Introduction: Cancer risk and screening data are limited in their ability to inform local interventions to reduce the burden of cancer in vulnerable populations. The San Francisco Health Information National Trends Survey was developed and administered to assess the use of cancer-related information among under-represented populations in San Francisco to provide baseline data for the San Francisco Cancer Initiative.
Methods: The survey instrument was developed through consultation with research and community partners and translated into 4 languages. Participants were recruited between May and September 2017 through community-based snowball sampling with quotas to ensure adequate numbers of under-represented populations. Chi-square tests and multivariate logistic regression were used between 2018 and 2019 to assess differences in screening rates across groups and factors associated with cancer screening.
Results: One thousand twenty-seven participants were recruited. Asians had lower rates of lifetime mammogram (p=0.02), Pap test (p<0.01), and prostate-specific antigen test (p=0.04) compared with non-Asians. Hispanics had higher rates of lifetime mammogram (p=0.02), lifetime Pap test (p=0.01), recent Pap test (p=0.03), and lifetime prostate-specific antigen test (p=0.04) compared with non-Hispanics. Being a female at birth was the only factor that was independently associated with cancer screening participation (AOR=3.17, 95% CI=1.40, 7.19).
Conclusions: Screening adherence varied by race, ethnicity, and screening type. A collaborative, community-based approach led to a large, diverse sample and may serve as a model for recruiting diverse populations to add knowledge about cancer prevention preferences and behaviors. Results suggest targeted outreach efforts are needed to address disparate cancer screening behaviors within this diverse population.
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http://dx.doi.org/10.1016/j.amepre.2019.08.024 | DOI Listing |
JAMA
January 2025
Department of Dermatology, University of California, San Francisco, School of Medicine, San Francisco.
JAMA
January 2025
Department of Emergency Medicine, Henry Ford Health, Detroit, Michigan.
Importance: The emergency department (ED) offers an opportunity to initiate palliative care for older adults with serious, life-limiting illness.
Objective: To assess the effect of a multicomponent intervention to initiate palliative care in the ED on hospital admission, subsequent health care use, and survival in older adults with serious, life-limiting illness.
Design, Setting, And Participants: Cluster randomized, stepped-wedge, clinical trial including patients aged 66 years or older who visited 1 of 29 EDs across the US between May 1, 2018, and December 31, 2022, had 12 months of prior Medicare enrollment, and a Gagne comorbidity score greater than 6, representing a risk of short-term mortality greater than 30%.
JAMA Netw Open
January 2025
Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
J Neurol
January 2025
Division of Child Neurology, Children's Hospital of Philadelphia, Departments of Neurology and Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Background: The presented study identified the appropriate ocrelizumab dosing regimen for patients with pediatric-onset multiple sclerosis (POMS).
Methods: Patients with POMS aged 10-17 years were enrolled into cohort 1 (body weight [BW] < 40 kg, ocrelizumab 300 mg) and cohort 2 (BW ≥ 40 kg, ocrelizumab 600 mg) during a 24-week dose-exploration period (DEP), followed by an optional ocrelizumab (given every 24 weeks) extension period.
Primary Endpoints: pharmacokinetics, pharmacodynamics (CD19 B-cell count); secondary endpoint: safety; exploratory endpoints: MRI activity, protocol-defined relapses, Expanded Disability Status Scale (EDSS) score change.
Handb Exp Pharmacol
January 2025
Genentech Inc, South San Francisco, CA, USA.
In this chapter, we envision the future of Quantitative Systems Pharmacology (QSP) which integrates closely with emerging data and technologies including advanced analytics, novel experimental technologies, and diverse and larger datasets. Machine learning (ML) and Artificial Intelligence (AI) will increasingly help QSP modelers to find, prepare, integrate, and exploit larger and diverse datasets, as well as build, parameterize, and simulate models. We picture QSP models being applied during all stages of drug discovery and development: During the discovery stages, QSP models predict the early human experience of in silico compounds created by generative AI.
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