1. NoelleAngelique M. Molinari, PhD Nidhi Jain, MD CDC
Human Papillomavirus Vaccine Initiation & Access to Care among US Teens, 2007
NoelleAngelique M. Molinari, PhD
Nidhi Jain, MD
CDC
“The findings and conclusions in this presentation are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention.”

2. Background
Advisory Committee on Immunization Practice recommended in June 2006
Quadrivalent vaccine (HPV) for females
3 dose series
Aged routine
Aged catch-up
Costly
$ per dose (private sector cost to provider) *
CDC cost = $ *
* CDC Vaccine Price List accessed 03/23/2009

3. Objective Examine HPV initiation among female teens in US, 2007
Identify factors related to HPV initiation
Income/Poverty status
Health insurance
Access to care
Provider type
Utilization

4. Data National Immunization Survey - Teen (NIS-Teen), 2007 RDD survey
4th Quarter National data only
RDD survey
CASRO* Response Rate ~ 56%
Interview Completion ~ 84%
Follow-up provider record check
Provider Response Rate ~ 89%
N = 1,441 females with provider-reported vaccine histories
* Council of American Survey Research Organizations (CASRO) Response Rate = Resolution Rate * Screening Rate * Interview Completion Rate

5. Data
Nationally representative sample of US teens aged 13 through 17 years
Includes information on:
Insurance status
Parent- & provider-reported
Well-child exams
Vaccination coverage
Demographics

6. Statistical Methods SUDAAN software used to calculate vaccine uptake
Identify factors associated with HPV vaccine initiation
Initiation is 1+ provider-reported HPV
Bivariate tests of association: simple logistic regression
Wald-F Chi-squared statistic
Unadjusted odds ratios
do not control for confounding

7. Results
N = 1,441
71%
29%

8. Results ~ Bivariate
Variables with No Evidence of Association (p > 0.15)
Age
Foreign born status
Language of interview
Mobility - moved since birth
Teen’s grade in school
Asthma diagnosis - proxy for health status
Number of providers - scattering of care

9. Results ~ Bivariate Variables with Possible Association
Census Region (p<0.001)
Metropolitan Statistical Area (MSA) (p<0.001)
Race/Ethnicity (p=0.12)
Mother’s Education (p=0.01)

10. Results: Census Region
Wald-F: p < 0.001 R E F N T
OR = 0.5
p < 0.001
OR = 0.5
p < 0.001
OR = 0.6
p = 0.03

11. Results: MSA Wald-F: p < 0.001 R E F N T OR = 0.8 p = 0.11 OR = 0.5

12. Results: Race/Ethnicity
Wald-F: p = 0.13 R E F N T
OR = 1.4
p = 0.11
OR = 0.9
p = 0.7
OR = 0.6
p = 0.12

13. Results: Mother’s Education
Wald-F: p = 0.01
OR = 1.2
p = 0.42 R E F N T
OR = 0.9
p = 0.54
OR = 0.7
p = 0.14

14. Results ~ Bivariate Variables with Possible Association Access to Care
Provider Type
Facility Type
Provider Specialty
Utilization
Parent-reported receipt of yr Checkup
Parent-reported # Visits in past 12 months (also proxy for health status)
Household Income
Health Insurance

15. Results: Facility Type
Wald-F: p < 0.001
OR = 2.4
p = 0.00
OR = 2.4
p = 0.00
Mixed = public, private, R E F

16. Results: Provider Specialty
Wald-F: p < 0.001 R E F N T
OR = 0.9
p = 0.72
OR = 0.2
p = 0.00

17. Results: 11-12 Year Checkup
Wald-F: p < 0.001
Provider contact  1+HPV coverage
OR =2.0
p = 0.00 R E F N T

18. Results: # Visits past 12 months
Wald-F: p < 0.001
As contacts rise
1+HPV coverage rises
OR = 5.3
p = 0.00
OR = 4.7
p = 0.00
OR = 2.4
p = 0.01 R E F

19. Results: Health Insurance
Wald-F: p < 0.001
As Out of Pocket (OOP) Price rises
1+HPV coverage falls
OR = 1.2
p = 0.25 R E F N T
OR = 0.3
p = 0.00

20. Results: Household Income
Wald-F: p = 0.02
As Income rises
1+HPV coverage rises
But note U-shape …
OR = 1.6
p = 0.04
OR = 1.5
p = 0.13 R E F N T
OR = 1.1
p = 0.77
OR = 0.8
p = 0.36

21. Summary Provider type may be important
All public facilities  low coverage
Other specialty provider  low coverage
Utilization  high 1+HPV coverage
Had checkup  higher coverage
More provider contacts  higher coverage

22. Summary Insured  HPV initiation Income  HPV initiation
Public has higher coverage than private
Public insurance precedes private insurance
Not insured  low coverage
Price effect: high OOP HPV vaccine  low coverage
Income  HPV initiation
U-shaped relationship
Un(der)insurance effect? Price effect?
As income rises
Lose access to public insurance
Gain access to private insurance, but poor quality

23. Next steps… Add 2008 data – 4 more quarters
Examine HPV coverage over time
Multivariate model to adjust for Selection/ Simultaneity bias
Provider type
Utilization

24. Next steps…
Examine relationship between insurance and utilization of care
What factors affect insurance status?
How does insurance influence access to care?
Provider type
Utilization

25. Thank You!