Presentation on theme: "Expanding insurance coverage: Financial and quality spillovers in local health care markets JA Pagán 9/15/2009."— Presentation transcript:
Expanding insurance coverage: Financial and quality spillovers in local health care markets JA Pagán 9/15/2009
Acknowledgment AHRQ’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.
Issues Health 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.
The uninsured population 45 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.
Rationale for providing access Good but not fully convincing argument: “It is the right thing to do” Enlightened self-interest argument: “It makes you and me better off”
Local communities and uninsurance Lower revenue for healthcare providers Increased public/ Private spending Financial instability of Providers/institutions Reduce charity/ uncompensated care Economic effects Access to care Population health Public health services
Unmet medical needs by community ranking and insurance status Pagán JA, Pauly MV. (2006). “Community-level uninsurance and the unmet medical needs of insured and uninsured adults,” Health Services Research, 41(3): 788-803.
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). Spillovers and vulnerability: the case of community uninsurance (Pauly and Pagán, Health Affairs 2007)
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. Non-pecuniary or real spillovers
Uninsured 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. Non-pecuniary, real spillovers in partially insured markets
Community 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. Example: Two communities of same size and number of uninsured
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. How can the insured reduce these negative effects?
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.
Report covers: (1)Health insurance coverage trends (2)Health effects (3) Community effects 2009 IOM Report
Pauly & Pagán: Spillovers of uninsurance in communities 2003 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).
Dependent variables Potential and realized health care access: (1) Respondent has a place to go when sick (2) Visited doctor (3) Visited doctor for routine preventive care Assessment of health care providers: (1) Satisfaction with choice of PCP (2) Satisfaction with health care received (3) Trust doctor
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).
Had a place to go when sick Had a doctor’s visit in the past year Had a visit for routine preventive care Has seen specialist in the last 12 months %% Community uninsurance Percent uninsured in community (Baseline=15.15%) 92.0184.9864.7998.25 Percent uninsured in community (New=25.15%) 87.9683.3762.5697.94 Percentage point change-4.05-1.61-2.23-0.31 Charity care Percent of uninsured population with no cost-related access difficulties (Baseline=67.71%) 92.1885.0564.9198.26 Percent of uninsured population with no cost-related access difficulties (New=77.71%) 92.0585.0864.6598.18 Percentage point change-0.130.03-0.26-0.08 Simulated Effects of Changes in Community Uninsurance and Charity Care on Potential and Realized Health Care Access
Very satisfied with PCP choice Very satisfied with health care Trust doctorsVery satisfied with choice of specialist seen %% Community uninsurance Percent uninsured in community (Baseline=15.15%) 62.6853.7372.1474.54 Percent uninsured in community (New=25.15%) 55.6251.0370.7671.82 Percentage point change-7.06-2.70-1.38-2.72 Charity care Percent of uninsured population with no cost-related access difficulties (Baseline=67.71%) 63.1053.9272.2274.67 Percent of uninsured population with no cost-related access difficulties (New=77.71%) 61.3452.6872.0574.59 Percentage point change-1.76-1.24-0.17-0.08 Simulated Effects of Changes in Community Uninsurance and Charity Care on Assessment of Health Care System Providers
Conclusion/caveats Model predictions depend on: – the degree of market segmentation, – the amount of charity care (or below cost care), and – the 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.
Different 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. Punchline
Agent-based model (D Damianov, JA Pagán) 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).
Future Health 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!