Tag: coronavirus

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1 AC: What the data tell us

Be wise today so you don’t cry tomorrow.

E A Bucchianari

We’re well into Year 2 AC – After Covid. Clearly, we don’t know all we need to know. Conversely, we are awash in data and probably know more – collectively – than we think we do. In this series of posts, I’ve been presenting my observations, preliminary conclusions they’ve led me to, and what might be better approaches to future pandemics and other disasters, from a community perspective. In this post, I want to focus on the restrictions placed on all of us in response to the pandemic. There is a lot of misinformation out there (particularly if you listen to the media or the rather unprofessional rantings of Rochelle Walensky). While we don’t have “final” data for the pandemic, I think we’re close enough to the end to draw some conclusions about the effectiveness – or not – of the restrictions that we’ve been living with.

Probably front and center in most people’s minds is “Did the lockdowns work?” We paid a high price in terms of our economy, our social fabric and our kids’ lives; we need to know whether we got value for the disruptions. In answering this question, we are faced with several important hurdles:
• Goals. Initially, we were told that lockdowns were necessary to flatten the curve. Then we were told that they were continued to control the pandemic (whatever that means).
• Data. We have case data that is not very good, primarily because we did so little testing early on. The death data seems to be better, but again has some biases because of differing protocols for attribution across jurisdictions, lies misstatements by some public officials, and some question about the accuracy of early data. As noted in my last post, a better early warning system could have helped us have better data early on. For this analysis, I’m going to look at both case and death data, current to 4/5/21.
• Lockdowns and NPIs. If it were simply a matter of looking at lockdowns vs no lockdowns, analysis would be so much simpler! Unfortunately, almost every state has had a mix of non-pharmaceutical interventions (NPIs – mask mandates, social distancing, quarantines and lockdowns) making it difficult to isolate the effects of lockdowns. Further, these have changed over time. There are at least two attempts to develop an index try to reflect this spectrum of NPI responses on a common scale – I’m going to use the one developed by WalletHub, and the values for 2/26/21 (Although I don’t present the data here, I’ve looked at the indices for a few dates. While the absolute values change, the general conclusions are the same.). I have renamed their index “State Openness.”

In the following figure, I’ve plotted both cases and deaths by states (treating both Puerto Rico and the District of Columbia as states). Clearly, there is no relation between “state openness” and the death rate (R2 ~ 0). The data suggests that the states in the red box might want to compare their practices with those in the green boxes – a factor of five fewer deaths! There is a rough correlation (R2 = 0.35) between the case rate and state openness, but it is heavily influenced by outliers, especially the cluster of states in the green box (The gray box represents the Standard Error.). Thus, it appears that the NPIs may reduce the case rate, but have little to do with the death rate. This makes sense because the case rate depends on the public’s actions; NPIs influence those. The death rate is more a reflection of the quality of the health care system; NPIs have little influence there.

I’ve also looked at county data. Ideally, if there were a significant predictor of cases, counties and states could be better prepared to deal with potential “hot spots.” I’ve based my search on the CDC’s 2019 county health rankings data, thinking those data were likely to be the best source for a predictor. I looked at all of the data – but I won’t bore you with a plethora of scatter plots! One predictor that was discovered early on still holds – population density is a good predictor of the number of cases, as is the total population of the county (well, duh!). However, the two counties with the highest incidence of covid-19 are Chattahoochee County, GA, and Crowley County, CO; neither large metro areas. For both about one-third of their residents were infected.

There appeared to be “fuzzy” relationships between median household incomes and the prevalence of both cases and deaths in a county. The number of cases and deaths per 100,000 residents were limited by increasing household incomes. This was true for all residents, as well as when broken done by race. Let me stress this was not a correlation, but rather it appeared that low median household incomes were necessary (but not sufficient) conditions for high case and death rates.

Beyond these , I didn’t find any other data that were correlated with either case or death data.* Perhaps most notably, neither the Covid Community Vulnerability Index nor the Social Vulnerability Index correlated with either cases or deaths. This is particularly unfortunate, because they are intended to indicate potential hot spots. At least at the county level, they don’t.

The county data was further broken down by the type of county. The CDC classifies counties as either large, middle and small metro centers; large fringe centers; micrometro centers or non-core (rural areas). Rather than plot all of the data (a confusing profusion of colors and shapes), I’ve plotted the best fit lines for each county type vs state openness. While there is not a good fit for any of these, the “bunching” of the lines for the case data indicates that the county type did not make much of a difference in terms of cases. However, as the second graph of this pair shows, non-core counties tended to have significantly more deaths than the other county types. I’ve plotted the raw data for the large metro counties (red) and the non-core counties (green) in the lower graph. The data suggests that the health care system in many of the rural counties – but not all – are simply inferior. This may be due to a lack of medical personnel and hospitals, or the distance between those who died and health care centers; i.e., poorer care or poorer delivery. As a matter of interest, all four of the large metro counties with the highest deaths per resident were in NY – Queens, Bronx, and Kings and Richmond Counties. Foard County, TX; Emporia, VA and Jerauld County, SD, were the highest of all counties.

Finally, I’ve looked at state unemployment numbers for February (latest available data). Again, there is a rough (negative; R2 ~ 0.4)) correlation between unemployment and state openness. The most interesting outlier (at least to me) is Vermont (lower left corner) – one of the states with the most restrictions (NPIs) and yet very low unemployment. Perhaps unsurprisingly, California and New York have very high unemployment; but surprisingly (to me) Hawaii has the highest unemployment – probably indicative of restrictions on travel.

So, what’s the data trying to tell us? Lockdowns and the other NPIs have had a modest impact limiting the number of cases but also lead to higher unemployment. The NPIs have no measurable impact on the number of deaths. In that sense, they have done nothing to control the pandemic – lots of pain for little gain. The data on cases and deaths by county type clearly show that there are major disparities in rural health care for virtually every state. Perhaps most unfortunately, the data don’t point to a good predictor of impacts at the county level. The CCVI and SVI were worthy attempts to provide this, but ultimately have not been shown to be useful. It could be useful if health professionals dug more into the relationships between cases and deaths and household incomes; there could be a pony in there!

Clearly, I’m not a health professional. I have tried to present the data in as apolitical way as I can because the messages from the media have been filtered through their political biases. As Ernie Broussard has said, Pain is inevitable, but suffering is optional. If our communities are to avoid unnecessary suffering when the next pandemic hits, we will have to make some hard decisions to take difficult steps to alter our approaches. Let us hope that our leaders will base those decisions on cold facts such instead of the hot passions of the moment, or the emotional push to “just do something.” Let us hope that they are wise, lest the rest of us shed tears.

*The data from the 2019 county health rankings that did not correlate with either cases or deaths were:
Life expectancy (overall and by race);
Age adjusted mortality;
Child and infant mortality;
% of the population experiencing frequent physical and mental stress;
% of the population with diabetes;
Number and prevalence of HIV cases;
Number and prevalence of food insecurity;
Number and prevalence of limited access to health care;
Number and prevalence of drug overdoses resulting in death;
Number and prevalence of deaths due to motorcycles;
% of the population with insufficient sleep;
Number and ratio of primary care physicians to residents;
% of the population who are disconnected youth;
% of the population on free lunch;
Segregation index;
Homicide rate;
Number and prevalence of firearms deaths;
Number and prevalence of homeowners;
Number and prevalence of sever housing cost burden;
Fraction of the population under 18;
Number and fraction of the population over 65 (overall and by race);
Number and prevalence of English as a second language;
Fraction of the population who are female;
Number and fraction of the population living in a rural area;
The individual themes and the overall CCVI;
The SVI.

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Covid-19: Disasters Have Direction

You can be sure of succeeding in your attacks if you only attack places which are undefended. You can ensure the safety of your defense if you only hold positions that cannot be attacked.

Sun Tzu

This is an updating of an old post.  The original did not have any specific examples; I think Covid-19 provides a good one.  I’m sure the concept “Disasters Have Direction” is obvious to many of you, but I’ve never seen it articulated.  As I try to show in the discussion of the pandemic, it can be a useful construct as we think about a community’s resilience.

For a few years, FEMA and DHS have championed the idea of an “All Hazards – Maximum of Maxima” approach to planning.  The basic premise is that if a community plans for the worst of the worst, then it will be prepared for whatever may actually happen. This is a deceptively simple tautology that I think deserves a little more analysis than it usually receives, especially in terms of community resilience.

Let’s start by looking at an idealized community.   A community can be thought of as an ecosystem.  There is a “human layer,” made up of individuals and families.  There is an institutional layer, consisting of private businesses and other economic institutions, and all of the other “human-serving” organizations in the community.  Then there is the physical, environmental, layer – containing the built and natural environment.  All of these are held together by the social capital within the community (some may argue whether the physical layer is bound to the community by its social capital, but that’s a subject for another post!).

Of course, this is an ideal community; real communities may have a strong economy but be weak in the human element.  Some have a decaying infrastructure but a flourishing natural environment.  Thus, we can depict a real community as follows.  This real community would be relatively weak in terms of its community institutions, have a somewhat stressed natural environment, but have a robust built environment.

Now let’s assume the community is hit by a pandemic.  There is no immediate physical damage.  Any that occurs most likely happens because the humans who normally maintain things –infrastructure, for example – are not able to do so.  This disaster has attacked individuals and families, and – because they are closely tied to the human layer – the community organizations that meet social needs.  For a pandemic, hospitals, clinics and the public health department would certainly be included.  Since, in this case, there is relatively little capacity in the community institutions (e.g., a rural community), they will be particularly hard hit – most likely overwhelmed. 

But what happens in a natural disaster?  The initial impact on the community is going to be on the physical layer; buildings are going to be blown down, debris will be strewn about, flooding may occur. The other parts of the community will be impacted because of these physical blows.  In our notional real community depicted above, there would be relatively little damage done to the built environment, but the natural environment would experience much greater damage (at least in relative terms) because it is weaker. 

A severe economic downturn attacks the community from another direction.  Businesses lay off workers; some close.  Many individuals and families experience severe economic hardship.  There is no immediate impact on the other parts of the community ecosystem.  Eventually, however, all will be affected.  In our example community, the economic impacts are less severe than for a community with a weak economy, or already burdened individuals and families.

Thus, disasters have a direction, as shown in the next graphic. It must be stressed that the graphic points out the initial point of attack.  If the magnitude of the initial impact is huge, or other parts of the community are weak, then the disaster is likely to ripple throughout the community with cascading impacts.

This simple concept is consistent with the idea that vulnerability to a threat depends on weakness at the point of attack.  This is shown in the next figure.  Threat X indicates a potential health crisis (e.g., a pandemic), while Threat Y is primarily a threat to the community’s economy.  As depicted, Threat X is more likely to lead to disaster than Threat Y because the greater relative strength of the community to withstand an economic downturn.

This simple picture of a community also has meaning in terms of recovery and community resilience.  If community resilience is measured by how fast – and effectively – resources are deployed to achieve community restoration and recovery, then the social capital within the community plays a crucial role.  Suppose Threat X above actually materializes.  The vulnerable part of the community has few available resources.  It is the community’s social capital – its connectedness – that provides the pathways for resources to be shifted within the community.  It is the community’s social capital that determines whether resources from outside the community are effectively brought to bear.  In a very real sense, it is the community’s social capital that determines whether the community actually recovers from disaster.

If we look at Covid-19 through this lens, clearly the pandemic attacked individuals and families, and community health organizations.  Its magnitude varied from community to community, but – initially – dealing with the pandemic exceeded the resources (e.g., PPE, ventilators) available to most communities, i.e., it was a disaster and it had a direction.  Communities had to rely on their connections (bridging and linking social capital) to others in the region and to the state (and, for the biggest cities, to the federal government) to get the resources they needed.  In a later post, I’ll outline a methodology that, if used, could have reduced the impact of the pandemic at the community level.

Our response to the pandemic triggered an economic disaster.  For those of you who remember my old post “Of Ice Storms, Interdependencies and their Impacts on Running a Bar” I pointed out that the number of businesses which could reopen after a disaster depended on how long they were closed.  In some places, the Covid-19 lockdowns lasted for months – and the economic consequences have been devastating.  I intend to update that post as well and expand upon it a little based on the knowledge we’ve gained from the pandemic.

Dan Alesch once said that we recall a disaster by the name of its triggering event, but remember it because of its impacts.  If that’s the case, Covid-19 will join the Dishonor Roll with Katrina, Deep Water Horizon, the Great Recession and so many others.  Each of these disasters were daggers that first pierced specific parts of the community, i.e., they had a direction.  Their impacts were determined by communities’ strengths at the point of attack and the force of the dagger’s thrust.  A community’s social capital determines how rapidly resources can be brought to bear to heal the wounds.  However, those who are not connected – without significant social capital – have to recover on their own:  resources won’t flow where messages don’t go.  In this way, the community’s social capital plays a crucial role in its recovery – and thus is a key component of the community’s resilience.

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Finding a Better Path

I’ve learned that the safest path is not always the best path and I’ve learned that the voice of fear is not always to be trusted.

Steve Goodier

If COVID-19 returns in the fall, do we really want to go down the same path we’ve been taking? In the US, we have focused on “flattening the curve” – not reducing the total number of deaths directly due to COVID-19, but rather spacing them out so that “indirect” deaths due to insufficient health care system capacity have been minimized. We took this path because our models initially predicted tens of millions of cases – and millions of deaths – that would have overwhelmed our emergency rooms. In taking this approach, all of us were faced with a situation for which we were unprepared – we had never been faced with this kind of crisis. Our stocks of many critical resources had been depleted but never replenished. The “Triple Header” of hurricanes almost three years ago had hinted at what we now know – our EM doctrines are clearly inadequate to handle disasters that go beyond the regional. We acted largely out of caution and a fear of the unknown.

Things will be different the next time. We know that the approach we’ve taken to the pandemic is fraught with unintended consequences – impacting our economy, our social compact and our mental health. We will have much greater testing capability and probably sufficient PPE, and capable supply chains for each, the next time. With luck, we may also have more effective treatments available for those infected.

The problem we face in crafting a new approach is simply that too much conflicting “information” is being fire-hosed at us; it’s damned difficult to find the nuggets of truth in the rushing torrent so that we can plot a better path for the future. I strongly believe we should empanel a national Board of Inquiry for the coronavirus chartered to develop a better approach for nationwide disasters such as this. It is important that we do this for next fall, but also because this crisis may impact our ability to manage severe weather events starting this summer.

Rather than bipartisan, the Board should be non-partisan – focused on policy and planning rather than personalities and politics; lasered in on developing a better approach rather than playing the Blame Game. Such a Board needs a strong chair, versed in both the strategic and operational aspects of dealing with crises; Craig Fugate, Honore Russell or Thad Allen come to mind. The Board should be independent of both the Executive and Legislative Branches, perhaps under the National Academies. The Board should include expertise in economics; health and health care; law; and federal, state and local decision-making.

Such a Board should pursue lines of inquiry such as:

• Situational awareness. Whatever plan we develop, we can be sure that it will have to be modified once case numbers start to rise. Those changes will be informed by the data at hand. I’m sure I’m not the only one who can’t figure out whether we’re severely undercounting or overcounting cases and deaths (My guess is undercounting cases and overcounting deaths, but who knows?). The federal government should provide useful guidance in terms of how to count and report, esp. considering that both the pub;lic and private sectors are likely to be involved.

• Federal, state, community and private sector roles. There seem to be as many opinions about who should lead and how, who should follow, and who should get out of the way as there are politicians and pundits, i.e., way too many. We need simple efficient processes at all levels, minimizing paperwork, and clear on what they are and how to follow them. We really began to have success when we let the private sector get involved. Our processes need to stop stymieing and start encouraging private sector participation.

• Urban areas. Most importantly, we need to understand why urban areas such as NYC were hit so hard. Less than 3% of US counties – all urban and accounting for half of our nation’s GDP – have had 60% of US cases. If NYC was a separate country, its mortality rate would exceed Italy’s! Over one-third of US deaths occurred within a 30 mile radius of NYC. We have to understand what happened in these urban areas to perform better next time.

• Foreign experience. The Board should consider successful responses from other countries. As of today, Taiwan has had only six deaths and less than 450 cases out of a population of 23+M. They learned some hard lessons from SARS – and acted on what they learned; we apparently haven’t.

• Lockdowns. Singapore, South Korea and Sweden all have approached the “Wu Flu” very differently than we have in the US, with arguably better results. One element these have in common is aggressive contact tracking; it seems that we can learn from what they have done. In these countries, the elderly and those potentially at greatest risk were urged to quarantine, but schools weren’t closed; restaurants weren’t closed; retailers weren’t closed. If we look at the responses across our states, lockdowns don’t seem to have had a quantifiable impact on health outcomes; population density does. Conversely, we will likely pay a terrible price later in deaths of those who didn’t go for their cancer treatments, who couldn’t afford to pay for their medications, who didn’t get the proper exercise. This doesn’t begin to address the incalculable impacts on our kids’ educations and future employability, or the millions of children worldwide who the UN estimates are going to die of starvation.

• Next points of attack. We now know that urban centers were the hardest hit in this first round. We need to determine whether this has resulted in “herd immunity.” The potential vulnerability of more rural populations also needs to be addressed.

There are other issues (e.g., border security and immigration) that also should be considered. What we need most now, however, is the wisdom to find those nuggets of truth, the wisdom to use them as signposts toward a better path, and the courage to follow those signs onto that path. Given our highly partisan polity, wisdom, will and courage will all be needed. The GOP and the Dems have both contributed to the problems; the best ideas from both are needed to solve them.

Once the parties start again

We’re now in the third month of dealing with the coronavirus pandemic in the US. In some ways our collective response to this has been effective (e.g., closing the borders), in some ways not (e.g., politicizing the pandemic). We are clearly learning as we go – as we should – and our response efforts are getting better focused. But the pandemic is both causing problems that will last long after we have the pandemic under control, as well as shining new light on existing problems that we haven’t solved.

Taiwan has done exceedingly well in dealing with the pandemic. Even though next door to China, only two deaths have been reported so far (By comparison, Italy’s per capita death rate from the virus is almost 1000 times higher.). Taiwan’s success is due in large part to their taking a hard look at their response to SARS in 2004. They built a crisis plan based on what they learned and have successfully implemented it. Their approach to the crisis has been different from ours and other countries (See here for a nice summary article and to get to a list of the actions they’ve taken.). I hope we in the US will do the same after this crisis passes. In this post, I want to pose some questions that I hope will be considered (starting with gathering appropriate data). I’m focusing on impacts to our communities; there are many others that need to be considered as well.

• What is the “aim point” for our response in the future? The current strategy in the US is aimed at limiting the number of deaths from the virus. Thus, we’re not really trying to prevent the virus from occurring; rather we’re accepting that people will catch the virus but trying to slow down its spread. If we are unsuccessful, then people will needlessly die because we don’t have enough ICU hospital beds, respirators and ventilators to treat the potential spike in cases. If we had a cure OR a vaccine OR more hospital beds and needed equipment, we could potentially employ a different strategy.

• How will those who live in cities and those in rural areas do? I must admit I have often been bemused by our country’s lurch toward urbanization. Cities concentrate risk – you’re more likely to be exposed to the virus if you live in a city (New York City is currently experiencing a death every hour.). Conversely, cities also concentrate resources – there are more hospitals, medical equipment and medicines in cities to deal with the sick. In the Spanish flu epidemics of 1917-19, mortality was less than 1% in urban areas (probably due to partial immunity from previous influenza outbreaks) compared to up to 90% in some rural communities. Right now, we have too few ICUs in rural communities and too many cases in some of our cities. We need to recognize that rural and urban health care needs are different and develop better means to address both. But to do that we need to have a better handle on what those needs are.

• How will the homeless fare? Most of the permanently homeless are in poor physical and mental health. Most of them are men. Drugs, alcohol, and poor environments have compromised their immune systems. They are likely at high risk. I’m fairly certain that our communications with the homeless are – at best – spotty. We need to consider what actions we ought to take to both communicate with and care for this slice of the homeless population.

• How useful were our models of the virus’ spread and mortality? As George Box famously said, “All models are wrong, but some are useful.” Our models for the spread of a pandemic are generally pretty good BUT like all models their accuracy depends on their input parameters. The ones we’re using are based on the Chinese experience, or what they’ve indicated was their experience. We don’t know how well that translates to American demographics or the American health care system.

• Is the approach we’re taking to social distancing the best overall? Taiwan, South Korea have taken a different approach to achieve the same ends as our draconian shutdowns of businesses and schools. While our approach may be best for containment of the virus, we need to know how it impacts other aspects of community life, e.g., businesses, education and other social facets. We are taking action to determine the impacts on small businesses and the economy at large; we need to have the same urgency about the pandemic’s impacts on our kids’ education and our communities’ social fabric.

• Can we track contacts more effectively? Tracking the contacts of those potentially infected is a key part of the strategy followed by Taiwan. This is much harder to do in our country with its patchwork of health departments at community, county, state and national levels. But I’m sick and tired of hearing the phrase “community spread” as a sort of code for “we don’t have a clue how Grandma was infected.” We can do better, but it will require that each of us takes a hard look at the balance between individual privacy and community health security.Along those same lines, we need to begin using Big Data techniques to determine potential future hot spots. There is all sorts of data indicating people flows; we need to start using them for future casting. We undoubtedly will initially stumble – make bad calls – but we can’t do better unless we start doing.

• How should we deal with those crossing our nation’s borders? Our immigration policy – such as it is – is a mess. Was and is, but we need to fix it for the future. Further, many of us Americans (like She Who Must Be Obeyed) have a lot of unsatisfied wanderlust. The government took what appears to be appropriate and relatively effective action to selectively close our borders but it is clear that foreign visitors or returning Americans triggered at least some of the hot spots. While I hate to contemplate it, we need to consider actions such as required medical screening at every border entry for anyone coming into the country.

This is a difficult time for all of us. The approach we’re taking toward the virus in the US is the one most likely to deplete our social capital, at least for a while. As I’ve often said, never underestimate the power of a party – I hope the human love of partying will help us to recapitalize our social infrastructure. But once the parties start again, we need to look back honestly at the crisis past, and be better prepared for the next one – knowing full well that it won’t be like the last one.

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In my next post, I’ll turn from crisis planning to putting together a plan for coming back. Given our approach to the pandemic, what sorts of things ought to be considered in planning for our communities’ recovery?

One more thing. With all of the guidance on hand washing and use of sanitizers, we tend to overlook the obvious: healthy people are going to fare better than those who aren’t, no matter their age. All of us need to find ways to keep fit while we’re isolated. During the week, I’m usually out by 630am walking 3-4 miles. Others are using video exercise or tai chi classes. Whatever you do, please make sure you, your elderly parents and your kids find ways to stay active even while avoiding unnecessary contacts.