Background: By mid-May 2020, there were over 1.5 million cases of (SARS-CoV-2) or COVID-19 across the U.S. with new confirmed cases continuing to rise following the reopening of most states. Prior studies have focused mainly on clinical risk factors associated with serious illness and mortality of COVID-19. Less analysis has been conducted on the clinical, sociodemographic, and environmental variables associated with initial infection of COVID-19. Methods: A multivariable statistical model was used to characterize risk factors in 34,503cases of laboratoryconfirmed positive or negative COVID-19 infection in the Providence Health System (U.S.) between February 28 and April 27, 2020. Publicly available data were utilized as approximations for social determinants of health, and patientlevel clinical and sociodemographic factors were extracted from the electronic medical record. Results: Higher risk of COVID-19 infection was associated with older age (OR 1.69; 95% CI 1.41-2.02, p < 0.0001), male gender (OR 1.32; 95% CI 1.21-1.44, p < 0.0001), Asian race (OR 1.43; 95% CI 1.18-1.72, p = 0.0002), Black/African American race (OR 1.51; 95% CI 1.25-1.83, p < 0.0001), Latino ethnicity (OR 2.07; 95% CI 1.77-2.41, p < 0.0001), non-English language (OR 2.09; 95% CI 1.7-2.57, p < 0.0001), residing in a neighborhood with financial insecurity (OR 1.10; 95% CI 1.01-1.25, p = 0.04), low air quality (OR 1.01; 95% CI 1.0-1.04, p = 0.05), housing insecurity (OR 1.32; 95% CI 1.16-1.5, p < 0.0001) or transportation insecurity (OR 1.11; 95% CI 1.02-1.23, p = 0.03), and living in senior living communities (OR 1.69; 95% CI 1.23-2.32, p = 0.001). Conclusion: sisk of COVID-19 infection is higher among groups already affected by health disparities across age, race, ethnicity, language, income, and living conditions. Health promotion and disease prevention strategies should prioritize groups most vulnerable to infection and address structural inequities that contribute to risk through social and economic policy.
Background By mid-May 2020, there were over 1.5 million cases of (SARS-CoV-2) or COVID-19 across the U.S. with new confirmed cases continuing to rise following the re-opening of most states. Prior studies have focused mainly on clinical risk factors associated with serious illness and mortality of COVID-19. Emerging risk factors in the U.S., including clinical, sociodemographic, and environmental variables associated with contraction of COVID-19 have not been widely studied to assess disparities across populations. Methods A multivariable statistical model was used to identify predictors associated with COVID-19 contraction in the study population of 34,503 patients, comparing laboratory confirmed positive and negative COVID-19 cases in the Providence Health System (U.S.) between February 28 and April 27, 2020. Publicly available data were utilized as approximations for social determinants of health, and patient-level clinical and sociodemographic factors were extracted from the electronic medical record. Results Higher risk of contraction was associated with older age (OR 1.69; 95% CI 1.41-2.02, p<0.0001), male gender (OR 1.32; 95% CI 1.21-1.44, p<0.0001), Asian race (OR 1.43; 95% CI 1.18-1.72, p= 0.0002), Black/African American race (OR 1.51; 95% CI 1.25-1.83, p<0.0001), Latino ethnicity (OR 2.07; 95% CI 1.77-2.41, p<0.0001), non-English language (OR 2.09; 95% CI 1.7-2.57, p<0.0001), high school education or less (OR 1.02; 95% CI 1.01-1.14, p=0.04), residing in a neighborhood with financial insecurity (OR 1.10; 95% CI 1.01-1.25, p=0.04), low air quality (OR 1.01; 95% CI 1.0-1.04, p=0.05), housing insecurity (OR 1.32; 95% CI 1.16-1.5, p< 0.0001) or transportation insecurity (OR 1.11; 95% CI 1.02-1.23, p=0.03), and living in senior living communities (OR 1.69; 95% CI 1.23-2.32, p= 0.001). Conclusions Risks associated with COVID-19 contraction reflect disparities across age, race, ethnicity, language, socioeconomic status, and living conditions. Health promotion and disease prevention strategies should prioritize groups most vulnerable to contraction and address structural inequities that contribute to risk through social and economic policy.
Determination of the full elastic constants (cij) of methane hydrates (MHs) at extreme pressure-temperature environments is essential to our understanding of the elastic, thermodynamic, and mechanical properties of methane in MH reservoirs on Earth and icy satellites in the solar system. Here, we have investigated the elastic properties of singe-crystal cubic MH-sI, hexagonal MH-II, and orthorhombic MH-III phases at high pressures in a diamond anvil cell. Brillouin light scattering measurements, together with complimentary equation of state (pressure-density) results from X-ray diffraction and methane site occupancies in MH from Raman spectroscopy, were used to derive elastic constants of MH-sI, MH-II, and MH-III phases at high pressures. Analysis of the elastic constants for MH-sI and MH-II showed intriguing similarities and differences between the phases' compressional wave velocity anisotropy and shear wave velocity anisotropy. Our results show that these high-pressure MH phases can exhibit distinct elastic, thermodynamic, and mechanical properties at relevant environments of their respective natural reservoirs. These results provide new insight into the determination of how much methane exists in MH reservoirs on Earth and on icy satellites elsewhere in the solar system and put constraints on the pressure and temperature conditions of their environment.
Background By mid-May 2020, there were over 1.5 million cases of (SARS-CoV-2) or COVID-19 across the U.S. with new confirmed cases continuing to rise following the re-opening of most states. Prior studies have focused mainly on clinical risk factors associated with serious illness and mortality of COVID-19. Emerging risk factors in the U.S., including clinical, sociodemographic, and environmental variables associated with contraction of COVID-19 have not been widely studied to assess disparities across populations. Methods A multivariable statistical model was used to identify predictors associated with COVID-19 contraction in the study population of 34,503 patients, comparing laboratory confirmed positive and negative COVID-19 cases in the Providence Health System (U.S.) between February 28 and April 27, 2020. Publicly available data were utilized as approximations for social determinants of health, and patient-level clinical and sociodemographic factors were extracted from the electronic medical record. Results Higher risk of contraction was associated with older age (OR 1.69; 95% CI 1.41–2.02, p < 0.0001), male gender (OR 1.32; 95% CI 1.21–1.44, p < 0.0001), Asian race (OR 1.43; 95% CI 1.18–1.72, p = 0.0002), Black/African American race (OR 1.51; 95% CI 1.25–1.83, p < 0.0001), Latino ethnicity (OR 2.07; 95% CI 1.77–2.41, p < 0.0001), non-English language (OR 2.09; 95% CI 1.7–2.57, p < 0.0001), high school education or less (OR 1.02; 95% CI 1.01–1.14, p = 0.04), residing in a neighborhood with financial insecurity (OR 1.10; 95% CI 1.01–1.25, p = 0.04), low air quality (OR 1.01; 95% CI 1.0-1.04, p = 0.05), housing insecurity (OR 1.32; 95% CI 1.16–1.5, p < 0.0001) or transportation insecurity (OR 1.11; 95% CI 1.02–1.23, p = 0.03), and living in senior living communities (OR 1.69; 95% CI 1.23–2.32, p = 0.001). Conclusions Risks associated with COVID-19 contraction reflect disparities across age, race, ethnicity, language, socioeconomic status, and living conditions. Health promotion and disease prevention strategies should prioritize groups most vulnerable to contraction and address structural inequities that contribute to risk through social and economic policy.
Background: Notable discrepancies in vulnerability to COVID-19 infection have been identified between specific population groups and regions in the United States. The purpose of this study was estimate likelihood of COVID-19 infection using a machine-learning algorithm that can be updated continuously based on health care data.Methods: Patient records were extracted for all COVID-19 nasal swab PCR tests performed within the Providence St. Joseph Health system from February to October of 2020. Several different machine learning models were tested to evaluate effects of sociodemographic, environmental, and medical history factors on risk of initial COVID-19 infection.Results: A total of 316,599 participants were included in this study and approximately 7.7% (n = 24,358) tested positive for COVID-19. A gradient boosting model, LightGBM (LGBM), predicted risk of initial infection with an area under the receiver operating characteristic curve of 0.819. Factors that predicted infection were cough, fever, being a member of the Hispanic or Latino community, being Spanish speaking, having a history of diabetes or dementia, and living in a neighborhood with housing insecurity. Conclusion: A model trained on sociodemographic, environmental, and medical history data performed well in predicting risk of a positive COVID-19 test. This model could be used to tailor education, public health policy, and resources for communities that are at the greatest risk of infection.
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