The Ontario Cancer Risk Factor Surveillance Program

1. The Ontario Cancer Risk Factor Surveillance Program Michael Spinks Senior Research Analyst Cancer Care Ontario at 5th Annual RRFSS WorkshopInstitute for Social Research, York UniversityJune, 2006

2. Contents • Risk Factor Surveillance at CCO • CCO analysis of RRFSS data • Generating complex survey estimates using SPSS • Risk factor indicator inference and trends • CCO Risk Factor Surveillance Reporting System • Next Steps

3. CCO Cancer Risk Factor Surveillance System • CCO is very supportive of RRFSS • Risk Factor Surveillance Project established at CCO • Important to liaise with suppliers and users of risk factor data

4. Risk Factor Surveillance MethodologyData Sources • RRFSS (monthly survey, available in 6 weeks) • CCHS (annual survey, available in 6 months) • Other Survey and Related Data (OHS, NPHS, OBSP, SHAPES, OSDUS) • Population Estimates and Projections • Census data

5. Risk Factor Surveillance MethodologyIndicator Development • Cancer 2020 project • Review of indicator definitions from other agencies – CWIG(APHEO), RRFSS, Statcan, camh • Develop indicators using flow diagrams and existing survey data • Indicator refinement and standardization(Beth Theis – CCO representative on CWIG)

7. Current Risk Factor Indicators by Survey

8. Risk Factor Surveillance MethodologySurvey Analysis Review • Single-stage sampling- random selection of individuals from the population is sampled- for a simple random sample, each sample of a given size is equally likely to be selected from the population - each individual has the same probability of being selected- computation of point and variance estimates relatively straightforward • Multistage sampling- units at the first stage are clusters of individuals(or clusters of smaller clusters)- mainly used for cost and logistical reasons- individuals have unequal probabilities of being selected- variability or estimates greater compared with simple random sample of same size- computations of point and variance estimates more complex

9. Risk Factor Surveillance Methodology RRFSS Survey Design At provincial level RRFSS considered to be a multistage cluster sample design stage 1 cluster (PHU) and stage 2 cluster (household)

10. PHU and CCO Weighting Procedures • What is the sampling weight - each individual represents other persons not in sample- computed as the inverse of the inclusion probability- used to obtain unbiased estimates of risk factor indicators • Sample weight used by PHU (monthly/annual)- inclusion probability of selecting an adult member from sample of households- weights total to number of respondents in sample • Sample weight used by CCO (annual)- inclusion probability of selecting an adult member in the population- adjusted so each month is equally represented- adjusted to size of population age/sex structure- weights total to number of adults in population

11. Respondents by PHU and Wave, 2004 Number of respondents vary slightly by month

12. Comparison of Estimates – PHU and CCO • Point estimates- Both methods yield almost identical point estimates • Variance estimates- Assuming simple random sampling (PHU)- Taylor’s series linearization (CCO) - Bootstrap resampling (CCHS)- Jack-knife resampling- Balanced half-sample

13. Comparison of estimates - PHU and CCO Approaches PHU approach underestimates variance of multistage survey design

14. Comparison of estimates - PHU and CCO Approaches Was the percentage of smokers in Durham significantly lower in 2003 than in 2001?

15. Tools for computing estimates from complex surveys • SAS (CCO) – proc surveyfreq, surveymeans, surveyreg, surveylogistic • SPSS (PHU) - CSPlan then - CSDescriptives, CSTables, CSTabulate, CSGLM, CSLogistic • Sudaan – proc crosstab, descript, ratio, regress, logistic • Stata – svyset, then svy: mean, proportion, ratio, total, regress, logit, etc.

16. Computing estimates from complex surveys in SPSS 1 3 SPSS Syntax * Analysis Preparation Wizard. CSPLAN ANALYSIS /PLAN FILE='M:\RRFSS\SPSS\rrfssplan.csaplan' /PLANVARS ANALYSISWEIGHT=fwgt /PRINT PLAN /DESIGN STRATA= h_unit CLUSTER= idnum /ESTIMATOR TYPE=WR. 2

17. Computing estimates from complex surveys in SPSS

18. Computing estimates from complex surveys in SPSS - Results

19. Comparison of estimates generated from SPSS and SAS% of current smokers, Durham Regional Health Unit, 2004 • Estimate of point statistic identical • Estimate of standard error identical to the 5th decimal place

20. CCO Risk Factor Measures Compute range of statistics for different indicators to be able to respond to the majority of analytical needs

21. Risk Factor Estimates at the Provincial Level • Almost 100% of population and 100% of Health Units represented in CCHS • 85% of population and 67% (24) Public Health Units represented in RRFSS 2004 • Estimates from RRFSS Public Health Units are not usually used as a proxy for the province • RRFSS not representative of northern PHUs

22. Significantly different at 5% • Data Source: CCHS 2.1, Statistics Canada Comparison of Risk Factor Estimates between RRFSS Health Units and Non-RRFSS Health Unitsusing CCHS 2.1 Prevalence of Selected Risk Factor Indicators with 95% CI

23. Comparison of Risk Factor Estimates • Overlapping confidence intervals • Compute age-standardized rates (age groups-12-17, 18-44, 45-64, 65+) • Funnel plots for comparing PHUs • Significance testing using logistic regression and controlling for age and sex differences

24. Risk Factor Surveillance MethodologyTrends • Annual plots of RRFSS and CCHS estimates • Quarterly plots of RRFSS estimates • Change point analysis • Control charts • Box-jenkins time series analysis

25. PrevCan - CCO Risk Factor Surveillance ReportingSystem

26. Next Steps • Collaboration with CE RRFSS Group • Establish agreement with RRFSS for sharing of data and technical support • Share developments with MOHLTC • Refine methods for testing and dissemination of results • Expand indicators to include socio-economic and environmental factors • Include GIS in risk factor surveillance