I am saddened and sickened to learn of the horrific events in Norway today. As I write this, the news is that a total of 80 have died, 7 in the bombing in Oslo and the rest, presumably, at the youth camp in Utoya Island. This is an unimaginable tragedy for the parents of these children and would be wherever such an event occurred. The impact on aggregate mortality just happens to be particularly acutely noticeable in a low-mortality country such as Norway. I look at Norwegian mortality data quite a bit because I use mortality change in Norway as an example in at least two classes I teach. To give a sense of what an enormous impact 80 violent deaths have on the overall mortality of a relatively small, and very low-mortality country like Norway, I plotted the number of deaths by age on semi-logarithmic axes for the latest year for which we have data (2009). I then added the 73 deaths (in red), assuming for simplicity that they all fell on 16 year-olds (since it was a youth camp). While clearly not true, this allows us to compare the scale of this mass murder with the pace of death in Norway as a whole.
It is plain to see that, beyond the clear impact such an event has on the families directly effected, this senseless act has a substantial effect on the aggregate pattern of mortality for the entire country of Norway.
A new study has found that the structural adjustments that the International Monetary Fund requires of countries to which it loans money increase tuberculosis incidence, prevalence, and mortality. The authors studied tuberculosis epidemiology in countries of Eastern Europe and the former Soviet Union that received IMF loans following 1989. This result suggests that there are real health trade-offs associated with the fiscal austerity that follows an IMF loan. This research is very welcome because it adds a reasoned empirical perspective to the often shrill debates over the relative merits of IMF policies. Harvard epidemiologist and political scientist Megan Murray and Gary King have written a companion Perspectives piece in the same issue of PLoS Medicine. There is also a nice news story on it in The New Scientist.
A new paper (discussed in this New York Times article) shows what many demographers have suspected for a while. Life expectancy in the nation’s poorest counties has actually declined since 1983, while life expectancy continues to rise for the more affluent counties. This finding stands in stark contrast with the trends recorded from 1961-1983. During this period, life expectancy increased in nearly every county in the United States. The basic idea is that if you were well off when Ronald Reagan entered the White House, you have done well in terms of mortality since. However, if you had the misfortune of being poor, then you have typically fared less well. The result? Diverging health fortunes among the haves and the have-nots in American society.
This finding relates to what my colleagues Shripad Tuljapurkar and Ryan Edwards refer to as “the ultimate inequality.” The manifestation of inequality does not get much more stark than through systematic differences in the number of years lived by people of different socioeconomic or ethnic backgrounds.
In the 1983-1999 period, mortality from the largest cause of death, cardiovascular disease, declined in both rich and poor counties. So what has caused the divergence in life expectancy between rich and poor counties? It appears to be driven largely by increased mortality among the poor from diabetes and other non-communicable diseases on the one hand, and lung cancer and chronic obstructive pulmonary disease (COPD; of which emphysema is an example) on the other. Among men in the poor counties, the probabilities of death from homicide or HIV/AIDS have also increased. While the probabilities of death due to either homicide or AIDS increased only a little, homicide and AIDS can have a big impact on life expectancy for a population. This is because victims of homicide and AIDS are usually young, so that every homicide or AIDS death leads to a relatively large number of lost years for the sub-population. So much for that goal of “accelerat[ing] CDC’s health impact in the U.S population and to eliminate health disparities for vulnerable populations as defined by race/ethnicity, socio-economic status, geography, gender, age, disability status, risk status related to sex and gender, and among other populations identified to be at-risk for health disparities.” We can do better…