Data Analysis

This study has used univariate, cross tabulation and logistic regression methods to meet the study objectives. Initially, univariate analyses method such as frequencies, percent distribution, means, and standard deviations were used to describe the individual characteristics of the sample women. In the next step, cross tabulation between the independent variables and the outcome variables (good newborn care practices) were carried out to examine the pattern of good newborn care practices by the study variables. The chi-square test was also used to examine the significant difference in newborn care practices (good practice and bad practice)

by the independent factors. Finally, logistic regression method was used to examine the impact of each of the independent variables on the three outcome variables separately and also the combined effect of the variables on each of the outcome variables separately.

4.8.1 Logistic regression

Both the simple and multiple logistic regression methods were used in the analysis of determinants of the dependent variables. Since the three outcome variables had binary outcomes, binary logistic regression models were used. The simple logistic regression analysis was carried out to examine the associations between each of the independent variables and the three outcome variables separately and the unadjusted odds ratios of the associations and the 95% confidence intervals of each independent variable with the outcome variable were obtained. The independent variables that were found not associated with the outcome variables at p<0.05 in the simple regression were not included in the multiple regression.

In multiple regressions, association of the independent variables with each of the three dependent variables were tested separately, while controlling for the confounding aspects of the other independent variables. Adjusted odds ratios that were obtained from the multiple logistic regression models compared individuals who differ in the characteristics of interest and have the values of all other variables constant (Hosmer and Lemeshow, 1989).

Logistic regression modelling is based on the assumption that the log of the dependent variable is a linear function of the independent variables. The multivariate regression models provided the odds ratio and 95% confidence intervals for each of the explanatory variables. The models used in this study were based on the following formula.

log [p/(1-p)] = α + β1 * X1 + β2 * X2 + ….. + βN * XN, where,

p is the probability of practising the outcome behaviour, α is the intercept,

β1 ….. βN is the regression coefficients.

The regression coefficients represent the change in the log odds of the outcome variable associated with a unit change in each corresponding independent variable while controlling the effects of the other independent variables. The exponentiated log odds denotes the odds ratio

for the outcome practices associated with a one unit change in the independent variable, while controlling for the effects of other independent variables.

Logistic regression was carried out in a series of steps. Initially, simple logistic regression was carried out to examine the association between each of the independent variables and the outcome variable separately. In the second step, the independent variable that was not significant at p<0.05 was discarded. The independent variables that form a set of variables and were significant in the simple regression were then regressed to examine its association with the outcome variable, and hypotheses were tested. In the third step, combined analysis of the independent variables was carried out, where a new set of variables was added one by one to the first variable set to see the combined effect of the independent variable sets on each of the outcome variables separately. The multiple logistic regression (including the combined analysis of the variables) tested the associations of the independent variables with each of the three outcome variables, while controlling for the confounding effects of other independent variable or variable sets. The variable sets were added as blocks and according to the conceptual framework of the study (Table 4.1). At the end, a full regression model was tested that comprised all the variables that were found significantly associated with the outcome variable in the simple logistic regression.

Table 4.1: Variable sets that were added in each step of the combined analysis Steps Independent variables

1 • Socio-demographic factors 2 • Socio-demographic factors • Socio-economic factors 3 • Socio-demographic factors • Socio-economic factors • Use of maternal health services

4

• Socio-demographic factors • Socio-economic factors • Use of maternal health services • Birth preparedness

5

• Socio-demographic factors • Socio-economic factors • Use of maternal health services • Birth preparedness

• Mothers’ knowledge of specific newborn care issues

• Socio-economic factors • Use of maternal health services • Birth preparedness

• Mothers’ knowledge of specific newborn care issues • Advice from a FCHV and counselling from a health worker

7

• Socio-demographic factors • Socio-economic factors • Use of maternal health services • Birth preparedness

• Mothers’ knowledge of specific newborn care issues • Advice from a FCHV and counselling from a health worker • Exposure to media

Table 4.1 displays the process of combined analysis of the variables. In the combined analysis of the variables, firstly the outcome variable was regressed on all the three socio-demographic factors. Secondly, the socio-demographic variables and the socio-economic status were regressed. In the third step, socio-demographic variables, socio-economic status and the maternal service utilisation variables were regressed together. In the fourth step, the socio- demographic variables, the socio-economic status, maternal service utilisation and the birth preparedness were regressed. In the fifth step the following variables- the socio-demographic, the socio-economic status, maternal service utilisation, the birth preparedness and knowledge of mothers were regressed. In the sixth step, the following variables- the socio-demographic, the socio-economic status, maternal service utilisation, the birth preparedness, knowledge of mothers and advice from FCHV/counselling from health workers were regressed. In the seventh and the final step all the independent variables (the socio-demographic, the socio- economic status, maternal service utilisation, the birth preparedness, knowledge of mothers and advice from FCHV/counselling from health workers, and exposure to media were regressed) were regressed. The changes in the covariates for the different models were examined to determine how each set of independent variables and the combinations thereof influenced the outcome variable. This pattern of analysis was carried on for each of the dependent variables separately and the adjusted odds ratio and 95% confidence intervals were obtained.

The statistical software package Statistical Package for Social Sciences (SPSS) version 17.00 was used for the analysis.