Table 2 Bradford Hill Criteria to evaluate trigger module of the cholera prediction system.
From: Combating cholera by building predictive capabilities for pathogenic Vibrio cholerae in Yemen
Criteria | Parameter | Fulfillment |
|---|---|---|
Strength | Strong association (correlation) is more causal than weak association | Correlation analysis (Fig. 3) |
Consistency | Consistent findings from other studies | Previous studies support the correlation (Huq et al.24, Khan et al.40, Lipp et al.21, Colwell1, Hashizume et al.16, Khan et al.22) |
Specificity | Causality of CPS is evaluated through Sensitivity, specificity, and accuracy | Figure 4a |
Temporality | Cause occurs before effect | A four-week lead time in hydroclimatic processes was observed to be the cause of cholera |
Biological gradient | Higher exposure leads to more public health burden | The gradient analysis was conducted in terms of PPV (precision) and NPV (Fig. 4b) |
Plausibility | Mechanism of cause | Previous studies have established precipitation and temperature as the mechanics of survival of cholera bacteria in the environment |
Coherence | Epidemiological findings match with laboratory/observational/analytical experiments | Previous studies have determined the presence of cholera bacteria in an aquatic environment (Louis et al.56, Neogi et al.19) |
Experiment | Experimental or analytical evidence | Direct dependence of increase in temperature and precipitation with the increase in cholera risk (Hood et al.64, Louis et al.56, Huq et al.24) |
Analogy | Are there any similarities/dissimilarities between the observed association to other processes? | A spatial analysis from India, Bangladesh, Nepal, Mozambique, Cameroon, Central African Republic, Congo, Zimbabwe shows a similar pattern of origin of cholera |
Reversibility | Do preventative actions lead to alteration of cause-effect or vice versa? | Preventative actions may have a positive cause-effect impact on the reduction of cholera cases in the year 2018 (Fig. S2) |
