Twenty additional years of epidemiologic literature have become available since the publication of two meta-analyses on farming and brain cancer in 1998. The current systematic literature review and meta-analysis extends previous research and harmonizes findings. A random effects model was used to calculate meta-effect estimates from 52 studies (51 articles or reports), including 11 additional studies since 1998. Forty of the 52 studies reported positive associations between farming and brain cancer with effect estimates ranging from 1.03 to 6.53. The overall meta-risk estimate was 1.13 (95% CI = 1.06, 1.21), suggesting that farming is associated with a 13% increase in risk of brain cancer morbidity or mortality. Farming among white populations was associated with a higher risk of brain cancer than among non-white populations. Livestock farming (meta-RR = 1.34; 95% CI = 1.18, 1.53) was associated with a greater risk compared with crop farming (meta-RR = 1.13; 95% CI = 0.97, 1.30). Farmers with documented exposure to pesticides had greater than a 20% elevated risk of brain cancer. Despite heterogeneity among studies, we conclude that the synthesis of evidence from 40 years of epidemiologic literature supports an association between brain cancer and farming with its potential for exposure to chemical pesticides.

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.