COVID-19: Website Communication from Pennsylvania, Michigan, Florida, Texas, and Georgia – Could the Florida Message be So good it saved lives?

Continuing to look at how states message the Covid-19 pandemic from the earlier review of the top 5 states by number of citizens with a positive diagnosis, we now look at states 6 – 10; Pennsylvania, Michigan, Florida, Texas and Georgia.

Florida’s COVID-19 mantra, “Safe. Smart. Step by step.” is so clear and supported with their protection of the elderly that it likely saved lives.

https://floridahealthcovid19.gov/

Trends

  • Florida leads the way with more of a ‘marketing style’ message. Yes, they share public health data, but their website has been hands down the clearest.
  • Florida’s messaging is so clear, and supported by the Massachusetts mortality numbers, that it changed my personal messaging – “Protect the elderly.”
  • Many states have the de facto ‘Public Health’ style update; here are the numbers, make of them what you will.
  • None of the states have made an attempt to report on ‘Recoveries’ – which would be a compelling aspect to focus on from a marketing standpoint and alignment of public incentive.

Pennsylvania – Coronavirus (COVID-19)

  • Good mapping.
  • Very few slogans, one that shows up is; “It Takes All of Us to Fight COVID-19”

Michigan – Coronavirus / Michigan Data

  • Daily data
  • ‘Public Health’ driven

Florida – Florida COVID-19 Response

  • Very clear
  • Focused on action, not data
  • Florida’s COVID-19 slogan, “Safe. Smart. Step by step.” is the clearest of any state
  • Could the power of Florida’s messaging be good enough to have saved lives?

Texas – Coronavirus Disease 2019 (COVID-19)

  • The Texas site is a bit more visually confusing – it doesn’t have the data front and center, rather the message is more about ‘opening up.’

Georgia – COVID-19 Daily Status Report

  • Georgia Department of Public Health Daily Status Report
  • This is a pure Public Health update from the get go.
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COVID-19: Understanding Mortality Rates from California and the California Coronavirus COVID-19 Statewide Update

California has the 5th highest number of positive diagnoses in the US, 67,939 as of Wednesday, May 13. When combined with the four states with the most diagnosed cases; New York, New Jersey, Illinois and Massachusetts – the daily California Coronavirus COVID-19 Response shows that the disease continues to be deadly in the elderly population.

95% of deaths come from those 50 and older in the top five states; New York, New Jersey, Illinois, Massachusetts, and California.

Differences in how states report age groups make it hard to compare – the data also indicates that 85% of the deaths come from those age 60 and older.

38,344 of the 40,227 deaths recorded to date – 95% – are from those age 50 and older.

  • The missing component in all of these reports is clearer visibility about the number and status of recovered patients.
  • COVID-19 kills senior citizens at a very high rate.
  • California, compared to the top 4 states, has very good data visualization. They use the Tableau system for Covid-19 reporting.
  • California, like New York, does not make it easy to access actual numbers.
  • California uses the lowest number of age buckets – only 4. The buckets chosen are most similar to New Jersey.
  • Like Illinois – California makes an at attempt at branding – using the taglines of ‘Resilience Roadmap’ and ‘California FOR ALL’.
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COVID-19 Kills Senior Citizens: Mortality Reporting from New York, New Jersey, Illinois and Massachusetts

New York, New Jersey, Illinois and Massachusetts represent the four states with the highest number of diagnosed cases of corona virus infection, 51% of all US diagnosed cases, and 57% of the deaths.

As a resident of Massachusetts, I’ve looked at the daily Massachusetts COVID-19 Reporting Dashboard and compared it to past Massachusetts death reports – the most recent being 2017. As we start to emerge out of the quarantine, comparisons to the other states show that the differences in reporting styles continue from state to state.

The Disease: Covid-19’s Impact

The disease is lethal to older member members of society. In Massachusetts #C19 will be the third leading cause of death in 2020 with a mortality rate of 70 per 100,000, and a rate of 1,009 for those 70 and older. The data from Illinois, New York and New Jersey show the same pattern. Each of the states also have special call outs for infections in assisted care facilities and nursing homes.

C19 gets into nursing homes and the elderly population and accelerates the rate of death significantly. This population will require special protection going forward.

The Style of Reports – Why are All the Age Brackets Different?

Ideally, these death reports would be summarized in the same way from state to state and also roll up to the CDC and Federal level in a consistent fashion. They don’t.

  • New Jersey uses different age brackets – closer to that of the CDC.
  • New York adds two age brackets that Illinois and Massachusetts don’t have – ages 0 – 9 and 80 – 89.
  • Illinois and New York have an ‘Unknown’ age bracket. Is that really possible? Is it really just ‘Not yet fully known with precision’? If we’re putting the deceased into 10 year age brackets, can an estimate be made?
  • Illinois has the only branded approach to C19, with their “Restore Illinois.”
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Constraints to COVID-19 Data Analysis: What would the ideal report look like?

Massachusetts circulates a great daily report on C19 every day at 4 PM – the “COVID-19 Dashboard” is posted every day and contains a very detailed, 30+ page summary of the State’s Covid response reporting.

It must be very difficult to pull together these reports, and they are very thorough compared to other states. Why are they so hard to compare to Massachusett’s own annual death reports?

The ideal mortality report shouldn’t be so singularly focused on deaths from SARS-COV-19 and the Corona virus.

  • Show the daily death totals.
  • Assign every death to a category.
  • The report should be an ‘accelerated’ reporting of the annual death data, just done on a daily basis.
  • The current reporting as done is very difficult to compare to any other death reporting. Why?

Why does the Massachusetts Data look so different than other sources of mortality data?

  • The age groups are different than the State’s own 2017 mortality report.
  • The State of Massachusetts’s 2017 mortality report tracks different age brackets than the CDC shows.
  • This data can’t be fixed by downloading the raw data / information – it still doesn’t line up.

The following are all constraints in how data is reported:

  1. Lag in Data Reporting
  2. Ability to Access Data Directly
  3. Use of ‘Estimates’ and algorithms in base data
  4. Inconsistent methods and terms with current data
  5. Use of different age brackets
  6. Region-to-region variability
  7. Death definitions (included vs “due to”)
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COVID-19 Mortality Data Comparisons Between Massachusetts and 2017 US and Same State

The State of Massachusetts releases daily updates on the status of COVID-19. Slide 11 of the “Massachusetts Department of Public Health COVID-19 Dashboard” has typically covered death statistics to date broken out by age group. (Data here is from the Saturday, May 9, 2020 Dashboard.)

The 4,840 deaths to date in Massachusetts, when adjusted by the current population of 6,939,373 and then back to the standard method shows a mortality rate of 69.7.

Massachusetts most recent posting with thorough analysis of causes of death in the state – the ‘2017 Death Report’ can be found in the states Death Data. If the 2020 year-to-date C19 data is used as a benchmark in the 2017 data – then C19 would be the third leading cause of death, behind only cancer (149.3 deaths per 100,000 people) and heart disease (134.7 deaths per 100,000 people).

Covid-19 Mortality is Highest in 70+ Year Old Population

Massachusetts age brackets share mortality in ages 70 – 79, and 80+ as being dramatically higher for the disease. Nationally, this data is tracked in different age brackets – with the two oldest brackets being 75 – 84 and then 85+. (I’m preparing a summary of all the ways that C19 data analysis is made difficult.) The Center for Disease Control publishes an annual summary, “National Vital Statistics Reports”, and the most recent summary is the 77 page, “Deaths: Final Data for 2017“.

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All Cause Mortality Data Shows C19 Impact, Right?

There is an enormous amount of confusion around Covid-19. The data is confusing. Media and personal interpretation make it more confusing. It is hard to understand what is really happening, and without knowing reality, it is hard to know how to react.

“Don’t force certainty on uncertain situations.”

Eli Goldratt – The Goal

What is the Base Rate?

Because of the rate of spread of the virus and the potential to overload hospital systems, most of the world is in quarantine. Because of all the safety precautions, fewer people overall are dying. The confusion about diagnosing the disease, detecting the disease in those who have passed away, and inconsistency in how mortalities are labeled still makes it hard to understand what is happening.

All cause mortality tracks how many people have died due to any cause – and that shows that something is happening in the population. The spike in New York City deaths has surpassed 9/11. As the charts below from the Financial Times and other sources show, several regions show a surge in all-cause mortality.

650,000 Italians died in 2019. 2,839,205 Americans died in the same year. If the citizens of both countries are sheltering-in-place, avoiding risks and less likely to die from other cuases – then any rise in ‘all-cause’ fatalities should be due to Covid-19.

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Confusion & Covid 19; Virus Behavior Driven by Human Interpretation

Thanks to the Chartstravaganza by @PlanMaestro and Germany’s Christian Drosten who directs the Institute of Virology at the Charité Hospital in Berlin for providing this background information.

Infectious disease is confusing. When Goldratt wrote The Goal, the hero Alex Rogo never wanted for good data. C19 has presented confusing, conflicting data around the goal. This calls into question measurement and observation methods. It makes decisions harder to make.

For all of the confusion that comes from the original data, we see an added layer of confusion in how this data is reported and interpreted. There are clear cases where the way in which the data is presented is driving towards a specific answer.

Case Rates

China’s case growth looks artificially flat. Presenting fatality or case rates which have not been normalized by population size seems like deliberate manipulation of data.

Flat China Data and no normalization for population size – is this legitimate data?
This data looks more useful; it is normalized by population and there is no data that ‘looks weird.’

Sweden – Good or Bad?

Sweden followed a different plan on social distancing. The graph on the left is presented to make Sweden look bad – more fatalities. The graph on the right is presented to make Sweden look good by choosing a peer group with less fatalities. The graphs below are based on the same information and presented by the same organization.

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