Ambient UVB and Melanoma Risk in the United States: A Case-Control Analysis

2007 ◽  
Vol 17 (6) ◽  
pp. 447-453 ◽  
Author(s):  
C. Suzanne Lea ◽  
Joseph A. Scotto ◽  
Patricia A. Buffler ◽  
Judith Fine ◽  
Raymond L. Barnhill ◽  
...  
2021 ◽  
Author(s):  
Mark W. Tenforde ◽  
Manish M. Patel ◽  
Adit A. Ginde ◽  
David J. Douin ◽  
H. Keipp Talbot ◽  
...  

Background: As SARS-CoV-2 vaccination coverage increases in the United States (US), there is a need to understand the real-world effectiveness against severe Covid-19 and among people at increased risk for poor outcomes. Methods: In a multicenter case-control analysis of US adults hospitalized March 11 through May 5, 2021, we evaluated vaccine effectiveness to prevent Covid-19 hospitalizations by comparing odds of prior vaccination with an mRNA vaccine (Pfizer-BioNTech or Moderna) between cases hospitalized with Covid-19 and hospital-based controls who tested negative for SARS-CoV-2. Results: Among 1210 participants, median age was 58 years, 22.8% were Black, 13.8% were Hispanic, and 20.6% had immunosuppression. SARS-CoV-2 lineage B.1.1.7 was most common variant (59.7% of sequenced viruses). Full vaccination (receipt of two vaccine doses at least 14 days before illness onset) had been received by 45/590 (7.6%) cases and 215/620 (34.7%) controls. Overall vaccine effectiveness was 86.9% (95% CI: 80.4 to 91.2%). Vaccine effectiveness was similar for Pfizer-BioNTech and Moderna vaccines, and highest in adults aged 18-49 years (97.3%; 95% CI: 78.9 to 99.7%). Among 45 patients with vaccine-breakthrough Covid hospitalizations, 44 (97.8%) were at least 50 years old and 20 (44.4%) had immunosuppression. Vaccine effectiveness was lower among patients with immunosuppression (59.2%; 95% CI: 11.9 to 81.1%) than without immunosuppression (91.3%; 95% CI: 85.5 to 94.7%). Conclusion: During March through May 2021, SARS-CoV-2 mRNA vaccines were highly effective for preventing Covid-19 hospitalizations among US adults. SARS-CoV-2 vaccination was beneficial for patients with immunosuppression, but effectiveness was lower in the immunosuppressed population.


2010 ◽  
Vol 129 (3) ◽  
pp. 713-723 ◽  
Author(s):  
Catherine M. Olsen ◽  
Michael S. Zens ◽  
Adele C. Green ◽  
Therese A. Stukel ◽  
C. D'Arcy J. Holman ◽  
...  

BMJ Open ◽  
2015 ◽  
Vol 5 (12) ◽  
pp. e009413 ◽  
Author(s):  
Lin Zhang ◽  
Kumar Narayanan ◽  
Vallabh Suryadevara ◽  
Carmen Teodorescu ◽  
Kyndaron Reinier ◽  
...  

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0252114
Author(s):  
Gareth Harman ◽  
Dakota Kliamovich ◽  
Angelica M. Morales ◽  
Sydney Gilbert ◽  
Deanna M. Barch ◽  
...  

The objective of the current study was to build predictive models for suicidal ideation in a sample of children aged 9–10 using features previously implicated in risk among older adolescent and adult populations. This case-control analysis utilized baseline data from the Adolescent Brain and Cognitive Development (ABCD) Study, collected from 21 research sites across the United States (N = 11,369). Several regression and ensemble learning models were compared on their ability to classify individuals with suicidal ideation and/or attempt from healthy controls, as assessed by the Kiddie Schedule for Affective Disorders and Schizophrenia–Present and Lifetime Version. When comparing control participants (mean age: 9.92±0.62 years; 4944 girls [49%]) to participants with suicidal ideation (mean age: 9.89±0.63 years; 451 girls [40%]), both logistic regression with feature selection and elastic net without feature selection predicted suicidal ideation with an AUC of 0.70 (CI 95%: 0.70–0.71). The random forest with feature selection trained to predict suicidal ideation predicted a holdout set of children with a history of suicidal ideation and attempt (mean age: 9.96±0.62 years; 79 girls [41%]) from controls with an AUC of 0.77 (CI 95%: 0.76–0.77). Important features from these models included feelings of loneliness and worthlessness, impulsivity, prodromal psychosis symptoms, and behavioral problems. This investigation provided an unprecedented opportunity to identify suicide risk in youth. The use of machine learning to examine a large number of predictors spanning a variety of domains provides novel insight into transdiagnostic factors important for risk classification.


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