| Methodology
Report #25: Relative Importance of Poverty and Education
in MEPS Weighting Adjustment Sadeq Chowdhury, PhD Abstract
The Medical Expenditure Panel Survey (MEPS) Household Component, a nationally representative sample of the U.S. civilian noninstitutionalized population, is conducted by the Agency for Healthcare Research and Quality (AHRQ). The MEPS Household Component provides national estimates of health care use, expenditures, sources of payment, and health insurance coverage. In computing sampling weights for MEPS, base sampling weights are adjusted for nonresponse and under-coverage in various weighting steps including a final raking adjustment using control totals for selected demographic and socio-economic characteristics obtained from the Current Population Survey (CPS). The MEPS Full Year (FY) weight is released in two steps—a preliminary weight is released first with the FY Population Characteristics file, which is revised later for the final FY Consolidated file. The preliminary weight is produced without using poverty status in the raking adjustment as the poverty status variable is not available at that stage. The final weight is produced by adjusting for poverty status a few months later when the poverty status variable becomes available along with health care expenditure variables. This report presents the results from research evaluating the impact of using education status of family reference person instead of family poverty status in the raking adjustment. Since education status is easy to derive and available early, the objective is to see if education status can be used to improve the preliminary FY weight or if education status can be used as an alternative to poverty status in producing the final FY weight. Suggested Citation:
The estimates in this report are based on the most recent data available at the time the report was written. However, selected elements of MEPS data may be revised on the basis of additional analyses, which could result in slightly different estimates from those shown here. Please check the MEPS Web site for the most current file releases.
The Medical Expenditure Panel Survey (MEPS) Background The Medical Expenditure Panel Survey (MEPS) is conducted to provide nationally representative estimates of health care use, expenditures, sources of payment, and insurance coverage for the U.S. civilian noninstitutionalized population. MEPS is cosponsored by the Agency for Healthcare Research and Quality (AHRQ), formerly the Agency for Health Care Policy and Research, and the MEPS comprises three component surveys: the Household Component (HC), the Medical Provider Component (MPC), and the Insurance Component (IC). The HC is the core survey, and it forms the basis for the MPC sample and part of the IC sample. Together these surveys yield comprehensive data that provide national estimates of the level and distribution of health care use and expenditures, support health services research, and can be used to assess health care policy implications. MEPS is the third in a series of national probability surveys conducted by AHRQ on the financing and use of medical care in the United States. The National Medical Care Expenditure Survey (NMCES) was conducted in 1977, the National Medical Expenditure Survey (NMES) in 1987. Beginning in 1996, MEPS continues this series with design enhancements and efficiencies that provide a more current data resource to capture the changing dynamics of the health care delivery and insurance system. The design efficiencies incorporated into MEPS are in accordance with the Department of Health and Human Services (DHHS) Survey Integration Plan of June 1995, which focused on consolidating DHHS surveys, achieving cost efficiencies, reducing respondent burden, and enhancing analytical capacities. To accommodate these goals, new MEPS design features include linkage with the National Health Interview Survey (NHIS), from which the sample for the MEPS-HC is drawn, and enhanced longitudinal data collection for core survey components. The MEPS-HC augments NHIS by selecting a sample of NHIS respondents, collecting additional data on their health care expenditures, and linking these data with additional information collected from the respondents’ medical providers, employers, and insurance providers. Household Component The MEPS-HC, a nationally representative survey of the U.S. civilian noninstitutionalized population, collects medical expenditure data at both the person and household levels. The HC collects detailed data on demographic characteristics, health conditions, health status, use of medical care services, charges and payments, access to care, satisfaction with care, health insurance coverage, income, and employment. The HC uses an overlapping panel design in which data are collected through a preliminary contact followed by a series of five rounds of interviews over a two and a half year period. Using computer-assisted personal interviewing (CAPI) technology, data on medical expenditures and use for two calendar years are collected from each household. This series of data collection rounds is launched each subsequent year on a new sample of households to provide overlapping panels of survey data and, when combined with other ongoing panels, will provide continuous and current estimates of health care expenditures. The sampling frame for the MEPS-HC is drawn from respondents to NHIS, conducted by NCHS. NHIS provides a nationally representative sample of the Medical Provider Component The MEPS-MPC supplements and validates information on medical care events reported in the MEPS-HC by contacting medical providers and pharmacies identified by house-hold respondents. The MPC sample includes all hospitals, hospital physicians, home health agencies, and pharmacies reported in the HC. Also included in the MPC are all office-based physicians: § Providing care for HC respondents receiving Medicaid. § Associated with a 75 percent sample of households receiving care through an HMO (health maintenance organization) or managed care plan. § Associated with a 25 percent sample of the remaining households. Data are collected on medical and financial characteristics of medical and pharmacy events reported by HC respondents, including: § Diagnoses coded according to ICD-9 (9th Revision, International Classification of Diseases) and DSMIV (Fourth Edition, Diagnostic and Statistical Manual of Mental Disorders). § Physician procedure codes classified by CPT-4 (Current Procedural Terminology, Version 4). § Inpatient stay codes classified by DRG (diagnosis related group). § Prescriptions coded by national drug code (NDC), medication names, strength, and quantity dispensed. § Charges, payments, and the reasons for any difference between charges and payments. The MPC is conducted through telephone interviews and mailed survey materials. Insurance Component The MEPS-IC collects data on health insurance plans obtained through private and public sector employers. Data obtained in the IC include the number and types of private insurance plans offered, benefits associated with these plans, premiums, contributions by employers and employees, and employer characteristics. Establishments participating in the MEPS-IC are selected through two sampling frames: § A Bureau of the Census list frame of private-sector business establishments. § The Census of Governments from the Bureau of the Census. Data from the two sampling frames are collected to provide annual national and State estimates of the supply of private health insurance available to American workers and to evaluate policy issues pertaining to health insurance. Since 2000, the Bureau of Economic Analysis has used national estimates of employer contributions to group health insurance from the MEPS-IC in the computation of Gross Domestic Product (GDP). The MEPS-IC is an annual panel survey. Data are collected from the selected organizations through a prescreening telephone interview, a mailed questionnaire, and a telephone follow-up for nonrespondents. Survey Management MEPS-HC and MPC data are collected under the authority of the Public Health Service Act. Data are collected under contract with Westat. Data sets and summary statistics are edited and published in accordance with the confidentiality provisions of the Public Health Service Act and the Privacy Act. The As soon as data collection and editing are completed, the MEPS survey data are released to the public in staged releases of summary reports, micro data files, and tables via the MEPS Web site: www.meps.ahrq.gov. Selected data can be analyzed through MEPSnet, an online interactive tool designed to give data users the capability to statistically analyze MEPS data in a menu-driven environment. Additional information on MEPS is available from the MEPS project manager or the MEPS public use data manager at the Center for Financing, Access, and Cost Trends, Agency for Healthcare Research and Quality, 540 Gaither Road, Rockville, MD 20850; 301-427-1406, or email MEPSProjectDirector@ahrq.hhs.gov. AHRQ Publications Clearinghouse Attn: (publication number) 800-358-9295 703-437-2078 (callers outside the 888-586-6340 (toll-free TDD service; hearing impaired only) To order online, send an email to: ahrqpubs@ahrq.gov. Be sure to specify the AHRQ number of the document or CD-ROM you are requesting. Selected electronic files are available through the Internet on the MEPS Web site: http://www.meps.ahrq.gov/ For more information, visit the MEPS Web site or email mepspd@ahrq.gov. The Medical Expenditure Panel Survey (MEPS), conducted by the Agency for Healthcare Research and Quality (AHRQ), provides nationally representative estimates of health care use, expenditures, sources of payment, and health insurance coverage for the U.S. civilian noninstitutionalized population. It consists of three survey components with the Household Component (HC) as the core survey. The MEPS Household Component (will be generally referred to as MEPS hereafter) also provides estimates of respondents' health status, demographic and socio-economic characteristics, employment, access to care, and satisfaction with health care. The MEPS is a complex national area probability sample survey. The sample for MEPS is selected from the National Health Interview Survey (NHIS) and contains the same design features as the NHIS. The details of the NHIS sample design can be found in Botman et al. (2000). A new panel is sampled for MEPS every year from the previous year’s responding households of the NHIS. A panel remains in the sample for two years, which is covered by five rounds of data collection. The first two interviews (Rounds 1-2) cover most of the first year, the last two interviews (Rounds 4-5) cover most of the second year, and the middle interview (Round 3) covers the end part of the first year and the beginning part of the second year. A MEPS annual file consists of two overlapping sample panels—the first year of a new panel and the second year of an old panel. Figure 1 shows an example of the overlapping of MEPS panels and data collection rounds. The survey can be used to produce estimates for persons and families as well as subgroups of the population. The details of the MEPS-HC sample design can be found in Ezzati-Rice et al. (2008). Figure 1. Overlapping of MEPS panels and data collection rounds
Annual Full Year (FY) files are released in two phases—the preliminary file (i.e., the FY Population Characteristics file) is released in the spring and the final file (i.e., the FY Consolidated file) is released about six months later. Two of the major types of MEPS estimates are use of health care services and medical expenditures. Since the processing and derivation of expenditure variables requires more time than that of population characteristics and utilization variables, a preliminary file is released earlier so that users can produce estimates related to population characteristics and health care utilization while waiting for the expenditure data. Later, when the expenditure variables become available, the preliminary file is replaced by the final file that includes use, expenditure, and income data. The MEPS FY weights are not identical in the preliminary and final FY files. The preliminary weight released with the preliminary file is replaced by the final weight when the final file is released. A series of weighting adjustments for nonresponse, under-coverage, and benchmarking to available control totals is applied to produce the weights for the MEPS FY files. The adjustments are applied separately for each panel at the end of Round 1, at the end of the first year, at the end of Round 3, and finally at the end of the second year. For a FY file, the adjusted weight at the end of the first year of the new panel and the adjusted weight at the end of the second year for the preceding panel are used. When the two panels are combined to form a FY file, the weights of each panel are scaled down by using an appropriate compositing factor to jointly represent the target population. A raking adjustment is then applied to the composite weight using a set of raking dimensions for which the control totals are obtained from the Current Population Survey (CPS). The variables used in forming the set of raking dimensions are: age category, sex, race/ethnicity, census region, metropolitan statistical area (MSA), and poverty status. The weight released with the preliminary file is adjusted for all available variables except for poverty status, which is not available for another four to six months because it takes longer to derive. The release of the preliminary file and weight allows users to produce and analyze some estimates early. However, a disadvantage is that when the final weight becomes available, if the estimates are not revised, there may be confusion at a later date about different estimates and results for the same year. So, it would be convenient to produce the weight in a single step without any revision. This means either the weight must be produced in a single step as a final weight when the poverty variable is available without producing any preliminary weight, or the preliminary weight must be improved to avoid the need for the revision. This research evaluates the impact of the poverty status adjustment on MEPS estimates and investigates if education status of family reference person can be used to improve the preliminary FY weight or as an alternative to poverty status in producing the final FY weight. Since the education variable is derived early, the final weight can be computed and released early. Another advantage of using education is that it is easier to collect and derive in a consistent manner with the source of control totals (i.e., CPS). The derivation of poverty status is a complex procedure that involves more editing and imputation that makes it harder to produce poverty status consistent with the CPS. Such inconsistency in the survey variable and the corresponding variable in the control total file can introduce noise to the adjustment process. Each MEPS panel is weighted separately for different rounds of nonresponse and coverage adjustments until the final step, when the two panels are combined and a raking adjustment is applied to the combined panels to produce the final FY weight. Figure 2 provides a flowchart of the weighting scheme used to produce the MEPS FY weights. Machlin, Chowdhury, et al. (2010) provides details of the weighting and estimation procedures used in the MEPS. The weighting of the most recent panel starts with computing the dwelling unit (DU) base weight, which is calculated by starting with the nonresponse adjusted NHIS household weight. A poststratified ratio adjustment is then applied to the DU base weight to ensure representativeness of the MEPS sample in terms of the full NHIS sample. The control total for this adjustment is derived from the household reference person’s weight in the NHIS sample. A nonresponse adjustment is applied to the poststratified DU weight to compensate for the DU nonresponse to the Round 1 interview. A family-level weight is derived by assigning the DU weight to each family within the DU and then a family-level poststratification adjustment is applied using control totals from the CPS. The Round 1 person weight is then derived by assigning the poststratified family weight to each person in the family and then applying a person-level poststratification adjustment. Return to Table of Contents
The year 1 person weight is derived by first applying a nonresponse adjustment to the Round 1 weight for person-level nonresponse up to the end of the first year (i.e., over Round 2 and year 1 portion of Round 3) and then applying a raking/poststratification adjustment using the control total for December 31 of the year derived from the subsequent March CPS. This produces the year 1 person weight for responding persons in the most recent panel. The year 2 weight for the persons in the preceding panel is derived by starting with the year 1 weight from the previous year and applying a nonresponse adjustment to compensate for nonresponse in year 2. A raking/poststratification adjustment is then applied to the nonresponse adjusted weight. This produces the year 2 weight for responding persons in the preceding panel. The two panels are then put together to create the FY file for the
current year. The panel specific annual weights are scaled down
by applying a compositing factor proportional to the sample size
in
each panel so that the composite weights of both panels jointly
adds up
to the size of the target population. Then another round of raking
adjustment is applied to produce the preliminary FY weight for
the cases in the combined panels using the same set of control
totals
used for raking of individual panels. As mentioned in the introduction,
poverty status is not used in the raking adjustment for producing
the
preliminary FY weight. About four to six months later, when the
poverty status becomes available, the raking adjustment is repeated
by adding
dimensions involving poverty status, in addition to the dimensions
used for producing the preliminary FY weights. This produces the
final person-level FY weight. This paper concentrates on these
preliminary and final raking adjustments to assess the impact of
poverty adjustment
and the possibility of replacing the poverty status by education
status
in the raking adjustment. The variables with their categories used in the final raking adjustment are: Census region (Northeast, Midwest, South, West), MSA/non-MSA, race/ethnicity (Hispanic, non-Hispanic black, Asian, others), sex (male/female), age category (<1 yr, 1-19 yr, 20-29 yr, 45-64 yr, 65+), and poverty status of the family (below poverty, 100-124 percent, 125-199 percent, 200-399 percent, 400+ percent). A total of 15 raking dimensions are used for preliminary FY weighting and an additional eight dimensions involving poverty status are included for the final FY weighting. For some raking dimensions, the 5-category age group is collapsed to 4-category or 3-category groups by collapsing some age categories. Table 1 presents the raking dimensions used for the preliminary and the final raking adjustments. For the current investigation, another weight (called education-adjusted weight) is produced by replacing poverty status with education status of the family reference person to assess the relative impact of adjustments by poverty and education. The four categories of education status used are: <12th grade, high school graduate or GED, some college or associate/vocational degree, and college graduate or higher. Table 1. Raking dimensions used in producing preliminary and final
FY weights
To assess the importance of poverty and education in the raking adjustment, using 2007 FY data, some exploratory analyses are conducted first and then selected estimates of insurance coverage rates and of utilization of health care and expenditures under different weighting schemes are compared. Under exploratory analyses, the correlation between education and poverty categories is analyzed first to get an idea of how powerful education is in explaining poverty status. A model-based analysis is then conducted of the variances of two important target variables—health insurance status and total medical expense. A logistic regression is fitted for insurance status and a general linear model (GLM) is fitted for total expense with all variables available for raking adjustment used as independent variables in both cases. Since this is an exploratory analysis, only main effects of different variables are considered in the analysis in order to get a broad idea of the relative importance of different variables. As part of the exploratory analyses, a preliminary assessment is also made of the relative impact of adjustment by poverty and/or education by examining the discrepancy between estimated population totals and the known control totals and variation in insurance coverage rates in different poverty or education categories. This gives an indication of the direction and extent of an impact on insurance coverage if an overall adjustment is made by poverty only, by education only, or by education in addition to poverty. The final assessment is made by comparing important MEPS estimates such as the estimates of insurance coverage and selected utilization and expense estimates under three raking adjusted weights. The three raked weights used in producing the estimates are as follows:
The first two weights are already available in the 2007 MEPS preliminary
and final FY files and did not require calculation. However, the education-adjusted
weight was calculated by using a raking algorithm that used the preliminary
FY weight as the starting weight and the raking dimensions included
all dimensions used in producing preliminary weights plus the dimensions
involving education instead of poverty. Another weight by simultaneously
including both poverty and education in the raking adjustment was considered
but was not produced due to the problem with convergence of the raking
algorithm with too many dimensions. However, a simple analysis that
does not require computing such raked weight is presented as part of
the exploratory analysis to assess the potential impact of such an
adjustment. Considering the poverty-adjusted weight as the current
standard, the estimates without adjustment by education or poverty
(i.e., preliminary estimates) and the estimates based on the education
adjustment (alternative estimates) are compared with the estimates
under the final weight (i.e., final estimates). The comparison of preliminary
and final estimates shows the impact of poverty status adjustment,
and the comparison of alternative estimates and final estimates shows
the effectiveness of the education adjustment as an alternative to
the poverty status adjustment. Figure 3 shows the actual CI coverage under three levels of bias (i.e., 1.96SE, 1.0SE, and 0.5SE) assuming that the SE is roughly the same for both biased and unbiased estimators. It shows that when the bias is 1.96SE the 95 percent CI coverage is only 50 percent instead of 95 percent, if the bias is 1.0SE then the CI coverage is about 83 percent, and when the bias is 0.5SE the CI coverage is 92.1 percent. Table 2 summarizes the relationship between bias, SE, CI coverage, and mean square error (MSE) over variance. It shows that CI coverage goes below 90 percent as bias/SE goes above 0.6SE. Figure 3. CI coverage under different levels of bias (The curve in black shows the distribution under the biased estimate,
the curve in red shows the distribution under the unbiased estimate,
and shaded blue area shows the type I error)
Cochran (1977) provides further discussion on this topic and suggests keeping |bias/SE| below 0.20 to keep the CI coverage as close as possible to 95 percent. In the case of this analysis, we will consider a bias up to 0.50SE (i.e., |bias/SE|<0.5) ignorable considering the negligible practical implications of such a bias. Table 2. Relationship between bias, SE, CI coverage, and mean square error over variance
Association between education and poverty Table 3. Association between education and poverty—percentage
distributions of poverty categories within each education category
and vice versa
Analysis of variance of target variables Tables 4 and 5 present analysis of variance of two important MEPS target variables in terms of all base raking variables plus poverty and education statuses. Table 4 presents an analysis of variance from a logistic regression model of health insurance status (no/yes). It shows that after including all base variables in the model, both poverty and education are still significant to explain further variation in insurance status. However, the values of chi-square indicate that poverty is considerably more powerful than education in explaining the variation in insurance status (even if the difference in degrees of freedom is taken into account). Overall, poverty status appears to be the second most important variable (after age) in explaining the variation in insurance status. Table 4. Type III analysis of variance for insurance status
Table 5 presents a similar analysis of variance from a general linear model of total health care expense. Poverty and education are both significant in explaining the variation in total expense but the values of chi-square show that these two variables are not as important for explaining variation in total expense as in the case of insurance status. Table 5. Type III analysis of variance for total health care expense
Preliminary assessment of impacts of adjustments A preliminary assessment of the impact of adjustments by poverty versus education is also made using a simple approach by comparing the variation of the insurance coverage rates and adjustment factors across the categories of poverty and education. Table 6 shows that the estimates of percentage not-insured (using the preliminary weight) vary considerably by the categories of poverty status. The third and fourth columns of the table present the estimated (using the preliminary weight) and the actual (from the CPS) population totals. The adjustment factors (i.e., the ratios of known population control totals over the estimated population totals before adjustment) also vary considerably. The population with high income is underrepresented in the sample while their insurance coverage is higher. In fact, there is a strong negative association between percentage not-insured and the adjustment factors, implying that the adjustment by poverty would reduce the overall estimate of percentage not-insured. The last row of the table shows that a simple adjustment by poverty status reduces the overall estimate of percentage not-insured from 18.22 percent to 17.62 percent. These overall estimates of percentage not-insured are obtained by weighting the percentage not-insured in each category by the estimated and actual population totals in each category, respectively. Table 6. Assessing the impact of adjustment by poverty status
Table 7 presents a similar analysis for an adjustment by education category. The last row of the table shows that a simple adjustment by education status reduces the overall estimate of percentage not-insured from 18.22 percent to 17.97 percent. Both adjustments by poverty and education would have a downward impact on the estimate of percentage not-insured but the impact of adjustment by poverty is greater than the impact of adjustment by education. Since this approach does not require computing the raked weight including poverty and education, table 8 presents a similar analysis to show the likely impact of adjustment by education on top of the adjustment by poverty. The correlation between percentages not-insured and adjustment factors by education categories is very low (after adjustment for poverty status), which is reflected in the small change in the overall estimate (17.62 versus 17.55). Table 7. Assessing the impact of adjustment by education status
Table 8. Assessing the impact of adjustment by education status in addition to poverty status
Tables 9 and 10 present a comparison of estimates under the three adjustment schemes. The differences of the estimates with the final poverty-adjusted estimates, SEs, and corresponding ratios of difference over SE (diff/SE) are presented. Differences in SEs of estimates under different adjustments are mostly very small, which is consistent with the coefficient of variations (CVs) of different weights. The CVs of the three weights are 71.1 percent for the preliminary weight and 73.2 percent for both education-adjusted and final weights. So we will mainly concentrate on the difference (bias) in estimates. As mentioned in the methodology section, in addition to statistical significance of the difference, implications of the difference on the confidence intervals will be considered. The differences significant at 0.05 or lower level are indicated by an asterisk. However, as discussed before, any |diff/SE| less than 0.50 will be considered ignorable even if the difference is statistically significant. Table 9 presents the percentage distribution across different insurance categories by age, race/ethnicity, and MSA status under different weighting adjustments. Most preliminary and education-adjusted estimates of percentages in insurance categories are significantly different than the final estimates. As noted in the table (marked with a dagger symbol), most of the significant differences are non-ignorable as |diff/SE| is greater than 0.50. For example, the overall estimate of private insurance coverage is 61.90 percent under the final weight, 60.32 percent (1.58 percentage points lower) under the preliminary weight and 60.91 percent (0.99 points lower) under the education adjusted weight. The differences are statistically significant (as indicated by asterisks) and |diff/SE| is substantially higher than 0.50 in both cases. Similarly, the estimate of never insured is 17.76 percent under the final weight, 18.36 percent under the preliminary weight, and 18.07 percent under the education-adjusted weight. The differences between estimates are significant and corresponding |diff/SE| in both cases are considerably greater than 0.50. The table presents similar comparisons by selected categories of age, race/ethnicity, and by MSA status. The pattern of differences is similar and in most cases the differences are both significant and non-ignorable. However, the differences between the education-adjusted and the final estimates are smaller (by 30 percent to 50 percent in most cases) than the differences between the preliminary and the final estimates. So, the comparison of estimates in table 9 shows that, for insurance coverage estimates, adjustment by poverty status is non-ignorable and the adjustment by education status makes some improvement from the preliminary estimates but is not as effective as the adjustment by poverty status. Table 10 presents similar comparisons for selected use and expenditure variables overall and by major age categories. In contrast to insurance coverage, the differences between preliminary or education-adjusted estimates and the final estimates are mostly not significant and ignorable as |diff/SE| is less than 0.50 in the majority of cases. For example, for mean overall health care expenditures, the final estimate is $4,404, the preliminary estimate is $4,399, and the education adjusted estimate is $4,399.7. These differences are not significant and also ignorable. The pattern is the same for most of the other expenditures and use estimates overall and for subpopulations, except for the mean number of dental visits, where the differences between the preliminary and the final estimates are not ignorable in all cases. In some cases, although the difference is significant, it can be ignored because |diff/SE| is less than 0.50. Table 9. Comparison of preliminary and education-adjusted estimates
with final (poverty-adjusted) estimates for insurance coverage status
Table 10. Comparison of preliminary and education-adjusted estimates with final (poverty-adjusted) estimates for selected use and expenditure variables
The MEPS final FY weights are produced by including dimensions involving family poverty status in the raking adjustment. Since the derivation of poverty status involves a complex procedure, it delays the production of the final FY weights. This study examines the option of using education status of family reference person instead of family poverty status in the final raking adjustment. Analyses of variances of some important MEPS target variables show that both poverty and education are significant in explaining variation in insurance status but not as important for use and expenditure variables. However, poverty status is substantially more effective than education status in explaining the variation in insurance status. A correlation analysis shows that education status is only moderately associated with poverty status. A comparison of estimates based on different weights shows that raking adjustment by poverty status appears to have significant and non-ignorable impacts for insurance coverage estimates. The differences between preliminary (not adjusted by poverty or education) and final (adjusted by poverty status) estimates are generally significant and non-ignorable. Adjustment by education status instead of poverty status puts the estimates closer to the final estimates but the differences with the final estimates are still significant and non-ignorable. For most use and expenditure estimates, differences between preliminary and final estimates are significant in some cases, but ignorable in most cases, and differences between education-adjusted and final estimates are both non-significant and ignorable in most cases. The estimates based on the weight from the raking adjustment using both education and poverty is not compared because of the difficulty with convergence with too many raking dimensions. A weight with some collapsing or modifications of raking dimensions could still be produced, however this was not done as the analysis of variance and a simple adjustment showed that the impact of adjustment by education in addition to poverty would be negligible. The overall analysis suggests that education status can be used
as a replacement for poverty status in raking adjustment for
use and
expenditure estimates, but is not effective enough as a replacement
for poverty
status for the estimates of insurance coverage. However, since
the education adjustment puts the estimates closer to the final
estimates,
one possibility is to add education status in the raking adjustment
to produce the preliminary weight. That will reduce the differences
between preliminary and final weights and estimates. Botman S.L., Moore T.F., Moriarity C.L. (2000). Parsons V.L. Design
and Estimation for the National Health Interview Survey, 1995–2004.
National Center for Health Statistics. Vital Health Stat 2(130).
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