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Causal Effect of Managed Care on Health Care Quality: Evidence from Cancer Screening Guideline Discontinuities Srikanth Kadiyala* Grant Miller** Harvard University Funding: *Sloan Foundation, **NIH.
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Causal Effect of Managed Care on Health Care Quality: Evidence from Cancer Screening Guideline DiscontinuitiesSrikanth Kadiyala*Grant Miller**Harvard UniversityFunding: *Sloan Foundation, **NIH
Dr. Sandy MacColl [one of the founders of GHC] wrote that he and his colleagues sought a “system of family care…directed towards a goal of good care, health maintenance and preventive services” Crowley,To serve the greatest number: A History of the GHC of Puget Sound
Managed Care • Held Great Promise for Quality Improvements • Lower Cost • Appropriate Use of Medical Care • Conventional View is that it has Failed • We Contend Jury is Still Out
Previous Research • Randomized Control Trial • Rand HI experiment (late 1970s) • Cross-Sectional Studies • Selection problem since assignment to insurance type is NOT random • Control for observables • Findings Equivocal
New Empirical Strategy Discontinuity design using age-specific preventive service guidelines • Within plan comparisons of preventive service use across guideline thresholds difference out selection effects • Guidelines are “bright lines”-No discrete increase in cancer risk at these ages
Cancer Screenings Recommendations • U.S Preventive Task Force (USPSTF) and American Cancer Society (ACS) • Colorectal Cancer • USPSTF & ACS – Both recommend screening for individuals age 40+ • No recommendation on screening technology • Breast Cancer • ACS-Recommended mammography for women ages 40+ since early 1980s • USPSTF-Recently switched to 40+, previously 50+ • Thus we look for changes over both the 40 and 50 year thresholds • Prostate Cancer • USPSTF-Does not recommend PSA • ACS-Physicians should offer PSA • Screening is Recommended for these diseases ONLY for asymptomatic people above a certain age • IOM/ Quality Chasm report: Cancer Screenings UNDERUSED
Natural Experiment Framework Managed Care FFS Pre- guideline Post Guideline Pre- guideline Post Guideline 49 49 50 50 Difference-In-Difference-In-Difference = [ (D-B)-(C-A) ] – [(H-F)-(G-E)]
Regression Discontinuity FFS Managed Care Post Guideline Post Guideline 49 49 50 50 • Diff.-In-Diff.=[(D-B)] – [(H-F)] • -This assumes that [(G-E)-(C-A)] is zero, which is a • plausible assumption
Data • National Health Interview Survey(NHIS): • National Sample of Individuals • Breast Cancer (N=6807,Years 1998-2000) • Colorectal Cancer (N=3426,Year 2000) • Prostate Cancer(N=1543,Year 2000) • Insurance Plan Types • Group/Staff Models, IPA, POS, PPO, Fee-For-Service(FFS) • Rich Set of Covariates • Income, Education, Race, Region, Marital Status • Also MarketScan Data 1997-2001(these results not reported)
Colorectal Cancer: Any Screening in Last Year by Plan and Age NHIS Data-Year 2000
Breast Cancer: Mammogram Use in Last Year by Plan and Age NHIS Data: 1998-2000
Breast Cancer: Mammogram Use in Last Year by Plan and Age NHIS Data: 1998-2000
Prostate CancerPSA Test Use in Last Yearby Plan and Age NHIS DATA: Year 2000
Regression Discontinuity Estimate using Colorectal Cancer: Means by Plan and Age Group
Regression Estimates of Screening Use Standard Errors in parantheses. Bold indicates point estimate is significant at the 5% level. Italics means significant at the 10% level. Regression models adjust for age,sex,race, education, income,marital status, region and time where appropriate.
Results from Cross-Section Regressions Standard Errors in parantheses. Bold indicates point estimate is significant at the 5% level. Regression models adjust for age,sex,race, education, income,marital status, region and time where appropriate.
Interpretation of Results • Change in Use across Age thresholds generally larger in Managed Care Plans • Large statistically significant differences for Colorectal and Breast Cancer screenings • No Statistically Significant differences for Prostate Cancer Screening • Strongest Results for the Group/Staff Managed Care Models
Supply or Demand • Survey data indicates individuals don’t know the right age cutoffs • We know whether people were offered screening services in the 2000 NHIS data • Using the same framework as above we find large statistically significant changes in Offer rates across the relevant age thresholds • This indicates that supply side responses drive changes in use over the age thresholds.
Future Work • How does Managed Care do it? • Plan Characteristics • Health Effects • Other treatments with Age Thresholds • Ex. Cholesterol Screening