Categories
Medicine

Why tracking COVID-19 hospitalization data makes sense

Every day, I post the updated Danish COVID-19 hospitalization data on Twitter and Facebook The latest version is always available at https://rpubs.com/EmilOWK/COVID19_Denmark. The raw data are here. The figures for March 26th look like this:

Why track hospitalizations instead of cases? Because the number of true cases is practically unknown due to lack of large-scale random testing (see prior post), but we can be confident that the number of hospitalizations is roughly correct since they test anyone with broadly matching symptoms. In other words, this tracking is based on this model of the COVID-19 cases:

We don’t know how many mild cases there are, but we do know the areas under the right tail by different cutoffs. Deaths are also decent to track, but since they are few in number, they provide unreliable statistics, and are quite delayed since the disease history takes a while to unfold. It should be said that the hospitalization approach is based on the assumption that the thresholds don’t change, which they could if the healthcare system gets overloaded to the point where they refuse to hospitalize anyone except the most severe cases, or refuse to hospitalize people who aren’t likely savable. Thus, one can probably use this approach to track the epidemic in Denmark, but it would be unwise to use it on Lombardian data.

Categories
intelligence / IQ / cognitive ability

Danish national IQ

Best estimate of Danish-Scandinavian IQ based on recent, large samples is ~103 relative to UK norms.

What is the Danish national IQ? If we look at Lynn and Vanhanen 2012, it is estimated at 97.2. If we look at Becker’s re-analysis, it’s given at 98.45 (version 1.2). However, this is based on only two studies, one of which is quite questionable (Buj 1981). Rindermann 2007 notes:

Only one international comparison study has been carried out using a uniform intelligence test measured over a short time period under more or less standardised conditions. This is the study with the Cattell Culture Fair Test 3 (CFT3) non-verbal scale (Buj, 1981), probably conducted in the 1970s in 21 European countries and Ghana. The tests were administered in capital cities or in the biggest town in each country. But researchers believe the data from this study are of dubious quality: nobody knows the author; he did not work at a university; the way he collected so much data is unknown; the description of samples and testing procedure is scanty; and only one single two-page-long publication exists.

Both are old: 1981 (collected in 1970s?), and 1968 (Vejleskov). Can it really be that there is no more data for Denmark? The given IQs are suspiciously low from the perspective of both the climatic models and in terms of development, where Denmark is almost always in top 10 in the world. One would expect something like 100-103, depending on how much success you want to attribute to special Scandinavian autism-altruism personality combo.

I know of at least one missing study because I was a co-author:

The Scandinavian (Sweden, Norway, Denmark) standardization of the WAIS IV on the matrices subtest is presented. The score of Scandinavia on the WAIS IV matrices is higher than Finland (weighted means 105.1 and 103.1, relative to a US norm of 100). However, the difference is not statistically significant. Finland scores higher than Scandinavia on PISA Creative Problem Solving 2012. We meta-analyze the data from both studies and estimate the Scandinavian Matrices IQ at 99.1 and the Finnish at 102.3 or 102.4 (based on US norms) depending on which sample sizes are used. Finally, we discuss theories that attempt to explain this difference.

We can probably assume equivalence of Scandinavian IQs, yielding an estimate of ~103 on the Greenwich IQ scale. US is 2 points below UK in the usual norms. Note though that this test was restricted to Danish speaking population (meaning Danes + second generation foreigners) and filtered out people with mental issues, causing some inflation of the IQ relative to true national average.

I also know of another recent comparison based on large-scale survey data:

The Flynn effect describes the observed improvement in cognitive performance over time among individuals of the same age. We examine if the Flynn effect varies across three European regions and whether there are sex differences in the extent of improvement over time. Using SHARE-data, with a study population of 34,300 non-institutionalized individuals, aged between 50 and 84 years, we find that the Flynn effect is larger in regions which experienced the most rapid pace of development over time (Southern Europe), than in regions with relatively higher levels of development but less change over time (Central and Northern Europe). With respect to sex differences in the Flynn effect, we find that women, on the whole in Europe, have a larger Flynn effect than men. In the regional analyses, non-significant trends indicate that women gain more than men in Northern and Central Europe.

Their table:

Authors note about the tests and standardization:

We investigated two cognitive tasks, assessing episodic and semantic memory, which were administered both in the 1st and the 5th wave. In the episodic memory task, ten words were read out about 5 min prior to asking the respondents to recall as many words as possible. In the category fluency task, assessing semantic memory, participants were asked to name as many animals as possible within one minute. We standardized the scores on both cognitive tasks with mean 100 and SD 15 to enable comparisons between the two measures within our analyses, as well as with broader literature based on IQ scores. Although we refrain from equating our two memory tasks with IQ scores, they could be viewed as assessments of fluid (episodic) and crystallized (semantic) intelligence.

The IQ here seems standardized to the full sample of individuals across waves, hence the approximate mean of 100 across the two waves (4 points of Flynn gains in 8-9 years? Suspicious). In any case, we see that Scandinavian IQ is at the top in both waves, as one would expect. Note that these IQs are not on the Greenwich national scale, but scaled to the rather unhelpful mean and SD of this particular west European mega-population (no Slavs represented). The within country SDs are a bit deflated due to this, but the adjustment would be small. The data then suggests a rather stark gap of ~9 points between Scandinavia and Italy-Spain, whereas other data would indicate perhaps 3-5 points. Perhaps it’s best to disregard the southern data as questionable, and stick to the comparison of the central-northern countries, which reveals a gap of ~3.75 (4, 6, 1, 4) points. We can probably assume central IQ is about the same as UK, hence giving a Scandinavian estimate of ~103.75.

What about the standard IQ test standardization samples? Since the Wechsler tests (WISC, WAIS) have been used in Denmark for decades and they require at least a translation, there should be norms for each version. What does Google Scholar find? In this study, I see references to Danish (1974) and Swedish (1977) standardizations of the WISC. This obscure 1972 study mentions some results from the Danish WISC. This study tested a representative sample of 621 Danish kids using the WISC-3 but no information given about the Danish version. Jensen mentions in an old study (1978) that there is no Danish version of the WAIS-1 test, but gives mean IQ of the non-verbal tests (WAIS non-verbal and Raven’s) as 106.8 and 98.8 (no Flynn correction) for a tiny sample of twins (n = 12). Probably should not be taken too seriously, as twins used to show decreased overall IQ, and the sample is tiny, but it means there’s probably other data for the non-verbal part of the WAIS-1 for Denmark. Actually, this book chapter says the WAIS-1 was translated to Danish in 1958, but apparently norms were never published.

I see a paper in someone digging around for more Scandinavian IQ data.

 

Categories
Differential psychology/psychometrics Immigration

Cognitive ability of Westerners and non-Westerners: data from the Danish military draft

Rnotebook: http://rpubs.com/EmilOWK/immigrant_IQ_Danish_draft

Because I was discussing this dataset with a friend, I decided to reexamine it. Examining this dataset was my first reviewed paper (Kirkegaard 2013). It was manually done in Excel with no statistical guidance. So I had to work out everything myself based on googling around for formulas. My first write-up was not very good, but the reviewers at Mankind Quarterly were very friendly and helpful, and eventually I managed to produce a write-up that’s half-way sensible.

I did not replicate all the analyses in R, just the main gap analysis. Did not find any errors, except possibly in the report the military published. The primary output are these two figures.

The first shows the distribution of scores, converted to the familiar IQ scale based on the Western subsample, with an assumed IQ = 100, SD = 15. Relative to this, the non-Westerns have a mean IQ of 86 and an SD of 17.1. As the paper showed, the gap size and the SD of the non-Western group was predictable from the country of origin composition of 18-19 year old men, which is presumably the population the draftees are sampled from.

The second shows the estimated cutoffs used to classify draftees as unsuitable and less suitable for service. The finding here is typical of those La Griffe du Lion produced, and which was extensively discussed by Jensen’s 1980 book.

Categories
Medicine Politics

Organ donation consent vs. actual rates

There is a famous paper arguing the case for libertarian paternalism by using organ donation consent rates.

Johnson, E. J., & Goldstein, D. (2003). Do defaults save lives?. Science, 302(5649), 1338-1339.

The main result is this:

organ_consent

So having opt-out drastically increases consent rates compared with opt-in. These countries have various other differences between them, but the effect size is huge.

But what about actual donation rates? As it is, there is a pan-Nordic organization for this, Scandiatransplant. They publish their data. So I downloaded the last 10 years worth of data and calculated the actual donation rates per 100k persons. They look like this:

organ_donation_rates

The line varies a lot for Iceland because the population is fairly small (about 300k). We see that the donation dates for Denmark and Sweden are quite comparable despite the huge difference in organ consent rates. So, apparently the bottom line did not change much despite the difference in consent rates. Since people still die on waiting lists (Danish data), there must be some other limiting factor.

Materials

organ_donation.csv data file

R code:

library(pacman)
p_load(readr, plyr, magrittr, ggplot2)

#data
d_organ = read_csv("organ_donation.csv")

#per capita x 100k
d_organ %<>% mutate(Donations_per_100k = (Donations / Population)*100000)

#plot
ggplot(d_organ, aes(Year, Donations_per_100k, color = Country)) +
  geom_line(size = 1) +
  scale_x_continuous(breaks = 2000:3000)
ggsave("organ_donation_rates.png")

 

Categories
Sociology

The performance of immigrants from Yugoslavia in Denmark and Norway

This is actually an older post, but by accident I posted it on the Danish language sister blog.

I don’t know what study that is, however, I do have numbers for the performance of Yugoslavians in Denmark and Norway. There are both numbers for persons from Yugoslavia when that was one legal entity (actually multiple different with the same name) as well as for some of the constituent countries.

First a brief review. Many studies have looked at immigrant performance by macro-origin and recently country of origin. The country of origin studies are more useful because immigrants from e.g. “Asia” (which may or may not include the Muslim countries such as Afghanistan) are not very homogeneous. Cambodians and Chinese are different, but both are East Asians. Afghans are very different, but are sometimes included in the category. Mixing these together in a hodgepodge makes for uninterpretable results, especially when who is included changes over time and from study to study. For instance, whether a country is considered Western may depend on EU membership, which means that lots of non-Western became Western recently.

Because I was unsatisfied with the existing macro-origin studies for Denmark (most only found in Danish, but I guess I should do an English-language review some day) I began carrying out a series of such immigrants by country of origin studies with the underlying goal being to test the spatial transferability hypothesis (Fuerst’s name), which is that 1) when people move, they generally retain their mean levels of psychological traits, 2) and as a consequence, the effects of these traits follow them as well. Selective immigration and emigration makes this more difficult to test.

The two best such studies cover Denmark and Norway. They are the best because they include a large number of countries of origin and have data for many socioeconomic outcome variables. In brief, the S factors were extracted from available information regarding. income, educational attainment, crime, employment and use of social benefits. See the original papers for details (e.g. with regards to imputation).

The correlation between the S scores from DK and NO is .78 [CI .64 to .86], N=55. N’s by country are 70 and 67, DK and NO respectively. The table below shows all the data.

Abbrev. Country S in DK S in NO
AFG Afghanistan -1.38 -1.09
ARG Argentina 0.75
AUS Australia 1.131 1.03
AUT Austria 0.947 1.02
BDI Burundi -0.54
BEL Belgium 1.089 1.16
BGR Bulgaria 0.811 0.17
BIH Bosnia and Herzegovina -0.913 0.49
BRA Brazil 0.457 -0.34
CAN Canada 1.145 1.03
CHE Switzerland 1.119 1.13
CHL Chile 0.279 0.25
CHN China 0.627 0.61
COG Congo Rep. -1.07
COL Colombia 0.26
CZE Czech Republic 0.249 0.43
DEU Germany 0.846 1.04
DNK Denmark 1
DZA Algeria -0.775 -1.52
EGY Egypt Arab Rep. -0.241
ERI Eritrea -0.43
ESP Spain 0.788 0.52
EST Estonia 0.717 0.19
ETH Ethiopia -0.586 -0.16
FIN Finland 0.891 0.78
FRA France 1.098 0.97
GBR United Kingdom 0.848 1.14
GHA Ghana 0.162 0.03
GMB Gambia The -0.84
GRC Greece 0.613 0.61
HRV Croatia -0.12 0.54
HUN Hungary 0.837 0.45
IDN Indonesia 0.126 0.33
IND India 0.528 0.63
IRL Ireland 0.876
IRN Iran Islamic Rep. -0.688 -0.35
IRQ Iraq -1.654 -2.26
ISL Iceland 0.555 0.76
ISR Israel -0.061
ITA Italy 0.775 0.86
JOR Jordan -1.191
JPN Japan 1.018
KEN Kenya 0.088 -0.24
KSV Kosovo -0.43
KWT Kuwait -2.619
LBN Lebanon -2.027 -1.03
LKA Sri Lanka -0.749 -0.14
LTU Lithuania 0.897 -0.08
LVA Latvia 0.685 0.06
MAR Morocco -1.031 -0.63
MKD Macedonia FYR -0.439 -0.19
MMR Myanmar -1.812 -0.27
NGA Nigeria 0.336 -0.53
NLD Netherlands 1.118 1.11
NOR Norway 0.842
NPL Nepal 0.75
PAK Pakistan -0.679 -0.87
PER Peru 0.1
PHL Philippines 0.362 0.58
POL Poland 0.463 -0.02
PRT Portugal 0.631 0.54
PSE West Bank and Gaza -3.8
ROU Romania 0.703 0.31
RUS Russian Federation 0.447 -0.44
SDN Sudan -1.52
SOM Somalia -2.054 -3.06
SRB Serbia -1.931 0.46
SUN USSR 0.166
SVK Slovak Republic 0.42
SWE Sweden 0.766 1.03
SYR Syrian Arab Republic -1.997 -1.62
THA Thailand -0.233 -0.03
TUN Tunisia -0.825
TUR Turkey -1.42 -0.52
TZA Tanzania -0.254
UGA Uganda -0.341
UKR Ukraine 0.686 0.34
USA United States 1.259 0.97
VNM Vietnam -0.582 -0.11
YU2 Former Yugoslavia2 (Found in some Danish sources) -1.611
YUG Former Yugoslavia -1.247
ZAF South Africa 0.731

 

I have marked the Yugoslavian countries in italics above. The table below shows the Yugoslavian subset table:

Abbrev. Country S in DK S in NO
BIH Bosnia and Herzegovina -0.913 0.49
YUG Former Yugoslavia -1.247
YU2 Former Yugoslavia2 (Found in some Danish sources) -1.611
KSV Kosovo -0.43
MKD Macedonia FYR -0.439 -0.19
SRB Serbia -1.931 0.46

 

In both countries, the immigrants don’t perform well Well here means around native levels which is around +1. The natives are not found in the tables above because they are not immigrants. They perform worse in Denmark, in some cases by no small amount, which is somewhat puzzling. An S difference of 2.4 is case of Serbia is much larger than would be expected by sampling error (1.4 for BIH). Maybe differential selection. Looks like Denmark received more refugees than Norway despite similar population size, consistent with lower selection threshold for DK.

Studies

Crime, income, educational attainment and employment among immigrant groups in Norway and Finland

Educational attainment, income, use of social benefits, crime rate and the general socioeconomic factor among 71 immigrant groups in Denmark.

Categories
Differential psychology/psychometrics intelligence / IQ / cognitive ability Sociology

The performance of African immigrants in Europe: Some Danish and Norwegian data

Due to lengthy discussion over at Unz concerning the good performance of some African groups in the UK, it seems worth it to review the Danish and Norwegian results. Basically, some African groups perform better on some measures than native British. The author is basically arguing that this disproves global hereditarianism. I think not.

The over-performance relative to home country IQ of some African countries is not restricted to the UK. In my studies of immigrants in Denmark and Norway, I found the same thing. It is very clear that there are strong selection effects for some countries, but not others, and that this is a large part of the reason why the home country IQ x performance in host country are not higher. If the selection effect was constant across countries, it would not affect the correlations. But because it differs between countries, it essentially creates noise in the correlations.

Two plots:

NO_S_IQ DK_S_IQ

The codes are ISO-3 codes. SO e.g. NGA is Nigeria, GHA is Ghana, KEN = Kenya and so on. They perform fairly well compared to their home country IQ, both in Norway and Denmark. But Somalia does not and the performance of several MENAP immigrants is abysmal.

The scores on the Y axis are S factor scores for their performance in these countries. They are general factors extracted from measures of income, educational attainment, use of social benefits, crime and the like. The S scores correlate .77 between the countries. For details, see the papers concerning the data:

  • Kirkegaard, E. O. W. (2014). Crime, income, educational attainment and employment among immigrant groups in Norway and Finland. Open Differential Psychology. Retrieved from http://openpsych.net/ODP/2014/10/crime-income-educational-attainment-and-employment-among-immigrant-groups-in-norway-and-finland/
  • Kirkegaard, E. O. W., & Fuerst, J. (2014). Educational attainment, income, use of social benefits, crime rate and the general socioeconomic factor among 70 immigrant groups in Denmark. Open Differential Psychology. Retrieved from http://openpsych.net/ODP/2014/05/educational-attainment-income-use-of-social-benefits-crime-rate-and-the-general-socioeconomic-factor-among-71-immmigrant-groups-in-denmark/

I did not use the scores from the papers, I redid the analysis. The code is posted below for those curious. The kirkegaard package is my personal package. It is on github. The megadataset file is on OSF.


 

library(pacman)
p_load(kirkegaard, ggplot2)

M = read_mega("Megadataset_v2.0e.csv")

DK = M[111:135] #fetch danish data
DK = DK[miss_case(DK) <= 4, ] #keep cases with 4 or fewer missing
DK = irmi(DK, noise = F) #impute the missing
DK.S = fa(DK) #factor analyze
DK_S_scores = data.frame(DK.S = as.vector(DK.S$scores) * -1) #save scores, reversed
rownames(DK_S_scores) = rownames(DK) #add rownames

M = merge_datasets(M, DK_S_scores, 1) #merge to mega

#plot
ggplot(M, aes(LV2012estimatedIQ, DK.S)) + 
  geom_point() +
  geom_text(aes(label = rownames(M)), vjust = 1, alpha = .7) +
  geom_smooth(method = "lm", se = F)
ggsave("DK_S_IQ.png")


# Norway ------------------------------------------------------------------

NO_work = cbind(M["Norway.OutOfWork.2010Q2.men"], #for work data
                M["Norway.OutOfWork.2011Q2.men"],
                M["Norway.OutOfWork.2012Q2.men"],
                M["Norway.OutOfWork.2013Q2.men"],
                M["Norway.OutOfWork.2014Q2.men"],
                M["Norway.OutOfWork.2010Q2.women"],
                M["Norway.OutOfWork.2011Q2.women"],
                M["Norway.OutOfWork.2012Q2.women"],
                M["Norway.OutOfWork.2013Q2.women"],
                M["Norway.OutOfWork.2014Q2.women"])

NO_income = cbind(M["Norway.Income.index.2009"], #for income data
                  M["Norway.Income.index.2010"],
                  M["Norway.Income.index.2011"],
                  M["Norway.Income.index.2012"])

#make DF
NO = cbind(M["NorwayViolentCrimeAdjustedOddsRatioSkardhamar2014"],
           M["NorwayLarcenyAdjustedOddsRatioSkardhamar2014"],
           M["Norway.tertiary.edu.att.bigsamples.2013"])


#get 5 year means
NO["OutOfWork.2010to2014.men"] = apply(NO_work[1:5],1,mean,na.rm=T) #get means, ignore missing
NO["OutOfWork.2010to2014.women"] = apply(NO_work[6:10],1,mean,na.rm=T) #get means, ignore missing

#get means for income and add to DF
NO["Income.index.2009to2012"] = apply(NO_income,1,mean,na.rm=T) #get means, ignore missing

plot_miss(NO) #view is data missing?

NO = NO[miss_case(NO) <= 3, ] #keep those with 3 datapoints or fewer missing
NO = irmi(NO, noise = F) #impute the missing

NO_S = fa(NO) #factor analyze
NO_S_scores = data.frame(NO_S = as.vector(NO_S$scores) * -1) #save scores, reverse
rownames(NO_S_scores) = rownames(NO) #add rownames

M = merge_datasets(M, NO_S_scores, 1) #merge with mega

#plot
ggplot(M, aes(LV2012estimatedIQ, NO_S)) +
  geom_point() +
  geom_text(aes(label = rownames(M)), vjust = 1, alpha = .7) +
  geom_smooth(method = "lm", se = F)
ggsave("NO_S_IQ.png")

sum(!is.na(M$NO_S))
sum(!is.na(M$DK.S))

cor(M$NO_S, M$DK.S, use = "pair")

 

Categories
Differential psychology/psychometrics Sociology

New paper out: Educational attainment, income, use of social benefits, crime rate and the general socioeconomic factor among 71 immigrant groups in Denmark

Educational attainment, income, use of social benefits, crime rate and the general socioeconomic factor among 71 immigrant groups in Denmark

http://openpsych.net/ODP/2014/05/educational-attainment-income-use-of-social-benefits-crime-rate-and-the-general-socioeconomic-factor-among-71-immmigrant-groups-in-denmark/

Categories
Differential psychology/psychometrics

Paper published: Criminality and fertility among Danish immigrant populations

Abstract
Criminality rates and fertility vary wildly among Danish immigrant populations by their country of origin. Correlational and regression analyses show that these are very predictable (R’s about .85 and .5) at the group level with national IQ, Islam belief, GDP and height as predictors.

Published in our new journal for psychology.

http://openpsych.net/index.php/diff/article/view/7

Peer review is here: http://openpsych.net/forum/showthread.php?tid=2&action=lastpost