MAY 9 STATE-BY-STATE JOB LOSS & MORTALITY REVIEW

Note: Early in the pandemic up through this post of May 11, 2020 E. D. Hovee posted weekly updates of current COVID mortality and job loss data. This post represent the most recent and last of five weekly updates.

This is an update to last week’s mortality data — now extended through the week of May 9/10. Updated mortality rates were posted with this blog May 11. U.S. Department of Labor (DOL) data is posted as of May 14 — but for the week ended May 9 — allowing for a more complete complete review.

We are now 8 weeks into the current economic crisis with this data review starting for the week ending March 21 with the initial surge of unemployment claims. This may be the last weekly posting of the unemployment/mortality comparison.

We have run out of space for the week-by-week graph comparisons. More substantively, blog posts will now turn to address opportunities and challenges associated with the business of re-opening America’s economy while mitigating mortality risk. As you have questions or suggestions, please email me at ehovee@edhovee.com.

New Jobless Claims Nationally

This is now the 8th straight week of continued massive counts of unemployment claims filed nationally — with another nearly 3.0 million claims (seasonally adjusted) filed the week ending May 9, 2020. Over these eight weeks, cumulative filings of initial unemployment claims as tallied by DOL now are approaching a cumulative total of 36.5 million.

Starting at over 3 million claims the week ending March 21, new filings doubled to the 6-7 million range each of the following two weeks, then eased off somewhat to the 5+ million level the week ending April 11, then to 4.5 million the following week, then to 3.9 million, declining further to 3.2 million new claims, and then to just under 3.0 million for for the most recent week ending May 9.

The analysis with this week’s posting again includes a comparison with continued unemployment claims. This reflects adjustments during the 2nd week after initial filing when counts are pared to continued claims eligible for unemployment insurance payments, as well as for persons finding re-employment. As these adjustments lag behind by one week, the number of continued claims nationally (as of the week ending May 2) was 22.8 million.

STATE-BY-STATE JOBLESS REVIEW

As in prior weeks, this update includes a comparison of experience for the 50 states plus two territories and the District of Columbia (DC) for the weekly unemployment periods ending March 21, March 28, April 4, April 11, April 18, April 25, May 2 and now May 9. Data is shown as a % of base pre-recession employment levels. Note: State-level DOL data is only available on a basis that is not seasonally adjusted.

Ten states now have cumulative 8-week initial unemployment filings that exceed 30% of their pre-recession covered job base — again led this most recent week by Georgia but at the extraordinary rate of 42% — followed by Kentucky, Connecticut, Hawaii, Rhode Island, Washington, Michigan, Nevada, Louisiana, and Pennsylvania. Nationally, claims average out to a 23% share of pre-recession employment.

Eight of the top 10 jobless states remain in the same relative position as was the case during the preceding week - with two exceptions. In one week, Connecticut leaped up from the group of states in the bottom one-third of jobless rates to the #3 highest rate. Unemployment claims went from a cumulative total of 18% joblessness to 36% based on filings of this most recent week. Reasons for this situation are not entirely clear. Also noted is that Washington state went from the 9th to 6th highest jobless claims rate based on a substantial number of new claims for this most recent week ending May 9.

The 10 states (plus DC) with the lowest unemployment rates are the same as noted for the prior week, though there have been some minor changes in rankings. Texas his improved its already low jobless claims ranking as has the District of Columbia (DC).

While the pace of unemployment filings slowed once again in the week ending May 9 for most states, there were seven states for which filings increased as compared to the prior week ending May 2. Those with more filings are Georgia, Connecticut, Washington, Florida, New York, Wisconsin, and South Dakota. This is a grouping for which there appears to be no clear consistency of geographic pattern.

CONTINUED UNEMPLOYMENT CLAIMS

With seven weeks of data now in hand, it is now more useful to review the experience of continued unemployment claims (for those determined to be covered by unemployment insurance and not yet re-employed). This appears to be a still somewhat volatile metric, depending in part on where various states stand in clearing their backlog of claims.

With continued claims lagging one week behind initial filings, the total number of insured unemployed for the nation totals 22.8 million (on a seasonally adjusted basis) for the week ending May 2 — up by just over 450,000 from the prior week. This equates to 15.7% of pre-recession covered employment.

State-by-state data is reported on a seasonally unadjusted basis. The pattern of states with the highest rates of continued unemployment for those insured is somewhat different than for states with the highest rates of initial filings. The #1 state for the week ending May 2 is Oregon with continued claims representing 26% of the pre-recession covered employment base.

The other nine places in the top 10 are Nevada, Washington, Michigan, Mississippi, Rhode Island, New York, Connecticut, Puerto Rico and Vermont. This reflects a combination of northeastern and western states together with outliers of Michigan, Mississippi and Puerto Rico.

The top 10 listing experienced one major change from May 2 to the week ending May 9. California went from #1 position with the highest continued claims rate to #18 as its continued claims dropped from 4.8 million to 2.9 million in one week.

Of this top 10 grouping, five are also in the top 10 with respect to initial filings. The outliers that rank higher with respect to jobless insured versus all claims are Oregon, Mississippi, New York, Puerto Rico and Vermont.

By comparison, seven of the 10 states with the lowest initial filings are also the states with the lowest rates of continued claims as a proportion of total covered employment. Three states — Idaho, Kansas and Montana rank higher in terms of initial unemployment filings than with continued claimant rates.

STATE-BY STATE MORTALITY REVIEW

Through the weekend of May 9/10, an estimated 79,320 COVID-19 deaths represent a less than a 3% add-on to the 2.8-2.9 million deaths (from all sources) typically occurring across the U.S. With 11,575 new deaths, the latest week’s mortality is the lowest it has been since the week ending April 4. Barring a resurgence, it appears the nation has come across and is now dropping down the backside of the COVID mortality curve.

This blog post has focused on providing an update of cumulative deaths per million residents for each state. As with prior weeks, mortality data is from the New York Times (NYT) daily log (which was apparently bumped up the prior week ending May 2 to include some New York deaths for which COVID-19 is now viewed as the the presumed but not definitive cause of death).

Our composite data set starts with COVID deaths up to March 29, then proceeds with added deaths for each of the six following weeks to achieve cumulative totals as of the week ending May 9/10. (Note: Detailed counts with the New York Times listing can vary over the course of a 24-hour period as counts are regularly updated and the numbers used by this blog reflect the time of day at which the data is pulled).

As illustrated by the following graph, the U.S. COVID mortality rate as of this most recent week is now at just under 240 deaths per million U.S. residents. There are 10 states plus the District of Columbia that are above the national average rate — led by New York at a figure approaching 1,380 deaths per million. There were only 2,215 COVID deaths recorded for New York state during this most recent week, less than half the average experienced across the five earlier weeks (since the week ending March 29).

There continues to be substantial variation between the state with the highest mortality rate (New York at 1,380 deaths per million population) — more than 125 times the state with the lowest mortality to date (for Alaska at less than 11 deaths per million residents).

There are 10 states plus the District of Columbia (DC) with COVID mortality rates that exceed the national average. This is a group that remains unchanged over the last week — albeit with DC now moving ahead of Michigan in terms of death rate per million residents. Of the 11 states, eight are situated in the mid-Atlantic to New England regions of the U.S. The anomalies are Louisiana, Michigan and Illinois.

Of these 11 geographies, there were only three for which this most recent week was their highest mortality week to date — Rhode Island, Pennsylvania, and Illinois. The other eight states have dropped down below prior mortality rates — some substantially so.

STATES WITH BELOW AVERAGE MORTALITY

As suggested by this wide spread in mortality, a better view of the experience for states with mortality below the national average is presented by the following more detailed graph covering 40 states and Puerto Rico. All are below the national average cumulative mortality toll of just under 240 deaths per million residents nationwide as of May 9/10.

There are eight states with death rates in the range of 100-240 per million, another 17 with rates of 50-100, and 15 plus Puerto Rico still in the lowest range of less than 50 COVID deaths per million in-state residents.

The week ending May 9 represented a peak weekly mortality rate for 14 states — down from 19 the previous week. Of these 14 states, 11 have below average mortality as compared with the entire nation.

States in the 100-240 deaths per million range (below the U.S. average) but experiencing this last week as their peak mortality period to date are Mississippi and Minnesota. In the 50-100 mortality range are New Hampshire, Iowa, Missouri, Alabama, Florida and Arizona at what are peaking rates.

Of the 15 states plus Puerto Rico that have mortality rates of under 50 deaths per million residents, four experienced peak mortality for this most recent week — North Dakota, South Dakota, Texas and Utah. Despite low overall mortality, these states evidence hot spot activity important to monitor.

This leaves 28 states plus Puerto Rico as double winners (up from 15 the prior week ) — experiencing overall average low mortality and no new peaking this last week. At present, these appear to offer the clearest cases to date for start or continuation of business re-opening.

SUMMARY NOTES

As described in more detail for the blog posting with the prior week ending May 2, it continues to appear that COVID-related mortality rates have leveled off and are now starting a downward trajectory. At the same time, unemployment claims — while still high from a historical perspective — are continuing to decline on a week-to-week basis.

Considerable variations are evident across the states — in terms of joblessness and even more so with COVID-19 related deaths. While there will undoubtedly be state- and local-level successes and failures along the way, each state and region of the country now is in position to tailor jurisdiction-specific approaches reflecting greater understanding of local needs and opportunities.

Re-opening can be expected to continue as an iterative process — speeding up or slowing the pace based on real-time experience monitoring with clearly articulated employment and mortality metrics.

WHAT PRICE CORONAVIRUS?

Note: This article has been updated from the initial post (as of April 22, 2020).

Nearly 2,000 years ago, a sage spiritual leader asked those who would take on any project of consequence to "first sit down and figure the cost so you'll know if you can complete it." Just to make sure that no one misunderstood the range of costs to be considered, this founder of what became the Christian movement offered up two illustrations : the cost of a physical construction project (building a tower), and the cost of going to war (assessing the capabilities of one's army against that of the enemy).

So, in our current pandemic, count not only the costs of those who will be directly stricken by an unseen enemy, but also those who will be affected by potential loss of livelihood and home. In 2020, the U.S. and much of the rest of the world have gone into virtual lockdown in a mad rush to avert or mitigate the mortality effects of COVID-19 virus — albeit with minimal consideration of the short and long-term cost necessary to beat this previously unknown foe.

As David Farragut, flag officer of the U.S. Navy declared in a battle of the American Civil War: "Damn the torpedoes, full speed ahead."

In 244 years of the American republic, there has never been an occasion when the U.S. and most states effectively shut down the social and economic life of the country. Not even in wartime have such radical steps been taken.

Yes, there's lip service given to mitigating the collateral damage, but no meaningful initiative to date to directly and honestly answer the threshold question: Is the cure worse than the disease? Is the price we are paying to combat this pandemic too high?

Or perhaps the question is better phrased as: What price is too high? 

COMPARING THE COST

For some perspective on the human toll of the virus, it is useful to make comparisons with other conditions affecting mortality in the U.S.

Consider this. As shown by the following graph, there were over 2.8 million deaths in the U.S. for calendar year 2018. By comparison, as of April 16, 2020, COVID-19 has claimed close to 31,000 American lives. The number of deaths attributable to coronavirus, to date, equates to about 1.4% of total annual mortality in the U.S.

New York Times figures do not include more than 4,800 people in New York City who died and are believed to have had the coronavirus. As reported by the Times, many of those patients died without being tested, a consequence of a strained medical system and a persistent lack of testing capacity.

As depicted by the graph, a few other selected indicators are of note. The number of people in the U.S. who have died of COVID-19 to date can be calculated as equivalent to:

• Approximately 5% of the number deaths of all persons age 85 and over who passed away in 2018 (for any and all reasons)

• 6% of the number of deaths for those age 75-84

• 6% of the number deaths of those who die of heart disease each year
  (the number 1 killer in the U.S.)

• 7% of the number of deaths attributable to cancer

• 24% of the number of deaths caused by accidents of all types
  (Just over 100% of the number of deaths attributable to vehicle accidents)

• 47% of the number of deaths attributable to diabetes

• 68% of the number of deaths attributable to flu & pneumonia

• 78% of the number of deaths attributable to kidney disease

• 83% of the number of deaths attributable to suicide

While lost jobs are not a form of physical mortality, they do represent human and economic loss. As of April 11, the increased joblessness of more than 22 million means that well over 500 jobs have been lost for every coronavirus death, to date. And like COVID-19 deaths, the number of unemployed has yet further to go on its upward trajectory.

Bottom line and while tragic, the number of deaths attributable to COVID-19 is only a small fraction of all mortality — only a small fraction of deaths attributable to the major causes of death in the U.S. Why this undue focus on an unseen killer which has, so far, added only marginally to the on-going death toll associated with the everyday cycle of life and death across America?

Would we lock America down like this to go all out to stamp out the causes of diabetes or cancer? What about to eliminate all car accidents by shutting down all motor vehicle transportation? Or to prevent all suicides?

What is it about COVID-19 that gives the fight to take on this pandemic a higher priority than addressing any other substantial form of mortality? Is this a battle worth impoverishing large segments of the American population for years to come?

As of mid-April, the chief economist of the International Monetary Fund (IMF) has stated that:

As countries implement necessary quarantines and social distancing practices to contain the pandemic, the world has been put in a Great Lockdown. The magnitude and speed of collapse in activity that has followed in unlike anything experienced in our lifetimes.

Do we care about the cost to America? Do we care about what the IMF now says will be the worst downturn since the Great Depression of nearly a century ago? Or is our answer to be that of the medical bureaucrats who, like Farragut, would command: "Damn the torpedoes, full speed ahead."

Damn the cost, damn the livelihoods lost. Damn the kids whose educations are disrupted. Damn the increased disparity between the haves and have nots. Damn the loss to public revenues essential to provide public services. Damn the death of small businesses and gig workers into the hands of an engorged  corporate America. Damn the deplorables to strengthen the self-proclaimed rule of the medical-bureaucratic elites.

When will anyone have the guts to answer these questions?

REJOINDERS

There are those who would undoubtedly say this is over-the-top hyperbole. Even if there has never been an explicit policy pronouncement that this fight is worth any cost, there seems to be some implied social contract to make this effort, no matter what price it takes.

And there are technical issues, like:

• This is a disease of unknown proportions unlike other diseases for which risk can be more readily measured and calibrated — so it's worth going all out to beat the unknown (unlike such known maladies as cancer, diabetes, car accidents or suicides for which risks are now well defined).

• What we do know is that the more than 40,000 deaths (as of April 21) will grow larger by the time this is over — maybe now to 100,000 or 200,000 or if we relax too much off measures like social distancing, conceivably increasing to less likely worst case scenarios of perhaps 1 - 2 million.

• And there may be recurrences, flare-ups in the infection rate, as a start-stop stutter process that continues indefinitely — at least until a vaccine is found.

There are counters to these likely responses. No choice of this magnitude should occur merely as part of some implied social construct. If cost be damned is to be the order of the day, that should occur via informed and explicit legislative actions at federal, state and local levels including a policy commitment to hold the rest of society harmless, not impoverished — no matter what it takes, whether short or long term.

And regarding the technical issues. While this is a disease with many unanswered questions, the unknowables have been pared back as the health care community learns more day-by-day. We certainly know that the major variables to managing the risks going forward involve slow and measured ease-off of social distancing, widespread testing for the virus and for antibodies, getting therapeutic drugs and vaccines quickly to market (to reduce and ultimately stop the ravages of this disease), and (quite possibly) contact tracing using the tracking powers of ubiquitous smart phones.

In instances where the private market is not responding quickly enough — whether with masks or testing equipment — the powers of the presidency could be more actively applied to compel production and distribution. Now, not later.

We even have learned enough from disease modeling to better understand the potential range of outcomes and how the key variables likely influence these outcomes. And the monitoring tools related both to COVID-19 and economic recovery are there to gauge what is happening in real time — then scale the regulatory mechanisms to ease-off or tighten accordingly.

But there's one step that is essential to make all this work. There needs to be some general and explicitly communicated consensus of what a reasonable mortality target should be. It's not good enough to say that we aim to bring the rate down as much as possible. That approach suggests that our resources are infinite and that the cost imposed to get that one extra life saved is worth the universe.

Rather, aim for realistic targets. Based on what is known today, it now appears reasonable to aim for a goal of less than 100,000 deaths before this is over - but accept the possibility of going as high as 200,000 (as within the range of variability). Note: Even if there were 200,000 COVID-19 deaths this year, annual deaths in the U.S. would increase by only about 7% — going from an underlying rate of about 2.8 - 2.9 million per year to perhaps 3.0 - 3.1 million.

Coronavirus mortality targets should ideally exclude estimates of co-morbidity where an elderly or immune-compromised individual is likely to experience near term death anyway, with or without the virus. The medical profession needs to come clean and quantify the extent to which co-morbidity is or is not occurring.

WHICH WAY FORWARD?

Maybe it’s time to pay a bit more attention to sage advice — historically proven. Count the cost before going into battle. Do it before continuing to spend extraordinary sums of funds while impairing business and household incomes with minimal regard to both foreseen and potentially unforeseen consequences. Not just the cost from one perspective, but from all relevant viewpoints before making decisions as to the most viable course of action.

Putting this in today's context, this could mean continuing to follow the course of continued lockdown if the cost to the rest of humanity is widely viewed as worthwhile to save a small percentage of deaths, including at least some who are likely to die anyway. Alternatively, tack toward a new course of COVID-19, social and economic recovery if this is the more acceptable price.

Either way, make the choice consciously and with the consent of the governed. The worst of all worlds will be to attempt to muddle through — putting the cart ahead of the horse. The interests of the self-anointed over those doing the work — on whom the future of our republic ultimately rests.

CORONAVIRUS: COMPUTING THE DEATH TOLL

On March 13, the New York Times reported that the U.S. Centers for Disease Control and Prevention (CDC) had prepared four scenarios of the potential medical impact of COVID-19 (details of which have not been fully published), indicating that anywhere between 200,000 to 1.7 million U.S. residents could die over the duration of the pandemic. And this could involve hospitalization of anywhere from 2.4 million to 21 million people. The hospitalization scenario is particularity concerning to CDC and the public considering that the U.S. only has a little more than 924,000 staffed beds.

What has been released by the CDC for publication are age-specific hospitalization, intensive care (ICU) and mortality rates of the U.S. population based on experience from February 12 to March 16 of this year. While this initial sample is relatively small (at 2,449 cases), it begins to provide a window into potential case-fatality, estimated to range between 1.8% to 3.4% of all persons infected.

The combination of these two studies make it possible to assess potential mortality scenarios relative to more normalized patterns of age-specific deaths in the U.S. That is the purpose of this blog.

We walk through this analysis in several steps:

• First, considering age-specific mortality for the U.S. in a typical year — in this case 2018 as the most recent year with data available from the National Center for Health Statistics (NCHS).

• Second, looking at what is publicly known, so far, about the CDC scenarios of potential U.S. deaths from the pandemic.

• Third, combining the CDC scenarios with recent case-fatality data as generated by the CDC to estimate the percentage distribution of deaths by age.

• Fourth, bringing all of these datasets together to compare how COVID-19 mortality projections compare to underlying existing (or normalized) death rates for the U.S.

As will be evident with the data presented, there is still a considerable range of estimates for many of the variables discussed. With more case experience, it hopefully will also become possible to refine and tighten the range of estimate.

Consequently, this blog may be updated to reflect new information as it becomes available. Questions and comments about the methodology used with this somewhat simplified but potentially informative review are also appreciated.

Age-Specific Mortality

This discussion begins with a review of age-specific mortality rates for the U.S. population (age 15+) as of 2018. As illustrated by the following chart, the annual death rate per 100,000 population ranges from just over 70 deaths per 100,000 persons age 15-24 to about 13,450 per 100,000 who are age 85 and over.

This data serves as a baseline as to the normalized pattern of mortality - independent of effects of a major pandemic such as coronavirus.

CDC Mortality Scenarios

As noted, the New York Times on March 13 headlined an article as: “Worst-Case Estimates for U.S. Coronavirus Deaths.” While details do not appear to have been publicly released of this CDC-sponsored teleconference with 50 experts from around the world, the Times reports that it obtained screenshots of the CDC presentation from someone not involved in the meetings. The newspaper then verified the data with scientists who did participate.

The discussion was aimed to address the question of how many people might be infected, need hospitalization, and/or die as the virus takeshold in the U.S. As reported by the Times:

One of the agency’s top disease modelers, Matthew Biggerstaff, presented the group on the phone call with four possible scenarios — A, B, C and D — based on characteristics of the virus, including estimates of how transmissible it is and the severity of the illness it can cause. The assumptions, reviewed by The New York Times, were shared with about 50 expert teams to model how the virus could tear through the population — and what might stop it.

While details were not provided for all four scenarios, the March 13 article brackets the range of scenarios with low and high estimates for an epidemic lasting for months or even over a year:

• The low estimate indicates that 160 million could be infected with 200,000 deaths.

• The high estimate involves up to 214 million U.S. residents infected with as many as 1.7 million deaths (essentially with a much higher mortality rate for those infested).

It would be useful to have more detail regarding all four scenarios and the assumptions that stand behind each alternative projection. However, even with these summary numbers, it is possible, on a preliminary basis, to model the age-specific implications of these low-to-high mortality estimates.

CDC Age-Specific Case-Fatality Analysis

Subsequent to CDC’s base mortality scenarios, the agency has released age-specific hospital, ICU admission and case-fatality information — covering U.S. cases over the period of February 12 to March 16. As detailed by the following chart, the resulting database comprises 2,449 cases, disaggregated by age group and providing range estimates for each of these three indicators of medical need and result:

• The lower bound of the range is estimated by CDC using all cases within each age group as denominators to calculating each incidence rate.

• The upper bound is estimated by using only cases with known information on each outcome as denominators.

The columns under the blue banners are directly from the CDC data base. The last three columns under the red banner involve supplemental calculations by E. D. Hovee:

• The first red column rate by age reflects the mid-point between the low and high estimates by CDC.

• The second column provides an imputed estimate of deaths in the CDC data base (not directly stated by CDC but calculated from the case rates divided by the mid-range case-fatality rate).

• The third column provides a distribution of the number of age-specific deaths as a percent of the total (and is applied with the next and final step to the analysis which now follows).

U.S Deaths by Age & Coronavirus Scenario

This final step combines the baseline historical mortality data with the death rate scenarios as consistent with the CDC datasets. The following table provides a summary of the results of these calculations:

• The first column provides the age group categories by which the data has been compiled - with the 0-14 age group excluded because it is not shown as part of the NCHS dataset and because no fatalities are indicated with the CDC coronavirus dataset as available, to date.

• The second column shows the number of deaths as of 2018 from the NCHS dataset as the baseline expectation of mortality that might be expected in the absence of COVID-19.

• The third column adds in the low estimate of coronavirus deaths totaling the control total of 200,000 deaths, assuming that there is no overlap of coronavirus deaths with baseline mortality (a topic described further below).

• The fourth column adds in the added increment of high estimate coronavirus deaths, assuming a hypothetical 50% overlap between baseline mortality and coronavirus deaths (also described below).

• The fifth column indicates the cumulative total of existing baseline + low estimate + high estimate added coronavirus related deaths.

• The final column calculates the ratio of cumulative deaths divided by existing baseline mortality.

As indicated by the above chart, the high estimate scenario is associated with an overall mortality rate for persons 15+ that is 130% of (or 30% greater than) the baseline of existing 2018 U.S. deaths:

• For persons age 15-14, the high estimate of death is only 7% greater than existing baseline conditions.

• Conversely, the mortality rate is 36% greater for those those over age 35 than with baseline conditons.

• Generally speaking and consistent with press reports, the mortality rate effect of COVID-19 increases for older age cohorts than younger. The exception is at the 75-84 year age bracket, perhaps a statistical anomaly due to the as-yet relatively small sample size of the CDC database.

These comparisons can also be made in graphic terms, as illustrated by the following graph.

With the low scenario, the addition to existing death rates is much smaller — adding only 1.6% to the death rate for 14-44 year olds and 8.5% to morality for 85+ year old seniors. Over all 15+ age groups, mortality increases by just over 7%.

A critical (and not yet known) variable included with these hypothetical scenarios is the degree of overlap between existing deaths (that would happen anyway) versus deaths that might be attributable solely to COVID-19. In between is a middle category of deaths that might have happened this year without coronavirus but for which the virus was another contributing factor. In some cases, coronavirus would accelerate the time of death, in others it might be a minor factor due to the seriousness of other underlying conditions.

No data has been made available from CDC that provides guidance as to how this may be attribution can best be made. And to a large extent, this may be an unknowable, for example, trying to ascertain to what extent alcoholism or heart disease or diabetes may have each contributed to a person’s demise.

As noted above, for sake of illustration and discussion, this analysis hypothesizes:

• No overlap with the low coronavirus estimate - assuming that coronavirus is the primary death factor due to its relatively low incidence relative to baseline mortality.

• 50% overlap with the high coronavirus estimate - assuming that a higher rate of infection and serious complication will inevitably involve a higher proportion of people with existing underlying issues.

If the 50% overlap estimate is off the mark so that there is little or no overlap with the high coronavirus death estimate, then we would be faced with a worst-worst scenario. In effect this could increase the total annual U.S. death toll from 3.7 million per year with coronavirus to as many as 4.5 million. This means that instead of COVID-19 accounting for a death rate increase of 30%, the increase in death rate would increase by 60%. and for persons 85+, the death rate attributable to coronavirus could increase by as much as 72% rather than 36%.

Conversely, it may be that the overlap is even greater than 50%. This possibility is supported by the observation from cases to date that most fatalities have involved individuals with existing (generally multiple) underlying health issues.

In effect, it may be the COVID-19 is not the sole cause of death but a contributing factor — in some cases the final tipping point, in others perhaps not. A more granular examination of mortality data might be useful to attempt to quantify how much a person’s life span, on average, is shortened as a result of this virus. Shortening a life by 1 month is much different than reducing the life span of a senior citizen by, say, five years. The appropriate policy choices may also differ substantially based on this type of determination.

IMPLICATIONS

The question of how much America’s coronavirus will increase deaths will have an obvious influence on the inherently no-win task of determining the appropriate trade-off between saving lives and salvaging the country’s economic and social well-being.

This blog intentionally avoids the question of what set of policies best provide the best balance of countering or mitigating two pressing and conflicting catastrophes. Rather, the implications of most immediate interest involve improvements to the base data so that the decisions made will be more informed. From a data perspective, the following suggestions are noted:

1. CDC should be more transparent by fully and publicly disclosing the research methodology and conclusions regarding the four scenarios of potential U.S. death toll already prepared. This information is essential to better understand what is required to achieve each scenario from medical, economic and societal perspectives.

2. Over the next 1-2 months as the virus reaches exponentially more people, it will be important for CDC to continuously and publicly update its databases — each time providing for a larger and clearer window into who will be most affected and how. And as more testing resources become available, conduct random sample monitoring to better benchmark infection and mortality rates.

3. Finally, to the extent possible, it would be extremely useful for more robust CDC datasets to parse out the degree to which death rates at specific age levels overlap with deaths likely due to existing age and underlying health conditions. This will be essential to gaining a better understanding of the true net effect of coronavirus on mortality going forward.

In the weeks ahead, E. D. Hovee will continue to monitor progress on the data side of the coronavirus challenge. And due to a clear economic and development bent, I may offer observations aimed to contribute to ongoing public policy discussion. So, look for updates and feel free to question or critique as the occasion arises.

Take care and be well,
Eric Hovee - Principal