COVID deaths in context by weeks

co-authored with Jens von Bergmann & cross-posted over at MountainMath

In our previous post on COVID mortality in context, we tried to place COVID deaths, as recorded so far this year, in the context of expected deaths from previous years. There have been a lot more developments since that post. And unfortunately a lot more deaths too.

Here we’re providing an update to our previous post, but also expanding on that post by talking a bit more about new mortality analyses and the progression of outbreaks in terms of expected deaths on a weekly basis. First, an update! We previously placed COVID deaths in the context of expected deaths at the national level, starting after the 20th death was recorded. What does that look like now?



As visible in the mortality data, Belgium has moved to the forefront of the COVID outbreak in Europe in terms of COVID deaths relative to expected deaths from years prior. Ireland, the UK, and the US appear to continue to climb. By contrast, Spain and Italy, early centres of the outbreak in Europe, have largely leveled off. Though the USA “leads” in deaths from COVID-19, this doesn’t (yet) show up in the relationship between COVID deaths and expected deaths because the USA is enormous, with a lot of expected deaths every year, and the outbreaks of COVID deaths have been heavily concentrated in a few locales so far.

Overall, and as mentioned previously, there’s still a lot we don’t know in these comparisons. For instance, we don’t know if we’re actually counting all of the deaths due to COVID. Lots of people don’t get tested, and cause of death is always tricky to determine in the best of times, let alone with an overloaded medical system and coroners’ offices. As a result, revisions to the data can add dramatically to the death toll, as happened recently in New York City. In addition to good COVID death data, we’d also like updated data on mortality overall. We’ve seen recent – and very preliminary – data out of NYC and scattered other locales suggesting that all-cause mortality has risen dramatically in places with severe COVID outbreaks.

Where we have it, we can put updated all-cause mortality in conjunction with COVID mortality and expected mortality all together. Putting this on a weekly basis really provides a sense of the progression of outbreaks and how overloaded they leave medical systems in terms of the normal deaths they have to deal with. Given some of the data from NYC, here’s roughly what that looks like.



We notice a downturn in deaths as recorded by the CDC FluView for the last week they report data (the week ending on 2020-04-12). This is not a REAL downturn. Rather it illustrates the reporting lag for data on deaths. It can take several weeks for the numbers to fill in and stabilize. We added the reported Covid-19 related deaths as assembled by the JHU for reference. JHU data was aggregated up the week ending 2020-04-19, so it’s nominally a week ahead of the FluView data. However, these deaths are coded by date reported, unlike the CDC data that is coded by date of death, which causes the JHU data to lead a bit. Even accounting for a possible time shift in JHU data, it appears that JHU data does not account for the full increase in all-case mortality, hinting at likely under-reporting of Covid-19 deaths in the JHU data.

Unfortunately we still don’t have updated all-cause mortality on the country level. As suggested by the lag in NYC data, it takes awhile to compile in the best of times (here’s a look at efforts to gather some of the European data). So here we’ll provide a replication of our previous analysis, but breaking out COVID deaths against expected deaths on a weekly basis for countries instead of across the entire length of the outbreak.




Overall, weekly COVID deaths as a percentage of expected deaths looks broadly similar to our earlier figure, which charted the rise in COVID deaths as a percentage of expected deaths since outbreak deaths began. But there are a few significant differences. The weekly chart better highlights the evolving overload on hospitals and health systems, as well as coroners’ offices, and this is reflected in the y-axis, demonstrating that COVID deaths in Belgium have more than doubled the expected deaths in the last week for which we have data. The weekly chart also more quickly identifies declines in the relative impact of COVID deaths in places where the worst of the outbreak has passed, like Spain, Italy, and France. It will take a long time for the expected death toll to diminish the impact of the overall death toll of COVID in our figures at the top of the post. But on a weekly basis, we can already see the toll of COVID receding in many places.

As we’ve noted previously, it will still take a long time to sort out the overall effects of COVID on mortality. Why? Well, we’re still nowhere near done with the outbreak, and we can expect deaths to continue until we have a vaccine and have reached some level of “herd immunity.” But we’ll also be sorting through the mortality data for years to come. Also important: the toll at national levels, while helpful in assessing cross-national differences, masks the impact at local levels where outbreaks often occur. So it is that the estimate from Belgium, where most recent weekly COVID deaths appear to have more than doubled expected mortality, is dwarfed by the estimate from New York City, where the most recent weekly COVID deaths appear to be more than six times the expected (pre-COVID) mortality.

As usual, the code for the post is available on GitHub in case anyone wants to refine or adapt it for their own purposes.

How many owner-occupiers can already defer their Property Taxes in BC?

We’re rolling around to property tax time, and municipalities are about to feel the COVID-19 crunch. The Mayors of Metro Vancouver have been leading an ask of the province to backstop municipal finances given that many residents and businesses may fail to pay their property taxes. Indeed, the City of Vancouver recently commissioned a survey indicating that due to job and income losses, some 25% of home owners in the city would be paying less than half of their 2020 property tax bills.

One ask from the Metro Mayors is for the province to expand it’s property tax deferral program to cover those not currently included. As they advocate:


This, of course, is a big ask! But just how big? Here I want to separate out the ask for businesses and non-profits (where the ask is very big indeed), and focus on homeowners. And after all, homeowners are where the City has focused its survey. So how many homeowners are not currently covered by provincial tax deferment options?

There are two programs covered under provincial tax deferment: the regular program and the program for families with children. The regular program is open to any property owners (of a primary residence) over the age of 55, as well as surviving spouses (of any age) and persons with disabilities. The province effectively puts a lien on your property to secure the debt and charges 1.95% interest on outstanding taxes owed. The families with children program is open to anyone living with or supporting children under age 18, or children enrolled in education (e.g. university), or children with disabilities of any age, and the interest charged under this program (3.95%) is higher.

Just focusing on the two main groups covered, homeowners age 55+ and families with children, we can draw upon census data from 2016 to roughly estimate how many owner-occupied households are likely covered by existing tax deferment options. The answer: the vast majority, over four-in-five. Why? Because home owners are especially likely to be old or have children. Here are owner-occupier households in BC by age of primary maintainer and presence of children*:


Overall this is good news! Most resident homeowners in BC are already covered under property tax deferment options. And the province will likely see a big uptake in deferments this year through existing programs. But those who fail to qualify also deserve provincial attention. And, of course, renters deserve a lot more attention too. I’d argue that it’s also well worth supporting expansion of the property tax deferral program more broadly since this also supports municipal finances at a very trying time. Moreover, if the province expands the program at family program interest rates, it may also help support provincial coffers down the road.


*- Here I lump the relatively tiny set of multiple family households into those without children, following the general household type categorization. See StatCan Table 98-400-X2016226 to play around with your own operationalizations.

So are you two a couple now? Asking for the BC Government

BC has been lauded for rolling out an assistance program for renters, unlike basically every other province. At the same time, BC’s also been criticized for the perceived inadequacy of that rental assistance program, as well as the fact that it literally goes straight to landlords. In conjunction with the temporary eviction moratorium, it would appear that the BC Temporary Rental Supplement (BC-TRS) is really aimed at supporting landlord incomes and easing tenant-landlord relations to avoid a rash of evictions once the moratorium has been lifted.

Here I want to question another aspect of the program, at least as we’ve seen it so far: What’s it got against couples?

The BC Temporary Rental Supplement, as announced today, provides $300 per single person or couple household, and $500 per household with dependents. But roommates can apply separately for benefits, and it would appear each roommate is eligible for a $300 or (if living with a dependent) $500 rent supplement. Here are relevant items from the FAQ:*

Rental_Assist_1This means the “household” definition being applied by the province – whereby roommates constitute separate households – best matches the “family” definition of the Census, whereby family is defined by a couple (married or common-law) or parent-child relationship. The Census considers roommates as members of the same household, but unrelated, and hence not members of a family.

Why does it matter? Well, what’s the distinction between roommates and a couple?** Because if you’re a COUPLE you max out at a $500 benefit with children or a $300 benefit without. But if you’re ROOMMATES, it appears you qualify for $300 each, or more if there are children involved, maxing you out at $600+. In effect, couples have their status turned against them in terms of government benefits.

Interestingly, this isn’t the first time the current BC government has zeroed in on couple status as a determinant of less than favorable policy treatment. The BC Speculation and Vacancy Tax hinges upon marital status in terms of whether overseas partner incomes get counted toward family incomes, distinguishing “satellite families” hit with higher property taxes from everyone else. In effect, if you own a home this is a huge disincentive for formalizing, declaring, or maintaining transnational relationships, at least if your partner potentially earns more than you. BC tax policy says it’s better for you to split up than stay coupled with anyone outside of Canada, just as BC renter support policies seem to tell us it’s better to be single (with a roommate) than part of a couple.

One way of looking at the government position on rentals is that couples might be considered more resilient than singles. So singles, including roommates as well as single parents (who get $500), need more help and more allowances. And as I wrote previously, with respect to rental supports this might well be correct. Singles and single parents make up the bulk of those in core housing need. I’m happy that the BC government is providing special help to those with dependents, even if I wish the amounts were higher.


It’s also the case, as in my past research auditing rental listings, that BC’s tipping of the scales against renting couples might actually counteract some of the beneficial treatment they usually receive in the rental market, where landlords tend to discriminate against single parents and some same-sex couples (who may, in some cases, have been taken for roommates). Finally, policy is being rolled out at a ridiculously fast speed, which is important and a success in its own right because people are in need of money now. But that speed is bound to come at a cost in terms of care in crafting policies. We’ll see plenty of mistakes and unintended consequences of fast policy roll-out in the days to come. We shouldn’t forget the urgency behind the roll-out, even as we offer up critiques and fixes.

That said, we’re left with a fun contrast. If Pierre Trudeau famously declared “there’s no place for the state in the bedrooms of the nation,” the government of BC still wants to know: are you two an item?


UPDATE (Apr 12, 2020):  Another interpretation (in this case my partner’s) is that the BC – TRS is geared entirely toward assumptions about how many bedrooms different kinds of households need and what the associated costs might be. The logic being that couples might only need 1BR, whereas parents with children need at least a 2BR, and roommates are (ironically) assumed to sleep in separate bedrooms, also requiring at least a 2BR. This interpretation actually mirrors the logic of the Canadian National Occupancy Standards defining the suitability aspect of the Core Housing Needs measure. Accordingly, BC-TRS payments could be designed simply to go up in response to anticipated bedroom need. I like this interpretation a lot, so I thought I’d share it too! (I hinted at the importance of considering bedroom need in my previous post on the renter benefit, only I didn’t think they’d adopt the couple assumption from the National Occupancy Standards, which I’ve also researched in the past! Kicking myself a little that I didn’t think of this interpretation first, but also patting myself on the back for settling down with someone more clever than me…)


*- Yeah, also your adult kids don’t qualify as roommates (item 18) and you don’t get any assistance if your landlord is also a family member (item 19).

**- As it happens, I asked just this question in my dissertation… though from a viewpoint embedded within demography (i.e. are people more likely to cohabit with an unmarried partner in response to housing shortages, making them like roommates, or less likely, making them act more like married couples?) In the context of Swedish demography, easier access to housing meant greater likelihood of cohabitation, providing evidence that cohabiting couples tended to be acting more like married couples than economizing roommates. BUT, there’s a lot of grey in there. Especially insofar as we usually leave it to people to define their own relationships.




Context for COVID-19 Mortality so far

co-authored with Jens von Bergmann & cross-posted over at MountainMath

Unfortunately, more and more people are dying due to COVID-19. We won’t know the full toll from COVID-19 for quite some time. But we can at least start to get a sense of its impact. One useful way of assessing the impact, of course, is just to plot deaths attributed to COVID-19. This highlights the real loss of human lives associated with outbreaks. But as any demographer can tell you, deaths are a normal part of life. Within a given population, we can reliably expect a certain number of deaths to occur over any given time period. So another way of visualizing COVID-19 deaths is also useful: How many deaths attribute to COVID-19 are occurring as compared to the deaths we would normally expect to occur?

Below we follow the rise in deaths attributed to COVID-19 through time relative to the expected number of deaths that likely would have occurred without COVID-19 during the same time. [UPDATE April 20: our newest post plots this on a weekly basis]




This visualization places deaths reported from COVID-19 in the context of expected deaths overall. This helps establish where we know the mortality toll has already been enormous. As of March 31, the end-point of the animation, Italy leads the overall count in deaths attributed to COVID-19. Here we can also report that in just over a month, Italy’s deaths so far attributed to COVID-19 already add more than 20% to its expected deaths. But Spain’s toll relative to its expected number of deaths is ever higher. In just over three weeks time, we can see that COVID-19 already accounts for more than a 30% rise over the deaths that would’ve been expected without COVID-19.




Unfortunately, most curves are still rising. So far. Initially curves grow exponentially, until aggressive containment or mitigation strategies flatten them. Curves that stabilize and flatten, or even begin to turn downward, reflect countries where deaths attributed to COVID-19 are being overtaken by deaths that might’ve been expected to occur anyway. Hopefully this reflects an outbreak coming increasingly under control – GOOD NEWS – rather than a data gap.

But the possibility for data gaps is very real. It will be quite awhile before we can properly estimate the overall toll from COVID-19. We already have preliminary data on deaths attributed to COVID-19 rolling in. But this data will be messy, excluding cases where COVID-19 was missed as a cause, despite being present, and possibly over-including cases where the cause was actually not COVID-19 (e.g. instead common influenza), or COVID-19 was present but the death should be attributed primarily to a different underlying condition claiming the life. Cause of death data is never clean to begin with. As COVID-19 overwhelms medical systems and coroners’ offices, we should fully expect that data quality will suffer further.

More concretely, COVID-19 deaths will show up in the mortality databases with code U07.1 or U07.2 in the current ICD-10 classification system (or RA01.0 and RA01.1 once ICD-11 comes into effect). But many will likely also get classifed as J11, J18 or J22. When the dust settles, we will have to check how these cases have evolved over time and estimate how many cases in 2020 (or late 2019 in the case of China) are likely misclassified COVID-19 cases.




We will also eventually get data about overall mortality. We will likely see deaths increase beyond those attributed directly to COVID-19. Deaths will rise both in response to complications introduced by COVID-19 in those with pre-existing conditions, and in response to people dying due to failure of overloaded medical systems to be able to respond to non COVID-19 cases they way they normally would. At the same time, some other non-COVID deaths may go down. This can happen when COVID-19 claims lives that otherwise might’ve been claimed by something else (e.g. an underlying condition). But it can also relate to deaths that don’t occur due to lockdown and the measures related to dealing with COVID-19. For instance, the regular toll of influenza may diminish in response to the lockdown targeted at Coronavirus (making it unclear what the “expected” baseline case count for 2020 should be). Similarly, fewer cars on the road will likely result in fewer deaths from car accidents. For references, see the most common causes of death in Canada in normal years here. A similar discussion of the eventual breakdown we’ll need in mortality data can be found in this demographer thread attempting to summarize some of this complexity via twitter feed.

The mortality data coming in bears watching, both in terms of COVID-19 attributed deaths and deaths overall. Some analysts (e.g. in Italy and Spain) as well as some China skeptics, are already drawing upon anecdotal mortality data to suggest that the toll from COVID-19 is far greater than revealed in the official data so far. These kinds of analyses are especially potent when applied to cities and regions as opposed to countries. But ultimately it will take years for demographers to sort this all out. In the meantime, we can at least get a rolling sense of COVID-19’s toll by looking at deaths attributed to Coronavirus relative to deaths otherwise expected based on past data from the same rough period of time.

As usual, the code for the post is available on GitHub in case anyone wants to refine or adapt it for their own purposes.


Update (2020-04-06)

It’s been a week since we posted this, and things are changing fast with covid-19 related deaths increasing exponentially and background mortality estimates only increasing linearly with time. The traces in the animated GIF already highlight this, but here is a quick update of what the graph looks like using data from a week later.


And for completeness, here is the static graph with the latest available numbers.