Expanding insurance coverage: Financial and quality spillovers in loca Slide Presentation from the AHRQ 2009 Annual ConferencSlide presentation from the AHRQ 2009 conference. On September 19, 2009, Jose A. Pagan made this presentation at the 2009 Annual Conference. Select to access the PowerPoint® presentation (2 MB) (Plugin Software Help).Slide 1 Expanding insurance coverage: Financial and quality spillovers in local health care marketsJA Pagán9/15/2009Slide 2 AcknowledgmentAHRQ's Minority Research Infrastructure Support Program.AHRQ's M-RISP Program funded the Health Services Research Initiative at The University of Texas-Pan American.Grant Number R24HS017003.Slide 3 Americans are becoming more cynical as to what they will gain from health care reform.Image: Line graph shows that number of people who consider they would be better off after health care reform has remained about the same (38% to 36%) between Feburary and August 2009. People who believe they would be worse off has risen from 11% to 31%, and people who believe it wouldn't make much difference has fallen from 43% to 27%.Slide 4 More Worry About Negative Effects Then About PassageWhich worries you more?51%: That Congress and the President will pass a reform bill that won't be good for you and your family39%: That Congress and the President won't be able to pass health care reform this year6%: Don't know/Refused4%: NeitherSlide 5 IssuesHealth care reform debate is confusing.Everyone wants to know: "What's in for me?"Understanding potential spillovers of insurance/ uninsurance on local health care markets is key.Empirical analyses based on data from the Community Tracking Study → Potential/realized access and assessment of health care providers.Policy modeling and implications.Slide 6 The uninsured population45 million nonelderly uninsured in 2009.54 million nonelderly uninsured by 2019.65% are from low-income families <200% FPL).35% of Latinos, 29% of Native Americans and 20% of African Americans are uninsured compared to 12% of whites.Sources: Congressional Budget Office, 2009; Kaiser Family Foundation (2006) The Uninsured, A Primer.Slide 7 Rationale for providing accessGood but not fully convincing argument:"It is the right thing to do"Enlightened self-interest argument:"It makes you and me better off"Slide 8 Percent Uninsured Under Age 65 - 2005 SAHIE ProgramImage: Map of the United States shows percentage of uninsured by state and county.Source: U.S. Census Bureau, Small Area Health Insurance Estimates (SAHIE) program, Oct. 2008Slide 9 Local communities and uninsuranceImage: Chart shows relationship between health services, access to care, and economic effects.Slide 10 Unmet medical needs by community ranking and insurance status UnisuredInsuredLow17.1%5.5%Mid-low18.8%6.1%Mid-high18.6%7.2%High17.2%8.l3%A 10%-point increment in the proportion of local uninsured adults was associated with a 29.3% increase in the likelihood that an insured adult would have had unmet medical needs within the last 12 months.Pagn JA, Pauly MV. (2006). "Community-level uninsurance and the unmet medical needs of insured and uninsured adults," Health Services Research, 41(3): 788-803.Slide 11 Spillovers and vulnerability: the case of community uninsurance Pauly and Pagn, Health Affairs 2007)IOM framework: High uninsurance results in higher uncompensated care (bad debt and charity care).End result: Insured people pay for this (10% higher premiums; higher prices; cost-shifting).This is a pecuniary community spillover (that is, a financial spillover that works its way through prices).Local pecuniary spillovers on the insured are likely to be small when money flows from the outside (e.g., Medicare DSP, state funds).Slide 12 Occur when the uninsured demand a lower quality and quantity of health care than the insured.Preference externalities: "distinct groups of consumers who have substantially different preferences [from others]. bring forth products with more appeal to themselves but less appeal to others" (Waldfogel, 2003).Caveat: Local health care markets must not be perfectly segmented.Slide 13 Non-pecuniary, real spillovers in partially insured marketsUninsured demand lower quality care.Even if the insured have differential access to health care (e.g., better facilities to go to), there are high fixed costs for quality differentiation.Slide 14 Example: Two communities of same size and number of uninsuredCommunity 1: Uninsured never use charity care and can only pay for low quality care.Only quality spillover.Community 2: Uninsured get charity care and do not skimp on quality.Only pecuniary spillover.Slide 15 How can the insured reduce these negative effects?By patronizing health care suppliers that do not provide care to the uninsured or who do not engage in cost shifting.This can only happen in large enough markets that can be segmented (e.g., in certain regions and for low-fixed-cost services).Quality spillovers are more likely in specialty care, care requiring expensive equipment (e.g., scanning), etc.Slide 16 Health Care Access, Utilization, And Quality Indicators Of Insured Adults, By Community Uninsurance, 2003Image: Sample page of report.Slide 17 Community uninsurance and mammography(J of Clinical Oncology April 2008)Women ages 40-69 from 2000-2001 CTS HS (n=12,595).Prob of mammography screening falls by 1.3% for every 10% increase in community uninsurance.Slide 18 2009 IOM ReportReport covers: Health insurance coverage trendsHealth effectsCommunity effectsSlide 19 Pauly & Pagn: Spillovers of uninsurance in communities2003 CTS Household Survey → Participants clustered in 60 communities (48 large metro areas, 3 small metro areas and 9 non-metro areas).Working age population (N = 31,935).Health care measure = f(Community uninsurance, Charity care, X, C).Slide 20 Dependent variablesPotential and realized health care access:(1) Respondent has a place to go when sick(2) Visited doctor(3) Visited doctor for routine preventive careAssessment of health care providers:(1) Satisfaction with choice of PCP(2) Satisfaction with health care received(3) Trust doctorSlide 21 Independent variables (distinguishing pecuniary and real spillovers)Community uninsurance rate → Proportion uninsured.Charity care → Proportion of uninsured respondents in a community with no cost-related problems obtaining medical care.Charity care proxy is correlated with different sources of free care (hospitals, doctors and FQCHCs; Herring, 2005).Slide 22 Table 1: Descriptive Statistics: Full Sample and by Community Uninsurance and Charity Care Use Community Uninsurance Charity Care UseIndividual-level variablesAllLowHighP-valueLowHighP-valueHave usual source of care (%)89.0691.3286.540.0090.1687.980.07Have a doctor's visit in the past year (%)79.6480.3878.820.1780.2779.030.27Have a doctor's visit for preventive care (%)60.1161.2658.830.0760.2859.940.8Very satisfied with choice of primary carephysician (%)67.1469.2664.770.0069.0465.260.02Very satisifed with health carea (%)63.5565.6361.190.0064.8562.270.09Trust doctors (%)69.3470.5367.960.0470.4468.230.06Fair or poor health (%)9.18.2610.030.018.649.550.2Age (years) (mean)40.8740.8540.890.9041.1540.60.13Education (years) (mean)13.9214.0313.80.0313.8913.950.61Race/Ethnicity (%) White76.6483.9568.530.0083.6969.740Black9.536.8712.480.026.6912.310.02Hispanic9.074.7513.860.004.5313.510Other4.764.435.130.495.094.440.53Married (%)71.4873.6169.110.0172.6870.30.2Female (%)50.9550.7951.120.7351.3450.560.39Income/family poverty level (mean)4.594.784.380.034.524.660.45Community-level variables Percent uninsured in community (%)15.1510.2020.640.0013.9816.280.13Percent of uninsured population with nocost-related access difficulties (%)67.7165.7769.860.1658.3176.910Median household income/1,000 (mean)47.5153.1141.290.0047.5747.460.97 Gini coefficient (mean)0.380.360.40.000.370.390.03Notes: a Wald test of differences in means/proportions by low/high community uninsurance and low/high charity care use (i.e., comparison between the 30 communities with low and hight community uninsurance and charity care use, respectively.Slide 23 Table 2: Adjusted Odds Ratios of Potential and Realized Health Care Access Had a place to go when sick or in need of advice about healthHad a doctor's visit in the past yearHad a doctor's visit for a routine preventive careIndividual-level variablesOR95% ciOR95% ciOR95% ciFair or poor health1.05(0.78, 1.41)2.27***(1.74, 2.95)1.20(0.99, 1.46)Age (years)1.02***(1.01, 1.03)1.02***(1.01, 1.02)1.03***(1.02, 1.03)Education (years)1.02(0.98, 1.06)1.06***(1.03, 1.08)1.04***(1.02, 1.06)Black0.85(0.65, 1.12)0.94(0.74, 1.18)1.73***(1.39, 2.15)Hispanic0.53***(0.42, 0.67)0.71***(0.59, 0.86)1.07(0.91, 1.25)Other0.48***(0.35, 0.64)0.68***(0.51, 0.91)1.02(0.83, 1.25)Married1.44***(1.15, 1.80)0.98(0.85, 1.12)0.99(0.89, 1.11)Female2.03***(1.81, 2.29)2.82***(2.56, 3.12)2.59***(2.35, 2.86)Income/family poverty level1.04(1.81, 2.29)1.07***(1.03, 1.10)1.04***(1.02, 1.07)Community-level variables Percent uninsured in community /100.63***(0.60, 0.68)0.89***(0.85, 0.92)0.91***(0.88, 0.94)Percent of uninsured population with nocost-related access difficulties /100.98(0.95, 1.01)1.00(0.99, 1.02)0.99*(0.98, 1.00)Median household income/1,0001.00(0.99, 1.01)1.00(1.00, 1.00)1.01***(1.00. 1.01) Gini coefficient36.43***(10.40, 127.63)9.07***(4.06, 20.26)7.00***(4.11, 11.91)Notes: *p<0.05p<0.01***p<0.001Slide 24 Table 3: Adjusted Odds Ratios of Assessment of Health Care System Providers Very satisfied with choice of primary care physicianVery satisfied with health careTrust doctors Individual-level variablesOR95% CIOR95% CIOR95% CIFair or poor health0.77*(0.62. 0.94)0.70***(0.58, 0.85)0.73***(0.60, 0.88)Age (years)1.01***(1.01, 1.01)1.01***(1.00, 1.01)1.01***(1.00, 1.01)Education (years)1.01(0.99, 1.04)1.00(0.97, 1.02)0.98*(0.95, 1.00)Black1.04(0.87, 1.25)0.93(0.76, 1.15)0.96(0.79, 1.16)Hispanic0.78**(0.66, 0.94)0.74*(0.58, 0.93)0.63***(0.52, 0.76)Other0.73**(0.59, 0.90)0.84(0.61, 1.16)0.87(0.67, 1.12)Married0.93(0.84, 1.03)1.07(0.94, 1.23)0.83**(0.74, 0.94)Female1.31***(1.18, 1.45)1.11**(1.04, 1.19)1.16**(1.04, 1.30)Income/family poverty level1.02(1.00, 1.05)1.07***(1.05, 1.10)1.00(0.98, 1.03)Community-level variablesPercent uninsured in community /100.75***(0.72, 0.78)0.90***(0.86, 0.94)0.93***(0.94, 0.96)Percent of uninsured population with no cost-related access difficulties /100.93***(0.91, 0.95)0.95***(0.93, 0.97)0.99(0.98, 1.00)Median household income/1,0000.99***(0.98, 0.99)1.00(1.00, 1.00)1.00*(1.00, 1.01) Gini coefficient14.32***(5.53, 3703)2.94*(1.22, 7.07)2.62***(1.57, 4.35)Notes: *p<0.05p<0.01***p<0.001Slide 25Simulated Effects of Changes in Community Uninsurance and Charity Care on Potential and Realized Health Care Access Had a place to go when sickHad a doctor's visit in the past yearHad a visit for routine preventive careHas seen specialist in the last 12 months %%%%Community uninsurancePercent uninsured in community (Baseline=15.15%)928564.898.3Percent uninsured in community (New=25.15%)8883.462.697.9Percentage point change-4.05-1.61-2.23-0.31Charity carePercent of uninsured population with no cost-related access difficulties (Baseline=67.71%)92.285.164.998.3Percent of uninsured population with no cost-related access difficulties (New=77.71%)92.185.164.798.2Percentage point change-0.130.03-0.26-0.08Slide 26Simulated Effects of Changes in Community Uninsurance and Charity Care on Assessment of Health Care System Providers Very satisfied with PCP choiceVery satisfied with health careTrust doctorsVery satisfied with choice of specialist seen %%%%Community uninsurancePercent uninsured in community (Baseline=15.15%)62.753.772.174.5Percent uninsured in community (New=25.15%)55.65170.871.8Percentage point change-7.06-2.7-1.38-2.72Charity carePercent of uninsured population with no cost-related access difficulties (Baseline=67.71%)63.153.972.274.7Percent of uninsured population with no cost-related access difficulties (New=77.71%)61.352.772.174.6Percentage point change-1.76-1.24-0.17-0.08Slide 27 Conclusion/caveatsModel predictions depend on: the degree of market segmentation,the amount of charity care (or below cost care), andthe source of resources to cover the cost of charity or below cost care.Empirical evidence points to both real and pecuniary spillovers.The theory is value neutral → Insurance-related quality spillovers could be good or bad.Slide 28 PunchlineDifferent approaches to cover the uninsured will lead to different effects on the insured.Ex 1: Increase support for safety net providers that cater to the uninsured (e.g., community health centers)? little positive real spillover on the insured.Ex 2: Tax credits for health insurance would have a positive real spillover on the insured.Slide 29 Agent-based model(D Damianov, JA Pagn)Objective: Understand consequences of various health care reform proposals.Approach: Modeling incentives to purchase insurance and use services based on the quality of the service provided, price, and risk/preferences.Model useful to understand complex social dynamics and learning between micro/macro-level processes.Agents respond to their social context, especially to the actions of other members in their community (local health care market).Slide 30 FutureHealth care reform plans are constantly changing: How do we muddle through and get our message across?In making the individual, business or social case for reform, the answer to the "What's in for me?" question should always consider the broader community/market effects.Thanks! Current as of December 2009 Internet Citation: Expanding insurance coverage: Financial and quality spillovers in loca: Slide Presentation from the AHRQ 2009 Annual Conferenc. December 2009. Agency for Healthcare Research and Quality, Rockville, MD. http://www.ahrq.gov/news/events/conference/2009/pagan/index.html