Annual Review of Public Health - Volume 33, 2012

In the past 20 years, public health initiatives on smoking cessation have increased substantially. Randomized trials indicate that pharmaceutical cessation aids can increase success by 50% among heavier smokers who seek help, and use of these aids has increased markedly. Quitlines provide a portal through which smokers can seek assistance to quit and are promoted by tobacco control programs. Randomized trials have demonstrated that telephone coaching following a quitline call can also increase quitting, and a combination of quitlines, pharmaceutical aids and physician monitoring can help heavier smokers to quit.

While quit attempts have increased, widespread dissemination of these aids has not improved population success rates. Pharmaceutical marketing strategies may have reduced expectations of the difficulty of quitting, reducing success per attempt. Some policies actively discourage unassisted smoking cessation despite the documented high success rates of this approach. There is an urgent need to revisit public policy on smoking cessation.

Complex systems abound in public health. Complex systems are made up of heterogeneous elements that interact with one another, have emergent properties that are not explained by understanding the individual elements of the system, persist over time, and adapt to changing circumstances. Public health is starting to use results from systems science studies to shape practice and policy, for example in preparing for global pandemics. However, systems science study designs and analytic methods remain underutilized and are not widely featured in public health curricula or training. In this review we present an argument for the utility of systems science methods in public health, introduce three important systems science methods (system dynamics, network analysis, and agent-based modeling), and provide three case studies in which these methods have been used to answer important public health science questions in the areas of infectious disease, tobacco control, and obesity.

Much of health services research seeks to inform particular policy choices and is best characterized as policy-driven research. The reverse, research-driven policy, occurs when studies alter how people perceive reality, which eventually leads to new policy. An example of the latter is nearly four decades of work by John Wennberg and colleagues. Observing variations in practice across small geographic areas led to the notion that some care is preference sensitive, whereas other care is supply constrained. For the former, patient, rather than physician, preferences should be honored, after acquiring and effectively communicating the best available information on the benefits and risks of treatment options. Finding that areas with high use of services have no better quality or outcomes than do areas with lower use led to the notion of accountable care organizations (ACOs). Eventually, both patient engagement and ACOs were written into the Affordable Care Act of 2010.

Suicide mortality varies widely across age, sex, race, and geography, far more than does mortality from the leading causes of natural death. Unlike the tight correlation between cancer mortality and the incidence of cancer, suicide mortality is only modestly correlated with the incidence of suicidal acts and other established risk factors for suicidal behavior, such as major psychiatric disorders. An implication of this modest correlation is that the proportion of all suicidal acts that prove fatal (the case fatality ratio) must account for a substantial portion of the (nonrandom) variation observed in suicide mortality. In the United States, the case fatality ratio is strongly related to the availability of household firearms. Findings from ecologic and individual-level studies conducted over the past two decades illustrate the importance of accounting for the availability of highly lethal suicide methods in efforts to understand (and ultimately reduce) disparities in suicide mortality across populations.

U.S. health care spending has increased dramatically in the past several decades, consuming 17.6% percent ($2.6 trillion) of GDP in 2010. Although historical spending drivers do not account for this recent increase, two major changes in population health—the rise in obesity and obesity-related chronic disease—provide a likely explanation. This article reviews the contribution that rising treated obesity-related chronic disease prevalence and its associated treatment (spending per treated case) has made to the growth in health care spending. We discuss trends in the clinical incidence of obesity and chronic disease as well as timely advancements in disease detection, treatment, and management. Evidence shows that rising obesity rates are influencing spending largely by increasing the treated prevalence of obesity-related chronic disease. Therefore, preventing individuals from becoming treated cases in the first place is one key way that our country can cut health care spending moving forward.

This review describes methods used in comparative effectiveness research (CER). The aim of CER is to improve decisions that affect medical care at the levels of both policy and the individual. The key elements of CER are (a) head-to-head comparisons of active treatments, (b) study populations typical of day-to-day clinical practice, and (c) a focus on evidence to inform care tailored to the characteristics of individual patients. These requirements will stress the principal methods of CER: observational research, randomized trials, and decision analysis. Observational studies are especially vulnerable because they use data that directly reflect the decisions made in usual practice. CER will challenge researchers and policy makers to think deeply about how to extract more actionable information from the vast enterprise of the daily practice of medicine. Fortunately, the methods are largely applicable to research in the public health system, which should therefore benefit from the intense interest in CER.