New and Exciting in PLoS Biology and PLoS Medicine

There's some cool new stuff in PLoS Biology and PLoS Medicine this week. Here are my picks and you look around and see what you are interested in....

The Evolutionary and Developmental Foundations of Mathematics:

Understanding the evolutionary precursors of human mathematical ability is a highly active area of research in psychology and biology with a rich and interesting history. At one time, numerical abilities, like language, tool use, and culture, were thought to be uniquely human. However, at the turn of the 20th century, scientists showed more interest in the numerical abilities of animals. The earliest research was focused on whether animals could count in any way that approximated the counting skills of humans [1,2], though many early studies lacked the necessary scientific controls to truly prove numerical abilities in animals. In addition, both the public and many in the scientific community too readily accepted cases of "genius" animals, including those that performed amazing mathematical feats. One such animal still lends its name to the phenomenon of inadvertent cuing of animals by humans: Clever Hans. Hans was a horse that seemed to calculate solutions to all types of numerical problems. In reality, the horse was highly attuned to the subtle and inadvertent bodily movements that people would make when Hans had reached the correct answer (by tapping his hoof) and should have stopped responding [3]. One consequence of this embarrassing realization was a backlash for the better part of the 20th century against the idea that animals could grasp numerical concepts. The second, more positive consequence, however, was that future researchers would include appropriate controls to account for such cues.

Fish Invasions in the World's River Systems: When Natural Processes Are Blurred by Human Activities:

As one of the major threats to biodiversity, the detrimental consequences of biological invasions are widely recognised. Despite this, a global view of invasion patterns and their determinants is still lacking in aquatic ecosystems, reducing our ability to initiate practical actions. Here we report the global patterns of freshwater fish invasion in 1,055 river basins covering more than 80% of Earth's continental surface. This allows us to identify six major invasion hotspots where non-native species represent more than a quarter of the total number of species. According to the World Conservation Union, these areas are also characterised by the highest proportion of threatened fish species. We also show that the natural factors controlling global biodiversity do not influence the number of non-native species in a given river basin. Instead, human activity-related factors, and particularly economic activity, explain why some river basins host more non-native species. In view of our findings, we fear massive invasions in developing countries with a growing economy as already experienced in developed countries. This constitutes a serious threat to global biodiversity.

Human Activity, not Ecosystem Characters, Drives Potential Species Invasions:

From the Asian tiger mosquito in the American South, to the Eurasian zebra mussel in the Great Lakes, to European quackgrass throughout the United States, invasions of non-native species can disrupt ecosystems, cause havoc with local economies, and even threaten health. A new study shows that, at least for freshwater fishes, the major driver of successful invasion is human development, not intrinsic ecological factors, suggesting that in the future, many more newcomers will be making their homes in foreign lands.

Competing hypotheses have been proposed to account for the establishment of non-native species. Human activities, from disrupting ecosystems to transporting exotic species, have clearly contributed to many invasions. But do ecosystems themselves play a part? The "biotic resistance" hypothesis suggests that species-rich environments can deter newcomers, while the "biotic acceptance" hypothesis suggests the opposite, that if it's good for the locals, it's good for the invaders.

Does Preventing Obesity Lead to Reduced Health-Care Costs:

In a study in this issue of PLoS Medicine, Pieter van Baal and colleagues used data from The Netherlands to simulate the annual and lifetime medical costs attributable to obesity [1]. They also compared these costs to those attributable to smoking as well as to the medical costs associated with healthy, living persons (defined as non-smokers with a body mass index in the range of 18.5 to less than 25 kg/m2). The researchers explored the question of whether reducing obesity would lead to reduced or increased health-care costs

Lifetime Medical Costs of Obesity: Prevention No Cure for Increasing Health Expenditure:

Background.

Since the mid 1970s, the proportion of people who are obese (people who have an unhealthy amount of body fat) has increased sharply in many countries. One-third of all US adults, for example, are now classified as obese, and recent forecasts suggest that by 2025 half of US adults will be obese. A person is overweight if their body mass index (BMI, calculated by dividing their weight in kilograms by their height in meters squared) is between 25 and 30, and obese if BMI is greater than 30. Compared to people with a healthy weight (a BMI between 18.5 and 25), overweight and obese individuals have an increased risk of developing many diseases, such as diabetes, coronary heart disease and stroke, and tend to die younger. People become unhealthily fat by consuming food and drink that contains more energy than they need for their daily activities. In these circumstances, the body converts the excess energy into fat for use at a later date. Obesity can be prevented, therefore, by having a healthy diet and exercising regularly.

Why Was This Study Done?

Because obesity causes so much illness and premature death, many governments have public-health policies that aim to prevent obesity. Clearly, the improvement in health associated with the prevention of obesity is a worthwhile goal in itself but the prevention of obesity might also reduce national spending on medical care. It would do this, the argument goes, by reducing the amount of money spent on treating the diseases for which obesity is a risk factor. However, some experts have suggested that these short-term savings might be offset by spending on treating the diseases that would occur during the extra lifespan experienced by non-obese individuals. In this study, therefore, the researchers have used a computer model to calculate yearly and lifetime medical costs associated with obesity in The Netherlands.

What Did the Researchers Do and Find?

The researchers used their model to estimate the number of surviving individuals and the occurrence of various diseases for three hypothetical groups of men and women, examining data from the age of 20 until the time when the model predicted that everyone had died. The "obese" group consisted of never-smoking people with a BMI of more than 30; the "healthy-living" group consisted of never-smoking people with a healthy weight; the "smoking" group consisted of lifetime smokers with a healthy weight. Data from the Netherlands on the costs of illness were fed into the model to calculate the yearly and lifetime health-care costs of all three groups. The model predicted that until the age of 56, yearly health costs were highest for obese people and lowest for healthy-living people. At older ages, the highest yearly costs were incurred by the smoking group. However, because of differences in life expectancy (life expectancy at age 20 was 5 years less for the obese group, and 8 years less for the smoking group, compared to the healthy-living group), total lifetime health spending was greatest for the healthy-living people, lowest for the smokers, and intermediate for the obese people.

What Do These Findings Mean?

As with all mathematical models such as this, the accuracy of these findings depend on how well the model reflects real life and the data fed into it. In this case, the model does not take into account varying degrees of obesity, which are likely to affect lifetime health-care costs, nor indirect costs of obesity such as reduced productivity. Nevertheless, these findings suggest that although effective obesity prevention reduces the costs of obesity-related diseases, this reduction is offset by the increased costs of diseases unrelated to obesity that occur during the extra years of life gained by slimming down.

Soft Targets: Nurses and the Pharmaceutical Industry:

The nursing literature has yet to pay much attention to the expansive reach of the pharmaceutical industry into the nursing profession. In this article, we examine some of the key literature on the influence of drug companies upon nurses, consider the limitations of this literature, and define a strategy for heightening awareness and strengthening the skills of nurses to manage the impact of commercial interests.