Ontario’s Experience with a Universal Influenza Immunization Program (UIIP)

1. Ontario’s Experience with a Universal Influenza Immunization Program (UIIP) Doug Manuel, MD MSc FRCPC Scientist Jeff Kwong, MD MSc CCFP Research Fellow October 25, 2005

2. Outline • Background • Effect of UIIP on vaccination rates • Effect of UIIP on hospitalizations • Discussion & conclusions

3. A brief geography lesson…

4. Population by province/territory as of July 1, 2004 30K 40K 30K 0.5M 7M 3M 1M 12M 0.1M 4M 1M 1M 0.7M

5. Influenza vaccination in Canada • All health care services are publicly funded and delivered, but programs vary by province • Ontario, pre-2000 • 1988: Targeted program initiated to provide influenza vaccination free for those at high risk of complications from influenza (elderly 65+, those with chronic medical conditions) • 1993: Program expanded to cover patient-care staff of long term care facilities (LTCF) • 1999: Program expanded to cover ALL health care workers

6. Ontario launches UIIP in 2000 • First large-scale program to provide free influenza vaccination to entire population aged 6 months or older • Stated goals: to decrease influenza-related morbidity and mortality, and to decrease ER overcrowding • All other provinces chose to maintain their targeted vaccination programs

7. Some details about the UIIP • Vaccines (trivalent inactivated vaccine) purchased centrally by Ministry of Health and Long-Term Care from 2 manufacturers: Sanofi-Pasteur and Shire Biologics • Vaccines delivered at MD offices, LTC facilities, hospitals, public health units, workplace clinics, pharmacies, schools, community centers, shopping malls, etc.

8. Locations where people were vaccinated in Ontario, 2000-01

9. More details about the UIIP • Extensive communications campaign by provincial government and local public health units to promote UIIP, including TV/radio/print advertising, newsletters, mailings, billboards, web sites, etc. • Response has been generally favorable by the public and those involved in delivery of vaccines

10. Estimated costs of the UIIP • Total program cost: ~$42M CDN* (2003-04) • Vaccine purchase (~54%) • Vaccine delivery & administration (~34%) • Communications (~12%) * $1CDN = $0.85US as of Oct 17, 2005

11. Effect of Ontario’s UIIP on vaccination rates

12. Research Questions • Did introduction of Ontario’s UIIP in 2000 lead to an increase in vaccination rates, compared to the 9 provinces that maintained targeted immunization programs? • If so, which population subgroups benefited the most?

13. Data sources • National Population Health Survey (NPHS) • Canadian Community Health Survey (CCHS) • Both conducted by Statistics Canada • Cover the household population • Exclude members of the Canadian Forces, native reserves, and some remote areas, those living in institutions (e.g., nursing homes, prisons)

14. Population health surveys

15. Unpublished results to be presented

16. Summary of findings • Influenza vaccination rates increased across Canada between 1996 & 2003 • Introduction of UIIP in Ontario associated with a significant increase in coverage rate of overall population aged 12+ years compared to other provinces, with the increase accounted for by those aged 12-64 years

17. Median coverage rates in LTC facility residents & staff, hospital staff • Since 1999, coverage rates as of Nov 15 provided annually by facilities to public health officials * Includes those vaccinated in Dec and Jan

18. Effect of Ontario’s UIIP on hospitalizations

19. Research Questions • Did introduction of Ontario’s UIIP in 2000 lead to a reduction in influenza-related hospitalizations, compared to the 9 provinces that maintained targeted immunization programs? • If so, which population subgroups benefited the most?

20. Methods • Study design: Interrupted time series with concurrent controls • Study population and setting: All residents of 10 Canadian provinces, September 1993 to March 2004

21. Hospitalization data • Obtained from Hospital Morbidity Database • Included admissions with the following conditions listed as 1 of the first 5 diagnoses

22. Definition of influenza season • From October to May, starting when each week accounted for ≥ 5% of the season’s total number of influenza virus isolates for 2 weeks and ending when the influenza isolates accounted for < 5% for 2 weeks *Adapted from Izurieta HS, et al. NEJM 2000; 342(4):232-9

23. Viral surveillance data • Respiratory virus detections • Weekly percentage of tests positive for influenza A, influenza B, RSV • Predominant influenza subtypes • Subtype considered predominant if detections accounted for ≥ 20% of the season’s isolates • Vaccine antigenic match • Compared mismatch between circulating strains and vaccine strains • Good match: < 20% circulating strains mismatched • Fair match: 20-50% circulating strains mismatched • Poor match: > 50% circulating strains mismatched

24. Statistical analysis • Poisson regression models* • Run separately for each condition and province, for 7 age groups: 0-4, 5-19, 20-49, 50-64, 65-74, 75-84, 85+ • Accounted for numerous covariates • Used generalized estimating equations to control for autocorrelation – AR(1) *Adapted from Thompson, et al. JAMA 2004; 292(11):1333-40

25. Poisson regression model Y = α exp (β0 + β1[UIIP_flu] + β2[sex*agegrp] + β3[%FluA] + β4[%FluB] + β5[%RSV] + β6[A(H1N1)] + β7[A(H3N2)] + β8[B] + β9[match] + β10[ICD-10] + β11[t] + β12[t2] + β13[t3] + β14-16[sin(2tkπ/52)] + β17-19[cos(2tkπ/52)] [where k=1, 2, 3] + ε)

26. Poisson model terms Y = weekly number of condition-specific hospitalizations for a given province-, sex-, and age group-stratum α = log of province-, sex- and age group-specific annual population size UIIP_flu = RR of hospitalizations during influenza seasons after 2000 vs. before sex*agegrp = sex & age group interaction %FluA/FluB/RSV = weekly % of regional tests positive for influenza A/B, RSV A(H1N1)/A(H3N2)/B = predominant influenza subtype(s) for a season [1/0] match = vaccine antigenic match [G/F/P] ICD-10 = introduction of ICD-10 t, t2, t3 = time trend terms [t=week number divided by 52] sin/cos = seasonal trend terms

27. Statistical analysis cont’d • Compared RR estimates of change in influenza season-associated hospitalizations over time for Ontario with pooled estimates for other provinces combined • Pooled RR estimates for separate age groups to estimate effect on overall pop. • Pooled RR estimates for separate conditions to estimate combined effect on the 3 influenza-related conditions

28. Unpublished results to be presented

29. Discussion & Conclusions

30. Limitations • Data quality concerns with health administrative data (coding validity/reliability) • No vaccine coverage data on children < 12 years or institutionalized elderly (outside ON) • Provincial-level analysis – may have blurred regional variations in timing and severity of influenza epidemics and hospitalization rates • Ecological study design – susceptible to ecological fallacy • Unmeasured confounders

31. Lessons learned • Implementation of a universal influenza vaccination program is feasible • Clear increases in vaccination rates observed in younger age groups, and increases generally sustained • Modest reductions in influenza-related hospitalizations observed in groups with larger increases in coverage rates • Suggests direct benefit of influenza vaccination • Uncertain about indirect benefits (herd immunity)

32. Critical information gaps • Suboptimal data on individual-level vaccination status (no immunization registry) • Effect of UIIP on other outcomes (outpatient MD visits, ER services, mortality) to be examined by early 2006 • Effect of UIIP on school and workplace absenteeism should also be assessed • No data on vaccination rates in those < 12 yrs • Economic evaluation based on empirical outcome data needed

33. Future studies • Examine other outcomes • Repeat time series analyses with additional data (i.e., more influenza seasons) • Cross-sectional study to examine the relationship between regional variations in vaccination rates and outcome rates in post-UIIP Ontario (to look for a dose-response relationship)

34. Acknowledgements • ICES • Therese Stukel, Jenny Lim, Laura Fazio • Statistics Canada • Helen Johansen, Christie Sambell • Public Health Agency of Canada • Peter Zabchuk • CDC • Bill Thompson, David Shay • Others • Ross Upshur, Allison McGeer, Susan Tamblyn, Irfan Dhalla

35. Financial support • This research was supported by a grant from the Public Health Agency of Canada • Doug Manuel is supported by a Career Scientist award from Ontario’s Ministry of Health and Long-Term Care • Jeff Kwong is supported by a CIHR Fellowship award from the Canadian Institutes of Health Research

36. Questions?