Recent reports on the burden of cardiovascular disease (CVD) in the USA indicate that despite significant declines in CVD mortality in the late 20th century, this decline is now decelerating and may be worsened by inequalities in health care. Social factors contribute to most of the cardiovascular health disparities documented to date. Hispanics/Latinos and African-Americans share a higher prevalence of cardiovascular risk factors and experience higher rates of poverty and social stressors than non-Hispanic Whites. We propose that the use of social and behavioral data beyond basic and sometimes loose identifiers of race/ethnicity, educational attainment, and occupation would inform clinical practice and greatly facilitate the provision of adequate guidance and support to patients regarding continuity of care, adherence to medications and treatment plans, and engagement of participants in future research. This perspective briefly highlights factors deemed to be critical for the advancement of Hispanic/Latino health and delineates pathways toward future applications.

Low probability risks create challenges for individual decisions and potential pressures for government regulation. This article reports original survey evidence regarding the public’s perception and valuation of water-related risks from plastic bottles with bisphenol A, residues in drinking water of the herbicide atrazine, and trace amounts of prescription drugs in water. People who believe that they face high water-related risks generally believe that the risks apply and, given that belief, are willing to pay more to limit the risk. However, the expressed willingness to pay for risk reductions is inordinately high even among those who are unsure of whether they are even exposed to the risk, and therefore may not be reliable as values for the actual benefits.

Regulatory impact analyses (RIAs) weigh the benefits of regulations against the burdens they impose and are invaluable tools for informing decision makers. We offer 10 tips for nonspecialist policymakers and interested stakeholders who will be reading RIAs as consumers.

1. 1. Core problem: Determine whether the RIA identifies the core problem (compelling public need) the regulation is intended to address.

2. 2. Alternatives: Look for an objective, policy-neutral evaluation of the relative merits of reasonable alternatives.

3. 3. Baseline: Check whether the RIA presents a reasonable “counterfactual” against which benefits and costs are measured.

4. 4. Increments: Evaluate whether totals and averages obscure relevant distinctions and trade-offs.

5. 5. Uncertainty: Recognize that all estimates involve uncertainty, and ask what effect key assumptions, data, and models have on those estimates.

6. 6. Transparency: Look for transparency and objectivity of analytical inputs.

7. 7. Benefits: Examine how projected benefits relate to stated objectives.

8. 8. Costs: Understand what costs are included.

9. 9. Distribution: Consider how benefits and costs are distributed.

10. 10. Symmetrical treatment: Ensure that benefits and costs are presented symmetrically.

In our opinion, the challenges of ongoing measurement, the ever-moving behavioral baseline, and strategic self-ignorance return us full-circle to a sensible point made by Peter Bohm–Benefit-Cost Analysis for environmental goods should use an interval method.

Two important developments in recent policy analysis are behavioral economics and subjective-well-being (SWB) surveys. What is the connection between them? Some have suggested that behavioral economics strengthens the case for SWB surveys as a central policy tool, e.g., in the form of SWB-based cost-benefit analysis. This article reaches a different conclusion. Behavioral economics shows that individuals in their day-to-day, “System 1” behavior are not expected utility (EU-) rational – that they often fail to comply with the norms of rationality set forth by EU theory. Consider now that the standard preference-based view of individual well-being looks to individuals’ rational preferences. If the findings of behavioral economics are correct, an individual’s answer to a question such as “How satisfied are you with your life?” is not going to tell us much about her rational (EU-compliant) preferences. Behavioral economics, by highlighting widespread failures of EU rationality, might actually argue for an objective-good (non-preference-based) view of well-being. However (except in the limiting case of an objective-good view positing a single mentalistic good, happiness), SWB surveys will not be strong evidence of well-being in the objective-good sense. In short, SWB surveys are no “magic cure” for the genuine difficulties in inferring rational preferences and measuring well-being underscored by behavioral economics.

Individuals who are compromised in their ability to either believe in or plan for their future will make very different valuations of future benefits than will those who have more means and capabilities. These valuations could apply across a wide spectrum, from health care and insurance to investments in education to retirement savings. Data based on time trade-offs and other hypothetical questions will lead to large gaps in contingency valuations which, taken at face value, would lead to regressive outcomes. Individuals who discount the future are unlikely to be responsive to information intended to mitigate risk or to nudges designed to guide behavior away from risky choices. For example, these differential responses result in particular preventive policies having much less than the intended benefit values. Subjective well-being metrics can help circumvent the problem by comparing the reported well-being of individuals who are actually in different arrangements, such as those who have taken up health insurance or not, or in different work arrangements. Still, subjective well-being metrics are a compliment and not a substitute for the standard data that is used in BCA.

The analysis of regulations affecting addictive or habitual goods has drawn considerable controversy. Some studies have suggested that such regulations have only small welfare benefits, as consumers value these goods despite health benefits from quitting, while other studies suggest that information or behavioral problems make existing consumption decisions a poor guide to welfare evaluation. We examine potential utility offsets to health benefits of regulations affecting addictive or habitual goods theoretically and empirically. Our analysis focuses on individuals who consume these goods only, ignoring other social costs and benefits. Theoretically, we show the importance of several factors including: money saved in addition to health improvements; differentiating steady-state utility losses from short-term withdrawal costs; lack of utility loss for people dissuaded from starting to consume the good; and accounting for utility consequences of explicit or implicit cost increases. Our empirical analysis considers regulations that affect smoking. To measure the welfare cost of smoking cessation, we divide the population into those with more and less rational smoking behavior and use the valuation of smoking from more rational smokers to impute values of losses for less rational smokers. Our results show that the utility cost of smoking cessation is small relative to the health gains in people for whom withdrawal costs are the main utility loss of quitting, and even among people who have some ongoing loss, the utility offsets represent 20%–25% of the health gains. While marginal smokers induced to quit by regulations can be expected to have low or no steady-state loss, even this higher estimate is far below prevailing estimates of the utility cost of smoking used by the Food and Drug Administration and other analysts.

This research tested a multicomponent self-help manual that contained nicotine fading and some behavioral techniques. It also evaluated the incremental effects of using higher amounts of therapist contact on quitting rates. 114 smokers were randomly assigned to one of four treatment groups: (1) A 10-session multicomponent program (n = 25); (2) a 5-session multicomponent program (n = 31); (3) a 5-session multicomponent plus a self-help manual program (n = 25); and (4) a self-help-manual-only program (n = 33). A control group (n = 48) did not receive any treatment. The distinctive characteristic of the treatments was the different amount of therapist contact. Common components of the programs were a refundable deposit, self-monitoring, information on smoking, stimulus control, CO feedback, nicotine fading, and strategies to avoid withdrawal symptoms. End-of- treatment quit rates ranged from 36% (self-help manual) to 68% (10-session multicomponent program). At 12-month follow-up there were significant differences between groups, and the most effective group was the 5-session plus manual group, with an abstinence rate of 48%.

Background: The study aim was to conduct a systematic review of the evidence from randomized, placebo controlled trials related to the efficacy of donepezil, rivastigmine and galantamine in the treatment of behavioral and psychological symptoms of Alzheimer's disease.

Methods: Electronic database searches of MEDLINE, EMBASE and the Cochrane Central Register of Controlled Trials were carried out using multiple search terms. Articles included were original publications of randomized, placebo-controlled trials of monotherapy of donepezil, rivastigmine or galantamine that reported a behavioral outcome measure.

Results: 14 studies were identified that matched inclusion criteria. Nine were of donepezil, three of galantamine and two of rivastigmine. Median study treatment length was 24 weeks (range 12–170). Most studies used the Neuropsychiatric Inventory as a behavioral outcome measure although three used specific scales for either agitation or apathy. Four studies were specifically designed to assess behavioral outcomes whilst in the majority of studies behavioral outcomes were only secondary measures. Three studies found statistically significant, albeit modest, differences in the change of NPI total score between drug and placebo. The interpretation of the results of many studies is limited by methodological considerations, including generally low NPI scores at baseline and the investigation of behavioral and psychological symptoms of dementia (BPSD) as secondary outcomes.

Conclusions: The evidence base regarding the efficacy of cholinesterase inhibitors in BPSD is limited, in part due to methodological considerations. In the absence of alternative safe and effective management options, the use of cholinesterase inhibitors is an appropriate pharmacological strategy for the management of BPSD in Alzheimer's disease.