Prescription Drugs, Medical Care, and Health Outcomes: A Model of Elderly Health Dynamics Zhou Yang, Emory University Donna B. Gilleskie, Univ of North.

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Presentation transcript:

Prescription Drugs, Medical Care, and Health Outcomes: A Model of Elderly Health Dynamics Zhou Yang, Emory University Donna B. Gilleskie, Univ of North Carolina Edward C. Norton, Univ of Michigan June 23, 2008 ASHE

The Big Picture Prescription Drugs Physician Services, Hospitalization Health: Morbidity, Mortality Supplemental Insurance, Rx Coverage

Age Health Sudden death: “extreme” health shock but no functional decline Terminal Illness: good functional health then health shock and certain decline in function Frailty: no health shock(s) or serious chronic condition, but slow decline in function Entry-re-entry: chronic condition(s) associated with multiple health shocks and expected decline in function Typical Patterns of Health Decline among the Elderly JAMA 289(18), 2003

A Preview of our Main Findings A change from Medicare with no drug coverage to a plan that covers prescription drugs reveals that: Drug expenditures over 5 years increase between 7 and 27%. Survival rates increase 1-2%. But the distribution of functional status among survivors shifts toward worse health. Marginal survivors spend significantly more than individuals who would have survived anyway. There is some contemporaneous reallocation of consumption (a cross-price effect), but changes in consumption are largely driven by changes in health and survival as people age.

Model of behavior of individuals age 65+ I t, J t StSt A t, B t, D t E t+1, F t+1 beginning of age t beginning of age t+1 insurance and drug coverage health shock medical care demand health production Ω t = (E t, F t, A t-1, B t-1, D t-1, X t, Z I t, Z H t, Z M t ) Ω t+1 = (E t+1, F t+1, A t, B t, D t, X t+1, Z I t+1, Z H t+1, Z M t+1 ) And we model the set of structural equations jointly, allowing unobserved components to be correlated

Empirical Model I t, J t StSt A t, B t, D t E t+1, F t+1 beginning of t beginning of t+1 insurance and drug coverage health shock medical care demand health production Multinomial logit:  Medicare only (parts A and B) ( 8%)  Medicaid dual coverage(12%)  Private plan supplement(64%)  Medicare managed care plan (part C) (16%) Logit: Rx coverage (63%) (conditional on private or Part C plan)

Empirical Model I t, J t SktSkt A t, B t, D t E t+1, F t+1 beginning of t beginning of t+1 insurance and drug coverage health shock(s) medical care demand health production Separate logits:  Heart/stroke event (ICD ) in period t(24.5 %)  Respiratory event (ICD ) in period t( 4.8 %)  Cancer event (ICD ) in period t( 5.7 %)

Empirical Model I t, J t SktSkt A t, B t, D t E t+1, F t+1 beginning of t beginning of t+1 insurance and drug coverage health shock(s) medical care demand health production Separate logit for any use and OLS log expenditures conditional on any:  Hospital use and expenditures in period t (20 % and $13,057)  Physician service use and expenditures in period t (84 % and $2,013)  Prescription drug use and expenditures in period t (90 % and $980)

Empirical Model I t, J t SktSkt A t, B t, D t E k t+1, F t+1 beginning of t beginning of t+1 insurance and drug coverage health shock(s) medical care demand health: ever had chronic condition k, functional status Multinomial logit for functional status entering period t+1:  Not disabled(no ADL or IADLs) (58%)  Moderately disabled (IADL or <3 ADLs) (28%)  Severely disabled (3 or more ADLs) (10%)  Dead ( 5%) Indicator for having ever had a chronic condition entering period t+1:  Heart/stroke(47%)  Respiratory(15%)  Cancer(19%)  Diabetes(20%) E k t+1 = E k t + S k t

Unobserved Heterogeneity Specification Permanent: risk aversion or attitude toward medical care use Time-varying: unmodeled health shocks or natural rate of deterioration u e t = ρ e μ + ω e ν t + ε e t where u e t is the unobserved component for equation e decomposed into permanent heterogeneity factor μ with factor loading ρ e time-varying heterogeneity factor ν t with factor loading ω e iid component ε e t distributed N(0,σ 2 e ) for continuous equations and Extreme Value for dichotomous/polychotomous outcomes

Medicare Current Beneficiary Survey (MCBS) Sample Survey and Event files from Overlapping samples followed from 2 to 5 years Exclude individuals ever in a nursing home Attrition due to death and sample design Sample: 25,935 men and women; 76,321 person-year obs

Actual and Simulated Annual Mortality Rate, by Age

Actual and Simulated Prescription Drug Expenditures, by Age and Death

Actual and Simulated Physician Services Expenditures, by Age and Death

Actual and Simulated Hospital Expenditures, by Age and Death

Simulations Start everyone off with a particular type of health insurance – Medicare only – Dual coverage by Medicaid – Private supplement without Rx coverage – Private supplement with Rx coverage – Medicare managed care (part C) without Rx coverage – Medicare managed care (part C) with Rx coverage Simulate behavior for 5 years Examine expenditures and health outcomes over 5 years Examine expenditures of 5-year survivors

Five-year Simulations – with unobserved heterogeneity

Five-year Simulations – without unobserved heterogeneity

Five-year Simulations – with unobserved heterogeneity

Sole Survivors vs. Marginal Survivors Rx expenditures triple or quadruple } With increases here, too Increases in expenditures are 3.5 to 5.5 times larger

Take home message Methodologically, we have built and estimated a comprehensive dynamic model of health behavior of the elderly as they age. Substantively, our model allows us to examine the effects of health insurance extensions not simply on prescription drug use but also on other types of care, as well as the impacts of this altered demand on health outcomes and subsequent behavior over time. Recently, the paper was accepted by JHR and is available from the authors if you are interested in our other results or the model details. (

Five-year Simulations – with unobserved heterogeneity

Five-year Simulations – without unobserved heterogeneity

Unobserved Heterogeneity Distribution

Actual and Simulated Prescription Drug Use and Expenditures, by Age

Actual and Simulated Hospital Use and Expenditures, by Age

Actual and Simulated Physician Services Use and Expenditures, by Age

Features of our Empirical Model Suggested by Theory Supplemental insurance coverage is chosen at the beginning of the period before observing health shocks, but with knowledge of one’s functional status, chronic conditions, and, most importantly, unobserved individual characteristics entering the period.

Features of our Empirical Model Suggested by Theory Permanent and time-varying unobserved individual characteristics affect annual demand for all three types of medical care. Adverse selection

Features of our Empirical Model Suggested by Theory Health transitions are a function of medical care input allocations and health shocks during the year. (Grossman) Adverse selection Jointly estimated demand

Features of our Empirical Model Suggested by Theory Previous medical care use may alter the utility of medical care consumption today; hence, lagged use affects current expenditures directly as well as indirectly through health transitions. Adverse selection Jointly estimated demand Dynamic health production

Features of our Empirical Model Suggested by Theory Adverse selection Jointly estimated demand Dynamic health production Dynamic demand for medical care