Imagine if the Olympic Games were currently being held in Wuhan, China. There would be widespread mentionings of it having been the starting place of the Covid19 pandemic, in December 2019.

But pandemics (not ‘global pandemics’; pandemics are global by definition, as are world wars) have two places of origin, though those two places could be the one-and-the-same. For Covid19, Wuhan was certainly the first place; the root source, to use a tree analogy. The second source is the base of the stem, the place from where a pandemic fans out and becomes almost unstoppable.

In the case of Covid19, the events in February 2020 in Milan and Cortina d’Ampezzo – the jewel of the Italian Alps – were the origins of the pandemic. Without their role, Covid19 might have been a contained epidemic such as SARS (2003).

Since the near-run-disaster that was the SARS-Cov1 panic in 2003, the amount of useful epidemiological work on coronaviruses has been minimal. There was clearly research work being done, including in Wuhan. But that was mainly on the zoonotic origins of coronaviruses, and not on the administration of outbreaks. SARS-Cov1 was a severe novel coronavirus. Novel respiratory viruses – such as the 1918 influenza pandemic – are lethal, spread fast, and are hard to contain. More lethal than Sars-Cov1 was MERS which emerged around 2012. Yet preparations for a respiratory-illness pandemic were focussed almost entirely on a new strain of influenza. No prep for a new novel coronavirus. SARS-Cov2 was ‘tricky’, in that – less lethal but more transmissible than SARS-Cov1 – it fell on the cusp between being dangerously lethal and dangerously transmissible.

Geographic Analysis

The pandemic events of 2020 were not – at least not in any popular awareness – subjected to a proper geographical analysis. Most of the initial outbreaks of the SARS-Cov2 virus which escaped China were largely contained. There were relatively small outbreaks in Japan, Hong Kong, Australia and Seattle in the United States’ northwest; in some cases transmitted by passengers from a few cruise ships. And larger but still largely contained outbreaks in South Korea and in Iran. These outbreaks came directly from China. The containment of the Iran outbreak was facilitated by the West’s generally hostile attitude towards that country as a geopolitical ‘bad guy’; Iran was easier than most countries for the West to quarantine.

More problematic were the outbreaks in Spain and Italy, which can also be traced back to January 2020. In Spain the initial outbreak, direct from China, was more in the south; most likely linked to escapees from China. There was relatively little subsequent movement across the land border into France, though Andorra experienced a separate outbreak. The main risk from the south of Spain was the United Kingdom, given that, for many British people, southern Spain is either their first or second home. It would have been relatively easy to quarantine British arrivals from Spain; the British authorities ‘dropped a ball’ by being tardy here.

The main blind spot was that Spain is a western country, and westerners had become ingrained in the supposition that pandemics (and all things bad) come from other countries; or, more accurately phrased, ‘countries of others’. Guard rails that were up for China or Iran or even Japan and South Korea, were not there for ‘threats’ from West European countries.

The notion came about that the pandemic radiated out of southern China, rather than having flowed out of all of the places which had experienced outbreaks. When eyes should have been watching Spain and Italy, they were still firmly focused on China, and in a finger-pointing way.

The West could have learned much from China’s data about the impact of the new virus in terms of the demographics of victims and non-victims, and the extent and duration of their exposures and their symptoms. However, the western countries were more predisposed to put up the shutters with respect to that amazing country.

A large part of the problem in the 1918 influenza pandemic was the high numbers of younger adults who caught it and died from it. Covid19 was never like that. Data from China showed that few younger people had died from Covid19; unless, that is, they had had sustained exposures. For younger people, and for society as a whole, it was better for otherwise healthy non-allergic people to have early and tentative exposures to Covid than to be on tenterhooks awaiting what became the inevitable, and would become worse the longer the wait.

Milano-Cortina

More problematic than Spain was the coronavirus outbreak around and to the east of Milan – the ‘tech’ centre of Italy, and the fashion centre – and the connection of Milan to the ski resorts during the peak of the ski season; indeed during the February school holidays in Europe. Milan is the most monied city in Italy. It is an important entry-point for affluent techies on business, and for sundry one-percenters. Once the epidemic began in Wuhan, many of the monied of and around Wuhan (many were foreign nationals) had the nous to ‘escape’ – including to Macao and Hong Kong – before the Chinese central government closed the ‘stable doors’.

Milan and environs became a hotspot for witting and unwitting coronavirus refugees – affluent exiteers – just at the time Europe’s ten-percenters were heading to and from the ski resorts.

Further, there was the World Economic Forum, at Davos, Switzerland; a one-percenter retreat. A few of the delegates may have, unknowingly, arrived with Covid. Following the Forum, many delegates – coming straight from a transmissible environment – will have visited the other hotspots for the rich and famous; the other alpine resorts, and the principalities Monaco, Liechtenstein, Andorra. And San Marino, which is a centre for the world’s semi-licit arms trade. All of these places had significant outbreaks of Covid19 during February and March 2020. These were perfect environments for the rapid spread of SARS-like coronaviruses. While coronaviruses are not winter viruses as such – compared to other cold and influenza viruses – they nevertheless thrive in winter when not obstructed by those other winter pathogens.

Essentially the most significant locations for amplifying Covid19 were greater-Milan, the Italian skifields centred on Cortina and Livigno; though Torino in the northwest – host of the 2006 Games – probably experienced its share of the unchecked Italian Covid19 flow. From these places it spread to neighbouring countries: Austria, Switzerland, France, and Bavaria in Germany.

Who else was there at those resorts? The managerial class – the bureaucrat and technocrat nine-percenters of the most affluent cities of northwest Europe, especially those cities hosting international (Geneva, Brussels, Luxembourg and Strasbourg) and national (eg Stockholm for one; and Paris and Berlin of course) governance organisations – were there with their older children. Fly-in, fly-out; a week’s break from the office with the family. In many cases parents on their own with the children while their spouses and ex-spouses enjoyed time apart from their children; elite parents and teenagers who would take the opportunities to socialise during the long après-ski evenings. They would mostly be back in their home countries by the first week of March.

Visitors from the Americas – from those same socio-business milieux – would have also been in these resorts at that time, and also in the capital cities of western Europe.

Covid19 didn’t stream into New York from China or from Seattle. It streamed in from the affluent centres of and close to alpine Europe, and from the business and political capitals of northwest Europe. Covid19 came into the Americas directly or indirectly from Italy to a much greater extent than it came from anywhere else.

Missing Maps

What was needed was good flow maps, much like those devised by John Snow, in London around 1850, to chart the cholera epidemics of 1849 and 1854. Instead, the statistics most available were nationally-compiled accumulations of cases and deaths; not international flow maps showing the sequences as Covid19 moved from some places and then on to other places. Individual countries were making their own imperfect maps, with their own make-believe boundary walls. In reality these European borders were for administrative purposes only. Herein lay the problem of visualising the flows of infection; unjoined maps. Further, these case-maps were often unadjusted for the population sizes of each country or province; many maps simply showed that there were cases where there were more people.

For flow maps, you must remove the dots which represent cases resolved by time or, for a small minority of cases, by death. And you must provide per capita data.

These administrators literally failed to join the dots between their own patches and their neighbours’ patches. A glance at any Europe-wide case-map would have shown, by April 2020, a large cluster of cases from Geneva north towards Strasbourg and Luxembourg, and then west towards Maastricht and Brussels; this cluster straddled six separate national borders. (Seven countries if you include Italy, which is close to Geneva.) The conclusions from such a map would have been as obvious as those revealed by John Snow’s case-map of Soho (London) during the 1854 cholera outbreak there.

In early 2020, it was senior public servants, their families including their elderly parents, their staff, and the people they had meetings (and eatings) with who had been most effectively spreading and succumbing to the virus.

First and Second Waves

By July 2020, the Covid19 outbreak was largely contained in Europe. But at a cost, not only in terms of disrupted income-earning opportunities to the small-medium businesses personnel who contracted the virus from the holidaying returnees and who were most disrupted by stay-at-home orders. And also, the latent cost of the first wave included the loss of those many natural immunisations that commuters in large cities experience most days of their working lives; especially cities with international airports.

Thus, the countries which had experienced multi-month shutdowns rebuffed the pandemic virus at a significant hidden cost; a weakening of the immunity of the population, increasing the susceptibility of the so-far uninfected to a new wave of respiratory contagion. Populations in urban centres – historically, and especially immigrants to those cities from the provinces – have always been vulnerable to transmissible diseases. By August 2020 this was especially so, especially in those countries in Eastern Europe (with older and poorer populations) which had been minimally exposed to both the first wave of Covid19 and the other pathogens they would normally have come into frequent contact with.

While the pandemic was contained in Europe by July 2020, it was far from contained in the United States. In the United States, the covid curve was flattened, but at a high plateau. The downside of flattening-the-curve is that you get an extended curve, creating a pathogen reservoir for a second wave of infections.

The Grand Tour and the second wave of Covid19

In the eighteenth century, a time of very high economic inequality in the British Isles and other parts of Europe, a tradition developed among the sons of the then one-percenters to do a Grand Tour. For a few, that tour was somewhat intrepid; Joseph Banks did his grand tour on the Endeavour with James Cook. Lord Byron was another, whose tour was somewhat intrepid and was never completed.

For the majority of these entitled young men, there was a tourist trail that developed; the grand tour became a kind of hedonist pilgrimage. Principal stops included Paris, the Rhine lands (including Heidelburg) and Switzerland. Some of these early bohemian tourists headed directly from Switzerland to Italy; others ventured into Austria (especially Vienna) and the Bohemian capital of Prague.

In Italy there were several must-visit cities, including Milan, Venice, Florence, Rome and Naples. The homeward journey likely included Sicily, southern France and places in Spain and Portugal.

Some grand tourists would also visit the ‘Near East’, the areas – including the Holy Land – defined by the Aegean and Eastern Mediterranean Seas.

The twenty-first (and late twentieth century) version of the grand tour is undertaken by the sons and daughters of American ten-percenters. In the United States in particular, working-life career-building requirements and surprisingly little annual leave strongly encourage this somewhat-elitist comparator to New Zealand’s OE. Young Americans have much less time than young Europeans to travel as tourists during their working lives.

In the modern Grand Tour, which lasts from mid-July to mid-September, young university-educated Americans with both left-elite (nine-percenter) and right-elite (one-percenter) backgrounds descend upon Europe. In 2020, this timing coincided with the re-opening of Europe after what the Europeans optimistically presumed was the end of the Covid19 pandemic. Further, European tourist hotspots were keen to welcome new waves of spending visitors, to help with their economic recoveries.

The second wave of the Covid19 pandemic began in August 2020, though this was not fully apparent until late September. The second wave was much more lethal than the first, and especially in Eastern Europe, where the (generally older) populations had largely escaped the first wave, but were particularly immunity-compromised as a result of the stay-at-home orders during the pandemic’s first wave.

The second wave began in places like Amsterdam, Paris, Barcelona, Switzerland, Czechia (especially Prague). And in Israel, another popular destination for American grand tourists. It was the American Grand Tour which brought the pandemic back to Europe, and with a vengeance; and which in turn instigated the further lethal waves of Covid19 around the world in 2021.

Unfortunately, thanks to inadequate specific-location-mapping and flow-mapping of the abundant Covid19 statistics, this flow of infections was only apparent to those who looked under the bonnet. By then, the national Wikipedia sites for Covid19 had lost their energy, showing increasingly outdated maps, and misplaced emphases on first-entry cases during the first wave. The accessible information was either too technical or too stale.

Popular Lore

In popular lore, the Covid19 pandemic was essentially a 2020 phenomenon. TV dramas and documentaries still emphasise that early period of the global crisis.

It was from the lethal second wave that the nasty new variants evolved, in 2021; and spread into and then from India, as the most spectacular example. Remember the Greek Alphabet soup, with the (British) Alpha and (Indian) Delta variants having been especially problematic.

The older Swedish scientists who emphasised the need to take a path – a path which accentuated the need for natural immunity to facilitate an early and complete end to the pandemic’s most dangerous phase – were proved correct as the pandemic raged through its most serious phase in 2021. Though you wouldn’t know it, probably too many interests did not want to make comparisons. Sweden’s politicians had been too slow to address the Stockholm outbreak in early 2020, when that country had an especially vulnerable elderly population; so, it looked as if the world had little to learn from that country. (Sweden had had significantly less influenza than most other countries, in 2018 and 2019; meaning that Sweden had unusually low death rates in the winters of those two years; meaning that they had plenty of ‘fuel’ for a tragic pandemic ‘fire’ in the spring of 2020.)

2021 also became the year of the Covid19 vaccine race; whereas 2020 had been the year of the missing PPE. The public health industry tends to place too much emphasis on immunisation through intervention versus immunisation through monitored natural exposures. This emphasis is valid for the most lethal of infectious conditions; the conditions for which we routinely vaccinate today. But for the below the radar circumstances of categories of common respiratory viruses with high complexity and low lethality – including known circulating viruses such as RSV, coronaviruses (the descendants of previous lethal coronaviruses), rhinoviruses, and influenzas –medicalised immunisations came to be emphasised while, with little awareness, simultaneous processes were lessening immunity to these types of virus. It was like taking one step forward and two steps back.

In the end, the pandemic was resolved through a natural immunisation process. 2022 was the year of Omicron. In 2022 the non-lethal Covid-Omicron variant ‘ripped through’ New Zealand and other places with previous minimal coronavirus exposure. This was a direct result of the failure and subsequent redundancy of the border-quarantine and other barrier methods of protection which were still in force in January 2022. Most New Zealand residents were exposed to covid that year.

Omicron had evolved in southern Africa in late 2021, from the earliest strain of Sars-Cov2. It became a natural immunisation force. Omicron was the invisible cavalry coming to the rescue; favoured in evolutionary terms over the Delta nemesis because it was more highly transmissible while being much less lethal than the previous covid varieties. More like the familiar but under-studied ‘common cold’ coronaviruses. Omicron stopped Delta dead in its tracks; a more effective weapon than the Pfizer and Moderna vaccines.

Lessons

I don’t think that western society has learned very much from the Covid19 pandemic. The importance of good mapwork and monitored natural immunisation barely formed any part of the long but largely useless narrative. Sweden’s alternative scientific path was forgotten, or derided, rather than learned from.

The next pandemic will probably also catch us unawares. It will be as different from the contemporary preoccupations of epidemiology, as Covid19 was. It may already be ‘hiding in plain sight’, as the coronavirus threat was in the 2010s. Family doctors should be routinely testing for all the various ‘bugs’ out there, and passing-on data about the various pathogens and cross-immunities which keep us healthy in daily life. We could perhaps have knocked out Covid19 in its early stages, by facilitating natural exposures of healthy people to low doses of already-circulating non-covid coronaviruses.

I think that future government-overreach mandates around lockdowns and mask-wearing will be hard to enforce, given the huge rightwards shifts in western politics this decade. But there may be opportunities for short smart protective measures, undertaken at local levels and in places such as retirement villages and rest homes. In particular, making high-grade (ie the more expensive types of) facemasks available to the vulnerable, with the warning that these should be worn mainly in high-risk environments, and not everywhere all the time.

Meanwhile

It’s great that Milan and Cortina d’Ampezzo, still popular hangouts of the rich and the not-so-famous, have been able to host a magnificent sporting event. These places have not been tainted by their association with the still recent pandemic. Despite being the places from which an outbreak of a significant new coronavirus fanned out to create a three-year pandemic that changed the world. That outbreak was probably containable, if we had acted with more nous and more knowledge of the common pathogens of daily life.

But who was looking at the Italian Alps in those heady ski-holiday days of February 2020? We were transfixed by China.

*******

Keith Rankin (keith at rankin dot nz), trained as an economic historian, is a retired lecturer in Economics and Statistics. He lives in Auckland, New Zealand.

This chart essentially shows the stresses that New Zealand’s public health system can expect to face. I have analysed the death data by age, covering all deaths from July 1998 to June 2024. For those years (using June years) I have looked at every age of death from 16 to 99 and every birth year from 1900 to 2022, and counted deaths by birth-year.

For death-age 95, the most frequent birth year was 1928. As we would expect, most deaths at these high ages occurred in 2022 or 2023, thanks to Covid19. Thus, birth years in the 1920s feature in the chart.

Birth years in the early 1930s don’t feature so much because of the low birth numbers in those years. With fewer people born in say 1933, then 1933 will not often feature as the most frequent birth year for any given age.

Birth years around 1950 do not feature. This is both because the classic baby boomer generation is a healthy generation, and also because there were not as many births in the decade after World War Two as there were in the following two decades. So, while classic baby boomers will place an increasing burden on the public health system, the biggest burdens will come from those born between 1955 and 1975. (Also, classic baby boomers have high levels of private health insurance; this will be less affordable for subsequent generations as they age.)

Birth years from 1955 to 1964 feature most strongly, mainly because births in New Zealand peaked in those years. This birth cohort will place massive pressure on New Zealand’s public health system. People dying since 1998 between age 37 and age 67 are most likely to have been born in the years either side of 1960.

The cohort born 1966 to 1974 will also place huge pressures on Te Whatu Ora (Health New Zealand), in part because there are likely to be very many new Aotearoans in this birth cohort. By and large, immigrants are healthier than the New Zealand born population, because their health is considered before New Zealand residency can be granted.

The late 1970s represents a ‘baby-bust’ generation, like the early 1930s. Hence these ‘Gen-Y’ people don’t feature in this chart. The frequencies for the late 1980s’ and early 1990s’ birth years reflect the ‘baby blip’ which began in 1987. Also, these birth years relate to death of young people, which, being less frequent, can also be a bit more random.

People born in 1939 turn 85 this year. From 1938, birth numbers generally increased each year until the early 1960s. The impact of an aging population on New Zealand’s public healthcare system is certainly beginning. This impact will escalate each year for at least the next 25 years. People born in 1961 will turn 85 in 2046.

By contrast, we have been lulled into complacency because the unusually small early-1930s’ birth cohort placed a substantially below-average pressure on public healthcare.

We note that death numbers are a proxy for the demand for high-intensity healthcare. People born after 1955 are already making considerable demands on Aotearoa New Zealand’s health care.

*******

Keith Rankin (keith at rankin dot nz), trained as an economic historian, is a retired lecturer in Economics and Statistics. He lives in Auckland, New Zealand.

On Monday morning on RNZ’s Morning Report, Revenue Minister Simon Watts admitted that it was a legitimate option for ‘landlords’ to leave their houses empty. (Refer Revenue Minister on mortgage tax deductions for landlords, RNZ 11 March 2024.) The official narrative of the elite political class is that when tenancies on rental properties end, the houses are retenanted or sold; sold either to an owner-occupier or to a landlord who lets the property to new tenants. They don’t usually admit to owning homes which are fully or substantially empty.

I might also mention that, in one of the leaders’ political debates before the 2023 election, both Chris Hipkins and Christopher Luxon were asked a question about whether they favoured a tax on owners of empty houses. Both leaders appeared discomforted, as if this was a naughty question that should not have been asked, and then recombobulated themselves by saying ‘no’; a response that was to be expected from Luxon, but which may have cost Hipkins a significant number of votes. (In addition to not including such an obvious policy in the Labour manifesto, Hipkins’ cold rejection of an empty-house tax revealed that Luxon is not our only tone-deaf political leader.) Clearly neither leader had been briefed on the issue, despite such taxes being adopted overseas and despite the policy idea circulating widely in the New Zealand non-mainstream media.

An additional feature of the Simon Watts interview was the Minister’s defensiveness towards Corin Dann’s use of the term “property speculator”. When Dann pressed Watts on the matter, Watts was unable to deny that some so-called ‘investors’ were indeed ‘speculators’, and sought to fudge the issue by saying that typical ‘landlords’ are ‘ma and pa investors’ with just one or two rentals (presumably in addition to their ‘ma and pa family home’. This seems at odds with his earlier admission that professional investors have a valid option to leave rental properties untenanted; because the popular image of ‘ma and pa’ landlords is of people housing tenants who they know and have a relationship with, not an image of ruthless speculators.

It is worth reminding ourselves about maverick economist Gareth Morgan’s 2017 comments about residential landlords: see A hard look at Gareth Morgan’s plan to save New Zealand’s renters, Madeleine Holden, The Spinoff, 9 August 2017. Quote from Morgan: ‘”Look at me, I own six houses, ” he stated on The Nation. “I don’t have tenants; they just make carpets dirty. I do it because I know you [other investors] want to get in on this as well, and so you’re going to bid the price of those houses up.”‘

Leverage: how it works to create speculator paradise

Ruthless property speculators – of which there were many from 2003 to 2008 and again from 2011 to 2017 – use the principle of financial leverage to get a return principally from capital gain. (Capital gains taxes in other countries did not stop this process.)

The context is that New Zealand’s housing crisis is principally one of market and government failure in private rental housing. And we should note that this failure is less an Auckland problem and more a problem of New Zealand’s provincial cities and towns, and Wellington. Additionally, it is a crisis of urban land prices, and – as in the later 2000s – a crisis aggravated by high interest rates. Into this mix we face a change to taxation which will aggravate rather than diminish the housing crisis.

The principle of financial leverage works like this. Mr S has a million dollars and wants to double his money in two years. He has been told by his financial adviser that residential property is appreciating in price by ten percent a year. Further a typical house costs one million dollars. Mr S buys five houses with his million dollars; that $200,000 per house of his own money. He borrows the remaining four million dollars. (He may then list his houses on Airbnb, as ‘short-term’ rentals; he may even let his properties to genuine tenants, or he may leave them empty.) In two years time he expects to sell all five houses for 1.2 million dollars; that’s six million dollars in total. After repaying his mortgages in full, he would get to keep two million dollars. That’s a doubling of his initial outlay of one million. (OK, there will be some expenses; nevertheless Mr S expects to be smiling all the way to the bank when he realises his near 100% capital gain over two years.

Mr S is an example of a leveraged landlord. And he will make even more money if he doesn’t have to pay tax on his mortgage interest ‘costs’. But not all landlords are that highly leveraged. Some are not leveraged at all; they are letting mortgage-free properties to tenants. These landlords cannot gain from the deduction of tax for mortgage interest costs. So, in the coming years, they will not charge lower rents on account of lower costs. And it is these unleveraged landlords who will set the market price for private residential rental houses. (And many of these price-setting landlords will be ‘ma and pa investors’ approaching retirement age.) The leveraged landlords, if actually renting out their properties, when setting rents will take their cue from the unleveraged landlords; therefore, they will accept the reinstated tax deductions as windfall profits.

Rents will be at whatever price the market will bear, and not discounted by individual landlords with falling tax costs.

Auckland Regional Fuel Tax

Something similar will most likely happen with the repeal of the Auckland regional fuel tax. At present with the regional fuel tax in place, petrol prices in Auckland are not much different from the rest of the country. That wider nationwide price will tend to be the main determinant of ongoing petrol prices in Auckland, meaning the petrol retailers will gain a windfall when the tax ends. Auckland petrol consumers will gain less than what Mobil and BP will keep.

More generally, we see a pattern from this government to replace proportional taxes with regressive charges; for example, favouring increased car-registration fees over fuel taxes. The cost burdens are increasingly placed on those least able to afford the costs. (This situation can also occur if fixed charges for water or electricity increase faster than usage charges.)

House Prices

The granting of a licence to speculate is likely to set-up the next round of residential property price appreciation. (My sense is that other global economic headwinds will limit the next property bandwagon to no more than three years.) The issue today is much as it was in 2005, with the downturn in the tradable economy, caused in large part by higher interest rates, pushing bank lending into the non-tradable economy, especially property.

Lending to the property sector is less sensitive to interest rates than lending to businesses which export or compete with imports. The New Zealand economy is now primed for a shift in lending towards the property sector. In addition, consumer lending will likely stay strong; this will be driven more by the budget shortfalls of financially stretched households than by interest rates; consumer lending is largely interest-insensitive.

As house prices rise, rents can be expected to rise as landlords – unleveraged and leveraged – seek to maintain their percentage yields on capital. Let’s say that the expectation is that a $500,000 house in the provinces is expected to yield a rent of $500 per week; landlords’ expectations would be that if the $500,000 house becomes a $600,000 house then it should earn a rent of $600 per week. (In reality, in times of property price booms but little employment growth, there is quite a lag in rent increases; renters are simply unable to pay such proportional rent increases.)

What is likely to happen in the next few years – when the speculator community has its mojo back following the removal of tax on mortgage interest – is rents increasing faster than they otherwise would.

‘Investors’ owning just one home.

There is one class of landlords who require special attention. This is people who own just one home, which they rent out to tenants, while themselves renting their own dwelling.

I was in this situation from 2009 to 2014, having received an inheritance a few months after moving into a rental house which particularly suited my family’s circumstances. Other people will be in this situation if they move out of their family home to accept employment in another city. And other people, wanting to own some property as a hedge against poverty, will want to buy a cheapish rental in another town or an outer suburb; yet will themselves want to keep renting closer to where they work or to where their children go to school.

These people should pay zero tax on their rental income if the rent that they pay is more than the rent which they earn. While there is a case to treat mortgage interest as a legitimate ‘business’ cost for property owners, if the rent they pay offsets the rent they earn, then the question should not arise; there is no income to tax. So the critical reform here, that the National Party should be leading the way on, is to deduct rent paid from rent earned. More generally, these ‘landlords’ – who own just one property – should be treated more as owner-occupiers than as investor businesses.

Fiscal Austerity? Despite tax repeals.

We are being promised public austerity alongside the tax repeals which foster increased private affluence for a few. Tougher times stifle the circular flows that underpin a prosperous economy; austerity begets more austerity in a downward spiral, until someone finally rediscovers Keynesian economics.

Cabinet Minister Tama Potaka unashamedly advocated “austerity” last week, on Newshub’s AM show (1 March 2024): see ‘Not a free ATM card’: Taxpayers won’t bear cost of saving Newshub, minister says | AM. (The ‘austerity’ quote comes 8’38” into the video-recording. Lloyd Burr, the interviewer, looks genuinely surprised at this candid admission.)

In relation to Potaka’s comment we have this on Newshub on 5 March 2024: Finance Minister Nicola Willis rules out increasing GST after Labour speculation. The web-story discusses public austerity in the light of Potaka’s comments. This interview was a response to Why Labour’s new finance spokesperson Barbara Edmonds thinks a tax hike could be on the cards on Newshub the day before. Nicola Willis knows better than Tama Potaka to avoid the ‘austerity’ word. But Nicola Willis is showing all the signs that she will be like her 1990s’ predecessor Ruth Richardson, who cut benefits and became Aotearoa New Zealand’s exemplar for fiscal austerity following her “mother of all budgets”.

Finally, Two questions for Christopher Luxon:

Prime Minister Christopher Luxon owns four investment properties as well as three residences.

Question: Are you a good landlord, Mr Luxon?

Supplementary Question: How many of your four investment properties are currently tenanted?

*******

Keith Rankin (keith at rankin dot nz), trained as an economic historian, is a retired lecturer in Economics and Statistics. He lives in Auckland, New Zealand.

Generally, more people die in winter. Not surprising, though some years have significantly more deaths than others, and the timing of ‘peak death’ each year varies between the wintery months. These charts show the deaths, determined from weekly data, of people named Smith, New Zealand’s most common surname last century.

The numbers shown are nine-week moving totals, meaning that for the last week of July the data runs from the beginning of July to the end of August. The next datapoint drops the first week of July, and includes the first week of September. (This method addresses the randomness of death, and the randomness associated with the Smith sample.)

Secular Trend?

It is somewhat surprising that the numbers of deaths in 1973 were not much higher than in 1950. The population of New Zealand in 1973 was 3.0 million; in 1950 it was 1.9 million. More people should mean more deaths. But the age structures were quite different. In 1950, there were relatively many older people – thanks to the 1870 to 1895 baby boom. In 1973, there were fewer older men thanks to both World War One, and to the deceleration in birth numbers from the 1890s. With the partial exception of the early 1920s, that reduced birth rate lasted from around 1900 to 1945; though there was variation, with say the early 1940s having many more births than the early 1930s.

But, what goes around comes around. There was another baby boom from 1945 to 1975; a boom that is only just starting, in the 2020s, to markedly influence death tallies. So, as annual death numbers have been only on a slow incline in the lifetimes of those alive today, annual death numbers are set to increase dramatically. Just as individuals die, so do generations. And big generations die bigly.

(We also note that, in the 1940s and 1950s, infant mortality was much higher in New Zealand than in the 1970s.)

Mortality Peaks

The higher peaks in these charts can be attributed to influenza outbreaks. In addition, the winter seasonal highs are linked to the set of viruses – including coronaviruses – which we collectively know as the ‘common cold’.

Superficially, these charts suggest that ‘the flu’ and ‘the common cold’ are New Zealand’s grimmest reapers; are, together, New Zealand’s biggest public health nuisance. Further, the peaks in these charts seem to be getting higher relative to the troughs in the more recent data. Should this be a matter of concern? Didn’t we, by the 1970s, reach a state of hubris about infectious diseases?

Old Age

Death and taxes are (allegedly) the two principal certainties of life. If we don’t die of one thing, we die of something else. So, an increase of deaths triggered by ‘minor’ respiratory viruses can be explained, mainly, by a relative decrease in deaths from other causes such as heart disease and cancer.

And there may be more to it than that. The seasonal circulation of non-novel respiratory viruses may represent a kind of natural vaccination programme. So, at least for otherwise healthy working-age (and younger) people, the presence of these viruses in our temperate ecosystems may be contributing to our increased longevity. Less smoking and sugar, combined with more (not less) exposure to respiratory viruses, may be the essence of why life expectancies have risen in recent decades.

If so, then, the presence of highly seasonalised death patterns may represent a collective solution rather than a collective problem. On balance, influenza may be our friend, not our enemy. It may determine the timing of death in old-age rather than be a significant cause of premature death.

(Tactfully, Queen Elizabeth’s death certificate in 2022 simply attributed her death to ‘old age’. Old Age is a real thing, and not an expression of casual ageism. Indeed, ‘old age’ was the most important and truthful part of her death story; though, as is usual, a single attribution is not the whole story of a person’s death.)

Dry Tinder

Outbreaks of influenza (and other respiratory) viruses work like forest fires. Thus, after years with relatively few winter seasonal deaths, there is a build-up of ‘fuel’ meaning that there will soon be a year or two of higher numbers of seasonal deaths. Followed by years of below-average winter deaths. This is a normal pattern. When there is a large build-up of people of advanced age, there will be more deaths from old age. That’s the normal cycle of life. How do people die of old age? More often than not, such deaths are triggered by a seasonal infection.The aim of public health policy is to maximise the numbers of people who die of old age; minimising the numbers who die prematurely.

These charts, to a large extent, represent deaths due to old age. They also indicate years of more virulent strains of influenza.

The Charts

In the purple 1946-1955 chart, we see 1950 and 1953 as the years of elevated winter deaths, suggesting more dangerous influenza strains. We also notice secondary death peaks in late spring, early summer. In 1949 and 1952 these secondary peaks were higher than the primary winter peak for that year. Presumably, the end of the year is a time when people circulate more; and there will be more vulnerable people if the winter death tally was unusually low.

Looking at the red 1955-1964 chart we see 1956 looking much like 1950, suggesting two low-mortality years would be followed by a higher mortality year, presumably the ‘dry tinder’ effect.

1957 and 1958 were the years of the ‘Asian Flu’ pandemic (a novel strain of influenza), and it shows in the New Zealand data for both years; higher death tallies in years which would otherwise have had significantly fewer deaths. Many of these additional deaths will be of people who would otherwise have lived a few years longer. (Unlike the extremely lethal 1918 strain, most non-elderly people with good general health seem to have weathered this pandemic OK.)

As is normal after a respiratory pandemic (and this is certainly true in Eastern Europe after Covid19, where public health measures substantially subsided in the latter part of 2020), the death tallies for the next couple of years (1959, 1960) is significantly down. 1961 and 1964 were higher winter mortality years, as per the three-year pattern. (1963 had a sharp mortality peak, probably a nasty flu strain, followed by unusually low spring mortality.)

Looking at the blue 1964-1973 chart, 1968 to 1970 reflects the ‘Hong Kong Flu’ pandemic. Whereas the 1957 influenza strain was first reported around January of 1957, the 1968 pandemic strain was first reported in the middle of that year. There was no sign of it in New Zealand in 1968, or in early 1969. Then, in mid-1969, with a mix of ‘dry tinder’ and a lethal influenza strain, there was a longer than usual mortality peak. Then, after a short pause, the pandemic really hit in December, and lasted until August 1970. Like the 1918 pandemic influenza peak, and some of the Covid19 peaks, this was a summer shock.

1971 and 1972 were also high mortality years, suggesting that many who died from influenza in the early months of 1970 had been of working age rather than old age. There were still many frail old people in the population after 1970; people born during the first baby-boom era.

By 1973, we start to get the impact of diminishing relative numbers of older people; a combination of Word War and falling birth rates around the year 1900.

Prognostication

These charts show pandemic and substantial epidemic influenza outbreaks in New Zealand. And they show how ‘old age’ deaths follow a seasonal pattern; commonly triggered by a respiratory virus which would be weathered by the vast majority of people who did not have the characteristics of old age. These viruses are part of the wider ecosystem of which humans are very much a part. Further, the ecosystem of seasonal viruses is maintained by periodic appearances of virulent novel viruses.

There is no reason to believe that life expectancies could be raised by taking public health measures to eliminate influenza and ‘cold’ viruses. Rather, these viruses fine-tune our immune systems, and without that fine-tuning, average life-expectancy would probably fall. Indeed, one cannot imagine the possibility of healthy populations in the crowded metropolises of the world without regular exposures to non-lethal respiratory viruses; exposures tantamount to natural vaccination.

Note

From 1974, not all historical deaths can be accessed online. The rule is that, for today (13 February 2024), only deaths of people born on or before 13 February 1944 will be accessible. This means that 1974 data will not be comparable with 1973 data, because it will miss about a half of infant deaths. We should also note, however, that by 1974 infant mortality rates were substantially lower than they were in the 1940s and 1950s; meaning that late 1970s’ Smith data will remain broadly comparable. A substantial majority of the ‘Smithometer’ Smiths after 1973 will be older people.

*******

Keith Rankin (keith at rankin dot nz), trained as an economic historian, is a retired lecturer in Economics and Statistics. He lives in Auckland, New Zealand.

In the European Union at least, mortality data is now available until close to the end of 2023. In northern Europe, mortality has been markedly higher than it should have been in the ‘Fall’ – autumn – of 2023. The main exception is Poland. Respiratory illness is most likely the culprit.

My working hypothesis is that excess deaths since mid-2021 have been mainly due to reduced immunity – general and covid specific – and that the extent of deficient immunity is largely a function of the duration of ‘public health’ lockdowns and facemask mandates. Other contributing factors would be the severity of the viruses in circulation, and the numbers of people vulnerable due to age or pre-infective morbidity.

Finland seems to have been hit particularly hard and early, this autumn. The deaths suggest an outbreak of Covid19 or something else towards the end of September. Finland fits the working hypothesis, with little sign of a Covid19 impact until mid-2021, and then a consistent pattern of excess deaths. Finland’s population is looking distinctly unhealthy, and with no evidence yet that 2024 will be much better. Finland had probably the strictest public health barrier mandates (lockdowns and facemasks) of all the Scandinavian countries.

Sweden is the opposite of Finland, with substantial excess mortality in 2020 and January 2021. From February 2021, Sweden’s mortality has looked normal. There were 2022 mortality waves synchronous with Finland, but smaller and briefer (except December 2022 which was high throughout Europe). Sweden has caught the autumn 2023 wave, but later and not as seriously as Finland. We’ll watch to see if there was a mortality fall-off there in December. (We note Sweden had a particularly benign 2019, meaning that it had in 2020 a group of older people who would have died in 2019 had 2019 been a normal year.)

Germany looks much like Finland, except that it experienced the winter 2020/21 wave which Finland avoided due to its public health restrictions. Germany experienced a ‘spiral of death’ in the second half of 2022. While most of 2023 has been about normal in Germany, the autumn mortality wave was serious and possibly ongoing. Germany is a country with weakened immunity, at least according to my hypothesis (given its extensive and prolonged public health barrier mandates), and which seems to have been exposed to the worst of whatever viruses – or virus strains – have been circulating recently.

Poland appears to be an enigma, though it does fit my working hypothesis. Poland showed no significant Covid19 mortality until September/October 2020. Like other Eastern European countries, it suffered twice as much as Western Europe in the winter of 2020/21. This, I have hypothesised, is due to the substantially weakened levels of general immunity in East Europe in the fall of 2020. Poland also suffered severely from waves of Covid19 in the spring of 2021 and the winter of 2021/22. Then, in 2022, Omicron Covid19 seems to have acted as a natural vaccination, making its experience much less severe than the experiences of Germany and Finland. 2023 looks to have been particularly benign in Poland.

We should note that Poland’s elderly took a hammering in 2020 and especially 2021, so there were fewer of them in 2022 and 2023. But, all of Eastern Europe has an older population, especially of postwar baby-boomers, in large part because the has been a westward drain of younger people. Poland’s population remains ‘oldish’, despite the demographic ‘haircut’ faced by that country in 2021. It’s population now looks remarkably healthy; presumably with high restored levels of general immunity.

(Another problematic country of North Europe, not shown here, is Ireland. Ireland imposed greater public health restrictions than the United Kingdom, but, like Finland, has had a distinctly queasy time of it since July 2021. Before the British pillory their government too much over its Covid19 public health response, a good comparative analysis, looking at all four years from 2020 to 2023, might suggest that the excess mortality data in the United Kingdom may not have been much better had they adopted a different set of public health policies.)

For interest, I have added charts for Greece and South Korea.

Greece got Covid19 late, and had a bad 2021, though not as bad as Poland. Early 2022 was worse in Greece though. We also note that Greece is unusual, because it has substantial summer death waves. Most of this will be due to the numbers of people passing through Greece, in the context that its visitor-to-local population ratio is unusually high each summer. Then, in 2022 and 2023 there were the big wildfires, which also contributed to excess mortality. There is no sign of excess mortality in Greece this autumn. It may not have received much of the virus infectivity that has been apparent in the north. Or, Greeks may have better restored their levels of general immunity than have Finns and Germans.

Finally, South Korea has this remarkable picture of having been little affected by Covid19 – at least in the death data – from March 2020 until July 2021. (It did have a significant early outbreak, in February 2020.) Things started to go wrong in Korea after July 2021, followed by a fullscale deathwave from February to April 2022. This was the less-severe Omicron variant, so clearly the Korean population was not prepared; Koreans must have had very low natural and vaccination immunity, owing to excessive and prolonged facemask wearing (and an insufficiency of vaccination boosters). Further, South Korea has had significant excess deaths since August 2022. It’s too early to say how the northern hemisphere autumn wave has affected South Korea; that country is tardy in releasing its data.

For the most part the data fits my working hypothesis, although Sweden is starting to converge more with its Nordic neighbours, all of which followed more restrictive policies. Sweden continues to have large numbers of very old people, reflective of its early exit from the Great Depression and its neutrality in World War Two.

(And, just an aside, it’s remarkable how many very old people are still alive in Japan, given its World War Two experience. Indeed I have visited the Nagasaki Atomic Bomb Museum, which I thought gave a very fair representation of the events of the war in Japan. And I visited the ‘ground zero’ Hypocenter Park. Sobering. Many Japanese will have survived due to the lack of a protracted ground war in that country. Korea, on the other hand, has a demographic structure significantly more determined by both World War Two and the active historical phase of the Korean War. South Korea’s excess mortality might have been substantially greater had it had a full quota of octogenarians and nonagenarians.)

*******

Keith Rankin (keith at rankin dot nz), trained as an economic historian, is a retired lecturer in Economics and Statistics. He lives in Auckland, New Zealand.

The above chart looks rather noisy, and so it should. Life and death are messy, and subject to random variations. But this chart, for females, and those that follow, are important charts. It’s worth looking through the ‘random noise’. These charts suggest that the cliché ‘we are all living longer’ is incorrect. Also, this representation of New Zealand’s mortality data highlights the experience of younger people, something hidden by most death and life expectancy data.

First, some technical information. Triennial death increases are three-yearly percentage increases in the actual number of deaths; in this case, the numbers of deaths for an age-cohort (aka. for a generation). These are 11-year age-cohorts; eg the 1970s’ age cohort here is made up of people born from 1969 to 1979. Hence it is listed as 1974±5 (where ± means ‘plus or minus’).

Consider the plot for people in the 1974±5 cohort who died aged 21. The number shown is 23%. Essentially, that means there were 23% more deaths of females in this 1970s’ birth cohort who died aged 21 compared to the number from that birth cohort who died aged 18. (This is equivalent to an annual increase in deaths of 7%. We note that the incidence of death among 18-21 year-olds remains very low, even if the numbers of deaths are increasing.)

The data has been ‘smoothed’ in the following way. The deaths of those aged 21 have been compared with the annual average of deaths for those people when they were aged 16 to 20. The reason for this smoothing is to remove distortion arising from random single-year impacts on comparison populations.

We note that, in the absence of immigration and emigration, each year the population of a birth cohort falls; the fall is slow when the birth cohort is young, and accelerates from middle-age. Certainly, from about age 30, the likelihood of death from natural causes increases, as the cohort population falls. Again ignoring net immigration for the time being, death increases with age should be positive (ie above zero percent); and approximately stable as the increased likelihood of death offsets the reduced cohort population size.

Adding emigration and immigration to the picture, for New Zealand at least, is likely to reduce the numbers of deaths of people aged in their early twenties (an age which typically features net emigration) and to raise the numbers of deaths of people in their thirties (an age which features net immigration).

A further point of interpretation. Where there are ‘spikes’ in the chart, it does not necessarily mean that death rates are falling in subsequent years. The spikes essentially show acceleration of death incidence. If death rates are high, then zero percent increases indicate that death rates are still high.

So, what does this first chart tell us?

First, we see that from age 30 onwards, each birth-cohort (ie each generation) has experienced about 20% more deaths every three years; this amounts to about a 6% annual increase in death numbers. The greater variability for women aged 30 to 50 most likely reflects variations in the rate of immigrant arrivals; and we should bear in mind that immigration in this age group includes large numbers of returnees, immigrants to New Zealand who were born in New Zealand.

There is no obvious sense that more-recently-born women are living longer or shorter than their elders. Though women born in the 1930s do seem to have been more likely to die age 35 to 45 than their daughters and granddaughters; probably a mix of aftereffects of childhood poverty in the Depression years, and of the high rates of smoking amongst that generation.

Second, all generation show at least one age from 20 to 25 where there were fewer deaths than three years previously. Emigration – in particular, extended ‘overseas experience’ – will have been one reason, though probably not the only reason.

Third, and perhaps most worrying, are the high increases in teenage death rates showing for people born after 1980. This will be partly due to falling death rates for people aged around ten. But is also likely to reflect the emergence of a growing underclass; child/teenage poverty in times in which underclass births have become a larger proportion of total births. While this data is for females, we’ll look at males shortly.

This second chart shows the same data as the first chart, though it’s plotted by year-of-death rather than age-at-death. This chart shows particular high-death or low-death years. 1957 and 1968 were influenza pandemic years, and it shows, especially for the 1930s-born age cohort. We can also see death peaks in 1977, 1980, 1987, 1995, 2003/04, 2011, 2017, and 2022.

We note the big fall in the late 1990s in deaths of the 1970s’ born. This will be due to particularly heavy emigration of young people in the 1990s; emigration resulting from the record-high unemployment levels in the early 1990s.

The other feature prominent in this chart is the experience of teenagers born from 1979 to 2009. These are most likely to be due to increased mental health issues faced by teenagers born from the 1980s, perhaps combined with other issues around childhood immunity to pathogens. Anecdotally, we do hear about increasing incidences of conditions such as asthma and allergies; conditions possibly due more to over-cleanliness than to exposure to pathogens.

Comparing males with females, we see that the ‘teenage issue’ is substantially more prominent with males, and more clearly extends back to older generations. In the case of teenage males, we note a greater propensity to risky behaviours, and also the greater likelihood of death by suicide. On the matter of risky behaviours, the most prominent feature of risk-taking was vehicle crashes.

(A particular note re my own memories. 1973 was the worst year ever for road deaths in New Zealand, and one group overrepresented were motorcyclists aged around 18 to 20. Look at the green 1950s’ birth cohort. While I neither died nor got injured from such a crash, that was me; born in 1953, and an active student motorcyclist. I knew a number of people who did have serious crashes. And I attended two funerals in 1973; both born in 1952, one in a mountaineering accident and one suicide.)

In the final chart, we again see no evidence that the younger generations are healthier – or more likely to be long-lived – than their parents’ generations. Further, those born in the 1990s are not showing the decline in deaths in their early 20s which characterised previous generations. Though this may be due to less emigration; ie to changes in the culture of ‘overseas experience’ with more young people taking short trips rather than an extended or indefinite period of absence from New Zealand.

The last year of data is 2022, the year of high Covid19 mortality in New Zealand. New Zealanders born in the 1970s appear to have been hard-hit by Covid19, whether by the infection itself, or as a result of other circumstances associated with the pandemic. A 40% triennial increase in deaths in 2022 cannot be explained entirely by immigration.

Finally, in terms of the charts, we see that for older people, cohort death increases have been less for males than for females. This is because there are fewer older men than older women; meaning that more ‘past deaths’ of older men means relatively fewer ‘present deaths’ of older men.

Explanatory Context

Finally, I should mention the work of prominent (and still living) twentieth-century demographer Richard Easterlin (b.1926). His central insights were that baby-bust generations have more successful life-outcomes, on average, than baby-boom generations. (This may be modified, by the conclusion that ‘trailing baby-boomers’ – eg Gen X – would do less well in life than ‘leading baby-boomers’.)

And that the advantages/disadvantages of each age-cohort would show up in death rates, and would persevere throughout their cohorts’ lives.

The recent data that I have shown here modifies the first insight. Young people born in the 1980s – a baby bust period – have higher teenage death rates than those born in the 1960s and 1970s. My modification to Easterlin’s conclusion is that increasing inequality and poverty within a nation-state will also have an adverse impact on the life outcomes of a generation; especially given that, for today’s younger generations, children are overrepresented in the poorest households.

On Easterlin’s second insight, it’s too early to tell if unusually high teenage death rates for recent birth cohorts will also translate to unusually high death rates for these generations when they reach middle-age and old-age. But it’s looking likely that triennial death toll increases for people born in New Zealand after (say) 1970 will continue higher than for people born in the 1940s, 1950s, and 1960s.

This means that I am questioning current official life-expectancy projections as too optimistic, given that they are biassed by the experiences of people born before 1970. Not only are we not all living longer, as the purveyors of retirement savings’ schemes claim, but my prediction is that true life-expectancy (an average) for people born after 1970 is actually lower than it is for people born between 1940 and 1970. Indeed, some United States data already shows this for that American country. I can see every reason to believe that New Zealand will follow this turning trend already apparent in official United States’ mortality data.

*******

Keith Rankin (keith at rankin dot nz), trained as an economic historian, is a retired lecturer in Economics and Statistics. He lives in Auckland, New Zealand.

The chart above looks at the changing numbers of deaths for New Zealand’s seven adult generations. The numbers are ‘triennial’ – three-year percentage increases. For example, in 2020, twenty percent (20%) more Generation-Y people died than three years earlier; with ‘three years earlier defined as the average for years 2015 to 2019, and ‘Generation-Y’ is defined as people born from 1976 to 1986.

Deaths in a given year increase because of the aging process, because of population increases for that age cohort, or because of adverse health outcomes (eg epidemics) particular to the years concerned.

We may consider 2016 a baseline of sorts, though each year has its quirks. 2016 was a year of low death rates in Aotearoa New Zealand.

In 2016, there was a small fall in Gen-M deaths (‘millennials’.) But deaths rose in the next three years. The main driving factor here will be net immigration, with there being a significant outflow of New Zealanders in their 20s. From 2017, the impact of New Zealanders returning from overseas will have been important. Also, we note that Gen-M (shown in blue) probably had less of the risky behaviours (compared with older generations) typically associated with males in their early twenties. We also note that for Gen-M, death numbers are very low, so random fluctuation will be present.

In the 2020s, Gen-M deaths fell, and then increased more slowly than other generations. Net migration will have played a large part here, as well as the Covid19 public health measures keeping people at home more.

Gen-Y (shown in orange) and Gen-X have substantial increases in mortality in the years at the end of the last decade. Net immigration will be the major reason; noting that net immigration probably peaks for people aged in their thirties. We should note that 2017 was the year that the global influenza epidemic struck New Zealand, affecting people of all age groups with pre-existing conditions of poor health.

Gen-X (shown in black) is of particular concern, because it shows substantial increases in death numbers throughout the period, even in 2020. For people born from 1966 to 1976, while death numbers are still low, they are not insubstantial. There was a marked increase in Gen-X deaths in New Zealand’s ‘year of covid’, 2022, despite high death numbers already registered in 2020 and 2021.

Generation Jones (Gen-J, shown in yellow) is the generation which most reflects the very high birth rates in the early 1960s. Boosted by immigration – but not as much as generations X and Y – death increases have been consistent for this generation, boosted in the final year by a mix of Covid19 and the natural aging of each generation.

Gen-B – postwar baby-boomers (in green) – are not much affected by net immigration since 2010. So, their deaths have reflected both the natural aging process and the behaviour changes (partly enforced) in 2020 arising from the Covid19 pandemic. We also see an impact of the 2017 influenza epidemic.

I am calling the generation who were babies or young children during World War 2 ‘Gen-W’. Shown in purple, Gen-W were clearly and adversely affected by the 2017 influenza epidemic. In 2021, many Gen-W people died who would have died in 2020 had 2020 been a normal year. We also note that death increases in 2022, while marked, are less marked than the three younger generations. This is because Gen-W is a diminishing generation, with deaths in previous years impacting substantially on the living Gen-W population.

This last impact is most prominent for Gen-D (children of the Depression), who saw substantially fewer deaths in 2020 and 2021, largely because of there being fewer of them, but also because of the pandemic public health measures.

In coming years, most likely the main determinant of increasing numbers of deaths in each birth cohort will be that natural aging of that cohort/generation. But high levels of both immigration and emigration will continue to play a role, as will ‘health crises’ arising from both changes in the health statuses of the generations, and arising from compromised health-care services.

*******

Keith Rankin (keith at rankin dot nz), trained as an economic historian, is a retired lecturer in Economics and Statistics. He lives in Auckland, New Zealand.

Analysis by Keith Rankin.

This chart shows total deaths in a number of comparable countries with high or highish life expectancies. The countries with most deaths have older populations.

New Zealand should perhaps be compared most with Ireland, Scotland, Denmark and Finland; all countries with just over five million people. And with Australia.

Australian mortality has been similar to New Zealand’s in recent years, though, as more New Zealand citizens migrate to Australia, in the next few years New Zealand will age faster than Australia.

Ireland is exceptional because of its relationship with United States’ high technology companies and its full membership of the European Union. Thus Ireland has many elite ‘tech’ workers at present. Further, in past years of difficulty – especially 2008 to 2014 – Ireland was able to unload much of its underclass to other countries. While the health and financial circumstances of Ireland’s sixty- and seventy-somethings requires further investigation, Ireland will neither have had as big a baby bust as New Zealand in the 1930s nor as intense a baby boom from 1946 to 1965. So, it is likely that the numbers of deaths in New Zealand will rise faster in coming years than the number of deaths in Ireland.

The high death numbers in Scotland, Denmark and Finland do not reflect lower life expectancies in those countries. Rather, they reflect populations with comparatively fewer younger people compared to older people. These countries’ mortality numbers in 2018-2022 are the best guide we have to what death numbers will be like in New Zealand in coming years, as the baby bust generation passes on and the 1940s’ and 1950s’ baby boomers reach the days in which they dominate death data.

Most importantly, the experience of these three countries suggests that we will see the demand for health care in New Zealand surge from now on – peaking in the 2030s and 2040s – at a time when current projections show that New Zealand’s healthcare workforce will trough. There seems to have been minimal, if any, demographic analysis of the implication in New Zealand of a baby bust generation giving way to baby boom generations. This is despite record numbers of policy analysts and cost analysts contracted by government.

Some Particular Comments about other countries

I have included Qatar and its near-neighbour Oman to show how low death numbers are at present in small Arabian countries with relatively large numbers of working-age residents. I think that this observation also applies to Israel.

We note also the death incidence in the higher life expectancy countries of Latin America – Costa Rica, Colombia and Chile – on account of their relatively low numbers of people in their eighties.

We note that Portugal and Japan have relatively high numbers of elderly people in their populations. Portugal has been a retirement magnet within Europe, with strong links to the United Kingdom. I have generally been puzzled as to why Japan has so many older people, though we should note that the generation which fought in World War II has largely passed on. I guess that, as in England, many Japanese children in the war were transported into the countryside so that they were not in the cities which suffered very intense bombing from the United States. Overall, Japan is one of the most age imbalanced countries; the low birth rates in recent decades contribute most to this.

Germany is a country which suffered particularly from Covid19 and similar diseases in 2022. But its high 2018 death tally suggests demographic causes which still need unravelling. Despite Germany being a major labour inflow country in Europe, it still has a median age about ten years higher than New Zealand’s (47 compared to 37).

On the flipside of Germany’s role as a labour-inflow country within the European Union, we have Finland and the other Baltic states as outflow countries. Hence the high death tallies in Finland and the Baltics relative to their resident populations.

Neither Finland nor Denmark look particularly happy in this chart. I predict that New Zealand’s death tally will soon be like Finland’s, given both countries’ propensities to lose labour to bigger neighbours. The situation of Greece is similar to that of the Baltic counties; too great a loss of their younger people to the employment centres of the European Union.

Re a few countries not in the chart, I can affirm that both England and Netherlands have population-adjusted death tallies very similar to the United States. And Canada’s adjusted numbers are very similar to Norway’s.

Finally, we should note Sweden, which was neutral in World War II. So Sweden does not have the extreme demographics of older people which New Zealand and other war participant countries exhibit. And, Sweden was less impacted by Covid19.

______________

Keith Rankin (keith at rankin dot nz), trained as an economic historian, is a retired lecturer in Economics and Statistics. He lives in Auckland, New Zealand.

Analysis by Keith Rankin.

These three countries are very useful comparators because they have broadly similar demographics – especially population age structures – to each other. Further they have comparable living standards. The Republic of Ireland has a population the same size as New Zealand (and a similar climate); Australia has close to five times the population of each of the others.

Of the above charts, Ireland best shows the three main waves of mortality in the Covid19 Pandemic. [Note that I will capitalise the word ‘pandemic’ for a WHO-declared pandemic. Otherwise uncapitalised.] There are very clear covid mortality peaks in Ireland in April 2020, January 2021, and December 2022. Other than these peaks there are clear periods of elevated mortality, the second half of 2021 and most of 2022. 2023 also, from March.

Ireland’s population has been growing more slowly this century than Australia’s and New Zealand’s. Death tallies before the Pandemic years were not noticeably growing from 2015 to 2019; compare Australia below. There was an influenza pandemic from late 2016 to about April 2018; the high numbers of deaths in Ireland in January 2017 and December 2017 reflect this. (I have omitted 2016 and 2018 to avoid chart clutter. For Ireland, influenza pandemic deaths actually peaked in January 2018, and extended into March of that year.)

The most obvious difference in the New Zealand chart is the southern hemisphere seasons. The second most obvious difference in New Zealand is the lack of obvious Covid19 waves.

The scales of the two charts are fully comparable, because of the near-identical populations of each country. But a careful look will show that ‘normal’ – ie baseline – summer deaths in Ireland are lower than in New Zealand. Ireland’s population may have better baseline health than New Zealand’s. Or, New Zealand may have more deaths because it has a higher population of post-war ‘baby-boomers’ than Ireland; a population which is now starting to die in greater numbers.

The New Zealand data are worrying in other ways, however. While 2017 clearly shows the 2016-2018 influenza pandemic, with its July 2017 mortality peak, summer data for 2017 and 2019 don’t show large increases in deaths arising from population growth. The period from March to July 2019, in the absence of known epidemic illness, nevertheless looks like a protracted period of deaths triggered by early seasonal viruses. (Indeed, I recall from my former workplace that there were a lot of ‘bugs’ around for parts of 2019.)

If we regard the April to June periods in 2017 and 2019 as having elevated death tallies, then 2021 looks like a normal year in New Zealand, even allowing for population growth. Yet it wasn’t a normal year. It was the peak year of the Covid19 panic; the year of the most extreme public health mandates, with an effectively shut international border and with face-masking required in many settings. The big question is to ask why 2021 was not more like 2020. In the winter of 2021, New Zealand had no Covid19 to speak of, and no influenza.

Elevated death tallies reappeared in March 2022, continuing through to January 2023. While these were clearly linked to Covid19, there was no mortality peak anything like that which Ireland experienced in December that year. My guess is that the timing of mortality in New Zealand reflected the timing of booster vaccinations against Covid19, whereas Ireland was caught unawares that December.

A good check on these tentative conclusions for New Zealand is Australia. (The scale is five times higher, reflecting that Australia’s population is five times greater.) Australia shows most of the same features as New Zealand in the years before 2020, though in a muted way. Australia shows more consistently than New Zealand the impact of population growth before 2020 being reflected in more deaths each year than the previous year. We see that in the spring months (September to November) Australian deaths are generally lower than New Zealand’s; probably because winter lingers for longer in New Zealand.

Australia shows the same problem in 2021 as New Zealand; normal winter deaths despite highly abnormal circumstances. As in New Zealand, there almost certainly were ‘killer viruses’ in both countries that year. Deaths to some extent will have been people who would otherwise have died in 2020, but avoided viruses then because of the lockdowns and physical distancing. Also, weakened immunity arising from the lack of normal exposure to respiratory viruses in 2020 will have increased the chances of vulnerable people dying in 2021 after contracting such a cold virus. The 2021 mortality peaks were higher in New Zealand than in Australia.

Australia shows a classic Covid19 mortality peak in January 2022, before the ‘Omicron’ variant of Covid19 was discovered in New Zealand. Australia had covid exposure peaks in December 2021, much of that being the Delta variant, pre-Omicron. In the autumn and early winter of 2022, Australian mortality data show a shorter and lower ‘Omicron wave’ than New Zealand data.

All three countries continue to show elevated levels of mortality this year, though this is obscured in New Zealand by the problematic numbers of deaths in the autumns and early winters of 2017 and 2019. New Zealanders in the first four months of this year have had slightly more deaths (adjusting for population) than Australians. This may reflect New Zealand’s relatively more overstretched healthcare system, noting from having myself spent some time in Australia this year that Australians also see their healthcare system as overstretched.

*******

Keith Rankin (keith at rankin dot nz), trained as an economic historian, is a retired lecturer in Economics and Statistics. He lives in Auckland, New Zealand.

The Pandemic can be assessed a bit like games of football, with deaths being the score. (Or, given that there are many ‘teams’ competing together, a better analogy may be a Marathon race. Nevertheless, I will use the language of the football metaphor.)

The winning country would be that with the fewest number of deaths attributable to Covid19. While this football metaphor is indeed useful, our perceptions of ‘who did best’ are strongly coloured by the pandemic’s first year, when media attention was greatest, when public health measures were most ‘in our faces’, and when the pandemic response was at its most bureaucratic.

As a result, the half-time scores are the scores that most seeped into public consciousness. Then, deaths which were classed as ‘covid-deaths’ were implicitly seen as more tragic, more requiring of daily tallying, than other deaths.

The two Tables below look at this Pandemic ‘World Cup’ through the simple demographic criteria of increases in deaths, all deaths. We may note four ‘ordinary-time’ phases of the pandemic. Together, they add up to a period of four years.

First, the warm-up, from May 2019 to February 2020. The warm-up, going back to 2019, is important to include because countries with unusually low numbers of deaths due to respiratory illnesses in 2019 would typically have higher death tallies in the next respiratory epidemic, whatever virus that might be.

Second is the first half of the Pandemic proper, which I would date as March 2020 to March 2021. Third is the second half, from April 2021 to April 2022, which includes the waves associated with the Greek-alphabet variants (especially Alpha, Delta and Omicron).

Following ‘ordinary-time’, there was ‘extra-time’ which I define as May 2022 to April 2023. We may note that the WHO declared the Pandemic to be over at around the end of April this year. So, we may formally categorise the period from May 2023 as ‘post-pandemic’.

Table 1 below indicates the score at the end of ordinary-time. It shows the percentage increase in deaths for a number of countries for the three years from May 2019 to April 2022, compared to the three years from May 2015 to April 2018. In the right-hand column is a counterfactual which is a best estimate of what the increase in deaths would have been in the pandemic period had there been no pandemic. (The counterfactual is calculated by comparing deaths in the 24 months ending April 2019 with deaths in the 24 months ending April 2017. I have used April years because, in both hemispheres, the period in late April and early May is generally free from epidemic respiratory deaths. This method minimises the impact to this calculation of the severe influenza global epidemic which lasted from late 2016 to early 2018.)

Table 2 indicates the ‘extra-time score’, comparing the year-to-April 2023 with the year-to-April 2019. It uses the same ‘trend’ counterfactual as Table 1. Whereas Table 1 is sorted to place the ordinary-time ‘winners’ at the top, Table 2 is sorted to place the ‘extra-time losers’ at the top. (We note that, for Table 2, some countries are laggards in publishing their mortality data; and also that the most recently published numbers are subject to upwards revision. The countries which are problematic in this regard have their data marked with asterisks, the number of asterisks indicating the degree of estimation required.)

In the pandemic proper, the two countries with easily the least increases in deaths were Norway and Sweden. The others in the ‘Top Eight’ (the ‘quarterfinalists’, to use the football metaphor) were the other Nordic countries, Australia and New Zealand, and Taiwan.

Based on the media coverage in New Zealand and the world news channels that New Zealanders mainly follow, the only surprise in that Top Eight would be Sweden, which pursued a very different policy response, especially in the ‘first-half’ of the Pandemic. In the 2020 New Zealand election campaign, political parties generally agreed that Taiwan was the exemplar for other countries to follow.

We may note that only New Zealand and Taiwan had counterfactuals showing higher projected increases in deaths than what actually happened. Thus, these two may be declared the ‘ordinary-time’ winners. The problem is that the Pandemic World Cup had ‘extra-time’. (It must also be noted, however, when we take the ‘non-death costs’ of the pandemic and its associated health policies, Sweden’s non-death costs were easily the lowest. So, on this basis, it could be argued that Sweden was the true ordinary-time winner, despite having been way behind at ‘half-time’.)

Extra-Time

When we look at Table 2 we see clearly that the East Asian countries performed very poorly. Most of these were deemed to be success stories in ordinary time. Taiwan is very prominent here. So is South Korea which has a conservative estimate in this table for its ‘extra-time’ increase in deaths. Macao is very important here, because it is the best proxy we have for China. Taiwan has had a recent resurgence in deaths in May 2023, and Macao has had a resurgence of Covid19 cases in recent weeks. So, these countries’ pandemic problems are far from over. (There are also signs that New Zealand’s seasonal death tally is picking up early this year.) The Macao situation, combined with other reports that all is not well in China right now, suggest that China may be presently going through a significant third wave of Covid19. This will add to global supply-chain problems.

New Zealand and Australia are in the top (ie worst) half of Table 2. So are two of the Nordic countries, Iceland and Finland, the Nordic countries which imposed more restrictive health mandates than their neighbours. So is Ireland near the worst, more restrictive in its public health mandates than the United Kingdom countries. Norway, top of Table 1, is in the middle of the Table 2 pack. Of the Nordic countries, only Sweden – easily the least restrictive in Europe, especially in the first-half of the Pandemic – performed well.

Quirky Counterfactuals

Creating consistent counterfactuals for each country is difficult because there are quirky demographics at play. First, we note that there are three main reasons why death increases might trend high for a given country. The first is a general increase in the population of a country: more people, more deaths. Second is the aging of a country, represented by increases in the median age of living persons. Third is a deterioration of general health, especially of those middle-age cohorts whose deaths ‘come under the radar’, given that deaths are dominated in most countries by people aged over 75.

It is likely that the high counterfactual for New Zealand is due to a mix of these. We know that New Zealand has some of the same issues of underclass deprivation as the United States, which include obesity, diabetes, and substance abuse. And we know that the United States has a lower life expectancy than other ‘western’ countries; a life expectancy now known to be falling.

The other two main quirks to look out for are birth rates in the troubled second quarter of the twentieth century. The Great Depression and World War Two were the main events that impacted on birth rates. There was also warfare in the 1950s in Korea and Malaysia. Sweden is an interesting case, comparable with Switzerland, neutral in World War Two, so having a lesser demographic impact from the War. Also, Sweden came out of the Great Depression early, meaning it will have had comparatively high birth numbers in the 1930s; Sweden’s peak deaths since 2015 will have been higher than otherwise, on that account.

While New Zealand is possibly the western country with the fastest population growth this century, this is offset by the fact that low birth numbers in the 1930s are translating to lower deaths since 2015. (See my recent ‘Smithometer’ analysis, in Granny Smith.) Aging and population growth are not the whole story of New Zealand’s upper quartile trend of increasing deaths. (Unlike, say Portugal, which is known to attract retirees in Europe as Florida does in the United States.)

New Zealand also has the additional factor of having, in June and July 2022, too many vulnerable people who were denied, for unexplained political reasons, a timely second booster Covid19 vaccination. The July 2002 mortality peak, almost entirely experienced by older European-ethnic New Zealanders – the Granny Smiths – came to a prompt end once these people became eligible for second-boosters. This sharp July peak – and drop-off – appears to have been a New Zealand specific phenomenon.

High counterfactual notwithstanding, New Zealand performed very poorly in extra-time, though not as badly as the East Asian countries which imposed the most ‘sterile’ public health policies on their people.

East Europe

I would like to note two other groups of countries. First, it was Eastern Europe which had the highest reported per-capita Covid19 death tolls. These countries do not look as bad in this analysis, though they (except the Baltic states) still look bad in Table 1, especially in light of their often negative counterfactual death trends. The main demographic problem that these countries have been facing is emigration of working-age adults, especially those Eastern European countries in the European Union. Generally, these countries look much better in Table 2, in extra-time.

Most of these countries successfully imposed severe public health restrictions in the first half of 2020, but abandoned those restrictions – or were unable to easily reimpose them – in the later stages of ‘the game’. The result was that these countries’ populations had substantially compromised immunity going into the winter of 2020/21. Their death peaks were much higher than the death peaks in the west earlier in 2020. The second problem was that, on account of their departed youth, their populations were aging as well as falling. Hence the high Covid19 per capita death tolls that savage winter.

In ‘extra time’, East Europe has ‘performed’ best. This would appear to be in part because so many of their most vulnerable people had already died; respiratory viruses had lost much of their human ‘fuel’. Also, these countries had re-established (the hard way) high levels of natural immunity to respiratory illnesses.

Russia continues to supply mortality data, though it excludes deaths in the Ukraine conflict zone; so its not included in the tables. And Ukraine has certainly stopped supplying data, due to governmental priorities as well as a lack of will to publicise its present demographic plight. Kazakhstan is probably the best proxy for assessing the impact of the Pandemic in Russia.

South America

These countries (plus Mexico) are among the worst performers in both Tables. Typically, they exhibit many of the ‘underclass’ socio-economic problems apparent in the United States, United Kingdom, and New Zealand: inequality, poverty, homelessness, obesity, crime, violence. It is likely that they will see ongoing increases in annual mortality on account of these factors; factors exacerbated by both the Pandemic and its associated mandates.

While Latin American populations are much younger than Eastern European populations – due to both higher births and less emigration – there will also have been a significant growth of numbers of people of peak-dying-age (over 75) contributing to ‘trend’ counterfactuals in some cases as high as New Zealand’s.

Another factor in these American countries is the high proportions of people living in or near the tropics at high altitudes. Under normal circumstances, these are unusually healthy environments, in which seasonal respiratory illnesses do not circulate as much as in temperate climes. But, it makes people living in these zones more vulnerable to pandemic respiratory illnesses when they do happen. It’s an old story that goes back to the time of Spanish colonisation in the sixteenth century.

This last factor is only apparent here in Colombia and Ecuador. Other similarly affected countries – Peru and Bolivia – had very high early death tolls, but (presumably due to political crises) have not released ‘extra-time’ data. Venezuela was even less forthcoming with useful data.

Africa including Qatar

Mortality statistics from Africa are rare. Egypt is now the best, and it certainly suffered. South Africa, which in ‘ordinary-time’ had a similar experience to that of East Europe, used to supply good quality data; but no more as its present economic crisis deepens. Signs are that the African continent was less impacted directly by Covid19 than other regions, though its more fragile economic supply-chains have become victims of the Pandemic’s ‘extra-time’ environment.

Qatar is an interesting case, because of its unusual demographics. Qatar’s resident population is heavily weighted towards the younger working-age population. So, while its death rates per capita have been very low, its percentage increases in deaths have not been low. We do need a good comparative analysis of the health impact of Covid19 on working-age populations, though made difficult by demographic data today still focusing on sex and ethnicity rather than age or occupation or labour force status.

South Asia

After China, the biggest Pandemic uncertainties relate to South Asia, with Inda being the largest country. We may also add the very populous country that is Indonesia. This region is a demographic black hole, which experiences high levels of emigration as well as of death. (We may note here – see Hundreds of Pakistanis dead in Mediterranean migrant boat disaster, CNN 19 June 2023 – that the majority of victims of the overcrowded refugee boat which sank last week off the coast of Greece were from Pakistan.) This region has suffered a huge upheaval since 2020, with the Pandemic a significant contributing factor.

Conclusion

There remains a lack of competent demographic analysis of recent and former pandemics, partly due to poor (sometimes politically-motivated) record-keeping, and partly due to the low status of demography among the social sciences. Analyses like mine here – amateur in the sense of being unpaid, but not in the sense of quality – help to fill the gap.

The most striking conclusion is that the ‘extra-time’ of the Pandemic gives a very different picture of the Pandemic’s human cost. The imposition by governments of sterile environments for long periods is not a recipe for good health outcomes, although it may give good headlines in the early phases of a pandemic when the Press is at its most attentive.

*******

Keith Rankin (keith at rankin dot nz), trained as an economic historian, is a retired lecturer in Economics and Statistics. He lives in Auckland, New Zealand.

Next month, on July 10, six months will have passed since Papua’s Governor Lukas Enembe was “kidnapped” and flown to Jakarta for charges over alleged one million rupiah (NZ$100,000) graft.

Despite his deteriorating health, he has been detained in a Corruption Eradication Commission’s cell (KPK) in the Indonesian capital — more than 3700 km from his hometown of Jayapura.

He is due to appear in court today, but that depends on his health status.

His drawn out ordeal has been full of drama and trauma. There has been indecisiveness around the case and the hearing date has been repeatedly rescheduled — from 20 more days, to 40 more days, and now into months.

There are no clear signs of any definite closure. For his family, friends, colleagues, and the Papuan people, this has been a nightmare.

While being held captive and tortured in the KPK’s prison cell in Jakarta, his kidney, stroke, and heart specialists in Singapore are concerned about what has been happening to their long-term patient.

In December 2020, Governor Enembe had a major stroke — for the fourth time. He lost his voice completely in Singapore, but his medical specialists at Mount Elizabeth hospital brought his voice back.

Since then, during a covid lockdown in 2021, he had another stroke, and was flown to Singapore.

Between 2020 and 2022 he had been receiving intensive medical assistance from Singapore. He was about to go to Singapore last September as part of his routine check-ups, only to discover that his bank account had been frozen, and his overseas travel blocked.

The trip in September was supposed to fix his already failing kidneys. He was unable to walk properly, his foot kept swelling and he began to lose his voice again.

He was on a strict diet as advised by his doctors in Singapore.

After Jakarta’s special security forces and KPK “abducted” him during a happy lunch hour at a local restaurant in his homeland on January 10, all his routine medical treatment in Singapore came to an abrupt halt.

Governor’s health
Following the abduction, medical specialists in Singapore expressed their concern in writing and requested that the medical report of his latest blood test from KPK Jakarta be released so that they could follow up on his critical health issues.

On 24 February 2023, the medical centre in Singapore wrote a medical request letter and addressed it directly to KPK in Jakarta.

The above mentioned (Lukas Enembe) is a patient at Royal Healthcare Heart, Stroke and Cancer Centre under Patrick Ang (Senior Consultant Cardiologist) and Dr Francisco Salcido-Ochoa (Senior Renal Physician). He was last reviewed by us in October 2022. As his primary physicians, we are gravely concerned about his current medical status.

We are aware that his renal condition has deteriorated over the last few months with suboptimal blood pressure control. We are humbly requesting a medical report on his renal parameters via biochemistry, blood pressure readings and a list of his current medications.

To date, however, KPK has prevented his trusted long-time Singaporean medical specialists and family members from obtaining any reports regarding his health.

The governor’s family in Jakarta have repeatedly requested for an independent medical team to oversee his health, but KPK has refused.

Only KPK’s approved medical team is allowed to monitor his health and all the results of his blood tests, types of medications he has been offered and overall report on his treatment since the kidnapping has not been released to the governor, his family, medical specialists in Singapore or the Papuan people.

Elius Enembe, spokesperson of the governor’s family said they want the panel of judges at the Tipikor Jakarta court to appoint a team of independent doctors outside the Indonesian Doctors Association (IDI) to check the governor’s health condition.

According to the family, it was important to ensure Enembe’s current health conditions are verified independently before the court hearing takes place. This is because “we consider IDI to no longer be independent”, Lukas Enembe’s brother, Elius Enembe, told reporters in Jakarta, reports Medcom.

“After all,” he continued, “Indonesia’s Human Rights Commissioner had issued a recommendation that Lukas continue his treatment, rights that had been obtained before being arrested by the KPK, a service to be received from the Mount Elisabeth Singapore hospital doctor’s team.”

An independent opinion of the governor’s actual health condition is critical before the hearing so that judges have a clear, objective picture on his health condition.

“If there is an independent doctor, then there is another opinion that could be considered by the judge to ensure the governor’s health condition. This is what we are hoping for, so that the panel of judges can objectively make its decisions,” said Elius Enembe.

The court hearing
One of his five times failed case hearing attempts was supposed to be held in Central Jakarta’s District Court at 10am last Monday, 12 June 2023. This highly publicised and anticipated hearing did not take place.

Two conflicting narratives emerged about why this was adjourned.

KPK’s view
According to the Corruption Eradication Commission (KPK), Lukas Enembe’s actions hampered the legal process. In fact, the head of the KPK news section, Ali Fikri, stated that his first session was met with a very uncooperative attitude.

“We regret the attitude of the defendant, which we consider uncooperative,” Fikri said in his statement quoted by Holopis.com on June 12.

“The confession of Lukas Enembe, who was ill and could not attend the trial, was considered strange and far-fetched by the KPK. The defendant can answer the judge’s questions and explain his situation, even though he later claims that he is ill,” he said.

Fikri also threatened Lukas Enembe by saying that the Governor would face consequences during the prosecution process.

“The KPK Prosecutor Team and the panel of judges will assess his attitude separately when conducting prosecutions or drafting charges,” he said. ‘

“Of course, there are aggravating matters or mitigating issues, which will be a consideration when a defendant is uncooperative in the trial process,” he continued.

“When the trial process takes place, the KPK will always include a doctor’s health report to anticipate Luke’s uncooperative attitude in the retrial,” Fikri said. “The KPK Prosecutor Team will convey to the court in detail the defendant’s health condition during the next [hearing],” he said.

The first hearing in Lukas Enembe’s gratuity case has been postponed until this week. The reason for this is that Lukas Enembe claimed he was sick and could not participate in the virtual trial.

The Governor’s legal team protest
The Governor’s legal team protested against the KPK, saying that it was a “deliberate attempt” by the agency to manipulate public opinion based on biased and inaccurate information about what actually happened on Monday, June 12.

The following is the account provided by the Governor’s legal team after KPK was accused of spreading media news that the hearing had failed due to an “uncooperative governor” in terms of the legal proceedings on that day.

Monday, 12 June 2023, around 9.30am local Jakarta time, a guard entered the KPK’s detention room where Papua’s Governor, Lukas Enembe, was detained. The guard was requested to accompany the detained Governor to the hearing room.

Upon arriving at the door, the Governor asked the guard where the hearing was being held. The guard explained that he was taking him to the online courtroom in the red and white KPK building (red and white symbolise the colours of Indonesia’s flag or Bendera Merah Putih in Bahasa Indonesian).

The Governor said he would not attend the hearing via tele link. The Governor wanted to attend the hearing in person, not virtually via a screen.

Afterwards, the Governor went to his detainee room and wrote a letter of protest, explaining his aversion to viewing the proceedings on television. After the letter was written, the guard accompanied the Governor to the detention room to inform them of his desire to appear in court physically.

The court hearing was scheduled for 10am that day. Guards from KPK’s detention arrived at 9.30am to escort the Governor, allowing him only 30 minutes to prepare.

The Governor’s legal team was waiting outside the KPK’s building. As 10am approached, the legal team (Petrus, along with Cosmas Refra and Antonius Eko Nugroho), went to KPK’s receptionist and asked why they were not called to enter the hearing room.

The receptionist replied that they were still in the process of coordination since Enembe was not yet awake. Moments later, officers took the legal team into the detention visiting room, where there were masses of visitors because it was visiting time.

At one corner of the room, Governor Enembe was surrounded by prison guards working on a laptop. The governor’s lawyers were then told that the hearing would begin when the audio system was fixed.

When the Governor and the legal team finally met, the legal team asked Enembe why he was wearing shorts and a T-shirt to court. Governor Lukas said he was annoyed at the guard for suddenly arriving to escort him without warning, which is why he had not dressed neatly. He could not wear sandals because his feet were swollen.

Governor Enembe refused to have an online hearing because he had not been informed in advance of Monday’s hearing and the summons was only signed once the hearing was opened by the judges.

If the KPK prosecutor had notified him at least the day before the hearing, Governor Enembe would have cooperated. But he was only notified 30 minutes earlier.

As the judge covered the trial, the legal team led by Petrus, informed Governor Enembe to appear before the court on 19 June 2023. The governor nodded in agreement.

“In light of this explanation, we must emphasise that Mr Lukas does not intend to be uncooperative in facing the alleged case,” said the legal team.

According to Petrus, “the detained Governor Lukas Enembe did not immediately leave the detention room because he was still writing a statement that the prosecutor had not informed him in advance of the trial scheduled for Monday, 12 June 2023”.

The Governor’s next court hearing has been rescheduled for today and whether he can physically attend will depend on his health.

However, the main issue is will he be found guilty of the charges? There is a lot at stake.

Yamin Kogoya is a West Papuan academic/activist who has a Master of Applied Anthropology and Participatory Development from the Australian National University and who contributes to Asia Pacific Report. From the Lani tribe in the Papuan Highlands, he is currently living in Brisbane, Queensland, Australia.

Article by AsiaPacificReport.nz

The above chart draws on the historical ‘births, deaths, and marriages’ dataset published by the Ministry of Internal Affairs. It’s a major resource for genealogical research.

The database for 13 June 2023 showed the deaths of all people whose deaths were registered in New Zealand on or before 13 June 1973. And, for 14 June 1973 to 13 June 2023, the deaths of people born on or before 13 June 1943. You need to enter a family name – and ‘wildcard’ spellings are not allowed – so I have used the name Smith as a sample of New Zealanders. (This dataset is useful in that you can get death numbers for individual dates, and it’s the date of death, not the date of registration. It would be good if you could use wildcards, as you can in most genealogical databases.)

The Smith sample used for the data for the above chart is biassed in several respects, but these biases are pertinent in the context of an evaluation of the demographic impact of the Covid19 pandemic in New Zealand.

The first and most obvious bias is that the data only includes older people; further the definition of ‘older’ changes for each year plotted, albeit in an orderly way. This in itself means that there will be more women counted, because the older population is more female. The second bias is that married women, including widows, will show up whether either their birth name or their final married name is ‘Smith’. So this survey is principally, though not only, of grandmothers (and great-grandmothers, and their sisters and female cousins).

The name Smith is a Pakeha name, common in Scotland as well as England, so will bias the data in favour of people (including many Māori) with English and Scottish ancestors. There will be bias against Māori though, because Māori life expectancy is still well below that for Pakeha. The octogenarian population underrepresents Māori. These factors will also bias the data in favour of the South Island relative to the North Island. Overall, this is very much a survey of the end-of-life circumstances of older Pakeha women in Aotearoa New Zealand.

The 2022 number includes people aged 79 and above who died in the 12-week period from 19 June to 10 September. The 2021 number includes people aged 78 and above who died in the 12-week period from 19 June to 10 September of that year. And so on. Thus the 1973 number includes people aged 30 or older when they died. Overall, the earlier the year the more younger person deaths; yet the overwhelming majority of deaths each year is of people who would have been regarded, when they died, as ‘elderly’.

The chart shows a discontinuity in the late 1990s, which I cannot explain for sure; a discontinuity that shows more Smith deaths in the early 2000s, when in fact I had expected fewer deaths. (Anti-vaxxers might interpret the discontinuity as being due to the introduction of the influenza vaccine. I think this is very unlikely to be the reason, and we would need numbers of people who actually received that vaccination each year. My suspicion is that significant numbers of people received influenza vaccinations only from the late-2000s.) My best guess is that in the early 2000s the most frequent ages of recorded deaths were the early 80s – from 80 to 82 years old. This corresponds to births in the 1920 to 1922 post World War 1 mini baby boom.

An important reason for this century’s trendline sloping downwards is the shortfall in births from 1932 to 1938, a result of the Great Depression. A person dying aged 89 (the most frequent age of death) in 2022 would have been born in 1932 or 1933, the peak years of the Depression. The deficit of births in the early 1930s would however have been offset to some extent by the immigration of ten-pound-poms in the 1950s and 1960s.

The peaks in the chart in most cases represent years of high levels of death from pneumonia and other conditions arising from winter respiratory infections, the most prominent being influenza though ‘common colds’ may also be significant triggers of mortality amongst people over 85 years old. (One year, 2013, appears to be a ‘rogue sample’ in which Smiths died, by chance, in greater numbers than would have been expected from overall deaths; a bad year for the Smith clan.)

The chart shows the impact of the deadly 2017 influenza ‘pandemic’, and it shows the substantial upturn in deaths in the winters of 2021 and 2022. The high 2022 number is known to be the mortality peak of Covid19 in New Zealand, a peak accentuated because most older people in New Zealand were required to wait too long for their second booster vaccination despite well-publicised forecasts of a winter peak. The high number in the chart for 2021 is more of a mystery, because neither Covid19 nor influenza were present in New Zealand that winter. The elevated number of deaths that year will be partly a reflection of the numbers of frail elderly who would have died in 2020 had that been a normal year; and partly older people with weakened immunity to common colds (arising from the lockdown and border quarantines).

The above chart shows the mortality experience in the last 50 years of, especially, older New Zealand women of Anglo-Scottish ancestry. Deaths have been falling in recent years due to both low birth numbers in the 1930s and to substantial health improvements enjoyed by New Zealanders born between 1918 and 1943. The trend from the late 2020s will be different, as post-1945 baby boomers start to appear in these kinds of data; and, possibly, as health improvements decline and quite possibly reverse in the face of increased inequality, which exists – especially around housing – among the older age cohorts as well as among families with young children. We protect older people by including them, not ignoring them.

Granny Smith had a good life. Long live Granny Smith.

*******

Keith Rankin (keith at rankin dot nz), trained as an economic historian, is a retired lecturer in Economics and Statistics. He lives in Auckland, New Zealand.

Over Easter I relistened to Jim Mora’s RNZ interview (17 May 2020) of Johan Giesecke, “world leading epidemiologist” and Professor Emeritus at the Karolinska Institute in Stockholm, Sweden. In the period April to June 2020, Sweden gained notoriety for its divergent public health policies with respect to the management of the Covid19 pandemic. People – including me – widely pointed to Swedish authorities then as being more concerned about retaining a pretence of their economic normality rather than caring about people’s lives.

Swedish exceptionalism became a thing, again; this time seemingly for all the wrong reasons. Hitherto ‘progressive’ New Zealanders had regarded Sweden as an exceptional policy exemplar; now it seemed to be an outlier of classical liberalism.

Here are two summary measures of pandemic and post-pandemic mortality; comparing Sweden with Finland, Germany, New Zealand and Japan:

We note that all these countries have rising populations of older people, so some increase in deaths was to be expected in all of them. Sweden had covid vaccination rates comparable with these other four representatives of ‘the rest of the civilised world’, so differences in vaccination uptake cannot explain its mortality difference.

It’s worth relistening to this Giesecke interview, now with the perspective of hindsight. The context is that, in the contest (as it was then framed) of Sweden versus the rest of the civilised world (with the World Health Organisation settling on the counter-Swedish majority view), Sweden has come out a clear winner. The scandal is the failure of ‘the rest of the civilised world’ to acknowledge the statistical reality.

(Note that I use ‘civilised world’ with mock irony. In New Zealand at least, few politicians or high-profile commentators believed that there could be anything New Zealand authorities could learn from the experiences of West Europe, South America, or Africa; instead, the policy elite contemptuously assumed such countries to be ‘basket cases’. See the use of this phrase in 1980s: Days of greed and glamour, NZ Herald, while noting that we are still waiting for a balanced history of the ‘Muldoon Years’ referred to.)

Highlights from the 2020 Interview, and the interview itself can be heard here, and read in synopsis form: Johan Giesecke: Why lockdowns are the wrong approach, Radio New Zealand, 17 May 2020.

Relating to points not covered in RNZ’s synopsis, Giesecke draws a direct comparison with Finland, which was pursuing a public health policy very close to New Zealand’s. His concern – shared by Finland’s state epidemiologist – was that the authorities’ actions were creating a significantly vulnerable population in Finland.

Giesecke, from that May 2020 perspective, mentions that if a good vaccine would come quite soon then New Zealand’s outcome might be better than Sweden’s. The irony is that, while a good vaccine did indeed come quickly, New Zealand’s authorities were slow to embrace the vaccine as the answer; having already decided that New Zealand had eliminated the virus as per the China policy. Then, after New Zealand’s people were vaccinated, the government doubled down on the lockdowns, not at all trusting the vaccine to work as a substitute for lockdowns. (Indeed, New Zealand only abandoned its border-quarantine policy in 2022 because that policy failed on its own terms. Had the border policy been implemented without error, New Zealand presumably would have followed a set of draconian restrictions through 2022, with a timeline similar to that of China. New Zealand’s border mishaps proved to be a blessing in disguise.)

In mid-2020, Johan Giesecke’s main expectation was that the mortality experience of all countries in the OECD (essentially the rich western plus the rich eastern countries) would all be about the same; and that Sweden’s major benefit would be in its substantially lesser disruption to normal life.

Where Giesecke was wrong was that the OECD ‘WHO countries’ (a label for the ‘civilised world other than Sweden’) ended up with substantially higher increases in deaths than did Sweden; he was wrong in a way that favoured Sweden rather more than he had expected.

The pandemic has nevertheless had an adverse impact on Sweden’s mortality. Sweden did experience the West European surge in deaths from respiratory illnesses late last year. Its people no more live in a bubble than do New Zealand’s. Overall though, Sweden got the win-win outcome: fewer deaths, and less social and economic dislocation. (David prevailed over Goliath.)

A very basic summary of the difference between the Swedish and the Goliath approaches is that Sweden focussed on its people whereas the prevalent strategy focused too much on the virus; the world by-and-large pursued a strategy of covid exceptionalism. (One consequence of covid exceptionalism was that a death clinically ascribed to Covid19 became a more noteworthy death than most other deaths.) Sweden focussed on having people with good levels of immunity, whereas in 2021 much of the rest of the world went down the unhelpful path of obsessing over the various mutant variants of the novel coronavirus.

Giesecke clearly had a better understanding the history of human coronaviruses than most other epidemiologists; these are viruses for which specific immunity is short-lived, from which we top-up immunity naturally through living our daily lives in a normal manner, and for which vaccine-conferred immunity would also be short-lived.

Sweden understood the science better; indeed, the interview tells us that there was a substantial scientific contest of interpretations of the evidence in Sweden, a good sign that actual science was taking place. Not only did a number of Sweden’s scientists prove to be among the better predictors of the future, people such as Johan Giesecke were also much more prepared to offer humility to their own people and to the world if they had got it wrong.

I am still waiting for our authorities – including the scientists – to do a proper retrospective comparison of New Zealand (and other countries, as in my table above) and Sweden. I am still waiting for a little gracious humility from our authorities in Aotearoa New Zealand. Humility is an important characteristic of civilised behaviour.

In the middle of the interview, Jim Mora noted: “Our readers are quite fascinated with Sweden; I think the world is”. When and why did that fascination stop? Or is it just that Goliath’s information mediators stopped being fascinated when the ‘contest’ moved in David’s favour?

————-

Keith Rankin (keith at rankin dot nz), trained as an economic historian, is a retired lecturer in Economics and Statistics. He lives in Auckland, New Zealand.

Analysis by Keith Rankin.

A pandemic can end in three ways. Either the death rates attributed to the pandemic disease cease, or at least drop back to pre-pandemic levels. Or normality is re-established, with the pandemic disease still present, but displacing other causes of death. Or a ‘new normal’ is established, with higher ongoing rates of death normalised.

So, to some extent, a pandemic’s duration is a state-of-mind; meaning that the post-pandemic period is when that ’emergency’ mindset has departed. To a large extent, that happens when the most burdensome public health restrictions become untenable; in New Zealand’s case, that was when the substantive closure of the international border finished. Deaths, covid or otherwise, may still be a problem, but they cease to be newsworthy!

For most of the world, the post-pandemic period started around February 2022. East Asia was the principal exception. Table 1 below shows mortality in the first year of the new normal.

Table 1: Back to Normal?
total deaths:	2018/19*	2022/23**	increase
Macau	2199	3596	63.53%
Hong Kong	47056	62056	31.88%
Singapore	21323	26829	25.82%
Taiwan	170483	212665	24.74%
Thailand	484272	603662	24.65%
Iceland	2180	2702	23.94%
South Korea	291529	357298	22.56%
Chile	107408	130970	21.94%
Colombia	236488	285944	20.91%
Ireland	30051	35650	18.63%
Brazil	1325677	1569617	18.40%
Japan	1360950	1607011	18.08%
Northern Ireland	14998	17504	16.71%
Australia	161466	188155	16.53%
Scotland	55633	64807	16.49%
Malaysia	171015	199069	16.40%
Finland	53458	62112	16.19%
New Zealand	33310	38682	16.13%
Netherlands	148356	171826	15.82%
Germany	925309	1069924	15.63%
England & Wales	515610	592677	14.95%
Norway	39819	45650	14.64%
Austria	80544	92325	14.63%
Canada	279510	319140	14.18%
Uruguay	34655	39512	14.02%
United States	2812658	3193088	13.53%
Greece	122940	139406	13.39%
Mexico	726738	819268	12.73%
Portugal	111815	124757	11.57%
Spain	415025	458846	10.56%
Switzerland	66396	73311	10.41%
Italy	641280	705564	10.02%
Israel	45488	49996	9.91%
Denmark	53578	58826	9.80%
Peru	157650	172825	9.63%
France	591364	647762	9.54%
Czechia	110671	120448	8.83%
Slovenia	20603	22307	8.27%
Belgium	107810	116284	7.86%
Slovakia	54017	58196	7.74%
Poland	405241	435401	7.44%
Sweden	88633	94436	6.55%
South Africa	527630	561992	6.51%
Egypt	570015	605500	6.23%
Croatia	52144	55093	5.66%
Albania	21717	22900	5.45%
Hungary	131229	135090	2.94%
Kazakhstan	131089	134709	2.76%
Bulgaria	109175	112080	2.66%
Serbia	101699	104390	2.65%
Romania	263338	270222	2.61%
Moldova	37314	36554	-2.04%
*	year ended April 2019
**	latest available 12-month period

Table 1 shows a number of countries’ most recent annual death tallies compared with the year ended April 2019, the best baseline period available. May 2018 to April 2019 was chosen because it represents the first full year after the silent influenza pandemic of November 2016 to April 2018. While not a media event, that largely invisible 2017 pandemic was a substantial mortality event, at least in the ‘global north’. A pandemic does not require an authentication from WHO to be an actual pandemic. A pandemic is simply a globally widespread experience of a disruptive contagious disease.

Broadly, Table 1 shows the countries which followed ‘elimination strategies’ near the top for post-pandemic mortality. Not only did countries in the east of the eastern hemisphere (including Aotearoa New Zealand) pursue the most stringent anti-covid policies (and practiced them for the longest time periods), some  prematurely claimed to have eliminated (though not eradicated) the disease. For some in East Asia, the 2003 experience of SARS was uppermost in health officials’ minds.

Table 1 also shows that some of the countries worst-hit by the pandemic (especially those in the Southeast European ‘Balkans’) have returned to death tallies comparable with base-year numbers. If South Africa and Egypt are a suitable guide, that return to health normality applies to Africa as well.

The only West European countries with post-pandemic deaths under nine percent more than pre-pandemic deaths are Sweden and Belgium, both countries with high covid death tallies in the first wave of the pandemic, but well below European mortality averages in the second year of the pandemic. Sweden’s 6.55% increase actually overstates its situation by about two percentage points, because, more than in most other countries, deaths there were particularly and unusually low from May 2018 until the start of the pandemic. Also, Australia’s 16.53% is an overstatement, probably by at least two percentage points; this is because tardy Australia’s most recent annual deaths’ data includes the months of December 2021 and January 2022, both high mortality months compared to the following December and January.

New Zealand’s most recent death data uses December 2022 and January 2023, not December 2021 and January 2022. In contrast to Australia, New Zealand’s Table 1 increased mortality experience is understated by a percentage point, because March and April 2019 (included in the pre-pandemic baseline year) had somewhat higher deaths than those same months in 2018. If we had used a baseline year from March 2018 to February 2019, then New Zealand would have had a mortality increase of 17.40%, not 16.13%.

Re East Asia, the numbers for Macau and Hong Kong give a hint of the recent reality in China. For that region we should note also that the South Korea increase in Table 1 (22.56%) is a substantial understatement of reality, because South Korea has not reported ‘total deaths’ after July 2022, and we know that Korea has had many covid cases since then.

We also note that post-pandemic death tallies are high for Japan, Ireland, Scotland, Iceland, and Finland. These are all countries which, for their regions, were known for their more restrictive public health policies. Finland was widely acclaimed for being the most restrictive of the Nordic countries during the pandemic years. (We also note that Finland had many more deaths than Norway both pre-pandemic and post-pandemic, despite having about the same population as Norway; it suggests that many more young Finns are working abroad than young Norwegians. Likewise, we see that New Zealand has more deaths than Ireland, despite both countries having essentially the same population.)

Germany, which has had a particularly worrying recent run of deaths, in Table 1 is not out of step with its western neighbours; although we should note that southern Western Europe has generally had a post-pandemic more normal than northern Western Europe (Sweden excepted).

The critical question, looking to 2023 and 2024, is whether, for the countries towards the top of Table 1, the pandemic has triggered a new normal with persistently higher mortality than in the 2010s’ decade. Or have these countries simply experienced a delayed pandemic mortality experience which will soon subside? If the latter, then we should expect a substantial mortality drop in East Asia and West Europe in the year to April 2024.

Demography and the challenges of predicting the pandemic’s influence on 2020s’ mortality

Demography is a complex subject. Pandemic death rates per capita were high in Eastern Europe because those countries have lost many of their young people to emigration. Increases in death tallies, however, were never so high in those countries. The demography of Europe is particularly complex because many of their older people were born either side of, or during, World War Two; a war with substantial demographic consequences which have not yet fully played out.

The Scandinavian countries in particular had diverse experiences in that war. Sweden was neutral, Norway and Denmark were occupied, while Finland was successively friend and foe to the allied powers. So the change in the number of older people may differ in Sweden compared to the others. Nevertheless, Sweden still compares well with the other neutral countries: Switzerland, Ireland, Spain and Portugal. (Though noting that Spain had its own especially large demographic trauma in 1936 to 1939.)

Another problem in unravelling the demographics of Europe is the substantial international migration between present and former European Union countries, and immigration from former (or present) empire countries. So many people these days die in different countries from which they were born. We know little about the different pandemic and post-pandemic death experiences of immigrants compared to people born in the country of their death.

In most countries deaths in 2022/23 would have been higher compared to 2018/19. The main determinant of death rates is the numbers of people in the oldest age cohorts. About half of all deaths in most countries are of people in their eighties. So the biggest increases, for reasons other than the pandemic, would be due to the rate of increase or decrease of a country’s population of octogenarians. Some countries will have significantly fewer octogenarians after the pandemic, because the pandemic itself took so many.

The second most important reason for changing death tallies is the underlying health of the people. Pandemics take more people in countries which already have substantial populations – especially populations in the 65 to 74 age group – with compromised pre-pandemic health or compromised general immunity. In pandemic years, the main reason for more death is worse underlying health. In other years changes in health status may either accentuate or offset changes in the numbers of people over eighty. While there are health-compromised people of all ages, compromised health – high morbidity or low general immunity – is more likely to have prematurely fatal consequences for people aged 65 to 74.

To summarise the two previous paragraphs, I would argue that the two main predictors of a country’s normal death tally are the numbers of octogenarians in the population, and the numbers of people in the population aged 65 to 74 with compromised health or general immunity. (In addition, some developing countries still have unacceptably high levels of infant mortality.)

The two key aspects of the health status of living populations are morbidity and immunity. The countries which fare best in a novel virus pandemic (or from wave pandemics of pathogens which induce only-short-lived specific immunity) are those with low morbidity and high general immunity. With respect to the present post-pandemic period, the covid coronavirus increased both the morbidity and the immunity of populations. Where these two increases balance out, then a new normal appears which looks substantially like the old normal.

Before the twentieth century, people living rurally were more likely to experience longevity. That changed in the twentieth century, when people living in metropolises gained super-high-immunity levels from living in close proximity to each other (improving immunity); and urbanised populations experienced reduced morbidity as a result of access to a wider range of foods, from more timely access to healthcare services, less exposure to conditions such as malaria, and safer supplies of drinking water.

Big cities still reduce life outcomes for people immigrating from rural areas; for people not yet adapted to city levels of exposure to pathogens, and often having to settle for inferior housing and employment experiences. When governments tamper with the finely-tuned immunity equilibria in our big cities, the potential for deadly unintended consequences has always been there. Such tampering may include the required overuse of facemasks, and the creation of fear around the use of public transport.

The post-pandemic experience of East Asia is not a particularly good advertisement for disruptive public health practices. Sweden was conspicuous by taking the opposite policy tack from that taken in East Asia, minimising disruptions from normal social interaction. Sweden’s different approach was not a result of its greater wisdom or greater laisse-faire liberality; rather it was a result of a mistaken assumption that, by mid-March 2020, many more people had already been infected by the new coronavirus (making it too late for restrictive policies) than actually had been infected.

The interregnum between the two recent respiratory pandemics

Finally, it is worthwhile to suggest reasons why deaths in much of the developed world were especially low from May 2018 to February 2020; a phenomenon particularly marked in Sweden. This was most likely because of the 2017 influenza pandemic – the invisible pandemic (invisible even to demographers, then more attentive to issues other than heightened seasonal mortality). This world disease event left populations more immune, and (because that pandemic took so many) it meant that the post-influenza 2018 population was more healthy and had more immunity than the pre-pandemic 2016 population.

It is normal for post-pandemic death rates to be low for a couple of years. Indeed, it was true around 1919 and 1920, after the great influenza pandemic of 1918. Will it prove to be so this time, from 2023 to say 2025? We should be watching aggregate mortality – in our own countries and other countries – with as great interest as we watch the inflation, unemployment and economic growth data.

*******

Keith Rankin (keith at rankin dot nz), trained as an economic historian, is a retired lecturer in Economics and Statistics. He lives in Auckland, New Zealand.

Analysis by Keith Rankin.

The ‘Young Elderly’ are in essence the post-war baby-boomers. An average young elderly person in these charts was born around 1950 to 1952.

The charts look at ‘quarterly excess deaths’, so do not show week-by-week fluctuations in deaths. For example, data for the very end of 2022 covers the whole of the last three months of 2022.

As in my previous recent charts (see my Spiralling Deaths in Germany, Evening Report, 14 March 2023; and Examples of Germany and Denmark, Evening Report, 12 March 2023), I have emphasised Germany, because late-pandemic mortality has been so bad there. And because Germany’s differences with the rest of Europe create a very useful point for epidemiological analysis.

In the first chart (above), of the countries shown only Germany and New Zealand had excess deaths in this age group below ten percent in the first six months of Covid19. The United Kingdom was easily worst then.

The United States had a really bad pandemic, for two years from April 2020 to April 2022. But, subsequently, for nearly two years since April 2021 Germany has been for the most part easily the most deathly of these countries, for the young elderly, with only the USA contesting Germany for this dubious honour. For some of 2022, New Zealand was in second place out of these seven countries.

We note that Belgium, Netherlands and France all had high death rates early in the pandemic, but have subsequently had much lower death rates than Germany for this age group.

The two obvious avenues for investigation are diet (the French surely have a more healthy diet?) and differences in the policy responses to the Covid19 pandemic. My understanding is that, while all countries had similar public health restrictions during the peak weeks of Covid19, Germany was much the slowest of these countries to remove mandated public health measures. Germany’s abundance of caution may have backfired big-time. Yet the only reason given here (on Deutcshe Welle) is: “that diseases other than Covid19 are bouncing back because fewer people are wearing masks amid a general relaxation of pandemic rules in comparison with the past two years”. There is no hint of comparative analysis in this particular DW media report.

The second chart compares Germany with its four nearest Eastern European countries, and with the two Mediterranean countries which were the first to experience substantially elevated pandemic death rates.

For the young elderly ‘boomers’, Spain and Italy show quite the opposite pattern to Germany; they started with high death rates and then moved to generally lower rates. Both had summer mortality peaks in 2022, to some extent due to the summer heat waves but mainly due to the rebounding of tourism with Covid19 still present. Covid19 flourishes in bars and restaurants, and in airport terminals.

The eastern countries had more deaths overall in the middle seasons of the pandemic, despite (or maybe because of) the success of the measures taken in the early months of the pandemic. Also, for these countries, with lower life expectancies than their western neighbours, the young elderly are on average closer to their eventual deathdays. It is important to note that these eastern countries had the fewest pandemic-related deaths after March 2022. Presumably their people most at risk of dying had already died, and the remainder had higher natural immunity to respiratory illnesses than did the older citizenry of Germany. (I am not aware that Polish, Bohemian or Hungarian cuisine is particularly noted for its health benefits, in contrast to the much-touted Mediterranean diets; so a better diet is probably not the reason.)

There is one other country with a similar pandemic death-profile to Germany; its southern geographic and cultural neighbour, Austria. The final chart here shows the smaller countries of western and central Europe, plus New Zealand. (Australia and Sweden do not provide age-group data.)

First we note that Austria and its neighbours Slovenia and Switzerland start out closely synchronised. Switzerland drops off Austria’s high young-elderly mortality path from March 2021, and Slovenia drops off a year later (though has a high summer peak in line with its Italian neighbour). The Scandinavian countries had generally low death rates for the young-elderly age group. (They did however see rising deaths from mid-2021 for the older elderly.)

Any valid epidemiological analysis for Germany’s 2022 tragedy needs to take into account the similar experience of Austria, as well as the generally different experiences of the other European and neo-European countries.

The United States still has a worse overall pandemic record than Germany, for the young elderly. The worry for Germany, though, is that the reasons for its really bad 2022 have not necessarily been resolved; 2023 may be just as bad. Time will tell; so long as an asteroid strike or a nuclear war don’t displace infectious diseases as drivers of excess mortality in Europe.

Keith Rankin (keith at rankin dot nz), trained as an economic historian, is a retired lecturer in Economics and Statistics. He lives in Auckland, New Zealand.

https://www.dw.com/en/winter-illnesses-burden-germanys-intensive-care-units/a-64135331

https://www.dw.com/en/top-german-virologist-says-covid-19-pandemic-is-over/a-64214994