Implications of the Medicaid Undercount in a High-Penetration Medicaid State: Implications of the Medicaid Undercount

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Research Objective. This study investigates the impact of misreporting by Medicaid recipients on estimates of the uninsured in Louisiana, and is based on similar work by Call et al. in Minnesota and Klerman, Ringel, and Roth in California. With its
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  State Level Special Issue–Part 1 Implications of the MedicaidUndercount in a High-PenetrationMedicaid State  R. Kirby Goidel, Steven Procopio, Douglas Schwalm, and Dek Terrell ResearchObjective.  This study investigates the impact of misreporting by Medicaidrecipients on estimates of the uninsured in Louisiana, and is based on similar work byCall et al. in Minnesota and Klerman, Ringel, and Roth in California. With its uniquecharityhospitalsystem,culture,andhighpoverty,Louisianaprovidesaninterestingandunique context for examining Medicaid underreporting. Study Design.  Results are based on a random sample of 2,985 Medicaid households.Respondents received a standard questionnaire to identify health insurance status, andindividual records were matched to Medicaid enrollment data to identify misreporting. Data Sources.  Data were collected by the Public Policy Research Lab at Louisiana State University using computer-assisted telephone interviewing. Using Medicaid en-rollmentdatatoobtaincontactinformation,theLouisianaHealthInsuranceSurveywasadministered to 2,985 households containing Medicaid recipients. Matching responsesonindividualsfromthesehouseholdstoMedicaidenrollmentdatayieldedresponsesfor3,199 individuals. Conclusions.  Results suggest relatively high rates of underreporting among Medicaidrecipients in Louisiana for both children and adults. Given the very high proportionof Medicaid recipients in the population, this may translate up to a 3 percent bias inestimates of uninsured populations. Implications.  Medicaid bias may be particularly pronounced in areas with highMedicaidenrollments.Misreportingratesandthusthebiasinestimatesoftheuninsuredmay differ across areas of the United States with important consequences for Medicaidfunding. Funding Source.  Louisiana Department of Health and Hospitals. Key Words.  Health insurance, uninsured, Medicaid bias, survey methods Scholars and state health administrators have long noted differences inadministrative enrollment records and survey-based estimates of Medicaidpopulations. In Louisiana, the 2005 Current Population Survey estimates r Health Research and Educational Trust DOI: 10.1111/j.1475-6773.2007.00794.x 2424   Medicaid enrollments for children (under 19 years old) at 35 percent or404,730 children, while state administrative data place enrollments at over51.5 percent or 665,454 children. 1 Overall nonelderly Medicaid enrollment in Louisiana is 836,442 or 21.2 percent of the population according to stateadministrativerecords,butis633,070or15percentofthepopulationbasedonCPS estimates. Such gaps——referred to as the Medicaid undercount——arenoteworthy in their own right, but are of primary interest because of theirpotential to create an upward bias in survey-based estimates of the uninsured.Research reported by the American Enterprise Institute and conductedby the Actuarial Research Corporation suggests that——due largely to the un-derreporting of Medicaid enrollment in survey research——the 2003 CPS es-timates of the uninsured may be inflated by as many as 9 million persons(O’Grady 2005). While the size of this estimate has been subject to dispute(Davern 2005; Giannerelli 2005), the fact that general population surveysunderestimate Medicaid enrollments and that this underreporting has thepotential to impact estimates of uninsured populations is not (Lewis, Ellwood,and Czajka 1998; Blumberg and Cynamon 1999; Call et al. 2002). Moreover,Medicaid misreporting may be becoming worse over time (Ku and Bruen1999; Blewett et al. 2005; Klerman, Ringel, and Roth, 2005).Recognizing these gaps, most states now commission their ownstate-level surveys to estimate uninsured populations. 2 Subsequent uninsuredestimates are generally lower than CPS estimates reflecting important differ-ences in methodology, including differences in question wording, populationcoverage and sampling, nonresponse bias, and data processing (Call, Davern,and Blewett 2007). The CPS, for example, gauges uninsured status by iden-tifying respondents who have been uninsured for the previous year, whilemost state-levelsurveys(includingthesurveysdescribedbelow) are ‘‘point-in-time’’estimatesreflectingcurrentinsurancestatus.StudiessuchasthosebytheActuarial Research Corporation and Urban Institute, likewise, model unin-surance in terms of year-long (rather than point-in-time) uninsured status.Moreover, most state-level surveys use random digit dialing leaving out households without telephone service and potentially underestimating unin- Address correspondence to R. Kirby Goidel, Ph.D., Professor, Reilly Center for Media & PublicAffairs, Manship School of Mass Communication, Louisiana State University, Baton Rouge, LA70803. Steven Procopio, Ph.D., Director of Research and Accountability, is with the Louisiana Department of Culture, Recreation, and Tourism, Baton Rouge, LA. Douglas Schwalm, Ph.D., iswith the Department of Economics, Illinois State University, Normal, IL. Dek Terrell, Ph.D.,Director, is with the Division of Economic Development and Forecasting, Department of Eco-nomics, Ourso College of Business, Louisiana State University, Baton Rouge, LA. Implications of the Medicaid Undercount 2425   sured populations. 3 Even accounting for these differences, state-level surveysare subject to individual misreporting which, in the aggregate, tends to un-derestimate Medicaid populations and overestimate uninsured populations.While there has been a notable increase of research in this area, thecauses and consequences of Medicaid underreporting——on estimates of un-insured populations——are not yet fully understood (Call et al. 2002; Eberly,Pohl, and Davis 2005; Klerman, Ringel, and Roth 2005). To the extent that Medicaid underreporting reflects individuals currently counted as not having health insurance, estimates of uninsured populations may be significantly in-flated (Callahan 2005; Giannerelli 2005). However, if these cases are reportedas having private insurance, estimates of uninsured populations may be large-lyunaffectedbyMedicaidundercounts(Calletal.2002;Klerman,Ringel,andRoth 2005; Peterson and Grady 2005).According to UrbanInstitute estimates, generalpopulation surveysmayoverestimateuninsuredpopulationsbyasmuchasmanyas3.6millionpeople(Lewis, Ellwood, and Czajka 1998; Giannerelli 2005). Estimates derived bythe Actuarial Research Corporation are even larger, suggesting that the CPSestimated 45 million uninsured Americans may be closer to 36 million. How-ever, the simulations used by the Urban Institute and the Actuarial ResearchCorporation have not been universally embraced, and many scholars believesuch adjustments overcorrect for the Medicaid undercount (Call et al. 2002;Davern 2005; Klerman, Ringel, and Roth 2005). Studies directly comparing general population survey estimates of the uninsured to administrative data have been fewer in number, and have yielded mixed results (Call et al. 2002;Davern 2005; Klerman, Ringel, and Roth 2005). The common denominatorhas been that estimates of the effect of Medicaid underreporting on uninsuredrates are much smaller than the simulated measures would indicate, largelybecause misreported cases are only partially attributed as uninsured.Comparingself-reported insurancestatusamong a sampleof Minnesota Medicaid enrollments, Call et al. (2002) find substantial misreporting of Med-icaidenrollment,butnegligibleeffects(approximately0.26percentagepoints)on estimates of the uninsured. 4 Matching enrollment data to individual CPSdata, Klerman, Ringel, and Roth (2005) find more substantial Medicaid un-derreporting in California and statistically and substantively significant effectson estimates of the uninsured. Medi-Cal enrollment increases by about 40 percent when adjusting for underreporting, and the estimated percent of uninsured Californians drops by approximately 2.7 percentage points foradultsand6.9percentagepointsforchildren. 5 Bothstudiesnotethelimitationsoftheirdataandimportanceofpopulationfactorsthatcannotbecapturedina  2426 HSR: Health Services Research   42:6, Part II   (  December 2007   )   single state study. A study in Maryland found a comparable 25 percent un-dercount when using a survey modeled after the CPS questionnaire, thoughchanges in question wording to better capture state specific programs signifi-cantly reduced the undercount (Eberly, Pohl, and Davis 2005).The issue of the Medicaid undercount also plays a role on state-levelestimates of the uninsured and comparisons of uninsured rates across states.The Medicaid population, degree of undercount, and impact of the under-count on estimates of the uninsured may vary significantly across individualsand geographic boundaries. Klerman, Ringel, and Roth (2005) find higherrates of misreporting among groups with lower coverage rates reflecting a ‘‘stigma-based’’ explanation of the undercount. Research examining parish-level differences within Louisiana found that the undercount was negativelyrelated to parish-level per capita income and positively related to the percent of the parish population receiving public assistance (Goidel et al. 2005). Onemight subsequently suspect that Medicaid underreporting would be morefrequent in states and geographic areas with a larger proportion of the pop-ulation on Medicaid and/or public assistance, and that the effects of the un-dercount onestimatesoftheuninsuredwouldvaryaccordingto the size of theMedicaid population.RecentstudiesbytheStateHealthAccessandDataAssistanceCenterat the University of Minnesota have focused on Minnesota, California, Penn-sylvania, and Florida (Blewett et al. 2005). The percent of Medicaid recipientsmisreporting their insurance status varies from 3.3 to 10.5 percent; while theeffect on uninsured rates varies from 0.1 to 0.9 percent. We add to this lit-eraturebyutilizingtheCalletal.(2002)methodologytoexamineself-reportedinsurance status among a random sample of Louisiana Medicaid householdsas identified by state administrative data. We differ from prior research in twoimportant ways. First, the survey questionnaire employs a household ap-proach in which respondents are asked whether anyone any in the householdhas health insurance provided by an employer, former employer, someonenot currently in the household, Medicare, Medicaid, LaCHIP, military insur-ance, or insurance purchased on their own. This contrasts with the Call et al.(2002) work, which utilized a person-level approach in which respondents areasked about insurance coverage for each individual. Prior research indicatesthat a household-level approach yields higher estimates of uninsured popu-lations (Hess et al. 2001). 6 Second, our methodology also requires using theexactLHIS surveyinstrument,whichdoesnotallow ustoidentify a particularmemberofthehousehold.Askingaboutaparticularmemberofthehouseholdmay inform the respondent that they have been identified in a nonrandom Implications of the Medicaid Undercount 2427   manner generating both sample selection and response bias. Third, ourmatching methodology also deviates from studies matching based on socialsecurity number such as Klerman, Ringel, and Roth (2005) and Card, Hild-reth, and Shore-Sheppard (2004).Studies linking survey reports to Medicaid enrollment data remain rel-ativelyrareduetolimitationsindataaccess.Inthepresentstudy,wemakeuseof state administrative data provided by the Louisiana Department of HealthandHospitalstolinksurveyreportsdirectlytoMedicaidenrollments,andaddto this growing literature. As a southern state with high Medicaid penetration,Louisiana provides an important context for this investigation. With over half ofitschildren(0–18)and6.4percent ofadults(19–64)enrolledinMedicaidorLaCHIP,thereispotentialforalargerbiasduetotheundercountinLouisiana even if misreporting is comparable with other states. If misreporting is morecommon in high-penetration Medicaid areas, bias due to the undercount maybe more substantial in Louisiana and other similar states than has beenreported in the existing research. M ETHODS The Louisiana Health Insurance Survey was administered to 2,985 randomlyselected Louisiana Medicaid households drawn from Louisiana Department of Health and Hospital administrative records. Each of the households in-cludes at least one Medicaid recipient. The survey administered was a ques-tionnaire identical to that used for a sample of 10,000 randomly selectedLouisianahouseholdsaspartofthe2005LouisianaHealthInsuranceSurvey. 7 State administrative records were collected in May 2005, and the Medicaidhousehold survey was conducted in June 2005. 8 The response rate for thesurvey was 37 percent and the cooperation rate was 54 percent. 9 While theresponse rate is not ideal, it does fall within the norm of academic surveyresearch (Kosicki, Marton, and Lee 2003), reflects a more general decline inresponse rates over time (Curtin, Presser, and Singer 2005), and is consistent with similar studies. Call et al. (2006) report a 41.7 percent response rate inCalifornia,29.8percentinFlorida,and55.9percentinPennsylvania.Evenso,the response rate remains a limitation of the study.The 2,985 Medicaid household respondents provided information on9,426individuals.However,notall9,426individualsareenrolledinMedicaidor LaCHIP. To ensure that our analysis only includes actual Medicaidrecipients, survey data were matched back to Medicaid enrollment data. We 2428 HSR: Health Services Research   42:6, Part II   (  December 2007   ) 
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