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ACA and the New Individual Segment: Profiling the Uninsured and Non-Group Insured Populations with MEPS 2010 and SAS Survey Procedures. Jessica Hampton September 2013. Presentation Outline. Introduction Statement of Purpose PPACA MEPS 2010 Survey Design Literature Review

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ACA and the New Individual Segment: Profiling the Uninsured and Non-Group Insured Populations with MEPS 2010 and SAS Survey Procedures

Jessica Hampton

September 2013


Presentation outline
Presentation Outline

  • Introduction

    • Statement of Purpose

    • PPACA

    • MEPS 2010

    • Survey Design

    • Literature Review

    • Intro to SAS Survey Procedures

  • Data Preparation

    • Selected Variables

    • Derived Variables

  • Statistical Analysis

    • PROC SURVEYMEANS

    • PROC SURVEYFREQ

    • Profiles (with PROC CORR, PROC SURVEYREG)

    • CART Models (SPSS)

    • PROC SURVEYLOGISTIC Models

  • Conclusions/Recommendations

  • References



Statement of purpose
Statement of Purpose

  • Use MEPS 2010 data (most recent available)

  • With SAS survey procedures to:

    • Identify drivers of total and out of pocket medical expenditures for adults 18-65

    • Discover significant predictors of uninsured and private non-group insured segments

    • Profile these populations of interest

    • Compare mean expenditures across populations with regard to insurance coverage status

    • Estimate the size of these segments as of 2010

  • Why?

    • Prior to ACA, underwriting practices denied coverage to high risk (high cost) individuals

    • Individual market is changing

    • Although underwriters no longer allowed to deny coverage, customer profiles are used to identify desirable characteristics (low risk/low cost) in order to target those individuals in direct marketing campaigns.

    • Profiles also useful in retention and engagement strategies


Ppaca aca
PPACA/ACA

  • Patient Protection and Affordable Care Act (March 2010)

  • Full implementation January 1, 2014

  • Guaranteed coverage for people with pre-existing conditions

  • Minimum standards for coverage

  • Phases out annual and lifetime maximums

  • Standardized premiums with regard to gender and prior medical diagnoses

  • Pricing based on age and smoking status

  • Smoothes age-based premium differences (younger people will pay more than they used to)

  • Income-based subsidies (up to 400% poverty level)

  • Health insurance exchanges for each state to facilitate purchase

  • Penalizes those who elect not to purchase health insurance

  • Health insurance sales tax

  • Dramatically expands the individual segment

  • Previously uninsured purchase insurance through the exchanges

  • Others lose their private employer group coverage


Medical expenditures panel survey meps
Medical Expenditures Panel Survey (MEPS)

  • Administered annually by the U.S. Department of Health and Human Services since 1996

  • Agency for Healthcare Research and Quality (AHRQ)

  • Anonymity protected by removing individual identifiers from the public data files

  • MEPS 2010 consolidated data file released September 2012

  • Multiple components (household, insurance/employer, and medical provider).

  • Household component (1,911 variables) covers the following topics:

    • Demographics

    • Household income

    • Employment

    • Diagnosed health conditions

    • Additional health status issues

    • Medical expenditures and utilization

    • Satisfaction with and access to care

    • Insurance coverage

  • 18,692 after excluding out of scope, negative person weights, under 18 and 65+

  • U.S. civilian, noninstitutionalized population

  • ~3% out of scope (birth/adoption, death, incarceration, living abroad)


Meps survey design methods
MEPS Survey Design Methods

  • MEPS is a representative but NOT a random sample of the population

  • Person weights must be used to produce reliable population estimates

  • Stratification:

    • By demographic variables such as age, race, sex, income, etc.

    • Goal is to maximize homogeneity within and heterogeneity between strata

    • Sometimes used to oversample certain groups under-represented in the general population or with interesting characteristics relevant to study

    • For example: blacks, Hispanics, and low-income households

  • Clustering:

    • By geography in order to reduce survey costs -- not feasible or cost-effective to do a random sample of the entire population of the U.S.

    • Within-cluster correlation underestimates variance/error -- two families in the same neighborhood are more likely to be similar demographically (for example, similar income)

    • Desire clusters spatially close for cost effectiveness but as heterogeneous within as possible for reasonable variance.

    • Multi-stage clustering used in MEPS:

      • sample of counties >> sample of blocks >> individuals/households surveyed from block sample


Survey design considerations
Survey Design Considerations

  • If person weights are ignored and one tries to generalize sample findings to the entire population, total numbers, percentages, or means are inflated for the groups that are oversampled and underestimated for others

  • In regression analysis, ignoring person weights leads to biased coefficient estimates

  • If sampling strata and cluster variables are ignored, means and coefficient estimates are unaffected, but standard error (or population variance) may be underestimated; that is, the reliability of an estimate may be overestimated

  • Or when comparing one estimated population mean to another, the difference may appear to be statistically significant when it is not

  • (Machlin, S., Yu, W., & Zodet, M., 2005)



Literature review kff 2011
Literature Review – KFF 2011

  • Most literature focuses on data available prior to 2012 which describes these segments (uninsured/non-group) through 2008

  • Kaiser Family Foundation (KFF) 2011 study, “A Profile of Health Insurance Exchange Enrollees,” based on MEPS 2007

  • Model simulation of demographic, health, and medical utilization profiles of the people expected to enroll in exchanges by 2019

  • Compares exchange population profile to privately insured and uninsured

  • Estimates the exchange population in 2019 to include:

    • 16 million formerly uninsured individuals

    • 5 million formerly employer group insured

    • 1 million formerly private non-group insured

  • Exchange population older, with lower education and income levels, more racially diverse than current privately insured population

  • Expect self-reported worse health but fewer pre-existing medical diagnoses, possibly due to lack of access to care

  • Expect utilization and expenditures to increase for the previously uninsured once they gain access to care – similar to those of non-group insured

  • Some higher income will continue purchasing non-group insurance outside of the exchanges – those with lower income will favor exchanges


Literature review kff 2010
Literature Review – KFF 2010

  • 2010 study by KFF: “Comparison of Expenditures in Nongroup and Employer Sponsored Insurance: 2004-2007

  • Recent focus on the non-group market prompted by healthcare reform, which would expand the non-group market

  • DiJulio and Claxton, study authors, use combined data from MEPS 2004-2007 for the nonelderly adult population

  • Non-group insured have lower premiums, higher out-of-pocket expenses and better (self-reported) health than the private employer group segment

  • Implies some combination of more cost-sharing, higher deductible levels, and/or less coverage for non-group

  • If lower income people are entering the group market, they may not be able to afford the high out-of-pocket costs

  • Plans may need higher premiums if the uninsured population is not as healthy as the non-group segment (DiJulio, B. & Claxton, G., 2010)


Literature review kff 2008
Literature Review – KFF 2008

  • 2008 Kaiser study, prior to ACA being signed into law: “How Non-Group Health Coverage Varies with Income”

  • Based on combined data from MEPS 2000-2003 for nonelderly adults

  • Finds that even higher income people may prefer to remain uninsured than to buy in the non-group market if they have no coverage offered through an employer

  • At 4x the poverty level, 25% purchase non-group

  • At 10x the poverty level, only 50% purchase non-group coverage

  • Although people are more likely to purchase non-group plans at higher income levels, study concludes that the non-group insurance market is unattractive to consumers

  • Insurers either need to improve their product or the government needs to subsidize such plans even for higher incomes in order to encourage participation


Literature review meps statistical brief 2009
Literature Review – MEPS Statistical Brief 2009

  • A MEPS statistical brief from 2009 examines trends in group and non-group private coverage

  • 1996-2007, nonelderly adult population

  • 65% had private group coverage at some time in 2007

  • Percentage private group has remained similar since 1996, with increasing overall numbers corresponding to population increase

  • Non-group coverage has declined in the same time period, falling from 6% to 4% (Cohen, J.W. & Rhoades, J.A., 2009).


Literature review o neill o neill 2009
Literature Review – O’Neill & O’Neill 2009

  • O’Neill and O’Neill (2009) conducted an analysis of the characteristics of the uninsured population

  • Employment Policies Institute study finds that a large portion of the uninsured are “voluntarily uninsured”

    • Percentage varies by state from as low as 27% to as high as 55%

    • Defined as nonelderly adults at or exceeding 2.5 times the poverty level

  • The media tends to portray the uninsured population as being in very poor health and without options, but the authors say this portrayal is exaggerated and does not present a full picture

  • Uninsured population has very different demographic characteristics from the privately insured:

    • young

    • low education levels

    • immigrants

    • lower medical utilization/expenditure levels

  • Higher mortality rates (about 3% higher than those of the privately insured segment after controlling for other risk factors such as smoking) among uninsured

  • Lack of health insurance coverage not the major contributing factor – other disadvantages associated with poor health, such as lack of education (O’Neill & O’Neill, 2009)


Literature review summary
Literature Review – Summary

  • Private non-group coverage declining over time

  • Non-group market unpopular and overpriced

  • Non-group insured have lower premiums, higher out-of-pocket expenses and better (self-reported) health than the private employer group segment

  • Large portion of the uninsured are “voluntarily uninsured”

  • Uninsured population has very different demographic characteristics from the privately insured

  • Exchange population characteristics will be driven by large influx of formerly uninsured

  • Lower education and income levels, more racial diversity than current privately insured population

  • Fewer pre-existing medical diagnoses

  • Conflicting information on whether uninsured population is actually healthier or not



Sas survey procedures1
SAS Survey Procedures

  • Intended for use with sample designs that may include unequal person weights, clustering, and stratification.

  • PROC SURVEYMEANS estimates population totals, percentages, and means. Includes estimated variance, confidence intervals, and descriptive statistics.

  • PROC SURVEYFREQ produces frequency tables, population estimates, percentages, and standard error.

  • PROC SURVEYREG estimates regression coefficients by generalized least squares.

  • PROC SURVEYLOGISTIC fits logistic regression models for discrete response (categorical) survey data by maximum likelihood.

  • PROC SURVEYMEANS and PROC SURVEYREG available starting with SAS version 8.

  • PROC SURVEYFREQ and PROC SURVEYLOGISTIC available starting with version 9.

  • PROC SURVEYSELECT for sampling which will not be used in this project


Proc surveymeans syntax
PROC SURVEYMEANS Syntax

PROCSURVEYMEANS DATA=PQI.MEPS_2010;

STRATA VARSTR;

CLUSTER VARPSU;

WEIGHT PERWT10F;

DOMAIN INSCOV10;

VAR TOTEXP10 TOTSLF10;

RUN;



Proc surveyfreq syntax
PROC SURVEYFREQ Syntax

PROCSURVEYFREQ DATA=PQI.MEPS_2010;

STRATA VARSTR;

CLUSTER VARPSU;

WEIGHT PERWT10F;

TABLES PRIEU10 PRING10 INSCOV10;

RUN;



Proc surveyreg syntax
PROC SURVEYREG Syntax

PROC SURVEYREG DATA=PQI.MEPS_2010;

STRATA VARSTR;

CLUSTER VARPSU;

WEIGHT PERWT10F;

MODEL &TARGET=&&VAR&I /SOLUTION;

ODS OUTPUT PARAMETERESTIMATES=PARAMETER_EST FITSTATISTICS=FIT;

RUN;


Proc surveylogistic syntax
PROC SURVEYLOGISTIC Syntax

PROCSURVEYLOGISTIC DATA=SASUSER.MEPS_2010;

STRATA VARSTR;

CLUSTER VARPSU;

WEIGHT PERWT10F;

MODEL TOTEXP_HIGH(EVENT='1')=AGE10X MARRIED--HISPANX POVLEV10--PHYACT53 OBESE--ADSMOK42 ADINSA42--LOCATN_ER;

ODS OUTPUT PARAMETERESTIMATES=WORK.PARAM;

RUN;


Proc surveylogistic reg output
PROC SURVEYLOGISTIC/REG Output

Default output (similar to PROC LOGISTIC and PROC REG):

  • fit statistics (AIC, Schwartz’s criterion, R-square)

  • chi-squared tests of the global null hypothesis

  • degrees of freedom

  • coefficient estimates

  • standard error of coefficient estimates and p-values

  • odds ratio point estimates

  • 95% Wald confidence intervals

    Does not include:

  • Option for stepwise selection

  • chi-squared test of residuals/tabled residuals (assumptions of normality and equal variance do not apply)

  • influential obs/outliers (person weights)











Summarized output from proc surveymeans freq
Summarized Output from PROC SURVEYMEANS/FREQ

  • N for Private Non-Group = 397

  • Private Non-Group continues decline (from 4% in 2007 – MEPS Statistical Brief, 2009)

  • Additional 247 with 65+ included

  • Total adult nonelderly US population ~ 191 million

  • Any Private/Only Public/Uninsured add up to 100% of total

  • Mean total expenditures $3,751.61

  • Confidence Intervals overlapping for public/uninsured OOP means, but look at OOP as percent of total expenditures


Population size estimates
Population Size Estimates

  • Using PROC SURVEYFREQ

  • Private Insurance largest Group

  • Any Private/Only Public/Uninsured add up to 100% of total

  • Private Non-Group/ Private Empl Group subsets of Any Private


Mean expenditures
Mean Expenditures

  • Using PROC SURVEYMEANS

  • Public has largest total expenditures

  • Uninsured has lowest total expenditures, followed by Private Non-Group

  • Private Non-Group pays the most out-of-pocket in absolute dollars


Oop expenditures percent of total
OOP Expenditures – Percent of Total

  • Using PROC SURVEYMEANS

  • Private Non-Group and Uninsured pay the most out of pocket as a percent of their total expenditures



Building profiles
Building Profiles

  • Three approaches

  • Unweighted PROC CORR

  • PROC CORR with person weights

  • “PROC SURVEYCORR” macro with PROC SURVEYREG:

    • Uses all survey design variables (strata/cluster/weight)

    • Iteratively runs simple regression models for each predictor variable

    • Builds table with r-squared, r, and p-values

    • Sorted by r

    • See NESUG paper/presentation for more about this approach

  • Similar results for all three approaches


Profile expenditures
Profile – Expenditures

Note: All profiles show characteristics ranked roughly by size of correlation and significance level.


Profile non group insured population
Profile – Non-Group Insured Population

Note: All profiles show characteristics ranked roughly by size of correlation and significance level.


Profile uninsured population
Profile – Uninsured Population

Note: All profiles show characteristics ranked roughly by size of correlation and significance level.


Profile private employer based insurance population
Profile – Private Employer-Based Insurance Population

Note: All profiles show characteristics ranked roughly by size of correlation and significance level.


Profile publicly insured population
Profile – Publicly Insured Population

Note: All profiles show characteristics ranked roughly by size of correlation and significance level.


By population number of diagnoses
By Population – Number of Diagnoses

  • Using PROC SURVEYMEANS

  • Public has highest mean # chronic conditions

  • Uninsured has lowest # chronic conditions

  • Private Non-Group second most healthy


By population age
By Population – Age

  • Using PROC SURVEYMEANS

  • Private non-group is oldest (but also relatively healthy for age – see previous slide)

  • Uninsured is youngest

  • Underwriting for ACA allowed only based on Age and Smoking status


By population education levels
By Population – Education Levels

  • Using PROC SURVEYMEANS

  • Public least educated, followed by Uninsured

  • Private Non-Group most educated


By population of poverty level
By Population – % of Poverty Level

  • Using PROC SURVEYMEANS

  • Private Group at highest % of the poverty level (over 500%)

  • Subsidies for ACA up to 400% poverty level


By population income variables
By Population – Income Variables

  • Using PROC SURVEYMEANS

  • Private Group has highest income

  • Skewed high because mean, not median

  • Private Non-Group has highest total income compared to wage income – oldest segment w/ possible early retirees or more income from investments/pensions


By population ethnicity
By Population – Ethnicity

  • Using PROC SURVEYFREQ

  • Uninsured highest percentage of Hispanic (~ 35%)


By population marital status
By Population – Marital Status

  • Using PROC SURVEYFREQ

  • Private Group highest % Married (> 60%)

  • Public and Uninsured lowest % Married, but also younger


By population gender
By Population – Gender

  • Using PROC SURVEYFREQ

  • Uninsured highest % Male

  • Public and Any Private highest % Female


By population risk profile
By Population – Risk Profile

  • Using PROC SURVEYFREQ

  • Public most obese (> 35%)

  • Private Non-Group and Uninsured least obese


By population risk profile1
By Population – Risk Profile

  • Using PROC SURVEYFREQ

  • Public least physically active (< 50%)

  • Private Non-Group most physically active


By population risk profile2
By Population – Risk Profile

  • Using PROC SURVEYFREQ

  • Public and Uninsured have highest % Smokers – around double the rate of Private

  • All Private categories similar (< 15%)

  • Private Non-Group has least behavioral risk, but Uninsured has fewer chronic conditions


By population insurance attitudes
By Population – Insurance Attitudes

  • Using PROC SURVEYFREQ

  • Uninsured/Private Non-Group most likely to agree/strongly agree


By population insurance attitudes continued
By Population – Insurance Attitudes (continued)

  • Using PROC SURVEYFREQ

  • Private Non-Group most likely to agree/strongly agree, followed by Uninsured

  • Higher prevalence of this attitude across the board; about twice as many feel that insurance is not worth the cost as feel that they are healthy enough to not need insurance



Cart model high total expenditures above mean
CART Model – High Total Expenditures (Above Mean)

  • Using person weights in SPSS

  • Inputs exclude Expenditures/Utilization variables (Slide 31)

  • ADAPPT42 = # of times respondent went to doctor’s office/clinic past 12 months

  • Ordinal values 1-6; top-coded with 5 = 5-9 times and 6 = 10+ visits

  • 5+ visits in the past 12 months -> above-average expenditures

  • < 5 times without multiple diagnoses for high blood pressure -> below-average expenditures

  • Combination of high blood pressure with diabetes as predictor of above-average medical costs

  • Consistent with Table 19 (see Appendix), showing ADAPPT42 high on the list of positive correlations with high total expenditures. Multiple blood pressure diagnoses is the second most highly-correlated diagnosis variable behind NUMDX (total number of diagnoses was not an input for this CART model) and arthritis diagnosis (ARTHDX).


Cart model insurance coverage status
CART Model – Insurance Coverage Status

  • Target INSCOV10 (1 = any private, 2 = public only, 3 = uninsured)

  • Utilization, expenditures, and income-related variables all significant

  • >200% of the poverty level + higher total expenditure -> private insurance

  • >200% of the poverty level + lower wage income < $27K + not full-time students + no usual source of care -> uninsured (voluntarily?)

  • < 200% of the poverty + low wage income (< $10K) + low OOP + higher total expenditures -> publicly insured

  • CHECK53 = how long since last physical (preventive care)

  • Divides b/t privately insured and uninsured at this % of poverty level.

  • If >3 years since last physical -> uninsured. If <=2 years since last physical + wage income < $5K + low total expenditures -> uninsured.


Proc surveylogistic high total expenditures
PROC SURVEYLOGISTIC – High Total Expenditures

  • Diagnoses:

    • EMPHDX (+)

    • STRKDX (+)

    • DIABDX (+)

    • ARTHDX (+)

    • CANCERDX (+)

    • CHDDX (+)

    • BPMLDX (+)

    • ASTHDX (+)

    • CHOLDX (+)

    • OHRTDX (+)

  • Demographics:

    • MALE (-)

    • FTSTU (-)

    • HISPANX (-)

    • EDUCYR (+)

    • POVLEV10 (+)

    • AGE10X (+) at 90% level

  • Insurance Attitudes:

    • ADINSA42 (-)

    • ADINSB42 (-)

  • Utilization/Preventive Care:

    • CHECK53 (-)

    • DENTK53 (-)

  • Risk:

    • PHYACT53 (-)

  • All chronic diagnoses have significant positive correlation at 95% level except:

    • MIDX (myocardial infarction)

    • ANGIDX (angina)

    • RADX (rheumatoid arthritis)

    • HIBPDX (single high blood pressure diag)

  • Demographics:

    • Males have lower expenditures

    • Hispanics have lower expenditures (also more likely to be uninsured)

    • Full-time students have low expenditures (<23 y.o. -- proxy for age? – age significant at 90%)

    • Higher education and income levels associated with higher expenditures

  • Insurance Attitudes:

    • Those with high expenditures do not feel health insurance is unnecessary or not worth the cost

  • Utilization/Preventive Care:

    • Higher levels of preventive care are associated with higher than average costs

    • Low values correspond to frequent dental visits and recent physical examinations

  • Risk:

    • Meeting recommended physical activity guidelines associated with lower costs

    • BMI and smoking insignificant (emphysema proxy for smoking?)


Proc surveylogistic private non group coverage
PROC SURVEYLOGISTIC – Private Non-Group Coverage

  • Demographics:

    • NATIVE_AMER (-)

    • BLACK (-)

    • HISPANX (-)

    • EDUCYR (+)

    • AGE10X (+)

  • Usual Source of Care:

    • LOCATN_OFFICE (+)

    • LOCATN_HOSP (+)

    • HAVEUS42 (-)

  • Insurance Attitudes:

    • ADINSA42 (+)

    • ADINSB42 (+)

  • Expenditures:

    • TOTSLF10 (+)

  • Income-Related Variables:

    • WAGEP10X (-)

    • TTLP10X (+)

  • Risk:

    • BMINDX53 (-) at 90% level

  • Demographics:

    • Less likely to be Native American, black, or Hispanic (not racially diverse)

    • Higher education levels

    • Older

  • Usual Source of Care:

    • Likely to be a doctor’s office or the hospital

  • Insurance Attitudes:

    • Negative

    • Feel health insurance is unnecessary and/or not worth the cost (smaller p value)

  • Expenditures

    • High out of pocket costs

    • Probably associated with negative attitudes toward insurance

  • Income:

    • Multiple income-related variables are present in the model, possibly leading to instability/multicollinearity issues

    • From the profile of this group, all income-related variables should have individual positive correlations with private non-group insured status

  • Lower BMI significant at 90% level



Recommendations conclusions
Recommendations/Conclusions

  • Limitations:

    • Only 4 SAS survey procedures

    • N = 397 for private non-group segment of the population means some sub-groups have small sample sizes (for example, Hispanics, unweighted N=41)

    • Some studies pool years to get larger N

    • Could include 65+ or examine separately (~250)

  • Hard to conclude whether addition of uninsured will increase/decrease costs:

    • Decreased short term costs due to influx of younger males without chronic medical diagnoses

    • Long term, behavioral risk factors such as smoking and physical inactivity may lead to higher costs

    • Engage population to lower risk: incentives, health risk assessments, risk stratification of the population, targeted outreach through health coaching programs

  • Evidence to support O’Neill & O’Neill’s “voluntarily uninsured”:

    • Based on insurance attitudes, target these groups with lower premium cost, high deductible health plans (HDHPs)

    • MEPS 2011 data available in 2013; first survey to include information specific to HDHPs (demographics of those who select HDHPs vs. traditional plans, OOP compared to total expenditures and utilization)


Recommendations conclusions1
Recommendations/Conclusions

  • Trend in domestic private individual/non-group coverage has been steadily decreasing market share

    • International non-group markets augment declining U.S. sales

    • Increased customer segmentation efforts as part of strategies to identify, understand, and target desirable (low-risk) clusters for IFP marketing

  • Underwriting practices are curtailed under ACA, but selective direct marketing to desirable segments will develop and increase in importance. Insurance companies are interested in purchasing behavior, socio-economic indicators, and demographics of potential customers, and should note the distinguishing characteristics (young, single, male, Hispanic, etc.) of the uninsured population in marketing, retention, and engagement strategies.

  • Individual segment is unpopular with customers:

    • Insurance companies need to demonstrate that they are adding value to consumers and differentiate themselves from competitors.

    • Example: Cigna’s “GO YOU” branding and advertising campaign (October 2011)

    • Dramatic switch to individual-focused, “customer-centric” business model touted as offering highly-personalized service to customers

    • Part of a strategy developed in anticipation of healthcare reform to increase competitiveness in the changing marketplace with its expanded individual segment



References1
References

  • Carrington, W. J., Eltinge, J. L., & McCue, K. (2000). An Economist’s Primer on Survey Samples. Working Paper no. 00-15. Suitland, MD: Center for Economic Studies, U.S. Bureau of the Census, October 2000. Retrieved from ftp://tigerline.census.gov/ces/wp/2000/CES-WP-00-15.pdf January 15, 2013.

  • Cohen, J.W., & Rhoades, J.A. (2009). Group and Non-Group Private Health Insurance Coverage, 1996 to 2007: Estimates for the U.S. Civilian Noninstitutionalized Population under Age 65. Medical Expenditure Panel Survey (MEPS) Statistical Brief #267. Agency for Healthcare Research and Quality, Rockville, MD. Retrieved from http://meps.ahrq.gov/data_files/publications/st267/stat267.pdf

  • DiJulio, B., & Claxton, G. (2010). Comparison of Expenditures in Nongroup and Employer-Sponsored Insurance: 2004-2007. Kaiser Family Foundation, Menlo Park, CA. Retrieved from http://www.kff.org/insurance/snapshot/chcm111006oth.cfm

  • Kaiser Family Foundation (2008). How Non-Group Health Coverage Varies with Income. Menlo Park, CA. Retrieved from http://www.kff.org/insurance/upload/7737.pdf

  • Machlin, S., & Yu, W. (2005). MEPS Sample Persons In-Scope for Part of the Year: Identification and Analytic Considerations. April 2005. Agency for Healthcare Research and Quality, Rockville, MD. Retrieved from http://www.meps.ahrq.gov /survey_comp/hc_survey/hc_sample.shtml


References continued
References (continued)

  • Machlin, S., Yu, W., & Zodet, M. (2005). Computing Standard Errors for MEPS Estimates. January 2005. Agency for Healthcare Research and Quality, Rockville, Md. Retrieved from http://www.meps.ahrq.gov/survey_comp/standard_errors.jsp

  • Medical Expenditure Panel Survey (MEPS). (2012). MEPS HC-138: 2010 Full Year Consolidated Data File. Rockville, MD: Agency for Healthcare Research and Quality (AHRQ), September 2012. Retrieved from http://meps.ahrq.gov/data_stats/download_data/pufs/h138/h138doc.pdf September 27, 2012.

  • Medical Expenditure Panel Survey (MEPS). (2012). MEPS HC-138: 2010 Full Year Consolidated Data Codebook. Rockville, MD: Agency for Healthcare Research and Quality (AHRQ), August 30, 2012. Retrieved from http://meps.ahrq.gov/mepsweb/data_stats/download_data_files_codebook.jsp?PUFId=H138 September 27, 2012.

  • Medical Expenditure Panel Survey (MEPS). MEPS-HC Panel Design and Collection Process. Agency for Healthcare Research and Quality, Rockville, Md. Retrieved from http://www.meps.ahrq.gov/survey_comp/hc_data_collection.jsp

  • Medical Expenditure Panel Survey (MEPS). Data Use Agreement. Agency for Healthcare Research and Quality, Rockville, Md. Retrieved from http://meps.ahrq.gov/mepsweb/data_stats/data_use.jsp


References continued1
References (continued)

  • O’Neill, J., & O’Neill, D. (2009). Who are the uninsured? An Analysis of America’s Uninsured Population, Their Characteristics, and Their Health. Employment Policies Institute, Washington, D.C.

  • SAS Institute Inc.(2008). SAS/STAT 9.2 User’s Guide. Chapter 14: Introduction to Survey Sampling and Analysis Procedures. Pp. 259-270. Cary, NC: SAS Institute Inc. Retrieved from http://support.sas.com/documentation/cdl/en/statugsurveysamp/61762/PDF/default/statugsurveysamp.pdf on January 15, 2013.

  • Trish, E., Damico, A., Claxton, G., Levitt, L., & Garfield, R. (2011). A Profile of Health Insurance Exchange Enrollees. Kaiser Family Foundation, Menlo Park, CA. Retrieved from http://www.kff.org/healthreform/upload/8147.pdf


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