Table 3.
Quality assessment of the cross-sectional studies using the non-randomized studies scale
| Author name | Year | Domain | |||||
|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | 6 | ||
| Arikan | 2008 | High | Low | High | Unclear | High | High |
| Ata-Ali | 2015 | High | Low | High | Unclear | High | High |
| Chaparro | 2020 | High | Low | High | Unclear | High | High |
| Chaparro | 2022 | High | Low | High | Unclear | High | High |
| Duarte | 2009 | High | High | High | Unclear | High | High |
| Kandaswamy | 2022 | High | Low | High | High | High | High |
| Milinkovic | 2021 | High | Low | High | Unclear | High | High |
| Rakic | 2013 | High | High | High | Unclear | High | High |
| Rakic | 2014 | High | High | High | Unclear | High | High |
| Song | 2022 | High | Low | High | Unclear | High | High |
Domain 1: Selection bias caused by inadequate selection of participants; Domain 2: Selection bias caused by inadequate confirmation and consideration of confounding variables (smoke habits and systemic diseases); Domain 3: Performance bias caused by inadequate measurement of intervention (exposure); Domain 4: Detection bias caused by inadequate blinding of outcome assessment; Domain 5: Attrition bias caused by inadequate handling of incomplete outcome data; Domain 6: Reporting bias caused by selective outcome reporting