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Variation in Health Care Spending: Target Decision Making, Not Geography (2013)

Chapter: Appendix D: Regression Model Specifications with "Clusters" of Predictors

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Suggested Citation:"Appendix D: Regression Model Specifications with "Clusters" of Predictors." Institute of Medicine. 2013. Variation in Health Care Spending: Target Decision Making, Not Geography. Washington, DC: The National Academies Press. doi: 10.17226/18393.
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Appendix D

Regression Model Specifications with “Clusters” of Predictors

Cluster 1: Partial-year enrollment (PYE), year, pharmacy benefit, age, sex, age–sex interaction (all subcontractors)

Cluster 2: Health status (All subcontractors) – BASELINE MODEL

a) PYE, year, pharmacy benefit, age, sex, age–sex interaction

b) Health status

Cluster 3: Race (All subcontractors)

a) PYE, year, pharmacy benefit, age, sex, age–sex interaction

b) Race

Cluster 4: Income (All subcontractors)

a) PYE, year, pharmacy benefit, age, sex, age–sex interaction

b) Income

Cluster 5: All common beneficiary independent variables (all subcontractors)

a) PYE, year, pharmacy benefit, age, sex, age–sex interaction

b) Health status

c) Race

d) Income

Suggested Citation:"Appendix D: Regression Model Specifications with "Clusters" of Predictors." Institute of Medicine. 2013. Variation in Health Care Spending: Target Decision Making, Not Geography. Washington, DC: The National Academies Press. doi: 10.17226/18393.
×

Cluster 6: Employer/insurance (Lewin and Harvard only)

a) PYE, year, pharmacy benefit, age, sex, age–sex interaction

b) Benefit generosity and payer/plan type (Harvard and Lewin only)

c) Data source (Harvard only)

d) Plan size (Lewin only)

Cluster 7: Market (all subcontractors)

a) PYE, year, pharmacy benefit, age, sex, age–sex interaction

b) Access to care, payer mix, hospital competition, supply of medical services, % population uninsured, malpractice environmental risk (all)

c) Physician competition (Harvard and Acumen)

d) Health professional mix (Lewin and Acumen)

e) Percentage of Medicare beneficiaries with supplemental insurance

f) MA/Medicaid penetration (Acumen only)

Cluster 8: Kitchen sink variables (Subcontractors included all independent variables)

All subcontractors

a) PYE, year, pharmacy benefit, age, sex, age–sex interaction

b) Race

c) Income

d) Health status and comorbidity adjuster

e) Access to care

f) Payer mix

g) Hospital competition

h) Supply of medical services

i) % population uninsured

j) Malpractice environment risk

Harvard additionally adjusted for:

•   Health
behavior

•   Data source

•   Payer/plan type

•   Benefit generosity

•   Physician composition

Acumen additionally adjusted for:

•   Medicare and Medicaid dual eligibility status

•   Supplemental Medicare insurance

•   Fee-for-service or Medicare Advantage

•   Part D enrollment

Suggested Citation:"Appendix D: Regression Model Specifications with "Clusters" of Predictors." Institute of Medicine. 2013. Variation in Health Care Spending: Target Decision Making, Not Geography. Washington, DC: The National Academies Press. doi: 10.17226/18393.
×

•   Percentage of Medicare beneficiaries with supplemental insurance

•   MA/Medicaid penetration

•   Physician composition

•   Health professional mix

Lewin additionally adjusted for:

•   Payer/plan type

•   Benefit generosity

•   Plan size

•   Health professional mix

Cluster 9: All common independent variables only (all subcontractors)

a) PYE, year, pharmacy benefit, age, sex, age–sex interaction

b) Race

c) Income

d) Health status and comorbidity adjuster

e) Access to care

f) Payer mix

g) Hospital competition

h) Supply of medical services

i) % population uninsured

j) Malpractice environment risk

Cluster 10: Cluster 1 and Medicare only IVs (Acumen only)

a) PYE, year, pharmacy benefit, age, sex, age–sex interaction

b) Medicare and Medicaid dual eligibility status

c) Supplemental Medicare insurance

d) Part D enrollment

e) Fee for service or Medicare Advantage

Suggested Citation:"Appendix D: Regression Model Specifications with "Clusters" of Predictors." Institute of Medicine. 2013. Variation in Health Care Spending: Target Decision Making, Not Geography. Washington, DC: The National Academies Press. doi: 10.17226/18393.
×

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Suggested Citation:"Appendix D: Regression Model Specifications with "Clusters" of Predictors." Institute of Medicine. 2013. Variation in Health Care Spending: Target Decision Making, Not Geography. Washington, DC: The National Academies Press. doi: 10.17226/18393.
×
Page 137
Suggested Citation:"Appendix D: Regression Model Specifications with "Clusters" of Predictors." Institute of Medicine. 2013. Variation in Health Care Spending: Target Decision Making, Not Geography. Washington, DC: The National Academies Press. doi: 10.17226/18393.
×
Page 138
Suggested Citation:"Appendix D: Regression Model Specifications with "Clusters" of Predictors." Institute of Medicine. 2013. Variation in Health Care Spending: Target Decision Making, Not Geography. Washington, DC: The National Academies Press. doi: 10.17226/18393.
×
Page 139
Suggested Citation:"Appendix D: Regression Model Specifications with "Clusters" of Predictors." Institute of Medicine. 2013. Variation in Health Care Spending: Target Decision Making, Not Geography. Washington, DC: The National Academies Press. doi: 10.17226/18393.
×
Page 140
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Health care in the United States is more expensive than in other developed countries, costing $2.7 trillion in 2011, or 17.9 percent of the national gross domestic product. Increasing costs strain budgets at all levels of government and threaten the solvency of Medicare, the nation's largest health insurer. At the same time, despite advances in biomedical science, medicine, and public health, health care quality remains inconsistent. In fact, underuse, misuse, and overuse of various services often put patients in danger.

Many efforts to improve this situation are focused on Medicare, which mainly pays practitioners on a fee-for-service basis and hospitals on a diagnoses-related group basis, which is a fee for a group of services related to a particular diagnosis. Research has long shown that Medicare spending varies greatly in different regions of the country even when expenditures are adjusted for variation in the costs of doing business, meaning that certain regions have much higher volume and/or intensity of services than others. Further, regions that deliver more services do not appear to achieve better health outcomes than those that deliver less.

Variation in Health Care Spending investigates geographic variation in health care spending and quality for Medicare beneficiaries as well as other populations, and analyzes Medicare payment policies that could encourage high-value care. This report concludes that regional differences in Medicare and commercial health care spending and use are real and persist over time. Furthermore, there is much variation within geographic areas, no matter how broadly or narrowly these areas are defined. The report recommends against adoption of a geographically based value index for Medicare payments, because the majority of health care decisions are made at the provider or health care organization level, not by geographic units. Rather, to promote high value services from all providers, Medicare and Medicaid Services should continue to test payment reforms that offer incentives to providers to share clinical data, coordinate patient care, and assume some financial risk for the care of their patients.

Medicare covers more than 47 million Americans, including 39 million people age 65 and older and 8 million people with disabilities. Medicare payment reform has the potential to improve health, promote efficiency in the U.S. health care system, and reorient competition in the health care market around the value of services rather than the volume of services provided. The recommendations of Variation in Health Care Spending are designed to help Medicare and Medicaid Services encourage providers to efficiently manage the full range of care for their patients, thereby increasing the value of health care in the United States.

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