**2002 SMART BRFSS MMSA Methodology**

**2002 Selected Metropolitan/Micropolitan Area Risk Trends from the
BRFSS **

**Creation of Metropolitan-level weights methodology**

This documentation describing the Behavioral Risk Factor Surveillance
System (BRFSS) Selected Metropolitan/Micropolitan Area Risk Trends
(SMART) project is based on a report produced for CDC by RTI
International. A documented and verified subset of the 2002 BRFSS has
been produced to provide some local area estimates. These local areas
are identified as metropolitan or micropolitan statistical areas (MMSA)
as defined by the Office of Management and Budget. The data set was
produced by adding new analysis weights designed to correspond to the
2002 population estimates for each eligible MMSA. The additional weights
were post-stratified to the MMSA-level. The process by which these new
weights were obtained is detailed in Appendix C, "Weight Class
Collapsing Rules."

# Selected Areas

Typically, BRFSS data are used to produce state-level estimates.
However, for the SMART project, BRFSS data were used to produce small
area-level estimates for MMSAs as defined by the Bureau of the Census.
On June 6, 2003, the Office of Management and Budget (OMB) issued new
definitions for metropolitan statistical areas, micropolitan statistical
areas, and metropolitan divisions (). A respondent was associated with a
particular MMSA on the basis of their county code. Missing county codes
were imputed from a value included in the purchased telephone sample
that represents the county most likely associated with the telephone
number. MMSA-level estimates have been produced from the BRFSS data for
98 MMSAs that have met the analysis criteria for the 2002 data year.

**Appendix A: List of Variables added to (2002 Data)**

**[Data Documentation for the 15 New Variables Added to the 2002 BRFSS
Data]{.underline}**

ADJMMSA -- MMSA-level post-stratification weight. This factor is
multiplied by the design weight (\_WT2) to get the final MMSA-level
weight (\_MMSAWT).

ADJMM_SS -- MMSA-level post-stratification split-sample weight. This
factor is multiplied by the design weight (\_WT2) to get the final
MMSA-level split sample weight (\_MMSWTSS). This variable is missing for
respondents who do not live in the Chicago-Naperville-Joliet, IL area,
the only metropolitan division with adequate sample size to provide a
split sample weight.

AGE_MMSA-- age categories used to set up the initial weighting classes
for the MMSA-level weights.

1 -- 18-24

2 -- 25-34

3 -- 35-44

4 -- 45-54

5 -- 55-64

6 -- 65+

AGE_M_F -- age categories used in the final weighting classes for the
MMSA-level weights.

1 -- 18-24

2 -- 25-34

3 -- 35-44

4 -- 45-54

5 -- 55-64

6 -- 65+

7 -- 18-34

8 -- 35-54

9 -- 55+

AGE_FSS -- age categories used in the final weighting classes for the
split-sample MMSA-level weights. This variable is missing for
respondents who do not live in the Chicago-Naperville-Joliet, IL
metropolitan division.

1 -- 18-24

2 -- 25-34

3 -- 35-44

4 -- 45-54

5 -- 55-64

6 -- 65+

9 -- 55+

ANLMMSA -- indicates whether a respondent lives in a MMSA that was
included in the reweighting.

0 -- not in a MMSA that got reweighted

1 -- in a MMSA that got reweighted

ANLMM_SS -- indicates whether a respondent lives in a MMSA that was
included in the split- sample reweighting (Chicago-Naperville-Joliet, IL
metropolitan division only).

0 -- not in Chicago-Naperville-Joliet, IL metropolitan division

1 -- in Chicago-Naperville-Joliet, IL metropolitan division

RACE_MMS -- race categories used to set up the initial weighting classes
for the mmsa-level weights.

0 -- Race not used

1 -- White, non-Hispanic

2 -- Nonwhite or Hispanic

RACE_M_F -- race categories used in the final weighting classes for the
MMSA-level weights.

0 -- Race not used

1 -- White, non-Hispanic

2 -- Nonwhite or Hispanic

RACE_FSS -- race categories used in the final weighting classes for the
split-sample MMSA-level weights. This variable is missing for
respondents who do not live in the Chicago-Naperville-Joliet, IL
metropolitan division.

1 -- White, non-Hispanic

2 -- Nonwhite or Hispanic

SEX_MMSA -- sex categories used to set up the initial and final
weighting classes for the MMSA-level weights (weight classes are never
collapsed across sex).

1 -- Male

2 -- Female

\_MMSA -- MMSA code of the metropolitan, micropolitan statistical area,
or metropolitan division if appropriate, where the respondent lives.
Metropolitan and micropolitan statistical areas and metropolitan
divisions are defined by OMB in Bulletin No. 03-04 ().

\_MMSANAM -- MMSA name of the metropolitan/micropolitan statistical
area, or metropolitan division if appropriate, where the respondent
lives. Metropolitan/micropolitan statistical areas and metropolitan
divisions are defined by OMB in Bulletin No. 03-04 ().

\_MMSAWT -- the new MMSA-level weight. This is the weight to use when
generating MMSA-level estimates (metropolitan or micropolitan
statistical areas or metropolitan divisions) for questions that were
asked of the whole sample.

\_MMSWTSS -- the new MMSA-level weight for the split-sample questions.
This is the weight to use when generating MMSA-level estimates
(metropolitan or micropolitan statistical areas or metropolitan
divisions) for the split-sample questions. This variable is missing for
respondents who do not live in the Chicago-Naperville-Joliet, IL
metropolitan division.

**Appendix B: List of the 98 MMSAs that have MMSA-level Weights in 2002
BRFSS Data**

**Metropolitan/Micropolitan Statistical Area or Metropolitan Division
Codes and Names**

  ------- --------------------------------------------------------------------------
  10420   Akron, OH Metropolitan Statistical Area
  10740   Albuquerque, NM Metropolitan Statistical Area
  11260   Anchorage, AK Metropolitan Statistical Area
  11700   Asheville, NC Metropolitan Statistical Area
  12060   Atlanta-Sandy Springs-Marietta, GA Metropolitan Statistical Area
  12580   Baltimore-Towson, MD Metropolitan Statistical Area
  12940   Baton Rouge, LA Metropolitan Statistical Area
  13644   Bethesda-Frederick-Gaithersburg, MD Metropolitan Division
  13820   Birmingham-Hoover, AL Metropolitan Statistical Area
  14260   Boise City-Nampa, ID Metropolitan Statistical Area
  14484   Boston-Quincy, MA Metropolitan Division
  14860   Bridgeport-Stamford-Norwalk, CT Metropolitan Statistical Area
  15540   Burlington-South Burlington, VT Metropolitan Statistical Area
  15764   Cambridge-Newton-Framingham, MA Metropolitan Division
  16540   Chambersburg, PA Micropolitan Statistical Area
  16620   Charleston, WV Metropolitan Statistical Area
  16940   Cheyenne, WY Metropolitan Statistical Area
  16974   Chicago-Naperville-Joliet, IL Metropolitan Division
  17140   Cincinnati-Middletown, OH-KY-IN Metropolitan Statistical Area
  17900   Columbia, SC Metropolitan Statistical Area
  18180   Concord, NH Micropolitan Statistical Area
  19124   Dallas-Plano-Irving, TX Metropolitan Division
  19380   Dayton, OH Metropolitan Statistical Area
  19740   Denver-Aurora, CO Metropolitan Statistical Area
  19780   Des Moines, IA Metropolitan Statistical Area
  19804   Detroit-Livonia-Dearborn, MI Metropolitan Division
  20100   Dover, DE Metropolitan Statistical Area
  20500   Durham, NC Metropolitan Statistical Area
  20764   Edison, NJ Metropolitan Division
  21604   Essex County, MA Metropolitan Division
  22020   Fargo, ND-MN Metropolitan Statistical Area
  22500   Florence, SC Metropolitan Statistical Area
  24660   Greensboro-High Point, NC Metropolitan Statistical Area
  25540   Hartford-West Hartford-East Hartford, CT Metropolitan Statistical Area
  25900   Hilo, HI Micropolitan Statistical Area
  26180   Honolulu, HI Metropolitan Statistical Area
  26420   Houston-Baytown-Sugar Land, TX Metropolitan Statistical Area
  26580   Huntington-Ashland, WV-KY-OH Metropolitan Statistical Area
  26900   Indianapolis, IN Metropolitan Statistical Area
  27140   Jackson, MS Metropolitan Statistical Area
  27260   Jacksonville, FL Metropolitan Statistical Area
  27980   Kahului-Wailuku, HI Micropolitan Statistical Area
  28140   Kansas City, MO-KS Metropolitan Statistical Area
  28180   Kapaa, HI Micropolitan Statistical Area
  29540   Lancaster, PA Metropolitan Statistical Area
  29820   Las Vegas-Paradise, NV Metropolitan Statistical Area
  30100   Lebanon, NH-VT Micropolitan Statistical Area
  30700   Lincoln, NE Metropolitan Statistical Area
  30780   Little Rock-North Little Rock, AR Metropolitan Statistical Area
  31084   Los Angeles-Long Beach-Glendale, CA Metropolitan Division
  31140   Louisville, KY-IN Metropolitan Statistical Area
  31700   Manchester-Nashua, NH Metropolitan Statistical Area
  32820   Memphis, TN-MS-AR Metropolitan Statistical Area
  33124   Miami-Miami Beach-Kendall, FL Metropolitan Division
  33340   Milwaukee-Waukesha-West Allis, WI Metropolitan Statistical Area
  33460   Minneapolis-St. Paul-Bloomington, MN-WI Metropolitan Statistical Area
  34980   Nashville-Davidson\--Murfreesboro, TN Metropolitan Statistical Area
  35084   Newark-Union, NJ-PA Metropolitan Division
  35300   New Haven-Milford, CT Metropolitan Statistical Area
  35380   New Orleans-Metairie-Kenner, LA Metropolitan Statistical Area
  35644   New York-Wayne-White Plains, NY-NJ Metropolitan Division
  35980   Norwich-New London, CT Metropolitan Statistical Area
  36260   Ogden-Clearfield, UT Metropolitan Statistical Area
  36420   Oklahoma City, OK Metropolitan Statistical Area
  36540   Omaha-Council Bluffs, NE-IA Metropolitan Statistical Area
  36740   Orlando, FL Metropolitan Statistical Area
  37964   Philadelphia, PA Metropolitan Division
  38060   Phoenix-Mesa-Scottsdale, AZ Metropolitan Statistical Area
  38300   Pittsburgh, PA Metropolitan Statistical Area
  38860   Portland-South Portland, ME Metropolitan Statistical Area
  38900   Portland-Vancouver-Beaverton, OR-WA Metropolitan Statistical Area
  39300   Providence-New Bedford-Fall River, RI-MA Metropolitan Statistical Area
  39660   Rapid City, SD Metropolitan Statistical Area
  39900   Reno-Sparks, NV Metropolitan Statistical Area
  40060   Richmond, VA Metropolitan Statistical Area
  40484   Rockingham County-Strafford County, NH Metropolitan Division
  41180   St. Louis, MO-IL Metropolitan Statistical Area
  41620   Salt Lake City, UT Metropolitan Statistical Area
  42580   Seaford, DE Micropolitan Statistical Area
  42644   Seattle-Bellevue-Everett, WA Metropolitan Division
  43620   Sioux Falls, SD Metropolitan Statistical Area
  44140   Springfield, MA Metropolitan Statistical Area
  44844   Suffolk County-Nassau County, NY Metropolitan Division
  45104   Tacoma, WA Metropolitan Division
  45300   Tampa-St. Petersburg-Clearwater, FL Metropolitan Statistical Area
  45780   Toledo, OH Metropolitan Statistical Area
  45820   Topeka, KS Metropolitan Statistical Area
  46060   Tucson, AZ Metropolitan Statistical Area
  46140   Tulsa, OK Metropolitan Statistical Area
  47260   Virginia Beach-Norfolk-Newport News, VA-NC Metropolitan Statistical Area
  47644   Warren-Farmington Hills-Troy, MI Metropolitan Division
  47894   Washington-Arlington-Alexandria, DC-VA-MD-WV Metropolitan Division
  48620   Wichita, KS Metropolitan Statistical Area
  48740   Willimantic, CT Micropolitan Statistical Area
  48864   Wilmington, DE-MD-NJ Metropolitan Division
  49180   Winston-Salem, NC Metropolitan Statistical Area
  49340   Worcester, MA Metropolitan Statistical Area
  49660   Youngstown-Warren-Boardman, OH-PA Metropolitan Statistical Area
          
  ------- --------------------------------------------------------------------------

**Appendix C: Weight Class Collapsing Rules**

**[MMSA]{.underline}[-level Weighting Methodology]{.underline}**

On June 6, 2003, OMB issued new definitions for metropolitan statistical
Areas, micropolitan statistical areas, and metropolitan divisions. See
(). Respondents were assigned to an MMSA on the basis of their county
codes. Missing county codes were imputed from a value included in the
purchased telephone sample that represents the county most likely
associated with the telephone number before the respondent identifies a
county during data collection.

All respondents in cities were then assigned to age, race, and sex
categories. If a respondent's age was missing, it was imputed by using
the variable \_IMPAGE available in the BRFSS public-use 2002 data file.
If a respondent's race was missing, it was imputed by using the majority
race for the MMSA in which the respondent lives. The six age categories
were 18-24, 25-34, 35-44, 45-54, 55-64, and 65+. The two race categories
were White, non-Hispanic, and Nonwhite or Hispanic.

Within each MMSA, respondents were assigned to weighting classes on the
basis of the age, race, and sex categories described above. Some states
do not use race in post-stratification. For the MMSA in states that do
not use race, only the age and sex groups were used to set up weighting
classes. For the MMSA in states that do use race, all three groups were
used to set up weighting classes. For the MMSA that cross state lines,
the post-stratification variables used by the state in which the
majority of the MMSA's population lives were used to set up weighting
classes. Thus, MMSA that use race had 24 initial weighting classes and
MMSA that do not use race had 12 initial weighting classes.

Weighting classes with fewer than 19 sample members were collapsed in
accordance with the following rules:

1.  For those MMSA that used race in post-stratification, the race
    categories within a sex category collapse if at least 80% of the age
    categories in that race /sex cross-classification (*i.e.* 5 out of 6
    the age categories) have fewer than 19 members. In MMSA that used
    race to create the initial weighting classes, the number of
    weighting classes was thus reduced from 24 to 12 if race was
    collapsed for both sexes and from 24 to 18 if race was collapsed for
    only one sex.

2.  Collapse the two youngest age categories in any age/sex or
    age/sex/race weighing class if either contains fewer than 19
    members. Do the same for the two middle and the two oldest age
    categories in each remaining weighting class.

3.  Do not collapse weighting classes across sex.

4.  Do not include an MMSA in the reweighting that still has weighting
    classes with fewer than 19 sample members after all collapsing rules
    have been applied.

There were 98 MMSA that had at least 500 respondents in the 2002 BRFSS
and at least 19 sample members in all final weighting classes. See
Appendix B in the Data Documentation for a list of these MMSA. Only the
respondents in these MMSA were given a MMSA-level weight. To calculate
the new MMSA-level weight, we applied a post-stratification adjustment
factor to the design weight (\_WT2) and created the adjustment factor by
taking the ratio of the total population over the sum of the design
weights for each weighting class within each MMSA. The new MMSA-level
weight (\_MMSAWT) should be used to generate estimates in these 98 MMSA.

Example SUDAAN Code:

For example, suppose we want an estimate for the Atlanta-Sandy
Springs-Marietta, GA Metropolitan Statistical Area (MMSA code = 12060).
Here's SAS/SUDAAN code that could be used to do this:

proc sort data=xxxx;

by \_STSTR \_PSU;

run;

proc descript data=xxxx filetype=sas design=wr;

nest \_STSTR \_PSU / missunit;

weight \_MMSAWT;

subpopn \_MMSA=12060 / name=" Atlanta-Sandy Springs-Marietta, GA";

var (your analysis variable);

catlevel (the level of your analysis variable for which you want an
estimate);

run;

**[MMSA]{.underline}[-Level Split-Sample Weighting
Methodology]{.underline}**

In 2002, Illinois used a split sample. This means they divided their
sample in half to ask two different versions of their questionnaire. One
version of their questionnaire was asked of half the sample and the
second version was asked of the other half of the sample. The \_MMSAWT
is appropriate to use for analysis of the questions asked on both
versions of their questionnaire. An additional weight was created to use
with questions that were asked on only one version of their
questionnaire. The MMSA-level split-sample weight (\_MMSWTSS was created
using the same methodology described above. The only difference was that
respondents in the Illinois MMSA were separated according to what
questionnaire version they received. Weighting classes were created for
each questionnaire version within each MMSA. Adjustment factors were
applied to the design weight (\_WT2) that forced the sum of the weights
for each half of the sample to sum to population totals.
Chicago-Naperville-Joliet, IL metropolitan division was the only MMSA
with adequate split-sample sizes to provide MMSA-level split-sample
weight.

Example SUDAAN Code:

For example, suppose we want an estimate for a split-sample question for
the Chicago-Naperville-Joliet, IL metropolitan division (MMSA code =
16974). Assume the question comes from questionnaire version 1
(\_QSTVER). Here's SAS/SUDAAN code that could be used to do this:

Data xxxx;

Set yyyy;

If (\_MMSA=16974 & \_QSTVER=1) then DUMMY = 1;

Run;

proc sort data=xxxx;

by \_STSTR \_PSU;

run;

proc descript data=xxxx filetype=sas design=wr;

nest \_STSTR \_PSU / missunit;

weight \_MMSWTSS;

subpopn DUMMY=1 / name=" Chicago-Naperville-Joliet, IL -- questionnaire
version 1";

var (your analysis variable);

catlevel (the level of your analysis variable for which you want an
estimate);

run;

**Note: In general, estimates for split-sample questions can only be
generated for MMSA that do not cross state lines. For example, the St.
Louis, MO-IL metropolitan statistical area lies in both Illinois and
Missouri. These new MMSA-level weights can not be used to generate a
prevalence estimate for a question that was asked of only half of the
Illinois residents in their sample but was asked of all of the Missouri
residents in their sample. For MMSA that cross state boundaries,
estimates can only be generated for questions asked of the entire sample
population in each state. **