Office excel 2007

Data collected were entered into a computer using Microsoft Office Excel 2007 and analyzed by using STATA version 13 (STATA Corp LP, USA). In the present study any patient with history of vaginal discharge or genital ulcer or clinically treated as case of STIs in the past was considered to have history of STIs. Presence of at least one viral infections was defined as being positive to HSV2 IgM antibodies or positive HIV status or positive HBsAg or positive HCV antibodies. Categorical variables (marital status, education level, occupation, history of STIs, use of condoms etc.) were summarized as proportions and were analyzed using the Pearson’s Chi-square test to observe the significant differences in distribution of viral infections among various groups like age, education levels etc. Continuous variables (age, gestation age, parity, number of sexual partners etc.) were summarized as median with inter-quartile range. Univariable and multivariable logistic regression model was used to determine the factors associated with HSV-2, HIV and HBV positivity. Variables with p-value less than 0.2 on univariable regression model were subjected into the backward multivariable logistic regression analysis adjusted by age. Odds ratio and 95% confidence interval were noted. Variables with p-value of less than 0.05 were considered statistically significant.

The data were analysed using Microsoft Office Excel 2007 and Stata/IC 14.1. We used descriptive statistics to characterise participants and testing during healthcare visits. We compared categorical variables using the chi-squared or Fisher’s exact tests with a significance level of less than 0.05.

The across-study bias (publication bias) was examined by funnel plots. In addition, the statistical significance was assessed by the Egger’s regression asymmetry test [28 (link)] and Begg rank correlation method [29 (link)]. The Duval & Tweedie non-parametric ‘fill and trim’ linear random method was used to test and adjust for publication bias [30 (link)]. To test the robustness of a pooled estimate, we evaluated the effect of each study on the pooled seroprevalence by excluding single studies sequentially (i.e. estimated based on 37 studies each time). A study was deemed to have no influence if the pooled estimate without it (i.e. n = 37) was within the 95% confidence limits of the overall seroprevalence (n = 38) [31 (link)].
Extracted data were entered into Microsoft Office Excel 2007 and Stata 12.0 was used in all statistical analyses.

Data were entered into a computer using Microsoft Office Excel 2007 and analyzed using the STATA version 11 (College Station, Texas, USA). Categorical variables were presented as proportions and analyzed using the Pearson’s Chi-square to observe the differences among the various groups. Continuous variables were summarized as median with interquartile ranges. Univariate analysis and multivariate logistic regression models were fitted to determine factors associated with rubella IgM seropositivity. We used a backward-stepwise selection model to select factors with a p-value <0.5 to be fitted into the multivariate logistic regression analysis. Odds ratio (OR) and 95 % confidence interval [95 % CI] were presented.

Data on the presence or absence of the different types and numbers of nematophagous fungi was entered into Microsoft Office Excel 2007 software, and STATA 14 was used for descriptive analysis such as frequencies and percentages. Chi-square test was used to determine the presence of an association between soil sample type, soil moisture, and agro-ecological climates, and P values ≤ 0.05 were considered as a significant association.

Descriptive statistical analyses were performed using different software packages (Microsoft Office Excel 2007, StataIC, StataCorp and R. To compare the data among the tested groups the one-way ANOVA (the aov function in R) with post-hoc pair wise comparisons was performed with Tukey’s Honestly Significant Difference (the TukeyHSDfunction in R). P < 0.05 was considered to represent a statistically significant difference.