Creating a 21st-Century Intelligent Health System

In most areas of life, Americans enjoy the ease and convenience offered by advances in technology, communications, and transportation. Every day we experience the 21st-century model of America, which is one of effectiveness, accuracy, speed, flexibility, efficiency, lower cost, more choices, and greater achievement. We can shop online, compare prices for goods and services, and when decisions need to be made, we have access to a wide array of information sources to assist in making those decisions. In short, Americans enjoy great latitude in our power to determine what is best for us.This is not, however, the case when it comes to health and healthcare. In our current healthcare system, individuals are dependent on a structure that has resisted the natural progress and modernization achieved by market-oriented, 21st-century industries. The information age has been leaving health behind.Although it is the nature of a science- and technology-based entrepreneurial free market to provide more choices of higher quality at lower cost, in the healthcare sector, prices continue to rise, quality is inconsistent, and individuals lack the information, incentives, and power to make choices.     

Evaluation of Diversity of Clinical Trials Informing Health Technology Assessments in the United States: A 5-Year Analysis of Institute for Clinical and Economic Review Assessments

ObjectivesThis study aimed to evaluate the diversity of clinical trials informing assessments conducted by the Institute for Clinical and Economic Review.MethodsThis was a cross-sectional study of pivotal trials included in completed Institute for Clinical and Economic Review assessments over 5 years (2017-2021). Representation of racial/ethnic minority groups, females, and older adults was compared with the disease-specific and US population, using a relative representation cutoff of 0.8 for adequate representation.ResultsA total of 208 trials, evaluating 112 interventions for 31 unique conditions, were examined. Race/ethnicity data were inconsistently reported. The median participant-to-disease representative ratio (PDRR) for Blacks/African Americans (0.43 [interquartile range (IQR) 0.24-0.75]), American Indians/Alaska Natives (0.37 [IQR 0.09-0.77]), and Hispanics/Latinos (0.79 [IQR 0.30-1.22]) were below the adequate representation cutoff. In contrast, Whites (1.06 [IQR 0.92-1.2]), Asians (1.71 [IQR 0.50-3.75]), and Native Hawaiian/Other Pacific Islanders (1.61 [IQR 0.77-2.81]) were adequately represented. Findings were similar when compared with the US Census, except for Native Hawaiian/Pacific Islanders, which was substantially worse. Relative to all trials, a higher proportion of US-based trials adequately represented Blacks/African Americans (61% vs 23%, P < .0001) and Hispanics/Latinos (68% vs 50%; P = .047), but a lower proportion adequately represented Asians (15% vs 67%, P < .0001). Females were adequately represented in 74% of trials (PDRR: 1.02 [IQR 0.79-1.14]). Nevertheless, older adults were adequately represented in only 20% of trials (PDRR: 0.30 [IQR 0.13-0.64]).ConclusionsThe representation of racial/ethnic minorities and older adults was inadequate. Efforts are needed to enhance the diversity of clinical trials. Standardized and transparent evaluation of trial diversity should be part of the health technology assessment process.     

Risk Assessments at Birth Predict Kindergarten Achievement and Involvement with Child Protective Services

Screening for family risk factors at birth is one way that early childhood programs and practitioners can identify families who might benefit from prevention efforts. Some prevention programs currently use cumulative risk (i.e., total number of risk factors present) to determine eligibility for services. More recently, however, person-centered approaches that take into account combinations of risk (versus cumulative risk) have emerged as an approach that could complement current risk assessment methods and illuminate the extent to which classes of family risk are associated with different outcomes in early childhood. Grounded in ecological theory, we tested cumulative family risk and latent classes of family risk at birth as predictors of kindergarten outcomes and mother-reported involvement with Child Protective Services (CPS). Families in the Fragile Families and Child Well-being Study were included in this study if they had medical records data available at the child’s birth as well as children’s age 5 kindergarten outcomes (N?=?757). Cumulative risk was positively associated with children’s attention problems, letter-word recognition skills, CPS involvement, and both covariates (i.e., child’s diagnosed disability status and mother’s ethnicity/race), but not aggression or social skill problems. In terms of latent classes, children from higher risk classes tended to fare significantly worse on kindergarten outcomes and were more likely to have reported involvement with CPS when compared to the lower risk classes. Implications are discussed related to primary prevention, the merits of screening for risk, and comparisons between cumulative risk and classes of risk approaches.

     

Systematic Review and Evidence Integration for Literature-Based Environmental Health Science Assessments

Background: Systematic-review methodologies provide objectivity and transparency to the process of collecting and synthesizing scientific evidence in reaching conclusions on specific research questions. There is increasing interest in applying these procedures to address environmental health questions.Objectives: The goal was to develop a systematic-review framework to address environmental health questions by extending approaches developed for clinical medicine to handle the breadth of data relevant to environmental health sciences (e.g., human, animal, and mechanistic studies).Methods: The Office of Health Assessment and Translation (OHAT) adapted guidance from authorities on systematic-review and sought advice during development of the OHAT Approach through consultation with technical experts in systematic review and human health assessments, as well as scientific advisory groups and the public. The method was refined by considering expert and public comments and through application to case studies.Results and Discussion: Here we present a seven-step framework for systematic review and evidence integration for reaching hazard identification conclusions: 1) problem formulation and protocol development, 2) search for and select studies for inclusion, 3) extract data from studies, 4) assess the quality or risk of bias of individual studies, 5) rate the confidence in the body of evidence, 6) translate the confidence ratings into levels of evidence, and 7) integrate the information from different evidence streams (human, animal, and “other relevant data” including mechanistic or in vitro studies) to develop hazard identification conclusions.Conclusion: The principles of systematic review can be successfully applied to environmental health questions to provide greater objectivity and transparency to the process of developing conclusions.Citation: Rooney AA, Boyles AL, Wolfe MS, Bucher JR, Thayer KA. 2014. Systematic review and evidence integration for literature-based environmental health science assessments. Environ Health Perspect 122:711–718; http://dx.doi.org/10.1289/ehp.1307972     

Addressing Human Variability in Next-Generation Human Health Risk Assessments of Environmental Chemicals

Background: Characterizing variability in the extent and nature of responses to environmental exposures is a critical aspect of human health risk assessment.Objective: Our goal was to explore how next-generation human health risk assessments may better characterize variability in the context of the conceptual framework for the source-to-outcome continuum.Methods: This review was informed by a National Research Council workshop titled “Biological Factors that Underlie Individual Susceptibility to Environmental Stressors and Their Implications for Decision-Making.” We considered current experimental and in silico approaches, and emerging data streams (such as genetically defined human cells lines, genetically diverse rodent models, human omic profiling, and genome-wide association studies) that are providing new types of information and models relevant for assessing interindividual variability for application to human health risk assessments of environmental chemicals.Discussion: One challenge for characterizing variability is the wide range of sources of inherent biological variability (e.g., genetic and epigenetic variants) among individuals. A second challenge is that each particular pair of health outcomes and chemical exposures involves combinations of these sources, which may be further compounded by extrinsic factors (e.g., diet, psychosocial stressors, other exogenous chemical exposures). A third challenge is that different decision contexts present distinct needs regarding the identification—and extent of characterization—of interindividual variability in the human population.Conclusions: Despite these inherent challenges, opportunities exist to incorporate evidence from emerging data streams for addressing interindividual variability in a range of decision-making contexts.     

Evaluating Uncertainty to Strengthen Epidemiologic Data for Use in Human Health Risk Assessments

Background: There is a recognized need to improve the application of epidemiologic data in human health risk assessment especially for understanding and characterizing risks from environmental and occupational exposures. Although there is uncertainty associated with the results of most epidemiologic studies, techniques exist to characterize uncertainty that can be applied to improve weight-of-evidence evaluations and risk characterization efforts.Methods: This report derives from a Health and Environmental Sciences Institute (HESI) workshop held in Research Triangle Park, North Carolina, to discuss the utility of using epidemiologic data in risk assessments, including the use of advanced analytic methods to address sources of uncertainty. Epidemiologists, toxicologists, and risk assessors from academia, government, and industry convened to discuss uncertainty, exposure assessment, and application of analytic methods to address these challenges.Synthesis: Several recommendations emerged to help improve the utility of epidemiologic data in risk assessment. For example, improved characterization of uncertainty is needed to allow risk assessors to quantitatively assess potential sources of bias. Data are needed to facilitate this quantitative analysis, and interdisciplinary approaches will help ensure that sufficient information is collected for a thorough uncertainty evaluation. Advanced analytic methods and tools such as directed acyclic graphs (DAGs) and Bayesian statistical techniques can provide important insights and support interpretation of epidemiologic data.Conclusions: The discussions and recommendations from this workshop demonstrate that there are practical steps that the scientific community can adopt to strengthen epidemiologic data for decision making.Citation: Burns CJ, Wright JM, Pierson JB, Bateson TF, Burstyn I, Goldstein DA, Klaunig JE, Luben TJ, Mihlan G, Ritter L, Schnatter AR, Symons JM, Yi KD. 2014. Evaluating uncertainty to strengthen epidemiologic data for use in human health risk assessments. Environ Health Perspect 122:1160–1165; http://dx.doi.org/10.1289/ehp.1308062     

International Journal of Disaster Risk Science

<正>The International Journal of Disaster Risk Science is supervised by the Ministry of Civil Affairs of the People’s Republic of China and supported by the National Disaster Reduction Center of the ministry.The Editorial Offi ce is at the Academy of Disaster Reduction and Emergency Management of Beijing Normal University.The journal is also supported by the State Key Laboratory     

International Journal of Disaster Risk Science

<正>The International Journal of Disaster Risk Science is supervised by the Ministry of Civil Affairs of the People’s Republic of China and supported by the National Disaster Reduction Center of the ministry.The Editorial Offi ce is at the Academy of Disaster Reduction and Emergency Management of Beijing Normal University.The journal is also     

Importance of a Patient's Personal Health History on Assessments of Future Risk of Coronary Heart Disease

Objective: Although many coronary heart disease (CHD) risk factors are known, the role of an individual's changing personal health history is unclear. We implemented this study to evaluate whether accounting for previous Framingham Risk Scores (FRSs) improves the predictive ability of a current FRS for future CHD in middle-aged adults. METHODS: We analyzed data from the Atherosclerosis Risk in Communities Study (ARIC), a longitudinal cohort of people 45 to 64 years old at entry (1986 to 1989 through 2001). FRSs were calculated for participants in the ARIC cohort (3901 men, 5406 women) at baseline (visit 3) and 3 and 6 years before. Using Cox regressions we evaluated the risk of CHD development for the FRS 6 years from baseline and then evaluated whether the addition of the change in FRS assessments from 3 and 6 years before the baseline improved the predictive ability of the FRS. Areas under the receiver operating characteristic (AUROC) curves were compared. RESULTS: The addition of the difference between the baseline FRS (eg, in 1995) and the FRS from 6 years earlier (eg, in 1989) to predict CHD development by 2001 for the entire cohort yielded an AUROC of 0.730, which was a significant improvement over just using the baseline FRS (P < .05). The effect was located primarily among women, with the AUROC curve improving from 0.667 to 0.709 (P < .05). There was no improvement for CHD risk prediction in men when the earlier FRS assessments were taken into account. Men seem to have less change in some risk factors over time. CONCLUSIONS: Accounting for an individual's history improves risk assessments based on current measures.     

A Framework for the Next Generation of Risk Science

Objectives: In 2011, the U.S. Environmental Protection Agency initiated the NexGen project to develop a new paradigm for the next generation of risk science.Methods: The NexGen framework was built on three cornerstones: the availability of new data on toxicity pathways made possible by fundamental advances in basic biology and toxicological science, the incorporation of a population health perspective that recognizes that most adverse health outcomes involve multiple determinants, and a renewed focus on new risk assessment methodologies designed to better inform risk management decision making.Results: The NexGen framework has three phases. Phase I (objectives) focuses on problem formulation and scoping, taking into account the risk context and the range of available risk management decision-making options. Phase II (risk assessment) seeks to identify critical toxicity pathway perturbations using new toxicity testing tools and technologies, and to better characterize risks and uncertainties using advanced risk assessment methodologies. Phase III (risk management) involves the development of evidence-based population health risk management strategies of a regulatory, economic, advisory, community-based, or technological nature, using sound principles of risk management decision making.Conclusions: Analysis of a series of case study prototypes indicated that many aspects of the NexGen framework are already beginning to be adopted in practice.Citation: Krewski D, Westphal M, Andersen ME, Paoli GM, Chiu WA, Al-Zoughool M, Croteau MC, Burgoon LD, Cote I. 2014. A framework for the next generation of risk science. Environ Health Perspect 122:796–805; http://dx.doi.org/10.1289/ehp.1307260     

新一代风险科学的框架(续完)

5.2.5 暴露评估 目前,人类暴露评估主要基于人类环境中环境因素的测量水平(美国EPA,2010);某些情况下,内部剂量测量值也可能通过生物监测(Hays 和Aylward,2008)或药代动力学模型(Barton等,2007)获取.在NexGen方法中,暴露评估将更侧重通过先进的生物监测技术(NRC,2012)结合新高通量方法,获得大量环境因素暴露指标(Jones等,2012),从而直接测量人类关键的毒性通路扰动.