Tag: COVID

I have sworn off blogging about COVID, but I just can’t help myself.

On Mondays, the Minnesota Department of Health (MDH) puts out a COVID-19 Vaccine Breakthrough Weekly Update, which reports the number of fully vaccinated people who test positive, are hospitalized, and die. It shows the percentages of the population by age groups graphically (in bar charts) in each of those categories and demonstrates that only small percentages of the vaccinated population test positive, are hospitalized (smaller yet), and die (even smaller).

What it does not show are the percentages of those groups that are vaccinated versus unvaccinated. For example, what percentage of those who died were vaccinated. Those percentages, by their nature, will be much higher because the denominators are lower. I understand the decision to do that (or assume that I do): given how the vaccines have become so politicized, the public health professionals do not want to put out information that could discourage the hesitant from becoming vaccinated. I agree with that. It’s also true that the exact denominator to use is unclear, because the appropriate period depends upon when one assumes the population could have been vaccinated.

But the downside is that it might cause the vaccinated to think they are at lower risk than they really are. To illustrate, this Monday’s report shows that 839 of Minnesota’s COVID-19 deaths were in fully vaccinated individuals. MDH separately reports that there have been just over 4,000 COVID-19 deaths in Minnesota in 2021. Thus, almost 21% of 2021 Minnesota COVID deaths have been of fully vaccinated people.  (That overstates the period in which the population could be fully vaccinated since the vaccines only became generally available in February 2021 and it took several months to vaccinate the willing. The real percentage is higher!) I don’t think most people realize the number is that high. I cannot recall seeing it reported in general news media. It certainly has not gotten much play to the extent it has been reported.

Vaccines reduce the risk of infection, serious illness, and death – especially the latter two – but they do not eliminate it. Thus, it is crucial for the vaccinated to remain vigilant about avoiding infections – wearing masks, socially distancing, avoiding large indoor gatherings, etc. There still is risk involved for fully vaccinated people, especially for elderly and those with comorbidities (diabetes, high blood pressure, etc.). My fear is that the public health community’s PR spin on breakthrough cases to avoid discouraging the hesitant from getting vaccinated may have an unintended and undesirable side effect of creating overconfidence among the vaccinated as to their immunity. The rise of variants makes this more worrisome.

A story in today’s STRIB (buried multiple graphs down) does make the point about the risk involved with breakthrough infections:

Fully vaccinated Minnesotans only made up 32% of coronavirus infections and 35% of COVID-19 deaths from May through September, but they made up 43% of infections and 45% of COVID-19 deaths in the five-week period ending Nov. 13, according to the most recent state data on Monday.

Relative risks remain highest among the roughly 1 million unvaccinated Minnesotans, who make up the majority of COVID-19 cases and deaths even though they make up one third of the state’s population.

Jeremy Olson, Minnesota sees post-holiday COVID-19 surge, hopes for peak

The main stream media stories (e.g., Unvaccinated patients are filling Minnesota’s intensive care units) correctly hammer at the risk to the unvaccinated. Unfortunately, the unvaccinated largely consume right wing media (Fox, Newsmax, Epoch Times, etc.), which do not do that. So, the risk is that the vaccinated are lulled into overconfidence by the MSM coverage.

In any case, continued COVID caution for the vaccinated, especially by oldsters like me, is advised.

After some initial posts pointing out the (then) lack of population adjustments in most media presentations of state COVID data and on Minnesota’s poor long term care facility record, I have refrained from writing about COVID out of respect for my lack of expertise. But COVID keeps dominating the news and much of my attention. So, I couldn’t refrain from doing one last post.

Overview

Because this post is ridiculously long and I cannot imagine anyone will read it (certainly not all of it), I will start with a bullet point summary of its highlights:

• Minnesota and Wisconsin are similar states that have adopted different policies for addressing COVID-19. Minnesota has modest public health restrictions; Wisconsin has very few after its Supreme Court invalidated the governor’s executive order in May. That provides an opportunity to assess the effects of their respective actions, a “natural experiment.” This post presents some raw data comparing the two states’ experiences. A full evaluation must await more complete data and sophisticated statistical analysis by experts who know what they are doing (not me). Preliminary raw data present, at best, an impressionist painting of the situation.
• For all of 2020, Minnesota has had many fewer cases (about 100,000 less) of COVID-19 but more deaths than Wisconsin after adjusting for population differences. Wisconsin has had more deaths than Minnesota following the invalidation of its public health restrictions but many fewer relative to its case rate than Minnesota.
• Minnesota’s unexpectedly higher death rate is not explained by the age of its population, which is modestly younger than Wisconsin’s. The lethality of COVID-19 increases with age, particularly for the elderly, so that would suggest Wisconsin should have a higher fatality rate. It does not.
• Minnesota’s higher minority population, groups who statistically are more susceptible to contracting and dying from COVID-19, also does not appear to explain its higher death rate.
• My best guess as to the culprit is that Wisconsin’s long term care industry practices and regulatory policies are besting Minnesota’s, based on sketchy data.
• On balance, Wisconsin’s looser health public health restrictions have resulted in much more sickness and modestly more deaths than in Minnesota.
• But they also have led to more economic activity than in Minnesota – smaller drops in employment, consumer sales, and small business revenues. The overall differences are small with much bigger differences showing up in certain sectors (e.g., leisure and hospitality).
• The imponderable is whether trading off more sickness and death (albeit mainly among the very old) for small increases in economic activity is a good choice. Much subjectivity (e.g., in assigning dollar values to pain, sickness, and death) is involved and my instinct is that where one comes down devolves to their philosophical priors and/or identification with a partisan tribe. Available data does not justify the vociferous self-assurance of many of the commentators and elected officials and should inspire more modesty, compromise, and cooperation.

COVID-19 data for the two states

This post compares data on Minnesota’s and Wisconsin’s experience in dealing with COVID-19. I have not seen these comparisons presented elsewhere but may simply have missed it. The two states have taken different policy paths to address the pandemic, mainly because of a Wisconsin Supreme Court decision (text of opinions) that nixed its governor’s statewide public health mandates. Since that decision Wisconsin has largely been “open for business” (starting May 14^th) other than a patchwork of local restrictions and a statewide mask mandate adopted by the governor to which legal challenges in process but have not yet invalidated. By contrast, Minnesota has taken a somewhat more activist approach, but well short of what states in the northeast and on the west coast have done.

In dealing with SALT issues during my career, Minnesota and Wisconsin occasionally presented opportunities for “natural experiments” in social science research speak. The two bordering states are similar in size, demographics, and other factors with some modest differences in their business profiles, rural/urban breakdown (Wisconsin is slightly more rural), minority populations (Minnesota’s is a couple percentage points higher), and similar. Overall, they are similar. Social science research typically cannot run controlled experiments, since there is no opportunity to give placebos to a control group and see how they differ from the treatment group. Thus, when the two states’ public policies diverge (e.g., Wisconsin long has had a capital gains exclusion while Minnesota has not; Wisconsin has only minor homeowner tax incentives while Minnesota’s are generous), it presents an opportunity to study what effects those differences have. The COVID-19 policies present a similar opportunity. Background differences between the two states that affect public health outcomes may be much greater than is the case with SALT policies – here again my ignorance counsels caution in reading much of anything into this exercise.

The following are some readily available data without analysis or conclusions, just my commentary and speculation. I have never studied epidemiology at even the most fundamental level. (As an aside, I have noticed a fair number others who are similarly unqualified but appear smugly confident in reaching conclusions. More troubling, some mainstream media seem happy to lend credibility to their fairly wild assertions – looking at you Strib editorial page for publishing, e.g., Lennes, Tice, Kersten – all of them critical of the state government based mainly on their political priors and not credible data and analysis. Qualified experts with similar views do exist, but the Strib found it easier go with unqualified locals, I guess.) In any case, the basic data are interesting and suggestive – even if potentially misleading to the uninitiated like me. I assume after the dust settles (2023?), competent people will do careful analyses that control for relevant factors, are peer-reviewed, and will be informative – even if each pandemic is sui generis. Cross-country comparisons (using Sweden, which has consciously taken an approach even looser than Wisconsin’s accidental policy, and the other Scandinavian countries, e.g.) will certainly be done.  See this FT story on the Sweden policy, which suggests its sponsors may be losing their nerve. (Quote: “Sweden has reported more than 2,000 Covid-19 deaths in a month and 535 in the past eight days alone. This compares with 465 for the pandemic as a whole in neighbouring Norway, which has half the population. As Sweden’s King Carl XVI Gustaf said just before Christmas: “We have failed.””)

The data are from the Minnesota Department of Health, the Wisconsin Department of Health Services (some of which are usefully aggregated by the Journal Sentinel), and the COVID Tracking Project.

Basic data on case and death rates show Wisconsin has more cases but fewer deaths, adjusted for population. The table shows population, testing, case, and fatality data (as of December 31, 2020) for the two states.

Variable	Minnesota	Wisconsin
Population (000)	5,611,179	5,822,434
Persons tested	2,972,804	2,822,063
Total tests	5,574,962	NA
% of population tested	53.0%	48.5%
Positivity rate	14.0%	18.4%
Number of cases	415,302	520,483
% of population	7.4%	8.9%
Total number hospitalized	21,864	21,207
Total in ICU	4,620	2,034
Number of deaths	5,323	5,242
% of population	0.09%	0.09%
Case fatality rate or CFR	1.3%	1.0%

The two states have similar populations, so population adjustments only make modest differences. Minnesota has proportionately fewer cases (about 17% less on a population adjusted basis), but more deaths (about 5% more on a population adjusted basis). Cases include those confirmed by both PCR and antigen tests. The number of cases is sensitive to the level of testing and how the testing is done (i.e., who is being tested). Since Minnesota is testing at a higher rate than Wisconsin, testing differences are unlikely to explain Wisconsin’s higher case rate. With higher testing rates, one would expect Minnesota to have higher case rates all else equal. Of course, Wisconsin’s testing policy might be directed at individuals more likely to be infected; one would expect fewer tests to be better targeted. So, on the surface it appears that Wisconsin has a higher incidence of infections than Minnesota and more community spread. That would be consistent with Wisconsin’s looser public health restrictions.

But cases in Minnesota are more likely to result in death, as reflected in its higher number of ICU admissions, deaths, and higher CFR (the percentage of positive tests that end in death). Death statistics are less subject to testing levels than case levels are, even though lower testing levels may cause some COVID deaths to be misattributed to other causes. Given that, I would trust the numbers more after the CDC statisticians have reviewed and adjusted death certificate data. But even preliminary death data are more reliable and obviously more consequential than case rates. So, that looks superficially like a modest advantage for the Badgers.

Interestingly, Wisconsin despite its higher case rate has lower hospitalization rates and much lower ICU rates. Those differences could be attributable to medical care practices or simply to the fact that Wisconsin has fewer cases with severe symptoms because more younger people are infected. The lower ICU rates are consistent with a lower death rate, but the difference in ICU rates is much larger than in the death rates. So, something else must be going on.

The age distributions of the state populations do not explain the differences. The lethality of the virus is strongly correlated with age; the older you are the more likely contracting the virus is to be fatal.  The power of this age effect is shown by the two tables below showing case and death rates by age group for Minnesota and Wisconsin. The death rate consistently rises with each successively older age group – by a lot for those over 70 (close to 10 percentage points per decade). The age distribution of Minnesota cases and deaths, as of December 31^st (note that the case total is lower than the state total in the table above, because MDH did not yet have age date for a few cases on December 31^st when I grabbed this data):

Age Group	Cases	% of total	Deaths	% of total	CFR
0 – 19 years	67,450	16.2%	1	0.0%	0.0%
20 – 29 years	79,534	19.2%	9	0.2%	0.0%
30 – 39 years	68,150	16.4%	30	0.6%	0.0%
40 – 49 years	59,896	14.4%	71	1.3%	0.1%
50 – 59 years	59,547	14.3%	222	4.2%	0.4%
60 – 69 years	41,322	10.0%	577	10.8%	1.4%
70 – 79 years	21,318	5.1%	1,145	21.5%	5.4%
80 – 89 years	12,479	3.0%	1,880	35.3%	15.1%
90 – 99 years	5,202	1.3%	1,309	24.6%	25.2%
100+ years	292	0.1%	79	1.5%	27.1%
Total	415,190	100%	5,323	100%	1.3%

The age distribution of Wisconsin’s cases and deaths, as of December 31^st (note: the Wisconsin death data is limited to confirmed deaths, which is why the total number of deaths is lower than in state total table which shows both types – I was lazy and used the Journal Sentinel table, rather than trying to construct my own from Wisconsin Department of Human Services API data and J-S reports on cases and deaths from PCR tests only for some unknown reason):

Age Group	Cases	% of total	Deaths	% of total	CFR
0 – 19 years	72,366	15.2%	2	0.0%	0.0%
20 – 29 years	91,532	19.2%	16	0.3%	0.0%
30 – 39 years	74,503	15.6%	35	0.7%	0.0%
40 – 49 years	68,012	14.3%	82	1.7%	0.1%
50 – 59 years	73,060	15.3%	266	5.5%	0.4%
60 – 69 years	51,996	10.9%	635	13.2%	1.2%
70 – 79 years	27,251	5.7%	1,208	25.1%	4.4%
80 – 89 years	13,387	2.8%	1,521	31.6%	11.4%
90 + years	5,185	1.1%	1,053	21.9%	20.3%
Total	477,292	100.00%	4,818	100.0%	1.0%

Thus, the age distributions of the two states’ populations could be a factor. A state with an older population, all else equal, is likely to have a higher death rate for the same infection rate. The table below shows the relative age distributions of the two populations. As can be seen, they do not differ much. Minnesota’s population distribution skews slightly younger (higher percentages in the under 20 group and lower in the 60 and over groups), so it moves in the opposite direction that one would expect if the age distribution explains the death rate difference. With proportionately more of its population in the younger groups, one logically would expect Minnesota’s fatality rate to be lower; it is higher. Blind alley.

Age group	MN population	MN %	WI population	WI %
0 to 19	1,444,186	25.7%	1,420,574	24.4%
20 to 29	736,599	13.1%	762,036	13.1%
30 to 39	768,081	13.7%	739,245	12.7%
40 to 49	663,497	11.8%	682,908	11.7%
50 to 59	749,491	13.4%	788,818	13.5%
60 to 69	654,005	11.7%	746,402	12.8%
70 to 79	368,852	6.6%	435,800	7.5%
80+	226,468	4.0%	246,651	4.2%
Total	5,611,179	100.0%	5,822,434	100.0%

Age distribution of cases and deaths: Minnesota’s higher death rate is concentrated in the oldest age brackets. Of course, the issue is not simply the age distribution of the population, but the age of individuals who are infected with the virus and who ultimately die. Here, we are stuck with the vagaries of testing data because that is the only way we know whether someone is infected or not. Of course, the real infection rate is some unknown multiple of the case rate (i.e., the number of positives/population), because many infected individuals are not tested. This multiple could be 5 to 10 times the case rate and is sensitive to the level of testing and the protocols used to select whom to test. The graph shows the comparable Minnesota (blue bars) and Wisconsin (red bars) case rates by age group as a percentage of each state’s respective populations. Since Wisconsin does not report probable deaths by age group and Minnesota does, I distributed its probable cases and deaths to age groups in proportion to the confirmed cases and deaths to be consistent with the Minnesota data.

Wisconsin’s higher cases are more concentrated in the lower age groups than Minnesota’s. Except for the oldest age group, the red bars are consistently longer than the blue bars. The percentage of the population that tested positive in each of the age groups below 60 are about 2 percentage points higher for Wisconsin than Minnesota. For age groups 60 and older, the effect starts to reverse. For 60- and 70-year old’s, Wisconsin’s case rate is about one percentage point higher. For those above 80, Wisconsin’s case rate is less than a half percentage point higher than Minnesota’s. This concentration of more Minnesota cases in those 80 and older group almost certainly explains why it has more deaths, despite its lower case rate. As shown in the tables above, death rates are much higher in the older age groups, especially those 80 and older.

The graph below shows the two states’ COVID death rates by age group (again, as a percent of the population of the age group). Aside from dramatically showing the higher death rates for older age groups, the graph shows that Minnesota’s death rate is higher than Wisconsin’s primarily in the oldest age group (80+). Its rates are still higher for those between 60 and 79 but reverse with slightly lower rates than Wisconsin for those below 60. This likely reflects Wisconsin’s higher case rates in those age groups. Since Minnesota is testing at higher rates than Wisconsin, its infection rates may be even higher for those younger age groups. (Note that is pure speculation, since the states likely have different testing protocols that could be a factor in the relationship between and distribution of case rates or positives relative to actual infection rates.) In any case, despite its higher testing rates, Minnesota’s CFRs are higher for the oldest age groups. That may suggest that more of Minnesota’s most vulnerable elderly are contracting COVID-19 than in Wisconsin.

On the surface, this does not look good for Minnesota’s more restrictive public health policy, as compared with the laisse faire Wisconsin Supreme Court’s approach. Minnesota’s restrictions appear to be better at controlling community spread of the virus but are not in preventing deaths among its elderly as effectively as Wisconsin’s. The latter seems more important and is what conservative critics have been harping on, albeit largely based on uninformed speculation. Minnesota’s success in minimizing community spread has not carried through to preventing its more vulnerable elderly from becoming infected and dying. If that is so, why is an important public policy question for legislators and executive branch public health officials. The next section explores the most obvious candidate, long term care facilities.

Long term care facilities (LTCF) may explain the two states’ differences. Why does Minnesota do a better job of preventing general community spread than Wisconsin, while many more of its most vulnerable population – those 80 and older – contract the virus? One possible answer lies in regulatory and business practices in the two states’ LTCFs (i.e., nursing homes, assisted living and memory care facilities) or in differences in the demographics and health status of the populations of those facilities. I have blogged about Minnesota’s abysmal LTCF COVID-19 record and the media has covered it extensively in many stories, including multiple stories in the Strib.

Unfortunately, as far as my unexpert eyes can tell, comparable state-by-state data on COVID-19 infections and deaths in LTCFs are not readily available. Data are available from several sources, but they are not comparable because of differences in reporting, state LTCF regulations and reimbursement practices that causes institutions and their resident populations to vary from state to state, and so forth. The CDC requires (as of May) reporting by skilled nursing facilities. But even those facilities likely vary considerably in their practices and populations from state to state. Moreover, reporting for other facilities, such as assisted living and memory care, is totally inconsistent. Some states report this data (e.g., Minnesota), while others do not (e.g., Wisconsin). Moreover, these facilities because they are more lightly regulated, probably vary even more than skilled nursing facilities, making comparisons of available data more problematic.

Despite all those caveats, available state-by-state data show that Minnesota and Wisconsin have such wide differences that a good part of the story of Minnesota’s higher COVID-19 death rate among the elderly must lie in LTCF policies, practices, and regulations. At least, that seems to be a reasonable conclusion. Data from the COVID Tracking project, for example, report that Minnesota has had 15,320 cases in LTCFs and 3,220 deaths; Wisconsin, by contrast, has had 5,976 cases and 1,109 deaths. The differences in both cases and deaths are staggering; Minnesota’s cases and deaths are more 2.5X higher. Some of difference is explained by reporting differences – i.e., because Wisconsin does not include assisted living facilities in its reporting and Minnesota does. But it seems very improbable that that accounts for the full difference.

I could not find an ongoing data source that breaks out Minnesota’s cases and deaths between nursing homes and assisted living and other care facilities. This weekly CDC MMWR (Nov. 20) reports that Minnesota had 1,744 COVID-19 cases in assisted living facilities as of October 15^th (Table 1). Minnesota deaths are not reported by the MMWR and it includes neither cases nor deaths for Wisconsin. CDC says it gathered this data from state websites. I have been unable to find on the MDH website a breakdown of cases between skilled nursing homes and assisted living facilities. So, I am unsure where CDC got its Minnesota data. In early June, MDH released data by type of facility under threat of a legislative subpoena. It showed that about 68% of the then LCTF COVID-19 deaths were in skilled nursing homes. I have been unable to find more recent breakdowns, now that LTCF COVID-19 deaths in Minnesota are more than triple the then June number of 896. That suggests most (maybe two-thirds) of Minnesota’s LTC COVID-19 cases and deaths are in nursing homes. If that is an accurate inference, Minnesota has significantly more cases in nursing homes than Wisconsin, despite Wisconsin’s higher population.

In any case, Minnesota is among the states with the highest proportions of its COVID-19 deaths attributable to residents and staff of LTCFs (64%) based on COVID Tracking Project data. Only four states had higher percentages. It seems safe to conclude that some set of differences attributable to LTCFs are a major explanation for Minnesota’s higher death rate among the elderly than Wisconsin’s. And that Wisconsin LTCF operators and regulators are doing a better job than their Minnesota counterparts.

A principal premise of reducing community spread is that doing so is essential to keeping the virus out of LTCFs. Otherwise, LTCF workers or visitors will bring the virus into LTCFs. That may be so, but if it is, Minnesota’s better job of reducing community spread appears to be, then, thwarted by some other factor or factors.

As an aside, see this WaPo story (Will England, For the first time, the U.S. will reward nursing homes for controlling the spread of infectious disease) on HHS incentive payments to LTCFs that have done a good job of controlling the virus in their facilities. The measure HHS uses is based on the differential between community spread and the level of the virus in LTCFs. Thus, CMS appears to have accepted the premise that controlling community spread helps LTCFs control infection rates. But as the article notes, this is controversial. It creates the opportunity for LTCFs in states with rampant community spread to get incentive payments by keeping their incidence low. Conversely, LTCFs in states that have done a good job of controlling community spread – e.g., Vermont and Maine – will rarely qualify. That is not obviously wrong to me, unless the measure rewards absolute differences. The article does not say. In any case, Wisconsin is getting a disproportionate amount of the payments – twice the rate its population would suggest it should get. This provides indirect support for the narrative that LCTFs policies and practices are an explanation for Wisconsin’s lower elderly death rates.

Minnesota’s higher minority population does not appear to be a factor. Minnesota has higher proportions of its populations and higher absolute numbers of minorities than Wisconsin. National data show that minorities suffer more severe COVID-19 cases, including deaths. For example, African-Americans experience death rates, when compared with whites, as if they were a decade older (Brookings Institution). Thus, Minnesota’s higher minority population (about 2 percentage points higher than Wisconsin’s) suggests that it should have a slightly higher death rate, all else equal. Both states publish case and death data by race (many states do not). The data reveal that despite its lower minority population, Wisconsin’s has essentially the same number of COVID-19 deaths of minorities as Minnesota and, of course, higher proportions relative to its total population. Minorities comprise about 13 percent of COVID-19 deaths in Wisconsin and 11.5 percent in Minnesota. Thus, this moves in opposite direction expected, suggesting the differences in the relative sizes of their minority populations do help explain the differences in death rates. Another dead end. This naturally points back to LTCFs as the likely culprit for Minnesota’s higher death rate.

What has happened since the two states’ policies diverged?

All the preceding data is for the entire period of the pandemic (for 2020 to be more accurate). But the two states policies began to diverge only after the Wisconsin Supreme Court invalidated the Governor Evers’ executive order in mid-May 2020. The natural experiment only really began in late May or early June. To control for this effect, the table and graphs below show the differences in cases and deaths from June to December. Because of the lag between exposure and when tests can detect an infection, June 1^st seems like a reasonable starting point. December seemed like a reasonable cutoff since vaccination availability, distribution, and administration policies may begin to affect matters starting sometime in early January. The Table shows COVID-19 cases and deaths for June – December 2020.

	Minnesota	Wisconsin
Cases	388,980	499,874
per 100k of pop	6,932	8,585
Deaths	4,273	4,650
per 100k of pop	76	80

Following the change in policy required by the Wisconsin Supreme Court, Wisconsin’s population-adjusted case rate is approximately 20% higher than Minnesota’s and its death rate is 5% higher. During March through May, Minnesota had higher case and much higher death rates. Post-May data continue to show Minnesota with a higher CFR than Wisconsin. Although Minnesota’s population-adjusted death rate is now lower than Wisconsin’s, it still seems too high given its lower case rate. Testing differences seems= unlikely to explain such a large gap. Again, my suspicions lie with LTCF differences between the two states.

The graphs show the weekly case and death rates for the two states for June through December. To normalize for population differences, I adjusted the Minnesota numbers upward so they would be proportional to Wisconsin’s higher population (about 4% higher).

The two states’ pattern of outbreaks have followed one another. Minnesota’s lower case counts (positive tests) are obvious (the large area between the two lines, of which the lower orange line represent Minnesota cases).  Minnesota’s better performance in deaths similarly show up in the next graph, albeit in more modest fashion, which follow the same pattern as cases with the characteristic lag (deaths typically occur two or more weeks after infection).

For the pre-June period, Minnesota’s COVID death rate was 84% higher than Wisconsin’s. Following the Wisconsin Supreme Court decision, it was 5% lower. One could hypothesize that the court’s nullification of Governor Evers’ executive order explains the difference. I would not leap to that conclusion since other factors may be at play. In particular, when and how extensively the virus appeared in the two states may be a factor. States subject to early and virulent outbreaks of COVID-19 (e.g., New York, New Jersey, and Louisiana) suffered much higher death rates because medical practitioners were still learning the best therapeutic techniques and death rates were generally higher. The Twin Cities, as a large corporate headquarters location and travel hub, likely suffered much higher early exposure to the virus than Wisconsin. If Minnesota was more heavily exposed in February through March, it naturally would have experienced higher death rates that regressed to the mean. If so, the big divergence in the two states’ experiences during March through May period may be partially attributable to that and the reversal in the differences thereafter less to Wisconsin’s change in policy. But Wisconsin’s wide-open approach undoubtedly also contributed to the acceleration of cases and deaths in the post-May period.

On balance, it is difficult to not infer that Minnesota’s modestly more robust public health mandates have reduced infections, as well deaths to a lesser extent. Factors other than the public health measures also affect the infections and deaths and may differ between the two states. Thus, the magnitude of the effect requires analysis by someone who understands what control variables will better identify the effects of the public health measures in employing statistical techniques like regression analysis. But it seems safe to say the Wisconsin Supreme Court decision resulted in increased sickness and death. I assumed that they (the Republican justices who struck down the governor’s order and the Republican legislators who brought the suit) knew that would occur but concluded it was justified, which brings us to the next issue.

Effect on Economic Activity

More thoughtful critics of Minnesota’s public health measures generally recognize that more sickness and death will result from the looser policies that they advocate. Their point is that the resulting expanded economic activity provides greater benefits than the costs of sickness and death. (As an aside, much of the public commentary I have read is clear about that; what the authors have left unsaid is how they value the “cost” of more infections and deaths, relative to some measure of the “benefit” of more economic activity or what they think the relevant magnitudes of each are. So, their assertions are highly general and ultimately unsatisfying, bordering on the tautological or meaningless. I say that because reading their stuff is often maddening for me – self-righteous and condescending toward the folks making life-and-death public health decisions – given the lack of real analysis or stated factual bases for their criticisms. That may sound harsh, but their commentaries are pretty harsh, in my opinion.) Thus, one needs to look at the other side of the cost-benefit equation: to what extent has Wisconsin’s policy resulted in higher levels of economic activity that justify the more adverse public health outcomes (deaths, short or long debilitating illness, crowding out others from access to health care, etc.).

As an aside, I would point out that putting a dollar value on human lives and sickness in such cost-benefit analyzes is controversial and can be highly charged. Most of the COVID-19 fatalities are old folks (really old, 80 or older). How does that factor into the value of loss of their lives? By implication, these hard-nosed conservatives implicitly would discount it, I assume, as Texas Lt. Governor Patrick colorfully asserted. How do you put values on sicknesses that do not result in death? To an extent, economic output may implicitly take that into account (i.e., people not working, higher health care expenditures, etc.) but that doesn’t come close to capturing the real “value.” An abstract way to do so would be to sum how much infected individuals would be willing to pay – after the fact – to avoid becoming sick. That is an unknowable number, of course. All of this just underlines the difficulty of the calculus that the critics are asserting are being miscalculated.

Economic benefits could appear as either a matter of level (total economic activity under some measure) or distribution (which businesses or individuals realize net benefits or losses). Restrictions affect businesses and individuals differentially as they cause some buyers to substitute other goods and services. Individuals who cannot go to health clubs may buy home exercise equipment. Savings from the inability to travel or to go to restaurants may cause more home remodeling or construction. If we can’t go to restaurants, maybe we should update our kitchen? Home construction and remodeling in the Twin Cities have had a remarkably good year. See Jim Buchta, “For Twin Cities builders, 2020 was year of the single-family home,” Strib, 1/5/21: “Single-family homebuilders in the Twin Cities had one of their best years since 2005.” Big box retailers, like Target, have also done well. Most of the rhetoric focuses on the level, but some on distribution as well. Republican opponents of the restrictions typically walk restaurant and health club owners up to the microphones at press conferences, so their (Republican) concerns likely have an element of distributional concerns.

There are multiple challenge in assessing the economic benefits. Some big factors are:

• Data lags. Unlike reporting of COVID test results, death, hospital admissions and so forth, reporting of economic data, particularly the best measures (such as gross state product, income measures, etc.) lag considerably. As a result, it will take a while to get the data necessary for econometricians to analyze the effects.
• Inherent complexity make assessing cause and effect difficult. Many background and other factors affect economic decisions, sorting that out and isolating the effects of varying public health restrictions will be a challenge. This is, in addition, to adding controls to make the natural experiment a better measure of the effects attributable to policy, rather than background factors that differ between the states.
• Distributional effects resist evaluation. These effects are real and hurt or help individuals as an unintended side effect of the restrictions. But there is really no principled way, for example, to value the fact that the reduction of one business’s sales (e.g., a restaurant) has help another (e.g., a home builder). Peter’s cost may be Paul’s benefit.

Given that and the fact that my goal in doing this is simply to provide an impression or first look, I simply assembled some comparative data on basic measures – jobs, sales, etc. from available sources. The easiest way to do this was to use the data from the website tracktherecovery.org, which assembles viturally real time data. Both MCFE and I (Webinar worth watching) have described this data. Since I’m doing this to get an impression, I took the easy route. The charts below are from that website.

The first shows the differences in Minnesota’s and Wisconsin’s employment. It shows Wisconsin with an initial smaller drop in total employment than Minnesota. The difference predates invalidation of Ever’s executive order (i.e., it starts showing up in April when the order was invalidated in May), so there must be a little more involved than the health policy differences. Minnesota’s heavier earlier exposure to the virus is a plausible explanation. Interestingly, the latest data show Minnesota has closed the difference and is doing slightly better than the Badgers in the last months of available data.

If one focuses on unemployment of low-income wage earners, the effect is quite different. Wisconsin is doing much better (8 percentage points) than Minnesota as seen in the graph below.  Many of the workers in the high touch industries (restaurants, bars, personal services, etc.) are low-wage workers and the effects of Wisconsin’s lack of restrictions on those businesses is obvious. Thus, if minimizing distributional changes or protecting low-wage workers is important, the Wisconsin policy appears preferable.

The next chart compares total consumer spending in the two states. It shows that Minnesota’s spending dropped considerably more than Wisconsin’s (through December 6^th) – by almost four percentage points. The immediately following graph shows the drop in spending by consumers in low-income zip codes. Reversing the pattern shown in employment, spending in these Minnesota areas dropped less than in Wisconsin (-0.1 versus -2%). A paradox – probably because more of the spending is by higher income consumers? The final graph shows the drop in restaurant spending, which shows the dramatic difference in the two states’ consumer spending on that sector as one would expect (Wisconsin’s spending dropped by 14 fewer percentage points). It is worth noting, however, that spending in Wisconsin is still down a whooping 38 percent, more than double its advantage over Minnesota. So, the lack of a public health restrictions are not a panacea for those business – two-thirds of their problem is people simply choosing to avoid the high risk activity of dining out, even if they are open.

A final set of three charts shows the differences in small business revenues, which highlight the differences in the structures of the two states’ economies. Factors that a careful econometric analysis, when more complete data is available, would need to take into account. The first chart shows the change in revenues for all small businesses. The differences are small and follow similar patterns; revenues of small businesses in Minnesota dropped by 0.4-percentage points more than in Wisconsin. A very small difference for a 30-percentage drop. But the second and third charts show breathtaking differences by sector. The professional and business sector in Minnesota declined by under 6%, while that Wisconsin sector dropped by 26%. Minnesota’s better performance likely reflects the benefits of its headquarters economy with multinational firms (in finance, health care, food, consumer products and similar) that have not been as hard hit by the pandemic. That pattern reverses for the leisure and hospitality small businesses, which saw a 13-percentage point larger drop in Minnesota than Wisconsin. This, of course, reflects the effect of Wisconsin’s comparative lack of public health restrictions on those businesses. But those business are sucking wind with revenue declines south of 60% in both states. So, most of the cause is the pandemic and the consumer response it to, not the public health orders. It appears Minnesota’s public health orders increases its leisure and hospitality small business’s revenue loss by, perhaps, 20 percent.

When I was working, my point of reference was to consider the potential effect on state tax revenues. That point of view provides one additional data point confirming that Wisconsin’s looser public health restrictions caused a smaller decline economic activity than in Minnesota. The Urban Institute reports on state revenues and shows that for April through September Wisconsin’s revenues dropped by 2.6 percentage points less than Minnesota’s. (Getting full access to the Urban data is more expensive than I’m willing to pay.) That is an extraordinarily crude measure economic activity because Urban is simply reporting year-over-year net revenues, unadjusted for tax changes, and the two states’ tax structures differ somewhat. (This NY Times Upshot blog post graphs additional Urban data for another month, which shows a similar pattern although Minnesota appears to be catching up a bit.) But it is something, especially since my instinct and available data suggest that the pandemic has affected Minnesota’s underlying economy less adversely than Wisconsin’s, implying the effect on Minnesota’s revenues should be smaller. The phasing-in of the revenue reductions from Minnesota 2019 tax cut could be a small factor in the April to September revenues.

Overall, it appears clear that Wisconsin’s looser public health restrictions have resulted in somewhat smaller reductions in levels of economic activity (employment, consumer sales, and small business revenue), when compared to Minnesota’s more widely applicable and slightly more robust restrictions. While the overall effects appear small, the distributional effects (particularly on the leisure and hospitality sector) are more dramatic. That combination may suggest that the structure of Minnesota’s economy, principally its sector mix, has insulated it more from the pandemic’s effects and/or that public health restrictions are inducing more substitution effects. More data and statistical analysis are obviously required to reach meaningful conclusions about the economic effects.

Observations

On balance, I am comfortable concluding that both (1) Minnesota’s modestly more robust public health restrictions saved lives and reduced the incidence of sickness compared to Wisconsin’s and (2) Wisconsin’s approach reduced the adverse economic effects of the pandemic a bit. The distributional effects of Wisconsin’s policy are probably bigger than the overall level of economic activity, though. But the imponderable factor is how to value the saved lives and reduced pain and sickness in the cost-benefit equation. There is a large subjective element inherent in that calculus – how should the age of fatalities factor in, how much one does one value avoiding the pain and suffering of a bout of COVID, aside from the pure economic costs of health care expenditures and lost output, etc.? So, the apparent results (tentative as they are) do not tell us a lot about whether the tradeoff makes policy sense.

As a healthy 70-something, I am happy that I live in Minnesota rather than Wisconsin. By contrast, if I lived in an LTCF or had a loved one in an LTCF, all else equal, I rather be in Wisconsin (to paraphrase W.C. Fields). Similarly, if I owned a restaurant I would likely prefer to be on the other side of the river.

When I first conceived of doing this post, I imagined awarding a “trophy” to the winning state (like Paul Bunyan’s Ax for the football game). But I couldn’t decide on an appropriate one (Nurse Ratched’s (“The Big Nurse”) syringe filled with a million doses of vaccine?) and, in any case, it would need to stay in the trophy case in the middle of the Mississippi or St. Croix River.

Of course, as pointed out at the outset, this at best a mere impression with greater clarity awaiting more data and sophisticated statistical analysis by expert economists and epidemiologists. At best it is like a Monet painting, while later high-quality analysis by experts might be closer to a low-resolution black and white photograph. Both of which are far from the goal of a high-resolution color photograph. In my mind, all of this points out the nonsense of the vociferous and self-assured nature of the political debate that is going on over these public health restrictions. These are difficult decisions for which there are no clear or certain answers.

I guess that pushes people back to the self-assurance of their priors. If you are a Republican who is skeptical of any government interventions in the “market” or of limits on private behavior, you revert to that mode and are convinced the restrictions are a poor choice. As an aside, I would tend to think “conservatives” (in the Burkean sense of conserving or preserving what is good in the status quo) would be more conflicted and might lean to being careful about preserving health and life. Their lack of conflict probably says something about the flavor of conservatism that now dominates the Republican Party (if it actually is conservatism). Democrats, by contrast, instinctively favor communal efforts and have higher levels of trust in government and, not coincidentally, control the state executive branch. As a result, they default to favoring Governor Walz’s more restrictive approach. So, much of this pitched fight is likely little more than the usual partisan philosophical fight carried out on a new battlefield.

I normally avoid podcasts and webinars – reading is so much faster and a more efficient way to acquire information – but I was glad that I made an exception for this one, The economic impacts of COVID-19: Real-time evidence from private sector data, by Raj Chetty, hosted by the Benheim Center for Finance at Princeton.

Chetty is well known economist (I believe he is back at Harvard after at stint at Stanford) who has done a number of notable and path breaking studies. He and a team of economists and grad students have assembled a real time set of economic data constructed from private sector data – from credit and debit card transactions, job postings, small business revenues, education data, and so forth – made available by a variety of companies.

Unlike traditional government economic data constructed from surveys or administrative data with inherent lags (monthly or quarterly reporting), the private sector data is available immediately (daily). That allows real time analysis of what is going on. In addition, because the data represent the universe or close to it, not a sample, they allow analysis at much lower geographic levels (e.g., zip codes) and for shorter time periods. Those are two big advantages over traditional government data. Of course, issues include how reliable the data are and how closely they track traditional government data. The Webinar addresses those issues.

This effort is similar to what Google and many other businesses have been doing with their data – mainly for their internal business purposes (i.e., to make money), but occasionally for the public good as well such as using Google search data for public health purposes. Many have advocated that those methods should be applied to help understand and address public policy problems. That is what Chetty and his team are doing. The data base are available for download or to simply play with using their web tools at the tracktherecovery.org website.

Chetty and his team used this data, as suggested by the presentation title, to look at the economic impacts of COVID-19 and to evaluate the policy responses to it – e.g., stimulus checks, PPP loans, effects of reopening businesses, and so forth. Much of it is predictable (to me anyway), but some of it is eye opening – particularly the one day spending response to the big issuance of stimulus payments. For someone from Minnesota, the presentation uses Minnesota and Wisconsin data to evaluate the effects of reopening businesses. Spoiler alert: not much, although the graphs do show a very small diversion in consumer spending (Wisconsin being slightly higher) in the period after the court decision invalidating the governor’s executive order. (The vigorous partisan arguments on this issue probably represent much ado about nothing – reflecting little more than the parties deep animosity for each other and a seeming tribal need to fight over something.) The PPP loans don’t appear to have much effect.

As an aside, it confirmed to me the illogic and wastefulness of untargeted stimulus, such as providing stimulus checks to higher income households or to any and all businesses.

The Webinar is worth spending an hour and 25 minutes on, in my judgment. Probably more efficient that wading through the jargon and mathematics of the articles academic economists typically produce (disclosure: I have not attempted to find the article that was the basis for the webinar, but plan to do that).

Finally, national data on cases and deaths in long term care (LTC) facilities will be collected and published by the federal government, according to a WaPo story. The Center for Medicare and Medicaid Services (CMS) will collect facility-specific data and published it weekly, starting by the end of May.

CMS will impose a fine ($1,000/week – seems low to me) for failure to report, so one would expect compliance to be good and the data reliable. The penalty does not begin applying immediately, though. So far, Minnesota has not reported death data by facility, just case data (not sure why that decision was made). The federal data will give a first look at that for Minnesota, as well as staff cases and deaths, as well as some other data on PPE etc.

As I have noted before, the incomplete death data (for 36 states) that the Kaiser Family Foundation (KFF) reports show Minnesota has the highest death rate (i.e., the percentage of its COVID-19 deaths comprised of LTC residents) of any state by 4 percentage points. Minnesota’s rate is about twice the national average under the KFF data (81% compared to 41%).

The additional data from the CMS reports (e.g., on staffing infections and deaths) should allow making more reliable and comprehensive national comparisons. I hope CMS also publishes some baseline statistics, such as the number of residents and staff for each facility, whether it is a skilled nursing facility, assisted living facility, for-profit or non-profit, and so forth, along with the COVID-19 data. That would make it easier to see correlations without pulling in other data sources. In any case, the data should allow one to easily see how concentrated cases and deaths are in a few facilities. That appears to be the case in Minnesota with two facilities accounting over a fifth of the deaths, according to media reports.

That COVID-19 has hit LTC facilities so hard should be a wake-up call for regulators and policy makers. The financing problems have been long recognized (few can afford to pay or have insurance, Medicaid reimbursement rates are low, workers are very low paid, etc.). But COVID-19 has revealed that there are quality of service problems as well, undoubtedly somewhat linked to the finance issues.

In a May 8^th Strib op-ed (“Minnesota must recover from its pandemic of fear”), Katherine Kersten asserts: “99.24% of [Minnesota’s COVID-19] deaths involve[e] nursing home residents or people with underlying medical conditions.”

Two days later, the STRIB in an unsigned editorial (“Minnesotans need to understand and act on COVID-19 risks”) used the same precise statistic to make a slightly different assertion: “Of all those who have died from the mysterious viral illness statewide, 99.24% had an underlying health condition.”

On May 12^th, the STRIB ran another unsigned editorial (“Making sense of COVID-19 fatality rates”) citing yet again the same precise statistic: “So far in Minnesota, about 4 of every 5 fatalities have occurred in nursing homes or assisted-living facilities, and 99.24% of those who have died had an underlying health condition (as discussed in an editorial Sunday).”

Aside for the obvious inconsistency in their uses (unless the editorial board considers residence in a nursing home to be “an underlying health condition” or Kersten was being redundant in that regard), using a percentage carried to the hundreds place in this context seems laughably ignorant of the underlying data. It implies that the user knows the precise number of Minnesotans who meet at least two of three conditions: (1) they died from COVID-19, and (2) were a resident of nursing home or (3) had an underlying medical condition.  That follows because Minnesota has had fewer than 500 deaths (when the columns were published) and because deaths must be whole numbers (not to put too fine a point on it, but a person is either dead or not – no fractions here).  Thus, hitting a precise percentage like 99.24% must mean that there were 3 such COVID deaths out of a total of 395 (the May 2^nd number reported by MDH: 392/395 = 99.24%). For any of MDH’s reported death totals after May 2^nd (through May 12^th), one cannot derive 99.24% using an integer for deaths.

Of course, each day the number changes as more people die. (Rounding to something reasonable and it might not, but that could soften the rhetorical point of how really, really small the number is.) The bigger issue is that the underlying data is inherently imprecise; undoubtedly individuals are dying of COVID-19 who do not appear in the MDH counts because they died at home and/or were not tested. It has been widely recognized that current COVID death data is subject to substantial undercounts. For example, see this AP report from April 30^th that reports 66,000 excess deaths, many/some of must be due to unreported COVID-19.  Moreover, it is also unclear how reliable MDH’s data on residence status and underlying health conditions is – in fact, MDH admits (listing “unknown/missing” category for residence data) that it doesn’t always have data on the residence status for all decedents, the seemingly easier of the two to verify.

Bottom line: The Strib should be ashamed of publishing numbers like that in its editorials; it evidenced either carelessness or a lack of understanding of the imprecision of the data and how research statistics work.  False precision can be inadvertent, but often implies a desire to mislead. Allowing contributors like Kersten to do so also seems negligent to me.

Kersten piece is a separate case. I think a fair (probably charitable) characterization is that it is an intemperate effort to advocate for the latest right-wing hobby horse (keeping the economy semi-shut down for public health reasons is foolish) and evidences an extreme degree of confidence in its conclusion, while failing even superficially to satisfy the standards she sets out for reaching such a conclusion.

Its intemperate nature is obvious from a selection of her language characterizing Governor Walz’s actions to limit, the media coverage of, or the public’s perceptions of the risks of COVID-19 and SARS-CoV-2:

• “coronavirus hysteria”
• “apocalyptic scenario”
• “irrational panic”
• “frenzied, overblown ‘body count’ headlines”
• “herded into a massive new regime of political control over the details of ordinary life”
• “pummeled by apocalyptic propaganda”
• etc.

Whew!

It fails by its own terms. Kersten correctly notes that the government officials have a “duty to responsibly balance the risks of COVID-19 with the shutdown’s * * * costs” and the appropriately way to do is with “objective, data-based cost/benefit analysis that is indispensable to responsible crisis management.” She damns the Walz administration for failing to do that (to be fair she only says there is little evidence that they did).

I will not defend the Walz administration’s efforts in that regard since I am not competent to do so.  That would require combined expertise in epidemiology and economics; I have neither.  However, I would observe that the administration does appear to be making concerted and regular efforts to measure and weigh risks and benefits. They have models and are regularly receiving advice based on analysis of evidence by experts in the relevant fields.

That is certainly more than can be said for Kersten. She obviously disagrees with the administration’s models and experts. But she provides virtually no evidence why beyond two data points – the 99.24% (most people who die live in nursing homes or have some medical condition) and only a very small percentage of younger New Yorkers have died (again carried out to the hundreds of a percentage point – but at least New York has 20,000+ COVID-19 deaths!).

With regard to doing a cost-benefit analysis (as she says is indispensable), her piece provides no evidence that she is relying on such an analysis. If one has been done that provide the basis for her confident assertions, she makes no reference to it. Rather, we are left with simply trusting on her conclusory statement – no supporting evidence beyond the fact that almost all the Minnesotans who die live in nursing homes or have some underlying health conditions and that the economic cost is obviously high. (Note, as I have pointed out, Minnesota is an outlier in that regard.) I guess that is enough for her. To me it is not even close to “an objective, data-based cost/benefit analysis” that she says is indispensable. On that I agree with her.

Where is her or the Center of the American Experiment’s (CAE, Kersten’s employer) model and projections? What R0, R, CFR, IFR, and so forth is her model using? How many more people does she think will die if the shutdown ends (as she says it “must”)? How many more will become gravely ill but recover? How many will suffer organ damage as a result? How much will medical costs increase as a result, including those paid by the public? What are her assumptions about the values of the lives that will be lost? Is she discounting them because they typically are old or have high blood pressure, are obese, etc.? What is she assuming for the values of the hours of lost work (much less pain and suffering) for the individuals who will fall sick but not die as a result of ending the shutdown? There are many more questions (especially if someone who is actually knowledgeable starts asking) – for which there is no evidence that she or anyone else in her organization have carefully tried to analyze (using evidence and credible models) and answer. I guess we need to take it on faith, Kersten’s faith, for whatever that is worth. That is why her piece fails by the standard that she sets out. It appears to me to be faith-based, not evidence-based.

I would observe that CAE appears to have a lot of resources. (Its 2018 Form 990 shows nearly $4 million in revenue.) If they want to make a useful contribution, they could use some of that money to hire reputable researchers (epidemiologists in this case), rather than just lawyers, wordsmiths, rhetoricians, and similar to advocate for positions that largely appear to be based on their priors, rather than evidence and analysis.

I wish someone would shed light on why Minnesota’s COIVID-19 death rate in long term care (LTC) facilities appears to be so much higher than elsewhere. Yesterday (5/7) when I heard the topic of the daily press briefing was the state’s plan for addressing LTCs, I listened hoping to be enlightened. My hopes were not realized.

Someone asked Commissioner Malcolm why Minnesota’s LTC death rate was so high relative to other states. Her response was that it may be a reporting issue. Specifically she said Minnesota reports for all types of these facilities – both skilled nursing and assisted living – while other states may be reporting for skilled nursing facilities only.

For the Kaiser Family Foundation (KFF) data that is available publicly, that is an implausible explanation. (The KFF data on deaths is for only 33 states and MDH may have more comprehensive data for all states from some other source.) The reason I think that it is not a plausible explanation is that Minnesota’s LTC cases are in line with the national average in the KFF data, while its deaths are more than double the national average. Minnesota’s LTC death rate is higher than any of the 33 states for which KFF has data. If it’s a matter of Minnesota reporting for more types of facilities, I would expect Minnesota’s case rate also to be higher, not just deaths. Moreover, one would think that the death data are more reliable (i.e., less subject to the vagaries of testing practices).

The KFF numbers (for 5/7/2020) are in the table below – the national numbers (37 states for cases, including Minnesota; 33 states for the deaths, excluding Minnesota); the table lists the 3 states in the KFF data with the highest percentage of their deaths attributed LTC residents or staffers. Rhode Island is the state with the highest testing rate in the nation (4.5X Minnesota’s rate), which probably explains why it has a low case rate and high death rate (similar to Minnesota’s). Its high testing has likely detected a higher percentage of cases in the general population, whereas Minnesota has concentrated its much low rate of testing in LTCs and hospital (up until recently). The other two states have the expected pattern – high case rates as well as high death rates.

Location	LTC cases as % of all cases	LTC deaths as % of all state COVID deaths
U.S.	15%	38%
MN	16%	81%
RI	16%	73%
NH	27%	72%
PA	22%	70%

Media reports have revealed that a couple of Minnesota LTC facilities (both skilled nursing homes, I believe) account for 55 and 44 of Minnesota deaths. See stories here and here. Those two facilities account for over a fifth of Minnesota’s LTC COVID-19 deaths. It is possible that this is a story about a few poorly managed facilities. But why would Minnesota have proportionately more of those than other states? As someone who isn’t knowledgeable abut the industry, I have no idea.

The NY Times has a story that points out nonprofit, faith related LTCs tend to have the highest quality ratings, while for profit, LLC owned ones the poorest. The two Minnesota facilities reported by the media to have the high number of deaths (both coincidentally located in New Hope) fit into each of these categories: the one with highest number of deaths is in the former and the other in the latter. Unfortunately, MDH does not report death data by LC facility, so it is hard to determine much beyond what the media reports. The mystery (to me anyway) continues.

As of 5/6/2020, MDH is reporting the number of deaths for residents of long term care (LTC) facilities, such as nursing homes and assisted living facilities, in its daily situation update. Previously, I gleaned some information on that from sporadic media reports, which I assumed were generated by reporters’ questions to MDH officials.

LTC residents comprise an extremely high percentage (81% for 5/6) of Minnesota’s total COVID-19 deaths. Since I last posted about this, I have discovered that the Kaiser Family Foundation (KFF) has been collecting data from the other states that report on the number of cases and deaths in LTCs. It can be accessed here. The KFF data show that Minnesota is an outlier. KFF reports data from 33 states, but nine of those states (including Minnesota for the latest KFF table) do not report deaths. So, we have data for 24 states. For these states, reported LTC facility deaths, which may include providers for some states, are 31% of all COVID-19 case deaths. Rhode Island has highest reported percentage at 71%. Those numbers illustrate how high Minnesota’s 81% rate is.

As usual, this may be due partially to reporting differences, rather than actual experience attributable to policy, management, or demographic issues. Media reports have raised questions about other states’ data: 5/5/2020 WaPo story about NY having 1,700 previously undisclosed LTC deaths (adding these deaths would raise KFF’s national LTC death numbers by more than 10%) and a 5/1/2020 Miami Herald story that raised questions about undercounts in Florida’s reported data on LTC deaths are just two examples I noticed.

In any case, at some point Minnesota regulators and the LTC industry should address why Minnesota’s death rate appears to be so much higher than the national average. Is this something that results from policy, management, demographics, or some other factor? Can or how should it be addressed?