Annual Review of Public Health - Volume 38, 2017

The wide variety of dissemination and implementation designs now being used to evaluate and improve health systems and outcomes warrants review of the scope, features, and limitations of these designs. This article is one product of a design workgroup that was formed in 2013 by the National Institutes of Health to address dissemination and implementation research, and whose members represented diverse methodologic backgrounds, content focus areas, and health sectors. These experts integrated their collective knowledge on dissemination and implementation designs with searches of published evaluations strategies. This article emphasizes randomized and nonrandomized designs for the traditional translational research continuum or pipeline, which builds on existing efficacy and effectiveness trials to examine how one or more evidence-based clinical/prevention interventions are adopted, scaled up, and sustained in community or service delivery systems. We also mention other designs, including hybrid designs that combine effectiveness and implementation research, quality improvement designs for local knowledge, and designs that use simulation modeling.

Uncontrolled confounding due to unmeasured confounders biases causal inference in health science studies using observational and imperfect experimental designs. The adoption of methods for analysis of bias due to uncontrolled confounding has been slow, despite the increasing availability of such methods. Bias analysis for such uncontrolled confounding is most useful in big data studies and systematic reviews to gauge the extent to which extraneous preexposure variables that affect the exposure and the outcome can explain some or all of the reported exposure-outcome associations. We review methods that can be applied during or after data analysis to adjust for uncontrolled confounding for different outcomes, confounders, and study settings. We discuss relevant bias formulas and how to obtain the required information for applying them. Finally, we develop a new intuitive generalized bias analysis framework for simulating and adjusting for the amount of uncontrolled confounding due to not measuring and adjusting for one or more confounders.

Population health interventions are essential to reduce health inequalities and tackle other public health priorities, but they are not always amenable to experimental manipulation. Natural experiment (NE) approaches are attracting growing interest as a way of providing evidence in such circumstances. One key challenge in evaluating NEs is selective exposure to the intervention. Studies should be based on a clear theoretical understanding of the processes that determine exposure. Even if the observed effects are large and rapidly follow implementation, confidence in attributing these effects to the intervention can be improved by carefully considering alternative explanations. Causal inference can be strengthened by including additional design features alongside the principal method of effect estimation. NE studies often rely on existing (including routinely collected) data. Investment in such data sources and the infrastructure for linking exposure and outcome data is essential if the potential for such studies to inform decision making is to be realized.

Surveillance is critical for improving population health. Public health surveillance systems generate information that drives action, and the data must be of sufficient quality and with a resolution and timeliness that matches objectives. In the context of scientific advances in public health surveillance, changing health care and public health environments, and rapidly evolving technologies, the aim of this article is to review public health surveillance systems. We consider their current use to increase the efficiency and effectiveness of the public health system, the role of system stakeholders, the analysis and interpretation of surveillance data, approaches to system monitoring and evaluation, and opportunities for future advances in terms of increased scientific rigor, outcomes-focused research, and health informatics.

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with lifelong impacts. Genetic and environmental factors contribute to ASD etiology, which remains incompletely understood. Research on ASD epidemiology has made significant advances in the past decade. Current prevalence is estimated to be at least 1.5% in developed countries, with recent increases primarily among those without comorbid intellectual disability. Genetic studies have identified a number of rare de novo mutations and gained footing in the areas of polygenic risk, epigenetics, and gene-by-environment interaction. Epidemiologic investigations focused on nongenetic factors have established advanced parental age and preterm birth as ASD risk factors, indicated that prenatal exposure to air pollution and short interpregnancy interval are potential risk factors, and suggested the need for further exploration of certain prenatal nutrients, metabolic conditions, and exposure to endocrine-disrupting chemicals. We discuss future challenges and goals for ASD epidemiology as well as public health implications.

The use of social network theory and analysis methods as applied to public health has expanded greatly in the past decade, yielding a significant academic literature that spans almost every conceivable health issue. This review identifies several important theoretical challenges that confront the field but also provides opportunities for new research. These challenges include (a) measuring network influences, (b) identifying appropriate influence mechanisms, (c) the impact of social media and computerized communications, (d) the role of networks in evaluating public health interventions, and (e) ethics. Next steps for the field are outlined and the need for funding is emphasized. Recently developed network analysis techniques, technological innovations in communication, and changes in theoretical perspectives to include a focus on social and environmental behavioral influences have created opportunities for new theory and ever broader application of social networks to public health topics.

Countermarketing campaigns use health communications to reduce the demand for unhealthy products by exposing motives and undermining marketing practices of producers. These campaigns can contribute to the prevention of noncommunicable diseases by denormalizing the marketing of tobacco, alcohol, and unhealthy food. By portraying these activities as outside the boundaries of civilized corporate behavior, countermarketing can reduce the demand for unhealthy products and lead to changes in industry marketing practices. Countermarketing blends consumer protection, media advocacy, and health education with the demand for corporate accountability. Countermarketing campaigns have been demonstrated to be an effective component of comprehensive tobacco control. This review describes common elements of tobacco countermarketing such as describing adverse health consequences, appealing to negative emotions, highlighting industry manipulation of consumers, and engaging users in the design or implementation of campaigns. It then assesses the potential for using these elements to reduce consumption of alcohol and unhealthy foods.

We have reviewed the distinctive features of excess weight, its causes, and related prevention and management efforts, as well as data gaps and recommendations for future research in low- and middle-income countries (LMICs). Obesity is rising in every region of the world, and no country has been successful at reversing the epidemic once it has begun. In LMICs, overweight is higher in women compared with men, in urban compared with rural settings, and in older compared with younger individuals; however, the urban–rural overweight differential is shrinking in many countries. Overweight occurs alongside persistent burdens of underweight in LMICs, especially in young women. Changes in the global diet and physical activity are among the hypothesized leading contributors to obesity. Emerging risk factors include environmental contaminants, chronic psychosocial stress, neuroendocrine dysregulation, and genetic/epigenetic mechanisms. Data on effective strategies to prevent the onset of obesity in LMICs or elsewhere are limited. Expanding the research in this area is a key priority and has important possibilities for reverse innovation that may also inform interventions in high-income countries.

Tobacco use remains the leading preventable cause of death worldwide. In particular, people with mental illness are disproportionately affected with high smoking prevalence; they account for more than 200,000 of the 520,000 tobacco-attributable deaths in the United States annually and die on average 25 years prematurely. Our review aims to provide an update on smoking in the mentally ill. We review the determinants of tobacco use among smokers with mental illness, presented with regard to the public health HAVE framework of “the host” (e.g., tobacco user characteristics), the “agent” (e.g., nicotine product characteristics), the “vector” (e.g., tobacco industry), and the “environment” (e.g., smoking policies). Furthermore, we identify the significant health harms incurred and opportunities for prevention and intervention within a health care systems and larger health policy perspective. A comprehensive effort is warranted to achieve equity toward the 2025 Healthy People goal of reducing US adult tobacco use to 12%, with attention to all subgroups, including smokers with mental illness.

To address the obesity epidemic, the public health community must develop surveillance systems that capture data at levels through which obesity prevention efforts are conducted. Current systems assess body mass index (BMI), diet, and physical activity behaviors at the individual level, but environmental and policy-related data are often lacking. The goal of this review is to describe US surveillance systems that evaluate obesity prevention efforts within the context of international trends in obesity monitoring, to identify potential data gaps, and to present recommendations to improve the evaluation of population-level initiatives. Our recommendations include adding environmental and policy measures to surveillance efforts with a focus on addressing underserved populations, harmonizing existing surveillance systems, including more sensitive measures of obesity outcomes, and developing a knowledgeable workforce. In addition, the widespread use of electronic health records and new technologies that allow self-quantification of behaviors offers opportunities for innovative surveillance methods.

The exposome comprises all environmental exposures that a person experiences from conception throughout the life course. Here we review the state of the science for assessing external exposures within the exposome. This article reviews (a) categories of exposures that can be assessed externally, (b) the current state of the science in external exposure assessment, (c) current tools available for external exposure assessment, and (d) priority research needs. We describe major scientific and technological advances that inform external assessment of the exposome, including geographic information systems; remote sensing; global positioning system and geolocation technologies; portable and personal sensing, including smartphone-based sensors and assessments; and self-reported questionnaire assessments, which increasingly rely on Internet-based platforms. We also discuss priority research needs related to methodological and technological improvement, data analysis and interpretation, data sharing, and other practical considerations, including improved assessment of exposure variability as well as exposure in multiple, critical life stages.

Climate change is causing increases in temperature, changes in precipitation and extreme weather events, sea-level rise, and other environmental impacts. It is also causing or contributing to heat-related disorders, respiratory and allergic disorders, infectious diseases, malnutrition due to food insecurity, and mental health disorders. In addition, increasing evidence indicates that climate change is causally associated with collective violence, generally in combination with other causal factors. Increased temperatures and extremes of precipitation with their associated consequences, including resultant scarcity of cropland and other key environmental resources, are major pathways by which climate change leads to collective violence. Public health professionals can help prevent collective violence due to climate change (a) by supporting mitigation measures to reduce greenhouse gas emissions, (b) by promoting adaptation measures to address the consequences of climate change and to improve community resilience, and (c) by addressing underlying risk factors for collective violence, such as poverty and socioeconomic disparities.

Great progress has been made in addressing global undernutrition over the past several decades, in part because of large increases in food production from agricultural expansion and intensification. Food systems, however, face continued increases in demand and growing environmental pressures. Most prominently, human-caused climate change will influence the quality and quantity of food we produce and our ability to distribute it equitably. Our capacity to ensure food security and nutritional adequacy in the face of rapidly changing biophysical conditions will be a major determinant of the next century's global burden of disease. In this article, we review the main pathways by which climate change may affect our food production systems—agriculture, fisheries, and livestock—as well as the socioeconomic forces that may influence equitable distribution.

The complexity of the human exposome—the totality of environmental exposures encountered from birth to death—motivates systematic, high-throughput approaches to discover new environmental determinants of disease. In this review, we describe the state of science in analyzing the human exposome and provide recommendations for the public health community to consider in dealing with analytic challenges of exposome-based biomedical research. We describe extant and novel analytic methods needed to associate the exposome with critical health outcomes and contextualize the data-centered challenges by drawing parallels to other research endeavors such as human genomics research. We discuss efforts for training scientists who can bridge public health, genomics, and biomedicine in informatics and statistics. If an exposome data ecosystem is brought to fruition, it will likely play a role as central as genomic science has had in molding the current and new generations of biomedical researchers, computational scientists, and public health research programs.

The market for organic food products is growing rapidly worldwide. Such foods meet certified organic standards for production, handling, processing, and marketing. Most notably, the use of synthetic fertilizers, pesticides, and genetic modification is not allowed. One major reason for the increased demand is the perception that organic food is more environmentally friendly and healthier than conventionally produced food. This review provides an update on market data and consumer preferences for organic food and summarizes the scientific evidence for compositional differences and health benefits of organic compared with conventionally produced food. Studies indicate some differences in favor of organic food, including indications of beneficial health effects. Organic foods convey lower pesticide residue exposure than do conventionally produced foods, but the impact of this on human health is not clear. Comparisons are complicated by organic food consumption being strongly correlated with several indicators of a healthy lifestyle and by conventional agriculture “best practices” often being quite close to those of organic.

Investigating a single environmental exposure in isolation does not reflect the actual human exposure circumstance nor does it capture the multifactorial etiology of health and disease. The exposome, defined as the totality of environmental exposures from conception onward, may advance our understanding of environmental contributors to disease by more fully assessing the multitude of human exposures across the life course. Implementation into studies of human health has been limited, in part owing to theoretical and practical challenges including a lack of infrastructure to support comprehensive exposure assessment, difficulty in differentiating physiologic variation from environmentally induced changes, and the need for study designs and analytic methods that accommodate specific aspects of the exposome, such as high-dimensional exposure data and multiple windows of susceptibility. Recommendations for greater data sharing and coordination, methods development, and acknowledgment and minimization of multiple types of measurement error are offered to encourage researchers to embark on exposome research to promote the environmental health and well-being of all populations.

Health is created largely outside the health sector. Engagement in health governance, policy, and intervention development and implementation by sectors other than health is therefore important. Recent calls for building and implementing Health in All Policies, and continued arguments for intersectoral action, may strengthen the potential that other sectors have for health. This review clarifies the conceptual foundations for integral health governance, policy, and action, delineates the different sectors and their possible engagement, and provides an overview of a continuum of methods of engagement with other sectors to secure integration. This continuum ranges from institutional (re)design to value-based narratives. Depending on the lens applied, different elements can be identified within the continuum. This review is built on insights from political science, leadership studies, public health, empirical Health in All Policy research, knowledge and evidence nexus approaches, and community perspectives. Successful integration of health governance, policy, and action depends on integration of the elements on the continuum.

Large-scale public policy changes are often recommended to improve public health. Despite varying widely—from tobacco taxes to poverty-relief programs—such policies present a common dilemma to public health researchers: how to evaluate their health effects when randomized controlled trials are not possible. Here, we review the state of knowledge and experience of public health researchers who rigorously evaluate the health consequences of large-scale public policy changes. We organize our discussion by detailing approaches to address three common challenges of conducting policy evaluations: distinguishing a policy effect from time trends in health outcomes or preexisting differences between policy-affected and -unaffected communities (using difference-in-differences approaches); constructing a comparison population when a policy affects a population for whom a well-matched comparator is not immediately available (using propensity score or synthetic control approaches); and addressing unobserved confounders by utilizing quasi-random variations in policy exposure (using regression discontinuity, instrumental variables, or near-far matching approaches).

Public health researchers and practitioners are calling for greater focus on external validity, the ability to generalize findings of evidence-based interventions (EBIs) beyond the limited number of studies testing effectiveness. For public health, the goal is applicability: to translate, disseminate, and implement EBIs for an impact on population health. This article is a review of methods and how they might be combined to better assess external validity. The methods include (a) better description of EBIs and their contexts; (b) combining of statistical tools and logic to draw inferences about study samples; (c) sharper definition of the theory behind the intervention and core intervention components; and (d) more systematic consultation of practitioners. For population impact, studies should focus on context features that are likely to be both important (based on program theory) and frequently encountered by practitioners. Mixed-method programs of research will allow public health to expand causal generalizations.

Public health practice in the twenty-first century is in a state of significant flux. Several macro trends are impacting the current practice of governmental public health and will likely have effects for many years to come. These macro trends are described as forces of change, which are changes that affect the context in which the community and its public health system operate. This article focuses on seven such forces of change: the Patient Protection and Affordable Care Act, public health agency accreditation, climate change, health in all policies, social media and informatics, demographic transitions, and globalized travel. Following the description of each of these, this article then turns to possible approaches to measuring, tracking, and understanding the impact of these forces of change on public health practice, including the use of evidence-based public health, practice-based research, and policy surveillance.

Many high- and middle-income countries (HMICs) are experiencing a burden of comorbidity and chronic diseases. Together with increasing patient expectations, this burden is raising demand for population health–oriented innovation in health care. Using desk review and country case studies, we examine strategies applied in HMICs outside the United States to address these challenges, with a focus on and use of a new framework for analyzing primary care (PC). The article outlines how a population health approach has been supported by focusing assessment on and clustering services around social groups and multimorbidity, with support for community roles. It presents ways in which early first contact and continuity of PC, PC coordination of referral, multidisciplinary team approaches, investment in PC competencies, and specific payment and incentive models have all supported comprehensive approaches. These experiences locate PC as a site of innovation, where information technology and peer-to-peer learning networks support learning from practice.

China experienced both economic and epistemological transitions within the past few decades, greatly increasing demand for accessible and affordable health care. These shifts put significant pressure on the existing outdated, highly centralized bureaucratic system. Adjusting to growing demands, the government has pursued a new round of health reforms since the late 2000s; the main goals are to reform health care financing, essential drug policies, and public hospitals. Health care financing reform led to universal basic medical insurance, whereas the public hospital reform required more complex measures ranging from changes in regulatory, operational, and service delivery settings to personnel management. This article reviews these major policy changes and the literature-based evidence of the effects of reforms on cost, access, and quality of care. It then highlights the outlook for future reforms. We argue that a better understanding of the unintended consequences of reform policies and of how practitioners’ and patients’ interests can be better aligned is essential for reforms to succeed.

The use of financial incentives to improve quality in health care has become widespread. Yet evidence on the effectiveness of incentives suggests that they have generally had limited impact on the value of care and have not led to better patient outcomes. Lessons from social psychology and behavioral economics indicate that incentive programs in health care have not been effectively designed to achieve their intended impact. In the United States, Medicare's Hospital Readmission Reduction Program and Hospital Value-Based Purchasing Program, created under the Affordable Care Act (ACA), provide evidence on how variations in the design of incentive programs correspond with differences in effect. As financial incentives continue to be used as a tool to increase the value and quality of health care, improving the design of programs will be crucial to ensure their success.

The Veterans Health Administration is unique, functioning as an integrated health care system that provides care to more than six million veterans annually and as a home to an established scientific enterprise that conducts more than $1 billion of research each year. The presence of research, spanning the continuum from basic health services to translational research, has helped the Department of Veterans Affairs (VA) realize the potential of a learning health care system and has contributed to significant improvements in clinical quality over the past two decades. It has also illustrated distinct pathways by which research influences clinical care and policy and has provided lessons on challenges in translating research into practice on a national scale. These lessons are increasingly relevant to other health care systems, as the issues confronting the VA—the need to provide timely access, coordination of care, and consistent high quality across a diverse system—mirror those of the larger US health care system.

The Patient Protection and Affordable Care Act (ACA) expands access to health insurance in the United States, and, to date, an estimated 20 million previously uninsured individuals have gained coverage. Understanding the law's impact on coverage, access, utilization, and health outcomes, especially among low-income populations, is critical to informing ongoing debates about its effectiveness and implementation. Early findings indicate that there have been significant reductions in the rate of uninsurance among the poor and among those who live in Medicaid expansion states. In addition, the law has been associated with increased health care access, affordability, and use of preventive and outpatient services among low-income populations, though impacts on inpatient utilization and health outcomes have been less conclusive. Although these early findings are generally consistent with past coverage expansions, continued monitoring of these domains is essential to understand the long-term impact of the law for underserved populations.

Injury is a leading cause of death globally, and organized trauma care systems have been shown to save lives. However, even though most injuries occur in low- and middle-income countries (LMICs), most trauma care research comes from high-income countries where systems have been implemented with few resource constraints. Little context-relevant guidance exists to help policy makers set priorities in LMICs, where resources are limited and where trauma care may be implemented in distinct ways. We have aimed to review the evidence on the impact of trauma care systems in LMICs through a systematic search of 11 databases. Reports were categorized by intervention and outcome type and summarized. Of 4,284 records retrieved, 71 reports from 32 countries met inclusion criteria. Training, prehospital systems, and overall system organization were the most commonly reported interventions. Quality-improvement, costing, rehabilitation, and legislation and governance were relatively neglected areas. Included reports may inform trauma care system planning in LMICs, and noted gaps may guide research and funding agendas.