The Transformation of Medical Care to Health

1. The Transformation of Medical Care to Health Lecture 3 Asst. Prof. Dr. İlker Daştan HEALTH ECONOMICS

2. Marginal and average productivity • We are moving on from the lifestyle effect • Production function for health • The characteristics of medical care as an input • Uncertainty of outcomes • Uncertainty in the choice of health interventions across doctors • Marginal productivity • Typical decreasing marginal productivity in a production process dH/dm < 0, where H=f(m) • First, increasing marginal productivity due to specialization, then decreasing • Eventually, average productivity (H/m) will also fall

3. Extensive margin • Marginal productivity of health care will fall as more resources are used • Iatrogenic (induced) illnesses => negative MP • One way to think • E.g. Breast cancer screening • Epidemiological studies can characterize risks per age and per other characteristics. True population ratio is f • Yield of positive cases per 1000 screenings depends on population and test accuracy • False negatives (miss the cancer) & false positives (no cancer in reality) • p –probability of detecting true cases: sensitivity • q –probability of false positives • Yield = f x p + (1-f) x q

4. Extensive margin • Extending the screening age limit from 50 to 40 • Marginal productivity declines as we extend: We may cause cancer if we extend the age to 20 • E.g. Back Surgery (65-80% of Americans will have serious lower back pain at some point in life) • 2% have surgery • Success rate varies across locations • Case selection methods vary. Some cases are clear: near unanimous surgery decision. For these patients success rates are high • Others may be due to doctor’s decision : By expanding the extensive margin this way, more surgeries could result in more back pain (marginal product of health care falls to negative)

5. Intensive (of use) margin • Population held constant, frequency increased • If women receive mammography too often or too infrequent, marginal productivity will fall • In summary, we gain a lot from medical care on average, but not a lot from a little more • We use health care to a point where marginal product is declining

6. Aggregate data comparisons (average productivity) • Cross-country or cross regional comparisons of health outcomes (necessarily broad measures like life expectancy) show that per capita income, per capita education, medical care use, and good health outcomes move together • Life expectancy, mortality, death rates for infants may depend more on infrastructure rather than medical care

7. Interrelation of factors • Per capita income- improved living conditions, sanitary water supply, safer roads, better nutrition, more purchasing power. • Higher per capita income <=> higher per capita education • Education=> better management of life, better utilization of medical resources • Better health => better utilization of school => higher income • Exception: for white males income and mortality are positively related (cross-state studies in the US) • Could be due to consumption of “bads” –but complicated

8. Statistics • Separating the effects is a challenge • Measuring how much medical care has been used is a challenge • Across countries: Convert local currency to dollars • But medical spending may not be a good proxy for medical care because of different systems such as reimbursement • Within country: Convert spending in time using CPI, still challenging to separate the effects • Hall and Jones (2007): health elasticity of medical spending (Figure 3.1) • Estimates of marginal productivity of medical care in increasing life expectancy • 0.3 means 10% increase in medical spending results in 3% increase in life expectancy • Remember the causes of teenage deaths: medical care is not too useful • Review Appendix

10. Marginal cost of saving a life and per life year saved • Figure 3.2: Shifts along extensive margin • High teenage and young adult numbers due to violence as cause of death

12. RAND HIS Experiment • Health producing effects of medical care • Randomized control trial –RAND HIS (RHIS) • Four cities, two rural sites, 20,000 person years of data, 3-5 years • Two purposes: 1) Measuring the effect of insurance (price), 2) Measuring the effect of medical care on health • Random assignment to health insurance plans • Some full coverage others with co-pay, varying the price of health care –not health care per se per person (unethical) • Price of medical care varied => utilization differences (low coverage group used 2/3 of medical care used by high coverage group) • Health outcome measures in RHIS • Activities of Daily Living, perceived physical and mental health • Sick-loss days • Modified physical for conditions health care is supposed to affect: weight, blood pressure… • All measurements led to a “health-status age,” a physiological measure

13. Results and Criticism • “Health-status” aged as expected • Not much difference due to usage reduction to 2/3 of low coverage group relative to full coverage group • Except: Better vision and lower blood pressure for low income full coverage group than partial coverage group • Almost all improvement in the full coverage group for persons with high health risks like obesity (%10 decrease in risk of dying) came from lower blood pressure • Conclusion: focus on reducing blood pressure with targeted investments, not free health care • Criticism: Too short a period • By the way, no changes in health habits were detected (medical care usage does not alter these habits, we don’t know what would) • In general mortality/life expectancy is not the best measure • It may distort the best use of medical resources • Improvements in mental health (except suicide) or otherwise quality of life (e.g. hip replacement surgery) need to be accounted for

14. Randomized Control Trials • Extending treatments to either extensive or intensive margins • Compared with “usual care,” uses fixed technology • Table 3.1 • Incremental CE ratios in 2008 dollars • Added cost of for using the intervention divided by the added life-years • Cost remains the same across cases (extended) • Ratios shows MP: high MP => low CE rate

16. Other approaches for understanding production of health • Dynamics (not fixed technology any more) • Associate per case spending with per case changes in health outcomes • Cutler et al. (1998) • Costs increase by $10,000 whereas life expectancy of heart attack victims increase by one year • Most change in both cost and health outcomes arise from technological change • Cutler et al. (2000) • 1950-1990 low birth infants • $40,000 increase and 12 years => CE = $3,300

17. Finer Outcome Measures • Quality of life v. length of life • The US Medicare population had about 5-8 times more invasive tests or heart surgery done within the first 30 days after the heart attack than Canadians (gap slightly narrowed in 180 days), mortality rates improved from 22.3% only to 21.4% a month later (but was similar a year later) • Marginal productivity was low • Revascularization interventions in the US were triple the rate of those in Canada and life quality was better (21% v 34% chest pain and 45% v 29% shortness of breath) (Mark et al., 1994)

18. Finer Outcome Measures • Incremental Costs / Incremental Effectiveness (ICER) • Quality-Adjusted Life-Year (QALY) • Disability-Adjusted Life-Years (DALYs) • Table 3.2: DALY disability weights • Figure 3.3: DALY age weighting function

21. Doctor disagreements • Disagreement seen in many countries • US, Norway, Canada, UK… • Mostly re: extensive margin (how many people should receive various treatments) • A series of studies showed this disagreement: • Fix the population in question and measure rate of hospitalization for a procedure, do not measure the rate of use in a region, because a large university hospital could attract many referrals as opposed to a rural hospital

22. Coefficient of Variation (COV) • Coefficient of Variation (σ/µ) is reported • Free of country of origin of data or medical activity • High COV  considerable disagreement about the marginal productivity of a type of medical care • Since populations are similar, disagreement is generally on the subgroup to receive treatment • Extensive margin • Example: Tonsillectomies differed a lot in UK • The absolute variation differs across studies, but relative variations are stable

23. Examples of disagreement in surgical procedures (Table 3.3) • Relative agreement on hernia repair • Although overall rates per 100,000 persons differ considerably across regions: 113 England, 282 US • Disagreements: • Removal of Tonsil and Adenoids • Removal of Hemorrhoid • Higher COVs in other studies re: hospitals • Within hospital dental extractions (0.73) • False labor (0.75)

25. More on variation • Variation also exists in hospitalizations of • Urinary tact infections • Chest pain • Bronchitis • Middle-ear infection • Skin biopsy • Pediatric hospitalizations (controlling for age-mix) • Back problems • Depression

26. Boston v New Haven • Harvard v Yale • Academic medicine –but still variation • Cities are similar in terms of age profile, income, non-white population etc. – • Boston had 55% more hospital beds per capita • 22% more employees per bed, paid 5% more • Age adjusted medical care use is higher in Boston –uniformly • Most variation in minor surgeries and medical diagnoses in which variations in admissions rates are high (see previous table)

27. More on variation • In some types of major surgery, New Haven had higher rates • See Table 3.4 • Also variation in admission rates other hospital market areas

29. Moving on to Physician-specific variation • Up until now… • Compare use of medical intervention across different regions • Must make sure differences in treatment rates are not due to differences in illnesses • Hence need large numbers in order to gain statistical confidence • In order to standardize age and gender differences an epidemiological method called “indirect standardization” is used

30. Direct Standardization • Regional (crude) death rate per 1000 persons for an age group * (Population of that age group in the US / Total US population) • Sum across age groups to obtain standardized death rate at a region

31. Indirect Standardization • Standard (crude) death rate per 1000 persons for an age group * Population of that age group in the Region • Sum across age groups to obtain Expected Deaths • Standardized mortality rate = SMR = • Total Deaths in Region / Expected Deaths

32. Standardizing Physician case mix • “No doctor in the US treats enough patients with a single disease to make comparisons meaningful” • Need to standardize doctors’ patient mix and severity of illness to estimate a doctor’s style • Too costly or not?

33. Physician style regression • Phelps et al. (1994): Data from Blue Choice HMO, expenses are authorized by PCP. • Individual annual medical care spending on all areas (excluding nursing homes) was regressed on • Case mix • Illness severity • Age, gender, demographic variables (representing patient mix) • Dummy for each physician • These fixed effects constituted physician styles (Figure 3.4) • A score of -0.1 means 10% less spending than average

35. Physician styles (profiling for cost-control) • Distributions of medical resource use (resource units billed by physicians) differed between states: Figure 3.5 (Oregon v Florida) • “Relative value was measured in relative-value units (RVUs), according to the resource-based relative-value scale used by Medicare in determining payments to physicians. The mean number of RVUs per admission was then adjusted for the physician's case mix according to the patients' assigned diagnosis-related groups.” • Attending physician is resident at that hospital • Mean 30 (~work of 30 routine office visits) v 46 • On average lowest cost 10% used half of the medical care resources than the highest cost 10% • Table 3.5

38. Disagreements re: intensive margin of use • Is Boston aggressive in extensive margin (hospitalize people) but not in intensive margin (the length of stay) • No relationship found so far • Coronary artery bypass (number of grafts unrelated to the overall rate) • Except: Roos et al. 1986: propensity to admit v length of stay had weak negative relationship • Marginal Productivity remains an open question