**Regression
Outcome**

**MEASLES EFFECTS ON NUTRITIONAL STATUS - KENYA**

First, the results for __measles alone__.

1. Open

keast4j.sav2. Click on

Statistics, Regression, Linear.3. Place

wazin theDependent Variablebox andhmeasandagein theIndependent(s)box and choose theMethod, Enter.4. Click on

OK.

**INTERPRETATION**: The results of the first regression are surprising, at
least initially. The variable for measles immunization was entered (yes or no) and the
outcome variable is WAZ score. Measles is significant, but in the direction opposite of
what is expected. It appears here that those with measles immunization have significantly
lower nutrition status (0.513 point decrease in percent with measles for each unit
decrease in WAZ score).

What could cause this? Well, as previously
discussed, it likely has to do with the effect of age on nutrition status as well as the
effect of age on measles immunization. Look below at the model with both __ age and
measles__. (Run the same model as above, but include the continuous
independent variable

What considerations should be made for the
child's age? Does a child's age not affect both the likelihood of immunization and the
outcome of nutrition status? It has been seen that as children grow older and are weaned
from breast milk and exposed to more pathogens, they falter in their growth. Now that age
is in the regression equation, see if it changes the size of the effect for measles
immunization. If it is both significant in the model for nutrition status and it changes
the effect for measles, it could be __confounding__ the results. It does appear to
decrease the size of the effect for measles, by decreasing the coefficient. So, in a sense
this has controlled for the effect of age and measured the effect of measles on WAZ
separately…or has it?

The problem here is that the INTERACTION has not been tested between age and measles immunization. One RULE to remember is that before Confounding can be tested, interaction must first be detected. Here is how to add the interaction variable.

1. Open

keast4j.sav2. Select

Transform, Computeand name the newTarget Variableasmeas_age3. In the

Numeric Expressionbox type enter the variableshmeasynandagewith the arrow key and place an asterisk between the two to make the equation read:hmeasyn*age

4. Click on

OK.

This will add the interaction variable for measles and age to test for interaction in the model. Now continue by creating an equation with measles, age and the measles/age interaction variable:

1. Open

keast4j.sav2. Click on

Statistics, Regression, Linear.3. Enter

wazas theDependent Variable4. Enter

hmeasyn, age,andmeas_ageas theIndependent Variables5. Select the

Enter Methodand click onOK.

See the results here:

**INTERPRETATION:**

As suspected, there is interaction between measles and age, and now that we have a look there is probably confounding of age as well. We suspect this because even now that the interaction variable has been accounted for, there is still a negative effect of measles immuniztion on nutrition status (age is suspiciosly looking like a confounder).The procedure now is to leave the significant interaction variable in the model and continue to look for a ways to control for confounding. In our next section, we will do just that.