Address Frames and Mail Surveys as Complements (or Alternatives) to RDD Surveys Michael W. Link, Michael P. Battaglia, Martin R. Frankel, Larry Osborn, and Ali H. Mokdad Second International Conference on Telephone Survey Methodology, Miami, FL

Problems Facing RDD Surveys Growing Nonresponse Frame coverage issues: Households with no telephones (1.6%) Cell phone only households (3.7%) Households in zero-banks (3-4%) Frame efficiency issues: Proliferation of telephone numbers Cell phone numbers in mixed-use exchanges Other issues: Erosion of geographic specificity at state and substate levels

Behavioral Risk Factor Surveillance System (BRFSS) Monthly state-based RDD survey of health issues 50 states, District of Columbia, Puerto Rico, Guam, and Virgin Islands 300,000 adult interviews conducted in 2005 Faced with declining response rates Need to identify best future design (frame & mode)

USPS Delivery Sequence File (DSF) as an alternative sampling frame File contains All delivery point addresses serviced by USPS Identifies address type Residential vs business City style vs PO box vs other types Format conforms to USPS addressing standards Initial assessments for survey use: Highest coverage in urban areas Potential for coverage to improve in rural areas

Potential Drawbacks of DSF Unknown level of coverage: Excludes households with no USPS mail delivery Must purchase through list vendor (not USPS) Updates/list maintenance may vary Some exclude addresses on request Includes simplified addresses: City, state, zip code only Other potential problems: Seasonal units, PO Boxes and multi-drop addresses

Key questions to address How do RDD and DSF-based mail surveys compare in terms of: frame efficiency response rates respondent demographics Estimates on key health issues Can DSF-based mail surveys reach households without telephones and cell phone-only households?

BRFSS 2005 DSF mail survey pilot Six states: CA, IL, NJ, NC, TX, WA Sampling frame: access to Delivery Sequence File (DSF) provided by Marketing Systems Group Mode: mail survey with telephone verification for respondent selection Mail survey fielded March 15-May 15, 2005 Compared to monthly RDD surveys from March- May, 2005

BRFSS 2005 DSF pilot: sample design Probability sample from DSF household frames in each state Excluded business addresses identified by USPS or Marketing Systems Group Included seasonal units, vacant units, PO Boxes, throwback units, and drop point units Stratified each state sample by county and address type Drew 1,680 addresses per state using systematic random sampling

Split sample treatment groups Postcard (after 7 days) Second questionnaire mailing (after 2 weeks) Surname on address label Alternative within household selection methods: any adult (non-probability) next birthday all adults

Frame coverage assessment and characteristics

Percentage of Counties with >10% Under-coverage by State NJCAILWATXNC % counties with >10% under- coverage

Percentage of Counties with >10% Under-coverage by Pct. Urban % counties with >10% under- coverage < 25% % % 75+% % of adults in county living in urban area

BRFSS DSF Pilot: Types of Addresses State Address Type City Style (%) PO Box (%) Seasonal Unit (%) Vacant Unit (%) Throw -back Unit (%) Drop Point Unit (%) California918<11 Illinois87503<15 New Jersey866<11 5 North Carolina 897<12 Texas898<12 Washington91602<1

Response rates

Design factors and probability of completed interview from total cases (adjusted odds ratios) AOR(95% CI) Address type Other type1.00 City style2.27( ) PO Box1.83( ) Postcard sent No1.00 Yes1.12( ) Second Questionnaire No1.00 Yes1.58( ) Surname on mailing No name available1.00 Name not used2.01( ) Name used1.84( ) Respondent selection Any adult1.00 Next birthday0.91( ) All adults0.91( ) (n)(10,080)

Comparison of RDD telephone and DSF mail survey response rates State Response Rates RDD telephone survey % (n) DSF mail survey: All cases % (n) DSF mail survey: Cases with 2 nd Mailing (n) California39.2 (4,318) 31.8 (1,266) 39.2 (597) Illinois38.7 (4,462) 36.2 (1,356) 42.8 (671) New Jersey33.8 (9,976) 23.2 (1,250) 30.5 (614) North Carolina56.0 (7,992) 36.3 (1,200) 42.5 (602) Texas43.6 (4,920) 35.5 (1,122) 44.4 (543) Washington45.7 (12,910) 39.9 (1,334) 44.9 (626)

Within household selection

Comparison of Equalized Weighted Gender Distributions: % Female Population51.4% Any Adult61.5% Next Birthday61.5% All Adults50.8%

Other demographics of DSF mail survey respondents

Percent some college or more

Percent white

Percent household income > $50,

Comparison of Survey Estimates

Health conditions / risk behaviors Unadjusted prevalenceAdjusted odds ratio Telephon Telephon Asthma Diabetes High blood pressure Obese (BMI > 30) ** * Current smoker Binge drinking *** *** Tested for HIV HIV risk behaviors ** ** Significance: * p<.05, ** p<.01, *** p <.001 Note: Data weighted for sample design and post-stratified to sex-age totals for each state. Final weights were ratio adjusted to equalize the number of cases across states. Logistic regression models adjusted for state of residence, sex, race, age, education, and having health care coverage.

Reaching cell-only and non-telephone households

Type of household telephone access 1 Based on interviews NHIS conducted July – December, Source: Stephen J.Blumberg, Julian V. Luke, and Marcie L. Cynamon (2005). The Prevalence and Impact of Wireless Substitution: Updated Data from the 2004 National Health Interview Survey. Presented at the 2005 American Association for Public Opinion Research Annual Conference, Miami Beach, FL. Household telephone accessNational Health Interview Survey 1 (%) BRFSS mail survey (%) Land line Landline only Landline and cellular phone Cellular phone only No telephone2.40.9

Effect of household telephone access on mail survey estimates Health condition / risk factor Type of household telephone access Landline only Landline and cell phone Cell phone only Asthma Diabetes High blood pressure Obese (BMI > 30) Current smoker *1.06 Binge drinking *1.90* Tested for HIV HIV risk behaviors ** Figures are adjusted odds ratios. Significance: * p<.05, ** p<.01, *** p <.001 Note: Data weighted for sample design and post-stratified to sex-age totals for each state. Final weights were ratio adjusted to equalize the number of cases across states. Logistic regression models adjusted for state of residence, sex, race, age, education, and having health care coverage.

Advantages of address-based design In low response rate states the address-based mail survey approach can yield response rates similar to RDD rates Telephone follow-up of non-respondents should raise rates Approach reaches households without land-line telephones Weighted prevalence estimates were similar for 5 of 8 risk factors Facilitates geocoding and mapping

Disadvantages of address-based design Coverage in rural areas is a potential problem Mail survey limits number of questions and complexity of survey Mail survey alone does not yield higher response rates than RDD Less control over within household selection Mail survey respondents tend to have higher SES

Next Steps 2006 production level pilot study in 6 states Test alternative sampling approaches: RDD sample reverse-matched for addresses Address-based sample matched for telephone numbers Test mixed-mode design: If address available: mail survey with telephone follow-up of nonrespondents If no address available: telephone survey

Contact: Michael Link