A Multilevel Approach to Investigate Relationships Between Healthcare Resources and Lung Cancer.
Darryl Somayaji, Young S Seo, Gregory E Wilding and 1 others
PMID 35510544WHAT IT FOUND
New York lung cancer patients were more likely to be diagnosed late if they were Black, Hispanic, male, aged 22 to 54 or 78 and older, lived in rural counties, or lived in counties with higher COPD.
Rural counties had fewer screening resources.
Key findings
01Black, Hispanic, and other-race patients had higher odds of late-stage lung cancer diagnosis than White patients.
02Male patients, patients aged 22 to 54 or 78 and older, rural county residence, and higher county COPD were associated with higher odds of late-stage diagnosis.
03Rural counties had fewer health care resources, and 19 of 24 rural counties versus 12 of 38 nonrural counties had lung cancer mortality rates greater than the median.
STILL TO COME
How it was doneWhat they foundWhat it means for RNs
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What it does not show
Cross-sectional design cannot show that fewer screening resources, rurality, or COPD caused late-stage diagnosis. County-level variables were applied to all patients in the same county, so associations may reflect area characteristics rather than individual patient experiences. The SEER data did not include patient-level smoking status, survival or mortality status, or precise addresses, so distance to screening centers and travel time could not be modeled. Patients with unknown stage were excluded, so the results apply only to those with known stage. County COPD was based on self-reported survey data, which can be inaccurate. Rurality was simplified into rural versus nonrural counties, and some RUCC categories had only one county, so the rural grouping may miss within-county variation.
Declared interests
The authors reported no conflicts of interest. The supplied publication types indicate NIH extramural research support.
The easy way to misread this
Do not conclude that rural counties' fewer screening resources caused late-stage lung cancer diagnosis. The study is cross-sectional and uses county-level data, so it shows associations but cannot prove causation.