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Unit 11 A: Research Analysis It can be very helpful to understand why different research methodologies are applied in different studies. This understanding can help guide your future research design. To prepare for this assignment, use a published research paper by Shaw et al. (2020) in which researchers conducted a telehealth intervention for in-home dementia care support. Then address the following: · Post an analysis of the results in comparison to each of the Hill Criteria** for causation, and discuss whether the evaluation design adequately supports the conclusions. The Hill Criterias: 1. Strength of association; 2. Consistency; 3. Specificity; 4. Temporality; 5. Biological gradient; 6. Plausibility; 7. Coherence; 8. Experiment; 9. Analogy. · Note threats to internal validity, bias, and confounding variables, and explain how you believe these were controlled by the study design. · Are there threats, biases, or confounding variables that are missing and therefore jeopardizing the strength of the conclusions? If so, what could have been done to compensate for them? **The Hill Criteria: See attached in a separate file. Total pages: 1. Reference Shaw, C. A., Williams, K. N., Lee, R. H., & Coleman, C. K. (2020). Cost-effectiveness of a telehealth intervention for in-home dementia care support: Findings from the FamTechCare clinical trial. Research in Nursing & Health. https://doi.org/10.1002/nur.22076 Applying the Bradford Hill criteria in the 21st century: how data integration has changed causal inference in molecular epidemiology Fedak et al. Emerg Themes Epidemiol (2015) 12:14 DOI 10.1186/s12982-015-0037-4 ANALYTIC PERSPECTIVE Applying the Bradford Hill criteria in the 21st century: how data integration has changed causal inference in molecular epidemiology Kristen M. Fedak1,3*, Autumn Bernal2, Zachary A. Capshaw3 and Sherilyn Gross3 Abstract In 1965, Sir Austin Bradford Hill published nine “viewpoints” to help determine if observed epidemiologic associations are causal. Since then, the “Bradford Hill Criteria” have become the most frequently cited framework for causal infer- ence in epidemiologic studies. However, when Hill published his causal guidelines—just 12 years after the double- helix model for DNA was first suggested and 25 years before the Human Genome Project began—disease causation was understood on a more elementary level than it is today. Advancements in genetics, molecular biology, toxicology, exposure science, and statistics have increased our analytical capabilities for exploring potential cause-and-effect rela- tionships, and have resulted in a greater understanding of the complexity behind human disease onset and progres- sion. These additional tools for causal inference necessitate a re-evaluation of how each Bradford Hill criterion should be interpreted when considering a variety of data types beyond classic epidemiology studies. Herein, we explore the implications of data integration on the interpretation and application of the criteria. Using examples of recently discovered exposure–response associations in human disease, we discuss novel ways by which researchers can apply and interpret the Bradford Hill criteria when considering data gathered using modern molecular techniques, such as epigenetics, biomarkers, mechanistic toxicology, and genotoxicology. Keywords: Causation, Causal inference, Data integration, Bradford Hill, Molecular epidemiology © 2015 Fedak et al. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/ publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Background In 1965, Sir Austin Bradford Hill gave the first President’s Address to the newly formed Section on Occupational Medicine, which was published within the Proceedings of the Royal Society of Medicine [1]. Hill began his address by pointing out a fundamental problem facing the Section members: how could they effectively practice preventative occupational medicine without a basis for determining which occupational hazards ultimately cause sickness and injury? Namely, Hill asked, “In what circumstances can [one] pass from [an] observed association to a verdict of causation?” [1]. He proceeded to propose nine “aspects of association” for evaluating traditional epidemiologic data. These aspects, which have since become fundamental tenets of causal inference in epidemiology, are often referred to as the Bradford Hill Criteria. The nine “aspects of association” that Hill discussed in his address (strength of association, consistency, specificity, temporality, biological gradient, plausibility, coherence, experiment, and analogy) have been used to evaluate countless hypothesized relationships between occupational and environmental exposures and disease outcomes. Yet, when Hill conceived these nine aspects (hereafter referred to as criteria), the mechanistic con- nections between exposure and disease were not well understood. Consider that Hill published his criteria just 12 years after Watson and Crick first suggested the double-helix model for DNA. Traditional epidemiologic study designs that were developed and used around the time of Hill’s speech treated the connection between exposure and disease as a ‘black box’—meaning that the biological mechanisms that occur between exposure and disease onset were unknown and therefore omitted in Open Access *Correspondence:
[email protected] 1 Department of Environmental and Radiological Health Sciences, Colorado State University, 350 West Lake Street, Fort Collins, CO 80521, USA Full list of author information is available at the end of the article http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/publicdomain/zero/1.0/ http://creativecommons.org/publicdomain/zero/1.0/ http://crossmark.crossref.org/dialog/?doi=10.1186/s12982-015-0037-4&domain=pdf Page 2 of 9Fedak et al. Emerg Themes Epidemiol (2015) 12:14 study design [2]. Over the past 50 years, advances in sci- entific fields (e.g., molecular genetics, genomics, molecu- lar toxicology) and technology (e.g., computers, software, statistics, analytical methods) have provided researchers with a much deeper and more complex understanding of how diseases initiate and progress, effectively allow- ing researchers to glimpse into the ‘black box’ of the exposure-to-disease paradigm. As a result, researchers considering causal inference have new and more diverse types of information to consider when establishing cau- sality beyond the traditional epidemiologic study designs that were available when Hill wrote his causal criteria. Data integration refers to the incorporation of data, knowledge, or reasoning from across multiple disciplines or approaches, with the goal of generating a level of understanding or knowledge that no discipline achieved alone [3, 4]. Data integration, while not always referred to by that term, has been discussed in light of causal infer- ence of disease for over a decade, and the epidemiologic community has generally welcomed these interdiscipli- nary collaborations [5–7]. For example, the preface of the 5th edition of the Dictionary of Epidemiology directly acknowledges the “positive blurring of the boundaries of epidemiological research methods” into other scientific disciplines. The preface welcomes non-epidemiologists to contribute to and use the Dictionary and inversely invites trained epidemiologists to utilize the concepts within the Dictionary in non-epidemiological initiatives [4]. Fur- thermore, numerous agencies, organizations, and aca- demics have recently attempted to establish frameworks or guidelines for data integration in the field of human health and ecological risk assessment. These frameworks consider how researchers should address, compare, and contrast the value and contributions of data that come from different evidence streams or scientific disciplines [8–11]. Hill aptly stated at the end of his speech that “[a]ll sci- entific work is incomplete… [and] liable to be upset or modified by advancing knowledge” [1]. Today, research- ers considering causal inference must integrate data from a variety of scientific disciplines. Herein, we discuss how data integration in the field of causal inference of diseases affects the application and interpretation of each of Hill’s criteria. Criteria 1: strength of association Hill’s first criterion for causation is strength of the associ- ation. As he explained, the larger an association between exposure and disease, the more likely it is to be causal. To illustrate this point, Hill provided the classic example of Percival Pott’s examination of scrotal cancer incidence in chimney sweeps. The tremendous strength of association between that occupation and disease—nearly 200 times greater than seen in other occupations—led to a deter- mination that the chimney soot was likely a causal factor. Contrarily, Hill suggested that small associations could more conceivably be attributed to other underlying con- tributors (i.e. bias or confounding) and, therefore, are less indicative of causation. Defining what constitutes a “strong” association is criti- cal to the assessment of potentially causal relationships. Advances in statistical theory and the computational processing power have allowed scientists to delineate strong versus weak associations using more defensible mathematical criteria than Hill had in mind. Strength is no longer interpreted as simply the magnitude of an asso- ciation. Furthermore, researchers have gained a greater appreciation for multi-factoral diseases and the existence of determinant risk factors that are small in magnitude yet statistically strong. Today, statistical significance—not the magnitude of association—is the accepted bench- mark for judging the strength of an observed association, and thus its potential causality. Yet, these same statistical and computational advances necessitate an added degree of scrutiny when interpret- ing study results. Modern tools have enabled research- ers to collect much larger datasets, access wide ranges of metadata, employ complex algorithms, and choose from a multitude of statistical approaches. As such, statistically significant results presented within a study are not always biologically meaningful or methodologically appropriate for contributing to causal inference. Conversely, failure to mathematically demonstrate statistical significance in a single study does not preclude the possibility of a meaningful exposure–response relationship in reality. Thus, assessing strength of association in causal inference requires examination of underlying methods, compari- son to the weight of evidence in the literature, and con- sideration of other contextual factors including the other criteria discussed herein. An example can be seen in the analysis and subse- quent re-analysis of pulmonary function in a cohort of 106 workers at a flavorings manufacturing facility that used a variety of chemicals, including acetaldehyde, ace- toin, benzaldehyde, butyric acid, and diacetyl [12, 13]. In the original study conducted by the National Institute for Occupational Safety and Health (NIOSH), research- ers retrospectively analyzed spirometry reports and job title records collected by the cohort’s employer [13]. The authors presented statistically significant effect estimates showing that employees in jobs with higher potential for flavoring chemical exposures had 2.8 times greater annual declines in forced expiratory volume (FEV) than employees