Covid-19 booster shots and the Delta variant

Switzerland is now offering a Covid-19 booster shot to to the 65+ and at-risk who has had their two Covid-19 vaccinations at least six months ago. It may soon be extended to all adults. I had my third shot last Thursday with no side effects (not even a localized sore spot) and was surprised to receive a Pfizer booster after being fully vaccinated earlier this year with the Moderna vaccine. The mRNA in both vaccines encode the same S-2P protein which differs from the covid19 spike protein by two amino acids only. These stabilize the spike protein so that it can train the immune system before it enters the host cell (see here for details).

However, they differ in the “packaging” regions around the actual genetic code for the protein. These leader and trailer regions are responsible for initiating and regulating the translation of the mRNA to produce the protein which stimulates an immune response. The other major difference between the two vaccines is that there is a much larger dose of vaccine (100 mg) in a Moderna dose compared to a Pfizer dose (30 mg). While the initial protectiveness of both vaccines is similar at around 94-95%, there is some evidence that the protectiveness may decline at a somewhat slower rate for Moderna than Pfizer.

There is also evidence that getting a different booster shot that the vaccine you originally received actually enhances the immune response more than getting the same type of vaccine for a third dose (see here and here). There is a small advantage mixing the two mRNA vaccines (Pfizer and Moderna) as I have done,  and a substantially enhanced immunogenicity when non-RNA vaccines (eg. Astrozenica or Johnson and Johnson).

So how effective is the booster shot? A large study published in the New England Journal of Medicine in October compared outcomes in 1.14 million Israeli adults aged 60 years and over who had received two Pfizer doses at least 5 months earlier, of whom around half received a Pfizer booster shot and the other half did not. The analysis controlled for possible confounding factors including age, sex, demographic group and the date of the second vaccine (to ensure there were not differences in the time since second vaccine between the control and booster group).

The results were dramatic. At least 12 days after the booster dose, the rate of confirmed infection was lower in the booster group than in the nonbooster group by a factor of 11.3; the rate of severe illness was lower by a factor of 19.5. These findings clearly show the effectiveness of a booster dose even against the currently dominant delta virus. Recent reports have suggested that the efficacy of a vaccine administered 6 months earlier  against the currently dominant delta virus is reduced by approximately 50% compared to the post-vaccination efficiency of 95% against the alpha variant (see here, here and here). So the susceptibility to the delta variant of a person who has received the third dose would be reduced from around 50% to 5%, and even less if they receive a different vaccine than their first two shots.

On July 30, 2021, Israel was the first country in the world to make available a third dose of the BNT162b2 vaccine against Covid-19 to all persons who were 60 years of age or older and who had been vaccinated at least 5 months earlier. Since then, Israel has extended the booster program to the entire population. The following graph shows trends in vaccination rates and booster doses for Israel and Austria (as a typical example of a Western European country). The fully vaccinated rate (two doses) is essentially identical for the two countries from August onwards at a little over 60%. Booster doses per 100 people rose from zero at the end of July to over 40 per 100 currently.

The next graph shows the daily new cases per million for Israel and Austria (which is currently experiencing one of the largest increases in Delta cases in western Europe).  Israel had a huge spike in infections this summer due to the highly infectious Delta strain, together with a combination of waning immunity five or six months after the second jab. Its clear that the booster shots were definitely a game changer.  Over time, there was a very steep reduction in infections, hospitalizations and deaths among the people that got the booster shot. Israel went from over 11,000 new cases a day at the peak, down to a few dozen today. Before the booster campaign started hospitalizations were rising among vaccinated people whose immunity had waned, but the overwhelming majority of hospitalizations now are of people who are unvaccinated.

The same combination of waning immunity and Delta are now causing rising cases in Europe, as illiustrated in the following plot. Austria, Belgium and the Netherlands are currently the hardest hit (see graph below), with average daily new cases well over 1,000 per million, higher than any previous wave. In most of the countries on this graph, fully vaccinated rates are somewhere between 60 and 75%. That means around 25 to 40 percent of these populations are unvaccinated and their much higher susceptibility to infection is driving the latest surge in Delta infections.

Germany, Austria and the German-speaking region of Switzerland have the largest shares of unvaccinated populations in all of Western Europe. About one in four people over 12 is unvaccinated, compared with about one in 10 in France and Italy, and almost none in Portugal.

Governments are struggling to address this shortfall in vaccination levels. Austria has introduced a lockdown for anyone over the age of 12 who is not vaccinated and foreshadowed compulsory vaccination for all adults. Germany is considering new restrictions and in Saxony the unvaccinated are already barred from non-essential shops and other locations. The Netherlands has flagged that they will move to full lockdown for the unvaccinated but not the vaccinated. Switzerland has a referendum next weekend on whether to continue with the vaccine mandate which allows only fully vaccinated people to go to theatres, restaurants, gyms and other indoor public venues.

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A fourth Covid-19 wave hits Europe and Switzerland has a second referendum to end vaccine certificate requirements

Covid-19 cases rose by 7% and deaths by 10% over the last week in Europe, as it enters a fourth (or fifth) wave and currently accounts for about two-thirds of infections reported globally. Belgium and the Netherlands, which have fully vaccinated 73-74% of their populations, have the highest new case rates in Western Europe, almost double those of Britain. The fully vaccinated rate is Switzerland is 64%, higher than the USA at 57% but lower than Australia now at 69%.

I drove past the UN Palais de Nations yesterday, which had a crowd of around 2000 anti-vaccination protesters outside it, apparently concerned about loss of “freedom”.  The Netherlands has just reimposed a partial lockdown to address the rapidly rising case numbers and Switzerland won’t be far behind if the protestors have their way. I am way more concerned about the potential loss of freedom of association, ability to work and earn money, for students to attend schools and universities, ability to participate in social, sporting and cultural events etc etc than the freedom of a minority to be evidence-averse idiots who incubate the virus to continue to spread it and make life difficult for the vaccinated (who still have a small but non-zero risk of catching Covid from the unvaccinated in which it is spreading like wildfire).

Later this month, Switzerland will be holding its second referendum in less than six months on the Covid-19 law under which the government has made the Covid-19 certificate compulsory since September 13 to access indoor spaces (movie theatres, gyms, restaurants etc). This law was accepted by 60.2% of voters on June 13, and opinion polls indicate it will be accepted again. The Covid certificate has allowed the (intelligent) population to return to an almost normal social life. I’ve gone to the cinema, been training at the gym mask-free, and met up with friends for coffee, lunch or dinner.  All at risk if the anti-vax people get more support than before. The proposed revisions to the law also put other covid initiatives at risk. If passed, they would end government financial support for big events and end all government funding programs for the development of drugs or other important medical goods.

While death rates are substantially lower than in previous waves, death rates are starting to rise reflecting rising case rates. And apart from death, there is significant disability with “long-Covid” which affects a significant proportion of people who are infected.

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Religiosity and atheism: revised estimates for 1980-2020

In previous posts, I have summarized various analyses of the World Values Survey (WVS) and the European Values Study (EVS), altogether including more than 110 countries, and focusing on trends and differences religious affiliation, religious beliefs and practices, as well as traditional and modern values.  I examined the prevalence of religious people, non-religious people and atheists across 110 countries in 2020 here, and trends from 1980 to 2020 here.

Checking in on the World Values website earlier this year, I discovered that the World Values Survey dataset has been updated to fix some errors in the coding of data for. Some data collected using a mobile phone app in the most recent US survey was incorrectly coded and this mainly affected the religiosity categories.  Comparison of the US prevalences for religiosity show that the coding errors resulted in an overestimate of the atheist and non-religious categories as shown in the following table:

Comparison of religiosity prevalence estimates
 for USA in year 2020

This post provides updated estimates of religiosity levels and trends for countries, regions and world based on the 2021 release of the combined data for the WVS and EVS in the Integrated Values Surveys (IVS) 1981-2021 [1-3]. In carrying out these updates, I also addressed some definitional issues which have resulted in mostly slight changes to estimates for other countries.

The atheist and other categories of religiosity are constructed from the following survey questions:

Survey questionResponse categories
Are you a religious person?A religious person / Not a religious person / A confirmed atheist
Importance [in life: religion] Very, Rather / Not very / Not at all
How often do you attend religious services?More than once a week / Once a week / Once a month / Only on special holy days / Once a year / Less often / Never, practically never
Pray to God outside of religious services?More than once a week / Once a week / At least once a month / Several times a year / Less often / Never
Belief in godYes / No / Don’t know
Importance [in life: God]1 (Very important), 2…9, 10 (Not at all important)
Religious affiliationNone, 9 or more religion/denomination categories, Other

Estimates of atheism prevalence based on WVS/EVS, or the Pew or Gallup surveys addressing this question often quote statistics based on question categories such as “confirmed atheist” or “not religious” or “religion: none” (see Wikipedia for more information on these). The first of these provides low estimates of overall atheist prevalence, since the qualifying adjective “confirmed” will result in many atheists avoiding this category. In the full IVS dataset (645,249 respondents), 5.5% of respondents said they were “confirmed atheists” whereas 13.3% stated they did not believe in God and a further 3.2% did not know whether they believed in God (note these figures are unweighted for sampling or country population size at this stage). In contrast, the “non-religious” and “religion: none” categories will include theists who have rejected organized religion as well as atheists.

I suspect the term “confirmed atheist” is intended to exclude agnostics (who say they don’t know whether God does or does not exist. I prefer to use the modern widely accepted definition of atheism as “lacking belief in God or gods”. This will include gnostic atheists (those who say they know gods don’t exist) and agnostic atheists (those who say they don’t know whether God or gods exist and lack a belief in any). So for the belief in God question I have recoded “Don’t know” to “No”, as a person is highly unlikely to have a belief in God which they don’t know about. This resulted in an overall distribution of 79%  Yes, and 21% No (comprised 16.8% No and 4.0% recoded Don’t know).

There is an additional complexity in defining religiosity for people who are affiliated with non-theist religions such as Buddhism, Jainism, or Confucianism. This mainly relates to Asian countries, as the proportion of people who are belong to non-theist religions is generally small in other regions. The religiosity categories I defined for previous analyses moved practicing and non-practicing non-theist religious people from the atheist category to the relevant religious categories, meaning that the atheism category essentially excluded atheists who practiced non-theist religions. Asian religious practice tends to be syncretic, so for example, many people in Japan practice follow both Buddhist and Shinto religious practices. Additionally, in some cultures and non-theist religious traditions, there are mythic gods and for many people, the historical Buddha is essentially considered to be a god. In the IVS dataset, 61% of Buddhist say they believe in God, and 39% say they do not believe in God.

To obtain a complete estimate of the prevalence of atheism (those who lack a belief in God or gods), I have assigned all people who do not believe in God to the atheist category. This will thus include some religious people who practice non-theist religions. The practicing religious and non-practicing religious categories who thus be understood to refer to religious practice/belief including a belief in God or gods, including some but not all followers of non-theist religious traditions.

The four religiosity categories have thus been defined as:

Practicing religious person: A religious person who believes in God and is practicing*, OR a non-religious person who believes in God, is practicing, and rates the importance of God in range 1-5.

Non-practicing religious person: A religious person who believes in God and is non-practicing OR a non-religious person who believes in God, is non-practicing, and rates the importance of God in range 1-6.

Non-religious: A non-religious person who believes in God but rates the importance of God as 7-10 at the not important end of a 10-point scale.

Atheist: A “confirmed atheist” and/or does not believe in God

* Respondents are classified as “practicing” if they attend religious services or pray to God outside of religious services at least once a month. Otherwise, they are classified as non-practicing.

For the 110 countries with IVS survey data for years 2000 or later, the prevalences of the four religiosity categories across survey waves were projected forward to 2020 (Endnote a). The following plot shows the estimated prevalence of all religiosity categories in 2020 for countries ranked in descending level of irreligion (atheist + non-religious).

There are 18 countries where more than half the population are estimated to be atheist in 2020. These include China,  South Korea, Japan, Vietnam and Thailand, all Asian countries with Buddhist and non-theist religious traditions. They also include all the Scandinavian countries and European countries such as France, the Netherlands and the United Kingdom, as well as Australia and New Zealand.     At the other end are six countries where the prevalence of irreligion is estimated at less than 1% of the population.

Full time series of religiosity trends from 1980 to 2020 were imputed for the 110 countries using the same methods as developed for the earlier analyses (see endnote a). The updated trends are generally similar to those previously posted (see here), except for the USA. The revised data for the USA shows that the prevalence of atheism has increased rapidly in the 21st century from around 6% to almost 23% in 2020 and the prevalence of non-religious has also increased from 2.2% to 6.9%. 

The prevalence of irreligion (atheists and non-religious) has increased in the USA by an estimated 21.5 percentage points over the last two decades, the fourth largest increase of any country included in this analysis. The largest increase occurred in neighboring Canada with a 36.7% increase since year 2000.  Apart from two Asian countries (South Korea and Singapore) and Hong Kong, all the other countries in the top 20 for increase in irreligion since 2000 are high income countries. And apart from Chile, Australia and New Zealand, all of these are in Europe and North America.

To compute regional and global trends in religiosity, I have also imputed religiosity for 75 countries not included in the IVS using data from Win/Gallup and Pew Research Centre surveys (Endnote b). These 75 mostly small countries (apart from in Africa) account for 8% of the world population, mostly in the Islamic East and Sub-Saharan Africa (23% and 41% respectively of the populations of those regions).

I have computed revised trends for the 11 culture zones used in previous posts. These are based on those originally defined by Welzel [4], with one modification. Because Australia’s and New Zealand’s culture values are much closer to the countries of the Reformed West than to those of the USA and Canada, I have included Australia and New Zealand in the Reformed West and renamed the New West as North America (see Endnote c for details). In summary, these are:

Reformed West — Western European societies strongly affected by the Reformation;
North America — USA and Canada;
Old West — Mostly Catholic parts of Western Europe being core parts of the Roman Empire;
Returned West — Catholic and Protestant parts of post-communist Europe returning to the EU;
Orthodox East — Christian Orthodox or Islamic parts of the post-communist world, mostly parts of former USSR;
Indic East — Parts of South and South East Asia under the historic influence of Indian culture;
Islamic East — Regions of the Islamic world formerly parts of the Arab/Caliphate, Persian and Ottoman empires;
Sinic East — Parts of East Asia under the historic influence of Chinese culture ;
Latin America — Central and South America and the Caribbean;
Sub-Saharan Africa — African countries south of the Sahara.
Oceania — Pacific Island states with predominantly Melanesian and Polynesian populations

The following plots show estimated religiosity trends for the world as a whole and for these 11 culture zones:

These plots illustrate the extreme diversity of religiosity trends across regions. Western countries (Reformed West, Old West, North America) are characterised by rapidly rising prevalence of atheism and corresponding decline in practicing religious. The former Eastern bloc countries (Returned West, Orthodox East) are characterised by a large drop in atheism prevalence and corresponding rise in religious categories following the collapse of the Soviet Union around 1991. More recently, atheism is rising and practicing religious falling in the Returned West, following a similar path to that taken by Western Europe.

The Orthodox East has had continuing decline in atheism and the non-practicing religious have become the dominant group at around 55% of the population, far more than the practicing religious. A 2018 Pew Research Center Report [5] examined this more closely and found that for most people in the former Eastern bloc, being Christian (whether Catholic or Orthodox) is an important component of their national identity, with many people embracing religion in the post-Communist period as an element of national belonging, even though they are not highly religious.

Latin America, the Islamic East and Sub-Saharan Africa are the “religious” culture zones characterised by very high prevalences of practicing and non-practicing religious people and very low prevalences of non-religious and atheists. The Indic East also has very low prevalences of the irreligious, but in contrast to the other “religious” zones, has had a shift from practicing to non-practicing religious. This may very well be largely reflecting the increasing levels of Hindu nationalism in India.

Iran and other Islamic countries generally report very low levels of atheism, 2.4% on average, and I suspect this is lower than reality because of the quite severe social and legal consequences in many Islamic countries. The WVS uses telephone interviews and its quite likely irreligious respondents would be fearful of being identified if they respond honestly to a telephone interview. A recent internet survey provides some support for this concern. A June 2020 internet-based survey collected responses from 40,000 Iranians living in Iran. Respondents took part in the survey anonymously, and would have felt safer to express their real  opinions than in telephone surveys or surveys conducted at respondents’ residence.

The contrast with the results of the most recent 2020 World Values Survey for Iran are extreme. The latter found that 43% of Iranians are practicing Muslims (similar to the online survey estimate for total Muslims, 53% are non-practicing and only 1.5% say they do not believe in God. In the WVS, 91% say they believe in life after death, 92% believe in heaven and 88% believe in hell. The online survey found that over 60% said they did not perform the obligatory daily Muslim prayers. This is in the same ballpark as the 53% who were classified as non-practicing in the WVS.

Reading between the lines of the WVS, and taking into account the severe consequences of being apostate or atheist in Iran, it supports the conclusion of Arab and and Maleki [6] that Iran is becoming much more secular. Around 53% of respondents in their online survey reported coming from practicing religious families but losing or changing their religion in their lifetime. This increasing secularity is also supported by the evidence of dramatic declines in the fertility of Iranian women over recent decades, with population growth in 2020 dropping below 1%. It’s quite likely that real levels of irreligion are higher in many other Islamic countries than the survey data suggest.

The Sinic East is of course dominated by China. Around 1980, apparently equal numbers of people in this region identified as atheist and non-religious. There has been a dramatic drop in the non-religious prevalence with a corresponding rise in the prevalence of atheism. The overall prevalence of irreligion has remained fairly stable and high. Its difficult to know what to make of this, or whether it reflects any real change, given the possibility that the Chinese language version of the WHS questions may have changed in some way over time, or their interpretation has changed given the overall lack of fit of the WVS religious questions with the non-theist religions that are most common in China.

At global level, the prevalence of practicing religious has barely changed over the last 40 years, as has the prevalence of atheism, but there has been a shift from non-religious to atheist and to non-practicing religious, the latter reflecting mainly the change in former Soviet bloc countries. The relatively small changes in prevalence of religiosity at global level over the last 40 years conceal quite substantial changes in developed countries and in former Soviet countries, in opposing directions. The following table summarizes global changes in the prevalence of religiosity categories over the 40-year period 1980 to 2020.

The overall global prevalence of irreligion (atheist plus nonreligious) has declined somewhat, but a substantially higher proportion of the irreligious identify as atheist in 2020 compared to 1980. Is irreligion likely to increase in the future. If the economies of developing countries continue to grow, with decreasing levels of poverty, and education levels continue to improve, it is likely that religiosity in these countries will decline in the longer term. But if the pandemic and global heating crises derail the historical development trends, then population growth due to the higher fertility levels of Islamic and African countries will ensure that the overall religiosity of the world will increase in the future. 

  If on the other hand, the very low levels of irreligion in Islamic countries and Africa do not increase, then the higher fertility levels of these regions will ensure that the overall religiosity of the world will increase in the future. In an era of joint global environmental and pandemic crises, with rising populism and rejection of science and global institutions, its entirely possible that the developing countries will not pass through the equivalent of the Western Reformation resulting in freedom of thought and religion and decreasing levels of premodern religious values (see previous post here).

Endnotes

a. Interpolation and projection of religiosity trends from 1980 to 2020

For countries with data for three or more waves of the IVS, trends in the prevalence of the four religiosity categories were estimated at country-level as follows:

  1. Values for single missing waves between two other waves were estimated using the geometric average for the two waves with data
  2. For countries in the Returned West or Orthodox East, the average change in prevalence due to the breakup of the Soviet Union was estimated using the average for those countries with data for the first wave in the 1980s and the second wave in the 1990s. This average jump was used to estimate values for the first or second wave for countries where one of these was missing.
  3. For countries where one or more early waves or one or more late waves were missing, prevalences were projected forwards or backwards using the median annual rate of change calculated at culture zone level for countries without missing waves.
  4. Population-weighted average trends were then calculated for each culture zone with the following two exceptions. For the Returned West and Orthodox East, prevalences were assumed constant prior to 1991. For Islamic East countries, flat trends were assumed from 1980 to earliest available data, as Turkey is the only Islamic country with data prior to 2000, and its trends are probably not representative.

For countries with data from only one or two waves, forward and back projections to 1981 and 2018 were made using the average trends calculated in step 4 above for each culture zone.

For projections from the Wave 7 survey year to 2020, a conservative approach was taken to avoid making extreme projections based on substantial differences between Wave 6 and Wave 7 estimates which may reflect variations in survey sampling or procedures rather than real change. For each country, the recent trend was estimated using data from survey waves in the period 2000-2020, with each earlier wave being given 15% less weight in the regression than the subsequent wave.  Average annual rates of change (aar) were also calculate from the Wave 6 and Wave 7 average prevalence estimates for each culture zone.

Where the regression aar were more extreme than the culture zone average aar, an average of the two was used for projection to 2020. Where the regression aar and the culture zone aar were of opposite signs, the regression aar was halved before use for projection. To be even more conservative, if the last Wave for a country was more than a year earlier than 2020, the rate of change from the survey year to 2020 was assumed to be aar1.5.

Finally, annual estimates for all years from 1980 to 2020 were prepared as follows:

  • Values prior to 1981 (the actual or projected first wave values) were assumed to be constant at the first wave values
  • Values beyond 2020 were also assumed to be constant at the 2020 value
  • Annual values between waves were estimated by linear interpolation
  • Annual values from 1980 to 2020 were smoothed using a 5-year moving average.

b. Imputation of religiosity for countries not included in the WVS/EVS.

Religiosity categories were imputed for 12 countries using data from Win/Gallup surveys for years 2012, 2015 and 2017 [7-9] which includes a question “Are you a religious person?” with the same response categories as the WVS/EVS question. These countries were Afghanistan, Cameroon, the Democratic Republic of the Congo, Cuba, Fiji, Kenya, Mongolia, Panama, Papua New Guinea, North Korea and South Sudan. Religiosity category prevalences for Israel were imputed from a Pew Research Centre survey which also included similar questions on religiosity [10].

For the remaining 63 mostly small countries, religiosity was imputed using culture-zone-specific regressions of the IVS religiosity prevalences against Pew Research Centre country-specific estimates for the year 2020 of religious affiliation distributions for 8 religious categories, including “other” and “none” [11].

c. Definitions of culture zones used to group countries

I am using the 10 culture zones defined by Welzel [4], with one modification. Because Australia’s and New Zealand’s culture values are much closer to the countries of the Reformed West than to those of the USA and Canada, I have included Australia and New Zealand in the Reformed West and renamed the New West as North America. The culture zones are defined as follows:

Reformed West — Western European societies strongly affected by the Reformation: Denmark, Finland, France, Germany, Iceland, Netherlands, Norway, Sweden, Switzerland, United Kingdom, plus Australia and New Zealand;
North America — USA and Canada;
Old West — Mostly Catholic parts of Western Europe being core parts of the
Roman Empire: Austria, Belgium, Cyprus, Greece, Ireland, Israel, Italy, Luxembrg, Malta, Portugal, Spain;
Returned West — Catholic and Protestant parts of post-communist Europe returning
to the EU: Croatia, Czechia, Estonia, Hungary, Latvia, Lithuania, Poland, Slovakia, Slovenia;
Orthodox East — Christian Orthodox or Islamic parts of the post-communist world,
mostly parts of former USSR;
Indic East — Parts of South and South East Asia under the historic influence
of Indian culture: Bhutan, Cambodia, India, Indonesia, Laos, Malaysia, Maldives, Myanmar, Nepal, Pakistan, Philippines, Singapore, Sri Lanka, Thailand, Timor-Leste;
Islamic East — Regions of the Islamic world that have been parts of the Arab/Caliphate,
Persian and Ottoman empires;
Sinic East — Parts of East Asia under the historic influence of Chinese culture: China, Hong Kong, Japan, Macau, Mongolia, North Korea, South Korea, Taiwan, Vietnam;
Latin America — Central and South America and the Caribbean;
Sub-Saharan Africa — African countries south of the Sahara
Oceania — Melanesian and Polynesian countries: Fiji, Kiribati, Nauru, Palau, Papua New Guinea, Samoa, Solomon Islands, Tonga and Vanuatu.

References

  1. EVS (2021): EVS Trend File 1981-2017. GESIS Data Archive, Cologne. ZA7503 Data file Version 2.0.0, https://doi.org/10.4232/1.13736
  2. EVS/WVS (2021). European Values Study and World Values Survey: Joint EVS/WVS 2017-2021 Dataset (Joint EVS/WVS). JD Systems Institute & WVSA. Dataset Version 1.1.0, doi:10.14281/18241.14.
  3. Haerpfer, C., Inglehart, R., Moreno, A., Welzel, C., Kizilova, K., Diez-Medrano J., M. Lagos, P. Norris, E. Ponarin & B. Puranen et al. (eds.). 2021. World Values Survey Time-Series (1981-2020) Cross-National Data-Set. Madrid, Spain  &  Vienna,  Austria:  JD  Systems  Institute  &  WVSA Secretariat. Data File Version 2.0.0, doi:10.14281/18241.15.
  4. Welzel C. Freedom Rising. Human Empowerment and the. Quest for Emancipation. 2013. Cambridge: Cambridge University Press. https://www.cambridge.org/core/books/freedom-rising/80316A9C5264A8038B0AA597078BA7C6
  5. Pew Research Center, Oct 29, 2018. Eastern and Western Europeans Differ on Importance of Religion, Views of Minorities, and Key Social Issues. https://www.pewforum.org/2018/10/29/eastern-and-western-europeans-differ-on-importance-of-religion-views-of-minorities-and-key-social-issues/
  6. Arab TA, Maleki A. Iran’s secular shift: new survey reveals huge changes in religious beliefs. The Conversation, Sept 10 2020. https://theconversation.com/irans-secular-shift-new-survey-reveals-huge-changes-in-religious-beliefs-145253
  7. WIN Gallup International. Global Index of Religiosity And Atheism – 2012.  WIN-Gallup International. 27 July 2012.
  8. WIN Gallup International. Losing Our Religion: Two Thirds of People Still Claim to Be Religious. WIN/Gallup International. 13 April 2015. https://www.gallup-international.bg/en/33531/losing-our-religion-two-thirds-of-people-still-claim-to-be-religious/
  9. WIN Gallup International. Religion Prevails in the World. WIN/Gallup International. 10 April 2017. https://www.gallup-international.bg/en/36009/religion-prevails-in-the-world/
  10. Pew Research Center, March 8 2018. Israel’s Religiously Divided Society. https://www.pewforum.org/2016/03/08/israels-religiously-divided-society/
  11. Pew Research Center, April 2 2015. Religious Composition by Country, 2010-2050. https://www.pewforum.org/2015/04/02/religious-projection-table/2020/number/all/
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Afghanistan, where the war on drugs and the war on terror meet

In the last couple of weeks, watching the end of the American war in Afghanistan and the Taliban takeover, I realized that Afghanistan is at the intersection of the war on terror and the war on drugs. I have been engaged for nearly 20 years now in work to update global estimates of conflict deaths and global estimates of deaths attributable to drug use.

Alfred McCoy has documented the role of opium production in the Afghanistan wars in his 2015 book In the Shadows of the American Century (see also how-the-heroin-trade-explains-the-us-uk-failure-in-afghanistan). After 20 years, the fighting (mostly) has ended, but western intervention has resulted in Afghanistan becoming the world’s first true narco-state. Opium harvesting along with US support sustained the Afghan resistance to the Soviet occupation in the 1980s, and the rise to power of the Taliban in the 1990s. In July 2000, the Taliban ordered a ban on all opium cultivation, and opium production fell by 94%. When the US invaded Afghanistan in 1991, they allied with the Northern warlords who had been active in the drug trade and smuggling. Opium production resumed and grew over the following two decades.

The UN Office on Drugs and Crime reported in its World Drug Report 2021 that Afghanistan reported a 37 per cent increase in the amount of land used for illicit cultivation of opium poppy during 2020 compared with the previous year. It was the third highest figure ever recorded in the coun- try and accounted for 85 per cent of the global total of opium production  in 2020. The increase follows a trend that has seen the global area  under opium poppy cultivation rise over the past two decades, particularly after 2009. In 2020, 43% of arable land in Afghanistan was under poppy cultivation. This was somewhat lower than the 60% peak in 2017. An estimated 95% of heroin in Europe comes from Afghanistan. Only a small proportion of heroin in the USA comes from Afghanistan, the majority comes from Mexico.

However, the US-led war on drugs with its attendant prohibition and criminalization keeps heroin prices and profits high, so that poppy cultivation remains far more profitable than other crops, and has played a significant role in funding both sides of the Afghan conflict. Narcotics are likely to have provided the Taliban with over half its revenues through organising cultivation, protecting harvests, and securing criminal supply routes into central Asia. Its military victory may now see a further expansion of the opiate economy. But what of the impact on the USA, where pharmaceutical and other synthetic opioids, particularly fentanyl have fueled an exponential increase in drug overdose deaths.

The CDC has recently released provisional estimates of US drug overdose deaths in 2020, and I have done a quick update of previous time series estimates for US opioid and other drug overdose deaths. The results are shown in the following plot. Dug overdose deaths (grey curve) have been rising exponentially for over three decades at an average annual growth rate of 10.4% (dotted grey curve) with a 29% jump in the pandemic year 2020 to 96,000 overdose deaths, of which 70,470 were due to opioids. Fentanyl and other synthetic opioids were responsible for most of these, heroin in 2020 was responsible for only around 15,400 deaths.

I have also done an approximate projection of total deaths attributable to drug use (yellow curve), which include overdose deaths, road injuries and suicide, as well as HIV and hepatitis B and C deaths associated with transmission through injecting drug use. The total attributable deaths in 2020 were estimated at around 140,000.

How does the mortality toll from the war on drugs compare with the deaths due to the Afghan conflict? Conflict death estimates for Afghanistan are hugely uncertain. Wikipedia has a review of various estimates for the Soviet war period of the 1980s, with 1.2 million deaths being a mid-range estimate. The post-Soviet period of civil war in the 1990s probably results in around another half million deaths. For the period from 2001, when the US commenced action against the Taliban and Al-Quaeda, to the end of US involvement in August 2021, I have updated earlier conflict death estimates prepared for WHO and UNICEF (see here for details) to include new data from ACLED, the Armed Conflict Location and Event Data Project. I have again drawn on the latest data from ACLED up to end of July 2021 to update estimates of total conflict deaths in Afghanistan from 1985 to 2021. For the years 2001 to 2021 inclusive, there were an estimated total of 483,800 conflict deaths.

A very approximate apportioning of this almost half a million deaths suggests that there were around 116,000 Afghan soldiers and police deaths, 51,000 Taliban fighter deaths and around 300,000 civilian deaths. Almost 2,500 US soldiers died, along with 1209 deaths among US allies (UK, Australia, Canada and EU forces), and almost 4,000 US civilian contractors.

These figures for deaths due to the Afghan war and for US drug-related deaths dwarf the current US total of just over 640,000 Covid-19 deaths to date, though of course these are concentrated into a much shorter period of one and a half years.

The table and figure below compares these death tolls by decade (and include 2021 for Covid-19 only):

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The current COVID Delta wave

The graph below summarizes the COVID-19 situation as of yesterday (19 August 2021) for a selection of countries of interest to me. Confirmed case numbers have been rising in all these countries except the Netherlands (where case numbers have dropped to 30% of the peak on 19 July). Almost all the cases in this latest wave are the Delta variant.

Current case rates per million population are more than 50% of the previous maximum country-specific rate for the UK and USA, and exceed the previous maximum by 6% in Australia. The apparent case fatality rate (deaths/cases) is highest for Australia at 1.24% followed by the USA 0.74% and lowest in the Netherlands (0.29%) and Switzerland (0.19%). Around 60% of the population are fully vaccinated in the Netherlands (62%), UK (61%), Germany (58%), and Italy (58%). These are followed by the France (54%), USA (51%), Switzerland (50%) and last Australia (23%).

Ignoring Australia (which has very low case numbers and deaths relative to the other countries), there are two main outliers for apparent case fatality rates: Switzerland with very low CFR at 0.19% and the USA with a high CFR at 0.74%. Vaccination rates are similar in both countries, but my sense is that there is a huge difference with mask wearing and other social restrictions to limit transmission. And also large differences in health system access and health insurance coverage and adequacy.

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Vaccination and the COVID Delta variant

The COVID Delta variant (B.1.617.2), which started spreading widely in India last October, is highly transmissible and might cause more severe illness and hence death. It has spread to many countries and is now the dominant strain in the UK. This website has up-to-date stats on the proportion of (tested) cases that are the Delta variant. In the week ending 24 June, Delta accounted for 16.7% of positive COVID-19 tests in Switzerland. In comparison it accounted for 62% of cases in Australia and 96% of cases in the UK. In terms of actual number of cases in the week, those percentages correspond to 52 cases in Switzerland, 9354 cases in the UK. And 16 cases in Australia.

The good news (at least for us in Switzerland) is that both the mRNA vaccines in use here (Moderna and Pfizer) are effective against the Delta variant. A single dose is 33% effective and two doses 88% effective. The AstraZenica vaccine in use in Australia and the UK is also about 33% effective for a single dose, and 60% effective after two doses.  All the vaccines appear to substantially reduce the likelihood of severe illness and death. Currently, around 55% of the Swiss population are fully vaccinated (two doses) and the government aims to raise that to 80% by the end of the summer. Currently less than 5% of Australians are fully vaccinated and new lockdowns are being implemented across Australia. Sydney, Perth, Darwin and now Brisbane are all in lockdown, and Victoria just exited one.  

The disastrous mishandling of the vaccine roll-out in Australia is entirely down to Scott Morrison, as the Australian prime minister has insisted on taking full federal responsibility for it. The folly of the government’s over-reliance on the AstraZenecca vaccine has now escalated into a public health disaster. Falsely claiming delays, EU blocks and supply issues, the problem of insufficient vaccines has been compounded as AstraZenecca is no longer advised for people under 50, leaving millions of Australians with no immediate prospect of vaccination. To make matters worse, the Prime Minister a couple of days ago off-the-cuff told reporters that any Australian over 16 could request vaccination by their GP. This directly contradicting the advice of government Chief Medical Officers, who had not been consulted, and left GPs confused as to whether this had involved any change to the legal indemnity rules for GPs (which currently would not cover GPs if a patient under 40 developed blood clots from the AstraZenica vaccination. 

Stephen Duckett, a former head of the Victorian Health Department, has examined how the vaccine rollout was comprehensively bungled in a recent article on The Conversation.

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Age Patterns of Religiosity and Atheism in the USA, Europe and Australia

It is well known that the proportion of people who are atheist or non-religious in high income countries is generally higher in younger people than older people and rising over time.  So is the decline in religiosity mainly a cohort or period phenomenon?

I have analysed age patterns in religiosity using data from the last three waves of the World Values Survey and the European Values Study [1-4]. See previous posts here and here for more details on the analysis of these surveys. I chose to look at the USA and compare it with the eight high income countries in which the prevalence of irreligion (atheism and the non-religious) exceeds 50%: Australia, Denmark, Finland, France, Netherlands, Norway, Sweden and the United Kingdom. The non-religious category is quite small in these countries, apart from Australia, and includes people who say they believe in God, but say they are non-religious and rate the importance of God as 8-10 on the unimportant end of a 10-point scale.

The proportion of people who are religious rises with age and the proportion who are irreligious declines. Trends and levels are quite similar for the three high-atheism countries. The following plot aggregates the overall prevalences by sex for the seven European countries and compares them with those for the USA.

Across all age groups apart from 15-29 years, the prevalence of irreligion is higher in men than women. The proportion of religious people who are non-practicing rises to middle age and then declines in older ages in the USA. In contrast, the proportion who are practicing religious remains fairly low in the European countries (ranging from 10 to 20%) whereas the proportion non-practicing rises with age. The prevalence of irreligion in the youngest age group (18-24 years) exceeds 50% in 2018 in all the countries analyzed, including the USA.

Next, I compare time trends across the three last waves of the surveys for these countries. The plot on the left below shows age-specific trends in the per cent of people who are irreligious and that on the right the trends in the per cent of people who are practicing religious.

Although the prevalence of irreligion is lower in all age groups in the USA compared to the European countries, it is rising rapidly in all age groups under 60 years, and will likely catch up to Europe soon. Trends are much smaller in Europe as much of the growth in irreligion occurred decades earlier, whereas it has really only started to increase substantially in the USA in the last decade.

Is the rise in irreligion dominated by cohort or period effects? As in the epidemiology of diseases [5], there are three time factors that are potentially explanatory:  the age of the person, the year (period) in which the population is surveyed, and the year in which a person is born (cohort). Potentially, in the absence of any other influences, the prevalence of irreligion might change with age (people may get disillusioned with religion as they get older, or perhaps more religious as they age). Perhaps the birth cohort is a major factor: as a marker of the social and religious milieu around the time when a person reaches adulthood and makes their own decisions about religious beliefs and practices as an adult. Or perhaps changes in the social, economic and political environment affect people across all ages and birth cohorts so that everyone becomes more or less religion over periods of time. These three factors are not independent, since cohort = period – age. Thus, it is not possible to statistically estimate the independent contributions of all three factors, unless one factor can be specified from external data or knowledge [5].

I will assume that there is no independent age effect. Given the fairly flat trends by age over periods in which social and religious conditions have been relatively stable, I think this is a reasonable first approximation that allows us to gauge the relative importance of cohort versus period effects. I ran fairly simplistic linear regressions of the form 

yx  = β0x +  β1x .period +  β2x .cohort + ε

where yx denotes the prevalence (%) of religiosity category x. I fitted this regression to the individual country data for years 2018 and 2003, where the prevalences in 2003, calculated by backprojecting the trend between 2018 and 2007. This allowed age, cohort and period all to be measured in units of 15 years, so that the coefficient β1x  gives an estimate of the average change in prevalence over a 15 year period and β2x  gives an estimate of the change in prevalence between two birth cohorts 15 years apart.

Given the simplicity of the model, and the simplifying assumptions of linear trend across age groups and periods, this gives no more than an indicative measure of the relative importance of period versus cohort effects, but it does show a very distinct difference between the USA and the high-atheism European countries.

Period effects dominate for the USA, whereas cohort effects are more important in the other countries for atheism and practicing religious. For the two middle categories, both period and cohort effects both play a relatively important role, though for smaller time trends. Almost certainly this reflects the fact that the European countries and Australia have fairly stable secular cultures and cohort effects dominate, whereas the USA over the last decade or two has become increasingly polarized on religious and cultural issues and this period effect has led to declining religiosity across all age groups that is generally larger than the cohort-specific declines, greater in younger age groups.

References

  1. Inglehart, R., C. Haerpfer, A. Moreno, C. Welzel, K. Kizilova, J. Diez-Medrano, M. Lagos, P. Norris, E. Ponarin & B. Puranen et al. (eds.). 2014. World Values Survey: All Rounds – Country-Pooled Datafile Version: https://www.worldvaluessurvey.org/WVSDocumentationWVL.jsp. Madrid: JD Systems Institute.
  2. Haerpfer, C., Inglehart, R., Moreno,A., Welzel,C., Kizilova,K., Diez-MedranoJ., M. Lagos, P. Norris, E. Ponarin & B. Puranen et al. (eds.). 2020. World Values Survey: Round Seven–Country-Pooled Datafile. Madrid, Spain & Vienna, Austria: JD Systems Institute& WVSA Secretariat[Version: http://www.worldvaluessurvey.org/WVSDocumentationWV7.jsp].
  3. Gedeshi, Ilir, Zulehner, Paul M., Rotman, David, Titarenko, Larissa, Billiet, Jaak, Dobbelaere, Karel, Kerkhofs, Jan. (2020). European Values Study Longitudinal Data File 1981-2008 (EVS 1981-2008). GESIS Datenarchiv, Köln. ZA4804 Datenfile Version 3.1.0, https://doi.org/10.4232/1.13486.
  4. EVS (2020): European Values Study 2017: Integrated Dataset (EVS 2017). GESIS Data Archive, Cologne. ZA7500 Data file Version 3.0.0,doi:10.4232/1.13511
  5. Holford TR. Understanding the effects of age, period, and cohort on incidence and mortality rates. Annu Rev Public Health. 1991;12:425-57. doi: 10.1146/annurev.pu.12.050191.002233. PMID: 2049144.
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Excess mortality in the USA and its causes 2000 – 2020

Sam Preston and Yana Vierboom published a short paper in April which showed that Americans die at higher rates than Europeans and this translated into roughly 400,000 additional deaths in 2017 that would not have occurred if the USA had Europe’s lower age-specific death rates. That is about 12% of all American deaths and higher than the COVID-19 death toll of around  380,000 in 2020. In a recent Guardian article, they summarized this as “The mortality penalty that the US pays every year is equivalent to the number of Americans who died of Covid in 2020”. They also note that because people tend to die at older ages than those characterizing excess deaths, the total potential years of life lost are three times greater for the excess deaths than for Covid in 2020 (13 million versus 4.4 million).

In the Guardian article, they identify a number of factors that contribute to these excess deaths: overweight and obesity, the deaths of despair identified by Case and Deaton (drug overdose, suicide and alcohol-related causes) and lack of universal health insurance (see here and here). These factors have been widely identified as contributing to higher mortality rates and lower life expectancy for the USA compared to high income European countries, but their comparison of this mortality gap with the Covid death toll was a compelling way to present it. So I was interested to replicate and update their analysis and try to make ballpark estimates of the contribution of these factors to the excess deaths.

Preston and Vierboom used data from the Human Mortality Database (HMD) to create a composite of the five largest European countries, whose combined population size is very similar to that of the United States: Germany, England and Wales, France, Italy, and Spain. Additionally, they argued that using these larger European countries  to provide a mortality standard would avoid unrealistic expectations for a larger and more diverse population that might result from comparisons including small countries where there may be exceptional combinations of factors affecting mortality (e.g., climate, diet, social history, and healthcare delivery) Their analysis covered the period 2000 to 2017, the latest year for which data were available for all six countries.  

I downloaded data for these countries from the HMD a few days ago, and mortality rates were available for all six up to 2017, for four for 2018, and only the USA for 2019. I projected (non-Covid) age-sex specific mortality rates forward to 2020 and added Covid deaths for 2020 using age-sex distributions published by European countries by INED. I then adjusted the projections and in some cases the added 2020 Covid deaths to match the sex specific life expectancies at birth and at age 65, published by Eurostat up to and including year 2020. Data for non-Covid and Covid mortality by age and sex in 2020 were taken from national statistical office sources for the UK and USA.

Figure 1 below shows the ratio of US death rates to the average death rates* for the five European countries (the “European standard”) by age, in 2000, 2010, 2019 and 2020. US mortality rates are consistently higher than the European standard for all ages below 80 years and the ratio has gotten progressively worse throughout the 21st century. The ratio peaks among 25-29 year olds at almost 3 in 2019 and 3.24 in 2020.

Figure 1. * Note I calculated death rates for the male plus female population of the five countries combined, whereas Preston and Vierboom calculated the arithmetic average of the death rates for the five countries.

Figure 2 below shows the annual trend in total excess deaths in the USA above the number than would have occured if the US population had been subject to the age-sex specific death rates of the European standard. This excess rose from 219,000 in the year 2000 to 410,000 in 2019 and 616,000 in 2020. Although there were over 380,000 Covid deaths in the USA in 2020, the European standard also includes substantial numbers of Covid deaths, and the Covid excess for the USA is “only” 136,000 deaths.

Figure 3 shows the age pattern of US excess deaths in 2000, 2010 and 2020. Although the death rate ratio is highest in the 25-34 year age group, overall death rates rise with age and the peak in excess deaths is at older ages from around 40 to 75 year. From age 90 onwards, US death rates are somewhat lower than the European standard. Thus the excess deaths below age 90 are actually higher than the total excess and in 2020 there were 764,000 excess deaths below 90, of which 99,000 were Covid-90 deaths.

In their Guardian article, Preston and Vierboom identified several factors that contribute substantially the US excess. I have made approximate estimates of the contributions of these factors using estimates from the Global Burden of Disease 2019 update (IHME GBD2019) of death rates by age and sex for the six countries for drug overdose, suicide, homicide, deaths attributable to overweight and obesity, deaths attributable to alcohol and drug use.

The 2019 age-sex specific percentages of deaths attributable to these causes were also applied to the 2020 non-Covid mortality estimates. Numbers for drug use were adjusted to reflect a 13% increase in drug overdose deaths in 2020 compared to 2019, an approximately 25% increase in homicide deaths and a 5% decrease in suicide deaths.

Unlike the USA, all the European countries have universal healthcare insurance. The number of working age Americans without health insurance rose from 38.7 million under age 65 in 2000 to 46.5 million in 2010 and then dropped to around 26 million following the introduction of Obamacare. Around 1% of Americans aged 65 or more are also uninsured. The number uninsured has risen to around 30 million in 2020 due to Republican efforts to restrict and dismantle Obamacare. I used an estimate of the hazard ratio for mortality among the uninsured compared with the insured, of 1.4 to calculate the excess mortality among the uninsured. This figure includes adjustments for age, sex, body mass index, alcohol use, smoking, and a number of other factors. The ratio was 1.8 adjusted for age and sex only.

In addition to the uninsured, a substantial number of Americans are underinsured, defined as having out-of-pocket and deductible expenses greater than 10% of their income. Apart from substantial problems with debt, around half of the underinsured do not get needed care because of cost and there are associated excess deaths (see here and here). The number of Americans underinsured has risen from around 16 million prior to Obamacare to around 30 million in 2020. I assume a mortality ratio of 1.1 among the underinsured.  

Figure 4 shows the approximate contributions of overweight and obesity, drug use and overdose, suicide, homicide, lack of health insurance and Covid to the US excess deaths relative to the European standard. Together these six factors account for around 80% of the excess deaths. Excess deaths due to alcohol use were not included as a separate factor in this graph because European and US death rates were quite similar, and the alcohol excess was small and actually negative in 2000.

For 2020, the leading cause of excess deaths was overweight and obesity (around 154,000 deaths), followed by Covid-19 (136,000 deaths), drug use and overdose (103,000 deaths) and lack of health insurance (74,000 deaths). Excess deaths due to homicide and suicide were smaller at 20,200 and 11,200 respectively. If the USA had the European standard death rates for gun homicides and gun suicides, it would have 15,900 fewer gun homicides and 19,200 fewer gun suicides. Around 42% of the latter would still commit suicide by other means.

Figure 4 also illustrates the dramatic rise in drug overdose deaths, the vast majority due to opioids both prescription and illicit, which has occurred over the last decade. In a previous post, I examined this in more detail and noted that, in 2019, the USA accounted for an astonishing 40% of estimated global drug deaths.

Figure 5 shows the age distribution of each of the six factors contribution to total excess deaths in 2020. Not surprisingly, excess deaths due to drug use fall predominantly among younger adults, whereas deaths due to overweight and obesity fall more in later middle and older ages.

Excluding excess Covid deaths, there were almost half a million excess deaths in the USA in 2020 compared to the US standard. While overweight and obesity is not an easy problem to address, the European experience shows that substantial reductions in excess death can be achieved through introduction of universal health insurance, effective public health and other policies relating to drug use, and effective gun control. Overall, the excess deaths resulted in a life expectancy reduction for Americans of 4.3 years for males and 5.8 years for females, in 2019 before Covid caused extra reductions – see the final figure below.


Note: Most of the estimates for 2019 and 2020 in this post are based on provisional figures and have varying degrees of uncertainty. I plan to refine and improve some of the inputs and calculations, and final results may change a little. The big picture is unlikely to change significantly. In particular, the estimated total Covid deaths for 2020 may change as improved analyses are carried out on complete death registration data. The US had a significant rise in death rates for causes other than Covid-19 in 2020 and some of this may turn out to be undiagnosed Covid deaths. This will not make major differences to the provisional decomposition shown above.

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New estimates of total global COVID-19 deaths from IHME

A few days ago, the Institute for Health Metrics and Evaluation (IHME) released estimates of total COVID-19 deaths for countries based on comparisons of total deaths from all causes during the pandemic period with the expected total deaths based on projections of deaths in years before 2020.

IHME estimate that by 3 May 2021, the total number of COVID-19 deaths globally was 6.93 million, more than double the reported number of deaths of 3.24 million. The estimated total for the USA was 905,289, 58% higher than the reported number of 574,043 deaths.

The figure below from the IHME website shows a map of the predicted ratio of total COVID-19 deaths to reported COVID-19 deaths for March 2020 to April 2021. Ratios range from very high levels in many Eastern European and Central Asian countries to ratios that are much closer to 1 in several high-income countries. For most countries in sub-Saharan Africa, which have reported relatively low numbers of COVID-19 deaths, the estimated ratios range from about 1.6 to 4.1, suggesting that the total number of COVID-19 deaths in the region is several times higher than previously thought. Similarly, India, the country with the most recent severe wave of cases and deaths, is estimated to have an overall ratio of 2.96, which implies that the total COVID-19 death toll to date is much higher than what has been reported.

IHME predicted ratios of total COVID-19 deaths to reported COVID-19 deaths

Its just on a year since they released projections of total COVID-19 deaths based on a clearly stupid modelling process (see my earlier post) and which was widely criticized by infectious disease modellers and epidemiologists (see here for example). There is evidence the IHME’s extraordinarily optimistic projections for total deaths were seized on by the Trump Whitehouse to minimize the need to do anything to address the pandemic.  So have they done a better job this time?

From reading the summary of the methods they have used (see here) it would seem so. They are much more in their comfort zone in the type of modelling needed.  They are taking an approach to estimate excess deaths based on total recorded deaths minus a counterfactual baseline determined from projection of death rates for previous years. But additionally, and beyond what has been done by other estimates of excess deaths, they say that they are aiming to take into account changes in death rates during the pandemic from the following causes:

  1. deaths caused by COVID-19 infection
  2. the increase in mortality due to needed health care being delayed or deferred during the pandemic;
  3. the increase in mortality due to increases in mental health disorders including depression, increased alcohol use, and increased opioid use;
  4. the reduction in mortality due to decreases in injuries because of general reductions in mobility associated with social distancing mandates;
  5. the reductions in mortality due to reduced transmission of other viruses, most notably influenza, respiratory syncytial virus, and measles; and
  6. the reductions in mortality due to some chronic conditions, such as cardiovascular disease and chronic respiratory disease, that occur when frail individuals who would have died from these conditions died earlier from COVID-19 instead.

They refer to a Netherlands study that suggested direct COVID-19 deaths may be higher than estimated excess deaths because deaths due to some other causes have declined during the pandemic.

This is all good stuff, but they then go on to say that there is insufficient data to estimate the impact on excess mortality rates of these causes other than COVID-19, and so they just calculate total excess deaths like everyone else has to date, as total deaths minus counterfactual expected deaths based on projection of previous years deaths and assume the excess is all due to COVID-19. Disappointing! They do make a back-of-the-envelope estimate that there may have been a reduction of up to 615,000 deaths globally, resulting from behavioral changes. 

For countries without available deaths data, and where the available information suggests substantial under-reporting of COVID-19 deaths, the IHME developed a covariate-based prediction model based on infection-detection rates and location-specific fixed effects derived from published studies. As usual, their models are complex and  not easily examined to understand exactly how they work and whether in fact they are producing defensible results.

The COVID-19 death statistics most commonly reported and available on websites such as or Our World in Data are mostly based on confirmed COVID-19 deaths, usually defined as a death within 28 days of a positive COVID-19 test, and reported by many countries daily or weekly. Clearly these statistics depend on testing rates and quality of reporting systems. In many countries particularly in the early phase of the pandemic deaths were only recorded from hospitals, missing many at home and in nursing homes.

A number of researchers and media organizations are estimating the different between total recorded deaths and the predicted expected deaths based on trends for earlier years, similar to the estimates just released by IHME. These estimates refer to varying time periods from the start of the epidemic or to weekly totals and can be quite sensitive to projection methods for estimating the counterfactual expected numbers of deaths.  Excess deaths also represent not only the impact of COVID-19  but also the mostly as yet unknown impacts of mortality increases and decreases from other causes discussed above.

I have compiled estimates of the ratio of total excess deaths to reported COVID-19 deaths from a number of sources for comparison with the IHME estimates. The table below shows estimates for selected European countries and the USA, all of which have essentially complete registration of deaths.

* Excess deaths calculated by me from total annual deaths data for years 2015 to 2020 using
ordinary regression of the log of total death rate against year
(1) Kontis et al. Nature 2020
(2) BBC News (2020)
(3) Karlinski and Kobak (2021)
(4) Our world in data (2021)
(5) The Economist (2021)
(6) IHME (2021)

The first thing to note is the quite wide variation in estimates from different sources. In some cases this may relate to different time periods, but for the lower four sources, which all relate to deaths up to either end 2020 or to Feb/Mar 2021, there are some quite wide variations such as a range from 1.2 to 1.6 for the USA, or 0.7 to 1.5 for the USA, and -1.5 to 1.5 for Norway. Norway illustrates a limitation of this approach in countries with low total COVID deaths (reported total around 436 for 2020) where small differences in projection methods can mean a negative versus positive estimate of the excess. Italy and Spain both had quite large epidemics, and their ratios are more consistent across sources.

In general, the IHME ratios are higher than those from other analyses, suggesting that they are projecting lower counterfactual deaths for the pandemic period. Their projection method is much more complex than any of the others, and possibly is doing a better job. But its inherently difficult to assess the “accuracy” of counterfactuals. In this case, I think what is needed is for another group to do a similarly more sophisticated set of projections from the same data, taking into account the various factors such as seasonality that IHME did, and see whether their results are similar. How much method dependence is influencing the differences above is as yet unclear.

Finally, there is a third type of COVID-19 mortality statistic which is starting to become available. Both the USA and UK have released statistics for the year 2020 based on death registration data which show numbers of deaths where the certificate mentioned COVID-19 as an underlying or contributory cause of death. The USA also released the number of deaths in which COVID-19 is specified as the underlying cause by the certifying doctor.  Both of these statistics will differ from either the reported (confirmed) COVID-19 deaths or the estimated excess deaths. COVID-19 was specified as the underlying cause in 91% of the deaths where COVID-19 was mentioned in the USA. The magnitude of this difference, and with other types of COVID-19 mortality estimates will depend in part on the ICD rules for determining underlying cause from all the contributory causes and in part on national (and individual doctor) idiosycracy in applying or modifying the ICD rules.

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Premodern/modern religious values and happiness

I recently came across rankings of countries by average reported happiness produced for The World Happiness Report which was released on March 20, 2021. This is a report produced by independent experts for the United Nations Sustainable Development Solutions Network and uses data from the Gallup World Poll to produce an average happiness score for over 150 countries for each year from 2005 to 2020.

This year’s report focuses on the change in average happiness from 2017-2019 to 2020 to examine the impact of the COVID-19 pandemic on happiness, and to assess the relationship of these changes to the country-level health impacts of COVID-19 and the government responses to it. However, I was more interested in examining the potential relationship of happiness to the latent variable measure I developed for premodern versus modern religious values (see earlier post here).

The main measure used for happiness in the World Happiness Report is based on the national average response to the question on life evaluation in the Gallup World Poll. The English wording of the question is

“Please imagine a ladder, with steps numbered from 0 at the bottom to 10 at the top. The top of the ladder represents the best possible life for you and the bottom of the ladder represents the worst possible life for you. On which step of the ladder would you say you personally feel you stand at this time?”

This measure is also referred to as the Cantril life ladder. It provides a more stable measure of the satisfaction of people with their lives than the two other measures also included in the Gallup World Poll: positive and negative affect. Positive affect is the degree to which a respondent experienced happiness, laughter and enjoyment yesterday. Negative affect is the degree to which a respondent experienced worry, sadness and anger yesterday.

For this analysis, I averaged the national average life ladder values for the three years 2017 to 2019 for each of 153 countries and territories. I also added modernity and religiosity latent values for 102 of these countries for years 2017-2020 from my previous analysis of the World Values Surveys and the European Values Study. I then did a regression analysis of the association of these latent values and a number of other covariates with the national average happiness, which I used to decompose the contribution of each of these factors to the overall national happiness. I describe that analysis in more detail below, but first I present a graph of the results which also gives the ranking of countries from highest to lowest happiness score.

Finland holds the rank of the happiest country in the world for the third consecutive year, with an average score of 7.81 on the 0-10 scale. It is followed by Denmark, Switzerland, Iceland and Norway. Among the 102 countries included in the chart above, Australia came in at 12th position with a score of 7.22 and the USA in 16th position with a score of 6.94. The countries with the lowest scores on the graph was Rwanda, with Zimbabwe, Yemen and India just above it. Afghanistan received the lowest score, followed by South Sudan, in the overall results for all the countries included in the World Happiness Report.

The World Happiness Report also includes the results of a regression analysis used to attribute the average reported happiness score of each country for years 2018-2020 to its average income, healthy life expectancy and four social factors: social support, freedom to make life choices, generosity and perceptions of corruption. I note that the healthy life expectancy measure used was that I produced for WHO Member States in 2016, when I worked for WHO.

I carried out a similar regression analysis including not only the above explanatory factors, but also the following potential factors:

modernlatent variable for premodern/modern religious values (see post)
religioslatent variable for religiosity (practicing, non-practicing, non-religious, atheist) (see post)
hchuman capital: average years of education of persons aged 25+ (International Futures Version 7.31)
gfrglobal fertility rate (UN Population Division 2017)
urbanper cent of population living in urban areas (UN 2018)
giniincome inequality index for 2017-2019 from World Bank (2020)
stuntingper cent of children aged 0-4 who are stunted (WHO 2017)
bmibody mass index – people aged 40-64 years (Kontis et al 2014)
riskaverage risk measure for five categories of natural disaster (INFORM)
fsifragile states index (The Fund for Peace)
democEIU democracy index (Economist Intelligence Unit)
ciriCIRI Human Rights Index (CIRI Human Rights Data Project)
ptsiPolitical Terror Scale (Worldwide Governance Indicators – WGI)
rlerule of law effectiveness (WGI)
geegovernment effectiveness (WGI )
pvepolitical stability and absence of violence (WGI )
ccecontrol of corruption effectiveness (WGI
ymenpctproportion of the population that are 15-30 year old males
(UN Population Division 2017)
hedbalhedonic balance calculated as positive affect score (GWP) minus negative affect score (GWP)
avelfaverage ethnolinguistic fractionalization (La Porta 1999)
frac_ethnethnic fractionalization ( Alesina et al 2003 )
frac_langlinguistic fractionalization ( Alesina et al 2003 )
frac_religreligious fractionalization (Alesina et al 2003)

The other potential covariates are those that were included in the World Happiness Report analysis:

lngdplatent variable for premodern/modern religious values (see post)
halelatent variable for religiosity (practicing, non-practicing, non-religious, atheist) (see post)
socsuppthe national average of the binary responses (either 0 or 1) to the GWP question “If you were in trouble, do you have relatives or friends you can count on to help you whenever you need them, or not?”
freedomfreedom to make life choices is the national average of responses to the GWP question “Are you satisfied or dissatisfied with your freedom to choose what you do with your life?”
generosgenerosity is the residual of regressing national average of response to the GWP question “Have you donated money to a charity in the past month?” on GDP
per capita
corruptthe corruption measure is the national average of the survey responses to two questions in the GWP : “Is corruption widespread throughout the government or not” and “Is corruption widespread within businesses or not?” The overall perception is just the average of the two 0-or-1 responses.

The following plots provide scatterplots for the happiness index versus lngdp (r = 0.8) and versus modern (r = 0.78).

I started with the regression model used in the World Happiness Report but with the average happiness for 2017-2019, and covariates relating to the same period. This regression explained 81% of the variance across countries in average happiness, though two of the variables, generos and corrupt, had estimated coefficients which were not significant at the 5% level and contributed only very marginally to explained variance. When I added modern to the regression and tested inclusion of other variables, I ended up with the following final model, which explains 84% of overall variance in average happiness across countries:

Note that healthy life expectancy (hale) is no longer included, and that the other new health variables stunting, bmi and risk (disasters) were also not significant explanatory variables. Similarly, the gini measure of income inequality and most of the fractionalization variables were also not retained in the model. Hedonic balance (hedbal) was also unrelated to variations in average happiness on the life ladder. The religious fractionalization variable, frac_relig, was marginally significant (p=0.049) when added to the final model, but this was highly dependent on other variables included. Religious fractionalization reflects the probability that two individuals randomly selected from the population of the country will have different religions. The estimated coefficient for frac_rel was -0.35, so that average happiness decreases with increasing religious fractionalization. I did not include this variable in the decomposition of the contributions of the various factors to the overall happiness of each country shown in the plot above.

The first six sub-bars for each country/territory in the plot above reflect my estimate of the contributions of the variables included in the regression analysis to the average happiness score in that country. The final dark blue bar includes two elements. The first is the residual error, the part of the national average that the model does not explain. The second is the estimated average happiness in a mythical country called dystopia, since its score is the model’s predicted score (1.82) for an imaginary country having the world’s worst observed values for each of the six variables. With dystopia and the residual included, the sum of all the sub-bars adds up to the actual average happiness score on which the rankings are based.

Across all 101 countries on average, GDP per capita and social support each explained around 17-18% of the total happiness score, followed by freedom to make life choices (10%), modern religious values (8%), population proportion of young men (8%) and state repression (7%). Explanatory values vary substantially across individual countries of course.

The proportion of young men (ymenpct) was included as a potential variable because it is an explanatory variable for higher rates of homicide. Based on this association, I expected the model to estimate a negative coefficient for ymenpct, but it was in fact positive, so higher proportions of population being young men are associated with increased happiness. I have no idea whether this reflects any sort of real causal association or not. The association of self-reported happiness with age is generally U-shaped with declining happiness to mid-life and then increasing again. So possibly higher proportions of young men in the population is simply a marker for higher proportions of happier people (male and females).

It is of course not possible to conclude that any of the associations found here are necessarily causal, or that the estimated contributions reflect the real overall contributions if causal pathways could be identified between the various predictors as well as with the outcome happiness measure. However, it seems likely to me that average income per capita, social support, freedom to make life choices, and more modern religious values are indeed all independent causes of increased happiness, although there may well be more complex causal pathways (such as higher income giving more freedom to make life choices, as would more modern religious values) and also that some of these variables may be acting in part as markers for other unmeasured causes of happiness.

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