DOCSLIB.ORG

Cohort Study, Tz Jay

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Cohort Study, Tz Jay 世代追蹤 Cohort Study 國立成功大學 臨床藥學與藥物科技研究所 林子傑 博士後研究員 2014.10.23 Outline • Overview of pharmacoepidemiology (PE) • Design and structure of cohort studies What is pharmacoepidemiology? • The study of the use of and effects of drugs in large numbers of people. -- Brain L. Strom, editor Pharmacoepidemiology 4th edition • The study of the frequency, distribution, and determinants of diseases in humans, with “determinants” being drugs, vaccines or devices. -- Hartzema AG, Tilson HH, Chan KA, editors. Pharmacoepidemiology and Therapeutic Risk Management Pharmacoepidemiology is the study of the use of and effects of drugs in large numbers of people Pharmaco- Providing information about the epidemiology beneficial and harmful effects of any drug, thus permitting a better assessment of the risk/benefit balance for the use Borrow the research of any particular drug in any methods particular patient Marriage Clinical Epidemiology pharmacology Epidemiology is the study of the distribution Pharmacology is the study of the effects of drugs. and determinants of diseases in populations Clinical pharmacology is the study of the effects of drugs in humans. PHARMACOEPIDEMIOLOGY 4.Ed, edited by BRIAN L. STROM What is cohort study ? • Cohort studies → Studies that identify subsets of a defined population and follow them over time, looking for differences in their outcome --- BRIAN L. STROM • In PE: population-based What a cohort study can do in PE? • Causal-relationship Effectiveness and safety of drugs • Incidence rate, Number needed to treat / harm Risk difference, R1 R2 Absolute risk reduction Treatment Control Absolute risk increase NNT / NNH R1- R2 1/ risk difference Why there is a need of cohort studies in PE ? ~Limitations of RCTs • Carefully selected subjects may not reflect real- life patients in whom drug will be used Age, disease severity • Study subjects may receive better care than real-life patients Monitor by research nurse/staff • Short duration of treatment • Smaller sample size Strengths of population-based cohort studies • Large sample size • Longer duration of follow-up than clinical trials • Increased methodological flexibility Multiple comparison/control groups Compare relative effectiveness/safety of drugs in the same/different pharmacological class • Increased generalizability Examine drug use under real-life settings • Need to combat with confounding and other biases in design and analysis phase Limitations of PE studies • Databases are not necessarily developed for research purposes For reimbursement purposes → miss / over- or under-coding of diagnosis Validity of diagnosis codes • Need to control bias/confounders Still hard to be controlled through varies study design/statistical methods in some circumstances • Limitations of claims database Insufficient socieoeconomic factors in NHIRD Lack of dental record / medical device in medicaid database • Pharmacy record represents the “refill conditions” instead of the actual patient drug-taking behaviors 10 11 Data sources • Claims databases Patient registries Veterans Affairs (VA) databases Kaiser Permanente Medical Care Program Medicare/Medicaid databases The UK General Practice Research Database (GPRD) National Health Insurance Research Database (NHIRD) Sweden, Denmark, Canada 12 Study population – (1) • Inception cohort – new user design Similar to RCTs Exclude prevalent users Who are prevalent users Survivors of early period of therapy Long-term users tend to be adherent to a therapy Amplify adherence bias Predictors of prevalent use Plausibly already affected by the therapy Could introduce bias by adjusting for factors on the causal pathway • Limitations Small sample size Over-representation of short-term users Challenging to assess long-term effect 13 Study population – (2) • Study period, baseline period, follow-up period • Index date, the date met inclusion criteria (the start of follow-up) • Continuous enrollment • Censored points 14 Censoring issues • Non-informative censoring (ignorable missing) Missing completely at random (MCAR):The propensity for a data point to be missing is completely random. i.e. The missing data are just a random subset of the data. Missing at random (MAR): the propensity for a data point to be missing is not related to the missing data, but it is related to some of the observed data. • Informative censoring (non ignorable missing) Missing not at random (MNAR): the propensity for a data point to be missing depends on the unobserved event time 15 16 Selection of comparison group • Should be comparable As similar as possible with respect to all factors other than the exposure status In RCTs, the comparability is assured by randomization In observational studies, need to collect data on any potential baseline differences that could affect the outcome Non-users, active controls (need to control confounding by indication) 17 Soko Setoguchi, ICPE 2010 18 Intent-to-treat vs. On-treatment ~ Clinical trials • ITT Include outcome data for all randomized participants regardless of their status regarding non-adherence to assigned treatment protocols and missed assessment encounters • As-treated; On-treatment To account for treatment non-adherence ITT On-treatment Goal The evaluation of the effectiveness To measure the effect of the of the treatment in terms of the experimental treatment relative public health benefits of to the control condition when all administering the treatment in the patients adhere to the assigned community in light of inevitable treatment condition treatment non-adherence 19 Intent-to-treat vs. On-treatment ~ real-life setting • ITT → exposed to drug throughout follow-up • On-treatment → censored at the time point of non- persistence Date filled + days supplied + grace period+ effect period Gap • Information bias Patient persistence with therapy is differential and linked to outcome Persistence of drug effects may differ between therapies 20 Defining Outcomes • If and how you can measure (define) the outcome Diagnosis codes Surrogates Clinical expert advice is critical and imperative • What is the validity of the definition ? 21 22 Statistical analyses – (1) • To identify differences between comparison groups Chi-square, Student t-test The significance levels are sensitive to sample size The differences may be significant but not clinically meaningful • Standardized differences or effect size Differences between groups are divided by the pooled standard deviation of the two groups Not sensitive to sample size, providing a sense of the relative magnitude of differences 23 Statistical analyses – (2) • Kaplan-Meier method Give you event rates and event curves Assumes censored patients would have the same probability of experiencing a subsequent event as non-censored patients (MCAR or MAR) 24 Statistical analyses – (2) • Cox proportional hazard model 25 Variables needed for Cox model • Follow-up time • Outcome • Exposure & covariates Proc PHREG DATA=test; MODEL time*outcome(0) = Exposure & covariates; RUN; 26 Assumption: “Proportional Hazards” 27 Always check for the PH assumption -1 • Graphical method The log(-log(survival function)) plots 28 Always check for the PH assumption -2 • Graphical method Use the PH graph in the ASSESS option of proc PHREG Cumulative sums of martingale residuals over follow-up times 29 Always check for the PH assumption -3 • Schoenfeld (1982) proposed the first set of residuals for use with Cox regression packages Instead of a single residual for each individual, there is a separate residual for each individual for each covariate 30 Always check for the PH assumption – (4) • Time-dependent covariates Add time*covariate “interactions” to the model to fit non-PH If the coefficient for the time-dependent variable is significantly different from zero, non-PH is present Stratification 31 Sensitivity analysis • Guidelines for Good Pharmacoepidemiology Practices (GPP) 2007 “Sensitivity analyses should be conducted to examine the effect of varying the study population inclusion/exclusion criteria, the assumptions regarding exposure, potential effects of misclassification, unmeasured confounders” 33 Method • Study design and population: cohort, new users • Alendronate, calcitonin/raloxifene Classification : the 1st exposure after their osteoporotic fractures Calcitonin/raloxifene : the reference group (active control) • Primary analysis → on-treatment scenario Patients were censored if they switched to other treatment groups after treatment initiation Non-persistent on their therapy (last date covered by drug plus 30 days, allowing for a 30-day gap between prescriptions) Excluded short-term users 34 Method – ONJ def. and covariates 1. Previous proposed possible ONJ diagnosis codes (ICD-9: 73008, 73000, 73340, 73349, 73018, 73010, 73020, 73345, 73399, 52689, 7339, 5264, 5289, 5259, 5269) 2. Adopted the AAMOS definition Cases with persisted ONJ symptoms for more than 8 weeks and no history of radiation to the jaws 3. Potential cases had to receive any broad-spectrum oral antibiotics • Co-morbid conditions previously proposed to be related to ONJ Diabetes, hyperlipidemia, pancreatitis, gingival and periodontal diseases; other diseases and conditions of teeth and supporting structures, dentoalveolar surgery, rheumatoid arthritis, systemic lupus erythematosus, renal disease, hypertension, Alzheimer’s disease Osteoporos Int, 2013. 24(1): p. 237-44 35 Method – statistical analysis • Cox regression, PS-matching, KM-plot • Sensitivity analyses 1. An intent-to-treat
Load more

Recommended publications
  • Drug Exposure Definition and Healthy User Bias Impacts on the Evaluation of Oral Anti Hyperglycemic Therapies
    Drug Exposure Definition and Healthy User Bias Impacts on the Evaluation of Oral Anti Hyperglycemic Therapies by Maxim Eskin A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Health Policy Research School of Public Health University of Alberta © Maxim Eskin, 2017 ABSTRACT Accurate estimation of medication use is an essential component of any pharmacoepidemiological research as exposure misclassification will threaten study validity and lead to spurious associations. Many pharmacoepidemiological studies use simple definitions, such as the categorical “any versus no use” to classify oral antihyperglycemic medications exposure, which has potentially serious drawbacks. This approach has led to numerous highly publicized observational studies of metformin effect on health outcomes reporting exaggerated relationships that were later contradicted by randomized controlled trials. Although selection bias, unmeasured confounding, and many other factors contribute to the discrepancies, one critical element, which is often overlooked, is the method used to define exposure. Another factor that may provide additional explanation for the discrepancy between randomized controlled trials and observational study results of the association between metformin use and various health outcomes is the healthy user effect. Aspects of a healthy lifestyle such as following a balanced diet, exercising regularly, avoiding tobacco use, etc., are not recorded in typical administrative databases and failure to account for these factors in observational studies may introduce bias and produce spurious associations. The influence of a healthy user bias has not been fully examined when evaluating the effect of oral antihyperglycemic therapies in observational studies. It is possible that some, if not all, of the benefit, observed with metformin in observational studies may be related to analytic design and exposure definitions.
    [Show full text]
  • 10. Sample Bias, Bias of Selection and Double-Blind
    SEC 4 Page 1 of 8 10. SAMPLE BIAS, BIAS OF SELECTION AND DOUBLE-BLIND 10.1 SAMPLE BIAS: In statistics, sampling bias is a bias in which a sample is collected in such a way that some members of the intended population are less likely to be included than others. It results in abiased sample, a non-random sample[1] of a population (or non-human factors) in which all individuals, or instances, were not equally likely to have been selected.[2] If this is not accounted for, results can be erroneously attributed to the phenomenon under study rather than to the method of sampling. Medical sources sometimes refer to sampling bias as ascertainment bias.[3][4] Ascertainment bias has basically the same definition,[5][6] but is still sometimes classified as a separate type of bias Types of sampling bias Selection from a specific real area. For example, a survey of high school students to measure teenage use of illegal drugs will be a biased sample because it does not include home-schooled students or dropouts. A sample is also biased if certain members are underrepresented or overrepresented relative to others in the population. For example, a "man on the street" interview which selects people who walk by a certain location is going to have an overrepresentation of healthy individuals who are more likely to be out of the home than individuals with a chronic illness. This may be an extreme form of biased sampling, because certain members of the population are totally excluded from the sample (that is, they have zero probability of being selected).
    [Show full text]
  • A User's Guide to Debiasing
    A USER’S GUIDE TO DEBIASING Jack B. Soll Fuqua School of Business Duke University Katherine L. Milkman The Wharton School The University of Pennsylvania John W. Payne Fuqua School of Business Duke University Forthcoming in Wiley-Blackwell Handbook of Judgment and Decision Making Gideon Keren and George Wu (Editors) Improving the human capacity to decide represents one of the great global challenges for the future, along with addressing problems such as climate change, the lack of clean water, and conflict between nations. So says the Millenium Project (Glenn, Gordon, & Florescu, 2012), a joint effort initiated by several esteemed organizations including the United Nations and the Smithsonian Institution. Of course, decision making is not a new challenge—people have been making decisions since, well, the beginning of the species. Why focus greater attention on decision making now? Among other factors such as increased interdependency, the Millenium Project emphasizes the proliferation of choices available to people. Many decisions, ranging from personal finance to health care to starting a business, are more complex than they used to be. Along with more choices comes greater uncertainty and greater demand on cognitive resources. The cost of being ill-equipped to choose, as an individual, is greater now than ever. What can be done to improve the capacity to decide? We believe that judgment and decision making researchers have produced many insights that can help answer this question. Decades of research in our field have yielded an array of debiasing strategies that can improve judgments and decisions across a wide range of settings in fields such as business, medicine, and policy.
    [Show full text]
  • An Analytics Approach to Debiasing Asset- Management Decisions
    An analytics approach to debiasing asset- management decisions Wealth & Asset Management December 2017 Nick Hoffman Martin Huber Magdalena Smith An analytics approach to debiasing asset-management decisions Asset managers can find a competitive edge in debiasing techniques— accelerated with advanced analytics. Investment managers are pursuing analytics- Richard H. Thaler, one of the leaders in this field aided improvements in many different areas of (see sidebar, “The debiasing nudge”). the business, from customer and asset acquisition to operations and overhead costs. The change Asset management: An industry facing area we focus on in this discussion is investment challenges performance improvement, specifically the Investment managers are facing significant debiasing of investment decisions. With the challenges to profitability. Demonstrating the help of more advanced analytics than they are value of active management has become more already using, funds have been able to measure difficult in a market where returns are narrowing. the role played by bias in suboptimal trading Dissatisfaction with active performance is causing decisions, connecting particular biases customers to migrate toward cheaper passive to particular decisions. Such discoveries funds (Exhibit 1). Actions active funds are taking in provide the necessary foundation for effective response include reducing fees, which has created countermeasures—the debiasing methods that can a competitive cycle that is compressing margins. bring significant performance improvements to a Some funds (such as Allianz Global Investors pressured industry. and Fidelity) are changing their fee and pricing structures to make them more dependent on Business leaders are increasingly recognizing the outperformance through the use of fulcrum-type risk of bias in business decision making.
    [Show full text]
  • National Science Advisory Board for Biosecurity
    NATIONAL SCIENCE ADVISORY BOARD FOR BIOSECURITY Written Public Comments (Dec. 13, 2015 – Jan 8 . , 2016) The following are written comments submitted to the National Science Advisory Board for Biosecurity (NSABB) for the period December 13, 2015 – January 8, 2016. Interested persons may file written comments with the Board at any time via an email sent to [email protected]. Written statements should include the name, contact information, and when applicable, the professional affiliation of the interested person. From: David Fedson Sent: Sunday, December 13, 2015 9:35 AM To: Viggiani, Christopher (NIH/OD) [E] Cc: Opal, Steven Subject: NSABB Meeting on GOF research on January 7-8, 2016 Christopher Viggiani, Ph. D. Executive Director, NSABB NIH Office of Science Policy Dear Dr. Viggiani, I have reviewed the agenda of the NSABB meeting on January 7-8, 2016. At this meeting, the NSABB will discuss its Working Group's overview of progress, preliminary findings and draft working paper on Gain-of-Function (GOF) studies. The Gryphon Scientific report - "Risk and Benefit Analysis of Gain of Function Research, Final Report - December 2015" - will be presented at this meeting. I would like to bring to your attention and that of the NSABB several important points. 1. If GOF research accidentally or deliberately creates a new highly virulent and highly transmissible influenza virus, it will spread throughout the world in a matter of months. The ensuing pandemic will be a global event, and it will require a global response. 2. Ron Fouchier has said that Mother Nature is the biggest bioterrorist. Pandemic influenza viruses can arise not only in nature but also in experimental circumstances.
    [Show full text]
  • Cognitive Bias in Clinical Medicine ED O’Sullivan1, SJ Schofi Eld2
    J R Coll Physicians Edinb 2018; 48: 225–232 | doi: 10.4997/JRCPE.2018.306 REVIEW Cognitive bias in clinical medicine ED O’Sullivan1, SJ Schofi eld2 ClinicalCognitive bias is increasingly recognised as an important source of medical Correspondence to: error, and is both ubiquitous across clinical practice yet incompletely ED O’Sullivan Abstract understood. This increasing awareness of bias has resulted in a surge in Department of Renal clinical and psychological research in the area and development of various Medicine ‘debiasing strategies’. This paper describes the potential origins of bias Royal Infi rmary of Edinburgh based on ‘dual process thinking’, discusses and illustrates a number of the 51 Little France Crescent important biases that occur in clinical practice, and considers potential strategies that might Edinburgh EH16 4SA be used to mitigate their effect. UK Keywords: cognitive bias, diagnostic error, heuristics, interventions Email: [email protected] Financial and Competing Interests: No fi nancial or competing interests declared Introduction Cognitive bias can lead to medical error The human brain is a complex organ with the wonderful An important concept in understanding error is that of power of enabling man to fi nd reasons for continuing to cognitive bias, and the infl uence this can have on our decision- believe whatever it is that he wants to believe. making.10–12 Cognitive biases, also known as ‘heuristics’, are cognitive short cuts used to aid our decision-making. – Voltaire A heuristic can be thought of as a cognitive ‘rule of thumb’ or cognitive guideline that one subconsciously applies to a Cognitive error is pervasive in clinical practice.
    [Show full text]
  • Pharmacoepidemiological Evaluation in Prostate Cancer—Common Pitfalls and How to Avoid Them
    cancers Review Pharmacoepidemiological Evaluation in Prostate Cancer—Common Pitfalls and How to Avoid Them Aino Siltari 1,2, Anssi Auvinen 3 and Teemu J. Murtola 1,4,* 1 Faculty of Medicine and Health Technology, Tampere University, 33520 Tampere, Finland; aino.siltari@helsinki.fi 2 Department of Pharmacology, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland 3 Faculty of Social Sciences, Tampere University, 33014 Tampere, Finland; anssi.auvinen@tuni.fi 4 Department of Urology, TAYS Cancer Center, 33520 Tampere, Finland * Correspondence: teemu.murtola@tuni.fi Simple Summary: Pharmacoepidemiologic research provides opportunities to evaluate how com- monly used drug groups, such as cholesterol-lowering drugs, may affect the prostate cancer risk or mortality. However, such studies need to be carefully designed in order to avoid biases caused by systematic differences between medication users and non-users. Similarly, data must be carefully analyzed and interpreted while acknowledging possible biases that can lead to erroneous conclusions. Here, we review common pitfalls in such studies and describe ways to avoid them in an effort to aid future research. Abstract: Pharmacoepidemiologic research provides opportunities to evaluate how commonly used drug groups, such as cholesterol-lowering or antidiabetic drugs, may affect the prostate cancer Citation: Siltari, A.; Auvinen, A.; risk or mortality. This type of research is valuable in estimating real-life drug effects. Nonethe- Murtola, T.J. less, pharmacoepidemiological studies are prone to multiple sources of bias that mainly arise from Pharmacoepidemiological Evaluation systematic differences between medication users and non-users. If these are not appreciated and in Prostate Cancer—Common Pitfalls and How to Avoid Them.
    [Show full text]
  • Hypothalamic Clock Involvement in Cluster Headache
    Faculty of Health Sciences Department of Clinical Medicine Hypothalamic clock involvement in cluster headache A study of chronobiology, sleep, and cranial autonomic function in cluster headache — Hilde Karen Ofte A dissertation for the degree of Philosophiae Doctor – October 2016 Faculty of Health Science Department of Clinical Medicine UiT – The Arctic University of Tromsø Hypothalamic clock involvement in cluster headache A study of chronobiology, sleep, and cranial autonomic function in cluster headache Hilde Karen Ofte, MD A dissertation for the degree of Philosophiae Doctor - 2016 1 The anatomy lesson of Dr Nicolaes Tulp. Rembrandt van Rejn, 1632 [On the case of Isak van Halmaal, who:] “… in the beginning of the summer season, was afflicted with a severe headache, occurring and disappearing daily on fixed hours, with such an intensity that he often assured me that he could not bear the pain anymore or he would succumb shortly. For rarely, it lasted more than two hours. And the rest of the day there was no fever, nor indisposition of the urine, nor any infirmity of the pulse. But this recurring pain lasted until the fourteenth day..” Nicolaes Tulp, Observationes Medicae,1641 2 Table of contents Table of contents ..................................................................................................................... 3 Aknowledgements ................................................................................................................... 5 Abbreviations ..........................................................................................................................
    [Show full text]
  • Statistical Issues Arising in the Women's Health Initiative
    Biometrics 61, 899–941 DOI: 10.1111/j.1541-0420.2005.00454.x December 2005 Statistical Issues Arising in the Women’s Health Initiative Ross L. Prentice,∗ Mary Pettinger,∗∗ and Garnet L. Anderson∗∗∗ Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, P.O. Box 19024, Seattle, Washington 98109-1024, U.S.A. ∗email: [email protected] ∗∗email: [email protected] ∗∗∗email: [email protected] Summary. A brief overview of the design of the Women’s Health Initiative (WHI) clinical trial and observational study is provided along with a summary of results from the postmenopausal hormone therapy clinical trial components. Since its inception in 1992, the WHI has encountered a number of statistical issues where further methodology developments are needed. These include measurement error modeling and analysis procedures for dietary and physical activity assessment; clinical trial monitoring methods when treatments may affect multiple clinical outcomes, either beneficially or adversely; study design and analysis procedures for high-dimensional genomic and proteomic data; and failure time data analysis procedures when treatment group hazard ratios are time dependent. This final topic seems important in resolving the discrepancy between WHI clinical trial and observational study results on postmenopausal hormone therapy and cardiovascular disease. Key words: Chronic disease prevention; Clinical trial monitoring; Genome-wide scan; Hazard ratio; Measurement error; Nutritional epidemiology; Observational study; Randomized controlled trial; Women’s health. 1. Introduction domly assigned 48,835 eligible women to either a sustained The Women’s Health Initiative (WHI) is perhaps the most low-fat eating pattern (40%) or self-selected dietary behavior ambitious population research investigation ever undertaken.
    [Show full text]
  • Primacy, Congruence and Confidence in Diagnostic Decision-Making
    Primacy, Congruence and Confidence in Diagnostic Decision-Making Thomas Frotvedt1, Øystein Keilegavlen Bondevik1, Vanessa Tamara Seeligmann, Bjørn Sætrevik Department of psychosocial science, University of Bergen 1: These authors contributed equally to the manuscript Abstract Some heuristics and biases are assumed to be universal for human decision-making, and may thus be expected to appear consistently and need to be considered when planning for real-life decision-making. Yet results are mixed when exploring the biases in applied settings, and few studies have attempted to robustly measure the combined impact of various biases during a decision-making process. We performed three pre-registered classroom experiments in which trained medical students read case descriptions and explored follow-up information in order to reach and adjust mental health diagnoses (∑N = 224). We tested whether the order of presenting the symptoms led to a primacy effect, whether there was a congruence bias in selecting follow-up questions, and whether confidence increased during the decision process. Our results showed increased confidence for participants that did not change their decision or sought disconfirming information. There was some indication of a weak congruence bias in selecting follow-up questions. There was no indication of a stronger congruence bias when confidence was high, and there was no support for a primacy effect of the order of symptom presentation. We conclude that the biases are difficult to demonstrate in pre-registered analyses of
    [Show full text]
  • Selection Bias in Epidemiological Studies
    Selection Bias in Epidemiological Studies Madhukar Pai, MD, PhD Assistant Professor Department of Epidemiology & Biostatistics McGill University, Montreal, Canada Email: [email protected] 1 Leading Indian News Magazine: One out of every 10 Indian males could be impotent! 2 India Today, 1998 On closer look… 3 India Today, 1998 Antidepressant medications: do they work? Growing evidence of selective publication and publication bias in trials of antidepressant drugs 4 Antidepressant medications: do they work? "I have countless examples of helping people with these medications. So I'm not ready to throw them out.“ - Psychiatrist "Well, my personal experience has been that these pills have been extremely effective in the right patients," – Family Physician So who is right - the authors of those two critical studies or the psychiatrists and GPs on the front lines? 5 Now lets define selection bias “Distortions that result from procedures used to select subjects and from factors that influence participation in the study.” Porta M. A dictionary of epidemiology. Oxford, 2008. “Error introduced when the study population does not represent the target population” Delgado-Rodriguez et al. J Epidemiol Comm Health 2004 Defining feature: Selection bias occurs at: the stage of recruitment of participants and/or during the process of retaining them in the study Difficult to correct in the analysis, although one can do sensitivity analyses 6 Hierarchy of populations 7 Hulley et al. Designing Clinical Research. 2nd Edition. Lippincott Williams
    [Show full text]
  • The Whiteness of the Mediterranean Diet
    RESEARCH Journal of Critical Dietetics ISSN 1923-1237 Vol 5, Issue 2 Copyright 2021 Toronto, ON The whiteness of the Mediterranean Diet: A historical, sociopolitical, and dietary analysis using Critical Race Theory By Kate Gardner Burt, Lehman College, City University of New York, Bronx, NY Corresponding author: Kate G. Burt, PhD, RD Assistant Professor in the Dietetics, Foods, and Nutrition Program at Lehman College, City University of New York. Mailing Address: 250 Bedford Park Boulevard West, G-431, Bronx, NY, 10468. Email Address: [email protected] ORCID: 0000-0002-6115-6450 Abstract The Mediterranean Diet (MedDiet) is widely accepted as a gold standard diet, yet its adoption and promotion as the healthiest cultural diet reflects systemic racism and inherently biased research rather than evidence-based science. This analysis establishes that while the Mediterranean region is multi-cultural and multi-ethnic, the MedDiet is a white diet. It also asserts that a lack of causal research and other methodologic issues in research about the MedDiet has resulted in a hyperfocus on the MedDiet over other cultural diets. Ultimately, promoting the MedDiet as a gold standard marginalizes people from non-white cultures by maintaining white culture as normative. In order to better serve and include Black, Indigenous, and People of Color, dietary recommendations need to become as diverse as the US population. Doing so will also improve cultural humility among professionals, beget anti-racist dietary research, and promote a more evidence-based dietary perspective. Keywords: dietary recommendations; Mediterranean & Martín-Calvo, 2016). The MedDiet is widely accepted diet; cultural humility; racism; diversity and inclusion; as a gold standard diet and promoted by the United critical race theory States (US) Departments of Agriculture and Health and Human Service’s Dietary Guidelines (2015-2020 Dietary Guidelines for Americans, 2015).
    [Show full text]