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Does Health Insurance Affect Health? Evidence of Medicare’s Impact on Cancer Outcomes Srikanth Kadiyala, Ph.D. RAND Erin Strumpf, Ph.D. McGill University.

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Presentation on theme: "Does Health Insurance Affect Health? Evidence of Medicare’s Impact on Cancer Outcomes Srikanth Kadiyala, Ph.D. RAND Erin Strumpf, Ph.D. McGill University."— Presentation transcript:

1 Does Health Insurance Affect Health? Evidence of Medicare’s Impact on Cancer Outcomes Srikanth Kadiyala, Ph.D. RAND Erin Strumpf, Ph.D. McGill University McGill Institute for Health and Social Policy March 27, Preliminary results: please do not cite Draft available at:

2 Health Insurance and Health Does health insurance affect health outcomes? –Lower price paid for medical care –Increased access to care –Higher quantity (quality?) of care consumed –If marginal health benefits from increased care are positive, then we expect an effect on health

3 Measuring the Impacts Empirically, identifying effects of health insurance on health is complicated –Many contexts have little to no variation in health insurance status (national HI systems) –In the U.S., insurance status is endogenous Are the risk-averse and healthy more likely to be insured? Or are the sick more likely? –If the benefits from marginal health care are small, detection of any health benefit is difficult

4 How do we study this question? –Experimental designs are rare RAND HIE (1970s), Oregon HIE (ongoing) –Descriptive studies lacking identification are plentiful –Better evidence from quasi-experimental studies* Find that insurance reduces mortality and improves health status Most focus on acute conditions: heart attack, stroke, car accidents, pregnancies –Suggests effects result from better treatment conditional on diagnosis Some examine impact of insurance on differences by race, education, or previous insurance status * Currie and Gruber 1996, Decker and Rappaport 2002, Decker 2005, Doyle 2005, Polsky et al 2006, Card et al 2009, McWilliams et al 2009

5 Our Research Question Does Medicare affect cancer health outcomes? –Approximately 12% of the U.S. population is uninsured at ages (~4 million)* –At age 65 nearly everyone becomes eligible for Medicare What is the effect of Medicare coverage on cancer detection? –Cancer is the 2 nd leading cause of death in the U.S. –Medicare accounts for 45% of all spending on cancer treatment** –Cancer care is ~10% of Medicare spending *Kaiser Family Foundation, Health Insurance Coverage for Older Adults, May 2009 Birnbaum and Patchias 2008 estimate 5% of the age 65+ population is ineligible for Medicare **Cancer Action Network, ACS, Cancer and Medicare Chartbook 2009

6 Mechanisms: Detection vs. Treatment Cancer is not necessarily a symptomatic condition –Does any effect of health insurance on health work via disease detection? –The fact that it’s not acute or symptomatic might make it more possible to identify differential effects for the uninsured Medicare reduces the price of both screening tests and physician visits –Screening rates and physician visits increase at age 65* *Lichtenberg 2002, McWilliams et al 2003, Ward et al 2007, Card et al 2008

7 Cancer Detection Data U.S. Surveillance Epidemiology and End Results (SEER) database, –Cancer detection from 25% of the U.S. population (12 states) CA, CT, GA, HI, IA, KY, LA, MI, NJ, NM, UT, WA –Detailed information on staging, size of the tumor and other measures of cancer severity Behavioral Risk Factor Surveillance System (BRFSS), –Provides covariate data at year*age level

8 Methodology Regression Discontinuity Design –Uses the discontinuity in insurance status at age 65 and compares cancer detection rates on either side of this age threshold Assumes: –Smoothness in other determinants of cancer detection across the cutoff Education, marital status, employment, etc –In the absence of treatment (insurance), smoothness in the outcomes (detection) Cancer risk or unobserved true incidence is smooth

9 Analysis Graphical Evidence Estimate the magnitude of the discontinuity using regression –Cancer detection = α + β 1 (Mcare) + β 2 (Mcare*a-65) + β 3 (1-Mcare*a-65) + … + ε –Adjust for age, age 2 and age 3 ; sex, race, education, income, marital status, employment at year*year-of- age level from BRFSS Examine cancers with screening tests separately from those that do not –Breast, colorectal, prostate, cervical (BCPC) Assess heterogeneous impacts by pre- Medicare insurance status

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11 Impact of Medicare on All Cancer Detection OLS Poisson Medicare Cutoff101.5***108.4***92.9*** Age Trend Above75.8*** Age Trend Below87.4***85.86***179.0 % Increase relative to Detection rate at age %6.8%5.9% Covariates Controls Included NoYes N77 N=77, * p<=.05, ** p<=.01, *** p<=.001; Data: U.S. SEER (12 states); Controls: sex, race, income quartiles, education (4), marital status (5), employment. Cutoff coefficient is robust to adding age 2 and age 3. Poisson model includes age 2 and age 3.

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14 Effect of Medicare on BCPC Detection OLS Poisson Medicare Cutoff65.7***70.3***63.2*** Age Trend Above21.4***22.8***192.3 Age Trend Below40.2***41.9***206.6 % Increase relative to detection rate at %9.6%8.7% Covariates Controls Included NoYes N77 * p<=.05, ** p<=.01, *** p<=.001; Data: U.S. SEER (12 states); Controls: sex, race, income quartiles, education (4), marital status (5), employment. Cutoff coefficient is robust to adding age 2 and age 3. Poisson model includes age 2 and age 3.

15 Effect of Medicare on Non-BCPC Cancer Detection OLS Poisson Medicare Cutoff35.8***38.0***29.7** Age Trend Above54.4***53.0***-31.8 Age Trend Below47.2***44.0***-17.8 % Increase relative to detection rate at age %4.5%3.5% Covariates Controls Included NoYes N77 * p<=.05, ** p<=.01, *** p<=.001; Data: U.S. SEER (12 states); Controls: sex, race, income quartiles, education (4), marital status (5), employment. OLS cutoff coefficient is robust to adding age 2 and age 3. Poisson model includes age 2 and age 3.

16 Stage at Detection for BCP Cancers 80% of newly detected breast cancer cases are at local or regional stages 65% of newly detected CRC cases are at regional or distant stages 81% of newly detected prostate cancer cases are at the local stage About 45% of newly detected cases for these three cancers are at “treatable” stages* where we expect diagnosis to lead to significant health benefits –Conservative estimate based on concerns about over-diagnosis and treatment * Local and regional for breast; in-situ, local and regional for colorectal; regional for prostate

17 Initial Conclusions Medicare increases the cancer detection rate by a substantial amount Medicare’s effects on cancer detection are larger for cancers with recommended screening tests –Breast cancer detection rate: 6% increase –Colorectal and prostate: 9% increases –No impact on cervical cancer detection Impacts for non-screening cancers as well An important share of newly detected cases are treatable and health improvements are likely

18 Effects by Pre-Medicare Insurance Status We expect differential detection effects for those uninsured pre-Medicare –Large change in insurance status –But quality of insurance may change for those previously insured (ie, screening mandates) SEER does not include insurance status

19 Insured vs. Uninsured Analysis State Insurance Rates –Variation in insurance rates at age 64 by state, % in Louisiana, 92% Michigan –Correlate RD estimates of the increase in insurance coverage from age 64 to 65 with RD estimates of the change in cancer detection –Control for state and time fixed effects

20 Correlation of RD Estimates

21 Detection Linked to Changes in Insurance Status State-level variation in health insurance discontinuities due to Medicare can explain 37-71% of the state-level variation in BCPC detection due to Medicare We did not identify a similar relationship with respect to Non-BCPC detection Suggests that the extensive margin is important, but it’s possible that quality of insurance coverage also plays a role

22 Conclusions Health insurance plays a role in improved health in the context of chronic, latent disease Medicare increases the cancer detection rate by 6.4%, about 100 cancers per 100,000 individuals Larger detection effects for screening cancers, but also effects for non-screening The increase in insurance rates at age 65 can account for a significant share of the increase in screening cancer detection


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