This study design does not allow for detailed cause-effect analyses, prospective surveillance and follow-up visits, or the assessment of mortality data.
In addition, given that the study was a cross-sectional analysis, no cause and effect analyses could be conducted.
In a few cases when an effect was also a cause of a different effect variable (i.e. a chain of cause-effect relations), we analysed this as two separate cause-effect relations.
The set of statements was thereafter analysed for cause-effect relations according to the method described by Axelrod [ 10]. (As the Free Reports were not related to the different cases, coding for decision arguments was not considered relevant).
Another study limitation is the correlational nature of the analyses, which precludes cause-effect relationship and allows for the possibility that the observed associations might be due to an unobserved variable or set of variables.
Sectorial analyses may be incorrect when relevant cause-effect relationships are shut out from consideration.
Statistical analyses showed distinct groupings of land use categories providing evidence of cause-effect pathways attributed to individual land uses.
For example, Luoma et al. (2001) provide eight case studies that specify cause and effect linkages using community analyses of macroinvertebrates to decipher the effects of multiple stressors.
This is important since one of the theoretical premises of the intervention is that behaviour change will occur if communities are able to analyse the cause and effect linkages of health problems, and then define ways in which they can influence these linkages.
However, for a more comprehensive assessment of safety, other methods also need to be used, including reporting and learning systems, root-cause analyses, failure mode effects analyzes and morbidity and mortality reviews [ 13, 33].
