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:
1 |
The person had any private insurance coverage any time during 1999 |
2 |
The person had only public insurance coverage during 1999 |
3 |
The person was uninsured during all of 1999 |
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:
1 |
American Indian |
2 |
Aleut, Eskimo |
3 |
Asian or Pacific Islander |
4 |
Black |
5 |
White |
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:
1 |
Negative or poor (less than 100%) |
2 |
Near poor (100% to less than 125%) |
3 |
Low income (125% to less than 200%) |
4 |
Middle income (200% to less than 400%) |
5 |
High income (greater than or equal to 400%) |
The data set also contains the total health care expenditure in 1999, TOTEXP99
, which is used as a covariate in the analysis.
Output 98.2.1 displays the first 30 observations of this data set.
Output 98.2.1: 1999 Full-Year MEPS (First 30 Observations)
Obs | SEX | RACEX | POVCAT99 | INSCOV99 | TOTEXP99 | PERWT99F | VARSTR99 | VARPSU99 |
---|---|---|---|---|---|---|---|---|
1 | MALE | WHITE | MIDDLE INCOME | PUBLIC ONLY | 2735 | 14137.86 | 131 | 2 |
2 | FEMALE | WHITE | MIDDLE INCOME | ANY PRIVATE | 6687 | 17050.99 | 131 | 2 |
3 | MALE | WHITE | MIDDLE INCOME | ANY PRIVATE | 60 | 35737.55 | 131 | 2 |
4 | MALE | WHITE | MIDDLE INCOME | ANY PRIVATE | 60 | 35862.67 | 131 | 2 |
5 | FEMALE | WHITE | MIDDLE INCOME | ANY PRIVATE | 786 | 19407.11 | 131 | 2 |
6 | MALE | WHITE | MIDDLE INCOME | ANY PRIVATE | 345 | 18499.83 | 131 | 2 |
7 | MALE | WHITE | MIDDLE INCOME | ANY PRIVATE | 680 | 18499.83 | 131 | 2 |
8 | MALE | WHITE | MIDDLE INCOME | ANY PRIVATE | 3226 | 22394.53 | 136 | 1 |
9 | FEMALE | WHITE | MIDDLE INCOME | ANY PRIVATE | 2852 | 27008.96 | 136 | 1 |
10 | MALE | WHITE | MIDDLE INCOME | ANY PRIVATE | 112 | 25108.71 | 136 | 1 |
11 | MALE | WHITE | MIDDLE INCOME | ANY PRIVATE | 3179 | 17569.81 | 136 | 1 |
12 | MALE | WHITE | MIDDLE INCOME | ANY PRIVATE | 168 | 21478.06 | 136 | 1 |
13 | FEMALE | WHITE | MIDDLE INCOME | ANY PRIVATE | 1066 | 21415.68 | 136 | 1 |
14 | MALE | WHITE | NEGATIVE OR POOR | PUBLIC ONLY | 0 | 12254.66 | 125 | 1 |
15 | MALE | WHITE | NEGATIVE OR POOR | ANY PRIVATE | 0 | 17699.75 | 125 | 1 |
16 | FEMALE | WHITE | NEGATIVE OR POOR | UNINSURED | 0 | 18083.15 | 125 | 1 |
17 | MALE | BLACK | NEGATIVE OR POOR | PUBLIC ONLY | 230 | 6537.97 | 78 | 10 |
18 | MALE | WHITE | LOW INCOME | UNINSURED | 408 | 8951.36 | 95 | 2 |
19 | FEMALE | WHITE | LOW INCOME | UNINSURED | 0 | 11833.00 | 95 | 2 |
20 | MALE | WHITE | LOW INCOME | UNINSURED | 40 | 12754.07 | 95 | 2 |
21 | FEMALE | WHITE | LOW INCOME | UNINSURED | 51 | 14698.57 | 95 | 2 |
22 | MALE | WHITE | LOW INCOME | UNINSURED | 0 | 3890.20 | 92 | 19 |
23 | FEMALE | WHITE | LOW INCOME | UNINSURED | 610 | 5882.29 | 92 | 19 |
24 | MALE | WHITE | LOW INCOME | PUBLIC ONLY | 24 | 8610.47 | 92 | 19 |
25 | FEMALE | BLACK | MIDDLE INCOME | UNINSURED | 1758 | 0.00 | 64 | 1 |
26 | MALE | BLACK | MIDDLE INCOME | PUBLIC ONLY | 551 | 7049.70 | 64 | 1 |
27 | MALE | BLACK | MIDDLE INCOME | ANY PRIVATE | 65 | 34067.03 | 64 | 1 |
28 | FEMALE | BLACK | NEGATIVE OR POOR | PUBLIC ONLY | 0 | 9313.84 | 73 | 12 |
29 | FEMALE | BLACK | NEGATIVE OR POOR | PUBLIC ONLY | 10 | 14697.03 | 73 | 12 |
30 | MALE | BLACK | NEGATIVE OR POOR | PUBLIC ONLY | 0 | 4574.73 | 73 | 12 |
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 98.2.2.
Output 98.2.2: MEPS, Model Information
Model Information | |
---|---|
Data Set | WORK.MEPS |
Response Variable | INSCOV99 |
Number of Response Levels | 3 |
Stratum Variable | VARSTR99 |
Number of Strata | 143 |
Cluster Variable | VARPSU99 |
Number of Clusters | 460 |
Weight Variable | PERWT99F |
Model | Generalized Logit |
Optimization Technique | Newton-Raphson |
Variance Adjustment | Degrees of Freedom (DF) |
Output 98.2.3 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 98.2.4 lists the three insurance coverage levels for the response variable INSCOV99
. The "UNINSURED" category is used as the reference category in the model.
Output 98.2.5 shows the parameterization in the regression model for each categorical independent variable.
Output 98.2.5: MEPS, Classification Levels
Class Level Information | |||||
---|---|---|---|---|---|
Class | Value | Design Variables | |||
SEX | FEMALE | 1 | |||
MALE | -1 | ||||
RACEX | ALEUT, ESKIMO | 1 | 0 | 0 | 0 |
AMERICAN INDIAN | 0 | 1 | 0 | 0 | |
ASIAN OR PACIFIC ISLANDER | 0 | 0 | 1 | 0 | |
BLACK | 0 | 0 | 0 | 1 | |
WHITE | -1 | -1 | -1 | -1 | |
POVCAT99 | HIGH INCOME | 1 | 0 | 0 | 0 |
LOW INCOME | 0 | 1 | 0 | 0 | |
MIDDLE INCOME | 0 | 0 | 1 | 0 | |
NEAR POOR | 0 | 0 | 0 | 1 | |
NEGATIVE OR POOR | -1 | -1 | -1 | -1 |
Output 98.2.6 displays the parameter estimates and their standard errors.
Output 98.2.7 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.
Output 98.2.6: MEPS, Parameter Estimates
Analysis of Maximum Likelihood Estimates | ||||||
---|---|---|---|---|---|---|
Parameter | INSCOV99 | Estimate | Standard Error |
t Value | Pr > |t| | |
Intercept | ANY PRIVATE | 2.7703 | 0.1906 | 14.54 | <.0001 | |
Intercept | PUBLIC ONLY | 1.9216 | 0.1562 | 12.30 | <.0001 | |
TOTEXP99 | ANY PRIVATE | 0.000215 | 0.000071 | 3.03 | 0.0026 | |
TOTEXP99 | PUBLIC ONLY | 0.000241 | 0.000072 | 3.34 | 0.0009 | |
SEX | FEMALE | ANY PRIVATE | 0.1208 | 0.0248 | 4.87 | <.0001 |
SEX | FEMALE | PUBLIC ONLY | 0.1741 | 0.0308 | 5.65 | <.0001 |
RACEX | ALEUT, ESKIMO | ANY PRIVATE | 7.1457 | 0.6976 | 10.24 | <.0001 |
RACEX | ALEUT, ESKIMO | PUBLIC ONLY | 7.6303 | 0.5024 | 15.19 | <.0001 |
RACEX | AMERICAN INDIAN | ANY PRIVATE | -2.0904 | 0.2615 | -7.99 | <.0001 |
RACEX | AMERICAN INDIAN | PUBLIC ONLY | -1.8992 | 0.2909 | -6.53 | <.0001 |
RACEX | ASIAN OR PACIFIC ISLANDER | ANY PRIVATE | -1.8055 | 0.2299 | -7.85 | <.0001 |
RACEX | ASIAN OR PACIFIC ISLANDER | PUBLIC ONLY | -1.9914 | 0.2285 | -8.71 | <.0001 |
RACEX | BLACK | ANY PRIVATE | -1.7517 | 0.1983 | -8.83 | <.0001 |
RACEX | BLACK | PUBLIC ONLY | -1.7038 | 0.1692 | -10.07 | <.0001 |
POVCAT99 | HIGH INCOME | ANY PRIVATE | 1.4560 | 0.0685 | 21.26 | <.0001 |
POVCAT99 | HIGH INCOME | PUBLIC ONLY | -0.6092 | 0.0903 | -6.75 | <.0001 |
POVCAT99 | LOW INCOME | ANY PRIVATE | -0.3066 | 0.0666 | -4.60 | <.0001 |
POVCAT99 | LOW INCOME | PUBLIC ONLY | -0.0239 | 0.0754 | -0.32 | 0.7512 |
POVCAT99 | MIDDLE INCOME | ANY PRIVATE | 0.6467 | 0.0587 | 11.01 | <.0001 |
POVCAT99 | MIDDLE INCOME | PUBLIC ONLY | -0.3496 | 0.0807 | -4.33 | <.0001 |
POVCAT99 | NEAR POOR | ANY PRIVATE | -0.8015 | 0.1076 | -7.45 | <.0001 |
POVCAT99 | NEAR POOR | PUBLIC ONLY | 0.2985 | 0.0952 | 3.14 | 0.0019 |
NOTE: The degrees of freedom for the t tests is 317. |
Output 98.2.7: MEPS, Odds Ratios
Odds Ratio Estimates | ||||
---|---|---|---|---|
Effect | INSCOV99 | Point Estimate | 95% Confidence Limits | |
TOTEXP99 | ANY PRIVATE | 1.000 | 1.000 | 1.000 |
TOTEXP99 | PUBLIC ONLY | 1.000 | 1.000 | 1.000 |
SEX FEMALE vs MALE | ANY PRIVATE | 1.273 | 1.155 | 1.404 |
SEX FEMALE vs MALE | PUBLIC ONLY | 1.417 | 1.255 | 1.599 |
RACEX ALEUT, ESKIMO vs WHITE | ANY PRIVATE | >999.999 | >999.999 | >999.999 |
RACEX ALEUT, ESKIMO vs WHITE | PUBLIC ONLY | >999.999 | >999.999 | >999.999 |
RACEX AMERICAN INDIAN vs WHITE | ANY PRIVATE | 0.553 | 0.339 | 0.903 |
RACEX AMERICAN INDIAN vs WHITE | PUBLIC ONLY | 1.146 | 0.601 | 2.185 |
RACEX ASIAN OR PACIFIC ISLANDER vs WHITE | ANY PRIVATE | 0.735 | 0.499 | 1.084 |
RACEX ASIAN OR PACIFIC ISLANDER vs WHITE | PUBLIC ONLY | 1.045 | 0.655 | 1.670 |
RACEX BLACK vs WHITE | ANY PRIVATE | 0.776 | 0.638 | 0.944 |
RACEX BLACK vs WHITE | PUBLIC ONLY | 1.394 | 1.129 | 1.721 |
POVCAT99 HIGH INCOME vs NEGATIVE OR POOR | ANY PRIVATE | 11.595 | 9.293 | 14.467 |
POVCAT99 HIGH INCOME vs NEGATIVE OR POOR | PUBLIC ONLY | 0.274 | 0.213 | 0.353 |
POVCAT99 LOW INCOME vs NEGATIVE OR POOR | ANY PRIVATE | 1.990 | 1.606 | 2.466 |
POVCAT99 LOW INCOME vs NEGATIVE OR POOR | PUBLIC ONLY | 0.492 | 0.395 | 0.615 |
POVCAT99 MIDDLE INCOME vs NEGATIVE OR POOR | ANY PRIVATE | 5.162 | 4.197 | 6.348 |
POVCAT99 MIDDLE INCOME vs NEGATIVE OR POOR | PUBLIC ONLY | 0.356 | 0.280 | 0.452 |
POVCAT99 NEAR POOR vs NEGATIVE OR POOR | ANY PRIVATE | 1.213 | 0.901 | 1.632 |
POVCAT99 NEAR POOR vs NEGATIVE OR POOR | PUBLIC ONLY | 0.680 | 0.526 | 0.878 |
NOTE: The degrees of freedom in computing the confidence limits is 317. |