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This is no virus for old men

Summary:
Statistics Canada has just released a dataset with detailed anonymized information on all confirmed COVID-19 cases in Canada, available for download here. Unlike many of the available trackers, the Statistics Canada data reports cases by date of onset, defined as "earliest date available from the following series: Symptom Onset Date, Specimen Collection Date, Laboratory Testing Date, Date reported to the province/territory or Date reported to Public Health Agency of Canada." Defined that way, the case numbers start to ramp up in the second week of March. The decline in late March almost certainly reflects the time lag between symptom onset and confirmed COVID-19 diagnosis, rather than any peaking of the curve.  Using the data, it is also possible to estimate hospitalization

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Statistics Canada has just released a dataset with detailed anonymized information on all confirmed COVID-19 cases in Canada, available for download here.

Unlike many of the available trackers, the Statistics Canada data reports cases by date of onset, defined as "earliest date available from the following series: Symptom Onset Date, Specimen Collection Date, Laboratory Testing Date, Date reported to the province/territory or Date reported to Public Health Agency of Canada." Defined that way, the case numbers start to ramp up in the second week of March. The decline in late March almost certainly reflects the time lag between symptom onset and confirmed COVID-19 diagnosis, rather than any peaking of the curve. 

Symptom onset

Using the data, it is also possible to estimate hospitalization probabilities, that is, the predicted likelihood that someone with a confirmed COVID-19 diagnosis will end up hospitalized. This is no virus for old men - roughly 70 percent of men over the age of 80 diagnosed with COVID-19 end up hospitalized [corrected]. This diagram does, of course, overstate the true hospitalization probabilities, as people with mild cases of COVID-19 are unlikely to be tested - or even eligible for testing - and also unlikely to be hospitalized.

Hospitalization probabilities

Here's a final graph - one that I found quite surprising, in fact - conditional upon being hospitalized with a confirmed COVID-19 diagnosis, age and gender seem to have little impact on the probability of receiving ICU treatment. The sample that this is based on is small, however - 152 individuals.

Icu

Here is how I created these charts:

  • First, I downloaded the data from here as a .csv file: https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=1310076701
  • Next, I opened the .csv file, removed the introductory text in the first few lines of the file, and the footnotes at the end of the file, and then saved it.
  • I imported the .csv into Stata, and then added variable names/cleaned the data using this code:

***merge separate month, day and year variables into a single date avariable
gen episode_date=mdy( episodedatemonth6, episodedateday6, year)
format edate %dM_d
***install a nice graphics scheme, to replace the crummy Stata default
ssc install blindschemes, replace all
set scheme plottig

**create histogram graph

hist episode_date, freq title("Earliest of Symptom Onset Date, Test Date or Reporting Date") subtitle("Number of Cases, Canada, March 29, 2020") note("Source: Statistics Canada. Table 13-10-0767-01 Detailed confirmed cases of COVID-19 (Preliminary)")

**rename gender and age group variables; add labels to them.

recode gender7 (1=1 "male") (2=2 "female") (3=3 "other") (7=7 "unknown") (9=9 "not stated"), gen(gender)

recode agegroup8 (1=1 "0 to 19") (2=2 "20 to 39") (3=3 "40 to 49") (4=4 "50 to 59") (5=5 "60 to 69") (6=6 "70 to 79") (7=7 "80 or older") (9="Not stated"), gen(age)

***create hospitalization variable
recode hospitilazation10 (1=1 "hospitalized") (2=0 "not hospitalized") (7=.a "unknown") (9=.b "not stated"), gen(hospitalized)

***run logit analysis; generate and plot marginal effects

logit hospitalized i.age#i.gender if age<8

margins i.age#i.gender

marginsplot, title("Probability of hospitalization by age and gender") subtitle("Canada, March 29, 2020") note("Source: Statistics Canada. Table 13-10-0767-01. Logit analysis of 1,583 cases")

***create 0/1 ICU variable
recode intensivecareunit11 (1=1 "in ICU") (2=0 "not in ICU") (7=.a "unknown") (9=.b "not stated"), gen(ICU)

***run logit analysis; generate and plot marginal effects

logit ICU i.age#i.gender if age<8 & hospitalized==1
margins i.age#i.gender
marginsplot, title("Probability of needing ICU care by age and gender") subtitle("Canada, March 29, 2020") note("Source: Statistics Canada. Table 13-10-0767-01. Logit analysis of 1,017 cases")

Frances Woolley
I am a Professor of Economics at Carleton University, where I have taught since 1990. My research centres on families and public policy. My most-cited work is on modelling family-decision making, measuring inequality within the household, feminist economics, and tax-benefit policy towards families. I hold a BA from Simon Fraser University, an MA from Queen’s, and completed my doctorate at the London School of Economics, under the supervision of Tony Atkinson.

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