1. Growing Healthy Kids in Kentucky November 5, 2003
You Do the Math!
How to Collect BMI for Your School
Growing Healthy Kids in Kentucky
November 5, 2003

2. Presenters Camille Powell & Nancy Hiner
Lexington-Fayette County Health Department
Teri Wood
Kentucky Department for Public Health
Cheri Tolle
UK Prevention Research Center

3. Why collect BMI data? Address obesity epidemic at its roots
Provide baseline data at local level
Determine the extent of the problem
Make evidence-based decisions
Target high-risk schools/districts

4. Why collect BMI data? Respond to trends
Compare with regional and national data
Educate students, teachers, parents, administrators, community members, media

5. Confidentiality
Consult with your employer’s privacy officer or person responsible for approving evaluation/data collection
Obtain permission from the county superintendent’s office and principals for each school

6. What about HIPAA?

7. Fe Fi Fo FERPA… Family Educational Rights and Privacy Act
Protects student records from being released to third parties
Exception — organizations conducting studies on behalf of the schools

8. How do you get in the door?
Enlist support from the district’s central office
Obtain written permission from superintendent’s office
Enlist support from each school to gather the data
Meet with the principal or vice principal, school nurse, family resource center coordinator, or other staff
Have dst staff send letter to each school
In some cases the Vice Principal or other administrative staff can give the okay, just be sure the principal is informed

9. How do you get in the door?
Offer a monetary incentive if possible
Promise to report back with findings and provide support

10. How do you get in the door?
Contact other agencies that have gathered data to learn their strategies
Barren River District Health Department
Fayette County Health Department

11. How do you get in the door?
Don’t approach schools about data collection during chaotic times of the year
Beginning or end of the school year

12. Collection forms Gender Race Month/year of birth Height Weight
Date of exam
Other data (i.e., immunization)

13. What do you do with the collected information?
Keep each school’s information confidential
Compile a district report
Compile individual school reports
Compare individual school data with district data – do not report comparisons among schools

14. What to do with the collected information?
Use data to support environmental and policy changes
Use data for selecting program activities
Use data for grant writing purposes
Thank participating schools

15. Why size matters When randomness is a good thing
What sample size will you want or need?
Do you want to look at schools or districts?
Having decided on 1 and 2 above, how do you draw a random sample?

16. Deciding sample size
How much error are you willing to accept in your study?
What time/money or other resource limits do you face?
What level of analysis are you interested in?
District
School

17. How does level of analysis affect sample size?
Examples of differing sample sizes for different size populations
800 6th graders in the district
Sample size = 260
200 6th graders in each of 4 schools
Sample size – 132 for each school
Total of 528

18. How to select a random sample
Decide on your sample size
Do a systematic random sample
Calculate a SAMPLING INTERVAL by dividing the population size by the sample size:
Sampling interval = population size/sample size
3=800/260
This means you select every third record until you have completed 260 records

19. How to select a random sample
Select the first record by using the random number chart (in handout)
Use your sampling interval number to select the remaining records

20. Lessons learned Race Age Date of data May be coded as a number
Often found on “attendance report” or class list
Age
Only record month and year because of FERPA regulations
Date of data
“Old” data may need to be thrown out

21. Lessons learned
Not having enough kids with records to complete the sample size
Obtain accurate class sizes
Charts being grouped with multiple grades
If more than 20% of records are incomplete, BMI data is not valid

22. Lessons learned Charts in transition during summer break
Forgetting to start with random number