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After employing imputations, we have information on model variables for 1,888 of these establishments (94.2%)e.
Kumar et al. (2013) used ISM to model variables for effective customer involvement in green concept implementation in supply chain management.
The model variables for the analysis were categorized under the determinants of vulnerability levels which encompassed exposure indicators (temperature change, rainfall variability and frequency of extreme climatic events), capacity and sensitivity indicators (farmland size, land location, erosion rate and land policy, access to and use of farm input, land management training).
The model variables for each cohort are described in the Supplemental Material, Table 2 (http://dx.doi.org/10.1289/ehp.1002918).org/10.1289/ehp.1002918
In Table 1, descriptive statistics for the model variables for each country of origin sample are presented.
We conducted a sensitivity analysis to identify the critical model variables for total exposure and for each exposure pathway.
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In this model, variables stands for the differences of symmetrized phase.
Although the proposed model adequately explains total district PHC pharmaceutical expenditure, its predictive value should be established as data for model variables becomes available for subsequent years.
Multivariate model: variables were adjusted for each other in the multivariate logistic regression model.
Table 2 provides descriptive statistics for model variables.
Initial values for model variables were determined as follows.
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