Example 85.2 The Medical Expenditure Panel Survey (MEPS)

The U.S. Department of Health and Human Services conducts the Medical Expenditure Panel Survey (MEPS) to produce national and regional estimates of various aspects of health care. The MEPS has a complex sample design that includes both stratification and clustering. The sampling weights are adjusted for nonresponse and raked with respect to population control totals from the Current Population Survey. See the MEPS Survey Background (2006) and Machlin, Yu, and Zodet (2005) for details.

In this example, the 1999 full-year consolidated data file HC-038 (MEPS HC-038, 2002) from the MEPS is used to investigate the relationship between medical insurance coverage and the demographic variables. The data can be downloaded directly from the Agency for Healthcare Research and Quality (AHRQ) Web site at http://www.meps.ahrq.gov/mepsweb/data_stats/download_data_files_detail.jsp?cboPufNumber=HC-038 in either ASCII format or SAS transport format. The Web site includes a detailed description of the data as well as the SAS program used to access and format it.

For this example, the SAS transport format data file for HC-038 is downloaded to 'C:H38.ssp' on a Windows-based PC. The instructions on the Web site lead to the following SAS statements for creating a SAS data set MEPS, which contains only the sample design variables and other variables necessary for this analysis.

proc format; 
   value racex  
      -9 = 'NOT ASCERTAINED'
      -8 = 'DK'
      -7 = 'REFUSED'
      -1 = 'INAPPLICABLE'
      1 = 'AMERICAN INDIAN'
      2 = 'ALEUT, ESKIMO'
      3 = 'ASIAN OR PACIFIC ISLANDER'
      4 = 'BLACK'
      5 = 'WHITE'
      91 = 'OTHER'
      ; 
   value sex  
      -9 = 'NOT ASCERTAINED'
      -8 = 'DK'
      -7 = 'REFUSED'
      -1 = 'INAPPLICABLE'
      1 = 'MALE'
      2 = 'FEMALE'
      ;       
   value povcat9h  
      1 = 'NEGATIVE OR POOR' 
      2 = 'NEAR POOR'
      3 = 'LOW INCOME'
      4 = 'MIDDLE INCOME'
      5 = 'HIGH INCOME'
      ;
   value inscov9f  
      1 = 'ANY PRIVATE'
      2 = 'PUBLIC ONLY'
      3 = 'UNINSURED'
      ;
run;

libname mylib '';
filename in1 'H38.SSP';     
proc xcopy in=in1 out=mylib import;
run;

data meps; 
   set mylib.H38;
   label racex= sex= inscov99= povcat99=
      varstr99= varpsu99= perwt99f= totexp99=;
   format racex racex. sex sex.
      povcat99 povcat9h. inscov99 inscov9f.;
   keep inscov99 sex racex povcat99 varstr99 
      varpsu99 perwt99f totexp99;
run;

There are a total of 24,618 observations in this SAS data set. Each observation corresponds to a person in the survey. The stratification variable is VARSTR99, which identifies the 143 strata in the sample. The variable VARPSU99 identifies the 460 PSUs in the sample. The sampling weights are stored in the variable PERWT99F. The response variable is the health insurance coverage indicator variable, INSCOV99, which has three values:

The demographic variables include gender (SEX), race (RACEX), and family income level as a percent of the poverty line (POVCAT99). The variable RACEX has five categories:

The variable POVCAT99 is constructed by dividing family income by the applicable poverty line (based on family size and composition), with the resulting percentages grouped into five categories:

The data set also contains the total health care expenditure in 1999, TOTEXP99, which is used as a covariate in the analysis.

Output 85.2.1 displays the first 30 observations of this data set.

The following SAS statements fit a generalized logit model for the 1999 full-year consolidated MEPS data:

proc surveylogistic data=meps; 
   stratum VARSTR99;
   cluster VARPSU99;
   weight PERWT99F;
   class SEX RACEX POVCAT99;
   model INSCOV99 = TOTEXP99 SEX RACEX POVCAT99 / link=glogit;
run;    

The STRATUM statement specifies the stratification variable VARSTR99. The CLUSTER statement specifies the PSU variable VARPSU99. The WEIGHT statement specifies the sample weight variable PERWT99F. The demographic variables SEX, RACEX, and POVCAT99 are listed in the CLASS statement to indicate that they are categorical independent variables in the MODEL statement. In the MODEL statement, the response variable is INSCOV99, and the independent variables are TOTEXP99 along with the selected demographic variables. The LINK= option requests that the procedure fit the generalized logit model because the response variable INSCOV99 has nominal responses.

The results of this analysis are shown in the following outputs.

PROC SURVEYLOGISTIC  lists the model fitting information and sample design information in Output 85.2.2.

Output 85.2.3 displays the number of observations and the total of sampling weights both in the data set and used in the analysis. Only the observations with positive person-level weight are used in the analysis. Therefore, 1,053 observations with zero person-level weights were deleted.

Output 85.2.4 lists the three insurance coverage levels for the response variable INSCOV99. The "UNINSURED" category is used as the reference category in the model.

Output 85.2.5 shows the parameterization in the regression model for each categorical independent variable.

Output 85.2.6 displays the parameter estimates and their standard errors.

Output 85.2.7 displays the odds ratio estimates and their standard errors.

For example, after adjusting for the effects of sex, race, and total health care expenditures, a person with high income is estimated to be 11.595 times more likely than a poor person to choose private health care insurance over no insurance, but only 0.274 times as likely to choose public health insurance over no insurance.