Escepticismo organizado

Independent variables to be controlled by the multivariate analysis in eliciting the effect of maternal education on child health were identified as informed by the literature review. In screening possible confounders to include in the analysis, the relationship among the study factor, dependent variables and

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independent variables was considered. Figure 3.1 summarises four types of possible relationship among the variables. The arrow heads in the diagram represent the direction of the relationship.

Figure 3.1: Confounder, mediator, effect modifier and covariate (Source: Kelson, 2014)

Confounders, by definition, are the variables that are associated with both dependent variable and study factor while they are not on the pathway between the former and the latter (Webb, 2011). On the other hand, mediators are the ones lying on the causal pathway between the study factor and the dependent variable mediating the effect of the former on the latter (MacKinnon et al., 2000). Inclusion of mediating variables or mediators in the multivariate analysis as independent variables can reduce the effect size of the study factor masking or diminishing its independent effect on the dependent variable (Richiardi et al., 2013). In the present research, possible mediators such as maternal occupation and mother’s parity which could be lying on the causal pathway between maternal education and child health outcomes were not included in the multivariate analysis although they were used as potential confounders in other studies identified by the literature review.

While some variables were standing out as possible confounders or mediators, some were found to be having multiple roles depending on the context and the way the relationship among the variables is conceptualised. For instance, paternal education could be conceptually regarded as a possible confounder since it could influence both maternal education and child health. On the other hand, it could be regarded as a mediating variable since educated women are more likely to get married with educated

Study factor Dependent variable Confounder Mediator Effect modifier Covariate

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men. However, the current analysis did not reveal such a strong pattern since only 48 % of women with higher education had husbands or partners at the same education level.

The household wealth status variable could be associated with maternal education and child health as a possible confounder in a way that women from more prosperous families could have better access to education while wealth also can lead to improved utilization of child health services. On the other hand, household wealth could also be a mediator lying on the pathway between maternal education and child health since better-educated women could contribute to improved household income which in turn enables better access to quality health care for their children. Nevertheless, the ‘household wealth status’ variable was included into the analysis as a possible confounder since it is important to control for its effects on child health outcomes and it is not easy to determine from the survey data to what extent the family wealth comes from mothers’ education level.

Mother’s parity could be a mediator in the relationship between maternal education and child health since, compared to uneducated ones, educated mothers may be more likely to adopt birth spacing practices and have fewer children which could result in better child health outcomes. Parity was excluded from the multivariate analysis.

Some variables can contribute to the relationship between maternal education and child health through effect modification or interaction. Effect modifiers are the variables that influence the established effect of the study factor on the dependent variable changing magnitude or direction of the relationship (Bland, 2002). A good example of an effect modifier is the residential status of the family either residing in urban or rural since it can modify the effect of maternal education on child health. For instance, a beneficial effect of high maternal education on child health could be enhanced due to the family’s urban residential status where health facilities are more accessible than in rural, while the effect could be reduced if they live in rural regions where access to quality health services is limited. The reverse can be true for uneducated mothers who live in urban areas where the family can readily access health services regardless of mother’s education level. On the other hand, rural/urban residence can be conceptualised as a confounder too due to its influence over both maternal education and child health.

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Covariates are the variables that are strongly associated with the dependent variable but are not associated with the study factor (Kelson, 2014). Examples of the covariates in the present research are age and sex of the children. While these variables are associated with their health status, it is not very plausible for them to influence maternal education. The variables such as the use of safe water supply, safe sanitation facilities, and biomass fuel in cooking can be either covariates or mediators. Although mothers' education level may influence the use of safe water and sanitary latrine, the choice of these facilities can depend on the households’ affordability. These variables were thus excluded in the analysis because of their strong association with household wealth status and they should not be analysed in multivariate models as independent variables together with household wealth (al-Mazrou et al., 1991; Shukr et al., 2009; Ntenda et al., 2014; Moschovis et al., 2013).

Some variables used in the studies included in the literature review were initially considered as possible confounders but excluded from the multivariate analyses of child mortality because of their recent nature. The data related to children ever born by the interviewed mothers aged 15-49 years could span over thirty years before the survey while the variables such as father’s occupation, mother’s occupation and living with grandmother (mother or mother-in-law) could be reflecting the present status of the families. Predicting the events such as child deaths that happened long time ago using the current status variables may not be conclusive and thus such variables were excluded from the analysis.

Independent variables used in the multivariate analysis are as below:

Paternal education: The education level of all interviewed fathers was measured by the DHS survey as the highest number of years of completed formal education which was categorised into four groups as per the national education system (DHS and MOHS, 2017). The coding was performed in the same way as maternal education mentioned above.

Paternal occupation: The primary survey collected information on the employment status of both mother and father. The employment groups are: no work, professional/managerial, clerical/sales, agricultural self-employed, agricultural employee, unskilled labour and skilled labour. A categorical variable was developed with those employment groups being coded accordingly. Paternal occupation

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was excluded from the mortality analysis since it reflects the current status and child mortality data spanned for almost three decades prior to the survey data collection. However, it was included in the analysis related to child morbidity and health services utilisation which are of more recent nature and use the data of children born within five years prior to the survey.

Maternal reproductive characteristics: The literature review identified that maternal reproductive characteristics which could influence child health are maternal age at childbirth, parity of mother, and preceding birth interval (Kilfoyle et al., 2016; Nusair et al., 2016; Brown et al., 2015). However, as discussed above, mother’s parity was excluded from the analysis.

 Maternal age at child birth in continuous variable was transformed into a categorical variable with four group: under 19 years, 20 – 29 years and 30 and above, and coded accordingly.

 Preceding birth interval in the number of years in the original dataset was converted into a categorical variable with three groups: no preceding birth; preceding birth less than two years; and two years and above.

Children’s characteristics: Age of child as at the time of the survey data collection and the child’s sex were included.

 Age of the child was transformed into a categorical variable with five groups denoting 1 for children who are under one year, 2 for those who are between one and two years, 3 for those between two and three years, 4 for between three and four years, and 5 for between four and five years.

 Child’s year of birth was included in the analyses to control for the variation of child mortality patterns over time.

 Sex of the child was denoted as a dichotomous variable with 1 for male and 0 for female. Household wealth status: The DHS survey did not collect household income because of poor reliability of the income information reported in the household surveys in the developing countries (DHS, 2012). The survey instead collected the number and type of the asset items owned by the households as observed by the survey team and calculated household wealth level (DHS, 2012). The asset items ranged

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from television, bicycle, motorcycle, car and radio to toilet facilities and materials used for roof, floors and walls of the dwellings. The factor analysis was used to calculate the score or wealth index of each household which were then ranked and grouped into five wealth quintiles to represent the wealth level of the surveyed households.

Urban-rural residential status of households was represented with a binary variable denoting 1 for urban residents and 0 for rural ones. The sampled areas and households of the primary survey were allocated into urban or rural regions in line with the master sampling frame produced by the National Population Census 2014 (DHS and MOHS,2017).