Annual Review of Public Health最新文献

Among health care researchers, there is increasing debate over how best to assess and ensure the fairness of algorithms used for clinical decision support and population health, particularly concerning potential racial bias. Here we first distill concerns over the fairness of health care algorithms into four broad categories: (a) the explicit inclusion (or, conversely, the exclusion) of race and ethnicity in algorithms, (b) unequal algorithm decision rates across groups, (c) unequal error rates across groups, and (d) potential bias in the target variable used in prediction. With this taxonomy, we critically examine seven prominent and controversial health care algorithms. We show that popular approaches that aim to improve the fairness of health care algorithms can in fact worsen outcomes for individuals across all racial and ethnic groups. We conclude by offering an alternative, consequentialist framework for algorithm design that mitigates these harms by instead foregrounding outcomes and clarifying trade-offs in the pursuit of equitable decision-making.

Life expectancy among American Indians and Alaska Natives (AI/ANs) has declined from 72 years in 2019 to 68 years in 2021. This current life expectancy for AI/ANs is equivalent to the overall life expectancy in the United States population in the 1940s. The significant and persistent nature of AI/AN health inequities, and the lack of clarity around what is driving them, requires urgent action. The purpose of this article is threefold. First, we provide a comprehensive conceptual framework of health that presents Indigenous perspectives of health and recognizes settler colonialism as a key structural determinant of the health of AI/ANs. Second, we underscore the importance of Tribal self-determination and sovereignty in public health research and practice and identify successful initiatives centering these efforts. Finally, we conclude with recommendations for future research, practice, and policy.

Estimation of the disease burden attributable to environmental factors is a powerful tool for prioritizing environmental and pollution management and public health actions around the world. The World Health Organization (WHO) began estimating the environmental disease burden in 2000, which has formed the basis for the modern estimation approach conducted in the Global Burden of Disease, Injuries, and Risk Factor (GBD) study. In 2021, environmental and occupational risk factors in the GBD were responsible for 18.9% (12.8 million) of global deaths and 14.4% of all disability-adjusted life years (DALYs), led by ambient PM_2.5 air pollution (4.2% DALYs, 4.7 million deaths) and household air pollution from the use of solid fuels for cooking (3.9% DALYs, 3.1 million deaths). Climate change exacerbates many environmental hazards, leading to increased disease burdens from heat, air pollution, vector-borne diseases, storms, and flooding. Other environmental risk factors not included in the GBD, such as poor indoor air quality, various chemical exposures, and environmental noise pollution, also significantly contribute to disease burden in many countries, though more efforts are needed to generate and integrate data resources for inclusion in global estimations.

This review explores the transformative potential of just-in-time adaptive interventions (JITAIs) as a scalable solution for addressing health disparities in underserved populations. JITAIs, delivered via mobile health technologies, could provide personalized, context-aware interventions based on real-time data to address public health challenges such as addiction treatment, chronic disease management, and mental health support. JITAIs can dynamically adjust intervention strategies, enhancing accessibility and engagement for marginalized communities. We highlight the utility of JITAIs in reducing opportunity costs associated with traditional in-person health interventions. Examples from various health domains demonstrate the adaptability of JITAIs in tailoring interventions to meet diverse needs. The review also emphasizes the need for community involvement, robust evaluation frameworks, and ethical considerations in implementing JITAIs, particularly in low- and middle-income countries. Sustainable funding models and technological innovations are necessary to ensure equitable access and effectively scale these interventions. By bridging the gap between research and practice, JITAIs could improve health outcomes and reduce disparities in vulnerable populations.

This article provides a guide for rigorous, theory-driven measurement approaches, proposing best practices for the scientific study of systemic racism in health research. We argue that the analytical crux of measuring systemic racism-a complex, interconnected, and dynamic system-lies in operationalizing the collective logics, properties, and mechanisms that undergird racial inequities. Misalignment between measurement tools and these fundamental features undermines research validity, as incongruent measures distort findings and obscure systemic racism's true impacts. To address this, we draw on interdisciplinary theories and evidence to dissect key features of systemic racism, emphasizing their implications for measurement. We further recommend incorporating temporal processes in health research by leveraging core principles of the life course perspective, which elucidate the intricate interplay of individual, historical, and societal trajectories. Our recommendations underscore the necessity of adopting robust, evidence-based methods to advance the understanding of how systemic racism operates and shapes public health.

This review describes employees working in low-wage industries in the United States, their health risks, and their access to health promotion and other health-related resources through their employers. We use publicly available datasets to illustrate how low-wage jobs affect employees' social determinants of health, health risk behaviors, and chronic conditions. We also discuss how the COVID-19 pandemic has shifted these employees' and employers' health-related priorities and work settings. We describe employees' access to health supports through federal programs and their employers and the potential ways in which low-wage employers could support employee health and well-being. We close with a brief research and practice agenda to improve health equity for employees in low-wage industries. The goal of this review is to help practitioners and researchers in workplace health promotion, occupational health, and public health reach employees and employers in low-wage industries with interventions that address employees' health risks and employees' and employers' health priorities.

Development and amplification of effective, culturally resonant, trustworthy, and evidence-based public health communication are urgently needed. Research evidence, theory, and practical experience from within and beyond the interdisciplinary field of health communication are well-positioned to help public health authorities, researchers, and advocates navigate the complex societal challenges that influence health and well-being in global contexts. This review offers a broad overview of the field, considers what constitutes "effectiveness" versus "effects" in public health communication, and describes core concepts of public health communication as a process rather than a product. We review domains and dominant foci of public health communication research, articulate challenges for health communication to advance health and social equity and address mis-/disinformation, and offer practical guidance on message development, audience segmentation, multilevel intervention, and evaluation of communication programs. We conclude by identifying important questions for future public health communication research, interventions, and funding.

While energy transitions have always taken place, shifts have historically been primarily from one type of fossil fuel to another (e.g., from coal to oil). Yet today we are experiencing transitions away from fossil fuel toward cleaner and sustainable energy sources. These transitions include shifts to renewable energy, electric vehicles, pedestrian pathways and more walkable cities, energy efficiency measures in the built environment, and increased public transportation. This restructuring of our energy system will bring impacts on public health (e.g., improved air quality from lower use of traditional gasoline-powered vehicles). Furthermore, the transition can lower emissions of greenhouse gases (GHGs), thereby helping slow climate change and minimize its anticipated impacts on public health. While more work is needed to understand the full implications of these energy transitions, including the implications for environmental justice in terms of who will share in the health benefits and potential detriments such as increased emissions from brake wear for electric vehicles, the existing literature indicates substantial benefits to public health. Moving away from fossil fuel may offer a rare win-win strategy for a healthier society with a cleaner environment and lower GHGs.

Estimators of biological age hold promise for use in preventive medicine, for early detection of chronic conditions, and for monitoring the effectiveness of interventions aimed at improving population health. Among the promising biomarkers in this field are DNA methylation-based biomarkers, commonly referred to as epigenetic clocks. This review provides a survey of these clocks, with an emphasis on second-generation clocks that predict human morbidity and mortality. It explores the validity of epigenetic clocks when considering factors such as race, sex differences, lifestyle, and environmental influences. Furthermore, the review addresses the current challenges and limitations in this research area.

Health is at the forefront of clean air and climate action. However, most existing studies of health impacts were based on additive single-exposure effects, which often oversimplify the relationships between atmospheric components and health outcomes. This review examines various atmospheric components' common sources and differential health effects, including greenhouse gases and major air pollutants such as fine particulate matter (PM_2.5). It emphasizes the need for a comparative assessment of health impacts across various atmospheric components. We further highlight black carbon as an illustrative example, given its higher toxicity compared with other major PM_2.5 components. By integrating the best available findings on the differential effects of particulate matter components with multiple gridded estimates of air pollution concentrations and population data, we conducted a risk assessment to quantify the health benefits of particulate matter reductions associated with China's clean air actions (2013-2020) and future climate mitigation scenarios (2020-2060). Our assessments indicate that, in regions or during periods where black carbon accounts for a higher proportion of exposure reduction relative to other PM_2.5 components, reducing per-unit concentrations of PM_2.5 can prevent more premature deaths. We propose a conceptual framework for a health-oriented strategy to enhance the effectiveness of clean air and climate initiatives.