The Ontario Cancer Risk Factor Surveillance Program

Michael SpinksSenior Research Analyst

Cancer Care Ontario

at

5th Annual RRFSS WorkshopInstitute for Social Research, York

UniversityJune, 2006

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

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

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

Risk Factor Surveillance Methodology

Indicator 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)

Current Risk Factor Indicators by Survey

Indicator CCHS RRFSS

Adult smoking

Teen smoking

Quitting smoking

Exposure to 2nd hand smoke

Adult obesity

Teen obesity

Physical activity

Alcohol consumption

Fruit & Vegetable Intake

Mammography screening

Cervical screening

Colorectal screening

Sun safety

Tanning equipment usage

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

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)

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

Respondents by PHU and Wave, 2004

Number of respondents vary slightly by month

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

Comparison of estimates - PHU and CCO Approaches

PHU approach underestimates variance of multistage survey design

Comparison of estimates - PHU and CCO Approaches

Was the percentage of smokers in Durham significantly lower in 2003 than in 2001?

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.

* 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.

Computing estimates from complex surveys in SPSS

SPSS Syntax

13

2

Computing estimates from complex surveys in SPSS

Computing estimates from complex surveys in SPSS - Results

Comparison of estimates generated from SPSS and SAS% of current smokers, Durham Regional Health Unit, 2004

Sex Age group

SPSS SAS

% se % se

male   

18-44 34.01798 3.080043 34.01798 3.080045

45-64 24.99574 3.380777 24.99574 3.380780

65+ 15.16691 4.413263 15.16691 4.413266

18+ 28.79788 2.123433 28.79788 2.123434

female   

18-44 28.44647 2.656429 28.44647 2.656430

45-64 21.90933 2.851722 21.90933 2.851724

65+ 9.18770 2.939705 9.18770 2.939707

18+ 23.49117 1.759892 23.49117 1.759893

• Estimate of point statistic identical• Estimate of standard error identical to the 5th decimal place

CCO Risk Factor Measures

Indicator definitions Point statistics Statistics for evaluating precision

Multiple combinations of

numerators and denominators as

requirede.g. for female low alcohol risk1. <=9 drinks/week2. <=9 drinks/week and <=2 drinks daily in last week

Counts/Prevalence ratiosSex and/or age specificCrude/Age standardizedPHU/LHIN/Province

95% confidence intervalsStandard errorsCoefficient of Variation (CV)Numerator/Denominator sample sizes

Compute range of statistics for different indicators to be able to respond to the majority of analytical needs

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

Risk Factor Estimates at the Provincial Level

Comparison of Risk Factor Estimates between RRFSS Health Units and Non-RRFSS Health Units

using CCHS 2.1

0.0

10.0

20.0

30.0

40.0

50.0

60.0

curr

ent

sm

okers

*

obese *

fruit/v

egeta

ble

(5+/d

ay)

*

physic

ally

active

curr

ent

sm

okers

*

obese *

fruit/v

egeta

ble

(5+/d

ay)

physic

ally

active *

male female

%

All RRFSS Health Units All Non-RRFSS Health Units

•Significantly different at 5%Data Source: CCHS 2.1, Statistics Canada

Prevalence of Selected Risk Factor Indicators with 95% CI

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

Risk Factor Surveillance Methodology

Trends

Annual plots of RRFSS and CCHS estimates Quarterly plots of RRFSS estimates Change point analysis Control charts Box-jenkins time series analysis

PrevCan - CCO Risk Factor Surveillance ReportingSystem

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