Canadian Community Health Survey

1

• Canadian Community Health Survey
• Cycle 1.1
• Overview of methodological issues and more...

2
Presentation Outline

• Sample Design
• Target population, sample allocation and frames
• Sampling strategies, oversampling of
  sub-populations
• Data collection, response rates
• Imputation
• Weighting
• Sampling error
• Sampling variability guidelines
• Variance estimation Bootstrap re-sampling
  technique
• CV look-up tables
• Analysis
• Examples
• How to use the Bootvar programs

3
CCHS - Cycle 1.1 Health Region-level survey

• Main objective
• Produce timely cross-sectional estimates for 136
  health regions
• Target population
• individuals living in private occupied dwellings
  aged 12 years old or over
• Exclusions those living on Indian Reserves and
  Crown Lands, residents of institutions, full-time
  members of the Canadian Armed Forces and
  residents of some remote areas
• CCHS 1.1 covers 98 of the Canadian population

4
CCHS - Sample Allocation to Provinces

• Prov Pop of 1st Step 2nd Step Total
• Size HRs 500/HR X-prop Sample
• NFLD 551K 6 2,780 1,230 4,010
• PEI 135K 2 1,000 1,000 2,000
• NS 909K 6 3,000 2,040 5,040
• NB 738K 7 3,500 1,650 5,150
• QUE 7,139K 16 8,000 16,280 24,280
• ONT 10,714K 37 18,500 23,760 42,260
• MAN 1,114K 11 5,500 2,500 8,000
• SASK 990K 11 5,400 2,320 7,720
• ALB 2,697K 17 8,150 6,050 14,200
• BC 3,725K 20 10,000 8,090 18,090
• CAN 29,000K 133 65,830 64,920 130,750
• The sampling fraction in some small HRs was
  capped at 1 in 20 households

5
CCHS - Sample Allocation to Health Regions

• Pop. Size of Mean
• Range HRs Sample Size
• Small less than 75,000 41 525
• Medium 75,000 - 240,000 60 900
• Large 240,000 - 640,000 25 1,500
• X-Large 640,000 and more 7 2,500

6
CCHS - Sample Allocation to Territories

• Population Sample
• Yukon 25,000 850
• NWT 36,000 900
• Nunavut 22,000 800

7
CCHS - Sample Frame

• CCHS sample selected from three frames
• Area frame (Labour Force Survey structure)
• RDD frame of telephone numbers (Random Digit
  Dialling)
• List frame of telephone numbers
• Three frames are needed for CCHS for the
  following reasons
• 1. To yield the desired sample sizes in all
  health regions
• 2. Have a telephone data collection structure in
  place to quickly address provincial/regional
  requests for buy-in sample and/or content at any
  point in time
• 3. Optimize collection costs

8
Area frame - Sampling of households

• 83 of CCHS sampled households
• Multistage stratified cluster sample design

1 Each health region is divided into
strata
Stratum 1
?
?
2 Clusters selected within strata (PPS
sampling) (1st stage)
?
?
?
?
?
3 Dwellings selected within clusters
(2nd stage)
Stratum 2
?
?
?
?
?
?
?
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RDD frame of telephone numbers Sampling of
households

• Elimination of non-working banks method
• 7 of CCHS sampled households
• Telephone bank area code first 5 digits of a
  7-digit phone
• 1- Keep the banks with at least one valid phone
• 2- Group the banks to encompass as closely as
  possible the health region areas - RDD strata
• 3- Within each RDD stratum, first select one bank
  at random and then generate at random one number
  between 00 and 99
• 4- Repeat the process until the required number
  of telephone numbers within the RDD stratum is
  reached

10
List frame of telephone numbers Sampling of
households

• Simple random sample of telephone numbers
• 10 of CCHS sampled households
• Telephone companies billing address files and
  Telephone Infobase (repository of phone
  directories)
• 1- Create a list of phone numbers
• 2- Stratify the phone numbers by health region
  using the residential postal codes
• 3- Select phone numbers at random within a health
  region
• 4- Repeat the process until the required number
  of telephone numbers is reached

11
CCHS - Sampling of persons

• Area frame
• Simple random sample (SRS) of one person aged 12
  years of age or older (82 of households)
• SRS sample of two persons aged 12 years of age
  or older (18)
• RDD / List frames
• SRS sample of one person aged 12 years of age or
  older

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CCHS - Sampling of persons

• Age 1996 LFS CCHS
• group Census sample simulated (all
  persons) sample
• ( only 1 person)
• 12-19 13.2 13.7 8.5
• 20-29 16.4 14.4 14.3
• 30-44 30.8 28.7 29.1
• 45-64 25.8 28.0 27.9
• 65 13.8 15.2 20.2
• averaged distribution over 100 repetitions
  using the May 99 LFS sample

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CCHS - Representativity of sub-populations

• To address users needs, two sub-population
  groups needed larger effective sample sizes
• Youths (12-19 years old)
• Decision gt Oversample youths by selecting a
  second person (12-19) in some households based on
  their composition
• Elderlies (65 years old and )
• Decision gt Do not oversample - let the general
  sample selection process address the issue by
  itself

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Sampling strategy based on household composition

• Number of persons aged 20 or over
• Number 0 1 2 3 4 5 of 12-19
• 0 - A A A A B
• 1 A A C C C B
• 2 A C C C C C
• 3 A C C C C C
• A Simple random sample (SRS) of one person aged
  12
• B SRS of two persons aged 12
• C SRS of one person in the age group 12-19 and
  SRS of one person 20

15
CCHS - Sample Distribution after Oversampling

• Age 1996 CCHS CCHS
• group Census simulated simulated sample sam
  ple
• ( only 1 person) ( some 2 persons)
• 12-19 13.2 8.5 14.9
• 20-29 16.4 14.3 13.1
• 30-44 30.8 29.1 28.1
• 45-64 25.8 27.9 26.3
• 65 13.8 20.2 17.6
• averaged distribution over 100 repetitions
  using the May 99 LFS sample

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CCHS - Initial data collection plan

• 12 monthly samples
• 12 collection months 1
• Area frame
• CAPI
• STC field interviewers
• targeted response rate 90
• anticipated vacancy rate 13

• (09 / 2000 - 08 / 2001) 09 / 2001
• RDD / List frames
• CATI
• STC call centres
• targeted response rate 85
• telephone hit rate 15-60

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CCHS data collection - Observed situation

• Field interviewers
• workload exceeded field staff capacity
• Call centres
• new collection infrastructure
• unequal allocation of work among call centres
• Descriptive paper
•  Preventing nonresponse in the Canadian
  Community Health Survey , Y. Béland, J. Dufour,
  and M. Hamel. 2001, Hull, Statistics Canada
  XVIIIth International Symposium.

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CCHS - Final response rates

• Field Call centres
  Total
• NFLD 86.6 89.3 86.8
• PEI 87.7 82.6 84.7
• NS 88.8 89.3 88.8
• NB 88.4 92.4 88.5
• QUE 85.7 84.8 85.6
• ONT 82.8 79.5 82.0
• MAN 90.0 85.0 89.5
• SASK 87.0 85.4 86.8
• ALB 85.2 84.9 85.1
• BC 83.9 86.7 84.7
• YUK 79.3 95.6 82.7
• NWT 89.6 85.4 89.2
• NUN 66.3 34.6 62.5
• CAN 85.1 83.1 84.7

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CCHS - Proxy interviews

• Definition When another person in the household
  responds
• to the survey on behalf of the selected
  person in the sample
• Acceptable in the following cases
• Out of the country for a long period of time
• Mental or physical state of health
• Language barrier
• Usually, 2 to 3 of proxy respondents
• Because of the field problems 6,3
• Higher rate in some health regions, for men and
  younger respondents
• Major consequence one third of the
  questionnaire is missing
• Personal or sensitive type of questions not
  asked
• Solution imputation

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Modules for proxy and non-proxy

• Alcohol
• Chronic condition
• Exposure to second hand smoke
• Food insecurity
• General health (Q1, Q2 and Q7)
• Health care utilization
• Health Utility Index (HUI)
• Height / Weight (Q2 and Q3)
• Injuries
• Restriction of activities
• Smoking
• Tobacco alternatives

• Two-week disability
• Household composition housing
• Income
• Labour force
• Socio-demographic characteristics
• Administration
• Drug use (optional)
• Home care (optional)

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Modules for non-proxy only

• Alcohol dependence / abuse
• Blood pressure check
• Breastfeeding
• Contacts with mental health professionals
• Mammography
• Fruit vegetable consumption
• General health (Q3-Q6, Q8-Q10)
• Height / Weight (Q4 only)
• PAP smear test
• PSA test
• Physical activities
• Patient Satisfaction
• Breast examinations
• Breast self examinations
• Changes made to improve health
• Depression

• Dental visits
• Distress
• Driving under influence
• Eye examinations
• Flu shots
• Mastery
• Mood
• Physical check-up
• Sedentary activities
• Self-esteem
• Sexual behaviours
• Smoking cessation aids
• Social support
• Spirituality
• Suicidal thoughts and attempts
• Use of protective equipment
• Work stress

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Imputation Strategy - 5 passes

• 1st pass Health prevention modules
• 3 modules imputed completely
• 6 modules imputed partially (some questions
  only)
• 2 modules not imputed
• 2nd pass Mental health modules
• 6 modules imputed completely
• 7 modules not imputed
• 3rd pass Sexual behaviours
• 4th pass Fruit and vegetable consumption
• 5th pass one question in the module height and
  weight
• Note that some modules and/or questions are not
  imputed (such as physical activity, distress,
  work stress, time since last flu shot, etc.)

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Imputation Strategy

• Strategy applied at each imputation pass
• 1. Create imputation classes
• Usually Province X Sex X Age groups X
  Filters
• The donor has to be in the same imputation class
  as the recipient
• Minimum donor rule donors / (donors
  recipients) gt 60
• 2. Identify a list of matching variables
• 3. Assign a weight to each matching variable
• Default weight 1, sometimes weight 2, 3 or
  more.
• 4. Find the nearest donor
• - Highest Total Weighted Match
• - If more than one possible donor, select one
  randomly from them
• - No imputation if no donor over a minimum number
  of matches

24
CCHS - Weighting and Estimation

• Estimation relates sample back to population
• MUST use weights in calculation of estimates to
  correctly draw conclusions about population of
  interest
• Sampling weight is related to the probability of
  selecting a person in the sample
• Persons are selected with unequal probabilities
  therefore have varying weights

25
CCHS - Weighting and Estimation

• Three separate weighting systems
• Area frame design
• RDD frame design
• List frame design
• Several adjustments
• non-response (household and person)
• seasonal factor
• etc...
• Integration of the two weighting systems based on
  design effects and sample sizes ( n / deff )
• Calibration using a one-dimensional
  poststratification adjustment of ten age/sex
  poststrata within each health region
• Variance estimation bootstrap re-sampling
  approach
• set of 500 bootstrap weights for each individual

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Weighting Estimation
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Weighting Estimation

• Initial weight Inverse of the probability of
  being selected

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Weighting Estimation

• Household nonresponse Distribute weight of
  nonresponding households to responding ones
• Using nonresponse classes such as HR, collection
  period and urban, rural/urban)

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Weighting Estimation

• No phone lines No coverage of hhlds without a
  phone line. Weights are boosted by a certain
  rate (specific to each HR)
• Rates of no phone lines calculated using area
  frame data

30
Weighting Estimation

• of people in hhld Convert the hhld-level
  weight into a person-level weight (multiply by
  the number of people)
• Depends on the of people selected (1 or 2), and
  their age

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Weighting Estimation

• Person level nonresponse Redistribute the weight
  of selected person who did not respond to the
  ones who responded
• Using classes (age, sex, person selected,
  collection period, etc)

32
Weighting Estimation

• Multiple phone lines More phone lines higher
  probability of being selected
• weight divided by the number of residential phone
  lines

33
Weighting Estimation

• Final weight Each frames final weight is each
  representative of the total population. To
  create a single set of weights, they are combined
  through Integration

34
Weighting Estimation

• Integration Combine the 2 sets of weights into
  one single set of weights
• Based on sample size and design effect of each
  frame

35
Weighting Estimation

• Seasonal effect Adjust weights so that each
  season contains 25 of the total population
• Based on the collection period(sept-nov /
  dec-feb / mar-may / june - aug)

36
Weighting Estimation

• Post-stratification Ensure the sum of weights
  matches the estimated population projections in
  each HR, for 10 age-sex groups
• 12-19, 20-29, 30-44, 45-64 and 65 crossed with
  two sexes

37
Weighting Estimation

• Final CCHS weight Final weight present on the
  CCHS master file

38
CCHS - Special Weights

• For various reasons, many other weights are
  produced
• Quarter 4 special weight
• PEI special weight
• Share weights (master, Q4 and PEI special)
• Link weights (master, Q4 and PEI special)

39
Sampling Error

• Difference in estimates obtained from a sample as
  compared to a census
• The extent of this error depends on four factors
• sample size
• variability of the characteristic of interest
• sample design
• estimation method
• Generally, the sampling error decreases as the
  size of the sample increases

40
Sampling Error

• Measures of precision associated to an estimate
• Variance
• Standard deviation (square root of the variance)
• 95 confidence interval (estimate 1.96 x
  standard deviation)
• Coefficient of variation
• Standard deviation of estimate x 100 / estimate
  itself
• CV allows comparison of precision of estimates
  with different scales
• Examples
• 24 of population are daily smokers, std dev.
  0.003
• gt CV0.003/0.24 x 1001.25
• gt 95 CI 0.240 1.96 x 0.003 0.234
  0.246

41
Sampling Variability Guidelines

• Type of estimate CV Guidelines
• Acceptable 0.0-16.5 General
  unrestricted release
• Marginal 16.6-33.3 General
  unrestricted release but with
  warning cautioning users of the high
  sampling variablitity.
  Should be identified by letter E.
• Unacceptable gt 33.3 No release.
• Should be flagged with letter F.

42
Sampling Error

• Measuring sampling error for complex sample
  designs
• Simple formulas not available
• Most software packages do not incorporate design
  effect (and weights adjustments) appropriately
  for calculations
• Solution for CCHS the Bootstrap re-sampling
  method

43
Bootstrap method

• Principle
• You want to estimate how precise is your
  estimation of the number of smokers in Canada
• You could draw 500 totally new CCHS samples, and
  compare the 500 estimations you would get from
  these samples. The variance of these 500
  estimations would indicate the precision.
• Problem drawing 500 new CCHS samples is
• Solution Assuming your sample is representative
  of the population, sample 500 new subsamples and
  compute new sampling weights for each subsample.

44
Bootstrap method

• How CCHS Bootstrap weights are created(the
  secret is now revealed!!!)

T 40Var ? (Bi - B)2 / 499
45
Bootstrap Method

• How Bootstrap replicates are built?
• The real recipe
• 1- Subsample clusters (SRS) within a design
  stratum
• 2- Apply (initial design) weight
• 3- Adjust (boost) weight for selection of n-1
  among n
• 4- Apply all standard weight adjustments
  (nonresponse, integration, share, etc.)
• 5- Post-stratification to population counts
• The bootstrap method intends to mimic the same
  approach used for the sampling and weighting
  processes

46
Bootstrap Method

• Sampling weight versus Bootstrap weights
• Sampling weight used to compute the estimation of
  a parameter (e.g. number of smokers)
• Bootstrap weights used to compute the precision
  of the estimation (e.g. the CV of the number of
  smokers estimation)

47
Bootstrap Method

• The process of variance estimation is divided
  into two phases
• Calculation of bootstrap weights
• Need to be produced only once
• Done by Statistics Canada methodologists

48
Bootstrap Method

• Variance estimation using bootstrap weights
• Done by anyone - internally or externally
• Bootstrap weights files distributed with all CCHS
  files,except Public-Use Microdata File (PUMF)
• Bootstrap weights are in a separate file (match
  using IDs)
• Not for PUMF because bootstrap weights reveal
  confidential info
• PUMF users must proceed through remote access to
  get  exact  variances or use the CV look-up
  tables

49
Bootstrap Method

• Variance estimation using bootstrap weights
• SAS and SPSS (beta) macro programs provided to
  users (BOOTVAR)
• Allow users to perform a few statistical analysis
  (totals, proportions, differences of proportions
  and regression analysis)
• Fully documented with examples
• Bootstrap hands-on workshop

50
How to use the Bootvar program

• STEP 1
• Create your analytical file

STEP 2 Compute your variances using Bootvar

• Read CCHS data file
• Prepare the necessary dummy variables
• Keep only the necessary variables
• Perform the analysis to obtain the point
  estimates
• (not essential but recommended)

• Specify the location of the files
• Your analytical file
• Bootstrap weights file
• Specify the level of geography
• Specify the analysis to perform
• Total, proportion, diff. of prop.
• Regression (linear logistic)
• Generalized linear model

51
How to use the Bootvar program

• Statistical analysis
• Using the NPHS cycle 3 (1998) cross-sectional
  dummy data, estimate the number of ontarians aged
  12, by gender, who perceive themselves as being
• - in poor or fair health,
• - in good health,
• - in very good health,
• - in excellent health.
• - Compute 95 confidence interval for each point
  estimate using the Bootvar program.

52
Necessary variables for the analysis

• Self-perceived health (GHC8DHDI)
• 0 poor, 1 fair, 2 good, 3 very good, 4
  excellent, 9 not stated
• Age (DHC8_AGE) Sex (DHC8_SEX)
• gt 12 1 male, 2 female
• Province (PRC8_CUR) Sampling weight (WT68)
• 35 Ontario
• Record identifier for the household (REALUKEY)
• Number identifying the person in the household
  (PERSONID)

53
Basic theoritical notions for estimating a
proportion

• Example of a data file
• ID Weight Sex Asthma Asthma_id
• A 50 M YES 1
• B 60 M NO 0
• C 50 M NO 0
• D 70 M YES 1
• E 50 M NO
  0
• (WeightA WeightD)
• (WeightAWeightBWeightCW
  eightDWeightE)
• (50 70) / (50 60 50
  70 50) 100 120 / 280 100 43

(asthma_men) ________________________________
__________ 100
54
Little trick for the statistical analysis

• Create your univariate dummy variable
• Men 1,0 (men)
• Good health 1,0 (good)
• Men in good health mgood men good
• men good mgood
• 1 0 0
• 1 1 1
• 0 0 0
• 0 1 0

55
Results of the statistical analysis

• Self-perceived health of ontarians aged 12 or
  older by gender
• in 1998
• (000) 95 CI 95 CI
  
• Men
• - Poor / fair 391 (330 452) 8.4 (7.1 9 .8)
• - Good 1,106 (1,007 1,204) 23.9 (21.7
  26.0)
• - Very good 1,764 (1,648 1,880) 38.1 (35.6
  40.6)
• - Excellent 1,373 (1,268 1,479) 29.6 (27.4
  31.9)
• Women
• - Poor / fair 480 (409 551) 9.9 (8.5 11.4)
• - Good 1,258 (1,151 1,364) 26.1 (23.9
  28.3)
• - Very good 1,846 (1,726 1,965) 38.2 (35.8
  40.7)
• - Excellent 1,243 (1,138 1,348) 25.8 (23.6
  27.9)

56
Why use the Bootstrap method?

• Other techniques
• Taylor
• Need to define a linear equation for each
  statistic examined
• Jacknife
• Number of replicates depends on the number of
  strata (large number of strata makes it
  impossible to disseminate)

57
Why use the Bootstrap method?

• BOOTSTRAP
• more user-friendly when there is a large number
  of strata
• sets of 500 bootstrap weights can be distributed
  to data users
• Recommended (over the jackknife) for estimating
  the variance of nonsmooth functions like
  quantiles, LICO
• Official reference
• Bootstrap Variance Estimation for the National
  Population Health Survey, D. Yeo, H. Mantel, and
  T.-P. Liu. 1999, Baltimore, ASA Conference.

58
CV Look-up Tables

• Alternative to bootstrap
• Approximate
• Can only be used for categorical variables, and
  for estimations of totals and proportions
• Available for every health region, province and
  Canada
• Provided with PUMF and Share file for some
  subpopulations

59
CV Look-up TablesExample

• National Population Health Survey - 1996/1997
• Approximate Sampling Variability
  Tables for Ontario Health AreaOTTAWA CARLETON -
  Selected members
• NUMERATOR OF
  ESTIMATED PERCENTAGE
• PERCENTAGE
• ('000) 0.1 1.0 2.0 5.0
  10.0 15.0 20.0 25.0 30.0 35.0
  40.0 50.0 70.0 90.0
• 1 48.6 48.4 47.6
  46.4 45.0 43.7 42.3 40.9 39.4
  37.8 34.5 26.8 15.5
• 2 34.4 34.2 33.7
  32.8 31.9 30.9 29.9 28.9 27.9
  26.8 24.4 18.9 10.9
• 3 28.1 27.9 27.5
  26.8 26.0 25.2 24.4 23.6 22.7
  21.9 19.9 15.5 8.9
• 4 24.3 24.2 23.8
  23.2 22.5 21.9 21.2 20.4 19.7
  18.9 17.3 13.4 7.7
• 5 21.7 21.6 21.3
  20.7 20.1 19.5 18.9 18.3 17.6
  16.9 15.5 12.0 6.9
• 6 19.8 19.7 19.4
  18.9 18.4 17.8 17.3 16.7 16.1
  15.5 14.1 10.9 6.3
• 7 18.4 18.3 18.0
  17.5 17.0 16.5 16.0 15.5 14.9
  14.3 13.1 10.1 5.8
• 8 17.1 16.8
  16.4 15.9 15.5 15.0 14.5 13.9
  13.4 12.2 9.5 5.5
• 9 16.1 15.9
  15.5 15.0 14.6 14.1 13.6 13.1
  12.6 11.5 8.9 5.2
• 10 15.3 15.1
  14.7 14.2 13.8 13.4 12.9 12.5
  12.0 10.9 8.5 4.9
• ...

60
Another example using the Bootvar program

• Statistical analysis
• Using the NPHS cycle 3 (1998) cross-sectional
  dummy data, determine whether or not the number
  of men aged 12 or older who perceive themselves
  as being in excellent health in Ontario is
  statistically different (at level ?5) than the
  number of women.

61
Basic theoritical notions for performing a Z-test

• M_excel estimated proportion of men in
  excellent health
• F_excel estimated proportion of women in
  excellent health
• Hypothesis test H0 M_excel F_excel
• H1 M_excel ? F_excel
• At level ? 0,05, we conclude H0 if z
  lt 1.96
• We conclude H1 otherwise.
• Z ( M_excel - F_excel )
• sd (M_excel-F_excel)
• We use the section difference of proportions of
  the BOOTVAR program to estimate the standard
  deviation of the difference between the two
  estimates.

__________________
62
Results

• M_excel 29.64 F_excel 25.75
  sd(M_excel-F_excel) 1.62
• Z ( M_excel - F_excel ) (29.64 -
  25.75) 3.89 2.40
• sd (M_excel-F_excel) 1.62
  1.62
• At ? 0,05 level , we conclude H1 because z
  2.40 gt 1.96 .
• We can then conclude that among the ontarians
  aged 12 or older there is a statistical
  difference between men and women with regard to
  the caracteristic self-perceived health
  excellent.

________________
_________
____
63
CCHS - Data Dissemination Strategy

• Wide range of users and capacity
• 136 health regions
• 13 provincial/territorial Ministries of Health
• Health Canada and CIHI
• Internal STC analysts
• Academics
• Others
• Data products
• Microdata
• Analytical products (Health Reports, How Healthy
  are Canadians, etc)
• Tabular statistics (ePubs, Cansim II, community
  profiles, etc)
• Client support (head and regional offices, CCHS
  website, workshops, etc)

64
CCHS - Access to microdata

• Master file
• all records, all variables
• Statistics Canada
• university research data centres
• remote access
• Share / Link files
• respondents who agreed to share / link
• provincial/territorial Ministries of Health
• health regions (through the STC third-party share
  agreement)
• Public Use Microdata File (PUMF)
• all records, subset of variables with collapsed
  response categories
• free for 136 health regions
• cost recovery for others

65
CCHS - Overview of Cycle 1.2

• Produce provincial cross-sectional estimates from
  a sample of 30,000 respondents
• Area frame sample only / one person per household
• CAPI only
• 90 minute in-depth interviews on mental health
  and well-being based on WMH2000 questionnaire
• Scheduled to begin collection in May 2002

66
CCHS - Future Plans

• Same two-year cycle approach
• health region level survey starting in January
  2003
• provincial level survey starting in January 2004
• New consultation process with provincial and
  regional authorities
• Flexible sample designs (adaptable to regional
  needs)
• Development of an in-depth nutrition focus
  content (Cycle 2.2)

67
CCHS Web site

• www.statcan.ca/health_surveys
• www.statcan.ca/enquetes_santé

68
Contacts in Methodology

• Yves Bélandyves.beland_at_statcan.ca
• François Brisebois francois.brisebois_at_statcan.c
  a