Effects of Extreme Temperatures From Climate Change on the Medicare Population: Preliminary Results

Effects of Extreme Temperatures From Climate Change on the Medicare Population: Preliminary Results

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  Presentation at theAssociation of Environmental and Resource Economists Summer Conference May 31, 2024 Jared Jageler, David Adler, Noelia Duchovny, and Evan Herrnstadt Microeconomic Studies Division and Health Analysis Division Effects of Extreme Temperatures From Climate Change on the Medicare Population: Preliminary Results For information about the conference, see www.aere.org/summer.
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  1 The information inthis presentation is preliminary and is being circulated to stimulate discussion and critical comment. Climate change is expected to increase average temperatures and affect the frequency of extremely hot and cold days. The net effects of those changing temperatures on health and federal health care spending are uncertain: ▪ Extreme heat and cold are associated with a range of negative health effects. ▪ Spending and utilization could increase or decrease depending on how health effects and behavior respond to extreme temperatures. ▪ Mortality could increase (because of more hot days) or decrease (because of fewer cold days). Those effects may be pronounced among elderly populations, with implications for the federal budget related to Medicare enrollees and spending. Introduction
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  2 Adaptation refers tohow areas with different climates respond to increasing temperatures differently, such as by installing more air conditioning. The Congressional Budget Office uses a complete nationwide data set of Medicare beneficiaries to examine health care utilization and spending as well as mortality. In particular, CBO investigates these questions: ▪ What is the effect of extreme temperatures on emergency department (ED) visits, ED spending, and mortality among Medicare beneficiaries? ▪ How will those outcomes be affected by projected temperatures resulting from climate change, after incorporating the effects of adaptation? This research is preliminary, and the results are subject to change. Research Questions
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  3 ▪ Nationwide studieson the effects of climate change on mortality – Heutel, Miller, and Molitor (2021) – Carleton and others (2022) ▪ Other recent research on health care utilization (ED visits) and mortality in California hospitals: – White (2017) – Gould and others (2024) ▪ A study estimating the effects of exposure to particulate pollution on similar outcomes in the Medicare population – Deryugina and others (2019) Related Research
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  4 Overall, CBO findsa daily weighted average of 11 deaths, 156 ED visits, and $388,000 in spending per 100,000 Medicare enrollees. ED = emergency department. Overview: Econometric Estimates Very hot days cause more ED visits and higher ED spending. By contrast, very cold days cause a reduction in ED visits and a smaller reduction in ED spending. Mortality is higher on both very hot and very cold days. The current estimates show the effect of weather only on outcomes for the same day and the following two days, which could exclude some delayed and compensatory effects. CBO plans to explore longer windows in future versions of this analysis, which could change the results.
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  5 Daily average temperatureis the simple mean of daily minimum and maximum temperatures. Medicare Outcomes ▪ Daily counts of ED visits and spending by zip code (2000 to 2019) for fee-for- service (FFS) enrollees – Also disaggregated to inpatient (IP) and outpatient (OP) visits and spending; IP involves an overnight hospital stay, but OP does not ▪ Daily deaths by zip code per 100,000 enrollees (2000 to 2019) – Includes FFS and Medicare Advantage Weather ▪ Daily average temperature and precipitation from the National Oceanic and Atmospheric Administration’s nClimGrid-Daily (2000 to 2019) ▪ The National Aeronautics and Space Administration’s Earth Exchange Global Daily Downscaled Projections (through 2099) – 25-kilometer x 25-kilometer grid – Daily projections of average temperature ▪ Both are matched to zip code tabulation areas (ZCTAs) and weighted by inverse distance to ZCTA centroid Medicare and Climate Data
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  6 Medicare data areaggregated to the zip code and temperature data to the ZCTA. Those levels are nearly identical, but CBO performs a crosswalk between the two to stay at the ZCTA level. 𝑦𝑖𝑠𝑡𝑦 = ෍ 𝑏 𝛽𝑏𝑡𝑒𝑚𝑝𝑏𝑖𝑠𝑡𝑦 + 𝛾𝑥𝑖𝑡 + 𝛼𝑖𝑑 + 𝛿𝑠𝑦 + 𝜖𝑖𝑠𝑡𝑦 ▪ yisty​ is the three-day sum of the health outcome (ED visits or ED spending per 100,000 Medicare FFS enrollees, or mortality per 100,000 Medicare enrollees) in ZCTA 𝑖, in state s, on date 𝑡, in year 𝑦. ▪ 𝛽𝑏 are coefficients of interest. ▪ 𝑡𝑒𝑚𝑝𝑏𝑖𝑠𝑡𝑦 are the daily average 5 degrees Fahrenheit temperature bins from 0 to 100 (60 to 65 degrees Fahrenheit is the omitted category), where 𝑏 indexes the bins. ▪ 𝑥𝑖𝑡 is a vector of controls for daily precipitation and fully interacted two- and seven-day temperature lags plus leads. ▪ 𝛼𝑖𝑑 and 𝛿𝑠𝑦 are ZCTA-day-of-year and state-year fixed effects. Each observation is weighted by ZCTA Medicare enrollment. Standard errors are clustered by ZCTA. Estimating Homogeneous Effects of Temperature Bins
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  7 ED = emergencydepartment; IP = inpatient; OP = outpatient; ZCTA = zip code tabulation area. Change in Three-Day Emergency Department Visits per 100,000 Fee-for-Service Medicare Enrollees All ED visits increase with higher-temperature days. ED visits decrease with cooler temperatures in the short term. OP visits drive that effect, because IP visits are more complex and therefore more unavoidable.
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  8 ED = emergencydepartment; IP = inpatient; OP = outpatient; ZCTA = zip code tabulation area. Change in Three-Day Emergency Department Spending per 100,000 Fee-for-Service Medicare Enrollees ED spending generally increases with higher temperatures. The increase is greater for IP visits than for OP visits, which is consistent with the greater complexity and cost of IP visits (which therefore result in a hospital stay). On cold days, an increase in IP spending drives an overall increase in ED spending. OP spending decreases slightly on cold days, suggesting that the decrease in ED visits may have been concentrated in lower-cost visits.
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  9 CDDs = coolingdegree days; ED = emergency department. Using Differences in Local Average Climate to Account for Adaptation Extreme temperatures do not affect all areas equally because of different levels of adaptation: For example, a daily temperature of 90 degrees Fahrenheit is likely to be more harmful in Minneapolis than in Dallas. CBO captures that difference by allowing the effect of each temperature bin to differ on the basis of a ZCTA’s average climate. CBO computes the average annual cooling degree days (CDDs) for each ZCTA and estimates a separate linear spline in CDDs for each temperature bin. Annual CDDs capture how often and by how much the daily temperature surpassed 65 degrees Fahrenheit.
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  10 ED = emergencydepartment. Change in Three-Day Inpatient and Outpatient Emergency Department Visits for Three Illustrative Cities Despite the overall pattern of fewer ED visits on cold days, places with warmer climates experience more ED visits when the temperature is below 20 degrees Fahrenheit. Places with mild and cold climates experience more ED visits on hot days.
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  11 ED = emergencydepartment. Change in Three-Day Inpatient and Outpatient Emergency Department Spending for Three Illustrative Cities Spending increases substantially at the extremes, with significant heterogeneity by climate.
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  12 CBO takes theunweighted average of NEX-GDDP daily downscaled projections from models reporting a full year of projections. Dallas’s CDDs in 2075 are estimated to be 3300, exceeding the limits of this figure. CDDs = cooling degree days; ED = emergency department; SSP = shared socioeconomic pathway. Projecting the Effects of Climate Change CBO computes the projected change in the future temperature distribution relative to the 2015–2019 average and applies that change to the agency’s historical weather data. To account for adaptation, CBO allows a ZCTA’s temperature response to vary in the future on the basis of its projected CDDs using spline estimates. For example, Minneapolis is gradually allowed to have a temperature response by 2075 that approaches the response of Washington, DC, today. CBO’s central climate scenario is SSP2-4.5, but the agency also presents cases with low (SSP1-2.6) and high (SSP3-7.0) emissions and warming.
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  13 The figure showsthe change in IP and OP visits relative to the 2015–2019 average. ED = emergency department; SSP = shared socioeconomic pathway. Projected Change in Annual Emergency Department Visits per 100,000 Medicare Enrollees Average annual ED visits per 100,000 enrollees from 2030 to 2075 are projected to increase by an average of 250 under SSP2-4.5. Under alternative scenarios, those visits are projected to increase by 175 (SSP1-2.6) to 285 (SSP3-7.0). Without adaptation, average annual ED visits are projected to increase by 415 per 100,000 enrollees.
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  14 The figure showsthe change in IP and OP spending relative to the 2015–2019 average. SSP = shared socioeconomic pathway. Projected Change in Annual Emergency Department Spending per 100,000 Medicare Enrollees Average annual ED spending per 100,000 enrollees from 2030 to 2075 is projected to decrease by an average of $750,000 under SSP2-4.5. The projected decrease in ED spending is driven by a decrease of $870,000 in OP spending that is partially offset by an increase of $120,000 in IP spending. Under alternative scenarios, average annual ED spending per 100,000 enrollees is projected to decrease by $510,000 (SSP1-2.6) to $810,000 (SSP3-7.0). Without adaptation, average annual ED spending (IP and OP) is projected to increase by $48,000 per 100,000 enrollees.
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  15 Total cumulative changesare based on Medicare enrollee projections and on the sum of five-year incremental estimates from 2030 to 2075. ▪ Average annual ED visits (IP and OP) are projected to increase by 235,000 per year from 2030 to 2075. – About 1 percent of total 2019 ED visits ▪ Cumulative changes in ED spending (IP and OP) through 2075 are projected to decrease by roughly $7 billion. – An increase of about $1.1 billion in OP spending and a decrease of about $8.1 billion in IP spending – An average annual decrease of $700 million, or about 1 percent of total 2019 ED spending ▪ Cumulative mortality through 2075 is projected to decrease by roughly 320,000. – An average annual decrease of about 32,000, or about 2 percent of total 2019 Medicare mortality Preliminary Projected Outcome Summary for SSP2-4.5, 2030 to 2075
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  16 ▪ Extend theoutcome observation window to a 28-day sum ▪ Examine differences in mortality effects by subgroups: – Age – Race – Dual enrollees (Medicare and Medicaid) – FFS vs. Medicare Advantage ▪ Perform sensitivity analyses of adaptation: – Medicare enrollment over time – Alternate spline specifications – Delayed adaptation in projections Next Steps
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  17 Tamma Carleton andothers, “Valuing the Global Mortality Consequences of Climate Change Accounting for Adaptation Costs and Benefits,” Quarterly Journal of Economics, vol. 137, no. 4 (November 2022), pp. 2037–2105, https://doi.org/10.1093/qje/qjac020. Tatyana Deryugina and others, “The Mortality and Medical Costs of Air Pollution: Evidence From Changes in Wind Direction,” American Economic Review, vol. 109, no. 12 (December 2019), pp. 4178–4219, https://doi.org/10.1257/aer.20180279. Carlos F. Gould and others, Temperature Extremes Impact Mortality and Morbidity Differently, NBER Working Paper 32195 (National Bureau of Economic Research, March 2024), pp. 4178–4219, https://doi.org/10.3386/w32195. Garth Heutel, Nolan H. Miller, and David Molitor, “Adaptation and the Mortality Effects of Temperature Across U.S. Climate Regions,” Review of Economics and Statistics, vol. 103, no. 4 (October 2021), pp. 740–753, https://doi.org/10.1162/rest_a_00936. Corey White, “The Dynamic Relationship Between Temperature and Morbidity,” Journal of the Association of Environmental and Resource Economists, vol. 4, no. 4 (December 2017), https://doi.org/10.1086/692098. References