Table 2.
Risk of bias ratings for cross-sectional studies.
| First author | Q1 | Q2 | Q3 | Q4 | Q5 | Q6 | Q7 | Q8 | Q9 | Q10 | Q11 | Totals |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Liu et al. (2014) | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 8 |
| Wang and Xue (2014) | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 5 |
| Xu et al. (2015) | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 6 |
| Liu (2016) | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 6 |
| Lv et al. (2017) | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 6 |
| Yang (2017) | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 6 |
| Zhu et al. (2017) | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 6 |
| Bai et al. (2018) | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 7 |
| Jin et al. (2018) | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 6 |
| Xu et al. (2019) | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 8 |
| Chen et al. (2019) | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 0 | 7 |
| Wang et al. (2020) | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 6 |
| Lv et al. (2020) | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 6 |
| Chen et al. (2021) | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 6 |
1. Is the source of information (survey, literature review) clearly identified? 2. Are inclusion and exclusion criteria for exposed and non-exposed groups (cases and controls) listed or referenced in previous publications? 3. Is the time period for identifying patients given? 4. Are the study subjects consecutive, if not a population source? 5. Are the evaluator’s subjective factors overshadowing any other aspects of the subject matter of the study? 6. Describes any assessments for quality assurance (e.g., testing/retesting of primary outcome indicators). 7. Explains the rationale for excluding any patients from the analysis. 8. Describes how measures of confounding were evaluated and/or controlled. 9. If possible, explains how missing data were handled in the analysis? 10. Summarizes the response rate of the patients and the completeness of the data collection. 11. If there was follow-up, identify the expected percentage of patients with incomplete data or the outcome of follow-up.