i. I was considering covering this study in a bit more detail, but I decided against it because workplace filters probably would not like it very much – it would contain words such filters do not like (no, I’m not thinking of words like ‘sociodemographic characteristics’ or ‘multiple regression analyses’). I know a few people sometimes read my blog from work and if you’re one of them, let me just say that you should probably not read this while at work.
“The age- and sex-adjusted incidence of myocardial infarction increased from 274 cases per 100,000 person-years in 1999 to 287 cases per 100,000 person-years in 2000, and it decreased each year thereafter, to 208 cases per 100,000 person-years in 2008, representing a 24% relative decrease over the study period. […]
The proportion of patients who underwent revascularization within 30 days after myocardial infarction increased from 40.7% in 1999 to 47.2% in 2008 (P<0.001 for trend). Among patients with ST-segment elevation myocardial infarction, 49.9% underwent revascularization in 1999 as compared with 69.6% in 2008 (P<0.001 for trend). Among patients with non–ST-segment elevation myocardial infarction, 33.4% underwent revascularization in 1999 as compared with 41.3% in 2008 (P<0.001 for trend) […]
The proportion of patients with myocardial infarction who were known to have undergone troponin I testing increased from 53% in 1999 to 84% in 2004, with stable testing rates between 2004 and 2008. […]
The age- and sex-adjusted 30-day mortality after myocardial infarction decreased from 10.5% in 1999 to 7.8% in 2008 (P<0.001 for linear trend). This decrease was driven by the case fatality rate for non–ST-segment elevation myocardial infarction, which decreased from 10.0% to 7.6% (P<0.001 for trend); there was no significant change over time for ST-segment elevation myocardial infarction (P = 0.81). The multivariable adjusted odds ratio for death at 30 days after myocardial infarction was 0.76 (95% confidence interval [CI], 0.65 to 0.89) in 2008 as compared with 1999.”
Short version: Fewer people got a(n ST-segment elevation) myocardial infarction even though more people were subjected to fancy testing, more people got access to fancy treatment, and the people in the sample who got a non-ST-segment MI during the study period were less likely to die from it. But…
“observed reductions in case fatality rates could be attributable to secular trends in ascertainment of myocardial infarction and decreased severity on presentation, as well as any improvements in management of acute myocardial infarction.44 The observation that mortality after ST-segment elevation myocardial infarction (which is less influenced by the use of highly sensitive biomarkers) did not decrease over time provides support for this hypothesis.”
This could still be considered good news because if decreased severity on presentation reduces mortality it’s probably a good idea to at least have a closer look at that variable; on the other hand it’s bad news because fancy testing is expensive. Another thing:
“given the integrated medical care delivery structure in the health system that we studied and the magnitude of recent improvements in the control of risk factors within our population, our results may not be fully generalizable to other health care settings.”
Good luck finding MSM-coverage of the study including this part. I’d probably have removed the word ‘fully’. The population risk factor development during the period is a major confound.
iii. International migration: A panel data analysis of the determinants of bilateral flows by Anna Maria Mayda.
“According to the international migration model, pull and push factors have either similarsized effects (with opposite signs), when migration quotas are not binding, or they both have no (or a small) effect on emigration rates, when migration quotas are binding. It is not clear, ex ante, which one of the two scenarios characterizes actual flows. Migration policies in the majority of destination countries are very restrictive, which should imply binding constraints on the number of migrants. On the other hand, even countries with binding official immigration quotas often accept unwanted (legal) immigration.8 Restrictive immigration policies are often characterized by loopholes, that leave room for potential migrants to take advantage of economic incentives. […]
My empirical analysis also finds that inequality in the source and host economies is related to the size of emigration rates as predicted by Borjas (1987) selection model. An increase in the origin country’s relative inequality has a non-monotonic effect on the size of the emigration rate: the impact is estimated to be positive if there is positive selection, negative if there is negative selection. Among the variables affecting the costs of migration, distance between destination and origin countries appears to be the most important one: Its effect is negative, significant and steady across specifications. On the other hand, there is no evidence that cultural variables related to each country pair play a significant role. Demographics – in particular, the share of the origin country’s population who is young – shape bilateral flows as predicted by the theory. Since the effect of geography and demographics works through the supply side of the model, their impact should be even stronger when migration quotas are relaxed, which is what I find in the data. […]
Since immigrants are likely to receive support from other immigrants from the same origin country already established in the host country, they will have an incentive to choose destinations with larger communities of fellow citizens. Network effects imply that bilateral migration flows are highly correlated over time, which is what the data shows.”
iv. Via npr:
“It’s a sound you would never want to hear in real life, but this a safe way to eavesdrop. Just one warning: For the first two minutes of this video, nothing happens, nothing I could hear, anyway. Then there’s a countdown, and at 2:24 from the top … the bomb bursts; at 2:54 the blast hits.”
v. Does Thinking Really Hard Burn More Calories? Interesting piece. Unfortunately(?), “for most people, the body easily supplies what little extra glucose the brain needs for additional mental effort.”
I would be very interested in seeing a study on this including type 1 diabetics. Hard thinking for extended periods of time – like, say, a four-hour chess game or an exam – impacts my blood glucose in a very significant way; it drops like a stone if I don’t take precautions. This is despite the fact that hard thinking under such circumstances is often, as mentioned in the article, linked to stress and the release of cortisol, one of the primary functions of which is to increase blood sugar.
vi. TV from a different world:
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