{Reference Type}: Journal Article
{Title}: A scoping review shows that no single existing risk of bias assessment tool considers all sources of bias for cross-sectional studies.
{Author}: Kelly SE;Brooks SPJ;Benkhedda K;MacFarlane AJ;Greene-Finestone LS;Skidmore B;Clifford TJ;Wells GA;
{Journal}: J Clin Epidemiol
{Volume}: 172
{Issue}: 0
{Year}: 2024 Jun 4
{Factor}: 7.407
{DOI}: 10.1016/j.jclinepi.2024.111408
{Abstract}: <B>OBJECTIVE: </B>Different tools to assess the potential risk of bias (RoB) for cross-sectional studies have been developed, but it is unclear whether all pertinent bias concepts are addressed. We aimed to identify RoB concepts applicable to cross-sectional research validity and to explore coverage for each in existing appraisal tools.<BR><B>METHODS: </B>This scoping review followed the Joanna Briggs Institute methodology. We included records of any study design describing or reporting methods, concepts or tools used to consider RoB in health research reported to be descriptive/prevalence survey or analytic/association (cross-sectional) study designs. Synthesis included quantitative and qualitative analysis.<BR><B>RESULTS: </B>Of the 4556 records screened, 90 were selected for inclusion; 67 (74%) described the development of, or validation process for, appraisal tools, 15 (17%) described methodological content or theory relevant to RoB for cross-sectional studies and 8 (9%) records of methodological systematic reviews. Review of methodological reports identified important RoB concepts for both descriptive/prevalence and analytic/association studies. Tools identified (n = 64 unique tools) were either intended to appraise quality or assess RoB in multiple study designs including cross-sectional studies (n = 21; 33%) or cross-sectional designs alone (n = 43; 67%). Several existing tools were modified (n = 17; 27%) for application to cross-sectional studies. The RoB items most frequently addressed in the RoB tools were validity and reliability of the exposure (53%) or outcome (65%) measurement and representativeness of the study population (59%). Most tools did not consider nonresponse or missingness appropriately or at all.<BR><B>CONCLUSIONS: </B>Assessing cross-sectional studies involve unique RoB considerations. We identified RoB tools designed for broad applicability across various study designs as well as those specifically tailored for cross-sectional studies. However, none of the identified tools comprehensively address all potential biases pertinent to cross-sectional studies. Our findings indicate a need for continued improvement of RoB tools and suggest that the development of context-specific or more precise tools for this study design may be necessary.