What is the impact of delayed lung cancer diagnosis on outcomes?

What is the impact of delayed lung cancer diagnosis on outcomes?





One would think that this question would be a relatively
easy question to answer empirically: simply compare patients with delays
against those without delays and see if there is a difference in health
outcomes (e.g., survival).  This
approach, however, is problematic as due to three key methodological concerns.

Measurement issues.  Ideally, one would like to compare patients
at a given stage who are diagnosed and treated for lung cancer against those
who are not.  However, the underlying lung
cancer stage is typically unobserved during portions of the delay in care either
because a lung cancer diagnosis is not made yet or perhaps it is made but full
staging has not yet been completed.  Mediators. A mediator is a variable lies
in the causal path between the exposure variable and the outcome of interest.  For instance, consider two patients (Patient
A and B) with stage III lung cancer, but this stage information is not yet known
to the researcher.  Consider the case where
Patient B’s tumor advances to stage IV during the diagnostic phase whereas
Patient A’s tumor remains at Stage III.  In
this case, it appears to the researcher that Patient A is a Stage III patient
and Patient B is a Stage IV patient. 
Here the worsening outcome (stage progression) leads to stage
advancement.  The problem is that the
researcher may compare Patient B with other Stage IV patients without a delay
and Patient B may have better outcomes than those regular Stage IV patients
since Patient B’s tumor only just advanced. Confounders.  Confounding occurs when a third variable
influences both the exposure and outcome variable.  For instance, it could be the case that when interventional
pulmonologists view a lesion, they may view it as high or low risk.  High risk patients may get diagnosed rapidly;
low risk patients may be more likely to have a diagnostic delay.  When comparing outcomes, it would thus appear
that those with diagnostic delays had better outcomes.  However, it is really a third variable (i.e.,
lesion risk) which is causing both the delay period and outcomes.

In part because of these methodological complexities, a systematic literature review by Zuniga and Ost (2021) finds some counterintuitive results.  They find that longer delays lead to worse survival for n=8 studies, no statistically significant effect of delay on survival for n=10 studies, and a paradoxical effect that longer delays lead to better survival for n=8 articles, and mixed results (some strata worse and others paradoxical or no effect) for n=9.  The degree to which we observe so many counterintuitive impacts clearly suggests that mediators and confounding are significant empirical issues that have not been fully addressed across studies. 

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Below are the relevant articles.  Articles in bold show shorter delay leads to better survival; italics show shorter delay lead have no effect; underlined means shorter delay leads to worse outcomes, and no formatting means that shorter delay leads to mixed survival outcomes depending on the group.

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