Pub Date : 1900-01-01DOI: 10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a3087
R. Gaurav, D. Anderson, S. Radio, K. Bailey, B. England, T. Mikuls, G. Thiele, H. Strah, D. Romberger, T. Wyatt, J. Dickinson, M. Duryee, D. Katafiasz, A. Nelson, J. Poole
RATIONALE: Interleukin-33 (IL-33) is a danger signaling alarmin with an integral role in wound repair, fibrosis, and remodeling processes. IL-33 is increased in the serum and airways in patients with chronic obstructive pulmonary disease (COPD) and in lung tissues of patients with idiopathic pulmonary fibrosis (IPF). Recently, elevated serum IL-33 levels have been associated with poor outcomes with severe acute respiratory syndrome coronavirus (SARS-CoV)-2, although there have been no studies examining IL-33 expression from involved lung tissues. The objective of this study was to characterize IL-33 expression in lung tissues of patients with severe COVID-19, comparing tissue expression with that observed in other inflammatory lung diseases. METHODS: Post-mortem lung sections of de-identified patients with COVID-19 (N=8), COPD (N=6), IPF (N=4), and from normal subjects (N=7) deemed unsuitable for transplant were stained for IL-33 with prosurfactant protein C (proSP-C), a marker of type II alveolar epithelial cells (AT2), or with vimentin, a mesenchymal cell marker increased with fibrosis. With fluorescence microscopy, 10 photographs of each section/patient were taken. Images were quantitated by measuring integrated densities (the product of area and mean gray value) of each protein with Image J. Averaged integrated densities of each patient were plotted for statistical analysis with Prism 9 using Mann-Whitney test versus control group with p<0.05 accepted as statistically significant. RESULTS: Tissue IL-33 expression was increased in IPF (6.57-fold, p=0.0012) and COPD (3.91-fold, p=0.0012) compared to control lungs, whereas COVID-19 patients had low to negligible lung IL-33 expression that was markedly reduced as compared to controls (0.03-fold;p=0.0003). Vimentin staining was increased in COVID-19 (2.15- fold, p=0.0093) and IPF (1.74-fold, p=0.0424) lungs as compared to controls with no difference between COPD and controls. AT2 was decreased in COVID-19 (0.01-fold, p=0.0003) and COPD (0.43-fold, p=0.0047) lungs marked by decrease in proSP-C staining with no difference between IPF and controls. CONCLUSIONS: These studies confirm an increase in expression of IL-33 in chronic lung diseases yet demonstrate a striking depletion of lung tissue IL-33 in severe COVID-19 coupled with increased vimentin staining and decreased AT2 cells. Because recent studies have demonstrated that serum IL-33 levels are increased at the time of hospital admission with COVID-19, longitudinal studies of convalescent patients would provide insight into how IL-33 might mediate SARS-CoV-2-induced adverse lung pathophysiology and/or recovery. Understanding the mechanisms and timing of IL-33 expression in biological compartments and regulation for promoting damage or driving wound repair processes could inform potential interventional strategies.
{"title":"Lung IL-33 Levels Depleted in COVID-19","authors":"R. Gaurav, D. Anderson, S. Radio, K. Bailey, B. England, T. Mikuls, G. Thiele, H. Strah, D. Romberger, T. Wyatt, J. Dickinson, M. Duryee, D. Katafiasz, A. Nelson, J. Poole","doi":"10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a3087","DOIUrl":"https://doi.org/10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a3087","url":null,"abstract":"RATIONALE: Interleukin-33 (IL-33) is a danger signaling alarmin with an integral role in wound repair, fibrosis, and remodeling processes. IL-33 is increased in the serum and airways in patients with chronic obstructive pulmonary disease (COPD) and in lung tissues of patients with idiopathic pulmonary fibrosis (IPF). Recently, elevated serum IL-33 levels have been associated with poor outcomes with severe acute respiratory syndrome coronavirus (SARS-CoV)-2, although there have been no studies examining IL-33 expression from involved lung tissues. The objective of this study was to characterize IL-33 expression in lung tissues of patients with severe COVID-19, comparing tissue expression with that observed in other inflammatory lung diseases. METHODS: Post-mortem lung sections of de-identified patients with COVID-19 (N=8), COPD (N=6), IPF (N=4), and from normal subjects (N=7) deemed unsuitable for transplant were stained for IL-33 with prosurfactant protein C (proSP-C), a marker of type II alveolar epithelial cells (AT2), or with vimentin, a mesenchymal cell marker increased with fibrosis. With fluorescence microscopy, 10 photographs of each section/patient were taken. Images were quantitated by measuring integrated densities (the product of area and mean gray value) of each protein with Image J. Averaged integrated densities of each patient were plotted for statistical analysis with Prism 9 using Mann-Whitney test versus control group with p<0.05 accepted as statistically significant. RESULTS: Tissue IL-33 expression was increased in IPF (6.57-fold, p=0.0012) and COPD (3.91-fold, p=0.0012) compared to control lungs, whereas COVID-19 patients had low to negligible lung IL-33 expression that was markedly reduced as compared to controls (0.03-fold;p=0.0003). Vimentin staining was increased in COVID-19 (2.15- fold, p=0.0093) and IPF (1.74-fold, p=0.0424) lungs as compared to controls with no difference between COPD and controls. AT2 was decreased in COVID-19 (0.01-fold, p=0.0003) and COPD (0.43-fold, p=0.0047) lungs marked by decrease in proSP-C staining with no difference between IPF and controls. CONCLUSIONS: These studies confirm an increase in expression of IL-33 in chronic lung diseases yet demonstrate a striking depletion of lung tissue IL-33 in severe COVID-19 coupled with increased vimentin staining and decreased AT2 cells. Because recent studies have demonstrated that serum IL-33 levels are increased at the time of hospital admission with COVID-19, longitudinal studies of convalescent patients would provide insight into how IL-33 might mediate SARS-CoV-2-induced adverse lung pathophysiology and/or recovery. Understanding the mechanisms and timing of IL-33 expression in biological compartments and regulation for promoting damage or driving wound repair processes could inform potential interventional strategies.","PeriodicalId":375809,"journal":{"name":"TP63. TP063 COVID-19 IN ENVIRONMENTAL, OCCUPATIONAL, AND POPULATION HEALTH","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130895737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a3076
R. Dhand, P. Terry, E. Heidel
Rationale: As of December 7, 2020, there have been over 66 million confirmed cases of COVID-19 worldwide and over 1.5 million deaths attributed to the pandemic. Health outcomes of people with COVID-19 range from no symptoms to severe illness and death. Asthma is a highly prevalent chronic inflammatory disease of the airways that afflicts over 330 million people worldwide. Because SARS-CoV-2 is primarily a respiratory virus, people with asthma are apprehensive that they may be at increased risk of acquiring COVID-19 and suffer poorer outcomes. However, data addressing this hypothesis have been scarce until very recently. Methods: We reviewed the epidemiologic literature related to asthma's potential role in COVID-19 severity. Studies were identified through the PubMed and medRxiv databases, and by cross-referencing citations in identified studies, available in print or online before October 8, 2020. Asthma prevalence data were obtained from studies of people with confirmed COVID-19. Meta-analyses were conducted to produce weighted pooled prevalence ratios (PR) of asthma for hospitalized versus non-hospitalized participants, those with severe COVID-19 versus non-severe COVID-19, and those who died vs. survived. Results: Eleven studies provided data on the prevalence of asthma in people who were hospitalized with COVID-19 and those who were deemed well enough to be sent home with the disease (Table 1). The prevalence of asthma in these two groups was 8.5% (95% CI=6.4-10.9) and 8.2% (95% CI=6.8-9.8), respectively. The pooled PR for hospitalized individuals vs. those not hospitalized was 0.94 (0.78-1.12), p=0.49. Likewise, twenty-four studies provided data on asthma prevalence among patients hospitalized with COVID-19 according to disease severity (Table 1). The prevalence of asthma in patients with “severe” and “not severe” COVID-19 was 8.2% (95% CI=6.2-10.5) and 7.0% (95% CI=5.8-8.3), respectively. The pooled PR for asthma according to COVID-19 severity was 1.10 (95% CI=0.90-1.35, p=0.35). Twelve studies provided data from those who either died of COVID-19 or survived (Table 1). The prevalence of asthma in these two groups was 6.1% (95% CI=3.8-8.9) and 7.5% (95% CI=5.3-10.0), respectively. The pooled PR for asthma among patients who died from COVID-19 vs. those who survived was 0.76 (0.52-1.10, p=0.15). Conclusions: The results of our analyses do not provide clear evidence of increased risk of COVID-19 diagnosis, hospitalization or severity, due to asthma. These findings should provide some reassurance to people with asthma regarding the novel coronavirus and its potential to increase their risk of severe morbidity from COVID.
{"title":"Prevalence and Risk of Severe Asthma in Adult Patients with COVID-19","authors":"R. Dhand, P. Terry, E. Heidel","doi":"10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a3076","DOIUrl":"https://doi.org/10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a3076","url":null,"abstract":"Rationale: As of December 7, 2020, there have been over 66 million confirmed cases of COVID-19 worldwide and over 1.5 million deaths attributed to the pandemic. Health outcomes of people with COVID-19 range from no symptoms to severe illness and death. Asthma is a highly prevalent chronic inflammatory disease of the airways that afflicts over 330 million people worldwide. Because SARS-CoV-2 is primarily a respiratory virus, people with asthma are apprehensive that they may be at increased risk of acquiring COVID-19 and suffer poorer outcomes. However, data addressing this hypothesis have been scarce until very recently. Methods: We reviewed the epidemiologic literature related to asthma's potential role in COVID-19 severity. Studies were identified through the PubMed and medRxiv databases, and by cross-referencing citations in identified studies, available in print or online before October 8, 2020. Asthma prevalence data were obtained from studies of people with confirmed COVID-19. Meta-analyses were conducted to produce weighted pooled prevalence ratios (PR) of asthma for hospitalized versus non-hospitalized participants, those with severe COVID-19 versus non-severe COVID-19, and those who died vs. survived. Results: Eleven studies provided data on the prevalence of asthma in people who were hospitalized with COVID-19 and those who were deemed well enough to be sent home with the disease (Table 1). The prevalence of asthma in these two groups was 8.5% (95% CI=6.4-10.9) and 8.2% (95% CI=6.8-9.8), respectively. The pooled PR for hospitalized individuals vs. those not hospitalized was 0.94 (0.78-1.12), p=0.49. Likewise, twenty-four studies provided data on asthma prevalence among patients hospitalized with COVID-19 according to disease severity (Table 1). The prevalence of asthma in patients with “severe” and “not severe” COVID-19 was 8.2% (95% CI=6.2-10.5) and 7.0% (95% CI=5.8-8.3), respectively. The pooled PR for asthma according to COVID-19 severity was 1.10 (95% CI=0.90-1.35, p=0.35). Twelve studies provided data from those who either died of COVID-19 or survived (Table 1). The prevalence of asthma in these two groups was 6.1% (95% CI=3.8-8.9) and 7.5% (95% CI=5.3-10.0), respectively. The pooled PR for asthma among patients who died from COVID-19 vs. those who survived was 0.76 (0.52-1.10, p=0.15). Conclusions: The results of our analyses do not provide clear evidence of increased risk of COVID-19 diagnosis, hospitalization or severity, due to asthma. These findings should provide some reassurance to people with asthma regarding the novel coronavirus and its potential to increase their risk of severe morbidity from COVID.","PeriodicalId":375809,"journal":{"name":"TP63. TP063 COVID-19 IN ENVIRONMENTAL, OCCUPATIONAL, AND POPULATION HEALTH","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123519537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a3088
D. Puebla Neira, A. Watts, J. Seashore, E. Hsu, Y. Kuo, G. Sharma
Rationale. The association between smoking status and severe Coronavirus Disease-2019 (COVID-19) remains controversial. To assess the risk of 14-day hospitalization, as a marker of severe COVID-19, in patients who are ever-smokers and tested positive for the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) compared to those who are never smokers and tested positive for the virus in a single academic health system in the United States. Methods. We conducted a retrospective cohort study of patients who tested positive for SARS-CoV-2 in the University of Texas Medical Branch Health System between March 1st and October 30th 2020 to identify the risk of 14-day hospitalization in ever-smokers compared to non-smokers. Results. In our study period, we identified 5,738 patients who met the inclusion criteria and had documentation of smoking habits. Out of this group, 636 (11%) were consider to be ever-smokers. One hundred and ninety one patients were current smokers and 445 were former smokers. Of the 5,738 patients, 35.1% were male, average age was 43.8 (SD± 17.6), 37.4% were Caucasian, 51.5% were obese (BMI≥30), 3.19 % had vaping history, and 76.5% had at least one comorbidity. We identified 624 (10.8%) patients who were admitted in 14 days and 49(0.8%) who died in 14 days during hospitalization. The percentage of ever smokers admitted in 14 days was greater than that of never smokers (17.9% vs 10%, p<0.0001). In addition, the percentage of smokers who died in 14 days was greater than that of never smokers (2.8% vs 0.6%, p<0.0001). However, after adjusting for other covariates the odds for 14-day hospitalization among ever smokers with COVID-19 was not significant (OR 0.96, 95% CI 0.7-1.2). Conclusions. In our single center study, smoking status was not associated with severe COVID-19 infection.
基本原理。吸烟状况与严重冠状病毒病-2019 (COVID-19)之间的关系仍存在争议。评估作为严重COVID-19标志的14天住院的风险，在美国单一学术卫生系统中，与从不吸烟且病毒检测呈阳性的患者相比，吸烟者和严重急性呼吸综合征冠状病毒2 (SARS-CoV-2)检测呈阳性的患者。方法。我们对2020年3月1日至10月30日期间在德克萨斯大学医疗分支卫生系统中检测出SARS-CoV-2阳性的患者进行了一项回顾性队列研究，以确定与不吸烟者相比，吸烟者住院14天的风险。结果。在我们的研究期间，我们确定了5,738名符合纳入标准并有吸烟习惯的患者。在这一群体中，636人(11%)被认为是长期吸烟者。191例患者为当前吸烟者，445例为前吸烟者。在5738例患者中，35.1%为男性，平均年龄为43.8 (SD±17.6)岁，37.4%为白种人，51.5%为肥胖(BMI≥30)，3.19%有吸电子烟史，76.5%至少有一种合并症。我们发现624例(10.8%)患者在14天内入院，49例(0.8%)患者在14天内死亡。曾经吸烟者在14天内入院的比例大于从未吸烟者(17.9% vs 10%， p<0.0001)。此外，吸烟者在14天内死亡的比例大于从不吸烟者(2.8% vs 0.6%， p<0.0001)。然而，在调整了其他协变量后，曾经吸烟的COVID-19患者住院14天的几率并不显著(OR 0.96, 95% CI 0.7-1.2)。结论。在我们的单中心研究中，吸烟状况与严重的COVID-19感染无关。
{"title":"Association of Smoking Status with Severe COVID-19","authors":"D. Puebla Neira, A. Watts, J. Seashore, E. Hsu, Y. Kuo, G. Sharma","doi":"10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a3088","DOIUrl":"https://doi.org/10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a3088","url":null,"abstract":"Rationale. The association between smoking status and severe Coronavirus Disease-2019 (COVID-19) remains controversial. To assess the risk of 14-day hospitalization, as a marker of severe COVID-19, in patients who are ever-smokers and tested positive for the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) compared to those who are never smokers and tested positive for the virus in a single academic health system in the United States. Methods. We conducted a retrospective cohort study of patients who tested positive for SARS-CoV-2 in the University of Texas Medical Branch Health System between March 1st and October 30th 2020 to identify the risk of 14-day hospitalization in ever-smokers compared to non-smokers. Results. In our study period, we identified 5,738 patients who met the inclusion criteria and had documentation of smoking habits. Out of this group, 636 (11%) were consider to be ever-smokers. One hundred and ninety one patients were current smokers and 445 were former smokers. Of the 5,738 patients, 35.1% were male, average age was 43.8 (SD± 17.6), 37.4% were Caucasian, 51.5% were obese (BMI≥30), 3.19 % had vaping history, and 76.5% had at least one comorbidity. We identified 624 (10.8%) patients who were admitted in 14 days and 49(0.8%) who died in 14 days during hospitalization. The percentage of ever smokers admitted in 14 days was greater than that of never smokers (17.9% vs 10%, p<0.0001). In addition, the percentage of smokers who died in 14 days was greater than that of never smokers (2.8% vs 0.6%, p<0.0001). However, after adjusting for other covariates the odds for 14-day hospitalization among ever smokers with COVID-19 was not significant (OR 0.96, 95% CI 0.7-1.2). Conclusions. In our single center study, smoking status was not associated with severe COVID-19 infection.","PeriodicalId":375809,"journal":{"name":"TP63. TP063 COVID-19 IN ENVIRONMENTAL, OCCUPATIONAL, AND POPULATION HEALTH","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127258567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a3092
A. Chin, K. Chin, T. Chin
Rationale: Predicting deaths from COVID-19 in the near-term has important public health implications. National models may not be applicable at the county level, where limited test availability and/or delays in test results may alter the relationship between COVID-19 diagnoses and deaths. Methods: Publicly available data for daily new COVID-19 cases and deaths from March 4th, 2020 to December 1st, 2020 in Dallas County was obtained from the Texas Department of State Health Services website on December 17th, 2020. COVID-19 cases were reported by local health departments based on the date of test results, while deaths were reported based on death certificates. Due to the lag in case and death reporting, the last two weeks prior to the date of download were excluded. A linear regression was performed using the 7-day rolling average of newly reported cases vs the 7-day rolling average of new deaths utilizing different lag periods. The lag period resulting in the highest R2 value was identified. A similar analysis was subsequently performed in three other Texas counties. Results: Dallas County, which has a population of 2.636 million, had 114,981 confirmed COVID-19 cases and 1708 COVID-19 related deaths over the study period. As shown in Figure 1A, The maximum R2 value was observed at a lag period of 10 days (R2 = 0.8158, p < 0.001). Spikes in cases were seen in July and late November, with deaths following shortly after (Figure 1B). Similar results were seen in Tarrant and Bexar counties, with a maximum R2 value occurring at a lag period of 12 and 7 days (R2 = 0.7323, R2 = 0.7800), respectively. However, Harris County had a maximum R2 value at a lag of only 2 days (R2 = 0.7324). Discussion: Potential contributors to the lag between diagnosis and death include the disease process itself as well as county specific delays in testing and/or testing reporting. In particular, in locations with large surges, cases may overwhelm testing capabilities such that mean case count is under reported, and more cases are identified late in the disease process. Conclusions: In all four counties, peaks in deaths from COVID-19 closely followed peaks in reported cases. In three of four counties, the lag was 7-12 days, consistent with the expected lag between diagnosis and death. In Harris county however, the lag was only 2 days, supporting the idea that national models may not be applicable at a county level.
理由:预测近期COVID-19的死亡人数具有重要的公共卫生意义。国家模式可能不适用于县一级，在县一级，有限的检测可用性和/或检测结果的延迟可能会改变COVID-19诊断与死亡之间的关系。方法:从2020年12月17日德克萨斯州卫生服务部网站获取2020年3月4日至2020年12月1日达拉斯县每日新增COVID-19病例和死亡的公开数据。当地卫生部门根据检测结果日期报告新冠肺炎病例，根据死亡证明报告死亡人数。由于病例和死亡报告滞后，因此不包括下载日期前最后两周的病例。使用新报告病例的7天滚动平均值与使用不同滞后期的新死亡的7天滚动平均值进行线性回归。确定了导致最高R2值的滞后时间。随后在德克萨斯州的其他三个县进行了类似的分析。结果:达拉斯县有263.6万人口，在研究期间确诊了114981例COVID-19病例，1708例COVID-19相关死亡。如图1A所示，滞后期为10 d时，R2值达到最大值(R2 = 0.8158, p <0.001)。7月和11月下旬出现病例高峰，随后不久出现死亡(图1B)。塔兰特县和贝尔县也出现了类似的结果，R2最大值分别出现在滞后12天和7天(R2 = 0.7323, R2 = 0.780)。而哈里斯县的R2最大值仅滞后2 d (R2 = 0.7324)。讨论:造成诊断和死亡之间滞后的潜在因素包括疾病过程本身以及国家在检测和/或检测报告方面的特定延迟。特别是，在大量激增的地区，病例可能超过检测能力，导致报告的平均病例数不足，并且在疾病过程的后期发现了更多病例。结论:在所有四个县，COVID-19死亡高峰与报告病例高峰密切相关。在4个县中，有3个县的滞后期为7-12天，与诊断和死亡之间的预期滞后期一致。然而，在哈里斯县，滞后时间仅为2天，这支持了国家模式可能不适用于县一级的观点。
{"title":"Correlation Between COVID-19 Cases and Deaths in Four Texas Counties","authors":"A. Chin, K. Chin, T. Chin","doi":"10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a3092","DOIUrl":"https://doi.org/10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a3092","url":null,"abstract":"Rationale: Predicting deaths from COVID-19 in the near-term has important public health implications. National models may not be applicable at the county level, where limited test availability and/or delays in test results may alter the relationship between COVID-19 diagnoses and deaths. Methods: Publicly available data for daily new COVID-19 cases and deaths from March 4th, 2020 to December 1st, 2020 in Dallas County was obtained from the Texas Department of State Health Services website on December 17th, 2020. COVID-19 cases were reported by local health departments based on the date of test results, while deaths were reported based on death certificates. Due to the lag in case and death reporting, the last two weeks prior to the date of download were excluded. A linear regression was performed using the 7-day rolling average of newly reported cases vs the 7-day rolling average of new deaths utilizing different lag periods. The lag period resulting in the highest R2 value was identified. A similar analysis was subsequently performed in three other Texas counties. Results: Dallas County, which has a population of 2.636 million, had 114,981 confirmed COVID-19 cases and 1708 COVID-19 related deaths over the study period. As shown in Figure 1A, The maximum R2 value was observed at a lag period of 10 days (R2 = 0.8158, p < 0.001). Spikes in cases were seen in July and late November, with deaths following shortly after (Figure 1B). Similar results were seen in Tarrant and Bexar counties, with a maximum R2 value occurring at a lag period of 12 and 7 days (R2 = 0.7323, R2 = 0.7800), respectively. However, Harris County had a maximum R2 value at a lag of only 2 days (R2 = 0.7324). Discussion: Potential contributors to the lag between diagnosis and death include the disease process itself as well as county specific delays in testing and/or testing reporting. In particular, in locations with large surges, cases may overwhelm testing capabilities such that mean case count is under reported, and more cases are identified late in the disease process. Conclusions: In all four counties, peaks in deaths from COVID-19 closely followed peaks in reported cases. In three of four counties, the lag was 7-12 days, consistent with the expected lag between diagnosis and death. In Harris county however, the lag was only 2 days, supporting the idea that national models may not be applicable at a county level.","PeriodicalId":375809,"journal":{"name":"TP63. TP063 COVID-19 IN ENVIRONMENTAL, OCCUPATIONAL, AND POPULATION HEALTH","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134163323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a3078
A. Diwadkar, K. Taquechel, S. Sayed, J. Dudley, R. Grundmeier, C. Kenyon, S. Henrickson, D. A. Hill, B. Himes
Rationale: The COVID-19 pandemic dramatically changed daily routines as well as healthcare utilization and delivery patterns in the United States. We sought to identify changes in pediatric asthma-related healthcare utilization and levels of air pollution i.e. particulate matter (PM2.5, PM10) and gaseous chemicals (NO2, O3) during the COVID-19 pandemic in Philadelphia. We hypothesized that declining utilization of asthma care and changed pollution levels during the early stages of the pandemic rebounded after the relaxation of COVID-19-related public health measures. Methods: For the time period Mar 17 to Dec 17 during the years 2015-2020, asthmarelated encounters and weekly summaries of respiratory viral testing data were extracted from Children's Hospital of Philadelphia (CHOP) electronic health records. Daily average estimates of PM2.5, PM10, O3, and NO2 for the same time period were obtained from AirData, an EPA resource that provides quality-assured summary air pollution measures collected from outdoor regulatory monitors across the United States. Patterns in encounter characteristics and viral testing in Philadelphia from Mar 17 to Dec 17, 2020, were compared to data from 2015-2019 as a historical reference. Encounter pattern results were summarized as percentage changes. Controlled interrupted time series regression models were created to identify statistically significant differences in pollution levels that differed in 2020 compared with historical time periods. Results: We present data on asthma encounters, viral testing, and air pollution from Mar 2020 through Dec 2020. Contrary to the early stages of the pandemic when in-person asthma encounters decreased by 87% (outpatient) and 84% (emergency + inpatient), asthma-related encounters rebounded with the relaxation of COVID-19-related public health measures. During the initial months of the pandemic, the daily average of PM2.5, PM10, and NO2 levels decreased by 29.0% (2.17 μg/m3), 18.2% (3.13 μg/m3), and 44.1% (6.75 ppb), respectively, whereas ozone levels increased by 43.4% (10.08 ppb), changes that were not statistically significantly different compared to historical trends. Levels of all pollutants considered remained similar during subsequent 2020 months compared to the 2015-2019 reference period. Conclusion: The COVID-19 pandemic in Philadelphia was accompanied by initial decreases in pediatric asthma healthcare activity. Concurrent with the relaxation of COVID-19-related public health measures, there was a subsequent increase in asthma healthcare activity. No substantial change in air pollution levels compared with historical patterns was observed during the time period considered, suggesting that other factors influenced changes in asthma trends during the COVID-19 pandemic.
{"title":"Trends in Philadelphia Asthma Encounters and Pollution During the COVID-19 Pandemic","authors":"A. Diwadkar, K. Taquechel, S. Sayed, J. Dudley, R. Grundmeier, C. Kenyon, S. Henrickson, D. A. Hill, B. Himes","doi":"10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a3078","DOIUrl":"https://doi.org/10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a3078","url":null,"abstract":"Rationale: The COVID-19 pandemic dramatically changed daily routines as well as healthcare utilization and delivery patterns in the United States. We sought to identify changes in pediatric asthma-related healthcare utilization and levels of air pollution i.e. particulate matter (PM2.5, PM10) and gaseous chemicals (NO2, O3) during the COVID-19 pandemic in Philadelphia. We hypothesized that declining utilization of asthma care and changed pollution levels during the early stages of the pandemic rebounded after the relaxation of COVID-19-related public health measures. Methods: For the time period Mar 17 to Dec 17 during the years 2015-2020, asthmarelated encounters and weekly summaries of respiratory viral testing data were extracted from Children's Hospital of Philadelphia (CHOP) electronic health records. Daily average estimates of PM2.5, PM10, O3, and NO2 for the same time period were obtained from AirData, an EPA resource that provides quality-assured summary air pollution measures collected from outdoor regulatory monitors across the United States. Patterns in encounter characteristics and viral testing in Philadelphia from Mar 17 to Dec 17, 2020, were compared to data from 2015-2019 as a historical reference. Encounter pattern results were summarized as percentage changes. Controlled interrupted time series regression models were created to identify statistically significant differences in pollution levels that differed in 2020 compared with historical time periods. Results: We present data on asthma encounters, viral testing, and air pollution from Mar 2020 through Dec 2020. Contrary to the early stages of the pandemic when in-person asthma encounters decreased by 87% (outpatient) and 84% (emergency + inpatient), asthma-related encounters rebounded with the relaxation of COVID-19-related public health measures. During the initial months of the pandemic, the daily average of PM2.5, PM10, and NO2 levels decreased by 29.0% (2.17 μg/m3), 18.2% (3.13 μg/m3), and 44.1% (6.75 ppb), respectively, whereas ozone levels increased by 43.4% (10.08 ppb), changes that were not statistically significantly different compared to historical trends. Levels of all pollutants considered remained similar during subsequent 2020 months compared to the 2015-2019 reference period. Conclusion: The COVID-19 pandemic in Philadelphia was accompanied by initial decreases in pediatric asthma healthcare activity. Concurrent with the relaxation of COVID-19-related public health measures, there was a subsequent increase in asthma healthcare activity. No substantial change in air pollution levels compared with historical patterns was observed during the time period considered, suggesting that other factors influenced changes in asthma trends during the COVID-19 pandemic.","PeriodicalId":375809,"journal":{"name":"TP63. TP063 COVID-19 IN ENVIRONMENTAL, OCCUPATIONAL, AND POPULATION HEALTH","volume":"306 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122970080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a3080
H. Bayram, Kayalar, A. Ari, G. Babuccu, N. Konyalilar, Doğan, F. Can, E. Gaga, L. Kuzu, P. Arı, M. Odabasi, Y. Tasdemir, S. Cindoruk, F. Esen, E. Sakın, B. Çalışkan, L. Tecer, M. Ficici, A. Altın, B. Onat, C. Ayvaz, B. Uzun, A. Saral, T. Döğeroğlu, S. Malkoc, Üzmez, F. Kunt, S. Aydın, M. Kara, B. Yaman, G. Doğan, B. Olgun, E. Dokumacı, G. Güllü, E. Uzunpinar, Şahin
RATIONALE: Coronavirus disease 2019 (COVID-19), which is caused by the SARS-CoV-2, has been affecting the world since the end of 2019. Turkey is severely affected with the first case being reported on March 11th 2020. Several studies suggest an association between air pollution and the spread of the infection, and that ambient particulate matters (PM) can present a potential, as virus carriers. The aim of the present study was to investigate the presence of SARS-CoV-2 RNA on ambient PM. METHODS: Ambient PM samples in various size ranges were collected from 13 sites including urban, urban background locations and hospital gardens in 10 cities including Istanbul, Ankara, Izmir, Zonguldak, Tekirdag, Eskisehir, Bolu, Bursa, Konya, and Antalya across Turkey, between 13th of May and 14th of June, 2020. The nucleocapsid (N) 1 gene and RNA dependent RNA polymerase (RdRP) gene expressions were analyzed in PM samples for the presence of SARS-CoV-2 by applying quantitative real time-polymerase chain reaction (qRT-PCR) and three dimensional (3D)-digital PCR methods. RESULTS: A total of 155 daily samples (Total Suspended Particulate [TSP], n=80;PM2.5, n=33;PM2.5-10, n=23;PM10, n=19;and 6 size segregated, n=48) were collected using various samplers in the each city. According to RT-PCR and 3D-RT-PCR analysis, dual RdRP and N1 gene positivity were detected in 20 of the samples (9.8 %). The highest percentage of virus detection on PM samples was from hospital gardens in Tekirda Zonguldak, and Istanbul, especially in PM2.5 mode. Samples collected from two urban sites, Ankara and Eskisehir, were also positive. CONCLUSIONS: These findings suggest that SARS-CoV-2 may be transported by ambient particles, especially at sites close to the infection hot-spots such as hospital gardens. Whether this has an impact on the spread of the virus infection remains to be determined.
{"title":"Presence of Severe Acute Respiratory Syndrome-Related Coronavirus 2 (SARS-CoV-2) RNA on Particulate Matters: A Multi Central Study in Turkey","authors":"H. Bayram, Kayalar, A. Ari, G. Babuccu, N. Konyalilar, Doğan, F. Can, E. Gaga, L. Kuzu, P. Arı, M. Odabasi, Y. Tasdemir, S. Cindoruk, F. Esen, E. Sakın, B. Çalışkan, L. Tecer, M. Ficici, A. Altın, B. Onat, C. Ayvaz, B. Uzun, A. Saral, T. Döğeroğlu, S. Malkoc, Üzmez, F. Kunt, S. Aydın, M. Kara, B. Yaman, G. Doğan, B. Olgun, E. Dokumacı, G. Güllü, E. Uzunpinar, Şahin","doi":"10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a3080","DOIUrl":"https://doi.org/10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a3080","url":null,"abstract":"RATIONALE: Coronavirus disease 2019 (COVID-19), which is caused by the SARS-CoV-2, has been affecting the world since the end of 2019. Turkey is severely affected with the first case being reported on March 11th 2020. Several studies suggest an association between air pollution and the spread of the infection, and that ambient particulate matters (PM) can present a potential, as virus carriers. The aim of the present study was to investigate the presence of SARS-CoV-2 RNA on ambient PM. METHODS: Ambient PM samples in various size ranges were collected from 13 sites including urban, urban background locations and hospital gardens in 10 cities including Istanbul, Ankara, Izmir, Zonguldak, Tekirdag, Eskisehir, Bolu, Bursa, Konya, and Antalya across Turkey, between 13th of May and 14th of June, 2020. The nucleocapsid (N) 1 gene and RNA dependent RNA polymerase (RdRP) gene expressions were analyzed in PM samples for the presence of SARS-CoV-2 by applying quantitative real time-polymerase chain reaction (qRT-PCR) and three dimensional (3D)-digital PCR methods. RESULTS: A total of 155 daily samples (Total Suspended Particulate [TSP], n=80;PM2.5, n=33;PM2.5-10, n=23;PM10, n=19;and 6 size segregated, n=48) were collected using various samplers in the each city. According to RT-PCR and 3D-RT-PCR analysis, dual RdRP and N1 gene positivity were detected in 20 of the samples (9.8 %). The highest percentage of virus detection on PM samples was from hospital gardens in Tekirda Zonguldak, and Istanbul, especially in PM2.5 mode. Samples collected from two urban sites, Ankara and Eskisehir, were also positive. CONCLUSIONS: These findings suggest that SARS-CoV-2 may be transported by ambient particles, especially at sites close to the infection hot-spots such as hospital gardens. Whether this has an impact on the spread of the virus infection remains to be determined.","PeriodicalId":375809,"journal":{"name":"TP63. TP063 COVID-19 IN ENVIRONMENTAL, OCCUPATIONAL, AND POPULATION HEALTH","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129328631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a3085
R. Stern, P. Koutrakis, M. Martins, B. Lemos, S. Dowd, E. Sunderland, E. Garshick
Rationale: The mechanism for spread of Coronavirus Disease 2019 (COVID-19) has been attributed to large droplets produced by coughing and sneezing. There is controversy whether smaller particles may transport Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19. Smaller particles, referred to as fine particulate matter (≤2.5 μm in diameter), can remain airborne for longer periods than larger particles and after inhalation will penetrate deeply into the lungs. Little is known about the size distribution and location of airborne SARS-CoV-2 RNA in a hospital setting. Methods: As a measure of hospitalrelated exposure, air samples of three particle sizes (>10.0 μm, 10.0-2.5 μm, and ≤2.5 μm) were collected at Veterans Affairs Boston Healthcare System from April to May 2020 (N=90 size-fractionated samples) using a custom-built cascade impactor. Locations included outside negative-pressure COVID-19 wards, a hospital ward not directly involved in COVID-19 patient care, and the emergency department. Results: SARS-CoV-2 RNA was present in 9% of samples and in all size fractions at concentrations of 5 to 51 copies m-3. Locations outside COVID-19 wards had the fewest positive samples. A non-COVID-19 ward had the highest number of positive samples, likely reflecting staff congregation. Among all locations, the probability of a positive sample was positively associated (r=0.95, p<0.01) with the number of COVID-19 patients in the hospital, which reflected (r=0.99, p<0.01) the number of new daily cases of COVID-19 in Massachusetts. Conclusions: More frequent detection of positive samples in non-COVID-19 wards than outside COVID-19 hospital areas indicates effectiveness of COVID-ward hospital controls in controlling air concentrations and suggests the potential for disease spread in areas without the strictest precautions. The positive associations noted between the probability of a positive sample, COVID-19 cases in the hospital, and cases in Massachusetts suggests that hospital air sample positivity was related to community burden. The finding of SARS-CoV-2 RNA in samples of fine particulate matter supports the possibility of airborne transmission over distances greater than six feet. The findings support guidelines that limit exposure to airborne particles including fine particles capable of longer distance transport and greater lung penetration.
{"title":"Characterization of Airborne SARS-CoV-2 in a Veterans Affairs Medical Center","authors":"R. Stern, P. Koutrakis, M. Martins, B. Lemos, S. Dowd, E. Sunderland, E. Garshick","doi":"10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a3085","DOIUrl":"https://doi.org/10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a3085","url":null,"abstract":"Rationale: The mechanism for spread of Coronavirus Disease 2019 (COVID-19) has been attributed to large droplets produced by coughing and sneezing. There is controversy whether smaller particles may transport Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19. Smaller particles, referred to as fine particulate matter (≤2.5 μm in diameter), can remain airborne for longer periods than larger particles and after inhalation will penetrate deeply into the lungs. Little is known about the size distribution and location of airborne SARS-CoV-2 RNA in a hospital setting. Methods: As a measure of hospitalrelated exposure, air samples of three particle sizes (>10.0 μm, 10.0-2.5 μm, and ≤2.5 μm) were collected at Veterans Affairs Boston Healthcare System from April to May 2020 (N=90 size-fractionated samples) using a custom-built cascade impactor. Locations included outside negative-pressure COVID-19 wards, a hospital ward not directly involved in COVID-19 patient care, and the emergency department. Results: SARS-CoV-2 RNA was present in 9% of samples and in all size fractions at concentrations of 5 to 51 copies m-3. Locations outside COVID-19 wards had the fewest positive samples. A non-COVID-19 ward had the highest number of positive samples, likely reflecting staff congregation. Among all locations, the probability of a positive sample was positively associated (r=0.95, p<0.01) with the number of COVID-19 patients in the hospital, which reflected (r=0.99, p<0.01) the number of new daily cases of COVID-19 in Massachusetts. Conclusions: More frequent detection of positive samples in non-COVID-19 wards than outside COVID-19 hospital areas indicates effectiveness of COVID-ward hospital controls in controlling air concentrations and suggests the potential for disease spread in areas without the strictest precautions. The positive associations noted between the probability of a positive sample, COVID-19 cases in the hospital, and cases in Massachusetts suggests that hospital air sample positivity was related to community burden. The finding of SARS-CoV-2 RNA in samples of fine particulate matter supports the possibility of airborne transmission over distances greater than six feet. The findings support guidelines that limit exposure to airborne particles including fine particles capable of longer distance transport and greater lung penetration.","PeriodicalId":375809,"journal":{"name":"TP63. TP063 COVID-19 IN ENVIRONMENTAL, OCCUPATIONAL, AND POPULATION HEALTH","volume":"262 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129477410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a3082
B. Robinson, R. Sandhu, J. Edwards, T. Kendzerska, A. Sarkar
Introduction: Many variations of the Kermack-McKendrick SIR model were proposed in the early stages of the SARS-CoV-2 pandemic to study the transmission of COVID-19. The current state-of-the-art 16 compartment model developed by Tuite et. al (2020) is used to simulate the influence of government policies and leverage early available clinical information to predict the dynamics of the disease. As much of the world is now experiencing a second wave and vaccines have been approved and are being deployed;it is critical to be able to accurately predict the trajectory of cases while integrating information about these new model states and parameters. Challenges for accurate predictions are two-fold: firstly, the mechanistic model must capture the essential dynamics of the pandemic as well provide meaningful information on quantities of interest (e.g. demand for hospital resources), and secondly, the model parameters need to be calibrated using epidemiological and clinical data. Methods: To address the first challenge, we propose a compartmental model that expands upon model developed by Tuite et al. (2020) to capture the effects of vaccination, reinfection, asymptomatic carriers, inadequate access to hospital resources, and long-term health complications. As the complexity of the model increases, the inference task becomes more difficult and prone to over-fitting. As such, the nonlinear sparse Bayesian learning (NSBL) algorithm is proposed for parameter estimation. Results: The algorithm is demonstrated for noisy and incomplete synthetic data generated from an SIRS model with three uncertain parameters (infection rate, recovery rate and the rate temporary immunity is lost). As an example, Figure 1 shows the calibration of the three uncertain model parameters within a Bayesian framework while avoiding over-fitting by inducing sparsity in the parameters. Assuming there is little prior information available for the parameters, they are first assigned non-informative priors. Before NSBL, the model (red curve) is over-parameterized, and fails to predict the decline of the (blue) infection curve. The NSBL algorithm makes use of automatic relevance determination (ARD) priors, and finds one of the model parameters to be irrelevant to the dynamics. Removing the irrelevant parameter and re-calibrating enables the model (green curve) to capture the peak of the infection curve. Conclusion: An optimally calibrated model will allow for the concurrent forecasting of many hypothetical scenarios and provide clinically relevant predictions.
{"title":"A Sparse Bayesian Model Selection Algorithm for Forecasting the Transmission of COVID-19","authors":"B. Robinson, R. Sandhu, J. Edwards, T. Kendzerska, A. Sarkar","doi":"10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a3082","DOIUrl":"https://doi.org/10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a3082","url":null,"abstract":"Introduction: Many variations of the Kermack-McKendrick SIR model were proposed in the early stages of the SARS-CoV-2 pandemic to study the transmission of COVID-19. The current state-of-the-art 16 compartment model developed by Tuite et. al (2020) is used to simulate the influence of government policies and leverage early available clinical information to predict the dynamics of the disease. As much of the world is now experiencing a second wave and vaccines have been approved and are being deployed;it is critical to be able to accurately predict the trajectory of cases while integrating information about these new model states and parameters. Challenges for accurate predictions are two-fold: firstly, the mechanistic model must capture the essential dynamics of the pandemic as well provide meaningful information on quantities of interest (e.g. demand for hospital resources), and secondly, the model parameters need to be calibrated using epidemiological and clinical data. Methods: To address the first challenge, we propose a compartmental model that expands upon model developed by Tuite et al. (2020) to capture the effects of vaccination, reinfection, asymptomatic carriers, inadequate access to hospital resources, and long-term health complications. As the complexity of the model increases, the inference task becomes more difficult and prone to over-fitting. As such, the nonlinear sparse Bayesian learning (NSBL) algorithm is proposed for parameter estimation. Results: The algorithm is demonstrated for noisy and incomplete synthetic data generated from an SIRS model with three uncertain parameters (infection rate, recovery rate and the rate temporary immunity is lost). As an example, Figure 1 shows the calibration of the three uncertain model parameters within a Bayesian framework while avoiding over-fitting by inducing sparsity in the parameters. Assuming there is little prior information available for the parameters, they are first assigned non-informative priors. Before NSBL, the model (red curve) is over-parameterized, and fails to predict the decline of the (blue) infection curve. The NSBL algorithm makes use of automatic relevance determination (ARD) priors, and finds one of the model parameters to be irrelevant to the dynamics. Removing the irrelevant parameter and re-calibrating enables the model (green curve) to capture the peak of the infection curve. Conclusion: An optimally calibrated model will allow for the concurrent forecasting of many hypothetical scenarios and provide clinically relevant predictions.","PeriodicalId":375809,"journal":{"name":"TP63. TP063 COVID-19 IN ENVIRONMENTAL, OCCUPATIONAL, AND POPULATION HEALTH","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115560201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a3081
S. Salcin, G. Kumar
Rationale: The SARS-CoV-2 virus responsible for COVID-19 is known to cause coagulopathy and thrombotic events in affected patients. In a recent meta-analysis, the rate of venous thromboembolism (VTE) in hospitalized COVID-19 patients was estimated to be about 17%. However, the incidence of VTE in COVID-19 is not clearly reported at the population level. We examined the incidence of VTE in COVID-19 at a population level in order to calculate incidence rates and make a comparison to rates from the same population in the previous year. Methods: We performed a retrospective analysis across a multi-center community-based hospital system for all adult patients (age ≥18 years) admitted with a positive COVID-19 test from March 1, 2020 to September 18, 2020. Patients were identified in the electronic medical record (EMR) using ICD10 codes for VTE (both pulmonary embolism and deep venous thrombosis). Chart review of the EMR was also used to obtain relevant demographic, clinical, and laboratory data. Patients with VTE confirmed by imaging studies were included. Incidence rates were calculated using total COVID-19 case count per county. The same methodology was then used to evaluate VTE from March 1, 2019 to September 18, 2019 in adult patients from the same counties. Comparison incidence rates were calculated using 2019 county population data. Results: During the 2020 study period, there were 1,258 total admissions for COVID-19. Of these, 51 patients with VTE were identified from 11 counties: 22 developed DVT and 29 developed PE (total = 51). The average calculated incidence rate of VTE in COVID-19 was 252 per 100,000 population (Graph 1). During the 2019 study period, 526 patients from the same 11 counties were diagnosed with VTE. The average incidence rate of total VTE was 60 per 100,000 population. The incidence rate of VTE in the same population was 4.2 times higher in patients with COVID-19. Conclusions: The incidence of VTE in COVID-19 is approximately 4.2 times higher than incidence rates among the same population without COVID-19 in 2019.
{"title":"Comparison of Incidence of Venous Thromboembolism (VTE) to Baseline During the COVID-19 Pandemic in a Community-Based Healthcare System","authors":"S. Salcin, G. Kumar","doi":"10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a3081","DOIUrl":"https://doi.org/10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a3081","url":null,"abstract":"Rationale: The SARS-CoV-2 virus responsible for COVID-19 is known to cause coagulopathy and thrombotic events in affected patients. In a recent meta-analysis, the rate of venous thromboembolism (VTE) in hospitalized COVID-19 patients was estimated to be about 17%. However, the incidence of VTE in COVID-19 is not clearly reported at the population level. We examined the incidence of VTE in COVID-19 at a population level in order to calculate incidence rates and make a comparison to rates from the same population in the previous year. Methods: We performed a retrospective analysis across a multi-center community-based hospital system for all adult patients (age ≥18 years) admitted with a positive COVID-19 test from March 1, 2020 to September 18, 2020. Patients were identified in the electronic medical record (EMR) using ICD10 codes for VTE (both pulmonary embolism and deep venous thrombosis). Chart review of the EMR was also used to obtain relevant demographic, clinical, and laboratory data. Patients with VTE confirmed by imaging studies were included. Incidence rates were calculated using total COVID-19 case count per county. The same methodology was then used to evaluate VTE from March 1, 2019 to September 18, 2019 in adult patients from the same counties. Comparison incidence rates were calculated using 2019 county population data. Results: During the 2020 study period, there were 1,258 total admissions for COVID-19. Of these, 51 patients with VTE were identified from 11 counties: 22 developed DVT and 29 developed PE (total = 51). The average calculated incidence rate of VTE in COVID-19 was 252 per 100,000 population (Graph 1). During the 2019 study period, 526 patients from the same 11 counties were diagnosed with VTE. The average incidence rate of total VTE was 60 per 100,000 population. The incidence rate of VTE in the same population was 4.2 times higher in patients with COVID-19. Conclusions: The incidence of VTE in COVID-19 is approximately 4.2 times higher than incidence rates among the same population without COVID-19 in 2019.","PeriodicalId":375809,"journal":{"name":"TP63. TP063 COVID-19 IN ENVIRONMENTAL, OCCUPATIONAL, AND POPULATION HEALTH","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124851118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a3094
C. Nayar, A. Bhatt, J. Hagedorn, N. Amoroso, R. Condos, E. Hasan, S. Brosnahan
Background The long-term effects of SARS-CoV-2 are just now coming to light. These remaining symptoms are sometimes referred to as “Post-COVID syndrome.” The types and incidence of prolonged symptoms from the acute viral illness are unknown. Yet understanding the prevalence and which symptoms persist would help normalize post COVID syndrome and help providers recognize these issues in their COVID survivors. Methods We conducted a single-center retrospective analysis with patients discharged from New York University (NYU) Langone Hospital with primary diagnosis of COVID-19. Each patient was then called and given a phone survey 45-60 days post discharge. In the survey they were consented and asked about residual symptoms. Study data were collected and managed using REDCap electronic data capture tools hosted at NYU hospital. Patient surveys were then merged with their medical record from their COVID hospitalization. All statistical analysis was processed in SPSS. The study was approved through our institutional IRB. Results Overall, 101 patients were surveyed post discharge. The median age was 59, with the most common co-morbidities being DM (N = 20) and HTN (N = 45). Most patients (N= 57) reported residual lethargy and malaise as compared to prior. Thirty-eight patients continued to have limited exercise tolerance. Thirty- eight patients experienced shortness of breath more than prior to getting COVID, while 24 patients continued to have shortness of breath while walking within their house. Some experienced chest pain with breathing (N=5), dry cough (N=14) and productive cough (N=5) that was not present prior to COVID infection. Conclusion We found that COVID patients continued to have symptoms 2 months post discharge. More than half of patients reached reported continued lethargy post discharge. Other symptoms were quite common, with 1/4-1/3 having continued shortness of breath and decreased exercise tolerance. The full pathophysiology between continued symptoms and post COVID syndrome is not yet known;however, clinicians need to understand the prevalence to treat patients accordingly. Physicians should help to normalize these symptoms to patients. Treatment should include supportive care such as rehab and physical therapy with consideration of referral to post COVID centers.
{"title":"Prevalence and Symptomatology of Post COVID Syndrome in Patients Who Required Hospitalization During Acute Illness","authors":"C. Nayar, A. Bhatt, J. Hagedorn, N. Amoroso, R. Condos, E. Hasan, S. Brosnahan","doi":"10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a3094","DOIUrl":"https://doi.org/10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a3094","url":null,"abstract":"Background The long-term effects of SARS-CoV-2 are just now coming to light. These remaining symptoms are sometimes referred to as “Post-COVID syndrome.” The types and incidence of prolonged symptoms from the acute viral illness are unknown. Yet understanding the prevalence and which symptoms persist would help normalize post COVID syndrome and help providers recognize these issues in their COVID survivors. Methods We conducted a single-center retrospective analysis with patients discharged from New York University (NYU) Langone Hospital with primary diagnosis of COVID-19. Each patient was then called and given a phone survey 45-60 days post discharge. In the survey they were consented and asked about residual symptoms. Study data were collected and managed using REDCap electronic data capture tools hosted at NYU hospital. Patient surveys were then merged with their medical record from their COVID hospitalization. All statistical analysis was processed in SPSS. The study was approved through our institutional IRB. Results Overall, 101 patients were surveyed post discharge. The median age was 59, with the most common co-morbidities being DM (N = 20) and HTN (N = 45). Most patients (N= 57) reported residual lethargy and malaise as compared to prior. Thirty-eight patients continued to have limited exercise tolerance. Thirty- eight patients experienced shortness of breath more than prior to getting COVID, while 24 patients continued to have shortness of breath while walking within their house. Some experienced chest pain with breathing (N=5), dry cough (N=14) and productive cough (N=5) that was not present prior to COVID infection. Conclusion We found that COVID patients continued to have symptoms 2 months post discharge. More than half of patients reached reported continued lethargy post discharge. Other symptoms were quite common, with 1/4-1/3 having continued shortness of breath and decreased exercise tolerance. The full pathophysiology between continued symptoms and post COVID syndrome is not yet known;however, clinicians need to understand the prevalence to treat patients accordingly. Physicians should help to normalize these symptoms to patients. Treatment should include supportive care such as rehab and physical therapy with consideration of referral to post COVID centers.","PeriodicalId":375809,"journal":{"name":"TP63. TP063 COVID-19 IN ENVIRONMENTAL, OCCUPATIONAL, AND POPULATION HEALTH","volume":"124 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133419464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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