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Data Update

Massachusetts Data Update October 31,2020

Happy Halloween. Our ghoulish story continues – no surprise there. Test positivity rates are up across the board – particularly among the newly tested. Overall test positivity has increased as well.  Roughly three-quarters of all tests are for repeat testers.

Table 1: Massachusetts Testing Statistics
7 Day  Trailing Average
October 31, 2020
         
Testing Statistic   Current 7 Days Ago 4 Weeks Ago
         
Test Positivity Rate (Individuals)   6.5% 5.8% 3.6%
Test Positivity Rate (Include Suspected)   7.1% 6.1% 3.8%
         
Test Positivity Rate (All Tests)   1.9% 1.7% 1.1%
Test Positivity Rate (Newly Tested)   6.5% 5.8% 3.6%
Test Positivity Rate (Repeat Testers)   0.4% 0.4% 0.2%
Percentage Repeat Testers   76.0% 74.9% 73.1%
         
Newly Tested (Lagged 1 Week)   16,494 15,234 14,649
All Tests (Lagged 1 Week)   65,827 62,587 57,189

 

Not surprisingly, hospitalizations are increasing as well,, but not as rapidly as the increase in cases, as the average age of new cases is lower than in the spring. We’re basically at the same point we were in early-to-mid July, but hospitalizations were decreasing then, and they are now increasing.

Table 2: Massachusetts Hospitalization Statistics
7 Day Trailing Average
October 31, 2020
         
Hospitalization Statistic   Current 7 Days Ago 4 Weeks Ago
         
Covid Patients Hospitalized   570 523 426
Covid Patients in ICU   107 99 85
Covid Patients Intubed   48 37 30
New Confirmed Patients   52 46 33
         
Percent ICU / Hospitalized   19% 19% 20%
Percent Intubated / ICU   44% 38% 35%

 

Cases continue to rise, with reported confirmed and suspected cases above 1,000 for ten consecutive days.  Deaths are also slowly increasing, but the trend now is for a lower percentage from long-term care facilities.

Table 3: Massachusetts Reported Case and Death Statistics
7 Day Trailing Average
October 31, 2020
         
Statistic   Current 7 Days Ago 4 Weeks Ago
         
Total Deaths Including Suspected   22 17 17
Total  Deaths Confirmed Only   21 16 16
Deaths in Long-Term Facilities (All Cases)   12 8 10
Percent from Long-Term Care   56% 51% 62%
         
Total Cases Including Suspected   1302 939 608
Total Confirmed Cases   1214 874 569

 

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Other

How is Massachusetts Doing Compared to Other States? Part II: Cases

This is the second post comparing Massachusetts’ Covid statistics to other states, but with a focus on cases. I’m using the daily historical data from the Covid Tracking Project (CTP) to perform calculations, but used the New York Times and Worldometers databases to check figures from the CTP.

 

Table 1: Largest Case Discrepancies Among Data Aggregators
Data through October 28, 2020
                 
  Data Sources
                 
  NYT/World   NYT/CovTrack   World/CovTrack
                 
Largest Alaska 5.6%   Iowa 10.3%   Iowa 10.5%
2nd Largest New York 4.6%   Wyoming 10.0%   Wyoming 10.0%
3rd Largest Wisconsin 4.2%   Kansas 9.5%   Kansas 9.2%
4th Largest Georgia 3.0%   Texas 8.0%   Texas 8.4%
5th Largest Louisiana 2.3%   Montana 7.8%   Missouri 8.1%

 

Table 1 summarizes the discrepancies between the three data aggregators for case data through October 28th.  The entries in the tables are the percentage difference in the case totals to date for each pair of aggregators.  As with deaths, the best agreement was between the New York Times and Worldometers, and the CTP differed more from the other two aggregators.

In the first post in this series, I defined three phases to the pandemic (Spring – ending May 28th, Summer – ending September 12th, and Fall – ongoing) based on peaks and troughs in national case data from the New York Times. Table 2 shows the case rates and rank for Massachusetts as well as the top-ranked and bottom-ranked states (including Washington D.C.) for each phase of the pandemic. (The W in the Massachusetts rankings stands for Worst).

Table 2: Case Rates per 100000 and Rank By Pandemic Phase
March 1 through October 28, 2020
                       
  Pandemic Phase
  Total   Spring   Summer   Fall
                       
MA 34th W 2,184   4th W 1,377   46th W 430   40th W 378
                       
Worst  ND 4,950   NY 1,885   FL 2,832   ND 2,961
2nd Worst SD 4,431   NJ 1,777   AZ 2,615   SD 2,573
3rd Worst LA 3,949   RI 1,381   LA 2,545   WI 1,990
4th Worst MS 3,867   MA 1,377   MS 2,528   MT 1,774
5th Worst AL 3,760   DC 1,203   AL 2,475   UT 1,486
                       
Best  VT 332   MT 45   VT 113   VT 63
2nd Best ME 461   HI 45   ME 197   ME 102
3rd Best NH 759   AK 58   NH 248   NH 197
4th Best OR 998   OR 97   CT 358   NY 266
5th Best HI 1,042   WV 106   NY 395   HI 294

 

Table 2 clearly shows how the geographic concentration of the pandemic has shifted over time.  The Northeast had the most per capita cases in the spring; the Southeast and Southwest in the summer; and parts of the Upper Midwest, Rocky Mountain, and Great Plains states in the fall. In fact, the Northeastern states generally had the lowest case rates in the summer and fall – only Hawaii in the fall broke the Northeast’s monopoly on the lowest five case rates.  Massachusetts fits this pattern as well, even if its case rate is not among the five best. 

The table also indicates how per capita case rates have increased after the spring, even though death rates were much higher in that period.  The spring 7 day average national peak was almost 32,000 cases per day, compared to almost 67,000 cases in the summer and 78,000 cases now.  Some of this increase is because more testing is being done now than in the spring, resulting in more cases being diagnosed. 

In Massachusetts, for example, the average daily number of tests more than doubled from the spring (starting on March 15th) to the summer, and then almost tripled from the summer into fall.  However, much of that summer to fall increase has been driven by higher education testing, which now accounts for about half of all testing in the state.  Nonetheless, the increase is significant.

These statistics, when compared to the death rate findings in the prior post,  point out one of the central puzzles with covid in Massachusetts.  Massachusetts is among the states with the lowest number of per capita cases, especially after the spring, but among the states with the highest number of per capita deaths overall, and higher than one would expect in either the summer or fall. 

Why? The obvious answer is the case fatality rate (CFR) – the percentage of cases that lead to death.  To look at CFRs across states, I compared case rates through the end of the spring and summer phases to deaths rates through the end of the corresponding trough in deaths.  For example, I looked at case totals through May 28th in each state, and compared them with deaths through July 5th (from Table 1 in the prior post that defines the phases).  Implicitly, I’m assuming about a five week time lag between diagnosis and death.  Since the fall phase is still underway, I only performed this analysis for the spring and summer.

Because I’m using about a five week time lag, I may be overstating the CFR for the two phases. Nonetheless, this exercise does provide insight into the Massachusetts conundrum. Table 3 summarizes the results.  Death data is only through October 16, as that is the trough in deaths from the summer phase.

 

Table 3: Case Fatality Rates and Rank By Pandemic Phase
March 1 through October 16, 2020
                 
  Pandemic Phase
  Total   Spring   Summer
                 
MA 3rd W    7.8%
   7th W 8.6%   1st W 5.1%
                 
Worst  CT 8.4%   CT 10.4%   MA 5.1%
2nd Worst NJ 8.3%   AZ 10.2%   WV 2.8%
3rd Worst MA 7.8%   MI 9.9%   NJ 2.8%
4th Worst NH 6.1%   PA 9.6%   MS 2.7%
5th Worst MI 5.9%   NJ 9.6%   NH 2.6%
                 
Best  AK 0.9%   SD 2.0%   VT 0.3%
2nd Best UT 0.9%   UT 2.1%   UT 0.7%
3rd Best WY 1.3%   NE 2.2%   AK 0.7%
4th Best NE 1.4%   WY 2.3%   NY 0.9%
5th Best ID 1.5%   HI 3.0%   NE 1.0%

 

The use of a five week lag in deaths does seem to jumble some of the reporting.  For example, Arizona has the second highest CFR during the spring.  This is almost certainly because some deaths from the summer phase are paired with cases from the spring phase when Arizona had a relatively low case rate.  

The table shows the dramatic reduction in CFRs from the spring to the summer.  Outside of the Massachusetts outlier, the worst CFRs in the summer aren’t much higher than the best CFRs from the spring.  There are likely several reasons for this.  As noted before, increased testing has presumably led to the identification of less ill patients.  Second, the average age of people diagnosed with Covid has decreased.  Finally treatment protocols have gotten better (https://www.nytimes.com/2020/10/29/health/Covid-survival-rates.html)

However, a very surprising result is the out-sized Massachusetts’ CFR in the summer, significantly higher than any other state.  Not all of this can be explained by the death lag improperly assigning cases and deaths to the wrong phase.  Even as Massachusetts’ relative case rate has come down, its death rate has  stayed relatively high.  Is this the long-term care issue again?

To check this, I performed the same hypothetical as in the prior post.  What if the percentage of deaths from long-term care facilities in Massachusetts were 40% instead of 70%?  In that case, Massachusetts’ overall CFR for the spring and summer would have dropped to 4.6%, ranking it 8th highest overall instead of 3rd.  Similarly, the state would have improved only to the 5th worst CFR in the summer period.  Some improvement, but the state is still a laggard.  There is something else going on.

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Other

How is Massachusetts Doing Compared to Other States? Part I: Deaths

I’ve focused most of my attention on tracking the internal dynamics of the coronavirus in Massachusetts, with only one previous post with a comparison to other states, (https://www.masscoronavirus.net/massachusetts-isnt-as-great-as-it-thinks-it-is/) back at the end of August. But one of the things that has stood out  is Massachusetts’ very high death rate relative to other states.  It has either the second or third highest (more likely 3rd, more on this later) per capita death rate among states over the course of the pandemic, but a high percentage of deaths in Massachusetts were during the spring.  As a consequence, I wanted to analyze how well Massachusetts has been handling the pandemic relative to other states since the spring – if possible examining cases, deaths, testing and test positivity.

Methodology

I used data from the Covid Tracking Project (CTP) (https://covidtracking.com/data) as my primary source, because it is easy to download daily historical state-by-state information from their website. However, I checked the CTP’s overall case and death statistics against the New York Times (https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html?action=click&module=Top%20Stories&pgtype=Homepage) and Worldometers (https://www.worldometers.info/coronavirus/country/us/) databases.  All data is as of October 28.

I defined three phases of the pandemic to date using the New York Times calculation of the 7-day average of the national total of cases, looking for troughs in the data to define the end of each phase of the pandemic. Table 1 summarizes peaks and troughs for both cases and deaths.

Table 1: Pandemic Phases Based on Data Peaks and Troughs
March 1 through October 28, 2020
       
  Peak and Trough Dates
       
  Beginning Peak Trough
       
Pandemic Phase Cases
       
Spring 1-Mar 10-Apr 28-May
Summer 29-May 25-Jul 12-Sep
Fall 13-Sep    
       
Pandemic Phase Deaths
       
Spring 1-Mar 17-Apr 5-Jul
Summer 6-Jul 1-Aug 16-Oct
Fall 17-Oct    

For example, based on national case data, the spring phase of the pandemic lasted from March 1st through May 28th, because May 28th is the day when total cases fell to a minimum after peaking on April 10th (they began increasing again on May 29th).  Based on national death data, the spring phase would have ended on July 5th.

There are some interesting tidbits in Table 1.  First, the troughs in cases coincide with a few days lag to the holidays that bookend the summer – Memorial Day and Labor Day.  This does not necessarily imply that increased social activities on those holidays led to upswings in cases, but they certainly may have played a part. 

Second, the death peaks for the spring and summer phases occur roughly one week after the case peaks, but the death troughs occur roughly five weeks after the case troughs.  I would not read too much into this, as the national statistics are just the sum of state statistics, each of which has its own ebb and flow of cases and deaths. In addition, this is based on a sample of only two phases for the pandemic (and let’s hope there are not many more).  However, the lag between the case and death troughs correspond more naturally to our understanding of how covid progresses from diagnosis to death.

Based on the peaks and trough data in Table 1, I defined the pandemic phases using troughs based on cases, even when I analyze death data.  This is primarily because the current (Fall) phase based on death data has had too few days to draw any meaningful conclusions (twelve days so far)

A few caveats about the data are in order.  First, the CTP generally obtains its data from state agencies charged with compiling and publishing Covid information for that state.  To the extent that different states have different procedures for counting cases and deaths, the data published by Covid Tracking is not uniform across jurisdictions. 

Second, the CTP (and Worldometers, for that matter) are not particularly adept at handling data restatements from the states.  For example, when Massachusetts dropped roughly 8,000 probable cases and 26 deaths from its tally in early September, both sources just showed one day drops in case and death totals reflecting those changes.  In other words, neither data aggregator restated the data historically, so that it appeared that there were a large number of negative cases in Massachusetts on September 2nd.  This means that although the total number of cases and deaths are accurate, they are improperly distributed into the three pandemic phases. 

Finally, although agreement among the three data aggregators is generally good, there are some large discrepancies in aggregate death and case statistics that call into question some of the results from the Covid Tracking Project. 

Deaths

Table 2, a comparison of the largest differences between the cumulative death rates among the three data sources, illustrates these issues.  The entries in the tables are the percentage difference in the death rate to date for each pair of aggregators.  As is evident from the table, the agreement was best between the New York Times and Worldometers, and second best between the CTP and Worldometers. 

Table 2: Largest Death Discrepancies Among Data Sources
March 1 through October 28, 2020
                 
  Data Sources
                 
  NYT/World   NYT/CovTrack   World/CovTrack
                 
Largest Alaska 5.6%   North Dakota 30.4%   New York 30.5%
2nd Largest Kentucky 4.6%   New York 28.5%   North Dakota 29.1%
3rd Largest Washington 4.2%   Colorado 9.4%   Colorado 8.0%
4th Largest Wisconsin 3.0%   Alaska 5.6%   Missouri 4.6%
5th Largest Georgia 2.3%   Washington 5.0%   Texas 3.3%

 

Table 2 points out particular issues for the Covid Tracking Project data for New York and North Dakota, where the agreement between the New York Times and Worldometers is close.  The CTP shows 25,773 deaths for New York, compared to over 33,000 for the other two aggregators.  The CTP data aligns with the NY State website, which excludes probable cases from its totals.   This implies that the CTP is under counting deaths in New York. (Is Cuomo fudging the stats?)

For less clear reasons, The CTP is also under counting deaths for North Dakota relative to Worldometers and the New York Times.  Death totals from these two sources align much more closely with the North Dakota state website.

These data discrepancies have implications for the results shown in Table 3, which show the death rates and rank for Massachusetts as well as the top-ranked and bottom-ranked states (including Washington D.C.) for each phase of the pandemic.

Table 3: Death Rates per 100000 and Rank By Pandemic Phase
March 1 through October 28, 2020
                       
  Pandemic Phase
  All   Spring   Summer   Fall
                       
Massachusetts  2nd Worst
 144.0   4th Worst
 96.3   9th Worst
 37.1   21st Worst
 10.6
                       
Worst  NJ 183.8   NJ 147.5   MS 66.9   ND 32.1
2nd Worst MA 144.0   NY 121.9   AZ 61.2   AR 30.0
3rd Worst NY 132.5   CT 107.3   LA 53.0   SD 22.7
4th Worst CT 129.1   MA 96.3   SC 49.9   MS 20.7
5th Worst LA 126.7   DC 64.2   FL 48.0   MO 19.0
                       
Best  VT 9.3   HI 1.2   VT 0.5   VT 0.0
2nd Best AK 9.7   AK 1.4   ME 3.8   ME 0.8
3rd Best ME 10.9   MT 1.6   AK 4.6   NY 2.0
4th Best WY 13.3   WY 2.6   WY 4.7   NH 3.2
5th Best HI 15.2   UT 3.3   HI 5.6   CO 3.4

 

Based on the CTP data, Massachusetts has the second highest per capita death rate from the pandemic in total.  However, Massachusetts would drop to 3rd place if New York State included probable cases.  Even so, Massachusetts has had a very high relative rate of deaths in each phase of the pandemic, only dropping to 9th worst during the summer, and to 21st worst for the fall pandemic to date.  Not an enviable record.

This table makes concrete how the pandemic has affected different areas of the country in the three phases to date.  As it well known, the first and deadliest phase in the spring affected the Northeast most heavily – of the eleven states (and D.C.) with the highest death rates, all but Michigan and Louisiana were in New England or the Mid-Atlantic.  Only the more rural states in New England were spared.

The summer phase impacted the sunbelt most heavily, with eight of the ten states with the highest death rates in the Southeast or Southwest.  But Massachusetts and Rhode Island were in the top ten as well. Finally, the current phase is more of a mixed bag, although the states with the highest death rates are generally more rural. There is less clarity about this phase to date.

Overall, the states with the lowest death rates overall are sparsely populated, or geographically isolated (Hawaii), or both.  This is true for the ten states with the lowest death rates overall, except for Oregon and Washington. While there have been some changes in the ranking of these low death rate states during different phases or the pandemic, for the most part this has been the case.

Why has Massachusetts fared so poorly?  An obvious thought is that this is from the high percentage of deaths in long-term care facilities in the state.  Almost 70% of Massachusetts deaths have been in long-term care facilities, compared to about 40% nationwide.

However, this is not the entire answer.  As a hypothetical, for each phase in the pandemic and in total, I adjusted Massachusetts’ deaths so that 40% instead of 70% were in long-term care facilities.  What would have changed?  In overall death rates, the state would have dropped from 2nd (or 3rd) overall to 9th, from 4th to 7th during the spring phase, from 9th to 28th during the summer phase, and from 21st to 30rd during the fall phase.  Improved, but not stellar. Because death rates during the spring were so high compared to later death rates, those early deaths dominate the overall results, even adjusting for long-term care deaths.

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Data Update

Massachusetts Data Update October 27, 2020

The cat’s out of the bag, and it isn’t going to be easy getting it back in. We hit two milestones today – 7 day average test positivity for the newly tested is now 6% and the 7 day average of reported confirmed cases is over 1000 per day. It took 16 days for test positivity to increase from 2 to 3%, 17 days to increase from 3% to 4%, then 7 days from 4 to 5%, and 6 days to reach 6%.

Table 1: Massachusetts Testing Statistics
7 Day  Trailing Average
October 27, 2020
         
Testing Statistic   Current 7 Days Ago 4 Weeks Ago
         
Test Positivity Rate (Individuals)   6.0% 4.8% 3.3%
Test Positivity Rate (Include Suspected)   6.4% 5.1% 3.5%
         
Test Positivity Rate (All Tests)   1.7% 1.4% 1.1%
Test Positivity Rate (Newly Tested)   6.0% 4.8% 3.3%
Test Positivity Rate (Repeat Testers)   0.4% 0.3% 0.2%
Percentage Repeat Testers   75.8% 76.2% 72.8%
         
Newly Tested (Lagged 1 Week)   16,008 15,768 14,423
All Tests (Lagged 1 Week)   67,301 59,430 55,015

 

All the hospital statistics hit highs not seen since early-to-mid July.  The 7 day average of hospitalized patients is up 8% from one week ago, and the count of patients in the ICU or intubated is up more than 25%.  New hospitalizations are up almost 20%.  Enough said.

Table 2: Massachusetts Hospitalization Statistics
7 Day Trailing Average
October 27, 2020
         
Hospitalization Statistic   Current 7 Days Ago 4 Weeks Ago
         
Covid Patients Hospitalized   545 502 393
Covid Patients in ICU   108 87 82
Covid Patients Intubed   42 33 28
New Confirmed Patients   49 41 28
         
Percent ICU / Hospitalized   20% 17% 21%
Percent Intubated / ICU   39% 38% 35%

 

As expected, cases are leading the charge, presumably indicating that even more hospitalizations and perhaps deaths will follow.  The 7 day new confirmed case average has increased by almost 50% in a week, and more than doubled from 4 weeks ago.  The number and proportion of deaths in long-term care facilities declined, but since the average number of deaths has stayed the same, this means that people dying are generally younger and healthier than before. 

 

Table 3: Massachusetts Reported Case and Death Statistics
7 Day Trailing Average
October 27, 2020
         
Statistic   Current 7 Days Ago 4 Weeks Ago
         
Total Deaths Including Suspected   19 18 14
Total  Deaths Confirmed Only   18 18 13
Deaths in Long-Term Facilities (All Cases)   8 12 9
Percent from Long-Term Care   45% 66% 64%
         
Total Cases Including Suspected   1085 724 517
Total Confirmed Cases   1009 676 482

 

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College Testing

Massachusetts College Testing Update October 26, 2020

Over the past three weeks, there has been little change concerning college testing.  Higher education institutions in Massachusetts continue to perform widespread testing and well outperform the state as a whole in terms of test positivity and case rates. Table 1 summarizes the testing statistics to date for the twelve Boston area institutions on which I’ve been focusing, as well as UMass Amherst .  Cumulative test positivity remains very low, with only Boston College above 0.2%.

Table 1: Greater Boston Area College Covid Testing
Cumulative Testing Results
October 25, 2020
           
  Initial         
  Results As Of Total Positive Positive
College/University Date Date Tests Tests Rate %
           
Babson 5-Aug 23-Oct 24,360 17 0.07%
Bentley 17-Aug 22-Oct 31,695 55 0.17%
Boston College 16-Aug 22-Oct 69,753 229 0.33%
Boston University 27-Jul 24-Oct 282,996 214 0.08%
Brandeis 12-Aug 22-Oct 53,051 16 0.03%
Emerson 6-Aug 22-Oct 32,157 26 0.08%
Harvard  1-Jun 24-Oct 140,731 80 0.06%
MIT 16-Aug 24-Oct 140,251 67 0.05%
Northeastern 17-Aug 23-Oct 298,848 150 0.05%
Suffolk 18-Sep 22-Oct 22,903 35 0.15%
Tufts 3-Aug 23-Oct 120,104 44 0.04%
UMass Amherst 6-Aug 23-Oct 108,097 157 0.15%
Wellesley 16-Aug 24-Oct 30,379 2 0.01%
           
Total     1,355,325 1092 0.08%

 

Table 2 shows testing statistics for the past week.  There have been very small outbreaks at Bentley College and Suffolk University, but the number of cases remain small.  Boston-area colleges are in very good shape.

Table 2: Greater Boston Area College Covid Testing
Latest Weekly Results
October 25, 2020
         
    Average Weekly  
  As Of Daily Positive Positive Test
College/University Date Tests Tests Percent
         
Babson 23-Oct 339 4 0.17%
Bentley 22-Oct 623 15 0.34%
Boston College 22-Oct 1,095 11 0.14%
Boston University 24-Oct 4,504 45 0.14%
Brandeis 22-Oct 642 2 0.04%
Emerson 22-Oct 497 4 0.12%
Harvard  24-Oct 2,439 7 0.04%
MIT 24-Oct 2,362 8 0.05%
Northeastern 23-Oct 4,681 18 0.05%
Suffolk 22-Oct 454 12 0.38%
Tufts 23-Oct 2,143 2 0.01%
UMass Amherst 23-Oct 1,764 5 0.04%
Wellesley 24-Oct 487 1 0.03%
         
Total   22,029 134 0.09%

 

Higher educational testing accounts for almost half of all testing in the state.  Test positivity rates are very low in higher education (both the cumulative rate and the rate over the past week are 0.09%).  As Figure 1 indicates, this means that test positivity outside of higher education is substantially higher than the “all test” positivity rate that the state highlights in the daily dashboard.  Even as the overall test positivity rate began to increase in late September, higher education test positivity rates remained low and steady. Outside of higher education, positivity rates are now about 2.5%. In addition, since August 15, only about 4% of all confirmed Covid cases in Massachusetts are associated with higher educational institutions.

 

 

Categories
Data Update

Massachusetts Data Update October 24, 2020

This is another graphical data update, similar to the one for October 3, because it provides long-term context to analyze Covid trends in Massachusetts. Unfortunately, we’ve once again passed a negative testing milestone – several days ago the 7 day average positivity rate for newly tested individuals reached 5% for the first time since June 3rd. Figure 1 shows testing positivity rates.

Figure 1 indicates that test positivity rates for newly tested individuals have been steadily increasing since late August after a long period in which they had been very low. In contrast, positivity rates for repeat testers have stayed low – increasing very slightly from 0.2% to 0.3% during October. The “all-test” positivity rate is a weighted average of the repeat tester and new tester positivity rate, and has increased for about a month, but relatively slowly. Over 75% of all testing is for repeat testers, holding the overall testing positivity rate in check.

Figure 2 shows hospitalization trends, with the total number of hospitalized patients on the right-hand axis, and the number of ICU patients, intubated patients, and new hospital admissions on the left hand axis. All the hospitalization metrics have been rising gradually since Labor Day, but at a slower rate than either cases or individual positivity. This may be in part because the case demographics have shifted younger compared to early in the pandemic, requiring fewer hospitalizations.

Figure 3 shows total confirmed and suspected cases on the right-hand axis and long-term care deaths and total deaths on the left-hand axis. The 7 day average number of cases has almost tripled since Labor Day and is now higher than on June 1 and rising very rapidly.  This case increase is not from increased testing, but is from higher positivity rates. Today’s newly reported confirmed cases topped 1000.  In contrast, deaths have stayed relatively constant since the end of July. Since June 1, 70% of all deaths have been from long-term care facilities. Of course, deaths (and hospitalizations) lag cases by at least several weeks, so it’s a bit early to discern longer-term trends.

Categories
Community Testing

Massachusetts Community Spread Update October 17, 2020

This is a follow-up to the first post documenting how the coronavirus has differentially impacted communities in Massachusetts, using the state’s weekly public health report for data. That post https://www.masscoronavirus.net/community-spread-in-massachusetts-september-26-2020/ described some of the issues with the weekly report, as well as my methodology for calculating how a particular city and town has contributed to the change in either case incident rates or test positivity rates in the state.

Table 1: Highest Case Incident Rates and Contribution to Case Incidence Increase
Period Ending October 17, 2020
               
Highest Case Incident Rates   Largest Contributors to Weekly Case Incidence Increase
               
City/Town 14 Day Case Count Daily Case Rate per 100,000   City/Town 14 Day Case Count Daily Case Rate per 100,000 % Contribution
               
Middleton* 89 61.6   Boston 1166 12.0 6.2%
Lawrence 567 45.9   Lynn 244 17.3 5.0%
Chelsea 150 28.4   Revere 230 27.0 4.2%
Revere 230 27.0   Gloucester 77 19.2 3.6%
Everett 159 23.4   Lawrence 567 45.9 3.5%
Kingston 41 21.6   Brockton 202 14.7 2.7%
Webster 49 20.4   Chicopee 76 9.5 2.2%
Marlborough 121 19.9   Saugus 71 17.8 2.1%
Acushnet 29 19.8   Methuen 136 18.1 1.9%
Gloucester 77 19.2   Wakefield 59 15.6 1.8%
               
Total/State 8980 9.2   Total/State 8980 9.2 33.2%

The three left hand columns focus on the communities with the highest per capita case incident rates for the two-week period ending October 17.  The state did make one change to the report, adding an asterisk to communities to indicate where some of the cases are institutional and not directly attributable to the local community.  A municipality has an asterisk if it has a long-term care facility, a higher-education facility, or a correctional facility that (1) has had more than ten cases in the past fourteen days, and (2) these cases are more than 30% of cases in the community over the past fourteen days.  In the case of Middleton, at the top of the list, Middleton House of Corrections has had a large covid outbreak.

The four right hand columns focus on the communities which contributed the most to the increase in the statewide case incidence rate.  As I discussed in the prior post, communities on this list tend to be either large in population (e.g., Boston, which is on the list because of its size), or have had a very large change in case incidence (e.g., Gloucester and Saugus, both with populations of abut 30,000).  Boston’s case incidence rate increased by about 8%, but Gloucester’s rate almost tripled. The case incidence rate increased statewide from 8.7 cases to 9.2 cases per 100,000 population per day.

The last column in Table 1 shows the percentage contribution of each community to the statewide increase. This column emphasizes that the case increase has been distributed widely across the state, rather than concentrated in just a few communities.

Table 2: Highest Positivity  Rates and Contribution to Positivity Rate Increase
Period Ending October 17, 2020
               
Highest Test Positivity Rate   Largest Contributers to Weekly Positivity Rate Increase
               
City/Town 14 Day Test Count Test Positivity Rate (%)   City/Town 14 Day Test Count Test Positivity Rate (%) Relative Impact (%)
               
Buckland 21 23.8%   Boston 173,409 0.8% 6.7%
Middleton* 1,273 8.9%   Lawrence 9,175 8.2% 5.9%
Lawrence 9,175 8.2%   Lynn 7,399 4.3% 5.6%
Revere 6,238 4.7%   Revere 6,238 4.7% 4.3%
Berkley 395 4.3%   Gloucester 2,953 2.8% 4.3%
Everett 4,580 4.3%   Brockton 8,417 3.0% 3.5%
Lynn 7,399 4.3%   Chicopee 4,090 2.3% 2.5%
Chesterfield 74 4.1%   Wakefield 2,816 2.6% 2.4%
Methuen 4,520 4.0%   Methuen 4,520 4.0% 2.1%
New Bedford 5,687 4.0%   Saugus 2,694 2.9% 2.0%
               
State 885,959 1.24%   Total/State 885,959 1.24% 39.2%

The left three columns in Table 2 focus on the communities with the highest test positivity rate.  There are some surprising names on the list, in particular Buckland and Chesterfield.  This is because they have high positivity rates with very limited testing – Buckland had five positive cases in the two weeks ending October 17, and Chesterfield  had three.  These are hardly hotspots.  However, four of the top ten communities for case incidence rates are also in the top ten for test positivity rates.

The right-hand columns focus on the communities making the largest contribution to the statewide positivity increase from 1.17% to 1.24%.  Boston tops this list as well, even with a very small increase in test positivity from 0.74% to 0.8%, because of its large volume of testing.  In the two weeks ending October 17, almost 20% of testing in Massachusetts was in Boston.  In contrast, Gloucester and Saugus are not large testers, but each had test positivity rate increases of about 1%. The communities driving the increase in case incidence are also driving the increase in test positivity – the top ten communities in Table 1 and Table 2 are the same.

 

Categories
Data Update

Massachusetts Data Update October 20, 2020

The adverse trend continues. The 7 day positivity rate for newly tested individuals is 4.5%. Overall testing has flattened out, at least in the short-term, both for repeat testers and new individuals. The percentage of repeat testers is closing in on 80%, which has helped keep the overall test positivity rate in check, as repeat tester positivity is still 0.3%.

Table 1: Massachusetts Testing Statistics
7 Day  Trailing Average
October 20, 2020
         
Testing Statistic   Current 7 Days Ago 4 Weeks Ago
         
Test Positivity Rate (Individuals)   4.5% 3.8% 2.4%
Test Positivity Rate (Include Suspected)   4.9% 4.0% 2.5%
         
Test Positivity Rate (All Tests)   1.3% 1.2% 0.8%
Test Positivity Rate (Newly Tested)   4.5% 3.8% 2.4%
Test Positivity Rate (Repeat Testers)   0.3% 0.3% 0.2%
Percentage Repeat Testers   77.5% 73.4% 73.8%
         
Newly Tested (Lagged 1 Week)   15,782 16,077 16,588
All Tests (Lagged 1 Week)   59,307 61,602 55,840

 

Overall hospitalizations are relatively stable and haven’t risen as rapidly as cases, even accounting for lags from diagnosis to hospitalization. In the last four weeks, the 7 day case totals have almost doubled, but hospitalizations are up only 38% and have been level for over a week. The number of patients in the ICU has held steady since the end of September.  In contrast, new confirmed hospital admissions are at their highest levels since the end of June.

Table 2: Massachusetts Hospitalization Statistics
7 Day Trailing Average
October 20, 2020
         
Hospitalization Statistic   Current 7 Days Ago 4 Weeks Ago
         
Covid Patients Hospitalized   502 508 362
Covid Patients in ICU   87 85 65
Covid Patients Intubed   33 29 28
New Confirmed Patients   41 36 23
         
Percent ICU / Hospitalized   17% 17% 18%
Percent Intubated / ICU   38% 34% 43%

 

The 7 day average of new cases continues to rise, roughly commensurate with the higher positivity rate.  Deaths in long-term care facilities are back near their long-term average of about 70%, and the overall number of deaths remains high.

Table 3: Massachusetts Reported Case and Death Statistics
7 Day Trailing Average
October 20, 2020
         
Statistic   Current 7 Days Ago 4 Weeks Ago
         
Total Deaths Including Suspected   18 13 15
Total  Deaths Confirmed Only   18 13 15
Deaths in Long-Term Facilities (All Cases)   12 8 11
Percent from Long-Term Care   66% 64% 72%
         
Total Cases Including Suspected   724 638 368
Total Confirmed Cases   676 601 349

 

Categories
Age Analysis

Massachusetts Covid Breakdown by Age Part V: Cumulative Death Rates

This post builds on the previous post to develop a better understanding of how covid has impacted death rates by age over time in Massachusetts.  As I’ve already commented ad nauseam, the state’s reporting of cases and deaths makes it extremely difficult to pull this information together.  This, coupled with the change from emphasizing individual-based positivity rates to test-based positivity rates makes me wonder if the state is deliberately obfuscating information to paint a rosier picture or to make it difficult to perform useful analysis. For example, Figure 1 shows the cumulative covid death rate by age over time.

 

 

This means that over 2% of seniors 80 and over in Massachusetts have died from or with Covid, regardless of whether they were living in long-term care, assisted living, or independently. But some have made the argument “they were going to die soon anyway”.  Aside from its callousness, is there any truth to this statement?  Table 1 sheds some light on this.

 

Table 1:  Estimated Covid Mortality Rate Increases in Massachusetts
Rates are Per 100,000
         
Statistic 20 to 39 Inclusive 40 to 59 Inclusive  60 to 79 Inclusive 80 and Over
         
Cumulative Covid Death Rate to October 10 1.8 25.4 235.3 2,043.8
Estimated Weekly Covid Death Rate Run Rate 0.0 0.4 2.5 20.4
         
Extrapolated Annual Covid Death Rate 2.5 33.8 292.2 2,514.1
2017  US Mortality Table Annual Death Rate 133.6 433.0 1,975.4 10,080.4
         
Percentage Death Rate Ratio 1.9% 7.8% 14.8% 24.9%

 

Table 1 requires explanation.  I’ve dropped ages under 20 from the table as there has been only one reported death in Massachusetts from covid in that age group. The first line is just the end point of the lines for each age cohort from Figure 1, converted from percentages to rates per 100,000.  For the 80 and over age cohort, for example, it shows the 2% figure for deaths.

The second line is an estimate of ongoing weekly date rates for each age cohort, based on the average number of weekly deaths since August 1st.  This assumes that deaths will continue at these levels – but rates could increase if the pandemic worsens in Massachusetts (there are signs of that since Labor Day), or decrease if treatment protocols become more effective or cases become less severe.  Using this average implies, for example, that 2.5 people per 100,000 between 60 and 79 will die each week from covid in Massachusetts going forward (this is about 32 people total). The third line uses the run rate from the second line to extrapolate the current death rates to a full year (it assumes 23 more weeks). This implies that about 0.3% of people 60 to 79 will have died from covid over one year.

The fourth line is the baseline mortality rate for each age cohort, based on the latest available mortality table for the US.  About 10% of people 80 and over in the US died from all causes in 2017, as did almost 2% of people from 60 to 79.  People in Massachusetts have a higher life expectancy than those in the US, so these age-specific death rates for the state might be somewhat, but not significantly, lower.  Finally, the last, and most important line, is the ratio of the estimated covid death rate in Massachusetts for one year to the total US death rate in 2017.

This emphatically does not mean that excess mortality from covid is close to 25% for those 80 and older, for example.  It certainly is true that some people in that age group who died from covid would have died anyway over the year – covid just hastened their death.  But that is certainly not true for all people 80 and over.  That applies to younger people as well (but the percentage of younger people who died prematurely from covid is certainly much higher). 

These ratios strike me as quite high for people 40 and older.  I was most surprised by the almost 8% ratio for 40 to 59 year old people, who are generally quite healthy, and have a baseline mortality rate of less then one-half of one percent.  For the most part, this age group has been overlooked in the reporting, which tends to focus on seniors or young adults.  In this cohort, it is more difficult to make the case that these covid deaths would have been likely to occur anyway.

A word on excess mortality, which is just the increase or decrease in all-cause mortality over some period compared to a baseline. This is a much better measure of the the overall impact of covid on death rates, as it includes second order effects of the pandemic.  These second order effects cut both ways.  Deaths almost certainly increased because medical appointments and procedures were delayed when hospital were overwhelmed with covid patients and shut down most non-covid care.  In contrast, deaths from some non-medical causes may have decreased because of less societal activity, including travel. But they may have increased as well, with higher suicide rates and more domestic violence. 

One more point.  I am focusing on deaths to explore the seriousness of covid for several reasons.  First, this is where much of the media and popular attention has focused.  Second, I think the hospitalization data by age group in Massachusetts is unreliable. I am very aware that deaths do not tell the whole story, as many people become ill from covid and partially recover but have long-lasting health issues (the so-called “long haulers”).  But at this point, there is little hard data detailing their issues.

Categories
Data Update

Massachusetts Data Update October 16, 2020

Well, I was clearly wrong about test positivity rates stabilizing.  The positivity rate for newly tested people is now over 4%.  That rate bottomed out at 1.4% on August 30, increased to 2% by September 8, rose to 3% on September 26, and hit 4% on October 13th.   Even the overall test positivity rate has increased to 1.4% after bottoming out at 0.8% on September 23.  And test positivity for repeat testers is now 0.4%, after holding steady at 0.2% for over a month until October 8th.  Not a good trend.

Table 1: Massachusetts Testing Statistics
7 Day  Trailing Average
October 16, 2020
         
Testing Statistic   Current 7 Days Ago 4 Weeks Ago
         
Test Positivity Rate (Individuals)   4.1% 3.5% 2.2%
Test Positivity Rate (Include Suspected)   4.5% 3.7% 2.4%
         
Test Positivity Rate (All Tests)   1.4% 1.1% 0.8%
Test Positivity Rate (Newly Tested)   4.1% 3.5% 2.2%
Test Positivity Rate (Repeat Testers)   0.4% 0.2% 0.2%
Percentage Repeat Testers   72.5% 73.7% 71.5%
         
Newly Tested (Lagged 1 Week)   17,365 15,976 14,638
All Tests (Lagged 1 Week)   65,978 59,072 49,123

 The total number of patients in the hospital, patients in the ICU, and patients intubated have held roughly steady for about a week, but at significantly higher levels than four weeks ago. The most troubling statistic here is the new hospital admissions, which are at their highest levels since June 30th. Given the rise in positivity rates and diagnosed cases over the past several weeks, this is not that surprising.

Table 2: Massachusetts Hospitalization Statistics
7 Day Trailing Average
October 16, 2020
         
Hospitalization Statistic   Current 7 Days Ago 4 Weeks Ago
         
Covid Patients Hospitalized   510 474 332
Covid Patients in ICU   85 84 62
Covid Patients Intubed   30 29 23
New Confirmed Patients   39 35 19
         
Percent ICU / Hospitalized   17% 18% 19%
Percent Intubated / ICU   35% 35% 37%

 

Diagnosed cases have not been this high since the end of May, with the 7 day average of confirmed cases over 600 for four days in a row.  The 7 day average number of reported deaths broke through the fairly tight range of 11 to 17 from mid-July (surprisingly, the 7 day average of reported deaths was 11 as recently as October 10).  Clearly, Massachusetts is headed in the wrong direction.  Is this the beginning of the second wave, or just an uptick in a continuing first wave?  Will Charlie Baker et. al. move to roll back openings? 

 

Table 3: Massachusetts Reported Case and Death Statistics
7 Day Trailing Average
October 16, 2020
         
Statistic   Current 7 Days Ago 4 Weeks Ago
         
Total Deaths Including Suspected   18 12 13
Total  Deaths Confirmed Only   17 12 13
Deaths in Long-Term Facilities (All Cases)   12 12 9
Percent from Long-Term Care   65% 93% 69%
         
Total Cases Including Suspected   665 581 371
Total Confirmed Cases   620 542 338