Linear extrapolation models of lung cancer risk associated with exposure to environmental tobacco smoke.

Regul Toxicol Pharmacol

Computational Epidemiology Laboratory, School of Computing Science, Burnaby, British Columbia, V5A 1S6, Canada.

Published: October 1998

This paper presents a model to estimate the number of lung cancer deaths due to ETS exposure among the 1992 U.S. never-smoking population, based on downward linear extrapolation from the estimated risks of active smokers. The model uses several recently available data sources including an extensive review of the published literature on indoor concentration of ETS constituents measured under real-world conditions and data from the National Mortality Followback Survey and the National Health Interview Survey which furnish nationally representative estimates of the distribution of the U.S. population and the persons who died from lung cancer by sex, age, and smoking status. The linear extrapolation model estimates that five male and six female excess lung cancer deaths due to ETS exposure would be expected in the 1992 U.S. population of over 52 million never smokers age 35 and over. Explanations for differences between the results of our downward extrapolation model and those of others are presented.

Download full-text PDF

Source
http://dx.doi.org/10.1006/rtph.1998.1216DOI Listing

Publication Analysis

Top Keywords

lung cancer
16
linear extrapolation
12
cancer deaths
8
deaths ets
8
ets exposure
8
extrapolation model
8
extrapolation models
4
lung
4
models lung
4
cancer
4

Similar Publications

Robotic-Assisted minimally invasive esophagectomy: Recommendations to textbook outcomes in clinical practice.

J Thorac Cardiovasc Surg

January 2025

Department of Thoracic Surgery I, Key Laboratory of Lung Cancer of Yunnan Province, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, China.

View Article and Find Full Text PDF

Tension-induced organelle stress: an emerging target in fibrosis.

Trends Pharmacol Sci

January 2025

Department of Surgery, University of California, San Francisco, San Francisco, CA, USA; Center for Bioengineering and Tissue Regeneration, University of California, San Francisco, San Francisco, CA, USA; UCSF Helen Diller Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA; Department of Radiation Oncology, Eli and Edythe Broad Center for Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA, USA.

Fibrosis accounts for approximately one-third of disease-related deaths globally. Current therapies fail to cure fibrosis, emphasizing the need to identify new antifibrotic approaches. Fibrosis is defined by the excessive accumulation of extracellular matrix (ECM) and resultant stiffening of tissue stroma.

View Article and Find Full Text PDF

Background: To evaluate the real-world surgical and pathological outcomes following neoadjuvant nivolumab in combination with chemotherapy in a multicentre national cohort of patients.

Methods: Retrospective analysis on consecutive patients treated in three tertiary referral hospitals in UK with neoadjuvant chemotherapy and immunotherapy (nivolumab) for stage II-IIIB nonsmall cell lung cancer (March 2023-May 2024). Surgical and pathological outcomes were assessed.

View Article and Find Full Text PDF

Objective: To determine the association between concurrent statin use with immune checkpoint inhibitors (ICIs) and lung cancer-specific and overall mortality in patients with nonsmall cell lung cancer (NSCLC).

Materials And Methods: SEER-Medicare was used to conduct a retrospective study of Medicare beneficiaries ≥65 years of age diagnosed with NSCLC between 2007 and 2017 treated with an ICI. Patients were followed from date of first ICI claim until death, 1 month from last ICI claim, or 12/31/2018, whichever came first.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!