Hello, I have a variable that has repeated observations over the life course. Research shows that the measurement of this variable (e.g., health, household income) during ealy childhood is a significant predictor of that variable during adulthood (e.g., health, household income). Accordingly, I'd like to see how average childhood health between the ages of 3 and 5 correlates with average adulthood health at various points in adulthood (e.g., 25-27, 28-30, 31-33, etc.). Therefore, I used the following to generate a measure of average childhood health during the ages fo 3 and 5.

Code:
bysort personid: egen mhealth35 = mean(health) if inrange(age,3,5)
I used the same code to generate a variable for each mean health status at later age intervals (e.g., 25-27, 28-30, 31-33, etc.). The result is that I get multiple variables indicating the mean health status for ages 25-27 (mhealth2527), ages 28-30 (mhealth2830), ages 31-33 (mhealth3133), and so on. However, when I regress either of these mean health status for adulthood variables on the mean health status for early childhood (mhealth35), I get no observations.

This seems to be because the code I used only displays the averages for when the respondent is within the ages specified within inrange(3,5). For example, if a person's average health status between the ages of 3 and 5 is 3.67, then the mean health status variable (mhealth35) only takes on the of 3.67 when the respondent is 3, 4, and 5 years old. However, when the respondent is not any of those ages, the value is missing. I'd like for the value to not be missing. How would I do that? I hope this is clear. Please let me know if I should clarify.

Below is an example of what my data looks like after using the code displayed above to generate the average health status variables for two separate age intervals (3-5 and 25-27). Rather than display the full set of ages available, I've truncated the observations for convenience.

What my data looks like:
Individual Id Health Status Average Health Status between ages 3 and 5 Age Average Health Status between ages 25 and 27
10001 2 3.67 3 missing
10001 4 3.67 4 missing
10001 5 3.67 5 missing
10001 4. missing 25 4.33
10001 4 missing 26 4.33
10001 5 missing 27 4.33
10001 2 missing 28 missing
10001 5 missing 29 missing
10001 3 missing 30 missing

Below is an example what I want my data to look like:
Individual Id Health Status Average Health Status between ages 3 and 5 Age Average Health Status between ages 25 and 27
10001 2 3.67 3 4.33
10001 4 3.67 4 4.33
10001 5 3.67 5 4.33
10001 4. 3.67 25 4.33
10001 4 3.67 26 4.33
10001 5 3.67 27 4.33
10001 2 3.67 28 4.33
10001 5 3.67 29 4.33
10001 3 3.67 30 4.33