Author Archives: Sanjay Basu

Converting EpiAnalysis for new media

As part of ongoing efforts to support MOOCs (massive open online courses), we’re closing our traditional EpiAnalysis blog and converting to a new, evolving epidemiology virtual course. While our older posts will remain available on this website, our newer effort will allow us to more flexibly discuss ongoing epidemiology controversies, new research and data, and analytical approaches. Stay tuned…

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Industry ties to medical expert panels

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Controversy has surrounded the latest publication of the Diagnostic and Statistical Manual (DSM) of mental health disorders, in part because of concerns that the guideline pathologizes many behaviors that some people might consider normal, theoretically increasing the opportunity to prescribe pharmaceuticals for non-pathological behavior. But beyond the field of psychiatry, there are increasing concerns that “medicalization” may be doing more harm than good for patients (especially where tests and therapies have marginal benefit but potentially great risks), and may be influenced by profit motives and desires to define disease so expansively as to intrude on normal living to a stifling degree. A recent comprehensive study of medical panels’ decisions about expanding disease definitions shed some light on this debate, and revealed some concerning findings… In 2009,  the Institute of Medicine published a landmark report entitled “Conflict of interest in medical research, education, and practice”. In the report, the IOM found  “widespread relationships with industry have created significant risks that individual and institutional financial interests may unduly influence professionals’ judgments” in medicine. Subsequent recommendations from the IOM revealed that a major area of concern was the writing of medical guidelines that physicians around the country (and, indeed, around the world) typically use to decide when to screen patients for diseases, how to define a disease, and when and how to treat such diseases.

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The Stanford Health 4 America Fellows Program

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We know health starts—long before illness—in our homes, communities, schools and jobs. But we devote most attention to medications and healthcare delivery. The Stanford Prevention Research Center is announcing the start of a new “Health 4 America” fellows program, whose goal is to train prevention experts to address health in  families, neighborhoods, schools, communities and the workplace… Many students interested in public health sign up for programs like Teach for America, or complete a public health master’s degree after college. But a persistent dilemma with these programs is that they’re either purely experiential (throwing you into the deep end without a lot of tools) or entirely classroom-based (throwing you into the slumber of statistics classes all day long, with no practicum in sight).

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The Disabled States of America: regional disparities in healthy life…

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The CDC recently released their latest data on healthy life expectancy across the US. The data reveal stark inequalities not only in overall death rates, but moreover in how extremely disabled various parts of the country are as compared to healthier areas.   The concept of “healthy life expectancy” (HLE) refers to the idea that life expectancy itself doesn’t capture common states of chronic disability; in theory, medicine and public health are not just striving to achieve longer lives, but happier, less painful ones. HLE measures both mortality (death rates) and morbidity (health status, or quality of life measures). In addition to helping predict where health services might be needed the most, HLE measures over time also give a sense of how well public health and healthcare systems might be functioning, and how heavily the “social determinants of health” (e.g., pollution, stress-related disease, injury from neighborhood violence, work-related injury) may be falling disproportionately on some populations over others.

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Bending the child obesity curve

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The Robert Wood Johnson Foundation has released a fascinating glimpse into data suggesting that childhood obesity may be declining in several US cities and counties. The report is preliminary and tracks cohorts over just a few time points across the US, but is consistent with other more population-representative, systematic reports suggesting that the obesity prevalence trajectory may be flattening or even declining in some US populations. Here’s the RWJ preliminary data: There are several questions that are intriguing here: (1) First, if these changes are meaningfully large and sustained, are there clear contrasts in policy we can use as “natural experiments” to analyze what changes might have occurred in these communities to drive the declines as compared to their neighboring cities/counties where the declines didn’t occur, controlling for other confounders? (Or even analyzing within the cohorts to examine why certain populations did/did not experience the declines). (2) Is there selection bias

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Tobacco control and chronic disease in rapidly-developing countries

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Today’s PLoS Medicine includes our recent study attempting to answer a simple question: given the rise in many chronic disease risk factors (high blood pressure, cholesterol, diabetes, etc.) in rapidly-developing countries like India and China, which interventions might avert the most deaths from cardiovascular disease? Some mathematical models have attempted to answer this question in the past, but a problem with prior assessments was that they often only had data at the level of whole countries (assuming all Indians or all Chinese are the same) or even whole regions (like all of South Asia). Yet we know there are vast disparities in risk between rich and poor, men and women, and regions within countries like India and China. Prior studies also made some debatable assumptions, such as assuming that 80% of people in developing countries would have access to medications over periods shorter than five years (!), and that patients treated with medications like statins will have perfect adherence to the medications as well as having healthcare providers who perfectly delivered the medications according to clinical guidelines.

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The data beneath The Farm Bill

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Numerous commentaries have debated aspects of the $100 billion dollar U.S. Farm Bill—the legislation that funds farm subsidies, food stamps, crop insurance policies, and potentially some international food aid as well. But what’s the impact of these various programs? We took a look at the data on Farm Bill payments and effects over the last several years… Food stamps First, a look at food stamps, or the Supplemental Nutrition Assistance Program (SNAP).

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Introducing The Body Economic

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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.

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Big data mining and new hypotheses in mental health research

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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

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University-based research and neglected diseases

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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.

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Soda and global obesity: are sugar-sweetened beverages relevant outside the…

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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

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Soda and global obesity: are sugar-sweetened beverages relevant outside the United States?

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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.

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Alcohol use during the Great Recession

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.

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Interpreting our findings from today’s study on sugars and diabetes

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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.

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