Data were recorded using Epi Info software version 3.1 and analyzed in SPSS® 20.0 (IBM Corporation, Armonk NY, USA) software. For continuous variables, means and standard deviations were calculated while for categorical variables we calculated proportions. Our primary outcome of interest was patients who had complication of cellulitis with abscess formation compare to those without abscess formation. Chi square or Fisher exact test were used when appropriate in univariate analysis. Multivariate backwards stepwise logistic regression analysis was performed to identify independent risk factors for the dichotomous outcome complication of cellulitis with abscess formation or absence of abscess. All variables significant during univariate analysis at a p-value less than 0.2 were then included in the multivariate analysis to assess the adjusted effect and derive the adjusted odds ratio (aOR) of each on the primary outcome. We allocated the value “1” to the dependent dichotomous variable if cellulitis becomes complicated with abscess formation, and the value 0 otherwise. A 95 % level of confidence was applied throughout.
Epi info software version 3
Manufactured by IBM
Sourced in United States, Denmark
Epi-info software version 3 is a data collection and analysis tool developed by the Centers for Disease Control and Prevention (CDC). It provides a platform for designing questionnaires, entering data, and analyzing epidemiological data.
Automatically generated - may contain errors
7 protocols using epi info software version 3
1
Abscess Risk Factors in Cellulitis
2
Dietary Diversity and Meal Frequency Analysis
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After data were collected, entering into Epi Info software version 3.1 (Lauritsen JM and Bruus M., Odense, Denmark, 2003–2005) was done and then exported to SPSS version 23 (IBM® SPSS® Statistics, Armonk, NY: IBM Corp) software for data processing and analysis. Descriptive statistics like frequency, proportions, mean and standard deviation was computed accordingly. The results were described using table, chart, graph and narration. Bi-variable logistic regression was carried out to screen candidate variables for the multiple logistic regression analysis and p-value ≤0.25 was taken as a cut-off point. Then multi-variable logistic regression was computed to identify independent predictors of inappropriate minimum dietary diversity and meal frequency feeding practices. A p-value <0.05 was used to declare statistical significance.
3
Factors Associated with Childhood Malnutrition
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Data were checked, coded and entered into Epi-info software version 3.5.3 and exported to Statistical Package for Social Science (SPSS) version 20 for analysis. The bivariate and multivariate analysis were done. All variables with a P value of less than 0.2 in bivariate analysis were entered to multivariate analysis to control the possible effect of confounders. Multi-co-linearity was checked for household income and latrine availability, and the result showed that the two independent variables had no significant co-linearity. Both crude odds ratio (COR) and adjusted odds ratio (AOR) with a 95% confidence interval (CI) were estimated to show the strength of association. In multivariate analysis, variables with a P value of ≤0.05 were considered as statistical significant.
4
Determinants of Male Involvement in PMTCT
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Data was entered into Epi-Info software version 3.5.2, edited, cleaned and analyzed using SPSS version 16.0. Male involvement is determined by the proportion of male partners accompanying their pregnant wives during ANC/PMTCT service. Additionally, descriptive statistics such as frequencies and proportion was used to describe the study population in relation to relevant variables. Explanatory variables found to be statistically significant in bivariate logistic regression analysis were entered into multiple logistic regression analysis (backward stepwise method) for adjustment of confounding effect between independent variables (Age, Educational status of mothers and their male partner, house hold income, previous knowledge of HIV status, marital status, duration of relationship, male partner as the first person to share HIV test result, ever tested and counseled together, husband informed about the availability of VCT in the ANC) and the dependent variable (Male involvement). Odds ratio with 95% confidence interval were computed to assess the presence and degree of association between the dependent variable (male partner involvement) and independent variables (Current knowledge of her sero status, Male partner as the first person to share HIV status, ever tested and counselled with her husband, Husband informed about the availability of VCT at the ANC/PMTC).
5
Epidemiological Data Analysis Methodology
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After data were collected clearly, code and entry were performed using the Epi Info software version 3 and then analyzed with SPSS software version 16. Codebook, analysis, and tabulation plans were developed in advance. Data analysis ranges from the basic description to identification of potential predicators. Associations between dependent and independent variables were assessed and presented using odds ratios (ORs) at 95% confidence intervals. Bivariate analyses were carried out for categorical variables, and ORs were used to quantify the strength of association between dependent and independent variables. Multivariate logistic regressions were utilized to manage the confounding effect of different variables as assessing the effect of each variable.
6
Orphanhood and Risky Sexual Behavior
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Data were entered and cleaned using Epi-Info software version 3.5.4 and then transported to SPSS software version 20 for analysis. Age, sex, orphan-hood due to any cause and due to HIV/AIDS related causes, HIV/AIDS related knowledge, attitude towards “ABC” rules to prevent HIV, prevalence of HCT uptake, substance use and other sexual behavior related variables were chosen for statistical analyses. Descriptive statistics such as frequencies and proportions were used to describe the study population in relation to relevant variables. Bivariate analysis between orphan-hood as a main exposure and history of having multiple sexual partners/one of UNAIDS indicators of risky sexual behaviors/as dependent variable was employed.
Then, a stepwise multivariate logistic regression model was used to assess the associations between the outcome and orphan hood as well as other predictors for multiple sexual partners. The variables in multivariate analysis were chosen based on existing theoretical knowledge on the variables and statistical significance found during bivariate analyses.
Then, a stepwise multivariate logistic regression model was used to assess the associations between the outcome and orphan hood as well as other predictors for multiple sexual partners. The variables in multivariate analysis were chosen based on existing theoretical knowledge on the variables and statistical significance found during bivariate analyses.
7
Dysphagia Predictors in Infants
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The qualitative or dichotomous variables were described as absolute measures and respective percentages. Continuous variables were described by using measures of central tendencies. The association between categorical variables, such as dysphagia (presence/absence), type of lingual frenulum, neurologic signs, presence of microcephaly at birth, and body weight for age was tested by w 2 or Fisher exact tests, when appropriate. A binomial logistic regression model was built to determine the effects of presence of a posterior lingual frenulum, pyramidal/extrapyramidal signs, cervical spine control, postural control, and global muscular tone (independent variables) on the likelihood that infants would present dysphagia (dependent variable). The level of significance was set at P .05. The descriptive analysis was performed by using Epiinfo software, version 3.5.4, and SPSS software, version 20.0 was used for association analysis and for the binomial logistic regression analysis.
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