Information on vaccine acceptance among healthcare workers is needed as health professionals provide front line care to COVID-19 patients. We developed and implemented an anonymous internet-based cross-sectional survey with direct solicitation among employees of a safety net health system. Items queried demographic and health-related characteristics, experience with and knowledge of COVID-19, and determinants of decisions to vaccinate. COVID-19 vaccine acceptance groups (acceptors, hesitant, refusers) were defined; an adapted version of the WHO vaccine hesitancy scale was included. The survey demonstrated good reliability (Cronbachs alpha = 0.92 for vaccine hesitancy scale; 0.93 for determinants). General linear and logistic regression methods examined factors which were univariately associated with vaccine hesitancy and vaccine acceptance, respectively. Multivariable models were constructed with stepwise model-building procedures. Race/ethnicity, marital status, job classification, immunocompromised status, flu vaccination and childhood vaccination opinions independently predicted hesitancy scale scores. Gender, education, job classification and BMI independently predicted acceptance, hesitancy and refusal groups. Among hesitant employees, uncertainty was reflected in reports of motivating factors influencing their indecision. Despite a strong employee-support environment and job protection, respondents reported physical and mental health effects. Appreciation of varied reasons for refusing vaccination should lead to culturally sensitive interventions to increase vaccination rates in healthcare workers.
Background Studies of inpatient COVID-19 mortality risk factors have mainly used data from academic medical centers or large multi-hospital databases and have not examined populations with large proportions of Hispanic/Latino patients. In a retrospective cohort study of 4,881 consecutive adult COVID-19 hospitalizations at a single community hospital in Los Angeles County with a majority Hispanic/Latino population, we evaluated factors associated with mortality. Methods Data on demographic characteristics, comorbidities, laboratory and clinical results, and COVID-19 therapeutics were abstracted from the electronic medical record. Cox proportional hazards regression modelled statistically significantly independently associated predictors of hospital mortality. Results Age ≥ 65 years (HR = 2.66; 95% CI = 1.90, 3.72), male sex (HR = 1.31; 95% CI = 1.07, 1.60), renal disease (HR = 1.52; 95% CI = 1.18, 1.95), cardiovascular disease (HR = 1.45; 95% CI = 1.18, 1.78), neurological disease (HR = 1.84; 95% CI = 1.41, 2.39), D-dimer ≥ 500 ng/ml (HR = 2.07; 95% CI = 1.43, 3.0), and pulse oxygen level < 88% (HR = 1.39; 95% CI = 1.13, 1.71) were independently associated with increased mortality. Patient household with multiple COVID-19 cases, and Asian, Black, or Hispanic compared to White non-Hispanic race/ethnicity were associated with reduced mortality. In hypoxic COVID-19 inpatients, remdesivir, tocilizumab, and convalescent plasma were associated with reduced mortality, and corticosteroid use with increased mortality. Conclusions We corroborate several previously identified mortality risk factors and find evidence that the combination of factors associated with mortality differ between populations.
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