Stata se version 16

STATA SE version 16.0 (StataCorp, College Station, TX, USA) was used to analyze data. Categorical data were analyzed by the Fisher exact test or chi-square test, and continuous variables were analyzed using unpaired t-test or Wilcoxon rank-sum test for data as appropriate. Univariate logistic regression analysis was used to determine the predictors associated with UOA. Multivariate logistic regression analysis with stepwise was performed and included variables with a p-value less than 0.10 from the univariate analysis. Significant difference was defined as p-value < 0.05. The Spearman coefficient and scatter plot were used to identify the correlation between the significant variable and the most common cause of unplanned admission. The receiver operating characteristic (ROC) curve with the Youden index was used to determine the cut-off value for the significant variable. The performance test of the prediction model was calculated to determine the sensitivity, specificity, and positive and negative predictive value in each model.

Prior to applying the k-means algorithm to cluster the adolescents, it was also made sure that the information was complete and there was no missing data. Further, after analysing the clustering tendency of the data and obtaining the optimum value of k, k-means algorithm was used to finally cluster the data into k clusters using the ‘sklearn’ library in Python [28 ]. Clustering was done based on seventeen available dietary items available in order to diminish subjectivity of these items. Evaluation of each cluster by background characteristics, using descriptive analyses was done. Phenotypic characteristics of each cluster was also analysed. Further, distribution of each component of metabolic syndrome and micronutrient deficiencies within all five clusters was analysed. Lastly, association of any metabolic syndrome and micronutrient deficiencies with clusters were also explored using chi square test for association.
The study followed the STrengthening the Reporting of Observational studies in Epidemiology (STROBE) reporting guidelines for cross-sectional study (Additional file 1). National weights of biomarker have been used while exploring and computational analysis. STATA(SE) version 16.0 software has been used for data analysis and data wrangling and “sklearn” library in python was used to perform k-means cluster analysis.

Linkage disequilibrium between CYP2C8*3 SNPs was calculated with the LDlink 4.1.0 LDassoc Tool [29 (link)]. Allele frequencies and Hardy Weinberg equilibrium were analysed through the Fisher’s exact test. Statistical associations between CYP2C8*2 and/or CYP2C8*3 allele carriers and treatment outcome or adverse events were assessed by Fisher’s exact test. All analyses were performed in STATA/SE version 16.0; statistical significance was defined as P < 0.05.

Data were processed, reviewed, sorted, and recoded using STATA/SE version 16.0. To account for the effects of the survey’s complex sampling design or the hierarchical nature of the EDHS dataset, to restore survey representativeness, and to obtain reliable statistical estimates, the data were weighted via applying the STATA command "svyset." This command was prepended to each analysis in this study.
Arc GIS version 10.8 software was used to visualize the spatial distribution and locate hotspot areas (clusters).To measure the deviation of the spatial arrangement of the uninformed choice from randomness, the global spatial autocorrelation (Global Moran’s I) was calculated [18 (link), 19 (link)]. A positive value of Moran’s I represent positive spatial autocorrelation (cluster together), whereas a negative value indicates dispersed arrangement.