Politicians have talked endlessly about deficits and finance during our ongoing economic crisis. But we’ve talked far less about achieving another major goal that is just as important, if not more so, than promoting stable financial markets: protecting our health and well-being during hard times and into the future. What policies are most effective in preserving our health during economic recessions—and can we afford them? That question, it turns out, can be answered through data and careful research on recessions both past and present.
This is a guest post by the computational epidemiologist Dr. John Ayers: Most of us are aware of the “big data” revolution fueled by electronic information. It has been suggested that big data, along with hypothesis-free methods popularized by films such as Moneyball, will allow for an unprecedented growth of knowledge across disciplines, including epidemiology and preventive medicine. While I am a bit more circumspect in expectations (there is no substitute for survey data in many cases), I do believe that electronic data collected for a fraction of the cost of survey data can work hand-in-hand with research derived from more traditional sources. Our study, published this month in the American Journal of Preventive Medicine, is a great example of the complementarity of big data approaches to mental health research
Back in 1999, the organization Médecins sans Frontières (MSF or “Doctors Without Borders”) received the Nobel Peace Prize and did something a bit surprising with it: they spent it on drugs. Or, more precisely, they invested in a new Drugs for Neglected Diseases Initiative (DNDi) that sought to develop an alternative model for the research and development (R&D) of new drugs for neglected diseases. By “neglected” we mean diseases that are only caught by people too poor to pay for medications: illnesses like malaria, visceral leishmaniasis (VL), sleeping sickness (human African trypanosomiasis, HAT), and Chagas disease. These sicknesses are currently treated by medications that are too expensive, no longer produced, highly toxic, or ineffective. Over a decade later, DNDi and other initiatives have highlighted some stark failures in the R&D process.
While sugar-sweetened beverages (SSBs) have garnered much attention in the US given their associations with obesity and diabetes in the Nurses Health Study and a number of other assessments, a key question is whether this effect also translates to low- and middle-income countries where both domestic and imported beverages are becoming increasingly popular. In an article just published in the American Journal of Public Health, we looked at this question using the soft drink industry’s own statistics, merged with comparative survey data on weight status and diabetes across the globe. First, we looked at the industry’s data to examine how much sales in low- and middle-income countries even made up significant business for soda companies. To our surprise, the majority of soft drink sales are indeed outside of North America and Europe, and the rate of increase in these sales is highest in low- and middle-income countries: Of course, merely correlating a rise in per-capita soda consumption to a rise in obesity or diabetes would be silly—there are many other changes taking place at the same time in low- and middle-income countries, such as urbanization and changes in the work environment that are associated with lower physical activity, changes in a number of other foods being consumed (like higher meat consumption, and higher overall calorie intake as incomes rise), and aging, among others. So instead of merely doing rough correlations, we looked at age-standardized estimates of overweight, obesity and diabetes, and corrected for other types of foods (e.g., other carbohydrates, fruits, vegetables, meats, fats, oils, and total calories), as well as aging, income and urbanization
There have been many theories and contradictory findings about how alcohol use changes during economic downturns. Will people drink less because they can’t afford it—a common refrain in economics journals? Or will the depression associated with unemployment lead to more binging? A recent article looking at alcohol use during the Great Recession provides an interesting, if unexpected, result.
In today’s edition of the journal PLoS One, we published an “open access” study on the relationship between sugars and diabetes. The study was an international analysis applying statistical techniques from the field of econometrics to public health data in order to understand the relationship between sugar availability and diabetes prevalence. It was peer-reviewed by five independent statisticians and diabetes experts. The study can be easily misinterpreted—for example, one doctor made the silly comment: “Well this is just like correlating the number of cups someone owns to their risk of diabetes, which is confounded by obesity”—which reflects that the doctor did not read the study or didn’t understand the statistical methods involved; obviously, as professors who teach statistics all day, we controlled for obesity and dealt with these kinds of issues up front.
Our data-visualization colleagues at Periscopic have released a new report on US gun statistics. They looked at the FBI’s Unified Crime Report, which describes gun murders from the year 2010 from police precincts across the country, and combined it with an estimate of the “expected life” of each victim based on standard age prediction tools using the UNSD Demographic Statistics database that provides estimates of the probability distribution of life expectancy among various groups in the population. The calculation provides an estimate of “years of life lost” from gun violence in the US. In total, their estimate suggests about 410,000 years of life lost from gun violence in 2010. See the full visualization, and the breakdown of years of life lost due to type of gun, race, sex, age, region, and type of murder (single victim or multiple) here.
Given the extensive interest these days about how public health decision-makers are being influenced by the soda industry, we decided to take a more systematic look at what institutions and people have close ties to the industry, and what sorts of relationships they have. It is no longer a secret that the Pan American Health Organization, a regional office of World Health Organization, accepts money from the Coca- Cola Company, PepsiCo, Kraft, Nestle, and Unilever. Similarly, some members of the WHO’s Nutrition Guidance Expert Advisory Group have food industry ties, particularly in the form of receiving funding. But who else in the public sphere of governance is linked to “Big Soda”, and how? We used the NNDB Mapper software (which we previously used to catalog relationships among major global health donors—see our paper in PLoS Medicine) to understand what kinds of linkages exist among prominent institutions, individuals, and major soda companies.
The Institute of Medicine has released a major new report today on the reasons why the United States seems to have poorer health, despite its greater wealth, as compared to other industrialized countries. The report reviews evidence on life expectancy and health in the United States, comparing U.S. data with statistics from 16 “peer” countries—other high-income democracies in western Europe, as well as Canada, Australia, and Japan. The report looks somewhat historically, but focuses mostly on data from the late 1990s through 2008.
This morning, The Lancet published the most comprehensive look at the Global Burden of Disease in over a decade. The “GBD 2010″ revealed major shifts in our understanding of global public health and what is causing disease worldwide. For those of you not planning to cure your insomnia tonight by reading all 196 pages of [...]
For several years, health advocates have tried to assemble a treaty to fund research and development on neglected diseases that predominate in poor countries. This week, US and EU negotiators gutted that goal.
A few weeks ago, the New England Journal published what we’d call the worst example of medical statistical misadventure we’ve seen in years: a paper claiming that “chocolate consumption enhances cognitive function” based on a correlation between chocolate consumption and the number of Nobel prize winners in a country (no, we’re not joking…it’s a real paper). Before we indulge in chocolate and a bit of other consumption during Thanksgiving, we thought it would be a good time to revisit a little lesson known as the ecological fallacy… The paper in the Journal correlated the amount of chocolate people consume on average (kg/person/year) to the number of Nobel Laureates from that country (per 10 million inhabitants, to correct for population size). The idea behind the paper was this: “Dietary flavonoids, abundant in plant-based foods, have been shown to improve cognitive function…A subclass of flavonoids called flavanols, which are widely present in cocoa, green tea, red wine, and some fruits, seems to be effective in slowing down or even reversing the reductions in cognitive performance that occur with aging…the total number of Nobel laureates per capita could serve as a surrogate end point reflecting the proportion with superior cognitive function and thereby give us some measure of the overall cognitive function of a given country.” The author then went on to find the following correlation (r=0.79, p<0.0001): It was concluded that “The principal finding of this study is a surprisingly powerful correlation between chocolate intake per capita and the number of Nobel laureates in various countries. Of course, a correlation between X and Y does not prove causation but indicates that either X influences Y, Y influences X, or X and Y are influenced by a common underlying mechanism. However, since chocolate consumption has been documented to improve cognitive function, it seems most likely that in a dose-dependent way, chocolate intake provides the abundant fertile ground needed for the sprouting of Nobel laureates.” The author dismissed the idea that there might be third variables involved, as “it is difficult to identify a plausible common denominator that could possibly drive both chocolate consumption and the number of Nobel laureates over many years.” Actually, it’s not.
Ever since the observation in 1995 that hunger and obesity co-existed among children, a flurry of research has sought to answer the question of whether those who are more “food insecure” are most likely to also become (ironically) obese. Many studies have now correlated participation in food stamp programs (since renamed the Supplemental Nutrition Assistance Program, or SNAP) with obesity. One in seven Americans are now on food stamps after the recession, so this finding may have important implications for the overall obesity epidemic. It may also seem like obvious selection bias to correlate food stamps to obesity; those who are poor are more likely to eat calorie-dense and cheap food, becoming both stuffed and starved.
A key observation from the ongoing global economic recession is that countries once thought to be highly financially stable are now teetering on the edge of long-term ruin. While it is now widely accepted that the decisions of a major investment banks precipitated the crisis, they are not the ones who appear to be paying [...]
