A new study suggests that up to a third of “excess deaths” in 2020 may not be attributable directly to COVID, but to healthcare disruptions resulting from the resources needed to address the pandemic. What is the lasting impact of this change in health spending and prioritization? How can patients and providers effectively protect the public from non-COVID risk factors that have not gone away? Vidit Munshi, BU Questrom Lecturer of Markets, Public Policy & Law, examines the data.
A recent study from experts at the Virginia Commonwealth University School of Medicine and Yale School of Public Health reported that two-thirds of the excess deaths that occurred this summer in the United States were directly related to COVID-19. What is more difficult to figure out are the causes of the one-third (nearly 75,000) excess deaths that were not directly attributable to the virus. The authors suggest some potential explanations for these excess deaths: Increased mortality from heart disease and dementia, inability to get appropriate care for acute conditions, and potentially delayed reporting of COVID-19 deaths. They also discuss long-term impacts such as delayed chemotherapy and diabetes treatment and delayed screening exams.
Accurately measuring indirect and long-term impacts can be tricky because we don’t know specifically what to look for. With COVID-19, there are many facets to think about. The negative impact of the pandemic on mental health has been well-established. What about impacts from changes in diet and exercise? Lifestyles have become more sedentary and will continue to do so into the winter months. This is particularly true of younger populations where the risks of obesity and long-term consequences are higher. What impact will this have on the onset of conditions that are associated with decreased exercise and changes in nutrition? Finally, what is the extent to which people have been willing to put up with conditions that, undetected or untreated, will lead to worse downstream consequences?
Current modeling efforts related to COVID-19 have rightfully focused mostly on estimating the spread of the disease and the effectiveness of mitigation and vaccination strategies. However, we will soon want to answer some important questions about the overall impact of the pandemic. These models will have to consider direct and indirect effects that were sustained over the majority of 2020. However, they will also have to estimate the impact of health and economic consequences, the timing and magnitude of which are uncertain, that we will face in the coming years and perhaps, decades. Simulation models are well-equipped to do this task by allowing for the synthesis of data from numerous sources. With small amounts of data to inform the models, we can make assumptions about how widespread certain behaviors were and what they will mean for future health outcomes. For example, what proportion of the population will suffer from health consequences as a result of the sedentary lifestyle required by stay-at-home orders and work-from-home? What are those consequences and how long will they last? Simulations can test across distributions for each of these parameters and allow for an understanding of a range of impacts we are likely to expect.
Another aspect of modeling often utilized in health care is the adjustment for quality of life (QoL). Many studies report health effects of interventions in terms of gained QALYs (Quality-adjusted life years). Surveys conducted during the pandemic will likely reveal loss of quality-of-life associated with the pandemic and potential health consequences downstream. However, there are limitations of QALYs to consider. It is difficult to determine exactly who to survey to get the best estimate for the quality of life associated with a condition. For example, people who have a limb amputation quickly report quality of life closer to normal as they have adjusted to life with their new condition, compared to healthy individuals speculating on what they expect the quality of life to be under the same condition hypothetically. However, QALYs can be helpful is assessing potentially detrimental effects of a situation that may not have an obvious health impact. Many during this pandemic might simply say “life has just been really tough this year” even though they are healthy, employed, and safe at home. Should we be capturing this sentiment as an effect of the pandemic? Can this potentially give us a more accurate understanding of the overall impact of this virus?
The indirect and long-term consequences of COVID-19 are complicated. They are affecting us in many ways, from a health standpoint to an economic standpoint. Understanding these impacts will become increasingly important as we get out of the pandemic so we can be better prepared for the potential downstream health-related consequences of the pandemic and better understand what kind of mitigation strategies our health systems are likely to need to combat the down-stream consequences of COVID-19.
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