I won’t talk much about these links or cover them in any detail – but I do encourage you to have a closer look if some of this stuff sounds interesting:
Given how long people have known about stuff like the Hawthorne effect, I almost can’t believe nobody ever got the idea of doing something like this at some point in the past. I however have no problem believing the results.
ii. Finnish war pics. Fascinating stuff.
iii. The kind of people who apparently receive elite research prizes in Denmark these years – exhibit B: Claudia Welz (Danish link). Unfortunately I couldn’t find a good English webpage describing her activities, in order to illustrate just how mad it is that a person like that receives that kind of money from the Danish taxpayers in order to do the kind of ‘research’ she does, and my life is definitely too short to translate the crap that’s put up at the Danish site.
Exhibit A is of course Milena Penkowa. Naturally more deserving people have received the prize as well this year – at least most of the recipients probably won’t feel any strong need to talk about imaginary entities in their publications.
Here’s a related link (in Danish). It’d be a lot cheaper to just give these people unemployment insurance. I’m sure not all of this research is equally useless, but even so my willingness to pay for this kind of stuff is, well, let’s put it diplomatically – not exactly super high. I don’t really understand why people can not just study that kind of stuff (and less useless stuff…) themselves, during their own time, when they’re not working.
iv. A few more Steven Farmer pharmacology lectures:
There’s a bit of annoying microphone-related noise in parts of the second video and parts of the third one, but aside from that they’re quite good and this should not stop you from watching the videos if you find the topics covered interesting.
Some reviews I had a look at after browsing the site:
i. Omega 3 fatty acids for prevention and treatment of cardiovascular disease (Review), by Hooper et al.
“Main results Forty eight randomised controlled trials (36,913 participants) and 41 cohort analyses were included. Pooled trial results did not show a reduction in the risk of total mortality or combined cardiovascular events in those taking additional omega 3 fats (with significant statistical heterogeneity). Sensitivity analysis, retaining only studies at low risk of bias, reduced heterogeneity and again suggested no significant effect of omega 3 fats. [...]
Authors’ conclusions It is not clear that dietary or supplemental omega 3 fats alter total mortality, combined cardiovascular events or cancers in people with, or at high risk of, cardiovascular disease or in the general population. There is no evidence we should advise people to stop taking rich sources of omega 3 fats, but further high quality trials are needed to confirm suggestions of a protective effect of omega 3 fats on cardiovascular health.”
(The review has 196 pages, so naturally there’s more stuff here if you’re interested…)
A total of 53 trials met inclusion criteria for one or more of the comparisons in the review. Thirteen trials compared a group programme with a self-help programme; there was an increase in cessation with the use of a group programme (N = 4375, relative risk (RR) 1.98, 95% confidence interval (CI) 1.60 to 2.46). There was statistical heterogeneity between trials in the comparison of group programmes with no intervention controls so we did not estimate a pooled effect. We failed to detect evidence that group therapy was more effective than a similar intensity of individual counselling. There was limited evidence that the addition of group therapy to other forms of treatment, such as advice from a health professional or nicotine replacement, produced extra benefit. There was variation in the extent to which those offered group therapy accepted the treatment. Programmes which included components for increasing cognitive and behavioural skills were not shown to be more effective than same length or shorter programmes without these components.
Group therapy is better for helping people stop smoking than self help, and other less intensive interventions. There is not enough evidence to evaluate whether groups are more effective, or cost-effective, than intensive individual counselling. There is not enough evidence to support the use of particular psychological components in a programme beyond the support and skills training normally included.”
“36 trials were included but most were small and of duration less than three months. Nine trials were of six months duration (2016 patients). These longer trials were the more recent trials and generally were of adequate size, and conducted to a reasonable standard. Most trials tested the same standardised preparation of Ginkgo biloba, EGb 761, at different doses, which are classified as high or low.
The results from the more recent trials showed inconsistent results for cognition, activities of daily living, mood, depression and carer burden. Of the four most recent trials to report results three found no difference between Ginkgo biloba and placebo, and one reported very large treatment effects in favour of Ginkgo biloba. There are no significant differences between Ginkgo biloba and placebo in the proportion of participants experiencing adverse events. [...]
Ginkgo biloba appears to be safe in use with no excess side effects compared with placebo. Many of the early trials used unsatisfactory methods, were small, and publication bias cannot be excluded. The evidence that Ginkgo biloba has predictable and clinically significant benefit for people with dementia or cognitive impairment is inconsistent and unreliable.”
Four trials with a combined total of 44,012 patients met the inclusion criteria and are included in this review. Acetylsalicylic acid (ASA) did not reduce stroke or ’all cardiovascular events’ compared to placebo in primary prevention patients with elevated blood pressure and no prior cardiovascular disease. In one large trial ASA taken for 5 years reduced myocardial infarction (ARR 0.5%, NNT 200), increased major haemorrhage (ARI 0.7%, NNT 154), and did not reduce all cause mortality or cardiovascular mortality. In one trial there was no significant difference between ASA and clopidogrel for the composite endpoint of stroke, myocardial infarction or vascular death.
In two small trials warfarin alone or in combination with ASA did not reduce stroke or coronary events.
The ATC meta-analysis of antiplatelet therapy for secondary prevention in patients with elevated blood pressure reported an absolute reduction in vascular events of 4.1% as compared to placebo. Data on the 10,600 patients with elevated blood pressure from the 29 individual trials included in the ATC meta-analysis was requested but could not be obtained.
Antiplatelet therapy with ASA for primary prevention in patients with elevated blood pressure provides a benefit, reduction in myocardial infarction, which is negated by a harm of similar magnitude, increase in major haemorrhage.
The benefit of antiplatelet therapy for secondary prevention in patients with elevated blood pressure is many times greater than the harm. [...]
Further trials of antithrombotic therapy including with newer agents and complete documentation of all benefits and harms are required in patients with elevated blood pressure.”
I have a paper deadline approaching, so I’ll be unlikely to blog much more this week. Below some links and stuff of interest:
“we surveyed the faculty and trainees at MD Anderson Cancer Center using an anonymous computerized questionnaire; we sought to ascertain the frequency and potential causes of non-reproducible data. We found that ~50% of respondents had experienced at least one episode of the inability to reproduce published data; many who pursued this issue with the original authors were never able to identify the reason for the lack of reproducibility; some were even met with a less than “collegial” interaction. [...] These results suggest that the problem of data reproducibility is real. Biomedical science needs to establish processes to decrease the problem and adjudicate discrepancies in findings when they are discovered.”
ii. The development in the number of people killed in traffic accidents in Denmark over the last decade (link):
For people who don’t understand Danish: The x-axis displays the years, the y-axis displays deaths – I dislike it when people manipulate the y-axis (…it should start at 0, not 200…), but this decline is real; the number of Danes killed in traffic accidents has more than halved over the last decade (463 deaths in 2002; 220 deaths in 2011). The number of people sustaining traffic-related injuries dropped from 9254 in 2002 to 4259 in 2011. There’s a direct link to the data set at the link provided above if you want to know more.
iii. Gender identity and relative income within households, by Bertrand, Kamenica & Pan.
“We examine causes and consequences of relative income within households. We establish that gender identity – in particular, an aversion to the wife earning more than the husband – impacts marriage formation, the wife’s labor force participation, the wife’s income conditional on working, marriage satisfaction, likelihood of divorce, and the division of home production. The distribution of the share of household income earned by the wife exhibits a sharp cliff at 0.5, which suggests that a couple is less willing to match if her income exceeds his. Within marriage markets, when a randomly chosen woman becomes more likely to earn more than a randomly chosen man, marriage rates decline. Within couples, if the wife’s potential income (based on her demographics) is likely to exceed the husband’s, the wife is less likely to be in the labor force and earns less than her potential if she does work. Couples where the wife earns more than the husband are less satisfied with their marriage and are more likely to divorce. Finally, based on time use surveys, the gender gap in non-market work is larger if the wife earns more than the husband.” [...]
“In our preferred specification [...] we find that if the wife earns more than the husband, spouses are 7 percentage points (15%) less likely to report that their marriage is very happy, 8 percentage points (32%) more likely to report marital troubles in the past year, and 6 percentage points (46%) more likely to have discussed separating in the past year.”
These are not trivial effects…
iv. Some Khan Academy videos of interest:
“Relative to developed countries, there are far fewer women than men in India. Estimates suggest that among the stock of women who could potentially be alive today, over 25 million are “missing”. Sex selection at birth and the mistreatment of young girls are widely regarded as key explanations. We provide a decomposition of missing women by age across the states. While we do not dispute the existence of severe gender bias at young ages, our computations yield some striking findings. First, the vast majority of missing women in India are of adult age. Second, there is significant variation in the distribution of missing women by age across different states. Missing girls at birth are most pervasive in some north-western states, but excess female mortality at older ages is relatively low. In contrast, some north-eastern states have the highest excess female mortality in adulthood but the lowest number of missing women at birth. The state-wise variation in the distribution of missing women across the age groups makes it very difficult to draw simple conclusions to explain the missing women phenomenon in India.”
A table from the paper:
“We estimate that a total of more than two million women in India are missing in a given year. Our age decomposition of this total yields some striking findings. First, the majority of missing women, in India die in adulthood. Our estimates demonstrate that roughly 12% of missing women are found at birth, 25% die in childhood, 18% at the reproductive ages, and 45% die at older ages. [...] There are just two states in which the majority of missing women are either never born or die in childhood (i e, [sic] before age 15), and these are Haryana and Rajasthan. Moreover, the missing women in these three states add up to well under 15% of the total missing women in India.
For all other states, the majority of missing women die in adulthood. [...]
Because there is so much state-wise variation in the distribution of missing women across the age groups, it is difficult to provide a clear explanation for missing women in India. The traditional explanation for missing women, a strong preference for the birth of a son, is most likely driving a significant proportion of missing women in the two states of Punjab and Haryana where the biased sex ratios at birth are undeniable. However, the explanation for excess female deaths after birth is far from clear.”
“The finding of abnormal lung function in some diabetic subjects suggests that the lung should be considered a “target organ” in diabetes mellitus; however, the clinical implications of these findings in terms of respiratory disease are at present unknown.”
Malcolm Sandler wrote this almost 25 years ago. What’s happened since then? Well, I should perhaps point out that you still today have a situation where highly educated individuals who’ve had diabetes for decades may not even be aware that their disease may affect the lung tissue – I should know, because until a few years ago I didn’t know this. You care about the kidneys, you care about the feet, the eyes, the heart, sometimes the autonomous nervous system – but your lungs aren’t very likely to be brought up in a discussion with the endocrinonologist unless you happen to be a smoker, and in that case the concern is cancer risk and cardiovascular risk.
One main explanation is likely that the effects of the disease are minor, and so do not have much influence on the quality of life of the patient:
“Clear decrements in lung function have been reported in patients with diabetes over the past 2 decades, and many reports have suggested plausible pathophysiological mechanisms. However, at the present time, there are no reports of functional limitations of activities of daily living ascribable to pulmonary disease in patients with diabetes. Accordingly, this review is directed toward a description of the nature of reported lung dysfunction in diabetes, with an emphasis on the emerging potential clinical implications of such dysfunction.” (my emphasis, quote from this review)
I am interested in this matter because, well, at least partly because I’m just the kind of person who takes an interest in such matters. But recently I’ve also started to become a bit curious about whether the disease may have already have had an impact on my own lung function, ‘compared to baseline’. It’s far from certain – most studies find that microvascular complications are correlated (say if your eyes start to display signs of damage, it’s more likely that one may also observe damage to the kidneys) and that the link between those complications and metabolic control is strong; and my metabolic control is close to optimal, and my eyes and kidneys look fine.
I’m a long-distance runner. I run ~35 km/week now (and increasing with ~3 km/week), so of course I should not have breathing difficulties walking up and down stairs, and I don’t. And as the quote above makes clear even for patients who may be impacted, the damage is not likely to be all that major. So the fact that I don’t have any overt lung problems isn’t relevant – we wouldn’t expect such to present anyway. But it is worth asking whether I perform as well as I would do without my disease when I run. The obvious answer would be ‘of course not’ – for reasons unrelated to my lungs (taking blood samples take time, loading up on carbohydrates during a run after the blood sample is taken takes time – and I can’t do these things while running). But is there an impact from the lungs as well? I don’t know. Maybe. You can’t observe the counterfactual.
Which is why I thought this recent-ish meta-analysis was interesting:
“Background: Research into the association between diabetes and pulmonary function has resulted in inconsistent outcomes among studies. We performed a metaanalysis to clarify this association.
Methods: From a systematic search of the literature, we included 40 studies describing pulmonary function data of 3,182 patients with diabetes and 27,080 control subjects. Associations were summarized pooling the mean difference (MD) (standard error) between patients with diabetes and control subjects of all studies for key lung function parameters.
Results: For all studies, the pooled MD for FEV 1 , FVC, and diffusion of the lungs for carbon monoxide were -5.1 (95% CI, -6.4 to -3.7; P<.001), -6.3 (95% CI, -8.0 to -4.7; P<.001), and -7.2 (95% CI, -10.0 to -4.4; P<.001) % predicted, respectively, and for FEV 1 /FVC 0.1% (95% CI, -0.8 to 1.0; P = .78). Metaregression analyses showed that between-study heterogeneity was not explained by BMI, smoking, diabetes duration, or glycated hemoglobin (all P<.05).
Conclusions: Diabetes is associated with a modest, albeit statistically significant, impaired pulmonary function in a restrictive pattern. [...]
Our metaanalysis shows that diabetes, in the absence of overt pulmonary disease, is associated with a modest, albeit statistically significant, impaired pulmonary function in a restrictive pattern. The results were irrespective of BMI, smoking, diabetes duration, and HbA1c levels. In subanalyses, the association seemed to be more pronounced in type 2 diabetes than in type 1 diabetes. Our study adds evidence for yet another organ system to be involved in bothtype 1 and type 2 diabetes. As a consequence of exclusion criteria, the levels of functional impairment fell within values that are generally considered to be normal. However, to place this in perspective, the magnitude of impairment found in our study closely resembles that of smoking per se.57 Similarly, given the relatively high prevalence of diabetes in COPD,58 it is tempting to speculate that (uncontrolled) diabetes may accelerate progressive lung function decline. However, from our metaanalysis summarizing crosssectional studies, it is difficult to draw conclusions on causality and progression into overt pulmonary diseases.” (my emphasis)
Whether you smoke or not is certainly not a trivial effect when you’re considering the fitness level of a long-distance runner! I know the effects are smaller for T1′s, but this is most certainly an effect to have in mind. Back when I ran my marathon three years ago both me and my brother were surprised that he did so much better than I did (he came in more than half an hour before I did, despite the fact that we both assumed beforehand that I was the one who was in better shape).
I consider some of the findings quite weird, and it’s hard to make heads or tails of some of this stuff:
“One would expect that a longer exposure to diabetes would proportionally increase the chance of connective tissue being nonenzymatically glycated. However, our study suggests that a longer duration is not necessarily associated with additional loss of pulmonary reserves. This is in line with previous longitudinal studies on this topic.59,60 [...]
It is intriguing to observe that the pulmonary system remains relatively spared in diabetes when compared with other organs with wide microvascular beds. It is speculated that the large pulmonary reserves protect against severe pulmonary dysfunction.
Because neither the duration of diabetes nor glycemic state appeared to influence the association in our study, one might question whether there is a causal relationship between diabetes and impaired pulmonary function.”
I’ll try to keep my eyes open for updates on this stuff – although the estimated effects may not be big enough for people to seek out medical advice, they’re huge if you’re a long-distance runner considering whether it’s even worth it to participate in future official runs solely for the sake of improving your performance in such competitions.
On a sidenote I should point out that I don’t (/no longer) run in order to obtain a faster time in an official run – I run because I like to run, and I no longer have much desire to participate in official runs – but I’d be lying if I said I didn’t care at all about that stuff some years back when I started out participating in such runs. Imagine what happens with your desire to participate in such official runs if you don’t seem to be able to improve your time much even with strict adherence to running schedules, especially considering the fact that other people who in other respects are similar to you can out-perform you without doing a lot of work. I was above 70 km/week and had several 30+ kilometer runs behind me before my marathon; my brother never even crossed the 40 km/week threshold. And he beat me by more than half an hour. Go figure. I had a bad run for diabetes-related reasons so during the day this was not a surprising outcome, but it was a profoundly annoying outcome. And no, I was not ‘overtraining’; I was rather at the point where a 25+ km run was the ‘standard running distance’ – you know, that distance you managed without thinking much about it every Tuesday, and Saturday, with a short 20 km run in between – and I decreased the kilometer count up to the run as advised by the plan I was following (more or less stringently, but compared to the people whom I entered the goal line with the word ‘more’ is by far the more accurate one). And no, it’s not like I hadn’t heard about interval training, and it’s not like this stuff is hard to implement in a hilly place like Aarhus.
I did make progress from I started running to the point where I decided not to really consider ‘official runs’ to be be worth it anymore – the first half-marathon took me more than 2 hours, the best one I did in an hour and 47 minutes (this performance was achieved at a point in time where I ran 65 km/week and at least cared somewhat about speed and time taken – so, yeah… Compare this again with my brother, whose next goal is 1.35, without ever having been near 50 km/week). Right now my ‘standard running distance’ is 12-15 km – I like to run, but I have a very limited desire to participate in official runs in the future. It’s not worth it – if I go back to very-high intensity training I may improve my official performances, but that could just as easily be due to factors completely unrelated to my actual shape, like whether I was lucky about the starting blood glucose (fewer tests during the run, less time wasted on that), or whether I’d slept well. Who cares? And it’s not like I need to participate in these runs to motivate myself to get out there – I find running enjoyable as it is, especially in the summer when the weather is nice.
But in case you’d forgotten because of all the personal stuff in the end – to just reiterate the main points that made me start out writing this post:
“Diabetes is associated with a modest, albeit statistically significant, impaired pulmonary function in a restrictive pattern. [...] the magnitude of impairment found in our study closely resembles that of smoking”.
This is perhaps also a good illustration of how dangerous diabetes is; the fact that the disease may impact the performance of the lungs in a manner not too dissimilar from smoking is not even considered clinically relevant; the patients have much bigger problems to worry about as it is.
i. Alone in the Crowd: The Structure and Spread of Loneliness in a Large Social Network, by Cacioppo, Fowler, and Christakis.
“The discrepancy between an individual’s loneliness and the number of connections in a social network is well documented, yet little is known about the placement of loneliness within, or the spread of loneliness through, social networks. We use network linkage data from the population-based Framingham Heart Study to trace the topography of loneliness in people’s social networks and the path through which loneliness spreads through these networks. Results indicated that loneliness occurs in clusters, extends up to three degrees of separation, is disproportionately represented at the periphery of social networks, and spreads through a contagious process. The spread of loneliness was found to be stronger than the spread of perceived social connections, stronger for friends than family members, and stronger for women than for men.”
I almost fell down my chair when I read the first half of this sentence: “The average person spends about 80% of waking hours in the company of others, and the time with others is preferred to the time spent alone (Emler, 1994; Kahneman, Krueger, Schkade, Schwarz, & Stone, 2004).” I really shouldn’t have been all that surprised because I’d seen numbers on related stuff before (“the percentage of people living in single person households [in Denmark is] 20,3 %.”) – here’s another link (in Danish), according to which 33% of adult Danes above the age of 25 do not have a cohabitating partner (as can be inferred from the other estimate, only a subset of these people actually live alone. However do note that not all people who do not ‘live alone’ actually interact socially with the people with whom they live; I’m a case in point, as for various reasons it’s exceedingly rare that I socially interact with my roommate). It’s presumably not surprising that someone like me would tend to underestimate how much time most normal people spend in the company of others during an average day, but the magnitude of the difference did catch me by surprise.
“Humans are an irrepressibly meaning-making species, and a large literature has developed showing that perceived social isolation (i.e., loneliness) in normal samples is a more important predictor of a variety of adverse health outcomes than is objective social isolation (e.g., (Cole et al., 2007; Hawkley, Masi, Berry, & Cacioppo, 2006; Penninx et al., 1997; Seeman, 2000; Sugisawa, Liang, & Liu, 1994). [...]
Loneliness has [...] been associated with the progression of Alzheimer’s Disease (Wilson et al., 2007), obesity (Lauder, Mummery, Jones, & Caperchione, 2006), increased vascular resistance (Cacioppo, Hawkley, Crawford et al., 2002), elevated blood pressure (Cacioppo, Hawkley, Crawford et al., 2002; Hawkley et al., 2006), increased hypothalamic pituitary adrenocortical activity (Adam, Hawkley, Kudielka, & Cacioppo, 2006; Steptoe, Owen, Kunz-Ebrecht, & Brydon, 2004), less salubrious sleep (Cacioppo, Hawkley, Berntson et al., 2002; Pressman et al., 2005), diminished immunity (Kiecolt-Glaser et al., 1984; Pressman et al., 2005), reduction in independent living (Russell, Cutrona, De La Mora, & Wallace, 1997; Tilvis, Pitkala, Jolkkonen, & Strandberg, 2000), alcoholism (Akerlind & Hornquist, 1992), depressive symptomatology (Cacioppo et al., 2006; Heikkinen & Kauppinen, 2004), suicidal ideation and behavior (Rudatsikira, Muula, Siziya, & Twa-Twa, 2007), and mortality in older adults (Penninx et al., 1997; Seeman, 2000).” [...]
“Lower levels of loneliness are associated with marriage (Hawkley, Browne, & Cacioppo, 2005; Pinquart & Sorenson, 2003), higher education (Savikko, Routasalo, Tilvis, Strandberg, & Pitkala, 2005), and higher income (Andersson, 1998; Savikko et al., 2005), whereas higher levels of loneliness are associated with living alone (Routasalo, Savikko, Tilvis, Strandberg, & Pitkala, 2006), infrequent contact with friends and family (Bondevik & Skogstad, 1998; Hawkley et al., 2005; Mullins & Dugan, 1990), dissatisfaction with living circumstances (Hector-Taylor & Adams, 1996), physical health symptoms (Hawkley et al., In press), chronic work and/or social stress (Hawkley et al., In press), small social network (Hawkley et al., 2005; Mullins & Dugan, 1990), lack of a spousal confidant (Hawkley et al., In press), marital or family conflict (Jones, 1992; Segrin, 1999), poor quality social relationships (Hawkley et al., In press; Mullins & Dugan, 1990; Routasalo et al., 2006), and divorce and widowhood (Dugan & Kivett, 1994; Dykstra & De Jong Gierveld, 1999; Holmen, Ericsson, Andersson, & Winblad, 1992; Samuelsson, Andersson, & Hagberg, 1998). [...] When people feel lonely, they tend to be shyer, more anxious, more hostile, more socially awkward, and lower in self esteem (e.g., (Berscheid & Reis, 1998; Cacioppo et al., 2006)).”
ii. Loneliness matters: a theoretical and empirical review of consequences and mechanisms, by Hawkley & Cacioppo. From the article:
“A growing body of longitudinal research indicates that loneliness predicts increased morbidity and mortality [12–19]. The effects of loneliness seem to accrue over time to accelerate physiological aging . For instance, loneliness has been shown to exhibit a dose–response relationship with cardiovascular health risk in young adulthood . [...] The impact of loneliness on cognition was assessed in a recent review of the literature . Perhaps, the most striking finding in this literature is the breadth of emotional and cognitive processes and outcomes that seem susceptible to the influence of loneliness. Loneliness has been associated with personality disorders and psychoses [23–25], suicide , impaired cognitive performance and cognitive decline over time [27–29], increased risk of Alzheimer’s Disease , diminished executive control [30, 31], and increases in depressive symptoms [32–35]. The causal nature of the association between loneliness and depressive symptoms appears to be reciprocal ” [...]
“Our model of loneliness [8, 9] posits that perceived social isolation is tantamount to feeling unsafe, and this sets off implicit hypervigilance for (additional) social threat in the environment. Unconscious surveillance for social threat produces cognitive biases: relative to nonlonely people, lonely individuals see the social world as a more threatening place, expect more negative social interactions, and remember more negative social information. Negative social expectations tend to elicit behaviors from others that confirm the lonely persons’ expectations, thereby setting in motion a self-fulfilling prophecy in which lonely people actively distance themselves from would-be social partners even as they believe that the cause of the social distance is attributable to others and is beyond their own control . This self-reinforcing loneliness loop is accompanied by feelings of hostility, stress, pessimism, anxiety, and low self-esteem  and represents a dispositional tendency that activates neurobiological and behavioral mechanisms that contribute to adverse health outcomes. [...]
Loneliness differences in immunoregulation extend beyond inflammation processes. Loneliness has been associated with impaired cellular immunity as reflected in lower natural killer (NK) cell activity and higher antibody titers to the Epstein Barr Virus and human herpes viruses [70, 80–82]. In addition, loneliness among middle-age adults has been associated with a smaller increase in NK cell numbers in response to the acute stress of a Stroop task and a mirror tracing task . In young adults, loneliness was associated with poorer antibody response to a component of the flu vaccine , suggesting that the humoral immune response may also be impaired in lonely individuals.”
iii. One of the studies also cited above: Women, Loneliness, and Incident Coronary Heart Disease, by Thurston & Kubzansky.
To examine associations between loneliness and risk of incident coronary heart disease (CHD) over a 19-year follow-up period in a community sample of men and women. [...]
Hypotheses were examined using data from the First National Health and Nutrition Survey and its follow-up studies (n = 3003). Loneliness, assessed by one item from the Center for Epidemiologic Studies of Depression scale, and covariates were derived from baseline interviews. Incident CHD was derived from hospital records/death certificates over 19 years of follow-up. Hypotheses were evaluated, using Cox proportional hazards models. [...]
Among women, high loneliness was associated with increased risk of incident CHD (high: hazard ratio = 1.76, 95% Confidence Interval = 1.17-2.63; medium: hazard ratio = 0.98, 95% Confidence Interval = 0.64-1.49; reference: low), controlling for age, race, education, income, marital status, hypertension, diabetes, cholesterol, physical activity, smoking, alcohol use, systolic and diastolic blood pressures, and body mass index. Findings persisted additionally controlling for depressive symptoms. No significant associations were observed among men.”
(The last sentence may be important.)
iv. The clinical significance of loneliness: A literature review, by Heinrich & Gullone. The first parts you can skip without missing out on anything, but there’s a lot of useful stuff in there as well and you shouldn’t give up on it just because the first part isn’t very good (IMO). I’ve quoted extensively from the paper because there’s a lot of stuff in there – from the article:
“With a particular focus on the adolescent developmental period, this review is organized into five sections: Drawing on developmental and evolutionary psychology theories, the nature of social relationships and the function they serve is first discussed. In the second section, loneliness is introduced as an exemplar of social relationship deficits. Here a definition of loneliness is provided, as well as an explanation of why it may pose a situation of concern. This is followed by a review of the prototypic features of loneliness through examination of its affective, cognitive, and behavioral correlates. The fourth section includes a review of theories related to the antecedent and maintenance factors involved in loneliness. Finally, methodological and theoretical considerations are addressed, and conclusions and proposals for future research directions are put forth.” [...]
“Empirical evidence [...] suggests that lonely and nonlonely people do not differ in either the daily activities they engage in, or in the amount of time they spend alone (e.g., see Hawkley et al., 2003).” [My initial reaction is to be very skeptical about that claim/finding.] “Thus, loneliness is clearly distinguishable from the objective state of solitude, social isolation, or being alone. Indeed, in a study examining adolescents’ perceptions of loneliness and aloneness, Buchholz and Catton (1999) found that loneliness was described as an aversive state arising from a sense of yearning for another person(s), and associated with negative feelings such as sadness and hopelessness. In contrast, however, aloneness was not viewed negatively. [...but I'm aware that this distinction is relevant and may be important.] In fact, whereas loneliness is by definition an undesirable condition, aloneness or solitude may actually be a desirable or positive condition fostering creativity, facilitating self-reflection, self-regulation, identity formation, concentration, thinking, and learning (Buchholz & Catton, 1999; Fromm-Reichmann, 1959; Larson, 1999; Larson, Csikszentmihalyi, & Graef, 1982; Storr, 1988; Winnicott, 1958). Burger (1995) and Larson (see Larson, 1999, for a review) have shown that college students and adolescents, respectively, may seek and appreciate solitude for such positive reasons, rather than as a means of avoiding possibly anxiety-provoking social interactions. However, Larson has also shown that while solitude may be associated with cognitive benefits, such as increased concentration, these benefits come at the cost of lowered mood states (e.g., sadness, irritability, loneliness, and boredom).” [...]
“loneliness has been found to be significantly associated with shyness, neuroticism, social withdrawal, and a lower frequency of dating, as well as extracurricular and religious participation (Hojat, 1982b; Horowitz, French, & Anderson, 1982; Jones, Freemon, & Goswick, 1981; Russell et al., 1980; Stephan, Faeth, & Lamm, 1988). Associations between loneliness and poorer social interaction quality have also been demonstrated (Hawkley et al., 2003, Jones et al., 1982, Rotenberg, 1994; Segrin, 1998; Wheeler et al., 1983). For example, Hawkley et al. (2003) found loneliness to be related to less positive and more negative feelings during social interactions. More specifically, loneliness was significantly correlated with less intimacy, comfort, and understanding, and more caution, distrust, and conflict. Importantly, Hawkley et al. also demonstrated that these effects of loneliness on social interaction quality were present after controlling for depressed affect and neuroticism.
Perhaps not surprisingly then, loneliness has also been linked to low social competence, peer rejection and victimization, a lack of high quality friendships, and more negative appraisals of social support (Crick & Ladd, 1993; Kochenderfer & Ladd, 1996; Parker & Asher, 1993; Riggio, Watring, & Throckmorton, 1993; Rubin & Mills, 1988). Larson (1999) has also observed that lonely adolescents are rated by parents and teachers as less well-adjusted. Moreover, loneliness has been found to be associated with higher school dropout rates (Asher & Paquette, 2003), poor academic performance (Larson, 1999; Rotenberg, 1999b; Rotenberg & Morrison, 1993), and juvenile delinquency (Brennan, 1982). However, perhaps most pertinent to the issue of psychosocial problems is the consistent finding that loneliness is associated with low self-esteem (Brage, Meredith, & Woodward, 1993; Hymel, Rubin, Rowden, & LeMare, 1990; Jones, 1982; Larson, 1999; Moore & Sermat, 1974; Olmstead, Guy, O’Mally, & Bentler, 1991; Paloutzian & Ellison, 1982; Schultz & Moore, 1988). Yet, despite the typically lower self-esteem of lonely people, Cacioppo et al. (2000) have reported that lonely people have no less social capital to offer than nonlonely people.” [...]
“it would appear lonely people experience predominantly negative affect, which can be summarized as four clusters of feelings: desperation, depression, impatient boredom, and self-deprecation. [...] while longitudinal investigations (e.g., Brage & Meredith, 1994; Cutrona, 1982; Olmstead et al., 1991) have suggested that low self-esteem plays a causal role in the development and maintenance of loneliness, it is likely that a reciprocal relationship exists between loneliness and low self-esteem (Peplau, Miceli et al., 1982). To elaborate, since social relationships constitute a major aspect of people’s self-conceptions (Parkhurst & Hopmeyer, 1999; Peplau, Miceli et al., 1982; Sippola & Bukowski, 1999), and given its relationship with social relationship deficiencies, loneliness may lead to negative self-conceptions thereby undermining one’s self-regard (Peplau, Miceli et al., 1982), and resulting in a vicious cycle wherein low self-esteem and loneliness reinforce one another.
Not surprisingly then, lonely people have been found to view themselves in a negative and self-depreciating manner, believing that they are inferior, worthless, unattractive, unlovable, and socially incompetent (Horowitz et al., 1982; Jones et al., 1981; Jones & Moore, 1987; Jones, Sansone, & Helm, 1983; Paloutzian & Ellison, 1982; Rubenstein & Shaver, 1982; Spitzberg & Canary, 1985; Zakahi & Duran, 1982, 1985). Lonely people have also been observed to hold greater discrepancies than nonlonely people between their actual selves (i.e., how they believe they are) and their ideal selves (i.e., how they would ideally wish to be; Kupersmidt et al., 1999; Eddy, 1961, cited in Peplau, Miceli et al., 1982).
Unfortunately, given Gardner et al.’s (2000) assertion that “the arousal of social hunger may direct attention toward and bias memory for social cues” (p. 487), and their observation that failure to meet belongingness needs gives rise to selective retention of social information, self-conceptions may also be more salient for lonely people than nonlonely people. In support of this notion, loneliness has indeed been found to be associated with self-consciousness and a heightened degree of self-focus (Goswick & Jones, 1981; Jones, Cavert, Snider, & Bruce, 1985; Jones et al., 1981, 1982; Moore & Schultz, 1983). Moreover,Weiss (1973) has argued that these inclinations may result in a “tendency to misinterpret or exaggerate the hostile or affectionate intent of others” (p. 21). This is a contention that has been at least partially supported by Cutrona’s, (1982) finding that lonely people are more sensitive to rejection.”
Numerous studies have indicated that the social behavior of lonely individuals is marked by inhibited sociability and ineffectiveness. For example, lonely people are typically shy (e.g., Anderson & Harvey, 1988; Cacioppo et al., 2000; Cheek & Busch, 1981; Dill & Anderson, 1999; Hojat, 1982a; Jackson, Soderlind, & Weiss, 2000; Jones et al., 1981; Kalliopuska & Laitinen, 1991; Qualter & Munn, 2002), introverted (Cutrona, 1982; Hojat, 1982a; Jones et al., 1981; Kalliopuska & Laitinen, 1991), less affiliative/sociable (Cacioppo et al., 2000; Cutrona, 1982), and less willing to take social risks (Hojat, 1982a; Jones et al., 1981; Moore & Schultz, 1983). Lonely people also seem to be less assertive than nonlonely people (Bell & Daly, 1985; Cutrona, 1982; Gerson & Perlman, 1979; Hojat, 1982a; Jones et al., 1981; Sermat, 1980; Sloan & Solano, 1984). [...] Jones et al. (1982) have revealed that, at least in mixed-sex college student pairs, lonely people make more statements focusing on themselves, respond more slowly to their partner, ask fewer questions, and change the discussion topic more often than nonlonely people. Thus, the self-focused behavior which lonely people appear to engage in during social interactions may undermine relationship development, furthering feelings of loneliness. [...]
Rubenstein and Shaver (1980, 1982) have observed that people’s responses to loneliness tend to fall into four categories: active solitude (e.g., study or work, write, listen to music, exercise, walk, work on a hobby, go to a movie, read, play music), spending money (e.g., spend money, go shopping), social contact (e.g., call a friend, visit someone), and sad passivity (e.g., cry, sleep, sit and think, do nothing, overeat, take tranquilizers, watch television, drink or get ‘stoned’). In coping with loneliness, they found that severely lonely people characteristically adopt a ‘sad passivity’ coping strategy, whereas people who are infrequently lonely tend to adopt the other three strategies. [...] perceived social skills are affected by loneliness, with greater loneliness being associated with lower self-perceived social competence. Therefore, coping behavior is influenced by perceived social skills, which in turn are negatively affected by loneliness. [...] to summarize, lonely people appear to behave in a self-absorbed, socially ineffective manner towards others, and are typically passive when faced with loneliness and stress.”
v. A Meta-Analysis of Interventions to Reduce Loneliness, by Masi, Chen, Hawkley & Cacioppo.
“In summary, meta-analysis of the randomized group comparison studies revealed a small but significant effect of the interventions on loneliness. Of note, interventions that addressed maladaptive social cognition had a sizable mean effect compared to the other intervention types. [...] The current study used meta-analytic techniques to determine quantitatively whether the outcomes of loneliness interventions varied based on study design, intervention type, or other study characteristic. Compared to single-group pre-post and nonrandomized group comparison studies, randomized group comparison studies had a small but significant mean effect size (–0.198, p < .05). Within this group, the mean effect size for interventions that addressed maladaptive social cognition was larger than that for interventions that attempted to improve social skills, enhance social support, or increase opportunities for social interaction. A primary criterion for empirically supported therapies is that they demonstrate efficacy in randomized controlled trials (Chambless & Hollon, 1998). By this criterion, our meta-analysis suggests certain interventions, particularly those that use CBT, can reduce loneliness. [...]
With an intervention effect size of –0.198, the average treatment group scored 0.198 standard deviations lower in loneliness, which is equivalent to 8.05 × 0.198 = 1.59 units on the UCLA Scale. Thus, with the control group mean at 41.17, the reduction in loneliness in the average treatment group was equivalent to a decrease from 41.17 to 39.58 on the UCLA Loneliness Scale. [...] Because clinical significance is defined as “returning to normal functioning” (Jacobson, Roberts, Berns, & McGlinchey, 1999), a 1.59-point decrease in the UCLA Loneliness score clearly did not return study participants to the level of healthy, community-living individuals. Moreover, a meta-analysis of 302 social and behavioral intervention meta-analyses (reviewed in Lipsey & Wilson, 2001) showed that, on average, interventions in this field have generated a mean effect size of 0.50. A mean effect size of –0.198 falls in the bottom 15% of this distribution, suggesting that loneliness interventions to date have not attained the degree of efficacy achieved by interventions targeting other social and behavioral outcomes.”
It takes way more time to cover this stuff in detail here than I’m willing to spend on it, but here are a few relevant links to stuff I’m working on/with at the moment:
iii. Kolmogorov–Smirnov test.
iv. Chow test.
vi. Education and health: Evaluating Theories and Evidence, by Cutler & Muney.
vii. Education, Health and Mortality: Evidence from a Social Experiment, by Meghir, Palme & Simeonova.
I’m currently writing a topic on ‘the causal effect of education on health’, so this is a topic I’ve looked at a bit – consider this post a ‘workblog’-post, even though it’s only tangentially related to what I’m working on.
This kind of stuff – health disparities related to education and income – pops up in the public debate every now and then, see e.g. this recent article (in Danish), or this analysis by AE-rådet (also in Danish). This is ‘politics’ to some extent (see the previous post), but it’s also a question about what’s actually going on in the world, and the latter type of question is the type of question I tend to be interested in answering. I’d like to make some general points here which are sometimes overlooked:
i. People with lower education are fatter. And being fat is bad for your health.
ii. People with lower levels of education smoke more: “Well-documented declines in smoking prevalence over time have not occurred evenly throughout society (12, 13). They have been most substantial among the most educated. Thus, the least educated form increasing proportions of those who remain smokers.” Regarding alcohol the picture is more complicated (as I’ve talked about before), however it should be noted that if the variance of the quantity consumed by the highly educated is lower than for the lower educated groups, as they claim in the article I link to at the beginning of this paragraph, then it would make sense if the highly educated people who die from alcohol-related diseases die later and lose fewer years of their life to the alcohol than does the group with low education (‘the uneducated alcoholic loses 20 years, the educated alcoholic loses five…’). Either way alcohol matters much less than smoking, and the differences aren’t that big in the former case. Incidentally the causal pathways of the smoking link are still unclear: “The causal pathways between education and smoking are both complicated and contested in the literature.” (link)
iii. Lifestyle differences among different educational groups make up a big part of the difference in health outcomes: “the mediating effects of health behaviors – measured by smoking, drinking, exercising and the body mass index – account in the short run for 17% to 31% and in the long run for 23% to 45% of the entire effect of education on health, depending on gender.”
iv. An additional point related to point iii.: I haven’t looked for studies on this because it’s obvious, but the health gradient is more sensitive to stuff like income level and employment status in countries like the US than it is in Denmark. So international (non-Scandinavian?) estimates of the magnitude of educational effects and income effects on health outcomes are likely to be biased upwards, compared to what the magnitude would be in a country like Denmark where ability to pay for medical services problems are unlikely to have much influence on life expectancy at this point.
v. I’ll spell out this point even though it should be obvious by now: Many of the reasons why people with a low education on average die too soon relate to the fact that they on average make poorer choices when it comes to their health. And the stuff mentioned above is just a small part of what’s going on; you also have related stuff like information channels and compliance differences, on top of stuff like ‘likelihood of seeking proper medical attention conditional on you actually needing it, and ability to verbalize complaints so that the doctor makes the correct inferences’ (e.g. a lot of T2 diabetics don’t get diagnosed, and this lowers their life expectancy significantly).
vi. Note that whereas it’s true that some jobs are still more unhealthy than others (a traditional mechanism most people think of when they’re thinking about these things), if the connection between type of work and health risks is known people employed in such jobs would be expected to earn a risk premium – this is not super relevant when you look at education and health, but it is something to have in mind when analyzing health and income stuff.
vii. It should be noted that if you get better over time at treating people for stuff that isn’t lifestyle-related and so stop a lot of people from dying early on of other causes, then lifestyle-stuff is going to become a big driver of health disparities.
i. Econometric methods for causal evaluation of education policies and practices: a non-technical guide. This one is ‘work-related’; in one of my courses I’m writing a paper and this working paper is one (of many) of the sources I’m planning on using. Most of the papers I work with are unfortunately not freely available online, which is part of why I haven’t linked to them here on the blog.
I should note that there are no equations in this paper, so you should focus on the words ‘a non-technical guide’ rather than the words ‘econometric methods’ in the title – I think this is a very readable paper for the non-expert as well. I should of course also note that I have worked with most of these methods in a lot more detail, and that without the math it’s very hard to understand the details and really know what’s going on e.g. when applying such methods – or related methods such as IV methods on panel data, a topic which was covered in another class just a few weeks ago but which is not covered in this paper.
This is a place to start if you want to know something about applied econometric methods, particularly if you want to know how they’re used in the field of educational economics, and especially if you don’t have a strong background in stats or math. It should be noted that some of the methods covered see wide-spread use in other areas of economics as well; IV is widely used, and the difference-in-differences estimator have seen a lot of applications in health economics.
ii. Regulating the Way to Obesity: Unintended Consequences of Limiting Sugary Drink Sizes. The law of unintended consequences strikes again.
You could argue with some of the assumptions made here (e.g. that prices (/oz) remain constant) but I’m not sure the findings are that sensitive to that assumption, and without an explicit model of the pricing mechanism at work it’s mostly guesswork anyway.
iii. A discussion about the neurobiology of memory. Razib Khan posted a short part of the video recently, so I decided to watch it today. A few relevant wikipedia links: Memory, Dead reckoning, Hebbian theory, Caenorhabditis elegans. I’m skeptical, but I agree with one commenter who put it this way: “I know darn well I’m too ignorant to decide whether Randy is possibly right, or almost certainly wrong — yet I found this interesting all the way through.” I also agree with another commenter who mentioned that it’d have been useful for Gallistel to go into details about the differences between short term and long term memory and how these differences relate to the problem at hand.
“An extensive body of prior research indicates an association between emotion and moral judgment. In the present study, we characterized the predictive power of specific aspects of emotional processing (e.g., empathic concern versus personal distress) for different kinds of moral responders (e.g., utilitarian versus non-utilitarian). Across three large independent participant samples, using three distinct pairs of moral scenarios, we observed a highly specific and consistent pattern of effects. First, moral judgment was uniquely associated with a measure of empathy but unrelated to any of the demographic or cultural variables tested, including age, gender, education, as well as differences in “moral knowledge” and religiosity. Second, within the complex domain of empathy, utilitarian judgment was consistently predicted only by empathic concern, an emotional component of empathic responding. In particular, participants who consistently delivered utilitarian responses for both personal and impersonal dilemmas showed significantly reduced empathic concern, relative to participants who delivered non-utilitarian responses for one or both dilemmas. By contrast, participants who consistently delivered non-utilitarian responses on both dilemmas did not score especially high on empathic concern or any other aspect of empathic responding.”
In case you were wondering, the difference hasn’t got anything to do with a difference in the ability to ‘see things from the other guy’s point of view’: “the current study demonstrates that utilitarian responders may be as capable at perspective taking as non-utilitarian responders. As such, utilitarian moral judgment appears to be specifically associated with a diminished affective reactivity to the emotions of others (empathic concern) that is independent of one’s ability for perspective taking”.
On a small sidenote, I’m not really sure I get the authors at all – one of the questions they ask in the paper’s last part is whether ‘utilitarians are simply antisocial?’ This is such a stupid way to frame this I don’t even know how to begin to respond; I mean, utilitarians make better decisions that save more lives, and that’s consistent with them being antisocial? I should think the ‘social’ thing to do would be to save as many lives as possible. Dead people aren’t very social, and when your actions cause more people to die they also decrease the scope for future social interaction.
v. Lastly, some Khan Academy videos:
(This one may be very hard to understand if you haven’t covered this stuff before, but I figured I might as well post it here. If you don’t know e.g. what myosin and actin is you probably won’t get much out of this video. If you don’t watch it, this part of what’s covered is probably the most important part to take away from it.)
It’s been a long time since I checked out the Brit Cruise information theory playlist, and I was happy to learn that he’s updated it and added some more stuff. I like the way he combines historical stuff with a ‘how does it actually work, and how did people realize that’s how it works’ approach – learning how people figured out stuff is to me sometimes just as fascinating as learning what they figured out:
(Relevant wikipedia links: Leyden jar, Electrostatic generator, Semaphore line. Cruise’ play with the cat and the amber may look funny, but there’s a point to it: “The Greek word for amber is ηλεκτρον (“elektron”) and is the origin of the word “electricity”.” – from the first link).
i. I had a doctor’s appointment today and got the results of my bloodwork back. My Hba1c was 48, or 6.5%. This is the lowest it’s been for as long as I can remember. I have had some trouble with hypoglycemic episodes now and then, but not significantly more than usual and I’ve had no major episodes. I believe the lowered Hba1c is probably mostly a result of lowered nocturnal blood glucose values. These have however at some points been uncomfortably low, so I’m not sure 6,5 is a realistic long-term goal and because of those uncomfortably low values I have made adjustments along the way which probably means that the Hba1c may be a bit higher next time if other things stay pretty much the same (which I know they won’t; for instance I’m planning on significantly increasing my running over the next four months). But even so I was very happy about this result, as I choose to believe that it means I’ll actually be able to obtain <7.0% results in the future without major adverse events if I’m careful and vigilant.
This recent post goes into more detail about the hypoglycemia risk and what it’s about. This Danish post has some data on the distribution of Hba1c results among Danish diabetics – the relevant figure is this one (with 6.5%, I’m in the 10% fractile).
ii. I’m now ‘officially’ a researcher. I have just become a member of Statistics Denmark’s research programme (-forskerordning), which means that I’ve obtained access to a specific data set which I’ll do work on during the next year. Danish registers contain a lot of good information compared to the registers of most other countries, so I may actually be able to look at stuff that a lot of researchers elsewhere are simply not able to analyze due to data issues – which is exciting. Unfortunately I’ll not be comfortable blogging anything about this stuff, as there are a huge number of restrictions on data access/sharing etc. – but I believe it’ll be interesting to work with this stuff and I’m looking forward to it.
iii. A couple of Khan Academy videos:
Abstract: “We analyzed one decade of data collected by the Programme for International Student Assessment (PISA), including the mathematics and reading performance of nearly 1.5 million 15 year olds in 75 countries. Across nations, boys scored higher than girls in mathematics, but lower than girls in reading. The sex difference in reading was three times as large as in mathematics. There was considerable variation in the extent of the sex differences between nations. There are countries without a sex difference in mathematics performance, and in some countries girls scored higher than boys. Boys scored lower in reading in all nations in all four PISA assessments (2000, 2003, 2006, 2009). Contrary to several previous studies, we found no evidence that the sex differences were related to nations’ gender equality indicators. Further, paradoxically, sex differences in mathematics were consistently and strongly inversely correlated with sex differences in reading: Countries with a smaller sex difference in mathematics had a larger sex difference in reading and vice versa. We demonstrate that this was not merely a between-nation, but also a within-nation effect. This effect is related to relative changes in these sex differences across the performance continuum: We did not find a sex difference in mathematics among the lowest performing students, but this is where the sex difference in reading was largest. In contrast, the sex difference in mathematics was largest among the higher performing students, and this is where the sex difference in reading was smallest. The implication is that if policy makers decide that changes in these sex differences are desired, different approaches will be needed to achieve this for reading and mathematics. Interventions that focus on high-achieving girls in mathematics and on low achieving boys in reading are likely to yield the strongest educational benefits.”
Abstract: “A cornerstone of modern biomedical research is the use of mouse models to explore basic pathophysiological mechanisms, evaluate new therapeutic approaches, and make go or no-go decisions to carry new drug candidates forward into clinical trials. Systematic studies evaluating how well murine models mimic human inflammatory diseases are non-existent. Here, we show that, although acute inflammatory stresses from different etiologies result in highly similar genomic responses in humans, the responses in corresponding mouse models correlate poorly with the human conditions and also, one another. Among genes changed significantly in humans, the murine orthologs are close to random in matching their human counterparts (e.g.,R^2 between 0.0 and 0.1). In addition to improvements in the current animal model systems, our study supports higher priority for translational medical research to focus on the more complex human conditions rather than relying on mouse models to study human inflammatory diseases.”
vi. Married men at the age of 40 can expect to live on average 7.1 years longer than unmarried men at the age of 40, and 6.6 years longer than divorced men at the age of 40. For women the life expectancy difference between the married and unmarried group is 4.8 years, and the difference between married women and divorced women is 4.3 years. The excess mortality for unmarried men in their forties (compared with married males) is around 250%, and for men in their fifties it’s still above 200%.
The data reported above is from a new publication by Statistics Denmark which you can read here. Here’s a related publication. Here is a recent publication on the education levels of Danish emigrants. All three publications are unfortunately in Danish.
vii. Nasa – The Tyranny of the Rocket Equation. This part was surprising to me, because I’d never really thought about this:
“If the radius of our planet were larger, there could be a point at which an Earth escaping rocket could not be built. Let us assume that building a rocket at 96% propellant (4% rocket), currently the limit for just the Shuttle External Tank, is the practical limit for launch vehicle engineering. Let us also choose hydrogen-oxygen, the most energetic chemical propellant known and currently capable of use in a human rated rocket engine. By plugging these numbers into the rocket equation, we can transform the calculated escape velocity into its equivalent planetary radius. That radius would be about 9680 kilometers (Earth is 6670 km). If our planet was 50% larger in diameter, we would not be able to venture into space, at least using rockets for transport.”
i. Better Colleges Failing to Lure Talented Poor, by David Leonhardt.
“Only 34 percent of high-achieving high school seniors in the bottom fourth of income distribution attended any one of the country’s 238 most selective colleges [...] Among top students in the highest income quartile, that figure was 78 percent. [...]
Among high-achieving, low-income students, 6 percent were black, 8 percent Latino, 15 percent Asian-American and 69 percent white [...]
The researchers defined high-achieving students as those very likely to gain admission to a selective college, which translated into roughly the top 4 percent nationwide. Students needed to have at least an A-minus average and a score in the top 10 percent among students who took the SAT or the ACT.
Of these high achievers, 34 percent came from families in the top fourth of earners, 27 percent from the second fourth, 22 percent from the third fourth and 17 percent from the bottom fourth. (The researchers based the income cutoffs on the population of families with a high school senior living at home, with $41,472 being the dividing line for the bottom quartile and $120,776 for the top.) [...]
If they make it to top colleges, high-achieving, low-income students tend to thrive there, the paper found. Based on the most recent data, 89 percent of such students at selective colleges had graduated or were on pace to do so, compared with only 50 percent of top low-income students at nonselective colleges.”
For people with access to nber papers, here’s the direct link to the study.
The p-value isn’t the only thing you should care about when evaluating small-N studies and larger N replication attempts. It shouldn’t be news, but lots of people get this stuff wrong. Do remember that even in the replication studies, N may be quite small.
“Seifert doubts we will ever have an injectable cocktail of molecules that triggers regeneration. There’s too much complexity in the transition from wound to blastema to new limb, he says. It will also be a lengthy process. [...] “Even if a human could grow a limb back, it might take 15-20 years,” says Seifert. A finger might be more realistic.”
iv. New insights into differences in brain organization between Neanderthals and anatomically modern humans. Razib Khan’s blog has some comments in case you’re curious.
iv. ‘The 99% percent’ weren’t really all that representative, it seems: The Geospatial Characteristics of a Social Movement Communication Network:
“Social movements rely in large measure on networked communication technologies to organize and disseminate information relating to the movements’ objectives. In this work we seek to understand how the goals and needs of a protest movement are reflected in the geographic patterns of its communication network, and how these patterns differ from those of stable political communication. To this end, we examine an online communication network reconstructed from over 600,000 tweets from a thirty-six week period covering the birth and maturation of the American anticapitalist movement, Occupy Wall Street. We find that, compared to a network of stable domestic political communication, the Occupy Wall Street network exhibits higher levels of locality and a hub and spoke structure, in which the majority of non-local attention is allocated to high-profile locations such as New York, California, and Washington D.C. Moreover, we observe that information flows across state boundaries are more likely to contain framing language and references to the media, while communication among individuals in the same state is more likely to reference protest action and specific places and times. Tying these results to social movement theory, we propose that these features reflect the movement’s efforts to mobilize resources at the local level and to develop narrative frames that reinforce collective purpose at the national level.”
“we investigated the relation between cognitive performance and heart rate variability as a function of fitness level. We measured the effect of three cognitive tasks (the psychomotor vigilance task, a temporal orienting task, and a duration discrimination task) on the heart rate variability of two groups of participants: a high-fit group and a low-fit group. Two major novel findings emerged from this study. First, the lowest values of heart rate variability were found during performance of the duration discrimination task, compared to the other two tasks. Second, the results showed a decrement in heart rate variability as a function of the time on task, although only in the low-fit group. Moreover, the high-fit group showed overall faster reaction times than the low-fit group in the psychomotor vigilance task, while there were not significant differences in performance between the two groups of participants in the other two cognitive tasks. In sum, our results highlighted the influence of cognitive processing on heart rate variability. [...] results suggested that the main benefit obtained as a result of fitness level appeared to be associated with processes involving sustained attention.”
N = 28, so it’s a small sample size. But at least the results “seem to support the idea that aerobic training produces selective benefits in cognitive performance.”
vi. How you behave online can tell (a lot? something? a bit? – people seem to disagree about how ‘impressive’ the findings are…) about who you are: Private traits and attributes are predictable from digital records of human behavior, by Kosinski, Stillwell & Graepel.
Figure 2 is probably the main figure from this paper – it “shows the prediction accuracy of dichotomous variables expressed in terms of the area under the receiver-operating characteristic curve (AUC), which is equivalent to the probability of correctly classifying two randomly selected users one from each class (e.g., male and female)”:
“Eighty percent of the antibiotics sold in the United States goes to chicken, pigs, cows and other animals that people eat, yet producers of meat and poultry are not required to report how they use the drugs — which ones, on what types of animal, and in what quantities. This dearth of information makes it difficult to document the precise relationship between routine antibiotic use in animals and antibiotic-resistant infections in people”
This is insane. I had no idea the problem in the US was this big.
viii. One of my guilty pleasures:
(If you just want to watch the chess, you can skip the first 3 minutes or so.)
i. Remember ‘the good old days’ of film-making? Here’s a reminder: The Hays Code.
“1. No picture shall be produced that will lower the moral standards of those who see it. Hence the sympathy of the audience should never be thrown to the side of crime, wrongdoing, evil or sin.
2. Correct standards of life, subject only to the requirements of drama and entertainment, shall be presented.
3. Law, natural or human, shall not be ridiculed, nor shall sympathy be created for its violation. [...]
The sanctity of the institution of marriage and the home shall be upheld. Pictures shall not infer that low forms of sex relationship are the accepted or common thing.
1. Adultery, sometimes necessary plot material, must not be explicitly treated, or justified, or presented attractively.
2. Scenes of Passion
a. They should not be introduced when not essential to the plot.
b. Excessive and lustful kissing, lustful embraces, suggestive postures and gestures, are not to be shown.
c. In general passion should so be treated that these scenes do not stimulate the lower and baser element. [...]
1. No film or episode may throw ridicule on any religious faith.
2. Ministers of religion in their character as ministers of religion should not be used as comic characters or as villains. [...]
The reason why ministers of religion may not be comic characters or villains is simply because the attitude taken toward them may easily become the attitude taken toward religion in general. Religion is lowered in the minds of the audience because of the lowering of the audience’s respect for a minister.”
ii. I’d love to see some corresponding Danish numbers:
“Italians born in 1970, who are about 43 now, will pay 50% more in taxes as a percentage of their lifetime income than those born in 1952, according to research from the Bank of Italy and the University of Verona. The research also found they will receive half the pension benefits that Italy’s 60-somethings are getting or are poised to get.” (link, via MR)
iii. Longevity AmongHunter-Gatherers: A Cross-Cultural Examination. Some main findings and conclusions from the paper:
“Post-reproductive longevity is a robust feature of hunter-gatherers and of the life cycle of Homo sapiens. Survivorship to grandparental age is achieved by over two-thirds of people who reach sexual maturity and can last an average of 20 years.
Adult mortality appears to be characterized by two stages. Mortality rates remain stable and fairly low at around 1 percent per year from the age of maturity until around age 40. After age 40, the rate of mortality increase is exponential (Gompertz) with a mortality rate doubling time of about 6–9 years. The two decades without detectable senescence in early and mid-adulthood appear to be an important component of human life span extension.
The average modal age of adult death for hunter-gatherers is 72 with a range of 68–78 years. This range appears to be the closest functional equivalent of an “adaptive” human life span.
Departures from this general pattern in published estimates of life expectancy in past populations (e.g., low child and high adult mortality) are most likely due to a combination of high levels of contact-related infectiousdisease, excessive violence or homicide, and methodological problems that lead to poor age estimates of older individuals and inappropriate use of model life tables for deriving demographic estimates.
Illnesses account for 70 percent, violence and accidents for 20 percent, and degenerative diseases for 9 percent of all deaths in our sample. Illnesses largely include infectious and gastrointestinal disease, although less than half of all deaths in our sample are from contact-related disease.
Comparisons among hunter-gatherers, acculturated hunter-gatherers, wild chimpanzees, and captive chimpanzees illustrate the interaction of improved conditions and species differences. Within species, improved conditions tend to decrease mortality rates at all ages, with a diminishing effect at older ages. Human and chimpanzee mortality diverge dramatically at older ages, revealing selection for a longer adult period in humans. [...]
Our results contradict Vallois’s (1961: 222) claim that among early humans, “few individuals passed forty years, and it is only quite exceptionally that any passed fifty,” and the more traditional Hobbesian view of a nasty, brutish, and short human life (see also King and Jukes 1969; Weiss 1981). The data show that modal adult lifespan is 68–78 years, and that it was not uncommon for individuals to reach these ages”
iv. What is it like when one of your parents gets Alzheimer’s? It’s not fun.
In people with impaired glucose tolerance interventions are clinically and cost effective
Screening for type 2 diabetes to allow early detection might be cost effective in certain groups
What this study adds
Modelling the whole screening and intervention pathway from screening to death shows that screening for type 2 diabetes and impaired glucose tolerance, followed by interventions, seems to be cost effective compared with no screening
Uncertainty still exists concerning the cost effectiveness of screening for type 2 diabetes alone
Screening populations with a higher prevalence of glucose intolerance might result in better clinical outcomes, although cost effectiveness seems unaffected”
vi. PLOS-ONE: Minimal Intensity Physical Activity (Standing and Walking) of Longer Duration Improves Insulin Action and Plasma Lipids More than Shorter Periods of Moderate to Vigorous Exercise (Cycling) in Sedentary Subjects When Energy Expenditure Is Comparable.
N is small but even so this is an interesting finding.
vii. “Commercial fishing operations in the past 40 years have precipitated a dramatic change in ocean fish stocks, with tuna and other big predators declining and small fish like anchovies and sardines surging. That’s the conclusion of the most ambitious study ever completed of fish populations in the Earth’s oceans, conducted by Villy Christensen of the University of British Columbia’s Fisheries Centre.In the past 100 years, 80% of the biomass of fish in the world’s oceans has been lost, Christensen says in a AAAS video that coincided with a symposium at the Annual Meeting. “Just in the last 40 years, we have lost 60% of the biomass,” he explained. “So we’ve seen some very serious declines, and there’s no doubt about what the cause is: We’re talking about overfishing—overfishing at the global scale.” [...] Christensen’s team of scientists based their conclusions on more than 200 marine ecosystem models and more than 68,000 estimates of fish biomass from 1880 to 2007, the Vancouver Sun reported, citing a University of British Columbia news release.”
“Polygynous animals are often highly dimorphic, and show large sex-differences in the degree of intra-sexual competition and aggression, which is associated with biased operational sex ratios (OSR). For socially monogamous, sexually monomorphic species, this relationship is less clear. Among mammals, pair-living has sometimes been assumed to imply equal OSR and low frequency, low intensity intra-sexual competition; even when high rates of intra-sexual competition and selection, in both sexes, have been theoretically predicted and described for various taxa. Owl monkeys are one of a few socially monogamous primates. Using long-term demographic and morphological data from 18 groups, we show that male and female owl monkeys experience intense intra-sexual competition and aggression from solitary floaters. Pair-mates are regularly replaced by intruding floaters (27 female and 23 male replacements in 149 group-years), with negative effects on the reproductive success of both partners. Individuals with only one partner during their life produced 25% more offspring per decade of tenure than those with two or more partners. The termination of the pair-bond is initiated by the floater, and sometimes has fatal consequences for the expelled adult. The existence of floaters and the sporadic, but intense aggression between them and residents suggest that it can be misleading to assume an equal OSR in socially monogamous species based solely on group composition. Instead, we suggest that sexual selection models must assume not equal, but flexible, context-specific, OSR in monogamous species.”
You sort of want to extrapolate out of sample (/…out of species?) here, but be careful:
“Our findings differ from those reported for some monogamous birds, where remaining life-time reproductive success (i.e., the expected future gains) of the individual that initiates or tolerates a ‘divorce’ was higher than if it remained with its initial partner. For example, in kittiwakes (Rissa tridactyla) and many other pair-living birds, but also in some human societies, it is sometimes advantageous to ‘divorce’, if partners prove incompatible , , . In contrast, our data strongly indicate that break-ups were associated with factors extrinsic to the pair, and that partners did not voluntarily leave or “divorce” as it has been reported for birds, gibbons, and (in at least one case) brown titi monkeys (Callicebus brunneus) –, , . On the other hand, in some species (oystercatchers, Haematopus ostralegus), the reproductive success of stable pairs is not only higher, but there are also accrued benefits with increased duration of the pair-bond, independent of effects of age or experience . This was not the case for owl monkeys, since the number of offspring produced did not change with increased duration of the pair-bond (Fig. 2).”
ii. Smbc (click to watch in a higher resolution):
“The ability to control fire was a crucial turning point in human evolution, but the question when hominins first developed this ability still remains. Here we show that micromorphological and Fourier transform infrared microspectroscopy (mFTIR) analyses of intact sediments at the site of Wonderwerk Cave, Northern Cape province, South Africa, provide unambiguous evidence—in the form of burned bone and ashed plant remains—that burning took place in the cave during the early Acheulean occupation, approximately 1.0 Ma. To the best of our knowledge, this is the earliest secure evidence for burning in an archaeological context.”
[Another reminder that SMBC is awesome: Here's a recent comic which is very handy here - it explains what a Fourier transform is, in case you don't know... (If you actually want to know there's always wikipedia...)]
iv. I never covered this here and though some of you may already have read it I thought I might as well link to Ed Yong’s write-up on replication studies in Nature published last year. A few quotes from the article:
“Positive results in psychology can behave like rumours: easy to release but hard to dispel. They dominate most journals, which strive to present new, exciting research. Meanwhile, attempts to replicate those studies, especially when the findings are negative, go unpublished, languishing in personal file drawers or circulating in conversations around the water cooler. “There are some experiments that everyone knows don’t replicate, but this knowledge doesn’t get into the literature,” says Wagenmakers. The publication barrier can be chilling, he adds. “I’ve seen students spending their entire PhD period trying to replicate a phenomenon, failing, and quitting academia because they had nothing to show for their time.
These problems occur throughout the sciences, but psychology has a number of deeply entrenched cultural norms that exacerbate them. It has become common practice, for example, to tweak experimental designs in ways that practically guarantee positive results. And once positive results are published, few researchers replicate the experiment exactly, instead carrying out ‘conceptual replications’ that test similar hypotheses using different methods. This practice, say critics, builds a house of cards on potentially shaky foundations.
These problems have been brought into sharp focus by some high-profile fraud cases, which many believe were able to flourish undetected because of the challenges of replication. Now psychologists are trying to fix their field.”
Good luck with that. I don’t see a fix happening anytime soon. A few numbers:
“In a survey of 4,600 studies from across the sciences, Daniele Fanelli, a social scientist at the University of Edinburgh, UK, found that the proportion of positive results rose by more than 22% between 1990 and 2007 (ref. 3). Psychology and psychiatry, according to other work by Fanelli4, are the worst offenders: they are five times more likely to report a positive result than are the space sciences, which are at the other end of the spectrum [...]. The situation is not improving. In 1959, statistician Theodore Sterling found that 97% of the studies in four major psychology journals had reported statistically significant positive results5. When he repeated the analysis in 1995, nothing had changed6.”
But maybe other fields are just as bad? Well, as already mentioned the space sciences do better – and that goes for other fields too (though I’d say there seems to be major problems in many areas besides psychology and psychiatry):
A major problem here is that unless you’re actually a researcher in the field or know whom to ask, the file drawer effect can be completely invisible to you.
v. Globalization of Diabetes – The role of diet, lifestyle, and genes. A new publication in Diabetes Care. As usual when they say ‘diabetes’ they mean ‘type 2 diabetes’. Some numbers from the article:
“According to the International Diabetes Federation (1), diabetes affects at least 285 million people worldwide, and that number is expected to reach 438 million by the year 2030, with two-thirds of all diabetes cases occurring in low- to middle-income countries. The number of adults with impaired glucose tolerance will rise from 344 million in 2010 to an estimated 472 million by 2030.
Globally, it was estimated that diabetes accounted for 12% of health expenditures in 2010, or at least $376 billion—a figure expected to hit $490 billion in 2030 (2). [...] Asia accounts for 60% of the world’s diabetic population. [Do note that this does not mean that Asian countries are on average overrepresented in the diabetes statistics. Asia also has roughly 60% of the World's population. - US] [...] In 1980, less than 1% of Chinese adults had the disease. By 2008, the prevalence had reached nearly 10% [...] in urban areas of south India, the prevalence of diabetes has reached nearly 20% [...] Compared with Western populations, Asians develop diabetes at younger ages, at lower degrees of obesity, and at much higher rates given the same amount of weight gain [...]
If current worldwide trends continue, the number of overweight people (BMI >25 kg/m^2) is projected to increase from 1.3 billion in 2005 to nearly 2.0 billion by 2030 (6). [...] the prevalence of overweight and obesity in Chinese adults increased from 20% in 1992 to 29.9% in 2002 (8) [...]
In the NHS (26), each 2-h/day increment of time spent watching television (TV) was associated with a 14% increase in diabetes risk. [...] Each 1-h/day increment of brisk walking was associated with a 34% reduction in risk [...] Cigarette smoking is an independent risk factor for type 2 diabetes. A meta-analysis found that current smokers had a 45% increased risk of developing diabetes compared with nonsmokers (29). Moreover, there was a dose-response relationship between the number of cigarettes smoked and diabetes risk. [That one I did not know about!] [...] Light-to-moderate alcohol consumption is associated with reduced risk of diabetes. A meta-analysis of 370,000 individuals with 12 years of follow-up showed a U-shaped relationship, with a 30–40% reduced risk of the disease among those consuming 1–2 drinks/day compared with heavy drinkers or abstainers (37). [...]
common variants of the TCF7L2 gene that are significantly associated with diabetes risk are present in 20–30% of Caucasian populations but only 3–5% of Asians [...] Conversely, a variant in the KCNQ1 gene associated with a 20–30% increased risk of diabetes in several Asian populations (43,44) is common in East Asians, but rare in Caucasians [...]
Several randomized clinical trials have demonstrated that diabetes is preventable. One of the first diabetes prevention trials was conducted in Daqing, China (58). After 6 years of active intervention, risk was reduced by 31, 46, and 42% in the diet-only, exercise-only, and diet-plus-exercise groups, respectively, compared with the control group. In a subsequent 14-year follow-up study, the intervention groups were combined and compared with control subjects to assess how long the benefits of lifestyle change can extend beyond the period of active intervention (59). Compared with control subjects, individuals in the combined lifestyle intervention group had a 51% lower risk of diabetes during the active intervention period, and a 43% lower risk over a 20-year follow-up.”
vi. Why chess sucks.
I don’t like when the blog isn’t updated for several days, so here are some links to stuff I’ve encountered on the internet in the recent past:
i. Diabetic Autonomic Neuropathy. An overview article which covers a lot of ground; it has approximately 1000 citations and I believe it’s one of the most read articles published in Diabetes Care, a journal you incidentally should know about if you’re diabetic or are interested in diabetes.
ii. Also diabetes-related and closely related to the above paper: The EKG in Diabetes Mellitus. This article is particularly relevant to me because I had an EKG last week and will be told the results of it tomorrow where I have a doctor’s appointment – reading stuff like this first makes it easier to ask the right questions. I jokingly explained to a friend yesterday that if the results of that test come out a specific way, it will be much easier for me to make pension plans (meaning I’d most likely be dead long before the official retirement age – naturally I do not hope for that outcome to happen). I’ll also learn the results of the standard Hba-1c blood test – which is measured 3-4 times a year – as well as the annual urin-sample analysis to check for microalbuminuria (kidney damage). Also, cholesterol levels and triglycerides. So I’ll learn more from this check-up than I usually do. I hope everything is fine but there’s a reason why they perform tests like these; I have no way of knowing myself if there’s a problem here.
Anyway, a few quotes from the paper:
“Fibrotic changes, especially in the basal area of the left ventricle, have frequently been observed in diabetic patients, even when cardiac involvement is clinically not yet evident. [...] The EURODIAB Insulin-Dependent Diabetes Mellitus Complications Study (EURODIAB IDDM)9 investigated 3250 type 1 diabetes patients with an average diabetes duration of >30 years; the prevalence of left ventricular hypertrophy was found to be 3 times greater than that reported in the general population of similar age. [...] Baroreflex dysfunction and disturbed heart rate variability are the most commonly used methods to assess CAN [Cardiovascular autonomic neuropathy, US]. [...]
Ong et al14 found the QTc to be shorter if patients had signs of neuropathy, although these patients’ heart rate was higher and their circadian patterns seemed to be preserved. Valensi et al15 found an unchanged QTc in mild neuropathy, although the circadian day/night QTc pattern was reversed. Pappachan et al16 expressed the view that the QTc interval can be used to diagnose CAN with reasonable sensitivity, specificity, and positive predictive value. Grossmann et al17 observed a prolonged QTc only in diabetic patients with CAN; late potentials were not recorded in any of these patients with CAN. CAN patients with prolonged variability in QTc, QT, or both had high incidence of sudden death.18 [...]
Myocardial ischemia is more often painless in patients with diabetes mellitus.19 Resting ECG abnormalities20 as well as cardiac autonomic dysfunction21 were found to be predictors of silent ischemia in asymptomatic persons with T1D.
In otherwise healthy diabetic men during an average follow-up of 16 years, an abnormal and even an equivocal exercise ECG response was associated with a statistically significant high risk for all-cause and cardiac mortality and morbidity, independently of physical fitness and other traditional risk factors; fit men had a higher survival rate than did unfit men.22 [One more reason why I shouldn't have that much trouble motivating myself to stay in shape.] [...]
The early stage of diabetic cardiomyopathy may already be associated with a range of metabolic abnormalities and even with abnormalities in diastolic function. Frequently, no structural cardiac abnormalities can be identified at this stage; the often subtle ECG alterations may be our only way to diagnose early diabetic cardiomyopathy. [...]
Even early in the course of diabetes mellitus, ECG alterations such as sinus tachycardia, long QTc, QT dispersion, changes in heart rate variability, ST-T changes, and left ventricular hypertrophy may be observed. ECG alterations help evaluate cardiac autonomic neuropathy and detect signs of myocardial ischemia even in asymptomatic patients. Prolonged myocardial fibrosis leads to diabetic cardiomyopathy, with peculiar ECG presentation. Electrocardiographic changes are already present in fetuses, children, and adolescents. The resting ECG, frequently complemented by exercise ECG, assists in cardiac screening of diabetic individuals and helps detect silent ischemia, assess prognosis, and predict mortality”
iii. Boredom Proneness: Its Relationship to Psychological- and Physical-Health Symptoms, by Sommers and Vodanovich.
“The relationship between boredom proneness and health-symptom reporting was examined. Undergraduate students (N 5 200) completed the Boredom Proneness Scale and the Hopkins Symptom Checklist. A multiple analysis of covariance indicated that individuals with high boredomproneness total scores reported significantly higher ratings on all five subscales of the Hopkins Symptom Checklist (Obsessive–Compulsive, Somatization, Anxiety, Interpersonal Sensitivity, and Depression). The results suggest that boredom proneness may be an important element to consider when assessing symptom reporting. Implications for determining the effects of boredom proneness on psychological- and physical-health symptoms, as well as the application in clinical settings, are discussed.”
I had no idea there was such a thing as a ‘Boredom Proneness Scale’! I found the literature overview in the beginning of the paper much more interesting than the study itself (one word: WEIRD). Judging from the reported results there, if you’re bored a lot and/or have a really boring job you may be well advised to do something about that – because being bored is associated with a lot of bad stuff:
“To date, the work on boredom proneness has focused on its association with negative affect, as well as problems in academic and work settings. For instance, significant positive relationships have been found between the tendency to experience boredom and depression, anxiety, hostility, anger, loneliness, and hopelessness (e.g., Ahmed, 1990; Farmer & Sundberg, 1986; Rupp & Vodanovich, 1997; Vodanovich, Verner, & Gilbride,
1991; Watt & Davis, 1991). Other researchers have reported boredom proneness to be related significantly to lower educational achievement, truancy rate, and poor work performance (e.g., Branton, 1970; Drory, 1982; Gardell, 1971; Maroldo, 1986; O’Hanlon, 1981; Robinson, 1975; Smith, 1981).
Limited work, however, has been devoted to investigating the association between boredom and psychological- and physical-health symptoms. Evidence for such a relationship can be inferred from studies reporting significant, positive correlations between boredom and substance abuse and eating disorders (e.g., Abramson & Stinson, 1977; Ganley, 1989; Johnston & O’Malley, 1986; Martin, 1989; Pascale & Sylvester, 1988).
Other researchers have established a connection between boredom and detrimental health effects in organizational settings. For instance, Smith, Cohen, and Stammerjohn (1981) found that workers in monotonous jobs reported more visual, musculoskeletal, and emotional-health complaints than those performing non-monotonous work. Samilova (1971) found that female Russian workers employed in repetitive tasks experienced higher incidence of health problems, including gastritis, peripheral neurological disorders, and joint, tendon, muscle, and cardiovascular disease, than workers in less-repetitive jobs. Ferguson (1973) found that telegraphists who complained of task monotony indicated a greater occurrence of physical-health problems, such as asthma, bronchitis, trunk myalgia, and hand tremors, as compared to other workers in less-monotonous positions.”
iv. Ideology, Motivated Reasoning, and Cognitive Reflection: An Experimental Study. I haven’t actually gotten around to reading this yet, but I bookmarked it for a reason; I probably will later during the week.
v. Media Use Among White, Black, Hispanic, and Asian American Children, by Rideout, Lauricella and Wartella. I’ve written about that stuff before but I haven’t written about this data. It’s survey data so it should be taken with a grain of salt. Even if it is, however, I think there’s some interesting information here. Some stuff from the report:
“Historically, scholars have been aware of differences in the amount of time that White and minority children spend with media, especially TV. But last year’s Generation M2 study indicated a large increase in the amount of time both Black and Hispanic youth are spending with media, to the point where they are consuming an average of 13 hours worth of media content a day (12:59 for Blacks and 13:00 for Hispanics), compared with about eight and a half hours (8:36) for White youth, a difference of about four and a half hours a day.” [my emphasis] [...]
The biggest differences are in the amount of time spent with a TV (a difference of about one to two hours of TV a day between White and minority youth), music (a difference of about an hour a day), computers (up to an hour and a half difference), and video games (from 30 to 40 minutes difference).”
Here’s the ‘big picture’, click to view full size:
vi. I really, truly dislike (and that’s putting it mildly) the new format for the discover magazine blogs, but I really liked this post by Razib Khan. Then again it was posted before the switch. I like a lot of his stuff so I tend not to link to individual posts (I’d have to link to a lot of stuff…) but I figure I should remind you now and then that you should be reading his blog. Even if the new format sucks.
“SUMMARY AND CONCLUSIONS
Documents provided by the Department of Energy reveal the frequent and systematic use of human subjects as guinea pigs for radiation experiments. Some experiments were conducted in the 1940s at the dawn of the nuclear age, and might be attributed to an ignorance of the long term effects of radiation exposure, or to the atomic hubris that accompanied the making of the first nuclear bombs. But other experiments were conducted during the supposedly more enlightened 1960s and 1970s. In either event, such experiments cannot be excused.
These experiments were conducted under the sponsorship of the Manhattan Project, the Atomic Energy Commission, or the Energy Research and Development Administration, all predecessor agencies of the Department of Energy. These experiments spanned roughly thirty years. This report presents the findings of the Subcommittee staff on this project.
Literally hundreds of individuals were exposed to radiation in experiments which provided little or no medical benefit to the subjects. The chief objectives of these experiments were to directly measure the biological effects of redioactive material; to measure doses from injected, ingested, or inhaled redioactive substances; or to measure the time it took radioactive substances to pass through the human body. American citizens thus became nuclear calibration devices.
In many cases, subjects willingly participated in experiments, but they became willing guinea pigs nonetheless. In some cases, the human subjects were captive audiences or populations that experimenters might frighteningly have considered “expendable”: the elderly, prisoners, hospital patients suffering from terminal diseases or who might not have retained their full faculties for informed consent. For some human subjects, informed consent was not obtained or there is no evidence that informed consent was granted. For a number of these same subjects, the government covered up the nature of the experiments and deceived the families of deceased victims as to what had transpired. In many experiments, subjects received doses that approached or even exceeded presently recognized limits for occupational radiation exposure. Doses were as great as 98 times the body burden recognized at the time the experiments were conducted.”
It seems that the Tuskegee syphilis experiment wasn’t quite as unique as I’d thought.
ii. Diuretic Treatment of Hypertension. Interesting, lots of stuff there I didn’t know.
“After adjusting for age, sex, education, and race/ethnicity, risk of death was higher in low-income than high-income group for both all-cause mortality (Hazard ratio [HR], 1.98; 95% confidence interval [CI]: 1.37, 2.85) and cardiovascular disease (CVD)/diabetes mortality (HR, 3.68; 95% CI: 1.64, 8.27). The combination of the four pathways attenuated 58% of the association between income and all-cause mortality and 35% of that of CVD/diabetes mortality. Health behaviors attenuated the risk of all-cause and CVD/diabetes mortality by 30% and 21%, respectively, in the low-income group. Health status attenuated 39% of all-cause mortality and 18% of CVD/diabetes mortality, whereas, health insurance and inflammation accounted for only a small portion of the income-associated mortality (≤6%).
Excess mortality associated with lower income can be largely accounted for by poor health status and unhealthy behaviors. Future studies should address behavioral modification, as well as possible strategies to improve health status in low-income people.”
iv. Influence of Opinion Dynamics on the Evolution of Games. I’ve only just skimmed this, but it looks interesting. Here’s the abstract:
“Under certain circumstances such as lack of information or bounded rationality, human players can take decisions on which strategy to choose in a game on the basis of simple opinions. These opinions can be modified after each round by observing own or others payoff results but can be also modified after interchanging impressions with other players. In this way, the update of the strategies can become a question that goes beyond simple evolutionary rules based on fitness and become a social issue. In this work, we explore this scenario by coupling a game with an opinion dynamics model. The opinion is represented by a continuous variable that corresponds to the certainty of the agents respect to which strategy is best. The opinions transform into actions by making the selection of an strategy a stochastic event with a probability regulated by the opinion. A certain regard for the previous round payoff is included but the main update rules of the opinion are given by a model inspired in social interchanges. We find that the fixed points of the dynamics of the coupled model are different from those of the evolutionary game or the opinion models alone. Furthermore, new features emerge such as the independence of the fraction of cooperators with respect to the topology of the social interaction network or the presence of a small fraction of extremist players.”
v. This is awesome.
“Determining the fitness consequences of sibling interactions is pivotal for understanding the evolution of family living, but studies investigating them across lifetime are lacking. We used a large demographic dataset on preindustrial humans from Finland to study the effect of elder siblings on key life-history traits. The presence of elder siblings improved the chances of younger siblings surviving to sexual maturity, suggesting that despite a competition for parental resources, they may help rearing their younger siblings. After reaching sexual maturity however, same-sex elder siblings’ presence was associated with reduced reproductive success in the focal individual, indicating the existence of competition among same-sex siblings. Overall, lifetime fitness was reduced by same-sex elder siblings’ presence and increased by opposite-sex elder siblings’ presence. Our study shows opposite effects of sibling interactions depending on the life-history stage, and highlights the need for using long-term fitness measures to understand the selection pressures acting on sibling interactions.”
Where did they get their data? Well, it was hard for people living in the 17th and 18th century to avoid death or taxes too:
“The demographic dataset from historical Finnish populations was compiled from records of the Lutheran church, which was obliged by law to document all dates of births, marriages and deaths in the population for tax purposes [25–29]. As migration events were relatively rare and the migration records maintained by the church allowed us to follow dispersers in the majority of the cases, these records provide us with relatively accurate information on individual survival and reproductive histories  (e.g. 91% of individuals with known birth date were followed to sexual maturity at age 15 years). Our study period is limited to the eighteenth and nineteenth centuries, before the transition to reduced birth and mortality rates .”
vii. I’ve posted about this topic before, here’s a new study on cancer screening procedures: Effect of Three Decades of Screening Mammography on Breast-Cancer Incidence. I think the results are depressing:
“The introduction of screening mammography in the United States has been associated with a doubling in the number of cases of early-stage breast cancer that are detected each year, from 112 to 234 cases per 100,000 women — an absolute increase of 122 cases per 100,000 women. Concomitantly, the rate at which women present with late-stage cancer has decreased by 8%, from 102 to 94 cases per 100,000 women — an absolute decrease of 8 cases per 100,000 women. With the assumption of a constant underlying disease burden, only 8 of the 122 additional early-stage cancers diagnosed were expected to progress to advanced disease. After excluding the transient excess incidence associated with hormone-replacement therapy and adjusting for trends in the incidence of breast cancer among women younger than 40 years of age, we estimated that breast cancer was overdiagnosed (i.e., tumors were detected on screening that would never have led to clinical symptoms) in 1.3 million U.S. women in the past 30 years. We estimated that in 2008, breast cancer was overdiagnosed in more than 70,000 women; this accounted for 31% of all breast cancers diagnosed.
Despite substantial increases in the number of cases of early-stage breast cancer detected, screening mammography has only marginally reduced the rate at which women present with advanced cancer. Although it is not certain which women have been affected, the imbalance suggests that there is substantial overdiagnosis, accounting for nearly a third of all newly diagnosed breast cancers, and that screening is having, at best, only a small effect on the rate of death from breast cancer.”
This new article is rather awesome, if for no other reason then because it involves so many people and follow them over such a long time-frame:
“Objective To estimate, in a national cohort, the absolute risk of suicide within 36 years after the first psychiatric contact.
Design Prospective study of incident cases followed up for as long as 36 years. Median follow-up was 18 years.
Setting Individual data drawn from Danish longitudinal registers.
Participants A total of 176 347 persons born from January 1, 1955, through December 31, 1991, were followed up from their first contact with secondary mental health services after 15 years of age until death, emigration, disappearance, or the end of 2006. For each participant, 5 matched control individuals were included.”
176.347 people followed for roughly two decades on average. That’s a lot of data. What did they find? Some of the main results:
“Results Among men, the absolute risk of suicide (95% confidence interval [CI]) was highest for bipolar disorder, (7.77%; 6.01%-10.05%), followed by unipolar affective disorder (6.67%; 5.72%-7.78%) and schizophrenia (6.55%; 5.85%-7.34%). Among women, the highest risk was found among women with schizophrenia (4.91%; 95% CI, 4.03%-5.98%), followed by bipolar disorder (4.78%; 3.48%-6.56%). In the nonpsychiatric population, the risk was 0.72% (95% CI, 0.61%-0.86%) for men and 0.26% (0.20%-0.35%) for women. Comorbid substance abuse and comorbid unipolar affective disorder significantly increased the risk. The co-occurrence of deliberate self-harm increased the risk approximately 2-fold. Men with bipolar disorder and deliberate self-harm had the highest risk (17.08%; 95% CI, 11.19%-26.07%).”
As mentioned they of course they didn’t just limit themselves to following ‘the sick people’ – they also needed people to compare them with… So:
“To estimate the cumulative incidence of suicide among people with no history of mental illness, we adopted a slightly alternative strategy. For each person with a history of any mental illness (as defined in the“Assessment of Suicide and Mental Illness” subsection), we randomly selected 5 people of the same sex and same birth date who had no history of mental illness (time matched). Using the described strategy, we followed up this healthy population (881 735 persons) to provide absolute suicide risks. Because this healthy population was selected at random among all 2.46 million people included in the study population, the estimates obtained represent the absolute risk of suicide among all 2.46 million people without a mental disorder.”
Again, that’s a lot of data – representativeness really is unlikely to be an issue here (at least when dealing with the situation in Denmark). As they put it in the paper: “This is the first analysis of the absolute risk of suicide in a total national cohort of individuals followed up from the first psychiatric contact, and it represents, to our knowledge, the hitherto largest sample with the longest and most complete follow-up.”
Results in a bit more detail:
(click to view full size). I’ve previously seen it argued in papers on anorexia that it’s the phychiatric disorder with the highest mortality rate, so I was a bit surprised by the relatively low numbers here. On the other hand that may be related to the fact that they tend to starve themselves to death rather than take their own lives in the traditional sense, which means that a lot of those excess deaths are not considered suicides. Note that a big majority of all suicides committed are committed by people with a mental illness and that the risk increase from a diagnosis is really quite significant; given the estimates, females with a mental illness are more than 8 times as likely to kill themselves than females without a mental illness, and males are 6 times more likely. Schizophrenic females are almost 20 times as likely to commit suicide than are females without a mental illness. Add substance abuse as well and these females are more than 30 times as likely to commit suicide (the absolute risk is around 7% in that case). The risk is substantially increased for almost all groups when you add substance abuse.
Do also note that not all people in the ‘mental illness’ group are actually people with a mental illness; personality disorders are not usually considered mental illnesses by health professionals, but the study includes in the group of people with mental illnesses people with: “any mental illness (any ICD-8 or ICD-10 code) if they had been admitted to a psychiatric hospital or had been in outpatient care with one of these diagnoses.” (The “any ICD-8 or ICD-10 code” means that people with personality disorders are included in the group as well). This is probably ‘fair enough’ given that at least some of these groups clearly have elevated suicide levels, but it’s worth having in mind that it should change the interpretation slightly. How about people who’ve attempted suicide?
The deliberate self-harm/attempted suicide group is obviously a high-risk group. The follow-up period is shorter than for the other estimates (30 years, rather than 36) so these estimates are perhaps best thought of as lower bounds. There’s some uncertainty regarding the estimates because the sample sizes aren’t that big (which is a good thing I think…), but roughly 1 in 6 Danish males with bipolar affective disorder killed themselves during the period. The absolute risks here are substantial; for the ‘any mental illness’ group, one in 12 committed suicide during the period. Although the female numbers are substantially lower for the group as a whole, for some illnesses the absolute risk is comparable to that of the males (and the excess risk much, much higher). More than one in ten females with schizophrenia and a suicide attempt in the past committed suicide during the follow-up period.
I should perhaps mention here that there may be some significant tail risk unaccounted for in the data, despite the long follow-up period which might lead you to think these are good estimates of the ‘lifetime probability of suicide’. The suicide-rate of Danish males above the age of 85 is the highest of all age groups, and it’s five times as high as the suicide risk of males at the age of 25-29 (Danish link). This is not just a Danish thing – similar dynamics have been observed elsewhere. Age matters a lot here, but people tend to care less when old people kill themselves than when young people do.
i. Temporal view of the costs and benefits of self-deception, by Chance, Nortona, Ginob, and Ariely. The abstract:
“Researchers have documented many cases in which individuals rationalize their regrettable actions. Four experiments examine situations in which people go beyond merely explaining away their misconduct to actively deceiving themselves. We find that those who exploit opportunities to cheat on tests are likely to engage in self-deception, inferring that their elevated performance is a sign of intelligence. This short-term psychological benefit of self-deception, however, can come with longer-term costs: when predicting future performance, participants expect to perform equally well—a lack of awareness that persists even when these inflated expectations prove costly. We show that although people expect to cheat, they do not foresee self-deception, and that factors that reinforce the benefits of cheating enhance self-deception. More broadly, the findings of these experiments offer evidence that debates about the relative costs and benefits of self-deception are informed by adopting a temporal view that assesses the cumulative impact of self-deception over time.”
A bit more from the paper:
“People often rationalize their questionable behavior in an effort to maintain a positive view of themselves. We show that, beyond merely sweeping transgressions under the psychological rug, people can use the positive outcomes resulting from negative behavior to enhance their opinions of themselves—a mistake that can prove costly in the long run. We capture this form of self-deception in a series of laboratory experiments in which we give some people the opportunity to perform well on an initial test by allowing them access to the answers. We then examine whether the participants accurately attribute their inflated scores to having seen the answers, or whether they deceive themselves into believing that their high scores reflect new-found intelligence, and therefore expect to perform similarly well on future tests without the answer key.
Previous theorists have modeled self-deception after interpersonal deception, proposing that self-deception—one part of the self deceiving another part of the self—evolved in the service of deceiving others, since a lie can be harder to detect if the liar believes it to be true (1, 2). This interpersonal account reflects the calculated nature of lying; the liar is assumed to balance the immediate advantages of deceit against the risk of subsequent exposure. For example, people frequently lie in matchmaking contexts by exaggerating their own physical attributes, and though such deception might initially prove beneficial in convincing an attractive prospect to meet for coffee, the ensuing disenchantment during that rendezvous demonstrates the risks (3, 4). Thus, the benefits of deceiving others (e.g., getting a date, getting a job) often accrue in the short term, and the costs of deception (e.g., rejection, punishment) accrue over time.
The relative costs and benefits of self-deception, however, are less clear, and have spurred a theoretical debate across disciplines (5–10). [...]
As we had expected, social recognition exacerbated self-deception: those who were commended for their answers-aided performance were even more likely to inflate their beliefs about their subsequent performance. The fact that social recognition, which so often accompanies self-deception in the real world, enhances self-deception has troubling implications for the prevalence and magnitude of self-deception in everyday life.”
ii. Nonverbal Communication, by Albert Mehrabian. Some time ago I decided that I wanted to know more about this stuff, but I haven’t really gotten around to it until now. It’s old stuff, but it’s quite interesting. Some quotes:
“The work of Condon and Ogston (1966, 1967) has dealt with the synchronous relations of a speaker’s verbal cues to his own and his addressee’s nonverbal behaviors. One implication of their work is the existence of a kind of coactive regulation of communicator-addressee behaviors which is an intrinsic part of social interaction and which is certainly not exhausted through a consideration of speech alone. Kendon (1967a) recognized these and other functions that are also served by implicit behaviors, particularly eye contact. He noted that looking at another person helps in getting information about how that person is behaving (that is, to monitor), in regulating the initiation and termination of speech, and in conveying emotionality or intimacy. With regard to the regulatory function, Kendon’s (1967a) findings showed that when the speaker and his listener are baout to change roles, the speaker looks in the direction of his listener as he stops talking, and his listener in turn looks away as he starts speaking. Further, when speech is fluent, the speaker looks more in the direction of his listener than when his speech is disrupted with errors and hesitations. Looking away during these awkward moments implies recognition by the speaker that he has less to say, and is demanding less attention from his listener. It also provides the speaker with some relief to organize his thoughts.
The concept of regulation has also been studied by Scheflen (1964, 1965). According to him, a communicator may use changes in posture, eye contact, or position to indicate that (1) he is about to make a new point, (2) he is assuming an attitude relative to several points being made by himself or his addresse, or (3) he wishes to temporarily remove himself from the communication situation, as would be the case if he were to select a great distance from the addressee or begin to turn his back on him. There are many interesting aspects of this regulative function of nonverbal cues that have been dealt with only informally. [...]
One of the first attempts for a more general characterization of the referents of implicit behavior and, therefore, possibly of the behaviors themselves, was made by Schlosberg (1954). He suggested a three-dimensional framework involving pleasantness-unpleasantness, sleep-tension, and attention-rejection. Any feeling could be assigned a value on each of these three dimensions, and different feelings would correspond to different points in this three-dimensional space. This shift away from the study of isolated feelings and their corresponding nonverbal cues and toward a characterization of the general referents of nonverbal behavior on a limited set of dimensions was seen as beneficial. It was hoped that it could aid in the identification of large classes of interrelated nonverbal behaviors.
Recent factor-analytic work by Williams and Sundene (1965) and Osgood (1966) provided further impetus for characterizing the referents of implicit behavior in terms of a limited set of dimensions. Williams and Sundene (1965) found that facial, vocal, or facial-vocal cues can be categorized primarily in terms of three orthogonal factors: general evalution, social control, and activity.
For facial expression of emotion, Osgood (1966) suggested the following dimensions as primary referents: pleasantness (joy and glee versus dread and anxiety), control (annoyance, disgust, contempt, scorn, and loathing versus dismay, bewilderment, surprise, amazement, and excitement), and activation (sullen anger, rage, disgust, scorn, and loathing versus despair, pity, dreamy sadness, boredom, quiet pleasure, complacency, and adoration). [...]
Scheflen (1964, 1965, 1966) provided detailed observations of an informal quality on the significance of postures and positions in interpersonal situations. Along similar lines, Kendon (1967a) and Exline and his colleagues explored the many-faceted significance of eye contact with, or observation of, another [...] These investigations consistently found, among same-sexed pairs of communicators, that females generally had more eye contact with each other than did males; also, members of both sexes had less eye contact with one another when the interaction between them was aversive [...] In generally positive exchanges, males had a tendency to decrease their eye contact over a period of time, whereas females tended to increase it (Exline and Winters, 1965). [...]
extensive data provided by Kendon (1967a) showed that observation of another person duing a social exchange varied from about 30 per cent of 70 per cent, and that corresponding figures for eye contact ranged from 10 per cent to 40 per cent. [...]
Physical proximity, touching, eye contact, a forward lean rather than a reclining position, and an orientation of the torso toward rather than away from an addressee have all been found to communicate a more positive attitude toward him. A second set of cues that indicates postural relaxation includes asymmetrical placement of the limbs, a sideways lean and/or reclining position by the seated communicator, and specific relaxation measures of the hands or neck. This second set of cues relates primarily to status differences between the communicator and his addressee: there is more relaxation with an addressee of lower status, and less relaxation with one of higher status. [...]
In sum, the findings from studies of posture and position and subtle variations in verbal statements [...] show that immediacy cues primarily denote evaluation, and postural relaxation ues denote status or potency in a relationship. It is interesting to note a weaker effect: less relaxation of one’s posture also conveys a more positive attitude toward another. One way to interpret this overlap of the referential significance of less relaxation and more immediacy in communicating a more positive feeling is in terms of the implied positive connotations of higher status in our culture. A respectful attitude (that is, when one conveys that the other is of higher status) does indeed have implied positive connotations. Therefore it is not surprising that the communication of respect and of positive attitude exhibits some similarity in the nonverbal cues that they require. However, whereas the communication of liking is more heavily weighted by variations in immediacy, that of respect is weighted more by variations in relaxation.”
I should probably note here that whereas it makes a lot of sense to be skeptical of some of the reported findings in the book, simply to get an awareness of some of the key variables and some proposed dynamics may actually be helpful. I don’t know how deficient I am in these areas because I haven’t really given body language and similar stuff much thought; I assume most people haven’t/don’t, but I may be mistaken.
iii. A friend let me know about this ressource and I thought I should share it here. It’s a collection of free online courses/lectures provided by Yale University.
iv. Prevalence, Heritability, and Prospective Risk Factors for Anorexia Nervosa. It’s a pretty neat setup: “During a 4-year period ending in 2002, all living, contactable, interviewable, and consenting twins in the Swedish Twin Registry (N = 31 406) born between January 1, 1935, and December 31, 1958, underwent screening for a range of disorders, including AN. Information collected systematically in 1972 to 1973, before the onset of AN, was used to examine prospective risk factors for AN.”
“Results The overall prevalence of AN was 1.20% and 0.29% for female and male participants, respectively. The prevalence of AN in both sexes was greater among those born after 1945. Individuals with lifetime AN reported lower body mass index, greater physical activity, and better health satisfaction than those without lifetime AN. [...]
This study represents, to our knowledge, the largest twin study conducted to date of individuals with rigorously diagnosed AN. Our results confirm and extend the findings of previous studies on prevalence, risk factors, and heritability.
Consistent with several studies, the lifetime prevalence of AN identified by all sources was 1.20% in female participants and 0.29% in male participants, reflecting the typically observed disproportionate sex ratio. Similarly, our data show a clear increase in prevalence of DSM-IV AN (broadly and narrowly defined) with historical time in Swedish twins. The increase was apparent for both sexes. Hoek and van Hoeken3 also reported a consistent increase in prevalence, with a leveling out of the trajectory around the 1970s. Future studies in younger STR participants will allow verification of this observation.
Several observed differences between individuals with and without AN were expected, ie, more frequent endorsement of symptoms of eating disorders. Other differences are noteworthy. Consistent with previous observations, individuals with lifetime AN reported lower BMIs at the time of interview than did individuals with no history of AN. Although this could be partially accounted for by the presence of currently symptomatic individuals in the sample, our results remained unchanged when we excluded individuals likely to have current AN (ie, current BMI, ≤17.5). Previous studies have shown that, even after recovery, individuals with a history of AN have a low BMI.59 Although perhaps obvious, a history of AN appears to offer protection against becoming overweight. The protective effect also holds for obesity (BMI, ≥30), although there were too few individuals in the sample with histories of AN who had become obese for meaningful analyses. Despite the obvious nature of this observation, the mechanism whereby protection against overweight is afforded is not immediately clear. Those with a history of AN reported greater current exercise and a perception of being in better physical health. One possible interpretation of this pattern of findings is that individuals with a history of AN continue to display subthreshold symptoms of AN (ie, excessive exercise and caloric restriction) that contribute to their low BMIs. Alternatively, symptoms that were pathologic during acute phases of AN, such as excessive exercise and decreased caloric intake, may resolve over time into healthy behaviors, such as consistent exercise patterns and a healthful diet, that result in better weight control and self-rated health.
Regardless of which of these hypotheses is true, another intriguing difference is that individuals with lifetime AN report a lower age at highest BMI, although the magnitude of the highest lifetime BMI does not differ in those with and without a history of AN. Those with AN report their highest lifetime BMIs early in their fourth decade of life on average, whereas those without AN report their highest BMIs in the middle of their fifth decade of life (close to the age at interview). On a population level, adults tend to gain on average 2.25 kg (5 lb) per decade until reaching their eighth decade of life.60 Although more detailed data are necessary to make definitive statements about different weight trajectories, our results suggest not only that individuals with AN may maintain low BMIs but also that they may not follow the typical adult weight gain trajectories. These data are particularly intriguing in light of recent reports of AN being associated with reduced risk of certain cancers61 - 62 and protective against mortality due to diseases of the circulatory system.63 - 64 Energy intake is closely related to fat intake and obesity, both of which have also been related to cancer development65 - 66 and both of which are reduced in AN. Further detailed studies of the weight trajectories and health of individuals with histories of AN are required to explicate the nature and magnitude of these intriguing findings.
Of the variables assessed in 1972 to 1973, neuroticism emerged as the only significant prospective predictor of AN. This is notable because there have been few truly prospective risk factor studies of AN.”
v. The music is a bit much for me towards the end, but this is just an awesome video. I think I’d really have liked to know that guy:
vi. Political Sorting in Social Relationships: Evidence from an Online Dating Community, by Huber and Malhotra.
I found these data surprising (and I’m skeptical about the latter finding):
“Among paid content, online dating is the third largest driver of Internet traffic behind music and games (Jupiter Research 2011).A substantial number of marriages also result from interactions started online. For instance, a Harris Interactive study conducted in 2007 found that 2% of U.S. marriages could be traced back to relationships formed on eHarmony.com, a single online dating site (Bialik 2009).”
Anyway I’ll just post some data/results below and leave out the discussion (click to view tables in full size). Note that there are a lot of significant results here:
The last few figures are also interesting (people really care about that black/white thing when they date (online)…). but you can go have a look for yourself. As I’ve already mentioned there are a lot of significant results – they had a huge number of data to work with (170,413 men and 132,081 women).
File under: Stuff you probably didn’t know about that actually matters a great deal.
“Generation of electricity using coal started at the end of the 19th century. The first power stations had an efficiency of around 1%, and needed 12.3 kg of coal for the generation of 1 kWh. [...] With increasing experience, in combination with research and development, these low efficiency levels improved rapidly. Increased technical experience with coal processing and combustion technology enabled a steady increase in the steam parameters ‘pressure’ and ‘temperature’, resulting in higher efficiency. In the years 1910, efficiency had already increased to 5%, reaching 20% by 1920. In the fifty’s, power plants achieved 30% efficiency, but the average efficiency of all operating power plants was still a modest 17%. [...] continuous development resulted around the mid 80′s in an average efficiency of 38% for all power stations, and best values of 43%. In the second half of the nineties, a Danish power plant set a world record at 47%. [...] The average efficiency of all coal power stations in the world is around 31%. [...] In the next 10 years [the paper is from 2005, US], efficiencies up to 55% can be expected.” [...]
Often, the question is asked why the ‘other 45%’ cannot be converted into electricity. This relates to the laws of physics: the absolute maximum efficiency is the so-called ‘Carnot efficiency‘. For a turbine operating with gasses of 600°C, it is 67%. Then we need to take into account the exergy content of steam (around 94%). Also combustion has an efficiency less than 100% (around 95%). The transfer of combustion heat to steam in the boiler is for example 96% efficient. Losses due to friction can be around 5% (efficiency 95%). The efficiency of a generator is about 98% on average . . . .
To obtain the combined efficiency, one needs to multiply the efficiency of each process. Taking the above mentioned components, one obtains 0.67 x 0.94 x 0.95 x 0.96 x 0.95 x 0.98 = 0.535 or 53.5%.
This does not yet take into account the efficiency of all components. The power station’s own power use for motors to grind coal, pumps, ventilators, . . . further reduces efficiency. In practice, net efficiency will be around 40 and 45%. Continuous load changes, i.e. following the load, and start-up/shutdown procedures further lower efficiency. The increasing variability of the load, through increased use of intermittent sources such as wind, will lead to increased swings in the load of the power station, reducing efficiency.”
ii. Allostatic load as a marker of cumulative biological risk: MacArthur studies of successful aging. From the abstract:
“Allostatic load (AL) has been proposed as a new conceptualization of cumulative biological burden exacted on the body through attempts to adapt to life’s demands. Using a multisystem summary measure of AL, we evaluated its capacity to predict four categories of health outcomes, 7 years after a baseline survey of 1,189 men and women age 70–79. Higher baseline AL scores were associated with significantly increased risk for 7-year mortality as well as declines in cognitive and physical functioning and were marginally associated with incident cardiovascular disease events, independent of standard socio-demographic characteristics and baseline health status. The summary AL measure was based on 10 parameters of biological functioning, four of which are primary mediators in the cascade from perceived challenges to downstream health outcomes. Six of the components are secondary mediators reflecting primarily components of the metabolic syndrome (syndrome X). AL was a better predictor of mortality and decline in physical functioning than either the syndrome X or primary mediator components alone. The findings support the concept of AL as a measure of cumulative biological burden.
In elderly populations, comorbidity in the form of multiple co-occurring chronic conditions is the norm rather than the exception. For example, in the U.S. 61% of women and 47% of men age 70–79 report two or more chronic conditions. These figures rise to 70% of women and 53% of men age 80–89 with 2+ chronic conditions (1). No single form of comorbidity occurs with high frequency, but rather a multiplicity of diverse combinations are observed (e.g., osteoarthritis and diabetes, colon cancer, coronary heart disease, depression, and hypertension). This diversity underscores the need for an early warning system of biomarkers that can signal early signs of dysregulation across multiple physiological systems.
One response to this challenge was the introduction of the concept of allostatic load (AL) (2–4) as a measure of the cumulative physiological burden exacted on the body through attempts to adapt to life’s demands. The ability to successfully adapt to challenges has been referred to by Sterling and Eyer (5) as allostasis. This notion emphasizes the physiological imperative that, to survive, “an organism must vary parameters of its internal milieu and match them appropriately to environmental demands” (5). When the adaptive responses to challenge lie chronically outside of normal operating ranges, wear and tear on regulatory systems occurs and AL accumulates.”
They conclude that: “The analyses completed to date suggest that the concept of AL offers considerable insight into the cumulative risks to health from biological dysregulation across multiple regulatory systems.” I haven’t come across the concept before but I’ll try to keep it in mind. There’s a lot of stuff on this.
“a few years ago, I learned that it’s actually pretty common to survive a plane crash. Like most people, I’d assumed that the safety in flying came from how seldom accidents happened. Once you were in a crash situation, though, I figured you were probably screwed. But that’s not the case.
Looking at all the commercial airline accidents between 1983 and 2000, the National Transportation Safety Board found that 95.7% of the people involved survived. Even when they narrowed down to look at only the worst accidents, the overall survival rate was 76.6%. Yes, some plane crashes kill everyone on board. But those aren’t the norm. So you’re even safer than you think. Not only are crashes incredibly rare, you’re more likely to survive a crash than not. In fact, out of 568 accidents during those 17 years, only 71 resulted in any fatalities at all.”
iv. Now that we’re talking about planes: What does an airplane actually cost? Here’s one article on the subject:
“As for actual prices, airlines occasionally let numbers slip, either because of disclosure requirements or loose tongues.
Southwest Airlines Co., LUV +0.11% for example, recently published numbers related to its new order for Boeing 737 Max jetliners in a government filing. Mr. Liebowitz of Wells Fargo crunched the data and estimated an actual base price of roughly $35 million per plane, or a discount of around 64%. He noted that Southwest is one of Boeing’s best customers and that early buyers of new models get preferential pricing. A Southwest spokeswoman declined to comment.
Air India, in seeking funding last year for seven Boeing 787 Dreamliners it expects to receive this year, cited an average “net cost” of about $110 million per plane. The current list price is roughly $194 million, suggesting a 43% discount. Air India didn’t respond to a request for comment for this article.
In March 2011, Russian flag carrier Aeroflot mentioned in a securities filing that it would pay at most $1.16 billion for eight Boeing 777s…”
100+ million dollars for a plane. I had not seen that one coming. File under: Questions people don’t seem to be asking, which I think is sort of weird. Now that we’re at it, what about trains? Here’s a Danish article about our new IC4-trains. A conservative estimate is at $1,09 billion (6,4 billion kroner) for 83 trains, which is ~$13,2 million/train (or rather per trainset (US terminology) or ~77 million Danish kroner. That’s much cheaper than the big airplanes, but it sure is a lot of money. What about busses? I’ve often thought about this one, perhaps because it’s a mode of transportation I use far more frequently than the others. Here’s one bit of information about the situation in the US, which is surely different from the Danish one but not that different:
“Diesel buses are the most common type of bus in the United States, and they cost around $300,000 per vehicle, although a recent purchase by the Chicago Transit Authority found them paying almost $600,000 per diesel bus. Buses powered by natural gas are becoming more popular, and they cost about $30,000 more per bus than diesels do. Los Angeles Metro recently spent $400,000 per standard size bus and $670,000 per 45 foot bus that run on natural gas.
Hybrid buses, which combine a gasoline or diesel engine with an electric motor much like a Toyota Prius, are much more expensive than either natural gas or diesel buses. Typically, they cost around $500,000 per bus with Greensboro, NC’s transit system spending $714,000 per vehicle.”
So of course you can’t actually compare these things this way because of the different way costs are calculated, but let’s just for fun assume you can: When you use the average price of a standard US diesel bus and compare it to the price of the recently bought Danish trains, the conclusion is that you could buy 44 busses for the price of one train. And you could buy 367 busses for the price of one of the Dreamliners.
v. A new blog you might like: Collectively Unconscious. A sort of ‘The Onion’ type science-blog.
vi. I was considering including this stuff in a wikipedia-post, but I thought I’d include it here instead because what’s interesting is not the articles themselves but rather their differences: Try to compare this english language article, about a flame tank designed in the United States, with this article about the same tank but written in Russian. I thought ‘this is weird’ – anybody have a good explanation for this state of affairs?
vii. The Emergence and Representation of Knowledge about Social and Nonsocial Hierarchies. I haven’t found an ungated version of the paper, but here’s the summary:
“Primates are remarkably adept at ranking each other within social hierarchies, a capacity that is critical to successful group living. Surprisingly little, however, is understood about the neurobiology underlying this quintessential aspect of primate cognition. In our experiment, participants first acquired knowledge about a social and a nonsocial hierarchy and then used this information to guide investment decisions. We found that neural activity in the amygdala tracked the development of knowledge about a social, but not a nonsocial, hierarchy. Further, structural variations in amygdala gray matter volume accounted for interindividual differences in social transitivity performance. Finally, the amygdala expressed a neural signal selectively coding for social rank, whose robustness predicted the influence of rank on participants’ investment decisions. In contrast, we observed that the linear structure of both social and nonsocial hierarchies was represented at a neural level in the hippocampus. Our study implicates the amygdala in the emergence and representation of knowledge about social hierarchies and distinguishes the domain-general contribution of the hippocampus.”
I’ve only actually watched the first 15 minutes (and I’m not sure I’ll watch the rest), but I assume some of you will find this interesting.
“This study analyzed the romantic content of a sample of 40 romantic comedy films using a basic grounded theory methodology. Analyses revealed that such films appear to depict romantic relationships as having qualities of both new and long-term relationships; that is, to be both novel and exciting, yet emotionally significant and meaningful. Furthermore, relationships were shown to have both highly idealistic and undesirable qualities but, for any problems or transgressions experienced to have no real negative long-term impact on relationship functioning. The potential for viewer interpretations is discussed and the need for future research highlighted. [...]
Of the 107 [romantic] gestures coded, male characters performed 90, they gave 35 of 37 gifts, performed 14 of 17 favors, and took more steps to initiate relationships (63 of 84). Such a proportion of effort could lead to the distinguishing of gender roles, identifying the man’s role to ‘‘take the lead’’ when it comes to relationships. A further implication could be female adolescent viewers’ forming of somewhat idealized relationship expectations. With films depicting male characters as frequently performing exaggeratedly romantic gestures [...], female adolescents may be led to believe that such behaviors are the norm. Furthermore, by preferring to focus on behaviors between couples such as the aforementioned, it is possible that such films may make these gestures more salient to adolescents as an indication of the extent of partners’ feelings for them and the quality of the relationship itself over factors such as communication and trust.
Although there were 61 coded instances of ‘‘open about feelings and intentions,’’ there were only 4 incidents coded pertaining to trust, with 3 of these demonstrating a character’s lack of trust in their partner. [...] The lack of depiction of trust becomes particularly notable when looking at the number of incidents of ‘‘deception’’ coded. There were 82 such incidents, occurring across all 40 films, ranging from white lies so as to spare partners’ feelings, to more serious acts of deception such as ulterior motives and direct lying for personal gains. These far outweighed characters confessing their lies and deceptive acts to their partners (9), with lies being discovered by partners typically by chance or indeed not at all. [...]
Another category to emerge at this stage of coding that may have the potential to influence viewer perceptions was ‘‘being single.’’ Although this was one of the smaller categories, each coded incident (15) was consistently negative. Individuals who were single were depicted as either lonely and miserable [...], frustrated [...], or made to feel insecure [...]. Two films [...] even suggested that being single might interfere with career progression. Such a consistently negative representation of being single could, therefore, have the potential to negatively influence viewers’ feelings toward being single themselves. [...]
It should be further noted that of the incidents of affection coded, a vast minority occurred between married couples. Married couples were typically portrayed as either unhappy with their spouse [...], or were implied as happy but did little to reflect this [...]. Of the depictions of affection between married couples that were coded, many were interspersed with episodes of arguing [...], and most were limited to gestures such as brief kisses or standing with an arm around one other. Such a representation of marriage may leave adolescent viewers to see marriage and romance as disparate entities and with affection between married couples as an exception instead of the norm. [...]
What is interesting to note about the behaviors comprising this category ['relationship issues'], however, is that, irrespective of seriousness, there appeared to be no real consequences for characters’ transgressions in their relationships. [...] Such depictions do not accurately reflect the actual emotions individuals typically experience in response to acts of deception and betrayal in their relationships, which can involve feelings of hurt, anger, resentment, and relational devaluation (Fitness, 2001). As a result, with characters’ negative behaviors either going undiscovered or having no long-lasting impact on their relationships, adolescent viewers may underestimate the consequences their behaviors can have on their own relationships.”
“This paper investigates, theoretically and empirically, a possibly fundamental aspect of technological progress. If knowledge accumulates as technology progresses, then successive generations of innovators may face an increasing educational burden. Innovators can compensate in their education by seeking narrower expertise, but narrowing expertise will reduce their individual capacities, with implications for the organization of innovative activity – a greater reliance on teamwork – and negative implications for growth. I develop a formal model of this “knowledge burden mechanism” and derive six testable predictions for innovators. Over time, educational attainment will rise while increased specialization and teamwork follow from a sufficiently rapid increase in the burden of knowledge. In cross-section, the model predicts that specialization and teamwork will be greater in deeper areas of knowledge while, surprisingly, educational attainment will not vary across fields. I test these six predictions using a micro-data set of individual inventors and find evidence consistent with each prediction. The model thus provides a parsimonious explanation for a range of empirical patterns of inventive activity. Upward trends in academic collaboration and lengthening doctorates, which have been noted in other research, can also be explained by the model, as can much-debated trends relating productivity growth and patent output to aggregate inventive effort. The knowledge burden mechanism suggests that the nature of innovation is changing, with negative implications for long-run economic growth.”
iv. Beyond Guns and God, Understanding the Complexities of the White Working Class in America. I haven’t read it and I don’t think I will, but I thought I should put the link up anyway. The link has a lot of data.
v. Some Danish church membership numbers. The site is in Danish but google translate is your friend and there isn’t much text anyway. Where I live almost 5 out of 6 people are members of the church. Over the last 20 years the national membership rate has dropped by ~0,5 percentage points/year. 4 out of 5 Danes are members of the national church, in 1990 it was 9 out of 10. Approximately 90% of the people who die are members, whereas ‘only’ approximately 70% of children being born get baptized. Children of non-Western immigrants make up less than 10% of all births (9,1% from 2006-2010) – so even though population replacement may be part of the story, there’s likely other stuff going on as well.
vi. Intelligence: Knowns and Unknowns. I may blog this in more detail later, for now I’ll just post the link.
vii. Theodore Dalrymple visited North Korea in 1989. The notes here about his visit to Department Store Number 1 are worth reading.
(link). He probably is going to say something stupid. According to a new paper: The Mere Anticipation of an Interaction with a Woman Can Impair Men’s Cognitive Performance. ‘Further studies needed’ etc., but I’m inclined to believe that they are right and that yes, males are actually that stupid and impressionable. Though effect sizes are important to have in mind too.
ii. I thought this was funny. Then again I’m weird.
iii. I’ve added Guam to my list of ‘places I don’t want to visit anytime in the near future’. Why? Because of this.
“Birds are dominant apex predators in terrestrial systems around the world, yet all studies on their role as predators have come from small-scale experiments; the top-down impact of bird loss on their arthropod prey has yet to be examined at a landscape scale. Here, we use a unique natural experiment, the extirpation of insectivorous birds from nearly all forests on the island of Guam by the invasive brown tree snake, to produce the first assessment of the impacts of bird loss on their prey. We focused on spiders because experimental studies showed a consistent top-down effect of birds on spiders. We conducted spider web surveys in native forest on Guam and three nearby islands with healthy bird populations. Spider web densities on the island of Guam were 40 times greater than densities on islands with birds during the wet season, and 2.3 times greater during the dry season. These results confirm the general trend from manipulative experiments conducted in other systems however, the effect size was much greater in this natural experiment than in most manipulative experiments. [...]
We compared the abundance of web-building spiders on Guam to that on Rota, Tinian and Saipan. At each site, we set up 1–3 transects, separated by at least 200 meters. The transects were 20 or 30 meters long, depending on the year. We counted all visible webs within 1 horizontal meter of each transect centerline and up to 2 vertical meters above the ground. Webs lacking a spider were considered abandoned, and not counted. [...] Guam, without birds, had a mean of 18.37 spider webs per ten meters in the wet season, compared to 0.45 webs per ten meters on nearby islands with birds [...]. In the dry season, Guam had 26.19 spider webs per ten meters compared to 11.37 webs per ten meters on nearby islands with birds”
iv. You Don’t Know Me, But I Know You: The Illusion of Asymmetric Insight, by Pronin, Kruger, Savitsky and Ross. Interesting. The abstract:
“People, it is hypothesized, show an asymmetry in assessing their own interpersonal and intrapersonal knowledge relative to that of their peers. Six studies suggested that people perceive their knowledge of their peers to surpass their peers’ knowledge of them. Several of the studies explored sources of this perceived asymmetry, especially the conviction that while observable behaviors (e.g., interpersonal revelations or idiosyncratic word completions) are more revealing of others than self, private thoughts and feelings are more revealing of self than others. Study 2 also found that college roommates believe they know themselves better than their peers know themselves. Study 6 showed that group members display a similar bias—they believe their groups know and understand relevant out-groups better than vice versa. The relevance of such illusions of asymmetric insight for interpersonal interaction and our understanding of “naive realism” is discussed.”
v. Crunching the data on human brain evolution. Which functional form fits the underlying process better is an interesting discussion but I’d like to note that what I first thought when seeing these (or rather, similar depictions elsewhere) was: ‘hey, look at that standard deviation!’
vi. Social rejection shares somatosensory representations with physical pain. Another fMRI-study. I don’t know enough about this stuff to comment on the validity of the conclusions, but I’ll probably bookmark it and keep it for later so that I’ll be able to use it to justify my decision not to ask out the hypothetical cute girl in class next semester (or whatever).
vii. Quality of Diabetes Care in Italy. I’m glad I don’t live in Italy:
“Of 126,163 diabetic individuals (prevalence of diabetes 5.8%, mean age 71 years), as many as 42% did not have their HbA1c measured for over a year. Even considering only insulin-treated people, this frequency remains disappointingly high (35%). The proportion of people having at least two annual tests for HbA1c was low (32.7%; 43.1% among insulin-treated patients). [...] Another disappointing finding is the very low proportion of subjects in whom microalbuminuria was tested (27%) in spite of its role as a strong predictor of cardiovascular diseases and dialysis. Annual testing for plasma total cholesterol (61.2%), creatinine (58.9%), eye examination (11.1%), electrocardiogram (25.1%), and arterial echo-Doppler (15.9%) were low.”
I recently posted some corresponding Danish numbers here, though unfortunately that post is in Danish. In Denmark approximately 95% of diabetics get their HbA1c measured at least once a year. I get my HbA1c tested 3-4 times a year and I’d have no clue what to do without these numbers. 92% of Danish patients cared for by the hospital outpatient clinics (diabetesambulatorier) and 55% of the patients treated by their local GP were tested for microalbuminuria at least once every two years. I’m tested once per year. I frankly found it shocking that the Italic HbA1c numbers were that low but I probably should have known better, given the variation in diabetes care across countries. Not all of the variables mentioned are equally important but Italy fails at the really basic stuff too. For a Danish diabetic to move to a place like (Southern) Italy (I’m almost certain the situation is far worse in the south than in the north) would be a bit like an old and frail person moving to a place where they haven’t heard about penicillin. This stuff is a big part of why I’m not very likely to move away from Denmark when I finish my education – a lot of places I basically consider ‘off limits’ because I’d be gambling with my health by moving there, and even a lot of relatively advanced societies still have diabetes treatment protocols which belong in the (metaphorical) Stone Age.
As indicated the second paper on my reading list is not as easy to cover here as was the first one. It’s quite a bit more technical than the first paper was and so there’s a lot of stuff which is harder to cover. The paper covers many of the same themes the first paper does (it’s written by the same people and published around the same time), but it handles some of the aspects in far more detail. The modelling will probably be a bit hard to understand if you’ve never worked with economic models before but I’ve tried to outline in the post what at least some of the model-building stuff that’s going on is aiming at.
I have decided that I want to try to blog at least all of the material this specific course in question deals with; I haven’t yet figured out if it’ll make sense to try to ‘workblog’ the other stuff I’m doing this semester yet but it takes time to write these posts and so I can assure you that I’ll not try to cover everything I’m supposed to learn this semester. In the post I’ve decided to just write some relevant stuff about the various aspects of the models and -results presented in the paper and keep it relatively superficial (I’ve included nothing which relates to the stuff in the appendix) – hopefully you’ll understand a bit about what’s going on. Of course I mainly write these posts for myself – I know that I learn stuff from writing these posts – but please don’t forget that I’m actually also providing you guys a valuable service here; the last post I wrote was a condensed version of an almost 40 page paper which took me at least a few hours to read and prepare notes for which you could read in just, what, 5-10 minutes?
So, this new paper – what’s it all about? In the paper there’s some introductionary stuff which closely relates to the previous paper, there’s some theoretical model-work, and then there’s a part which handles some model simulations and numerical illustrations. I’ve spent most of my time with the model-work (both in the post and when working with the paper previously). A key policy challenge for decisionmakers is to find and settle for a ‘proper’ balance between incentives and insurance in the labour market, and part of what this paper does is to have a closer look at different aspects of workfare in order to figure out how workfare is likely to affect incentive structures in the labour market and thus labour market outcomes. If you haven’t read the first post, you should probably start there before going any further. As in the last paper, the (now no longer implicit) model operates with three groups: Employed, unemployed and people in activation. Again you have a threat effect, a lock-in effect and a wage effect. The paper disregards human capital considerations so the post-programme effect is absent and state dependence is not addressed. The modelling framework takes benefit levels as a given and then proceed to question whether workfare elements can change insurance/incentives-aspects of the model and improve labour market performance. In the previous paper I did not feel that it was completely clear how the different workfare dimensions worked and how they differed, but in the formalized presentation here it is made very explicit; the two main policy instruments are i) the probability that an unemployed person will be required to participate in an activation measure [P(au)] and ii) the activation work requirement [l(a)]. The latter refers to how much work you’re required to do while activated – the larger this is, the more time/effort you’re required to spend on activation. One way to think about it is that one is the probability of X and the other the effect size of X. Going away from a unidimensional workfare requirement isn’t just something they do ‘to add complexity to the model’; it is shown in the paper that the two variables can be expected to affect different groups in different ways and it is emphasized for this reason that the overall effects of various changes in the workfare requirements depend critically on the total policy package and the specific mixing of the two policy variables.
The utility functions are standard leisure-income specifications and the main variable of interest in the analysis is the search effort (and how this relates to unemployment). As already mentioned the model work illustrates (in more detail) the effects also covered in the previous paper, for example the threat effect [∂S(u)(∂l(a))>0, ∂S(u)/∂P(au)>0], and it also illustrates much more precisely the reasons why using a multidimensional specification of the workfare scheme is important when evaluating the effects of changes to the workfare requirements: ∂S(a)/∂P(au) is negative in the model, meaning that the effect on the job search effort of people in activation given a marginal increase in the workfare intensity is the opposite of the effect such a marginal increase would have on the job search effort of people who are unemployed (and not in activation).
Given the model specification, people in activation spend more time on the work requirement and job search combined than unemployed people spend on job search, but which group actually spends more time searching is ambiguous. Searching is of course only half of the story as there also needs to be some jobs that people who search can find. Unemployed search for jobs, firms have vacancies where people can get employed and the unsurprising equilibrium conditions are briefly outlined. The job finding rate (α) of people searching is decreasing in the wage rate. Wage determination takes place according to a Nash bargaining solution where the bargaining power is taken to be exogenous. A key variable when dealing with the matching aspect of the model is the labour market tightness, θ = v/s (where v is number of job vacancies available and s is the effective search volume).
How workfare affects job search incentives is important, but the main interest is of course rather the impact on (un)employment. The main fact to take away from that part of the formal analysis is that ‘things are complicated’. The net effect on the number of people who are unemployed, in activation and in employment from a given policy change “depends on the balance between counteracting effects”. The effective job finding rates (α*s) [job finding rate conditional on search times search volume] of the two main groups (unemployed and activated) are key, and their contribution can be decomposed into an indirect wage effect, which is unambiguously positive and will thus increase the effective job finding rate for both unemployed and activated, and a direct search effect the sign of which depends on the workfare dimensions and the groups in question. This is another reason why a general equilibrium framework is required to fully understand the effects involved (more below); as also mentioned in the previous paper, in analyses which do not include the indirect wage effect workfare elements will generally be perceived of as worse performing than they do in analyses which include the indirect effects.
The effects of a workfare policy change is not surprisingly dependent on the initial level of workfare introduced into the system; it is for example shown that if no workfare elements exists ex ante, a policy maker can decrease total unemployment by introducing workfare elements and holding the benefit level constant. The dynamics of the level-dependencies involved are made more explicit in the model simulations, where one of the conclusions is that at a low intensity of workfare intensity [P(au)] the threat and wage effects dominate (i.e. a marginal increase in the workfare intensity will impact employment positively) whereas when at a higher level the locking-in effect dominates (i.e. a marginal increase in the workfare intensity will impact employment negatively). On the other hand, when it comes to the work requirement [l(a)] total unemployment is unambiguously decreasing in the work requirement. Welfare goes down for all three groups analyzed, including the people in employment, when workfare is increased, although employers benefit from workfare because it impacts their profit share positively. As workfare can be thought of as to some extent effectively introducing slack into the budget constraint of the government, this party is of course another entity which gains from the introduction of the scheme.
The next paper in the series is Short-Run Equilibrium Dynamics of Unemployment, Vacancies, and Real Wages by Christopher Pissarides (who got the Nobel Prize two years ago). I’ve unfortunately not been able to find a non-gated version of this paper online.
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