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    • br Statistical analyses We estimate

    2018-11-05


    Statistical analyses We estimate one model for each health outcome. For the physical markers of health, as well as self-rated health we estimate model 1, while for the health behaviour outcomes we estimate model 2:where Y is the outcome variable; MAB is the maternal age at the time of birth for the respondent; MAB2 is a quadratic term for maternal age at the time of birth; BO is the variable for the birth order of the respondent; SEX is the variable for the gender of the respondent; DEM_SES is the vector of demographic and parental socioeconomic status characteristics (size of the sibling group of origin of the respondent, paternal occupational class at the time of birth of the respondent, maternal educational attainment when respondent was aged 19, paternal logged income when respondent was aged 19); HEALTH_BEHAV is the vector of health behaviour variables (alcohol consumption, smoking, exercise behaviour); and MENTALHEALTH is the vector of the respondent’s self-reported experience of depression and anxiety in the past 12 months. Although we also estimated models without a quadratic term for maternal age at the time of birth, previous research indicates that there is a U-shaped relationship between maternal age at the time of birth and health outcomes (Myrskylä & Fenelon, 2012), and these models were also of a better fit than the models that we estimated without the quadratic term. In this phenylephrine hydrochloride study we use seven different outcome variables that have a range of different distributions. For examining height, alcohol consumption, and self-rated health we used ordinary linear regression. For examining smoking behaviour we used a multinomial logistic regression as the proportional odds assumption was violated when we estimated the models using an ordered logistic regression model. For examining the binary variables for being overweight, being obese, and exercising regularly, we used logistic regression. For each outcome variable we also estimated a semi-parametric regression model (Lokshin, 2006) so as to graphically illustrate the relationship between maternal age at the time of birth and the outcome measure for the various models that were described above. The non-parametric part of the regression is the association between maternal age and the outcome variable, and the parametric part involves the adjustment for the control variables included in the model. This semi-parametric regression model imposes no shape on the association between maternal age at the time of birth and the outcome variable, instead basing the shape of the function on locally weighted data to smooth the curve. In these analyses we therefore do not impose any assumption about the shape of the relationship between maternal age and the outcome variables, and the plotted line is completely data driven.
    Results
    Discussion The results from this study show for the first time that maternal age at the time of birth is associated with the health behaviours of young adults in Sweden, and consistent with previous research, finds that those who are born to younger and older mothers have lower self-rated health. Furthermore, those born to the youngest and oldest mothers are shorter, and those born to older mothers are more likely to be obese or overweight. The results from the semi-parametric regressions indicate that this is true even after controlling for parental SES, various socio-demographic characteristics, as well as exercise behaviour and alcohol and smoking patterns. One explanation for these patterns of results could be a lasting disadvantage from poor peri-natal outcomes, as fermentation is known that those born to younger and older mothers are at greater risk of low birth weight, pre-term delivery, and other pregnancy complications, which can have long-term negative consequences (Black et al., 2007). While most of the results from our various OLS, logistic regression, and multinomial regression analyses did not show a statistically significant relationship between maternal age and the various outcomes that we study, that is likely to be due to the relatively low statistical power of this study. This low statistical power makes it more difficult to pick up statistically significant differences where they are likely to be small, such as with height, and for outcomes that are relatively rare amongst young adults in Sweden, such as being obese and overweight. Although rates of being overweight and obese are increasing in Sweden, they still lag far behind the rates found in the trend leader, the United States. It may well be that as weight increases with age, those weight gains may be particularly concentrated amongst those born to younger and older parents.