The COVID-19 Pandemic and the $16 Trillion Virus Cutler, David M; Summers, Lawrence H
JAMA : the journal of the American Medical Association,
10/2020, Letnik:
324, Številka:
15
Journal Article
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The COVID-19 pandemic is expected to cost the United States about $16 trillion. These losses, which cover reduced output and health care costs, can be mitigated by increased investment in testing and ...contact tracing.
Various studies in the U.S. and Europe show that the overall effect of precision medicine centered on cancer treatment has been modest. From an economic standpoint, precision medicine has reduced the ...proportion of patients with a cancer type using each medication. How insurers and pharmaceutical firms have been clashing over coverage of expensive new therapies is also discussed.
As many as half of all patients do not adhere faithfully to their prescription-medication requirements, and the result is more than $100 billion spent each year on avoidable hospitalizations. David ...Cutler and Wendy Everett discuss how to improve medication adherence.
Poor adherence to treatment regimens has long been recognized as a substantial roadblock to achieving better outcomes for patients. Data show that as many as half of all patients do not adhere faithfully to their prescription-medication regimens — and the result is more than $100 billion spent each year on avoidable hospitalizations.
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Nonadherence to medication regimens also affects the quality and length of life; for example, it has been estimated that better adherence to antihypertensive treatment alone could prevent 89,000 premature deaths in the United States annually.
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What is less clear is why adherence to the 3.8 billion prescriptions written . . .
Using a variety of data sets from two countries, we examine possible explanations for the relationship between education and health behaviors, known as the education gradient. We show that income, ...health insurance, and family background can account for about 30 percent of the gradient. Knowledge and measures of cognitive ability explain an additional 30 percent. Social networks account for another 10 percent. Our proxies for discounting, risk aversion, or the value of future do not account for any of the education gradient, and neither do personality factors such as a sense of control of oneself or over one's life.
Using Eurobarometer data, we document large variation across European countries in education gradients in income, self-reported health, life satisfaction, obesity, smoking and drinking. While this ...variation has been documented previously, the reasons why the effect of education on income, health and health behaviors varies is not well understood. We build on previous literature documenting that cohorts graduating in bad times have lower wages and poorer health for many years after graduation, compared to those graduating in good times. We investigate whether more educated individuals suffer smaller income and health losses as a result of poor labor market conditions upon labor market entry. We confirm that a higher unemployment rate at graduation is associated with lower income, lower life satisfaction, greater obesity, more smoking and drinking later in life. Further, education plays a protective role for these outcomes, especially when unemployment rates are high: the losses associated with poor labor market outcomes are substantially lower for more educated individuals. Variation in unemployment rates upon graduation can potentially explain a large fraction of the variance in gradients across different countries.
•High unemployment rates at graduation permanently lower income and health.•Education plays a protective role when unemployment rates are high.•Overall losses are smaller for more educated individuals.•Labor market conditions partially explain the variance in education gradients.
Underserved populations experience higher levels of pain. These disparities persist even after controlling for the objective severity of diseases like osteoarthritis, as graded by human physicians ...using medical images, raising the possibility that underserved patients' pain stems from factors external to the knee, such as stress. Here we use a deep learning approach to measure the severity of osteoarthritis, by using knee X-rays to predict patients' experienced pain. We show that this approach dramatically reduces unexplained racial disparities in pain. Relative to standard measures of severity graded by radiologists, which accounted for only 9% (95% confidence interval (CI), 3-16%) of racial disparities in pain, algorithmic predictions accounted for 43% of disparities, or 4.7× more (95% CI, 3.2-11.8×), with similar results for lower-income and less-educated patients. This suggests that much of underserved patients' pain stems from factors within the knee not reflected in standard radiographic measures of severity. We show that the algorithm's ability to reduce unexplained disparities is rooted in the racial and socioeconomic diversity of the training set. Because algorithmic severity measures better capture underserved patients' pain, and severity measures influence treatment decisions, algorithmic predictions could potentially redress disparities in access to treatments like arthroplasty.
A key question about aggregating payments to health care providers is determining whether to bundle services for individual, if extended, episodes of care or for whole patients. What are the ...tradeoffs of the two approaches, and how much money could be saved?
In the quest to manage the spiraling cost of U.S. health care, one approach has generated great interest. The philosophy behind much current policy — including the Affordable Care Act (ACA) — is that aggregating fee-for-service reimbursement into payments for broader bundles of care will lead to greater efficiency in the provision of care and thus lower costs.
Under the accountable care organization model, perhaps the best-known example of this strategy, medical reimbursements are aggregated to the person-year level. Other programs aggregate reimbursement for episodes of care — for example, care for a particular cardiovascular or orthopedic condition. The Episode . . .
IMPORTANCE: More patients with cancer use hospice currently than ever before, but there are indications that care intensity outside of hospice is increasing, and length of hospice stay decreasing. ...Uncertainties regarding how hospice affects health care utilization and costs have hampered efforts to promote it. OBJECTIVE: To compare utilization and costs of health care for patients with poor-prognosis cancers enrolled in hospice vs similar patients without hospice care. DESIGN, SETTING, AND PARTICIPANTS: Matched cohort study of patients in hospice and nonhospice care using a nationally representative 20% sample of Medicare fee-for-service beneficiaries who died in 2011. Patients with poor-prognosis cancers (eg, brain, pancreatic, metastatic malignancies) enrolled in hospice before death were matched to similar patients who died without hospice care. EXPOSURES: Period between hospice enrollment and death for hospice beneficiaries, and the equivalent period of nonhospice care before death for matched nonhospice patients. MAIN OUTCOMES AND MEASURES: Health care utilization including hospitalizations and procedures, place of death, cost trajectories before and after hospice start, and cumulative costs, all during the last year of life. RESULTS: Among 86 851 patients with poor-prognosis cancers, median time from first poor-prognosis diagnosis to death was 13 months (interquartile range IQR, 3-34), and 51 924 patients (60%) entered hospice before death. Matching yielded a cohort balanced on age, sex, region, time from poor-prognosis diagnosis to death, and baseline care utilization, with 18 165 patients in the hospice group and 18 165 in the nonhospice group. After matching, 11% of nonhospice and 1% of hospice beneficiaries who had cancer-directed therapy after exposure were excluded. Median hospice duration was 11 days. After exposure, nonhospice beneficiaries had significantly more hospitalizations (65% 95% CI, 64%-66%, vs hospice with 42% 95% CI, 42%-43%; risk ratio, 1.5 95% CI, 1.5-1.6), intensive care (36% 95% CI, 35%-37%, vs hospice with 15% 95% CI, 14%-15%; risk ratio, 2.4 95% CI, 2.3-2.5), and invasive procedures (51% 95% CI, 50%-52%, vs hospice with 27% 95% CI, 26%-27%; risk ratio, 1.9 95% CI, 1.9-2.0), largely for acute conditions not directly related to cancer; and 74% (95% CI, 74%-75%) of nonhospice beneficiaries died in hospitals and nursing facilities compared with 14% (95% CI, 14%-15%) of hospice beneficiaries. Costs for hospice and nonhospice beneficiaries were not significantly different at baseline, but diverged after hospice start. Total costs over the last year of life were $71 517 (95% CI, $70 543-72 490) for nonhospice and $62 819 (95% CI, $62 082-63 557) for hospice, a statistically significant difference of $8697 (95% CI, $7560-$9835). CONCLUSIONS AND RELEVANCE: In this sample of Medicare fee-for-service beneficiaries with poor-prognosis cancer, those receiving hospice care vs not (control), had significantly lower rates of hospitalization, intensive care unit admission, and invasive procedures at the end of life, along with significantly lower total costs during the last year of life.
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