Growing Up In Scotland: Maternal mental health and its impact on child behaviour and development

Appendix B: Regression output

Logistic regression models are used to assess whether there is reliable evidence that particular variables are associated with each other.

Regression analysis aims to summarise the relationship between a 'dependent' variable and one or more 'independent' explanatory variables. It shows how well we can estimate a respondent's score on the dependent variable from knowledge of their scores on the independent variables. This technique takes into account relationships between the different independent variables (for example, between education and income, or social class and housing tenure).

The Sweep 4 Maternal Mental Health Report uses logistic regression - a method that summarises the relationship between a binary 'dependent' variable (one that takes the values '0' or '1') and one or more 'independent' explanatory variables. The regression results are presented as odds ratios for each independent variable. Odds ratios estimate the effect of each individual independent variable on the outcome variable, adjusted for all other independent variables in the regression model. Logistic regression compares the odds of a reference category (shown in the tables in brackets) with that of the other categories. An odds ratio of greater than one indicates that the group in question is more likely to demonstrate this characteristic than is the chosen reference category, an odds ratio of less than one means they are less likely. For example, in the second column of Table B.1, which contains the results of the regression model seeking to identify factors related to the cohort child's mother having a repeated poor mental health record, the category of poor couple relationship returns an odds ratio of 1.71. This indicates that the odds of respondents with a poor couple relationship having repeated poor mental health are 1.71 times greater than they are for respondents with a good couple relationship.

The significance of differences between the reference category and other categories are indicated by 'p'. A p-value of 0.05 or less indicates that there is less than a 5% chance we would have found such a difference just by chance if in fact no such difference exists, while a p-value of 0.01 or less indicates that there is a less than 1% chance. p-values of 0.05 or less are generally considered to indicate that the difference is highly statistically significant. As shorthand to aid interpretation, we have used symbols to summarise statistically significant differences:

• '*' denotes results that are significant from 0 at the 5% level (p = 0.015 - 0.05)
• '**' denotes results that are significantly different from 0 at the 1% level (p = 0.0015 - 0.01)
• '***' denotes results that are significantly different from 0 at the 0.1% level (p = 0.001 or below)
• ' NS' denotes results that are not significantly different from the reference category.

It should be noted that the final regression models reported below were produced following a process involving several stages of analysis:

1 First, forward stepwise regression analysis was conducted in SPSS 15.0. Further details of variables entered into this first stage can be found below.

2 Second, those variables found to be significantly associated with the dependent variable in the forward stepwise model were entered into a forced entry regression which was able to account for the survey's complex sample design (in particular, the effects of clustering and associated weighting) when calculating odds ratios and determining significance values. The models shown in Tables B.1 and B.2 include only those variables found to be significant after the forced entry regression models taking into account the complex survey design.

3 In some cases, two models were run for one dependent variable - for example, running a model including demographic factors only in the first instance, then running a second model including significant demographic factors from the first stage plus subjective factors such as views of the couple relationship, support networks and hardship. Running the analysis in these stages allowed for the exploration of how much each additional set of factors added to the ability to explain the dependent variable. Further, it revealed interesting demographic variations that might have been masked had self-rated health and hardship been included in this analysis from the outset.

4 Where more than one model was created for one dependent variable, only the final model has been reported below. These include significant factors after all the various demographic and attitudinal variables listed have been taken into account.

Stage 1 explanatory models

The following variables were entered into forward stepwise models before the final forced entry model was performed for each outcome: ¹⁴

• Mental health (3 groups: brief poor, repeated poor, good/average)
• Number of children in the household (grouped)
• Family type (Lone parent/couple)
• Age of mother at birth of cohort child (grouped)
• Whether household lived in persistent poverty
• Tenure
• Highest education level of mother
• Household employment status
• Mother's employment status
• Socio-economic classification ( NS- SEC 6 category)
• Urban/rural index
• Scottish Index of Multiple Deprivation
• Equivalised income
• Whether respondent is receiving incapacity benefit
• Strength of couple relationship
• Strength of social support networks
• Whether maternal grandmother of cohort child is alive
• Whether child is attending pre-school
• Sex of child

Where the p value is blank in Tables B.1 and B.2, these variables were not entered into the final model for that outcome.

Table B.1 Associations between key socio-economic and psycho-social characteristics and brief or repeated poor maternal mental health

* = p < 0.001, ** = p < 0.01, *** = p < 0.05, NS = Not significant, empty cells indicate variables not entered in the final model.

Table B.2 Associations between key demographic, socio-economic, psycho-social and maternal mental health characteristics and poorer cognitive and developmental outcomes

* = p < 0.001, ** = p < 0.01, *** = p < 0.05, NS = Not significant, ~ = Borderline significant (just over 0.05), empty cells indicate variables not entered in the final model.