020 to November 2022. Laparoscopic diagnosis of endometriosis was documented prospectively in main cohort questionnaires before the pandemic (1993-2020) with high validity. SARS-CoV-2 infection (confirmed by antigen, polymerase chain reaction, or antibody test) and long-term COVID-19 symptoms (≥4 weeks) defined by the Centers for Disease Control and Prevention were self-reported during follow-up. Among individuals with SARS-CoV-2 infection, we fit Poisson regression models to assess the associations between endometriosis and risk of long COVID-19 symptoms, with adjustment for potential confounding variables (demographics, body mass index, smoking status, history of infertility, and history of chronic diseases). RESULTS: Among 3650 women in our sample with self-reported SARS-CoV-2 infections during follow-up, 386 (10.6%) had a history of endometriosis with laparoscopic confirmation, and 1598 (43.8%) reported experiencing long COVID-19 symptoms. Most women were non-Hispanic White (95.4%), with a median age of 59 years (interquartile range, 44-65). Women with a history of laparoscopically-confirmed endometriosis had a 22% greater risk of developing long COVID-19 (adjusted risk ratio, 1.22; 95% confidence interval, 1.05-1.42) compared with those who had never been diagnosed with endometriosis. The association was stronger when we defined long COVID-19 as having symptoms for ≥8 weeks (risk ratio, 1.28; 95% confidence interval, 1.09-1.50). We observed no statistically significant differences in the relationship between endometriosis and long COVID-19 by age, infertility history, or comorbidity with uterine fibroids, although there was a suggestive trend indicating that the association may be stronger in women aged <50 years (<50 years: risk ratio, 1.37; 95% confidence interval, 1.00-1.88; ≥50 years: risk ratio, 1.19; 95% confidence interval, 1.01-1.41). Among persons who developed long COVID-19, women with endometriosis reported on average 1 additional long-term symptom compared with women without endometriosis. CONCLUSION: Our findings suggest that those with a history of endometriosis may be at modestly increased risk for long COVID-19. Healthcare providers should be aware of endometriosis history when treating patients for signs of persisting symptoms after SARS-CoV-2 infection. Future studies should investigate the potential biological pathways underlying these associations.

Adherence to Healthy Lifestyle Prior to Infection and Risk of Post-COVID-19 Condition.

Importance: Few modifiable risk factors for post-COVID-19 condition (PCC) have been identified. Objective: To investigate the association between healthy lifestyle factors prior to SARS-CoV-2 infection and risk of PCC. Design, Setting, and Participants: In this prospective cohort study, 32 249 women in the Nurses' Health Study II cohort reported preinfection lifestyle habits in 2015 and 2017. Healthy lifestyle factors included healthy body mass index (BMI, 18.5-24.9; calculated as weight in kilograms divided by height in meters squared), never smoking, at least 150 minutes per week of moderate to vigorous physical activity, moderate alcohol intake (5 to 15 g/d), high diet quality (upper 40% of Alternate Healthy Eating Index-2010 score), and adequate sleep (7 to 9 h/d). Main Outcomes and Measures: SARS-CoV-2 infection (confirmed by test) and PCC (at least 4 weeks of symptoms) were self-reported on 7 periodic surveys administered from April 2020 to November 2021. Among participants with SARS-CoV-2 infection, the relative risk (RR) of PCC in association with the number of healthy lifestyle factors (0 to 6) was estimated using Poisson regression and adjusting for demographic factors and comorbidities. Results: A total of 1981 women with a positive SARS-CoV-2 test over 19 months of follow-up were documented. Among those participants, mean age was 64.7 years (SD, 4.6; range, 55-75); 97.4% (n = 1929) were White; and 42.8% (n = 848) were active health care workers. Among these, 871 (44.0%) developed PCC. Healthy lifestyle was associated with lower risk of PCC in a dose-dependent manner. Compared with women without any healthy lifestyle factors, those with 5 to 6 had 49% lower risk (RR, 0.51; 95% CI, 0.33-0.78) of PCC. In a model mutually adjusted for all lifestyle factors, BMI and sleep were independently associated with risk of PCC (BMI, 18.5-24.9 vs others, RR, 0.85; 95% CI, 0.73-1.00, P = .046; sleep, 7-9 h/d vs others, RR, 0.83; 95% CI, 0.72-0.95, P = .008). If these associations were causal, 36.0% of PCC cases would have been prevented if all participants had 5 to 6 healthy lifestyle factors (population attributable risk percentage, 36.0%; 95% CI, 14.1%-52.7%). Results were comparable when PCC was defined as symptoms of at least 2-month duration or having ongoing symptoms at the time of PCC assessment. Conclusions and Relevance: In this prospective cohort study, pre-infection healthy lifestyle was associated with a substantially lower risk of PCC. Future research should investigate whether lifestyle interventions may reduce risk of developing PCC or mitigate symptoms among individuals with PCC or possibly other postinfection syndromes.

Associations of Depression, Anxiety, Worry, Perceived Stress, and Loneliness Prior to Infection With Risk of Post-COVID-19 Conditions.

Importance: Few risk factors for long-lasting (≥4 weeks) COVID-19 symptoms have been identified. Objective: To determine whether high levels of psychological distress before SARS-CoV-2 infection, characterized by depression, anxiety, worry, perceived stress, and loneliness, are prospectively associated with increased risk of developing post-COVID-19 conditions (sometimes called long COVID). Design, Setting, and Participants: This prospective cohort study used data from 3 large ongoing, predominantly female cohorts: Nurses' Health Study II, Nurses' Health Study 3, and the Growing Up Today Study. Between April 2020 and November 2021, participants were followed up with periodic surveys. Participants were included if they reported no current or prior SARS-CoV-2 infection at the April 2020 baseline survey when distress was assessed and returned 1 or more follow-up questionnaires. Exposures: Depression, anxiety, worry about COVID-19, perceived stress, and loneliness were measured at study baseline early in the pandemic, before SARS-CoV-2 infection, using validated questionnaires. Main Outcomes and Measures: SARS-CoV-2 infection was self-reported during each of 6 monthly and then quarterly follow-up questionnaires. COVID-19-related symptoms lasting 4 weeks or longer and daily life impairment due to these symptoms were self-reported on the final questionnaire, 1 year after baseline. Results: Of 54 960 participants, 38.0% (n = 20 902) were active health care workers, and 96.6% (n = 53 107) were female; the mean (SD) age was 57.5 (13.8) years. Six percent (3193 participants) reported a positive SARS-CoV-2 test result during follow-up (1-47 weeks after baseline). Among these, probable depression (risk ratio [RR], 1.32; 95% CI = 1.12-1.55), probable anxiety (RR = 1.42; 95% CI, 1.23-1.65), worry about COVID-19 (RR, 1.37; 95% CI, 1.17-1.61), perceived stress (highest vs lowest quartile: RR, 1.46; 95% CI, 1.18-1.81), and loneliness (RR, 1.32; 95% CI, 1.08-1.61) were each associated with post-COVID-19 conditions (1403 cases) in generalized estimating equation models adjusted for sociodemographic factors, health behaviors, and comorbidities. Participants with 2 or more types of distress prior to infection were at nearly 50% increased risk for post-COVID-19 conditions (RR, 1.49; 95% CI, 1.23-1.80). All types of distress were associated with increased risk of daily life impairment (783 cases) among individuals with post-COVID-19 conditions (RR range, 1.15-1.51). Conclusions and Relevance: The findings of this study suggest that preinfection psychological distress may be a risk factor for post-COVID-19 conditions in individuals with SARS-CoV-2 infection. Future work should examine the biobehavioral mechanism linking psychological distress with persistent postinfection symptoms.

A prospective study of the association between SARS-CoV-2 infection and COVID-19 vaccination with changes in usual menstrual cycle characteristics.

BACKGROUND: Despite anecdotal reports, the impacts of SARS-CoV-2 infection or COVID-19 vaccination on menstrual health have not been systemically investigated. OBJECTIVE: This study aimed to examine the associations of SARS-CoV-2 infection and COVID-19 vaccination with menstrual cycle characteristics. STUDY DESIGN: This study prospectively observed 3858 premenopausal women in the Nurses' Health Study 3 living in the United States or Canada who received biannual follow-up questionnaires between January 2011 and December 2021 and completed additional monthly and quarterly surveys related to the COVID-19 pandemic between April 2020 and November 2021. History of positive SARS-CoV-2 test, COVID-19 vaccination status, and vaccine type were self-reported in surveys conducted in 2020 and 2021. Current menstrual cycle length and regularity "before COVID-19" were reported at baseline between 2011 and 2016, and current menstrual cycle length and regularity "after COVID-19" were reported in late 2021. Pre- to post-COVID change in menstrual cycle length and regularity was calculated between reports. Logistic or multinomial logistic regression models were used to assess the associations between SARS-CoV-2 infection and COVID-19 vaccination and change in menstrual cycle characteristics. RESULTS: The median age at baseline and the median age at end of follow-up were 33 years (range, 21-51) and 42 years (range, 27-56), respectively, with a median follow-up time of 9.2 years. This study documented 421 SARS-CoV-2 infections (10.9%) and 3527 vaccinations (91.4%) during follow-up. Vaccinated women had a higher risk of increased cycle length than unvaccinated women (odds ratio, 1.48; 95% confidence interval, 1.00-2.19), after adjusting for sociodemographic and behavioral factors. These associations were similar after in addition accounting for pandemic-related stress. COVID-19 vaccination was only associated with change to longer cycles in the first 6 months after vaccination (0-6 months: odds ratio, 1.67 [95% confidence interval, 1.05-2.64]; 7-9 months: odds ratio, 1.43 [95% confidence interval, 0.96-2.14]; >9 months: odds ratio, 1.41 [95% confidence interval, 0.91-2.18]) and among women whose cycles were short, long, or irregular before vaccination (odds ratio, 2.82 [95% confidence interval, 1.51-5.27]; odds ratio, 1.10 [95% confidence interval, 0.68-1.77] for women with normal length, regular cycles before vaccination). Messenger RNA and adenovirus-vectored vaccines were both associated with this change. SARS-CoV-2 infection was not associated with changes in usual menstrual cycle characteristics. CONCLUSION: COVID-19 vaccination may be associated with short-term changes in usual menstrual cycle length, particularly among women whose cycles were short, long, or irregular before vaccination. The results underscored the importance of monitoring menstrual health in vaccine clinical trials. Future work should examine the potential biological mechanisms.

Depression, worry, and loneliness are associated with subsequent risk of hospitalization for COVID-19: a prospective study.

BACKGROUND: Pre-pandemic psychological distress is associated with increased susceptibility to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, but associations with the coronavirus disease 2019 (COVID-19) severity are not established. The authors examined the associations between distress prior to SARS-CoV-2 infection and subsequent risk of hospitalization. METHODS: Between April 2020 (baseline) and April 2021, we followed 54 781 participants from three ongoing cohorts: Nurses' Health Study II (NHSII), Nurses' Health Study 3 (NHS3), and the Growing Up Today Study (GUTS) who reported no current or prior SARS-CoV-2 infection at baseline. Chronic depression was assessed during 2010-2019. Depression, anxiety, worry about COVID-19, perceived stress, and loneliness were measured at baseline. SARS-CoV-2 infection and hospitalization due to COVID-19 was self-reported. Relative risks (RRs) were calculated by Poisson regression. RESULTS: 3663 participants reported a positive SARS-CoV-2 test (mean age = 55.0 years, standard deviation = 13.8) during follow-up. Among these participants, chronic depression prior to the pandemic [RR = 1.72; 95% confidence interval (CI) 1.20-2.46], and probable depression (RR = 1.81, 95% CI 1.08-3.03), being very worried about COVID-19 (RR = 1.79; 95% CI 1.12-2.86), and loneliness (RR = 1.81, 95% CI 1.02-3.20) reported at baseline were each associated with subsequent COVID-19 hospitalization, adjusting for demographic factors and healthcare worker status. Anxiety and perceived stress were not associated with hospitalization. Depression, worry about COVID-19, and loneliness were as strongly associated with hospitalization as were high cholesterol and hypertension, established risk factors for COVID-19 severity. CONCLUSIONS: Psychological distress may be a risk factor for hospitalization in patients with SARS-CoV-2 infection. Assessment of psychological distress may identify patients at greater risk of hospitalization. Future work should examine whether addressing distress improves physical health outcomes.

Seasonality and reduced nitric oxide titration dominated ozone increase during COVID-19 lockdown in eastern China

With improving PM2.5 air quality, the tropospheric ozone (O3) has become the top issue of China’s air pollution control. Here, we combine comprehensive observational data analysis with models to unveil the contributions of different processes and precursors to the change of O3 during COVID-19 lockdown in the Yangtze River Delta (YRD), one of the most urbanized megacity regions of eastern China. Despite a 44 to 47% reduction in volatile organic compounds (VOCs) and nitrogen oxides (NOx) emissions, maximum daily 8-h average (MDA8) ozone concentrations increase from 28 ppbv in pre-lockdown to 43 ppbv in lockdown period. We reproduce this transition with the WRF-Chem model, which shows that ~80% of the increase in MDA8 is due to meteorological factors (seasonal variation and radiation), and ~20% is due to emission reduction. We find that daytime photochemistry does not lead to an increase but rather a decrease of daytime O3 production during the lockdown. However, the reduced O3 production is overwhelmed by the weakened nitric oxide (NO) titration resulting in a net increase of O3 concentration. Although the emission reduction increases O3 concentration, it leads to a decrease in the Ox (O3 + NO2) concentration, suggesting reduced atmospheric oxidation capacity on a regional scale. The dominant effect of NO titration demonstrates the importance of prioritizing VOCs reduction, especially from solvent usage and the petrochemical industry with high emission ratios of VOCs/NOx.

Mix Contrast for COVID-19 Mild-to-Critical Prediction.

OBJECTIVE: In a few patients with mild COVID-19, there is a possibility of the infection becoming severe or critical in the future. This work aims to identify high-risk patients who have a high probability of changing from mild to critical COVID-19 (only account for 5% of cases). METHODS: Using traditional convolutional neural networks for classification may not be suitable to identify this 5% of high risk patients from an entire dataset due to the highly imbalanced label distribution. To address this problem, we propose a Mix Contrast model, which matches original features with mixed features for contrastive learning. Three modules are proposed for training the model: 1) a cumulative learning strategy for synthesizing the mixed feature; 2) a commutative feature combination module for learning the commutative law of feature concatenation; 3) a united pairwise loss assigning adaptive weights for sample pairs with different class anchors based on their current optimization status. RESULTS: We collect a multi-center computed tomography dataset including 918 confirmed COVID-19 patients from four hospitals and evaluate the proposed method on both the COVID-19 mild-to-critical prediction and COVID-19 diagnosis tasks. For mild-to-critical prediction, the experimental results show a recall of 0.80 and a specificity of 0.815. For diagnosis, the model shows comparable results with deep neural networks using a large dataset. Our method demonstrates improvements when the amount of training data is small or imbalanced. SIGNIFICANCE: Identifying mild-to-critical COVID-19 patients is important for early prevention and personalized treatment planning.

A Deep Learning Radiomics Model to Identify Poor Outcome in COVID-19 Patients With Underlying Health Conditions: A Multicenter Study.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) has caused considerable morbidity and mortality, especially in patients with underlying health conditions. A precise prognostic tool to identify poor outcomes among such cases is desperately needed. METHODS: Total 400 COVID-19 patients with underlying health conditions were retrospectively recruited from 4 centers, including 54 dead cases (labeled as poor outcomes) and 346 patients discharged or hospitalized for at least 7 days since initial CT scan. Patients were allocated to a training set (n = 271), a test set (n = 68), and an external test set (n = 61). We proposed an initial CT-derived hybrid model by combining a 3D-ResNet10 based deep learning model and a quantitative 3D radiomics model to predict the probability of COVID-19 patients reaching poor outcome. The model performance was assessed by area under the receiver operating characteristic curve (AUC), survival analysis, and subgroup analysis. RESULTS: The hybrid model achieved AUCs of 0.876 (95% confidence interval: 0.752-0.999) and 0.864 (0.766-0.962) in test and external test sets, outperforming other models. The survival analysis verified the hybrid model as a significant risk factor for mortality (hazard ratio, 2.049 [1.462-2.871], P < 0.001) that could well stratify patients into high-risk and low-risk of reaching poor outcomes (P < 0.001). CONCLUSION: The hybrid model that combined deep learning and radiomics could accurately identify poor outcomes in COVID-19 patients with underlying health conditions from initial CT scans. The great risk stratification ability could help alert risk of death and allow for timely surveillance plans.

A Concise Review and Required Precautions for COVID-19 Outbreak in Diagnostic and Interventional Radiology.

A global outbreak of a novel coronavirus (COVID-19) pneumonia began in December 2019 in Wuhan, China. The World Health Organization (WHO) announced a pandemic on March 11, 2020. The rapid rise in the case numbers and mortality led to the saturation of hospitals in many countries. COVID-19 patients usually present with fever, fatigue, dry cough, and dyspnea. Given the shortage of diagnostic kits in many countries and very high sensitivity of computed tomography (CT) for diagnosis of COVID-19 in clinically suspicious patients, the chest CT has been implemented among the primary initial methods of diagnosis before the confirmatory laboratory tests. This puts radiologists and radiology staff on the front line of this alarming pandemic. This report summarizes chest CT findings of COVID-19 patients to facilitate diagnosis and reviews a list of necessary precautions and safety measures for diagnostic and interventional radiology personnel. These precautionary plans are extremely important to avoid contamination of the health-care providers, as well as cross-contamination between patients.