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Statistical analysis
This protocol is extracted from research article:
Inequalities in maternal malnutrition in Ethiopia: evidence from a nationally representative data
BMC Womens Health, Jan 2, 2021;

Procedure

Descriptive analysis was used to examine the characteristics of the sample. Bivariate logistic regression was conducted to select the variables with p values < 0.2. Multiple logistic regression analyses were conducted to examine the association between selected predictors and the two maternal nutritional morbidity variables, adjusting for confounders. The logistic regression model is given by the equation.

where P is the probability that the event y occurs, at p(y = 1); and p/[1−p] is the “odds ratio”. We used a p ≤ 0.05 to ascertain statistical significance [22]. Hosmer–Lemeshow test was used to check the goodness of fit in our final model[23]. We used STATA 13 for data management, and data were weighted for descriptive analysis using DHS recommendation.

Inequalities in maternal malnutrition was estimated using a combination of regression-based absolute and relative measuring tools, namely Slope Index of Inequality (SII), Relative Index of Inequality (RII) and Population Attributable Fractions (PAFs). The SII is an absolute measure of the difference of inequality among socioeconomic groups within of a population of interest. The RII is a relative measure, derived from the SII, and considers the size of the population and the relative disadvantage experienced by different subgroups [24]. The computation of the SII and RII started with computing the prevalence of maternal undernutrition and overweight/obese by socioeconomic subgroups (wealth and parental education). Scores were then assigned based on the midpoint range in the cumulative distribution within the population. The SII were estimated by Weighted Least Square (WLS) regression considering the relative rank in the cumulative distribution of the wealth and parental education [24]. The SII is the linear regression coefficient or slope of the regression line given by:

where Yij: the mean value of overweight/obesity, Xij: the relative rank of the wealth quantile I, β0j: the slope showing the relationship between a group’s and its relative socioeconomic rank. eij: is the error distribution/unexplained error.

The fact that we used socioeconomic groups (analogous to individual ranking data), the regression error term in these Ordinary Least Square (OLS) model presented above becomes less reliable in terms of fulfilling the heteroscedasticity assumption. The relative index of inequality, RII, is derived from SII and the population mean (µ) of the health outcome, given by:

Population Attributable Fractions (PAFs) were used to estimate the inequalities in undernutrition across several risk factors to assess the burden at the population level. We used logistic regression estimates to get adjusted PAFs [2426].

The PAFs are directly obtained from logistic regression which was introduced by Greenland and Drescher[25]. The basic idea behind this approach is to estimate a logistic regression model with all known/available risk factors. Ruckinger et al. [26] provided the following steps for calculating the PAF of the risk factor of interest: a)The risk factor has to be coded dichotomously, and then 'removed' from the population by classifying all individuals as unexposed, irrespective of their real status b) predicted probabilities for each individual should be calculated using this modified dataset, given by pp = 1/ 1 + exp(−α + β xi); where α represents the estimate for the intercept of the logistic regression model, β denotes the parameter vector for the covariates included in the model, and xi denoting the observations of the covariates for each individual, however, with the 'removed' covariate set to zero for all individuals c) Computing the adjusted number of cases of the disease (i.e. overweight or obesity) that is obtained by summing up all predicted probabilities that would be expected if the risk factor was absent in the population, and d) the PAF is then calculated by subtracting these expected cases from the observed cases and dividing by the observed cases[26].

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