Better information, better health: the value of clinical laboratory innovation

1. Better information, better health:the value of clinical laboratory innovation Frank R. Lichtenberg Columbia University and National Bureau of Economic Research

2. Role of new goods in economic growth • Grossman and Helpman, Innovation and Growth in the Global Economy: “innovative goods are better than older products simply because they provide more ‘product services’ in relation to their cost of production.” • Bresnahan and Gordon, The Economics of New Goods: “New goods are at the heart of economic progress” • Bils: Measuring the Growth from Better and Better Goods, “Much of economic growth occurs through growth in quality as new models of consumer goods replace older, sometimes inferior, models.”

3. R&D intensity in 1997: Medical equipment and supplies industry vs. all industries Note: Percentages are total (Federal plus company and other) funds for industrial R&D performance in the U.S. as a percent of net sales of companies that performed industrial R&D in the U.S., 1997 Source: National Science Foundation/Division of Science Resources Statistics, Survey of Industrial Research and Development: 2000, http://www.nsf.gov/sbe/srs/nsf03318/pdf/taba19.pdf

4. Clinical laboratory innovations • Examine the impact of a subset of the new products generated by this industry—clinical laboratory products—on the longevity and quality of life of Americans. • FDA data indicate that, in the last decade, about 100 of these new products have been introduced. • I hypothesize that these new products have improved the quality of information physicians and patients have about patients’ medical conditions, and have therefore enabled more appropriate and effective treatment of those conditions.

5. Outline • Describe the general framework I will use to assess the impact on health of clinical laboratory innovations. • Explain how disease-specific measures of laboratory innovation can be constructed by combining FDA regulatory data with health insurance claims data. • Use these measures and Vital Statistics-Mortality Detail data to examine the impact of clinical laboratory innovation on longevity (age at time of death). • Use the laboratory innovation measures and data from the National Health Interview Survey to examine the impact of laboratory innovation on “quality of life” (activity limitations and disability days).

6. Difference-in-differences model HEALTHit = blab CUM_PRODit + gi Zit + ai + dt + eit HEALTHit= a measure of health outcomes associated with disease i in year t CUM_PRODit= an index of the cumulative number of laboratory products related to diagnosis i that have first appeared by year t Zit= a vector of other attributes of disease i in year t The fixed disease effects (ai‘s) control for any permanent between-disease differences in health determinants. The fixed year effects (dt‘s) control for changes over time in health determinants that are common across diseases. If the estimate of blab is positive and statistically significant, that indicates that there were above-average improvements in health outcomes of diseases with above-average increases in the cumulative number of clinical laboratory products.

7. Construction of disease-specific measures of laboratory innovation • A variety of clinical laboratory procedures are used to diagnose and treat people with a given disease. • I constructed an index of the cumulative number of products related to patients with diagnosis i that have first appeared by year t. • This index is based on all of the clinical laboratory procedures that could be linked (using Federal regulation numbers) to clinical laboratory products appearing in the FDA’s Premarket Notification Database.

8. Example: regulation 862.1170 chloride test systems CFR Title 21 Database PART 862 CLINICAL CHEMISTRY AND CLINICAL TOXICOLOGY DEVICES

9. Products regulated by CFR Title 21 862.1170 (chloride test systems) The number of products covered by this regulation increased from 3 in 1979 to 5 in 1990, and has remained constant since then.

10. Laboratory procedure coding specialists at the Laboratory Corporation of America ascertained that three diagnostic lab procedures (CPT codes)—82435 (Chloride; blood), 82436 (Chloride; urine), and 82438 (Chloride; other source)—involve the use of chloride test systems.

11. Suppose, for simplicity, that chloride test system procedures are used in connection with disease A but not in connection with disease B. • Then chloride test system innovation (as manifested in new products) is likely to benefit people with disease A, but not those with disease B. • In reality, chloride test system procedures may be used in connection with both diseases, but to different extents. • If chloride test system procedures account for twice as great a fraction of all clinical lab procedures for disease A as they do for disease B, then chloride test system innovation is likely to benefit people with disease A twice as much as it benefits those with disease B.

12. Estimation of SHAREip CUM_PRODit = ∑p SHAREip CUM_PRODpt Estimates of SHAREip were constructed from the MEDSTAT MarketScan Commercial Claims & Encounters and Medicare Supplemental Research Databases. The MarketScan databases capture person-specific clinical utilization, expenditures and enrollment across inpatient, outpatient, prescription drug, and carveout services from approximately 45 large employers, health plans, and government and public organizations.

13. In the year 2000, almost 14 million outpatient and inpatient claims were diagnostic lab claims; each of these claims included both a procedure code (usually a CPT code) and a diagnosis (ICD9) code. • I computed the frequency of diagnostic lab procedures, by (2-digit) diagnosis code. Let N_CLAIMip = the number of claims for lab procedure p associated with diagnosis i, and N_CLAIMi. = ∑p N_CLAIMip = the total number of claims for lab procedures associated with diagnosis i. Then SHAREip = N_CLAIMip / N_CLAIMi.

14. Leading clinical lab procedures: diabetes vs. hypertension

15. Relative utilization of procedures differs across diagnoses • The Glycated Hemoglobin Test is the procedure most frequently associated with a diabetes Dx, accounting for 18% of all procedures, but is not among the top ten procedures associated with a hypertension Dx. • Similarly, Assay of Thyroid Stimulating Hormone accounts for twice as large a fraction of procedures among hypertension patients as it does among diabetes patients.

16. Effect of laboratory innovation on longevity (age at time of death) AGE_MEASUREit = blab CUM_PRODit + ai + dt + eit AGE_MEASUREit = a measure (e.g., the mean) of the age distribution of deaths caused by disease i in year t

17. Controlling for pharmaceutical innovation AGE_MEASUREit = blab CUM_PRODit + bdrug CUM_DRUGit + ai + dt + eit CUM_DRUGit = the cumulative number of drugs launched to treat disease i in year t.

18. I estimated these equations using three alternative measures of the age distribution of deaths: • mean age at death • the fraction of deaths occurring before age 65 • the fraction of deaths occurring before age 75 • These measures were computed from the CDC’s Multiple Cause-of-Death Mortality Data Files for the years 1979-1998. Deaths during 1979-1998 were classified according to the ICD9 disease classification. Since 1999, deaths have been classified according to the ICD10 classification, causing a discontinuity in the data. Each file contains data on approximately 2 million deaths. • All equations were estimated by weighted least squares, weighting by the number of deaths caused by that disease in that year.

19. Implications Both laboratory and pharmaceutical innovation have increased longevity, but they have done so in different (complementary) ways. • Laboratory innovations have primarily reduced the risk of dying before the age of 65 • Pharmaceutical innovations have primarily extended the lives of older people.

20. Consistency with utilization data

21. We can calculate the effect of laboratory innovation on the overall increase in mean age at death between 1979 and 1998 by multiplying the estimate of blab in column 2 (0.336) by the difference between weighted (by number of deaths) mean CUM_PROD in 1979 and 1998. • This implies that laboratory innovation during the period 1979-1998 increased mean age at death by 0.44 years. This represents about 12% of the total increase in mean age at death (3.57 years) during the period.

22. Cost per life-year gained from laboratory innovation • There were 2.16 million deaths in the U.S. in 1998. Hence the laboratory innovation of the previous 20 years resulted in a gain of about 952,000 life-years (0.44 life-years/death * 2.16 million deaths) in 1998. • We would like to know the cost per life-year gained from laboratory innovation. This requires information about expenditure on new laboratory procedures (i.e. procedures that were not available in 1979). This information is not readily available. • However, we can calculate an upper bound on this, by using data on expenditure on all (new plus old) laboratory procedures. According to the Census Bureau, in 1998 the total revenue of medical and diagnostic laboratories (NAICS 6215) was $18.4 billion. • This implies that $19,340 (=$18.4 billion / 952,000 life-years) is an upper-bound estimate of the cost per life-year gained from laboratory innovation. • Even this figure is well below the cost-effectiveness thresholds proposed by most medical decision-makers. Moreover, if expenditure on new procedures accounts for half of total laboratory expenditure, then the cost per life-year gained from laboratory innovation is only half as great. • This is the average cost per life-year gained from all laboratory innovation. Undoubtedly some innovations have incurred higher costs, and others lower costs, per life-year gained.

23. Effect of laboratory innovation on activity limitations and disability days • Now I will analyze the effect of laboratory innovation on the quality of life, i.e. on the extent of disability and activity limitations in the U.S. population. • To do this, I will use data from an additional source, the National Health Interview Survey (NHIS). • The NHIS is the principal source of information on the health of the civilian noninstitutionalized population of the United States and is one of the major data collection programs of the National Center for Health Statistics (NCHS). • While the NHIS has been conducted continuously since 1957, the content of the survey has been updated about every 10-15 years. • The survey remained the same during the period 1982-1996. • During that period, it collected information about 1.2 million chronic and acute medical conditions afflicting about 1.6 million Americans.

24. I used these data to construct the following variables, for each (2-digit ICD9) condition in each of the years 1982-1996: • N_CASES: the number of times the condition appeared in the NHIS Condition File • LIMITED: the number of people whose ability to perform their usual activity was limited, mainly due to the condition • UNABLE: the number of people who were unable to perform their usual activity, mainly due to the condition • RADAYS: the aggregate number of restricted activity days caused by the condition in the 2 weeks preceding the interview • BDDAYS: the aggregate number of bed days caused by the condition in the 2 weeks preceding the interview • WLDAYS: the aggregate number of work-loss days caused by the condition in the 2 weeks preceding the interview

25. Model log(Yit) = blab CUM_PRODit + bdrug CUM_DRUGit + g Zit + ai + dt + eit where Y is one of the variables indicated above (e.g. N_CASES or RADAYS), and Z is a vector of demographic covariates (mean age, mean education, and percent male)

26. Model was estimated via weighted least-squares, with the weight equal to the mean (across years) value of Y for condition i, i.e. Yi. = (1/15) ∑t Yit. • I weight the observations by Yi. because measured percentage changes of Y are more reliable, the larger is Yi.. • In this model, blab may be interpreted as the percentage response of Y to one additional clinical laboratory product. • Suppose, for example that when Y = UNABLE, blab = -0.2. This would imply that one additional clinical laboratory product for a condition reduces the number of people who were unable to perform their usual activity, mainly due to the condition, by about 20%.

27. The coefficients on both CUM_PROD and CUM_DRUG in the log(N_CASES) equation are negative and statistically significant, consistent with the hypothesis that both laboratory and pharmaceutical innovation for a condition reduce the number of people who report that they have that condition at a given time. • This could be due, in part, to innovation-induced reductions in the duration of medical conditions. If condition incidence (the number of new cases) remains constant, but condition duration is reduced, then condition prevalence (the number of people who have a condition at a given time) is reduced.

28. During the period 1990-1996, CUM_PROD increased by about 0.033 per year, and CUM_DRUG increased by about 0.99 per year. • This implies that each kind of innovation • reduced the number of people who were limited in their ability to perform major activities by about 1.5% per year • reduced the number of people who were unable to perform major activities by about 2.1% per year

29. Assessing the benefits in 1996 • Assess the benefits in 1996, in terms of reduced activity limitations and disability days, of the laboratory innovation that occurred since the beginning of the sample period (1982) • Compute ratio of the counterfactual value of the dependent variable in 1996, had no laboratory innovation occurred during 1982-1996, to the actual value of the dependent variable in 1996. This is calculated as exp(0.462 * blab). • The estimates imply that, in the absence of any clinical laboratory innovation during 1982-1996, the number of people with limited ability to perform major activities in 1996 would have been 26% higher than it actually was, and the number of restricted-activity days in 1996 would have been 5% higher than it actually was.

30. The estimates imply that, in the absence of laboratory innovation during 1982-1996, the probability of being limited in one’s major activity would have been 2.6 percentage points higher in 1996—12.6% rather than 10.0%. • The probability of being unable to perform one’s major activity would have been 1.7 percentage points higher in 1996—6.4% rather than 4.7%. • Since the U.S. population was about 265 million in 1996, this means that 4.4 million (= 1.7% * 265 million) more people would have been unable to perform major activities. • The number of restricted-activity days per person per year would have been 0.8 days higher—15.3 days rather than 14.5 days.

31. Estimate that, in 1996, per capita expenditure on medical and diagnostic laboratory services was about $64. • Per capita expenditure on new medical and diagnostic laboratory services (those introduced since 1982) was probably much lower. Suppose that half of total expenditure on medical and diagnostic laboratory services was expenditure on new services, i.e. per capita expenditure on new laboratory services was $32. • By spending the hypothetical $32 per year on new laboratory services, the average person’s probability of being unable to perform a major activity is reduced by 1.7 percentage points. The net benefit of laboratory innovation is positive as long as the value of being able to perform a major activity is at least $1882 (= $32 / 1.7%). • In 1996, average annual employee compensation (including fringe benefits) was about $34,000/year. Moreover, reduced activity limitations increase the value of each hour of leisure time as well as the amount of time people can work.

32. Summary Longevity • Overall, both laboratory and pharmaceutical innovation have increased longevity, but they have done so in different (complementary) ways. • Laboratory innovations have primarily reduced the risk of dying before the age of 65. • Pharmaceutical innovations have primarily extended the lives of older people. • Evidence about the age distribution of laboratory procedure and pharmaceutical utilization was consistent with these findings. • Laboratory innovation is estimated to have increased mean age at death by 0.44 years during the period 1979-1998. • This represents about 12% of the total increase in mean age at death (3.57 years) during the period. • An upper-bound estimate of the cost per life-year gained from laboratory innovation is well below the cost-effectiveness thresholds proposed by most medical decision-makers.

33. Summary Activity limitations and disability days • Laboratory and pharmaceutical innovation both reduced activity limitations and disability days. • Both types of innovation were estimated to reduce the number of people who were unable to perform major activities by about 2.1% per year. • In the absence of laboratory innovation during 1982-1996, the probability of being unable to perform one’s major activity would have been 1.7 percentage points higher in 1996—6.4% rather than 4.7%. • The value of the reduction in disability attributable to laboratory innovation appears to exceed expenditure on new laboratory procedures by a substantial margin.