**B. Data analysis**

Appendix B provides additional information on outlier analysis and treatment, and multicollinearity assessment.

**Outliers and winzorization**: Box plots based on the interquartile range and skewness and kurtosis were used to identify outliers (where skewness >1 and kurtosis >3.5 indicate potential outliers [52]. There was a high number of data points outside the interquartile range, and indicators with high skewness and kurtosis. However, only nine points in total were determined to be outliers and errors based on the local expert knowledge of one of the authors. These were treated with winzorization which is a common method to manage outliers [50]. As the data was not treated with the intention to create normally distributed data (as this would alter the characterization of vulnerability and risk beyond what is the true situation in Myanmar), the median as a measure of central tendency for each indicator. **Table B1** outlines which data points were treated and which value they were given.


#### **Table B1.**

*Treated indicators and their values.*

**Table B2.**

*Results of multicollinearity assessment using Kendall's tau using SPSS (IBM SPSS statistics).*

**Multicollinearity assessment**: A multicollinearity assessment was conducted to determine if there were any redundant indicators. Using Kendall's Tau suitable for non-normal data (Puth et al. 2015), and two tailed significance, no issue of collinearity was detected based on a threshold of 0.9. **Table B2** outlines the results from the analysis.
