Model

Formula for the Model

I used a Bernoulli model with two outcomes: Yes and No, for whether the individual has one or more health conditions.

$$health\mathrm{\_}conditio{n}_i\sim Bernoulli(\rho =0.56)$$

$$\begin{array}{rl}health\mathrm{\_}conditio{n}_i=& {\beta }_0\\ +& {\beta }_1\cdot ag{e}_i\\ +& {\beta }_2\cdot se{x}_i\\ +& {\beta }_3\cdot marital\mathrm{\_}statu{s}_i\\ +& {\beta }_4\cdot limited\mathrm{\_}acces{s}_i\\ +& {\beta }_5\cdot num\mathrm{\_}of\mathrm{\_}childre{n}_i\\ +& {ϵ}_i\end{array}$$

Table of Coefficients

The intercept (${\beta }_0$) is for calculating $\rho$ using the average aged adult female who has never married, does not have limited access to healthcare, and has zero children.

• The more positive beta is, the greater the likelihood of having a health condition
• Likewise, more negative beta values mean a lower likelihood of having a health condition

To calculate the specific probability for a given characteristic, use the logit regression $\rho =\frac{e^{\beta }}{1+e^{\beta }}$ where $\beta$ is the characteristic’s corresponding $\beta$ value added to the (Intercept).

Some results were expected. As age increases, the probability of having a health condition is higher. Similarly, individuals who have had difficulty accessing healthcare are more likely to also be the ones who have a chronic health condition. This relationship could potentially be mutually reinforcing.

Furthermore, divorced individuals were more likely to have a health condition. It could be that their health condition resulted in the divorce, or the divorce caused the onset of a health condition, or there was an entirely different confounding variable.

Interestingly, males, married individuals, and individuals with one to three children were less likely to have a health condition.

Posterior Predictive Check

Each bar represented one of the two potential outcomes for health_condition. The ten replicates had little variation between them and precisely captured the actual data.