The returned questionnaires were checked for completeness, cleaned manually, coded and entered into EPI Data version 3.1software and then exported to SPSS windows version 23 for further analysis. Bivariate analysis was used primarily to check which variables have an association with the dependent variable individually. Variables which were found to have an association with the dependent variable (p-value ≤ 0.2) were then entered into Multiple Logistic regression for controlling the possible effect of confounders and finally the variables which have significant association were identified based on AOR, with 95%CI and p-value ≤ 0.05 to fit into the final regression model.
Epi data version 3.1software
Manufactured by IBM
EPI Data version 3.1 is a software application developed by IBM for laboratory equipment. The software provides data management and analysis capabilities for scientific experiments and research activities. The core function of EPI Data is to collect, organize, and process experimental data from various laboratory instruments and equipment.
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Lab products found in correlation
5 protocols using epi data version 3.1software
1
Data Analysis of Survey Responses
2
Determinants of Maternal Mortality in Ethiopia
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Data were cleaned, coded, and entered into Epi Data version 3.1software before being exported to SPSS version 25 software for further analysis. Descriptive statistics such as frequency, percentages, and summary measures were carried out, and the results were presented using narrative form and tables. Both the crude odds ratio and adjusted odds ratio with a 95% confidence interval were calculated to see the association and strength of the association, respectively.
Variables with a p-value < 0.25 in the bivariate analysis were transferred to multi-variable logistic regression model to control the effect of confounders. Lastly, in multivariable analysis, variables with a p-value of < 0.05 were declared statistically significant factors. The Hosmer-Lemeshow goodness of fit test was above the level of significance. A multi-colinearity test was checked by using the variance inflation factor and colinearity statistics. Then the final results were presented in table, figure, and narrative form.
Variables with a p-value < 0.25 in the bivariate analysis were transferred to multi-variable logistic regression model to control the effect of confounders. Lastly, in multivariable analysis, variables with a p-value of < 0.05 were declared statistically significant factors. The Hosmer-Lemeshow goodness of fit test was above the level of significance. A multi-colinearity test was checked by using the variance inflation factor and colinearity statistics. Then the final results were presented in table, figure, and narrative form.
3
Data Analysis of Participant Groups
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Data were first entered into the Epi-Data version 3.1 Software before being moved to the SPSS version 25 software for analysis [12 ]. Data were cleaned after coding, categorization, and variable merging. The study’s participants and critical variables were described using descriptive statistical analysis techniques like frequencies, percentages, and cross-tabulations, and they were then displayed as tables and figures. To determine whether there was a difference between the participant groups, a Chi-square test was utilized.
4
Bivariate and Multivariate Analysis of Factors
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The questionnaires were checked for completeness, coded and entered into EPI Data version 3.1software and then exported to SPSS windows version 23 for analysis. Bivariate analysis was used primarily to check variables which have an association with the dependent variable individually. Hosmer and Lemeshow goodness of fit test was checked, and it was found to be 0.989 on the final model. Variables which were found to have an association with the dependent variable at a P-value of < 0.2 were then entered into multivariable logistic regression for controlling the possible effect of confounders and finally the variables which have significant association were identified based on AOR, with 95%Confidence Interval (CI) and P-value < 0.05.
5
Severe Acute Malnutrition Treatment Outcomes
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Recovered: Patient that has reached the discharge criteria.
Discharge Criteria: Age 6 month-5years- W/H> = 85% on more than one occasion or MUAC ≥120mm (Two weeks for out-patients) and no oedema for 14 days (out-patient) for edematous,
Average weight gain: sum of weight gains/No of children 6–59 months who were cured.
Average length of stay: sum of length of stay/No of children 6–59 months who were cured.
RUTF consumption appropriate for weight: appropriate amount of RUTF was calculated based on the national SAM management protocol standing from weight of respective children.
Weight gain (g/kg/day): is average weight (in gram) increase for every Kg of body weight of the child per day. It is determined by;
Individual weight gains in marasmic patients were calculated with:
Data processing and analysis
Data were entered in to EpiData version 3.1 software and analyzed using SPSS version 23. Survival curve and hazard curve was used to display the survival (time to recovery) among different characteristics. Log rank test was done to compare median recovery time among different groups. Cox proportional hazard model with both bivariate and multivariate analysis was done. P-value < 0.05 was considered as statistically significant.
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Individual weight gains in marasmic patients were calculated with:
Data processing and analysis
Data were entered in to EpiData version 3.1 software and analyzed using SPSS version 23. Survival curve and hazard curve was used to display the survival (time to recovery) among different characteristics. Log rank test was done to compare median recovery time among different groups. Cox proportional hazard model with both bivariate and multivariate analysis was done. P-value < 0.05 was considered as statistically significant.
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