Table 2 CHANGE Tool Step 2: Study quality and bias assessment
Category | Question | Rating from 1–4; “1” indicating highest scientific rigor and “4” indicating poor or no scientific rigor. “5” indicates unknown |
|---|---|---|
Transparency | Does the study clearly specify the research question? | |
Does the study clearly state the inclusion and exclusion criteria? | ||
Is the research reproducible? Does the study present a full description of study design, including a clear rationale for the spatial scale at which exposure was measured and data availability? | ||
Does the study clearly assess the “risk of bias?” | ||
Selection Bias | Are the individuals selected to participate in the study likely to be representative of the target population? | |
Covariate Variable Selection | For non-predictive models: Did the study design or analysis account for the minimally sufficient set of confounding and covariate variables | |
For predictive models: Was the selection of predictor variables clearly described and following best standards? | ||
Detection Bias | Was the measurement ascertainment of the climate variable well described with detail of the technology, developer, detailed usage, and measurement error of the sensor? | |
Was the measurement ascertainment of the climate variable well described with detail of the frequency and recording of location updates, and the method, period, and duration of data collection clearly specified? | ||
Was the climate variable well described with detail of the data availability? | ||
Was there a clear justification for the chosen climate exposure and the method of exposure assessment? | ||
Can we be confident in the exposure characterization? Confidence requires valid, reliable, and sensitive methods to measure exposure applied consistently across exposure groups. | ||
Were confounding variables assessed consistently across groups using valid and reliable measures? | ||
Selective Reporting Bias | Were all measured outcomes (based on the research aims) reported? |