Abstract
Background
In 2015, Collins and Varmus articulated a vision for precision medicine emphasizing molecular characterization of illness to identify actionable biomarkers to support ...individualized treatment. Researchers have argued for a broader conceptualization, precision health. Precision health is an ambitious conceptualization of health, which includes dynamic linkages between research and practice as well as medicine, population health, and public health. The goal is a unified approach to match a full range of promotion, prevention, diagnostic, and treatment interventions to fundamental and actionable determinants of health; to not just address symptoms, but to directly target genetic, biological, environmental, and social and behavioral determinants of health.
Purpose
The purpose of this paper is to elucidate the role of social and behavioral sciences within precision health.
Main body
Recent technologies, research frameworks, and methods are enabling new approaches to measure, intervene, and conduct social and behavioral science research. These approaches support three opportunities in precision health that the social and behavioral sciences could colead including: (a) developing interventions that continuously “tune” to each person’s evolving needs; (b) enhancing and accelerating links between research and practice; and (c) studying mechanisms of change in real-world contexts. There are three challenges for precision health: (a) methods of knowledge organization and curation; (b) ethical conduct of research; and (c) equitable implementation of precision health.
Conclusions
Precision health requires active coleadership from social and behavioral scientists. Prior work and evidence firmly demonstrate why the social and behavioral sciences should colead with regard to three opportunity and three challenge areas.
Developing interventions that provide support that is matched to each person, healthcare system, or community’s needs, which is being called “precision health,” requires active co-leadership from the social and behavioral sciences along with others, such as biomedical research. Specifically, the social and behavioral sciences are needed to meaningfully target and improve root cause determinants of health, such as behavioral, social, and environmental factors.
Diabetes is a common, chronic, and costly condition that currently affects millions of individuals in the United States and worldwide with even greater numbers at high risk for developing the ...disease. Dramatic increases in diagnosed diabetes are projected for the decades to come meaning that most people will be affected by diabetes; either personally or through a family member. This article introduces the special issue of the American Psychologist focused on diabetes by providing an overview of the scope of diabetes and the importance of psychologists for improving disease management and quality of life. This includes an overview of the contributions of the behavioral and social sciences toward improved diabetes prevention and treatment. Finally, the article will point to opportunities for psychologists to close the gaps in the research, develop practice competencies, and increase training opportunities to meet the challenges of diabetes today and in the future.
Objective: Given the critical role of behavior in preventing and treating chronic diseases, it is important to accelerate the development of behavioral treatments that can improve chronic disease ...prevention and outcomes. Findings from basic behavioral and social sciences research hold great promise for addressing behaviorally based clinical health problems, yet there is currently no established pathway for translating fundamental behavioral science discoveries into health-related treatments ready for Phase III efficacy testing. This article provides a systematic framework for developing behavioral treatments for preventing and treating chronic diseases. Method: The Obesity-Related Behavioral Intervention Trials (ORBIT) model for behavioral treatment development features a flexible and progressive process, prespecified clinically significant milestones for forward movement, and return to earlier stages for refinement and optimization. Results: This article presents the background and rationale for the ORBIT model, a summary of key questions for each phase, a selection of study designs and methodologies well-suited to answering these questions, and prespecified milestones for forward or backward movement across phases. Conclusions: The ORBIT model provides a progressive, clinically relevant approach to increasing the number of evidence-based behavioral treatments available to prevent and treat chronic diseases.
Developing and testing more effective health-related behavioral interventions is critical to making progress in improving disease prevention and treatment. One way to achieve this goal is to use a ...systematic and progressive framework that outlines the steps needed to translate theories, findings, and basic understandings about human behavior into risk factor and disease management or mitigation strategies. Although several frameworks and process models have been designed to inform the development and optimization of health-related behavioral interventions, little guidance is available to compare key aspects of these models, clarify their common and unique features, and aid in selecting the best approach for a specific research question. This article describes the major frameworks that focus on early phase translation-that is, approaches that address the design and optimization of behavioral interventions before testing in Phase III efficacy trials. Differences between and common features of these models are described, opportunities for combining frameworks to maximize their impact are noted, and guidance is provided to enable investigators to choose the most useful model(s) when designing and optimizing health-related behavioral interventions. The goal of this article is to promote the consistent use of frameworks that encourage a systematic, progressive approach to behavioral intervention development and testing as one way to encourage the creation of well-characterized, optimized, and potentially more effective health-related behavioral interventions.
To address the vast gap between current knowledge and practice in the area of dissemination and implementation research, we address terminology, provide examples of successful applications of this ...research, discuss key sources of support, and highlight directions and opportunities for future advances. There is a need for research testing approaches to scaling up and sustaining effective interventions, and we propose that further advances in the field will be achieved by focusing dissemination and implementation research on 5 core values: rigor and relevance, efficiency, collaboration, improved capacity, and cumulative knowledge.
Medication adherence plays an important role in optimizing the outcomes of many treatment and preventive regimens in chronic illness. Self-report is the most common method for assessing adherence ...behavior in research and clinical care, but there are questions about its validity and precision. The NIH Adherence Network assembled a panel of adherence research experts working across various chronic illnesses to review self-report medication adherence measures and research on their validity. Self-report medication adherence measures vary substantially in their question phrasing, recall periods, and response items. Self-reports tend to overestimate adherence behavior compared with other assessment methods and generally have high specificity but low sensitivity. Most evidence indicates that self-report adherence measures show moderate correspondence to other adherence measures and can significantly predict clinical outcomes. The quality of self-report adherence measures may be enhanced through efforts to use validated scales, assess the proper construct, improve estimation, facilitate recall, reduce social desirability bias, and employ technologic delivery. Self-report medication adherence measures can provide actionable information despite their limitations. They are preferred when speed, efficiency, and low-cost measures are required, as is often the case in clinical care.
The Primary Care Behavioral Health (PCBH) model of service delivery is being used increasingly as an effective way to integrate behavioral health services into primary care. Despite its growing ...popularity, scientifically robust research on the model is lacking. In this article, we provide a qualitative review of published PCBH model research on patient and implementation outcomes. We review common barriers and potential solutions for improving the quantity and quality of PCBH model research, the vital data that need to be collected over the next 10 years, and how to collect those data.
Sightings of previously marked animals can extend a capture-recapture dataset without the added cost of capturing new animals for marking. Combined marking and resighting methods are therefore an ...attractive option in animal population studies, and there exist various likelihood-based non-spatial models, and some spatial versions fitted by Markov chain Monte Carlo sampling. As implemented to date, the focus has been on modeling sightings only, which requires that the spatial distribution of pre-marked animals is known. We develop a suite of likelihood-based spatial mark-resight models that either include the marking phase ("capture-mark-resight" models) or require a known distribution of marked animals (narrow-sense "mark-resight"). The new models sacrifice some information in the covariance structure of the counts of unmarked animals; estimation is by maximizing a pseudolikelihood with a simulation-based adjustment for overdispersion in the sightings of unmarked animals. Simulations suggest that the resulting estimates of population density have low bias and adequate confidence interval coverage under typical sampling conditions. Further work is needed to specify the conditions under which ignoring covariance results in unacceptable loss of precision, or to modify the pseudolikelihood to include that information. The methods are applied to a study of ship rats Rattus rattus using live traps and video cameras in a New Zealand forest, and to previously published data.
Team-based care has been increasingly used to deliver care for patients with chronic conditions, but its effectiveness for managing diabetes has not been systematically assessed.
RCTs were identified ...from two sources: a high-quality, broader review comparing 11 quality improvement strategies for diabetes management (database inception to July 2010), and an updated search using the same search strategy (July 2010–October 2015).
Thirty-five studies were included in the current review; a majority focused on patients with Type 2 diabetes. Teams included patients, their primary care providers, and one or two additional healthcare professionals (most often nurses or pharmacists). Random effect meta-analysis showed that, compared with controls, team-based care was associated with greater reductions in blood glucose levels (–0.5% in HbA1c, 95% CI= –0.7, –0.3) and greater improvements in blood pressure and lipid levels. Interventions also increased the proportion of patients who reached target blood glucose, blood pressure, and lipid levels, based on American Diabetes Association guidelines available at the time. Data analysis was completed in 2016.
For patients with Type 2 diabetes, team-based care improves blood glucose, blood pressure, and lipid levels.
1. Observed and predicted declines in Arctic sea ice have raised concerns about marine mammals. In May 2008, the US Fish and Wildlife Service listed polar bears (Ursus maritimus) - one of the most ...ice-dependent marine mammals - as threatened under the US Endangered Species Act. 2. We evaluated the effects of sea ice conditions on vital rates (survival and breeding probabilities) for polar bears in the southern Beaufort Sea. Although sea ice declines in this and other regions of the polar basin have been among the greatest in the Arctic, to date population-level effects of sea ice loss on polar bears have only been identified in western Hudson Bay, near the southern limit of the species' range. 3. We estimated vital rates using multistate capture-recapture models that classified individuals by sex, age and reproductive category. We used multimodel inference to evaluate a range of statistical models, all of which were structurally based on the polar bear life cycle. We estimated parameters by model averaging, and developed a parametric bootstrap procedure to quantify parameter uncertainty. 4. In the most supported models, polar bear survival declined with an increasing number of days per year that waters over the continental shelf were ice free. In 2001-2003, the ice-free period was relatively short (mean 101 days) and adult female survival was high (0·96-0·99, depending on reproductive state). In 2004 and 2005, the ice-free period was longer (mean 135 days) and adult female survival was low (0·73-0·79, depending on reproductive state). Breeding rates and cub litter survival also declined with increasing duration of the ice-free period. Confidence intervals on vital rate estimates were wide. 5. The effects of sea ice loss on polar bears in the southern Beaufort Sea may apply to polar bear populations in other portions of the polar basin that have similar sea ice dynamics and have experienced similar, or more severe, sea ice declines. Our findings therefore are relevant to the extinction risk facing approximately one-third of the world's polar bears.
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