Worthwhile Canadian Initiative

Rapid Vaccinations Can Help Avoid the `Third Wave’ in Ontario

The following is a guest post by Miguel Casares (Universidad Publica de Navarra, Spain), Paul Gomme (Concordia University), and Hashmat Khan (Carleton University)

Of utmost importance is improving our understanding of the complex interactions between: (a) the epidemiology that describes the evolution of the coronavirus/COVID-19; and (b) the social and economic choices of individuals.  Towards this end, we have developed a model that captures some salient elements of this interaction. The parameters of the model are calibrated to match epidemiological and economic developments in Ontario in the first half of 2020. We then evaluate the contributions of health and socioeconomic policies and the lockdowns since last March. 

Interactions between epidemiology and socioeconomics. Epidemiology describes how the coronavirus spreads through the Ontario population. Key departure from the standard SIR (susceptible-infectious-recovered) epidemiological model: (a) the number of daily contacts (from the socioeconomic side of the model) affects the number of new COVID cases; and (b) asymptomatic transmission of the coronavirus. Socioeconomics characterizes how self-interested actors respond to their environment, including the prevalence of the coronavirus. These actors like consuming and socializing; they dislike working and the risk of catching COVID.

A key implication of our model is that individuals respond to the coronavirus even in the absence of government interventions. In fact, they did. For example, restaurant reservations in Ontario made through the Open Table website fell off more than a week before the declaration of a state of emergency on March 17, 2020.  This is depicted in Figure 1 (percentage change relative to previous year). These individual-level actions can collectively affect the spread of the disease.

Figure 1: Restaurant reservations in Ontario made through the Open Table website

 

Fear of death in preferences provides a simple, elegant way of describing how COVID affects individual decision making. The fear of death introduces two wedges. First, it affects the work-consumption margin: the fear of death reduces the value of consuming since buying more goods implies more daily contacts; and the fear of death increases the disutility of working, again due to increased daily contacts. Second, the fear of death reduces the value of social activity, again because of the effect of such activity on daily contacts. As the number of COVID cases increases, individuals reduce their hours of work, their consumption, and their social activity.

First lockdown. We model government policy to fit with events from March 24 and June 11. Health-related interventions include measures that reduce contagion, like mask mandates, social distancing, increased hygiene; and testing and contact tracing. Socioeconomic policies operate through: busines shutdowns, and restrictions on socializing. While the socioeconomic interventions are very effective at reducing case numbers, they also lead to higher unemployment. The importance of the health protocols is in reducing the growth in COVID cases in the longer term. In Figure 2, the yellow dots are daily deaths, the blue line is the model’s fit, and the grey shaded areas are the lockdown periods. 

Figure 2: Projected mortality with and without COVID lockdowns

Second lockdown. The model predicts rising COVID cases and mortality starting in the autumn, as is seen in the data. We then imposed a second lockdown: only essential businesses open and minimal social activity. The model predicts a rapid drop in COVID cases and mortality, saving about 4,000 accumulated deaths compared to the forecast from the scenario without the second lockdown (the red line in Figure 2).

Third wave. Absent vaccinations, the model predicts a third wave peaking in the summer. These results not only point to the importance of COVID vaccines, but also of getting as many people vaccinates as quickly as possible.

Miguel Casares (Universidad Publica de Navarra, Spain), Paul Gomme (Concordia University), and Hashmat Khan (Carleton University)

Can the Great Barrington proposal save the economy?

The Great Barrington Declaration argues against universal lock-downs:

Those who are not vulnerable should immediately be allowed to resume life as normal. Simple hygiene measures, such as hand washing and staying home when sick should be practiced by everyone to reduce the herd immunity threshold. Schools and universities should be open for in-person teaching. Extracurricular activities, such as sports, should be resumed. Young low-risk adults should work normally, rather than from home. Restaurants and other businesses should open. Arts, music, sport and other cultural activities should resume. People who are more at risk may participate if they wish, while society as a whole enjoys the protection conferred upon the vulnerable by those who have built up herd immunity.

Setting aside the epidemiological merits of the Declaration, the question remains: how much of an economic impact would a targeted easing up of restrictions have?

To answer that question, we need to figure out how much spending power is in the hands of "young low-risk adults", and how much is in the hands of "people who are more at risk." COVID-19 risk depends upon a variety of factors, including sex, blood-type, body mass index, and so on. However age appears to be a key risk factor - 40 to 49 year olds in the US are 10 times more likely to die from COVID than people between 18 and 29. The odds of COVID death for 50 to 64 year olds are 30 times the odds for someone between the ages of 18 and 29 (source). So, while age is not a perfect measure of COVID risk, we can use it to get a rough sense of how much of the purchasing power in our economy is in the hands of people who are vulnerable to COVID.

There are a couple of different data sets that can be used to estimate the distribution of after-tax income by age in Canada. The first is the Canada Revenue Agency's Taxation Statistics. These report the total amount of income declared by every Canadian taxfiler, broken down into five year age categories, as well as the taxes paid. They are not a perfect measure of after-tax income. They only include personal income reported for income tax purposes, not corporate income, nor any incomes not declared for tax purposes. Also, the taxation statistics do not include a measure of after-tax income, so I estimated it by calculating net income minus total taxes paid. Moreover, these numbers are from 2016, so are somewhat out of date.  But they serve to make a point.

First, even if young low-risk adults can work normally, it won't make much difference to the amount of money spent on restaurants, arts, music, sports and other cultural activities, because young low-risk adults don't earn much money. Many are in school or just starting out in the labour market. Young people's wages are relatively low, and their unemployment rates are relatively high.

In 2016, the age group that controlled the most economic resources was 50 to 54 year olds: the after-tax income of all Canadians in that age group added up to a total of $114 billion. The next highest earning age group 55 to 59 year olds, raking in a collective $110 billion after taxes, followed by 45 to 49 year olds. This should come as no surprise.  These cohorts are large. In 2016, people aged 50 to 54 were the single largest age group in Canada, followed by people 55 to 59. Moreover, people in their 50s and 40s are at the peak of their earnings potential.  

The next figure presents this same information a slightly different way. It shows the percentage of total income received by people under the ages of 20, 25, 30, and so on. For example, the graph shows that people under the age of 20, who reported a total of $9 billion of after-tax income in 2016, accounted for less than 1 percent of the total after-tax income reported that year. People under the age of 30 accounted for 11 percent of the reported after-tax income. Less than half of after-tax income was received by people under the age of 50. The bulk of income was received by people 50 and over.

As has already been noted, CRA's Taxation Statistics are not a perfect measure of after-tax income, so I double-checked the analysis using the 2016 Census PUMF. Again, the same pattern holds - roughly 10% of after-tax income is in the hands of people under 30, and the age group with the greatest after-tax income is 50-54 year olds:

Again, this data is slightly dated - but the aging of the baby boomers would be expected to push the bulk of purchasing power even higher up the age distribution.

It could be argued that people in their 20s and 30s don't earn that much money, but they're the ones who really keep the economy going. They're the ones buying homes, buying cars, buying consumer durables. This point has merit, but it only serves to strengthen the argument made in this post. Canada's housing market is on fire right now. Car sales are strong, as are sales of consumer durables. It's the service sector that's hurting - plays, music, restaurants, museums, art galleries, and so on. And, generally speaking, it's middle-aged people who have the disposable income to keep these sectors going.

Perhaps if middle-aged people were allowed to assess the risks of various activities and participate if they chose, enough would opt in to make a real difference to the health of the Canadian economy. But I wouldn't count on it.

Download data used to create this post here

A Simple Micro/Macro Corona Tax Model

I want something you could teach to first or second year economics students. Using tools they already have in their toolkit.

MICRO

Start with a Demand and Supply model of the market for haircuts.

If we put a $1 per haircut tax on buyers of haircuts, the demand curve shifts vertically down by $1, reducing Q* and P*.

If we put a $1 per haircut tax on sellers of haircuts, the supply curve shifts vertically up by $1, reducing Q* and increasing P*.

If we put a tax on both buyers and on sellers of haircuts, both the demand and supply curves shift. We know that Q* falls. We don't know whether P* rises or falls. It depends on which tax is bigger; and it depends on whether demand or supply is more elastic.

The coronavirus is like a tax. Every haircut bought and sold comes with an extra cost to buyer and seller: the buyer risks getting sick; and the seller risks getting sick. (But the "tax revenue" isn't a gain to the government; it just gets thrown away as a net loss to everyone.)

I've drawn the above diagram with a roughly equal "Corona tax" on buyers and on sellers. Because I don't know which is bigger. But I have drawn a demand curve that is more elastic than the supply curve. Because I think that is roughly right, at least for the Short Run. And if demand is more elastic than supply, and the two taxes are the same, then P* falls.

And if the price of haircuts is sticky, and takes time to adjust to the new lower equilibrium price P*, we would see an excess supply of haircuts.

If the Corona tax is big enough, Q* may drop to zero, and the market for haircuts closes down completely.

Even if the Corona tax is not big enough to close down the market, the government might decide to close it down by law. Because the Corona tax also creates a negative externality, because if the buyer or seller gets sick, he or she might also infect other people who aren't buying or selling a haircut. So the Marginal Social Benefit of buying a haircut is less than the Private Marginal Benefit, and the Marginal Social Cost of selling a haircut is greater than the Private Marginal Cost. So the efficient quantity of haircuts Q^ might be zero, even if the market equilibrium quantity Q* is still positive.

Obviously, this will depend on the particular good. It might be efficient to ban the sale of haircuts, but allow people to buy and sell apples. Some markets close, or are closed; other markets stay open.

MACRO

That was Micro. Now lets do Macro, for those markets that do stay open, all lumped together in aggregate. So we are talking about Aggregate Demand and Aggregate Supply.

Eventually (we hope) the risks of buying and selling goods will go away, or at least get smaller. The Corona tax is a temporary tax. We can avoid the tax, by waiting to buy, or waiting to sell, until it goes away (or gets smaller). And the rate of interest is the extra price we pay for buying goods now, rather than waiting till next year; and it's the extra price we get for selling goods now, rather than waiting till next year. (Strictly, it's the real interest rate, which is the nominal interest rate adjusted for inflation.)

So put the real interest rate on the vertical axis, and relabel the curves "AD" and "AS".

Will the equilibrium rate of interest r* rise or fall? In other words, will the Bank of Canada need to raise or lower the rate of interest it sets to keep AD equal to AS (even though both AD and AS are lower than before). In other words, which curve shifts leftwards more, AS or AD?

That depends, on two things: it depends on whether the AD curve shifts vertically down more or less than the AS curve shifts vertically up (is the Corona tax bigger on buyers or on sellers); and it depends on which curve is more elastic (but here we are talking about intertemporal or interest-elasticity -- on how willing buyers and sellers are to postpone buying and selling until next year).

As before, I don't know whether the Corona tax is bigger on buyers or on sellers, so I've drawn it roughly equal. But I've assumed the AD curve is more interest-elastic than the AS curve, so r* falls, and the Bank of Canada needed to cut r. Because that seems plausible to me, and that is what the Bank of Canada actually did.

One big thing this model leaves out is this: buyers of goods expect to be able to buy a much wider variety of different goods (and services) in future than they can buy now. That also shifts the AD curve leftwards for the remaining goods. It's like they learn that they will be able to buy some fancy new device next year, that they can't buy now, and so want to save part of their current income because there are better things to spend their money on next year. See my previous post.

Update 1: A second big thing this model leaves out is that some sellers get hit much worse than others. Unemployed hairdressers might be borrowing constrained, and unable to buy as many apples as they want, because fully-employed apple producers aren't willing to lend to those with a bad credit risk.

[Update 2: I messed up slightly when drawing the two diagrams. If the "tax" on buyers is exactly equal to the "tax" on sellers, the point where the red S curve crosses the blue D curve should be exactly above the point where the blue S curve crosses the red D curve (same Q for both points). Which is the tax incidence irrelevance thing. Oh well.]

Public Health Spending and Pandemic Preparedness

As the first wave of the COVID-19 pandemic in Canada begins to peak, there have several discussions and perspectives offered on how prepared Canada was for this pandemic as well as whether we moved quickly enough to address the situation. Obviously, the situation has improved markedly given that there is now time for these retrospectives and a not insignificant amount of finger pointing.

In part, this is shaping the approach to moving forward by at least Alberta’s Premier who is instructing his provincial health officials to proceed with new drugs or treatments without waiting for lengthy bureaucratic approval.  One suspects the other provinces may be doing the same but more quietly given what is now perceived as a long lag time by the federal government in moving to nip the spread of the pandemic in the bud as well as shortages in what was thought to be a national stockpile of critical supplies.

There appears to have been weakness in our efforts at preparations ranging from having plans that were not followed to lack of critical supplies of personal protective equipment at both the federal and provincial levels. According to a story in the Toronto Star, federal Health Minister Patty Hajdu essentially agreed with this but laid the blame on underfunding public health preparedness.  As the key part of the story reads:

Hajdu said the “exact numbers” of medical equipment in the stockpile being distributed to provinces changes “day to day as we dispense equipment across the country.”  However, she acknowledged that “we likely did not have enough. “I think federal governments for decades have been underfunding things like public health preparedness and I would say governments all across the world are in the same situation.”

Toronto Star, Monday April 13^th, 2020

This is no virus for old men

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 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.

Regional disparities in hospital bed access across Canada

Thank you to Kayle Hatt and Kevin Andrew for generating some of the numbers and charts behind this post, and for helpful suggestions.

The number of acute care hospital beds per capita is an imperfect measure of a health care system's ability to respond to the COVID-19 pandemic. After all, beds are easy to acquire, as are hotels or similar buildings to put beds in. It is ventilators, IV drips, heart monitors, masks, gloves, and, above all, skilled health professionals that are the crucial resource constraints in this crisis. However if most hospitals operate with more or less the same ratio of nurses/beds, IV drips/beds, ventilators/beds and so on, looking at the number of beds per capita is a pretty good way to compare different countries', or regions', health care capacity.

In my last post and this piece for IRPP I discussed Canada's low number of beds relative to other OECD countries, and interprovincial disparities in bed numbers. In this post, I drill down to the health region level, show just how much hospital bed numbers differ across regions within the same province. This gives a useful indicator of Canada's regional disparities in health care funding and access to health care - and thus raises questions about the overall structure of health care financing in this country, and the allocation of health care resources.

The most dramatic regional differences in beds per thousand people can be found in Ontario:

Covid-19, Italy and Lessons for Canada

by Thomas Barbiero, Ryerson University and Livio Di Matteo, Lakehead University

As Italo-Canadese and members of the large Italian diaspora throughout the world, we have found the COVID-19 situation in Italy truly heart-wrenching.  As we write, Italy is the hardest hit country outside of China with over 60,000 confirmed cases and over 6,000 deaths. The hardest hit region is that of Lombardia in the north, which now has about half of the infected population and over 60 percent of the fatalities. The regions where our families originated, Campania and Abruzzo, have also been hit with about 1,500 cases combined. 

Hospitals in the hardest hit regions of Italy are overwhelmed, with mortality rates especially pronounced among seniors over the age 70.  The rapid spread of the virus and the high mortality rates in the last two weeks are particularly disconcerting with important lessons for us in Canada as we ramp up efforts to deal with the virus.  Based on current Canadian case numbers, we are approximately where Italy was in mid to late February. Canada currently has over 1,400 confirmed cases. A crucial difference is that we have had more warning and are taking things somewhat more seriously than they were at the start of their crisis.

We offer the following observations of the Italian situation and their application to Canada.  We believe the severity of the Italian situation is a function of several factors which have come together. 

First, Italy is an open economy heavily dependent on both trade and tourism and substantial international economic and travel ties. It is densely populated country -about the size of southern Ontario with 60 million people -  that receives over 60 million tourists a year. By comparison, Canada receives about 20 million international tourists a year but Canadians themselves are big international travellers especially to the US. Moreover, in just the last few days alone, about one million Canadians have returned home.

Ontario and British Colombia, together currently represent over half of those infected in Canada. Ontario, like Lombardia is an economic heartland with substantial international trade, travel and commercial ties.  B.C. has a thriving trade and commercial sector and again a major international travel destination as well as close links across the border. 

Canada’s decision to limit flights and close borders is an important one given that the virus spreads through personal contact and travel is the key factor in initial transmission and spread. Hopefully, this has not been delayed too substantially.

Unfortunately, Covid-19 spread in northern Italy before the authorities fully mobilized as there was reluctance to admit its seriousness.  A major politician in Italy in February even pronounced that there was no reason for Italians to give up their pleasurable aperitivo with friends in the evening. He was later found to have the virus and is now undergoing treatment.

Second, evidence has emerged that infection and mortality rates are higher in older Italian men. Smoking rates in Italian men are much higher than Canada.  Given COVID-19 has major impacts on the respiratory system, this is undoubtedly an aggravating factor.  Moreover, Italy’s population over age 65 is one of the highest in the world, representing almost a quarter of its population, compared to about 16 percent in Canada.  Mortality in COVID-19 is concentrated in the older population.

Third, Italian culture involves substantial social interaction outside of the house.  Italians are always out and about visiting the local bar to have a coffee or play cards, going for a passegiata and gathering on park benches after dinner. Italian culture is highly social and involves substantial physical contact when greeting.  Elders in Italian culture particularly expect the respect of warm physical embraces and greetings, and can be offended if that respect is not shown. Families are close and often get together for meals.

Indeed, the New York Times recently reported that 7 members of an Italo-American family in New Jersey were infected by the virus and 4 of them died Apparently, only one guest at one of the family dinners had the virus and spread it to these other family members.

Finally, Italians have a notorious irreverence for rules and compliance with government edicts, and the resistance to following quarantine and lockdown guidelines has been substantial based on the anecdotal evidence and news stories we are familiar with. Such behaviour has come with a price and is an important lesson to those of us in Canada who still are ignoring the need to social distance.

When it comes to what needs to be done to plank the curve in Canada, Italy in our opinion is a prime example of why “social distancing” combined specifically with “physical distancing” is paramount.  We all need to play our part.  It needs to be done immediately and it is important that the rules be complied with – now.

How prepared is your hospital for COVID-19?

I've written a blog post for the Institute for Research on Public Policy (IRPP).  It begins:

One widely touted response to the COVID-19 pandemic is to “flatten the curve” — to spread COVID-19 infections over time, so that the medical system can cope with them. Yet a cold hard look at the numbers suggests our hospitals cannot cope with the most flattened of curves. Indeed, they cannot cope with any kind of curve at all.

Canada has 1.95 acute care hospital beds per 1,000 people, fewer than any other OECD country but Mexico. (Italy, which has been overwhelmed by this coronavirus, has 34 percent more beds per capita than we do: 2.62 per 1,000 people.) Nationwide, the occupancy rate for Canada’s hospital beds is over 90 percent. (To put that number into perspective, the occupancy rate for US hospital beds is 64 percent.) Canada’s bed numbers and occupancy rates together imply that the “spare capacity” in our health care system is, at best, around 2 acute care beds per 10,000 people. If even a small fraction of Canadians contract COVID-19, and a non-trivial portion of those require hospitalization, our system will be overwhelmed.

How did Canada end up in this situation, and what can be done about it? (read the rest here).

 

The provincial level calculations in the post are based on Canadian Institute for Health Information (CIHI) data on the number of beds per hospital, per health region, and per province. You can download the raw data, plus a pivot table showing bed numbers at the health region and province level, here: Download Beds per health region

 

What would be really interesting is to do some calculations at the health region level, and find out which health regions are particularly short of beds. Unfortunately I've been unable to find data on the number of people per health region in Canada. Statistics Canada does produce a correspondence file that maps health regions to census districts, see here: https://www150.statcan.gc.ca/n1/pub/82-402-x/82-402-x2018001-eng.htm. Population per census district can be found here: https://www12.statcan.gc.ca/census-recensement/2016/dp-pd/hlt-fst/pd-pl/Table.cfm?Lang=Eng&T=703&S=87&O=A#, or calculated from the Census PUMF, which is can be freely downloaded here: https://www150.statcan.gc.ca/n1/en/catalogue/98M0001X (click through the links, it is actually free and downloadable. (I tried pulling numbers from the Canadian Community Health Survey, but that doesn't seem to work).

 

A project for someone with a computer and time on their hands?

Rising Health Spending Is Not Just About Seniors

The Canadian Institute for Health Information (CIHI) has released its 23^rd annual report on health spending in Canada - National Health Expenditure Trends, 1975 to 2019.  As a member of the CIHI National Health Expenditures advisory panel, it is always great to see the wealth of data on trends in health spending across Canada.   Total health spending in Canada in 2019 is expected to reach $264.4 billion which represents an increase of 3.9 percent over last year and also accounts for 11.6 percent of Canada’s GDP – a figure also up slightly from last year.  After a period of zero average annual growth in real per capita total health spending from 2010 to 2014, the period since 2014 has averaged about 1.4 percent a year.  This, however is lower than the average annual growth rate from 1996 to 2010 at 3.3 percent.  Health spending growth has resumed but on what seems like a more sustainable trajectory given that real per capita GDP growth is closer to 2 percent. 

Much of the concern about rising health spending has focused on the effects of population aging.  Health spending does rise with age as Figure 1 below shows rather dramatically.  Aside from those aged less than 1-year, average per capita provincial/territorial government health spending is well below $5,000 until the 60-64 age group when it starts to rise above that threshold reaching over $30,000 for those aged over 90 years.  Yet, despite this surge after age 60, what is also interesting is that when the drivers to rising health spending are broken down, in 2019, aging per se only contributes 0.8 percentage points out of the 3.8 percent growth in public sector health spending – about 21 percent – with general inflation, population growth and other factors (eg. Technology and utilization) accounting for the rest.  It does lead one to wonder whether this is because today’s seniors are generally quite healthy compared to the past or perhaps there are unmet needs.

What is also interesting and seldom noted is that while provincial/territorial government per capita health spending is highest among seniors, over the last two decades, the rates of growth in per capita spending have not been for seniors.  Indeed, between 2000 and 2017, the highest average annual growth rates have been for children and youth aged 5 to 19, followed by children under age 1-year and adults aged 35-39 as shown in Figure 2. 

Indeed, per capita spending for adults between the ages of 35 and 64 has been growing at a faster rate than those aged 65 to 89.  While it is true that much lower per capita amounts are being spent on those below age 65, this demographic has been seeing faster growth in health spending.  Again, this leads one to wonder given scarce resources, whether there is an implicit transfer of resources underway away from seniors when it comes to new spending growth or whether younger people today have more health problems or utilize health care more than similarly aged groups in the past.  Given the epidemic of obesity and mental health issues among the young, perhaps this is having an impact on health spending needs and expenditures. 

If a significant cohort shift in health care needs and utilization is underway is an interesting question. I suppose fully knowing if this is a recent development or has been underway for the last 50 years requires per capita age spending data going back quite a ways - I am only aware of the CIHI data going back to the mid 1990s or so.  This is an important issue.  While an aging population may only be contributing 21 percent of the increase in health spending now , if younger cohorts at present have deteriorating health status or more health issues than in the past, they may be poised to be a more important driver of health spending both now and in the future.

The Hospitalization Life Cycle

The Canadian Institute for Health Information has just released a report on Hospital Stays in Canada which provides a plethora of interesting tables on hospital stays in Canada at a national and provincial level.  Both the age-standardized hospitalization rate (per 100,000 population) and the age standardized average length of stay (in days) in Canada have fallen since 2013-14 going from 8,205 to 7,944 and 7.0 to 6.8 respectively.  The summary graphic for their report lists the top 5 reasons for inpatient hospitalizations as:1) Giving birth (362,700), 2) COPD and bronchitis (93,353), 2) Acute myocardial infarction (71,192), 4) Pneumonia (70,149) and 5) Heart failure (68,972) with the total number of inpatient hospitalizations in brackets beside each reason.  These five causes make up 21.6 percent of total hospitalizations in 2017-18.

Guest Post: Comparing Technological Change in the Health and Taxi Industries

Well, here is a guest post forwarded to me by Ruolz Ariste - a colleague whom I originally met during my annual interactions with the CIHI and with whom I have co-authored.  Ruolz Ariste is currently pursuing a PhD in Industrial Relations in a program run by run jointly by Université Laval and Université du Québec en Outaouais.  Enjoy.

Becoming Sustainable: The Six Stages of Provincial-Territorial Government Health Spending

The Canadian Institute for Health Information (CIHI) has released its 22^nd annual edition of  National Health Expenditure Trends covering the period 1975 to 2018 and the basic highlights are as follows:

• Total health expenditure is expected to reach $253.5 billion or $6,839 per Canadian in 2018.

• In 2018, total health expenditure is expected to rise by 4.2%, a slight increase in the rate of growth compared with earlier in the decade

• Provincial per capita health expenditures vary with total health expenditure per capita expected to range from $7,552 in Alberta and $7,443 in Newfoundland and Labrador to $6,597 in British Columbia and $6,584 in Ontario.

• Drugs expenditure growth is the fastest among 3 largest health spending categories with Hospitals (at 28.3% of spending), Drugs (at 15.7% of spending) and Physicians (a 15.1% of spending) together expected to continue to account for close to 60% of total health spending in 2018.

The bulk of health spending in Canada is done at the provincial-territorial level of government which in 2018 is forecast at $162.7 billion out of $253.5 billion or about 64 percent of total health spending – with remainder being private ($78.7 billion) or remaining public sector spending ($12.1 billion).  While much of the focus by the public on this annual release of health information is the current rate of increase and state of spending, a longer-term perspective is useful given the constant preoccupation with health care system fiscal sustainability. A historical retrospective of the evolution of provincial-territorial government spending since the dawn of Medicare is in order using Statistics Canada data for the 1965 to 1974 period and CIHI data since 1975.

It's time to blunt dentists' incentives to use general anesthetics

I recently consulted a dentist about getting a tooth extracted. The dentist recommended getting it done under general anesthetic. I responded, "I've had four wisdom tooth extracted under local anesthetic. I've given birth to two children without medication. I think I can handle it." "Ah, but some patients say dental pain is worse than child birth."

Eventually the dentist agreed to extract the tooth under a local anesthetic, and I was presented with an estimate for procedure - $350. "Why so low?" I asked the receptionist, remembering past estimates for dental work. "It's because you're not having a general anesthetic - if you were the bill would be more like $700." Indeed, I checked against the Alberta Dental Association fee guide (one of the few available on-line). The recommended charge for straightforward extraction done by a general dentist in Alberta is $242.25; the recommended charge for two units of general anesthetic is $228.62. A general anesthetic just about doubles the amount a dentist can charge for an extraction.

The Long Restructuring of Ontario's Health Spending

Ontario’s hospital sector has made a submission to the provincial finance committee making the case that overcrowding has become so serious that there is a need for more funding.  They are seeking a 4.55 percent increase in operating funds for the 2018-19 fiscal year in their pre budget submission.  According to numbers calculated from data from the Canadian Institute for Health Information(CIHI), over the period 2010 to 2017, the average annual growth of nominal provincial government hospital spending in Ontario has been 1.9 percent while provincial government health spending as a whole has averaged 2.6 percent.

Part of what is going on here is the usual maneuvering for more funds as part of any budgetary process which this year is expected to be a bit more active given that Ontario is apparently out of the deficit woods and balancing its books.  This should provide fuel for any number of claims across the province that there is a crisis and more funding is needed. The provincial government health care sector has seen its nominal spending growth since 2010 restrained to just over 2 percent annually (compared to about 7 percent annually between 1997 and 2009) as part of the Ontario government’s attempt to slay its deficit.  However, a look at the longer-term numbers also suggests that the hospital sector in Ontario has borne a disproportionate share of spending restraint over the last few decades.

Provincial Government Health Spending: The Force Awakens?

The Canadian Institute for Health Information (CIHI) recently released its 2017 edition of its National Health Expenditure Trends and its worth a trip to its website for the downloadable data on all things related to health spending.  I've been on an advisory group to the CIHI with respect to its national health expenditures for a number of years now and I always enjoy the release of the product and the assorted materials provided that can shed light on recent trends in health spending. 

The last few years have seen a flattening out of real per capita ($1997) provincial government health spending and a fall in growth rates as Figures 1 and 2 show.  However, after a decline from 2010 to 2014 that saw real per capita provincial government health spending fall from $2,589 to $2,508 - a drop of 3.1 percent - spending is expected to increase to $2,585 by 2017 for an increase of 3 percent since 2014.

Health Spending and System Characteristics in Canada and Spain

I gave a talk at Memorial University in Newfoundland & Labrador last week sponsored by the Department of Economics and the Collaborative Allied Research in Economics Initiative (CARE). My talk was based on joint research currently underway with David Cantarero Prieto at the University of Cantabria in Spain comparing the determinants of government health spending in Canada and Spain and particularly the role of physicians as a factor in that spending.

Add dentists to Millennials' list of victims

Diamonds. Napkins. Marriage. Relationships. Fashion. All are being killed by Millennials. Now it's the dental industry's turn.

 Millennials don't visit the dentist - at least not at the rates that their sweet and adorable younger siblings do, or at the rate of their responsible and sensible parents.

Now some Millennial reading this might start whining and complaining and making feeble excuses. "My employer doesn't offer dental insurance." "Other generations didn't visit the dentist when they were young." "I floss daily, have great teeth, and don't need to go." "I've got a crippling load of student debt, and can barely afford to pay the rent, let alone get a dental check-up." 

Seriously? Let's take apart that student debt thing. What about those poor dentists? Dental tuition in Canada currently averages $21,012 a year. How will young dentists pay off their hundreds of thousands in student loans if other Millennials don't use their services?

Nope, it's time for the Millennials to smarten up, find themselves good jobs with benefits, and end the carnage before it's too late.

Update: here are the 2013/14 numbers beside the 2003 ones. This says more about who has steady jobs with benefits than anything else.

 

 

 

No Health Deal. Now What?

Well, I just finished watching the federal health and finance ministers discuss the failed federal transfer health deal on the news.  I suppose coming just a few days before Christmas, a dispute over federal health transfers can become a new sort of Canadian holiday tradition given it has happened before with the December 2011 unilateral transfer decision announced by the federal government.  

New CIHI National Health Expenditure Numbers Out

The Canadian Institute for Health Information has released the latest version of its annual report on public and private health expenditure at both the provincial and federal levels. As always, the CIHI provides a wealth of health data and information resources and its site is an enormous asset to health researchers, health care administrators and policy makers across the country.*

Is Ontario Spending Too Little on Hospitals?

Earlier this month, the Ontario Council of Hospital Unions issued a media release for their The Fewer Hands, Less Hospital Care report, that made the following statement: “Based on the latest figures from the Canadian Institute for Health Information (CIHI), Ontario government funding for hospitals is $1,395.73 per capita. The rest of Canada, excluding Ontario, spends $1,749.69 per capita. In other words, provincial and territorial governments outside of Ontario spend $353.96 more per person than Ontario does. That is a whopping 25.3 percent more than Ontario. Overall there would be an additional 45,500 hospital employees, 15,200 of them nurses, in Ontario if funding was on par with the average for the rest of Canada.” The report goes on to say that this would amount to an additional 4.8 billion dollars for Ontario hospitals.