Do we know enough about the effect of low-dose computed tomography screening for lung cancer on survival to act? A systematic review, meta-analysis and network meta-analysis of randomised controlled trials

Variation in Model-Based Economic Evaluations of Low-Dose Computed Tomography Screening for Lung Cancer: A Methodological Review

OBJECTIVES

There is significant heterogeneity in the results of published model-based economic evaluations of low-dose computed tomography (LDCT) screening for lung cancer. We sought to understand and demonstrate how these models differ.

METHODS

An expansion and update of a previous systematic review (N = 19). Databases (including MEDLINE and Embase) were searched. Studies were included if strategies involving (single or multiple) LDCT screening were compared with no screening or other imaging modalities, in a population at risk of lung cancer. More detailed data extraction of studies from the previous review was conducted. Studies were critically appraised using the Consensus Health Economic Criteria list.

RESULTS

A total of 16 new studies met the inclusion criteria, giving a total of 35 studies. There are geographic and temporal differences and differences in screening intervals and eligible populations. Studies varied in the types of models used, for example, decision tree, Markov, and microsimulation models. Most conducted a cost-effectiveness analysis (using life-years gained) or cost-utility analysis. The potential for overdiagnosis was considered in many models, unlike with other potential consequences of screening. Some studies report considering lead-time bias, but fewer mention length bias. Generally, the more recent studies, involving more complex modeling, tended to meet more of the critical appraisal criteria, with notable exceptions.

CONCLUSIONS

There are many differences across the economic evaluations contributing to variation in estimates of the cost-effectiveness of LDCT screening for lung cancer. Several methodological factors and evidence needs have been highlighted that will require consideration in future economic evaluations to achieve better agreement.

Psychological Impact of Ambiguous Health Messages about COVID-19

Perceiving ambiguity in health information-that is, uncertainty elicited from believing information lacks credibility, reliability, or adequacy-is typically associated with pessimistic appraisals (e.g., high perceived disease risk) and behavioral avoidance. We examined the effect of ambiguous health information about COVID-19 on health cognitions and vaccination intentions, and tested a "normalized-uncertainty" intervention. Two studies with identical methodology (online adult sample: n = 299, undergraduate sample: n = 150) were conducted in March to April 2020. Participants were randomly assigned to read one of three health messages about COVID-19 that emphasized what was currently unknown (ambiguity condition), what was currently unknown but that scientific uncertainty is expected (intervention condition), or what was currently known (control condition). The ambiguity condition led to greater perceived ambiguity than the control condition and perceived ambiguity in the intervention condition was comparable to the ambiguity condition. There were few differences in health cognitions, and no differences in vaccination intentions, when examining pairwise comparisons across the three conditions. Correlational analyses collapsing across condition indicated evidence of pessimistic appraisal but not behavioral avoidance among individuals who perceived greater ambiguity. Future research should examine longer, more detailed normalized-uncertainty interventions.