ArticleBMC public health2026
Deviations from pre-pandemic lung cancer mortality trends in the United States: a 25-year counterfactual analysis (1999-2023).
Article in BMC public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe COVID-19 pandemic coincided with substantial disruptions to cancer care services worldwide. However, comprehensive population-level assessments of deviations from expected lung cancer mortality trends during the pandemic period using rigorous counterfactual methods remain limited, particularly regarding heterogeneity across demographic and geographic subgroups.
methodsWe performed a counterfactual analysis examining lung cancer mortality trends among United States adults aged 45 years and older (1999–2023) using CDC WONDER mortality data. Joinpoint regression characterized pre-pandemic trends (1999–2019) to project expected mortality rates for 2020–2023. Mortality gaps were calculated as differences between observed and expected age-adjusted rates across 66 demographic and geographic strata. Interrupted time series analysis evaluated pandemic-associated trend modifications. Population-weighted linear regression assessed dose-response relationships between state-level COVID-19 burden and lung cancer excess mortality.
resultsNational age-adjusted lung cancer mortality rates declined from 156.03 per 100,000 in 1999 to 94.90 in 2019 (annual percent change: -4.56, 95% CI: -4.92 to -4.20). During the pandemic period, cumulative excess lung cancer mortality totaled 11.26 per 100,000, with statistically significant positive gaps emerging in 2021 (+ 3.67), 2022 (+ 2.77), and 2023 (+ 4.99). Non-Hispanic White populations exhibited three-fold greater 2023 lung cancer excess (+ 6.21) compared to Hispanic populations (+ 1.81). Age-stratified analysis revealed sixteen-fold gradients in lung cancer mortality gaps, from + 1.53 per 100,000 in the 45–54 years cohort to + 24.75 in those aged 85 years and older. State-level cumulative lung cancer mortality gaps ranged from − 40.72 to + 63.16 per 100,000. A significant positive ecological association was observed between state-level cumulative COVID-19 mortality and cumulative lung cancer mortality gaps (β = 0.056, 95% CI: 0.010–0.103, P = 0.022).
conclusionsThe COVID-19 pandemic was associated with deviations from expected lung cancer mortality trends, exhibiting substantial demographic and geographic heterogeneity. These patterns may reflect differences in healthcare system capacity across populations. Population-specific surveillance systems and infrastructure investments warrant consideration to maintain cancer care continuity during future public health emergencies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.