The Ontario Cancer Risk Factor Surveillance Program

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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

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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

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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

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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

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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)

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(No Transcript)
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Current Risk Factor Indicators by Survey
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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

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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)
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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

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Respondents by PHU and Wave, 2004
Number of respondents vary slightly by month
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Comparison of Estimates PHU and CCO

• Point estimates- Both methods yield almost
  identical point estimates
• Variance estimates- Assuming simple random
  sampling (PHU)- Taylors series linearization
  (CCO) - Bootstrap resampling (CCHS)- Jack-knife
  resampling- Balanced half-sample

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Comparison of estimates - PHU and CCO Approaches
PHU approach underestimates variance of
multistage survey design
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Comparison of estimates - PHU and CCO Approaches
Was the percentage of smokers in Durham
significantly lower in 2003 than in 2001?
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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.

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Computing estimates from complex surveys in SPSS
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SPSS Syntax
Analysis Preparation Wizard. CSPLAN ANALYSIS
/PLAN FILE'M\RRFSS\SPSS\rrfssplan.csaplan'
/PLANVARS ANALYSISWEIGHTfwgt /PRINT PLAN
/DESIGN STRATA h_unit CLUSTER idnum
/ESTIMATOR TYPEWR.
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Computing estimates from complex surveys in SPSS
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Computing estimates from complex surveys in SPSS
- Results
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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

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

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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
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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

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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

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PrevCan - CCO Risk Factor Surveillance
ReportingSystem
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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