Posts tagged with "MIT Sloan"

Covid and health illustration

Environmental Effects × COVID-19

MIT Sloan School of Management study shows potential long-term environment effects from COVID-19 and the findings show a decrease in clean energy investment could exacerbate health crisis

While the COVID-19 pandemic has reduced air pollution in the U.S., the longer-term impact on the environment is unclear. In a recent study, MIT Sloan School of Management Prof. Christopher Knittel and Prof. Jing Li analyzed the short- and long-term effects, finding that the actual impact will depend on the policy response to the pandemic. Their study suggests that pushing back investments in renewable electricity generation by one year could outweigh the emission reductions and deaths avoided from March through June 2020.

“The pandemic raises two important questions related to the environment. First, what is the short-run impact on fossil fuel consumption and greenhouse gas emissions? Second – and more important but harder to answer – what are the longer-term implications from the pandemic on those same variables? The health impacts from the pandemic could stretch out for decades if not centuries depending on the policy response,” says Knittel.

In their study, the researchers analyzed the short-term impact of the pandemic on CO2 emissions in the U.S. from late March to June 7, 2020. They found a 50% reduction in the use of jet fuel and a 30% reduction in the use of gasoline. The use of natural gas in residential and commercial buildings declined by almost 20% and overall electricity demand declined by less than 10%. However, the professors point out that the shutdown also halted most investment in the transition to low-carbon energy. In addition, clean energy jobs decreased by almost 600,000 by the end of April.

“The short-term impact of the pandemic is clear, but the long-term impact is highly uncertain,” says Li. “It will depend on how long it takes to bring the pandemic under control and how long any economic recession lasts.”

The best-case scenario, according to the researchers, is a swift and low-cost strategy to control the virus, allowing the economy to reopen by the end of 2020. In this scenario, investment trends prior to the pandemic will continue.

“Unfortunately, we view a second scenario as more likely,” notes Knittel. “In this scenario, the consequences of the pandemic will be greater, with many more deaths and deeper disruptions to supply chains, and a persistent global recession. The need to backpedal on the reopening of the economy due to flare-ups could destroy rather than defer the demand for goods and services.”

In this scenario, the delays in investments in renewables and vehicle fuel economy could lead to an additional 2,500 MMT of CO2 from 2020-2035, which could cause 40 deaths per month on average or 7,500 deaths during that time.

“Our findings suggest that even just pushing back all renewable electricity generation investments by one year would outweigh the emissions reductions and avoided deaths from March to June of 2020. However, the energy policy response to COVID-19 is the wild card that can change everything,” they wrote in an article for Joule.

Li explains that budgets will be strained to pay for the costs of the virus, making it challenging to invest in clean energy. And if a recession persists, there may be pressure to lessen climate change mitigation goals. However, stimulus packages could focus on clean energy, increasing clean air, clean jobs, and national security.

“Just stabilizing the economy can go a long way to putting clean energy trends back on track. We need to solve the pandemic and continue to address climate change. Otherwise, it will lead to even more tragedy,” adds Knittel.

Li and Knittel are coauthors of “The short-run and long-run effects of COVID-19 on energy and the environment” with Kenneth Gillingham and Marten Ovaere of Yale University and Mar Reguant of Northwestern University. Their paper was published in a June issue of Joule.

Mina Tocalini, 360 Magazine, COVID-19

Covid Death Reports

New research by a team at the MIT Sloan School of Management estimates that COVID-19 cases and deaths are 12 times and 1.5 times higher than official reports, respectively.  The study examined 84 of the most affected nations, spanning 4.75 billion people.  The researchers estimate 88.5 million cases and 600 thousand deaths through June 18, 2020. 

Despite substantial under-reporting, however, these nations remain well below the level needed for herd immunity.  Absent breakthroughs in treatment or vaccines, and with only mild improvements in policies to control the pandemic, the researchers estimate a total of 249 million (186-586) cases and 1.75 million (1.40-3.67) deaths by Spring 2021.

Earlier and stronger policies to reduce transmission when the pandemic was first declared, together with the deployment of extensive testing, could have averted approximately 197,000 deaths, nearly one third of the estimated total.

However, they say, future cases and deaths are now less dependent on testing and more contingent on the willingness of communities and governments to reduce transmission, such as by reducing contacts with others, physical distancing, and better hygiene, including masks. 

The nations with the highest estimated percentage of their populations infected to date include Ecuador (18%), Peru (16.6%), Chile (15.5%), Mexico (8.8%), Iran (7.9%), Qatar (7.3%), Spain (7.1%), USA (5.3%), UK (5.2%), and the Netherlands (4.8%).

The paper, Estimating the Global Spread of COVID-19, is co-authored by MIT Sloan’s Hazhir Rahmandad, Associate Professor of System Dynamics; Professor John Sterman, Director of the MIT Systems Dynamics Group; and Ph.D. candidate Tse Yang Lim.

Using data for all 84 countries with reliable testing data (spanning 4.75 billion people), they developed a dynamic epidemiological model integrating data on cases, deaths, excess mortality and other factors to estimate how asymptomatic cases, disease acuity, hospitalization, and behavioral and policy responses to risk affect COVID transmission and the Infection Fatality Rate (IFR)—the probability of death after becoming infected—across nations and over time. IFR depends not only on the age and health of the population, but on the adequacy of health care and the effectiveness of protections for the most vulnerable, including the elderly.  The researchers estimate IFR to be 0.68% on average (0.64%-0.7%), but find it varies substantially across nations: approximately 0.56% for Iceland, 0.64% New Zealand, 0.99% for the USA, 1.59% for the UK, and 2.08% for Italy.

Follow MIT Sloan: Facebook | Instagram | Twitter | YouTube

Covid and health illustration

MIT COVID-19 Research

Why does the coronavirus kill some Americans, while leaving others relatively unscathed?

A new study by researchers at the MIT Sloan School of Management sheds light on that question. The study, by Christopher R. Knittel, the George P. Shultz Professor of Applied Economics at MIT Sloan, and Bora Ozaltun, a Graduate Research Assistant in the Center for Energy and Environmental Policy Research (CEEPR) lab, correlates COVID-19 death rates in the U.S. states with a variety of factors, including patients’ race, age, health and socioeconomic status, as well as their local climate, exposure to air pollution, and commuting patterns.

The findings have important implications for determining who is most at risk of dying from the virus and for how policymakers respond to the pandemic.
Using linear regression and negative binomial mixed models, the researchers analyzed daily county-level COVID-19 death rates from April 4 to May 27 of this year. Similar to prior studies, they found that African Americans and elderly people are more likely to die from the infection relative to Caucasians and people under the age of 65. Importantly, they did not find any correlation between obesity rates, ICU beds per capita, or poverty rates.

“Identifying these relationships is key to helping leaders understand both what’s causing the correlation and also how to formulate policies that address it,” says Prof. Knittel.

“Why, for instance, are African Americans more likely to die from the virus than other races? Our study controls for patients’ income, weight, diabetic status, and whether or not they’re smokers. So, whatever is causing this correlation, it’s none of those things. We must examine other possibilities, such as systemic racism that impacts African Americans’ quality of insurance, hospitals, and healthcare, or other underlying health conditions that are not in the model, and then urge policymakers to look at other ways to solve the problem.”

The study, which has been released as a Center for Energy and Environmental Policy working paper and is in the process of being released as a working paper on medRxiv, a preprint server for health sciences, contains additional insights about what does, and does not, correlate with COVID-19 death rates. For instance, the researchers did not find a correlation between exposure to air pollution. This finding contradicts earlier studies that indicated that coronavirus patients living in areas with high levels of air pollution before the pandemic were more likely to die from the infection than patients in cleaner parts of the country.

According to Prof. Knittel, the “statistical significance of air pollution and mortality from COVID-19 is likely spurious.”

The researchers did, however, find that patients who commute via public transportation are more likely to die from the disease relative to those who telecommute. They also find that a higher share of people not working, and thus not commuting, have higher death rates.

“The sheer magnitude of the correlation between public transit and mortality is huge, and at this point, we can only speculate on the reasons it increases vulnerability to experiencing the most severe COVID-19 outcomes,” says Prof. Knittel. “But at a time when many U.S. states are reopening and employees are heading back to work, thereby increasing ridership on public transportation, it is critical that public health officials zero in on the reason.”

The proportion of Americans who have died from COVID-19 varies dramatically from state to state. The statistical models that Knittel and Ozaltun created yield estimates of the relative death rates across states, after controlling for all of the factors in their model. Death rates in the Northeast are substantially higher compared to other states. Death rates are also significantly higher in Michigan, Louisiana, Iowa, Indiana, and Colorado. California’s death rate is the lowest across all states.

Curiously, the study found that patients who live in U.S. counties with higher home values, higher summer temperatures, and lower winter temperatures are more likely to die from the illness than patients in counties with lower home values, cooler summer weather, and warmer winter weather. This implies that social distancing policies will continue to be necessary in places with hotter summers and colder winters, according to the researchers.

“Some of these correlations are baffling and deserve further study, but regardless, our findings can help guide policymakers through this challenging time,” says Ozaltun. “It’s clear that there are important and statistically significant difference in death rates across states. We need to investigate what’s driving those differences and see if we can understand how we might do things differently.”

MIT Sloan Study of COVID-19

MIT Sloan study shows public health interventions in COVID-19 Pandemic could lead to faster economic recovery

The COVID-19 pandemic has raised critical questions about the impact of public health responses on the economy. An important issue for policymakers is whether current interventions like social distancing have economic costs. In a recent study by MIT Sloan School of Management Prof. Emil Verner, he analyzed the economic effects of the 1918 Flu Pandemic and found that public health interventions have no adverse effect on local economic outcomes. His study shows that cities that intervene earlier and more aggressively experience a relative increase in economic activity after the pandemic.

In normal times, nonpharmaceutical interventions (NPIs) like social distancing and quarantines are bad for the economy. They make it more difficult for economic activity to take place, like going to work. This leads to the notion of a tradeoff between public health interventions and the economy. Policymakers are in uncharted territory, with little guidance on what the expected economic fallout will be and how the crisis should be managed,” says Verner.

To address these issues, Verner and his colleagues studied the economic effects of the largest influenza pandemic in U.S. history, the 1918 Flu Pandemic. They looked at the severity of the pandemic as well as the speed and duration of NPIs, which resemble many of today’s NPIs, including school, theater, and church closures, public gathering bans, and restricted business hours. The team analyzed how the pandemic impacted manufacturing employment, manufacturing output, bank assets, consumer durables, and mortality.

Their study highlighted that areas that were more severely affected by the 1918 Flu Pandemic saw a sharp and persistent decline in real economic activity. The data showed an 18% reduction in state manufacturing output for a state at the mean level of exposure. Exposed areas also saw a rise in bank charge-offs, reflecting an increase in business and household defaults.

“The patterns are consistent with the notion that pandemics depress economic activity through reductions in both supply and demand. Importantly, we also found that the more affected areas of the country remain depressed relative to less exposed areas from 1919 through 1923,” he said.

Comparing the speed and duration of NPIs across cities, the researchers found that cities that intervened more aggressively performed better economically the year following the pandemic. Reacting 10 days earlier to the arrival of the pandemic in a city led to an increase in manufacturing employment by around 5% after the pandemic. Similarly, implementing NPIs for an additional 50 days increased manufacturing employment by 6.5%.

“It’s clear from this data that NPIs work and there is no evidence that they lead to a worse economy. They are the most beneficial way to protect our health and the health of our economy. We can’t go back and act more quickly in the COVID-19 pandemic, but we can apply this information now. Lifting restrictions too early could make the economy worse by leading to a resurgence of the virus in an even more destructive pandemic,” he said.

Verner added, “This isn’t a choice about saving lives or saving the economy. We won’t have a normal economy if we lift restrictions too early.”

Verner is a co-author of “Pandemics depress the economy, public health interventions do not: Evidence from the 1918 Flu” with Sergio Correia of the Federal Reserve Board, and Stephan Luck of the Federal Reserve Bank of New York.

The MIT Sloan School of Management is where smart, independent leaders come together to solve problems, create new organizations, and improve the world. Learn more at mitsloan.mit.edu

MIT Study: Clinical Decision Support Software

As concerns mount about the overuse of powerful and costly diagnostic imaging tests, such as CT scans and MRIs, a new study from MIT suggests that software designed to help doctors make better decisions could decrease certain scans by about 6%. The results of the study are published in the journal PLOS ONE.

“There is a lot of debate about the health risks and high costs that stem from the overuse of potentially inappropriate tests,” says Joseph Doyle, the Erwin H. Schell Professor of Management and Applied Economics at the MIT Sloan School of Management, and one of the authors of the study. “Our research shows that technology can improve healthcare delivery by helping physicians make the right decisions about which diagnostic scans to use when.”

This is the first large-scale study where physicians and other healthcare providers were randomized to receive Clinical Decision Support (CDS) software to guide imaging decisions. The CDS provides information about whether a test they order for a given patient is appropriate based on guidelines from the American College of Radiology. Beginning next year, the Centers for Medicare and Medicaid Services (CMS) have put in place new regulations that require imaging orders to be accompanied by a CDS recommendation in order to be reimbursed by Medicare.

Doyle and his colleagues—Sarah Abraham, Laura Feeney, and Amy Finkelstein of MIT, and Sarah Reimer of Aurora Health Care—conducted a yearlong trial of CDS software at Aurora Health Care, the largest healthcare system in Wisconsin. The study involved 3,511 healthcare providers, half of whom were randomly assigned to receive the tool. The control group continued to order images as they had prior to when the trial began.

“Going in, we didn’t know whether doctors in the treatment group would be receptive to the technology,” says Doyle. “If the system recommended a different test from the one that was ordered, would the physician consider changing course? We also worried about the potential of alert fatigue, which happens when people are exposed to a large number of frequent alarms and consequently become desensitized to them.”

The researchers found that CDS helped reduce targeted scans by about 6% relative to the control group.  CT scans—the most common high-cost imaging type, which also carries the greatest concerns about over-ordering—were responsible for four-fifths of the overall reduction in targeted scans. While the software changed the nature of image orders, it did not change the number of images ordered overall. The effects persisted over time, suggesting that this type of alert can continue to affect ordering even with concerns about alert fatigue more generally.

The study was supported by Arnold Ventures. The philanthropy funds randomized controlled trials in healthcare, justice, and education, to understand problems and identify policy solutions, and it was conducted in coordination with J-PAL North America[LF1] , which also supports such randomized evaluations.  With the impending wave of digital-health tools designed to guide physician decision-making, randomizing the rollout of these tools provides a golden opportunity to test how doctors respond to the information in a rigorous way.

“Our study was meant to understand whether software alone has potential to help doctors improve their decision-making around ordering these expensive and often risky tests because such an intervention is easily scaled,” says Prof. Doyle. “This is especially the case for diagnostic testing given the imminent mandate that CDS be used for high-cost imaging to be eligible for Medicare reimbursement. Further understanding of the most effective ways to employ the technology beyond simply showing the information about the guidelines remains an important area for future research.”