Are the Lancet authorship criteria adequate?

In May, the Lancet published a peer-reviewed study claiming to analyse data from nearly 15,000 patients who had received the drug hydroxychloroquine for treatment of Covid-19. The study concluded that patients who received the drug were dying at a higher rate than 81,000 controls who did not receive the drug and this led to the WHO suspending its clinical trials of the drug.

The paper was later retracted by its academic authors after they were unable to gain access to the study dataset, supposedly held by a private company, Surgisphere. There are doubts whether this dataset even exists, given it is not consistent with publicly available data and the hospitals from which the data supposedly came say they know nothing about it.

The Lancet recently updated its authorship policy to prevent this situation occurring again, so that it now requires that for papers which are the result of an academic and commercial partnership, all authors will be asked to sign author statements to confirm that they had full access to the data reported in their paper.  Here is the relevant paragraph of the Lancet announcement of this change.

“Changes to the signed declarations by authors in the author statements form will require that more than one author has directly accessed and verified the data reported in the manuscript. We will require that the authors who have accessed and verified underlying data are named in the contributors’ statement. For research Articles that are the result of an academic and commercial partnership, one of the authors named as having accessed and verified data must be from the academic team. In addition, all authors will be asked to sign the author statements form to confirm they had full access to the data reported in their Article, and accept responsibility for submitting the Article for publication.” Lancet, Learning from a Retraction, 17 Sep 2020

This issue of access to the study data was exactly the issue which led to the withdrawal of WHO and several external academic members from the Core Team of the IHME Global Burden of Disease Study (GBD) in 2012. The WHO subsequently declined to endorse the results of the first GBD 2010 results, published in the Lancet in December 2012, as it was unable to access the data used.

As the head of the unit responsible for WHO global health statistics, I was closely involved in this issue of WHO engagement with GBD work and the IHME, funded by the Bill and Melinda Gates Foundation to carry out Global Burden of Disease work, and recently published a paper summarizing WHO work on global burden of disease statistics over the last thirty years (see also earlier post here).

Subsequent updates of the IHME GBD have been published in the Lancet, with author lists including many hundreds of collaborators. For example, the most recent GBD cause of death paper has 1,020 authors. On advice from IHME, I signed up as a collaborator in order to obtain regular updates on IHME GBD activity and prior to submission of GBD papers to the Lancet would receive emails requesting me to state whether I wished to be listed as an author. From discussion  with various academics who did sign up to be listed as author, I know that the IHME authorship criteria did not require  that the collaborator had necessarily made a substantive contribution to the work, or even necessarily made a comment on the draft paper that was acted on, let alone had access to all the study data. There are clear incentives for “collaborators” to sign up as authors, particularly authors without an already strong publication record, as they receive authorship for highly cited papers in one of the highest impact medical journals. In turn, they lend the IHME GBD project an image of wide collaboration, particularly with country-level academics and experts, which may give the impression that the country-specific data has received scrutiny from in-country experts.

The IHME is unusual in not allowing access to data by collaborators. In my time working for WHO, I collaborated with many other academic groups, and never had an academic group refuse access to study data when I requested it as part of a collaborative effort. Will the new authorship policy of the Lancet address this practice?  I doubt it. Although the IHME is essentially a privately-funded research group, it is affiliated with the University of Washington and considered an academic institute.  It is quite unclear why the policy that all authors should have had access to study data would not apply to large multi-organizational studies in general, rather than just those where the study data is held by a commercial organization.

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Religiosity and atheism in 2020

The World Values Survey (WVS) and the European Values Study (EVS) include questions on religious affiliation, religious beliefs and practices, as well as on the influence of religion on attitudes to government, politicians and science [1-4].  These questions played a major role in the analysis of traditional values versus secular-rational values across countries and time (see my previous posts here and here).

This post takes a closer look at what the WVS/EVS data for 110 countries tells us about levels of religiosity and atheism. Quite often, estimates of the prevalence of atheism (the lack of belief in God or gods) have been based on either self-report of no religious affiliation, or self-identification as “non-religious” or “confirmed atheist” (both types of questions are included in the WVS/EVS). The WVS/EVS also includes a direct question on belief in God (Yes/No) and questions on frequency and type of religious practices. I’ve used these to estimate the prevalence of atheism for all countries of the world in 2020 using multiple questions to develop a more rigorous classification of religiosity and atheism. This is a work in progress and these are provisional results which may be further revised.

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

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.  Excluding people who state an affiliation with a non-theist religion (a very small proportion of respondents in most countries outside parts of Asia), the categories are defined as follows:

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

Non-religious:  A non-religious person who believes in God, but rates the importance of God as 8-10 at the not important end of a 10-point scale.

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 1-7.

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 1-7.

Modified versions of these definitions are used for persons with non-theist religion and in the predominantly Buddhist countries (see Endnote a). A larger scale map of variations in atheism prevalence for European countries is shown in Endnote b.

For the 104 countries with survey data for years 2005 or later (Endnote c), prevalences of the four religiosity categories across survey waves were projected forward to 2020 (Endnote d). Of the 104 countries, 76 have data for year 2017 or later, and another 17 have latest year of data on or after 2010. For the maps, I have also imputed religiosity for 56 countries not included in the WVS/EVS (Endnote e).

The following figure shows examples of these trends for four countries. Data for the USA suggest that the prevalence of atheism has increased rapidly in the 21st century from around 6% to 23% and the prevalence of non-religious people has also increased from less than 2% to 12%.  The prevalence of irreligion (atheists and non-religious) has increased by an estimated 30 percentage points over the last two decades, the largest increase of any country included in this analysis. Closely behind is South Korea, followed by Iceland, the Netherlands, Spain, Finland, Norway, the UK and Australia (15 percentage point increase). The overall prevalence of irreligion is higher in these other countries, but the USA is catching up. The plot for Russia illustrates the pattern of decline in atheism and the increase in practicing and non-practicing religious people that is typical of former Soviet-bloc communist countries. China has the largest prevalence of atheism in the world at an estimated 78% but as the plot shows there has been a substantial shift from the non-religious category to the atheist category and its difficult to interpret this given the lack of fit of the WVS questions for the non-theist religions and practices that are most common in China.

The following plot shows the estimated prevalence of all religiosity categories in 2020 for countries ranked in descending level of irreligion.

The next figure shows the estimated average prevalence of atheism and religiosity for Welzel’s culture zones defined in my previous posts. As Australia and New Zealand have religiosity distributions very similar to those of northern Europe, they have been included with the Reformed West and the New West relabeled as North America.

The Orthodox East includes former Soviet-bloc countries that are predominantly Orthodox Christian or Muslim. These countries are characterized by a much higher prevalence of non-practicing religious people than other culture zones. 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) s 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.

The following figure plots the reported religious affiliation of religious people by culture zone, using 2020 estimated prevalence of religious people together with the distribution of religious affiliation reported in the most recent wave survey for people classified as religious.

It is difficult to assess time trends for religiosity as its very difficult to distinguish variations in measurement error across surveys from real trends for some countries. In a following post, I will examine time trends and the question of whether irreligion is increasing or decreasing for the world and for various culture zones.


a. Defining religiosity and atheism for non-theist religions

The WVS/EVS questions, and those of other similar survey programs, are biased towards monotheistic religions and do not adequately take the non-theistic religions into account (these include Buddhism, Confucianism, Taoism, Jainism). Thus “confirmed atheist” is a separate category to “religious person” although around half of practicing Buddhists in the surveys said they do not believe in God. For respondents who report affiliation with a non-theistic religion, the religiosity categories are modified as described in footnote b. A more accurate label for the “Atheist” category that takes non-theist religious people  into account would be “Non-religious atheist”.

Atheist: A confirmed atheist”, practicing once a year or less and considers religion to be “not at all important”, OR a religious or non-religious person who does not believe in God, is not practicing and considers religion to be “not at all important”

Non-religious: A not religious person, who believes in God but considers religion not at all important, and is not practicing.

Non-practicing religious person: A religious person who is non-practicing OR a non-religious person who practices less than once a month and more than once a year, and rates the importance of religion greater than “Not at all”

Practicing religious person: A religious person who is practicing OR a not religious person or confirmed atheist who is practicing and considers religion to be more than “Not at all important”

This is not entirely adequate to deal with the non-theist issue, as adherents of non-theist and folk religions may often interpret the question on religious affiliation as referring to the major established religions, and respond “None”.  This is only an important issue for Sinic countries and other countries in Asia where Buddhism is a major religion. The religiosity results for these countries should be treated with caution, they vary substantially across the countries and across waves and time, and its likely that there are considerable measurement issues resulting from variable interpretation of questions framed largely for theist religious beliefs.

b. The prevalence of atheism in Eurasian countries, 2020

c. Countries without data for years 2005 or later

Of the 110 countries for which data is included in the WVS or EVS, six have been excluded as they have no data for any years from 2005 onwards. These are the Dominican Republic, El Salvador, Saudi Arabia, Tanzania, Uganda and Venezuela.

d. Projection of religiosity prevalences to year 2020

Trends in prevalence of atheism and other religiosity categories were projected forward to year 2000 using logistic regression of country-specific prevalences against time with weights decreasing at 5% per year for years prior to 2020 (in order to give higher priority to recent trend).  Projected trends are sensitive to variations across waves due to survey and sampling variations and outlier trends were adjusted towards the 75th percentile trend for each culture zone.

e. 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 [6-8] 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. Prevalences for religiosity category for Israel were imputed from a Pew Research Center survey which also included similar questions on religiosity [9].

For the remaining 43 countries with population greater than 1 million in 2020 (28 of these in Africa), religiosity was imputed using culture-zone-specific regressions of the WVS/EVS religiosity prevalences against Pew Research Center country-specific estimates for the year 2020 of religious affiliation distributions for 8 religious categories, including “other” and “none” [10].


  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: 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:].
  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,
  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. Pew Research Center, Oct 29, 2018. Eastern and Western Europeans Differ on Importance of Religion, Views of Minorities, and Key Social Issues.
  6. WIN Gallup International. Global Index of Religiosity And Atheism – 2012. WIN-Gallup International. 27 July 2012.
  7. WIN Gallup International. Losing Our Religion: Two Thirds of People Still Claim to Be Religious. WIN/Gallup International. 13 April 2015.
  8. WIN Gallup International. Religion Prevails in the World. WIN/Gallup International. 10 April 2017.
  9. Pew Research Center, March 8 2016. Israel’s Religiously Divided Society.
  10. Pew Research Center, April 2 2015. Religious Composition by Country, 2010-2050.
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The second wave of the Covid-19 pandemic

Today, I took another look at the second wave of the Covid-19 pandemic using data on confirmed cases downloaded from the Johns Hopkins University CCSE data repository. This post focuses mostly on western European countries, with a few others like USA and Australia included. The plots below show four countries where the second wave has peaked and is coming down. Australia is somewhat unique in that its second wave peaked considerably higher than the first. Croatia and likely Spain will join that club.

The next two plots show 8 European countries in which a second wave has started and average new cases per day are growing. I have calculated average growth rates per day for the last 14 days and from that estimated Reff, the effective reproduction rate of the virus: the average number of secondary infections resulting from a single infection. Values over 1.0 mean the number of new cases will grow over time, values under 1.0 mean that new cases per day will decline. I calculated Reff using a simple approximate method [1], with an average serial interval between successive infections assumed to be 4.8 days [2,3].

New cases are growing in Switzerland, but at a relatively slow rate with Reff = 1.12. The current new case rate is one quarter of that at the peak of the first wave, and if new cases continue to grow at the same rate, Switzerland will reach the same level as the peak of the first wave in 55 days. With children having just gone back to school, its possible the effective reproduction rate will increase. Perhaps more alarmingly, the Reff is substantially higher in all four countries neighboring Switzerland: France, Germany, Austria and Italy. The UK has imposed mandatory quarantine for travellers from Switzerland, who join those from France and Spain. The UK has the slowest growth rate of the countries in this group (among those I have looked at) and Croatia the highest, having already substantially exceeded its first wave peak more than threefold.

The last figure shows four countries who have had an extended first wave, now declining, and may or may not yet have a second wave. It is no coincidence that three of the four countries in this category  – Brazil, Russia and the USA – are led by far-right nationalists who use technology as a tool for disinformation, demonize minorities and ignore climate change.  Sweden was one of the few European countries not to impose a compulsory lockdown and has had a much more extended epidemic as a result. It did ban gatherings of more than 50 people, but other measures were voluntary. Though I saw a post from a Swedish man recently, saying he was having a lot of trouble coping with the social distancing of 2 metres and asking how soon he could go back to his usual social distance of 5 metres.

I haven’t classified the USA as having two waves, because the first small plateau resulted from the peaking and decline of the epidemic in New York largely, while transmission continued to increase rapidly elsewhere, particularly in the South. I read an article today that described the USA as suffering from twin pandemics: covid-19 and stupidity.

Russia’s numbers are likely considerably under-reported. And all of these plots are for cases identified by testing. Trends may be affected by changes in testing rates, particularly for the earlier period of the first wave. And actual numbers of infections in the population are almost certainly much higher, likely by factors around 10-fold (it is 9-fold in Geneva).

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WHO and global health statistics

My paper “History of global burden of disease assessment at the World Health Organization”  has just been published in Archives of Public Health. It reviews WHO work on Global Burden of Disease over the last 20 years and the challenges facing it. While new global health actors and funders have arisen in recent decades, funding to carry out WHO’s expanding mandate has declined. For many Member States, the WHO still retains a unique mandate and accountability for global health statistics and a moral authority as a setter for norms and standards that is not available to academic or NGO groups.  It is unclear whether WHO will continue to have the resources or will to carry out its own mandate in a world which at present seems to be increasingly turning away from multilateral global institutions, rules and norms.

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The climate crisis has not gone away

The return of the summer wildfires in California reminds us that the climate crisis has not gone away just because there is a pandemic. The USA has recently recorded the hottest ever measured temperature of 54.1 C (130 F) in Death Valley and 2020 is on track to be the hottest year on record for global average temperatures. A recent paper (Slater et al 2020) estimated total ice loss over the last 25 years at 28 trillion tonnes, which matches the worst-case-scenario predictions of the Intergovernmental Panel on Climate Change (IPCC).

And yet a substantial proportion of people in many countries deny the science, not only for human-caused global warming, but for coronavirus and vaccines. A recently published reconstruction of the earth’s global climate over the last 12,000 years illustrates starkly how different the rising global temperatures of the last decades have been from natural cycles. The figure below was prepared by Alexander Radtke (@alxrdk) with captions added by using temperature data for the 12k->2k years ago from, for the 2k->170 years ago data: and for 1850-2020 it is an equal blend of HadCRUT 4, GISTEMP, Berkeley Earth, Cowtan and Way and NOAA datasets.

Climate change denialists like to argue that climate changes naturally. It does, but the graph makes clear how different the rate of change has been since the mid-20th century, and by hom much it exceeds the limits of natural climate change. The climate fluctuates naturally in a roughly 10,000 year cycle and its clear from the graph that we should still be in a cold period, not the warmest by far in the last 12,000 years.

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Trends in cultural values across 105 countries: 1980 to 2020

In a previous post, I examined variations in cultural values across 105 countries in year 2019 using data from all 7 waves of the World Values Survey (WVS)and all 5 waves of the European Values Survey (EVS) [1-4]. I developed an improved method for estimating latent variables f1 and f2 for two dimensions of human values: a traditional values (parents,state, religion) versus secular values dimension and a survival values versus emancipative values dimension. These two dimensions were defined for earlier waves of the WVS and EVS by Inglehart and Welzel using factor analysis.

The regression procedure described in the previous post (for projections to 2019 for all countries) was also used to interpolate a complete time series for each country from 1980 to 2020. For some countries where f1 and f2 varied substantially across waves, the time trends were not smoothly varying. I thus applied a Loess regression with bandwidth 0.5 to smooth trajectories to a moderate degree. Using 10 “culture zones” defined by Welzel to group countries, I computed population-weighted averages of f1 and f2 for the culture zones and these are shown in the Figure below.

Trends in cultural values from 1980 to 2020 for 10 culture zones (see below).

The dots on the trajectories denote values for years 1980, 1990, 2000, 2010, 2020. For all trajectories, except for the Islamic East, the leftmost dot denotes 1980. For the Islamic East, the lower of the two rightmost dots is for 1980 and the upper for 2020. The culture zones are defined as follows:

Reformed West — Western European societies strongly affected by the Reformation;
New West — English-speaking countries (UK, Ireland and former overseas colonies);
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 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;
Latin America — Central and South America and the Caribbean;
Sub-Saharan Africa — African countries south of the Sahara.

Some of the kinks and twists in these trajectories relate to variation in f1 and f2 across waves for some individual countries.  The following graphs illustrate the cross-wave trends for selected countries.

Trends in values of f1 and f2 across waves (indicated by the dots) for selected countries, 1980-2020.

There are some countries where values are reasonably smoothly evolving apart from a single wave (eg. Hungary) and others where value vary substantially up and down across waves (eg. China, Turkey).  It is possible some of these represent real changes in cultural values over time, but also likely that in many cases it reflects variations in survey quality and representativeness. To get a better idea of the extent of this issue, I summarized the net deviations of intermediate waves values of f1, f2 from the values obtained by carrying out the covariate regression described in the previous post and adjusting its predictions so they ran through the first and last wave values for f1 and f2.

Net deviations were calculated in f1-f2 space as the distance between the survey wave values and the regression-based values. The root mean square deviation δ for all intermediate waves was calculated by country. The following table shows the percent of countries within each culture zone with δ > 0.12 (a value one standard deviation above the mean δ for all countries).

It is clear that there are much larger deviations from smoothly evolving time trajectories in developing regions (except the Sinic East) than in high income regions with the exception of the Old West. It seems to me highly probable that most of the larger deviations across waves are likely due to variations in survey implementation, sampling and quality. Various factors such as lack of good population data to establish sampling frames, language translation issues, interviewer training, difficulties in finding funding, and security issues in some countries, can all contribute to systematic differences across surveys.

The following graph bypasses these issues of comparability across waves to show the net trend in culture values from 1980 to 2020 as straight lines joining those two points.

Net trends in vulture variables f1 and f2 from 1980 to 2020 for 10 culture zones.

This graphs shows a very clear contrast between the evolution of cultural values for the West plus Latin America and the other culture zones. The West regions and Latin America have all moved quite strongly towards more emancipative values and also away from traditional values to more secular-rational values. In contrast, while the other regions have also moved somewhat rightward in emancipative values, they have moved downwards away from secular-rational values towards to more traditional values. The Islamic East is the major exception with very little change in either dimension.

In his 1996 book, The Clash of Civilizations and the Remaking of the World Order, Samuel Huntington described a global revival of religion in the second half of the twentieth century (La Revenche de Dieu) claiming that the trend towards secularization went into reverse in the 1970s in every region of the world [5]. At least to the extent the second latent dimension (or indeed Inglehart and Welzel’s traditional-secular factor) measure the degree of secularization, the trends from 1980 to the present do not fit with his conclusion. He is correct in identifying a return to religion in the former Soviet countries with predominantly Orthodox Christian or Islamic religious tradition. He also points to an increasingly Hindu orientation of India, whereas the story from the WVS-EVS data is somewhat more complex with a fairly stable level of religiosity (f2) from 1990 to 2000 associated with an increasing level of emancipative values, but from 2000 to 2020 an increasing degree of religiosity associated with a declining level of emancipative values. This correlates broadly with the rise of Hindu extremism and the election of the BJP Party with its Hindu nationalist orientation in 2014.

As a result of this initial analysis, I plan to revisit the conceptualization of latent culture variables in order to develop clearer operationalizations of important culture dimensions such as religiosity/secularism that make better use of more of the data collected in the WVS. I will also look at developing better methods of extrapolating and interpolating time series for countries with considerable deviations across waves, either by leaving out a single deviant wave, or perhaps by more broadly averaging across the waves. And perhaps, to revisit the grouping of countries into culture zones.


  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: 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:].
  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,
  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. Samuel Huntington. The Clash of Civilizations and the Remaking of the World Order. London: Simon and Schuster 1996.


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An update on coronavirus in Geneva and Victoria

Switzerland currently bans entry from countries with more than 60 new infections per 100,000 population in the last 14 days. Geneva passed that threshold on 27th July, and passed 100 new cases per 100,000 on 1 August. So if Geneva was a separate country, residents would be forced to quarantine upon entering the rest of Switzerland. Or if they were not considered to be Swiss citizens, they would not be able to enter at all. Belgium has banned travel to and from Geneva, along with Valais and Vaud, as a result of the rising infections.

The overall new infection rate for the last 14 days is 18 per 100,000 for the rest of Switzerland, excluding Geneva, and 23 per 100,000 for Switzerland as a whole (still well below the Swiss government’s high risk threshold). Why is it so much higher in Geneva?  As a Swiss epidemiologist explained: “population density, airport and border with France”. Geneva has the second highest population density after Basel, the second-busiest airport, and many cross-border workers (325,000 crossed the border into Geneva each day in 2019). However, the increase in cases in Geneva has as only reached 20% of the peak in the first wave.  With any luck, it will not reach the level of the first peak as much more has been learnt about how to use social distancing and masks to reduce the effective reproduction rate of the virus since then.

The state of Victoria in Australia is also experiencing a second wave of infections, much stronger than the first wave. As of Tuesday 4 August, Victoria experienced a total of 6101 new infections in the last 14 days, giving it a rate of 94 per 100,000 population, almost as high as Geneva. The new infection rate for the rest of Australia is very low at 2.4 per 100,000 in the last 14 days.

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Variations in cultural values across 105 countries in 2019

Working for the World Health Organization over the last 20 years and dealing with national health authorities, I’ve been quite fascinated by the variations across cultures and countries in values and beliefs, and the huge influence they have on willingness to accept or implement evidence-based public health interventions. More broadly, it seems fairly clear that people’s beliefs play a key role in economic development, the emergence and flourishing of democratic institutions, the rise of gender equality, and the extent to which societies have effective government.

So I was particularly interested to learn recently that data from the most recent wave of the World Values Survey was being released in July. This wave has been interrupted by the coronavirus pandemic, but the data for 48 countries whose survey were completed by early 2020 has recently been released on the WVS website.

Data from previous waves of the World Values Survey were used by political scientists Ronald Inglehart and Christian Welzel to identify two major dimensions of cross cultural variation across the world. They refer to these as Traditional values versus Secular-rational values and Survival values versus Self-expression values. The map below uses data from the 6th wave of the WVS carried out during 2010-2014 to plot countries according to their factor values for these two dimension. Moving upwards on this map indicates increasing secular values and moving to the right increasing self-expression values.

Inglehart-Welzel Culture Map 2010-2014. Source:

Traditional values emphasize the importance of religion, parent-child ties, deference to authority and traditional family values. Secular-rational values place less emphasis on religion, traditional family values and authority.

Survival values place emphasis on economic and physical security. It is linked with a relatively ethnocentric outlook and low levels of trust and tolerance. Self-expression values are associated with gender equality, relative acceptance of divorce, abortion, and homosexuality and rising demands for participation in decision-making in economic and political life.

Inglehart and Welzel have used factor analysis to extract these two dimensions from WVS data for 12 questions. The final factors are rotated, and all 12 questions contribute to the factor scores, which are correlated. I was interested to examine their approach more closely and attempt to use it, or a related approach, for an analysis of all the WVS data, including the recent release.

There have been seven waves of the World Values Survey, the first in 1980-1982 and the seventh underway since 2017. Additionally, the recent 2017 wave of the European Values Survey includes many of the same items as the WVS and has data for 30 countries. Together the recent wave of the WVS+EVS include data for 77 countries.

I downloaded all unit record data for the WVS from their website [1-4]. In total, the World Values Surveys plus the European Values Surveys now includes data for 117 countries or territories and over 638,000 respondents.  The dates of the waves, and the number of respondents by wave and survey type are shown in the table below.

I first set out to replicate the Inglehart-Welzel analysis of the two cultural dimensions based on data for 12 questions. I immediately ran into problems as the various documentation on the website has several differing lists of variables included in the factor analysis, and trying all three, none replicated the values for the factors in the data files. And indeed, there were at least three different sets of factor values in various places. I think what has happened is that the Welzel analysis and summary indicators have evolved over time.

I did my best to match his description of the analysis of waves 1-6 using factor analysis, and my resulting factor values for the wave 6 countries correlate highly with Welzel’s secular values index (svi) and emancipative values index (evi) with correlation coefficients of 0.92 and 0.97 respectively. See scatterplots below. So my factor analysis gives somewhat different results but very highly correlated with Welzel values. Its quite possible that the factor procedure I am using in stata does not exactly replicate the factor analysis or scoring produced by Welzel using SPSS.

In his recent book, Freedom Rising [6], Welzel mostly abandons factor analysis in favour of explicitly constructed indicators based on conceptual alignment of variables with the target latent construct. However, as with the factor analysis, he is assigning equally spaced scores between 0 (lowest) and 1 (highest) for categorical response categories to questions in the WVS/EVS. So for example,

How important is religion in your life?       Questionnaire Responses     Welzel rescale
Very important                     0
Rather important                  0.333
Not very important               0.667
Not at all important              1

When jobs scarce: men should have more right to job

Agree                                      0
Neither                                   0
Disagree                                 1

This a priori forces the factor analysis to treat all the categories for each question as equally spaced on the underlying factor even though responses for different questions may have very different means and be skewed across the response categories quite differently. In other words, the end categories for questions may map to very different values on an underlying latent construct, but are all forced to values 0 and 1.

A recent paper has highlighted the dangers of using factor analysis on ordered-categorical survey data (e.g., Likert items). The authors conclude that the common practice of factor analysing ordered-categorical data such as Likert items leads to very high risks of incorrect and misleading diagnoses of the latent structure of the items, with dire consequences for conceptualisation, replication and comparability, and evidence-based interventions, behaviour and policy [5]. They advise analysts to investigate latent structure with IRT models instead.  When the latent trait is continuous, but the data is a set of ordered categorical responses, the appropriate approach is to use Item Response Theory (IRT) models; this is the most common term in applications in educational or psychological testing, where these models are very widely used.

I have implemented a form of IRT analysis using the stata procedure gsem, for structural equation modelling, with ordered probit regression. For f1, the first latent variable (survival-emancipative dimension), I used data for three questions of gender equality (jobs, politics, education) and three questions on acceptance of homosexuality, abortion, and divorce. I estimated the second latent variable (f2 — traditional-secular dimension) using data for three questions on sources of authority (nation pride, government, parents) and three questions on religion (importance, belief, practice

Country-wave average values for f1 and f2 were computed taking sampling weights into account. Because the year of survey varied within waves, and also not all countries were in each wave, I used a projection procedure to estimate culture values for year 2019 by projecting forward from the most recent country-year available since 2005. Values were regressed on time and the following covariates: GDP per capita (PPP dollars), average years of education (adults 25+), general fertility index, UNDP gender inequity index and regional indicator variables, with random effects at country level. The following map shows the resulting estimated cultural latent variables f1 and f2 for 2019.

Because there are 105 countries displayed on this map, it becomes harder to draw nice coherent cultural groupings as in the Inglehart-Welzel map shown before. I’ve also adopted and adapted the 10 cultural groupings used by Welzel in his more recent book [6], into 8 groupings as follows:

New West and West —  Western Europe and overseas offshoots of Western Europe

Returned West —  Catholic and Protestant parts of post-communist Europe  returning to the EU

Orthodox —  Christian Orthodox or Islamic parts of the post-communist world, mostly parts of former USSR

South Asia —  Parts of South Asia under the historic influence of Indian culture

South East Asia —  Parts of South East Asia excluding those under historic influence of Chinese culture

Sinic East —  Parts of East Asia under the historic influence of Chinese culture

Latin America —  Central and South America and the Caribbean

African-Islamic —  African countries south of the Sahara, together with regions of the Islamic world that have been parts of the Arab/Caliphate, Persian and Ottoman empires

The general topology of this map is similar to the Inglehart-Welzel map, with Scandinavian countries to the top right, Sinic countries to the top left, and African-Islamic countries to the bottom left. However, there are some considerable differences in the locations of countries relative to each other, and the positions of some individual countries – no doubt reflecting the difference between arbitrary metrization of Likert scales versus estimation of latent values using IRT methods.

I have also made a more recent post which examines regional and country-specific trajectories in cultural values over the forty-year period 1980 to 2020.  A more interesting issue, and one that I may spend some time on, is to develop a better set of parsimonious values latent variables from the rich data of the World and European Values Survey.  At a time when democratic values, free speech and human rights seem to be under increasing attack in all regions, it would be nice to develop better tools to describe how these are valued across the world.


  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: 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:].
  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,
  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. van der Eijk C, Rose J (2015) Risky Business: Factor Analysis of Survey Data – Assessing the Probability of Incorrect Dimensionalisation. PLOS ONE 10(3): e0118900.
  6. Welzel C. Freedom Rising. Human Empowerment and the. Quest for Emancipation. 2013. Cambridge: Cambridge University Press.
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Drug overdose deaths – another exceptional US epidemic

Earlier this week, CDC released provisional figures for drug overdose deaths in the USA in 2019. They estimate almost 72,000 deaths, the highest annual number yet seen. Initially driven by prescription opioid painkillers, users migrated first to heroin and then to fentanyl, which is cheaper (much of it illicitly made). Synthetic opioids accounted for an estimated 36,500 deaths.

The WHO Global Health Estimates  included estimates of direct deaths due to drug use disorders, including accidental poisoning by drugs and overdose deaths, as well as deaths coded to drug dependence (see Technical Note for methods and data).   Last year I updated these to year 2017 using the latest death registration data from the WHO Mortality Database, as well as recent reports for selected countries, and estimates of drug deaths from the IHME GBD 2017. It was apparent at the time that the USA was a massive outlier globally, responsible for a disproportionate proportion of global overdose deaths. I’ve increasingly noticed that the USA is an outlier from other developed countries for a number of health and social indicators, most recently the coronavirus pandemic, police killings, gun homicides and incarceration rates. I was interested to see how the USA compared with other countries for drug-related deaths, given its enormous investment in and the human cost of the “war on drugs”. I’ve done a quick update as described below, but I am fairly sure it gives a reasonably good general picture, not too different from the earlier 2017 update for WHO or the IHME GBD 2017 estimates.

I estimate that globally, drug use disorders caused close to 181,000 deaths in 2019, of which 40% were in the USA. The global average death rate for drug use disorders was 15 per million population, compared with 219 per million for the USA. In other words, the USA rate is 15 times higher than the global average.

I have projected recent trends forward to 2019 and updated drug death estimates for the USA, Canada, Australia and China based on recent reports. The analysis of death registration data is complicated by the need to distribute the accidental poisoning category for “other and unspecified chemicals and noxious substances” (X49) to the specific categories for alcohol and drug use disorders (opioids, cocaine, amphetamines, cannabis and “other drugs”) and to accidental poisoning (non-drug and non-alcohol). Additionally, there is a category F19 in the mental health chapter for “multiple drug use and unspecified drug use disorders” which is used to code deaths in some countries and also must be redistributed appropriately. So there is some uncertainty associated with the estimates for countries with death registration data, and even more for countries without good data. The GHE includes cannabis use disorders as a category, but I have not shown it in the graphs here as there are no overdose deaths.

In terms of the overall drug disorder death rate in 2019, the four leading countries were the USA (219 per million), Canada (121), Australia (71) and Russia (63). Opioids are responsible for many more deaths than other types of drugs.  The following graph shows the drug use death rates for the 15 leading countries in 2019, with the contributions of opioids, cocaine, amphetamines and other drugs.

The following graph shows time trends for opioid death rates in selected countries from 2000 to 2019.

The USA and Canada stand out as having substantially increasing opioid overdose death rates over the last decade. In fact, the opioid death rate for the USA has been growing exponentially since the 1980s until 2017, when there was a 5% drop for 2018, followed by a rise to slightly higher than the 2017 peak in 2019.  Its still too early to say whether the US drug death rates are stabilizing or will start to fall. A recent paper by Jalal et al (Science 2018) shows that the US drug overdose death rate fits an exponential curve from 1980 to 2016 resulting from multiple subepidemics with changing patterns of overdose deaths by age distribution (see graph below).

Source: Jalal et al. Changing dynamics of the drug overdose epidemic in the United States from 1979 through 2016. Science. 2018 

The following regional graphs highlight how different the situation in North America is to the rest of the world.

In this post, I have focused on direct deaths associated with drug use. However, drug use also raises risks for road injury deaths and suicide, and injecting drug users also face increased risks of death from HIV, hepatitis B and hepatitis C. Together, direct and indirect deaths associated with drug use account for close to half a million deaths annually in the world, and the great majority of these (74%) are associated with opioid use [conference-presentations #131].

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Covid-19: which countries are succeeding and not succeeding?

There is now sufficient data on social distancing, lockdown, and other measures that researchers are starting to assess the effectiveness of these measures. The July 4th edition of The Economist has an article summarizing what is becoming clear.  Extensive testing and tracing of contacts was carried out by the Asian countries earliest affected, China and South Korea. China also locked down Wuhan and largely succeeded to confining the epidemic there. Italy was the earliest European country affected, and it locked down Lombardy on February 22, and succeeded in largely confining the epidemic to the north of the country. Some European countries, Australia and New Zealand locked down hard and very early while there were still very few deaths.  These were the most successful. As were Switzerland, Germany and Austria which locked down while they still had fewer than 60 deaths. Other European countries that waited till they had hundreds of deaths before locking down, such as Britain, were much less successful and the virus spread throughout the country.

The Imperial College modelling group have estimated that Europe’s lockdown policies prevented 3 million deaths by May 4th. The first wave in the USA, which primarily hit New York and New Jersey, was fairly successfully contained as in the European countries, but other states which had not closed their borders or successfully reduced the spread of the virus, opened prematurely and the US is now facing a runaway growth in new cases and deaths (see my previous post).

The country plots below show that it only takes about 5-7 weeks of strong interventions to get rid of the majority of cases. Taking half-measures as the US has done does not work. Particularly when the President repeatedly contradicts the advice of the public health experts and refuses to provide national guidance.

A couple of months ago, I was producing country plots of time trends in cases and deaths to see how the epidemic was evolving across the world. Since then, a very nice site has been doing these plots and updating them almost daily. The site groups countries into three groups: (1) those that are beating Covid-19, (2) those that are nearly there, and  (3) those that need to take action. The plots show new cases per day averaged over the last week and each graph has been normalized so all the peaks are the same height. This better shows the trend in the data and the degree to which the epidemic is being controlled, though not the relative magnitudes of the epidemics in different countries.

I’ve selected the plots for some countries of interest to me, to show differences in the impact of control measures in the three groups of countries, using data up to 4 July. The trends in some countries in the second group look quite similar to those in the first. The reason they are still in the second group is that the total number of new cases per day is higher that in the first group, and that can allow the epidemic to still escape control fairly easily.

Some countries that are beating Covid-19

Some countries that are nearly there

Countries that need to take more action

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