Musculoskeletal Disorders in the Workplace: Principles and Practice, 2nd Edition PDF
Preview Musculoskeletal Disorders in the Workplace: Principles and Practice, 2nd Edition
1600 John F. Kennedy Blvd. Ste 1800 Philadelphia, PA 19103-2899 MUSCULOSKELETAL DISORDERS IN THE WORKPLACE: ISBN-13: 978-0-323-02622-2 PRINCIPLES AND PRACTICE ISBN-10: 0-323-02622-2 Copyright © 2007, 1997 by Mosby Inc., an affiliate of Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permissions may be sought directly from Elsevier’s Health Sciences Rights Department in Philadelphia, PA, USA: phone: (+1) 215 239 3804, fax: (+1) 215 239 3805, e-mail: [email protected]. You may also complete your request on-line via the Elsevier homepage (http://www.elsevier.com), by selecting ‘Customer Support’ and then ‘Obtaining Permissions’. Notice Knowledge and best practice in this field are constantly changing. As new research and experience broaden our knowledge, changes in practice, treatment, and drug therapy may become necessary or appropriate. Readers are advised to check the most current information provided (i) on procedures featured or (ii) by the manufacturer of each product to be admin- istered, to verify the recommended dose of formula, the method and duration of administra- tion, and contraindications. It is the responsibility of the practitioner, relying on their own experience and knowledge of the patient, and to make diagnoses, to determine dosages and the best treatment for each individual patient, and to take all appropriate safety precautions. To the fullest extent of the law, neither the Publisher nor the Editors assume any liability for any injury and/or damage to persons or property arising out of or related to any use of the material contained in this book. The Publisher Library of Congress Cataloging-in-Publication Data Musculoskeletal disorders in the workplace: Principles and practice / [edited by] Margareta Nordin, Gunnar B.J. Andersson, Malcolm H. Pope. —2nd ed. p. ; cm. Includes bibliographical references and index. ISBN 0-323-02622-2 1. Musculoskeletal system—Diseases. 2. Occupational diseases. I. Nordin, Margareta. II. Pope, M. H. (Malcolm Henry), 1941- III. Andersson, Gunnar, 1942- [DNLM: 1. Musculoskeletal Diseases—therapy. 2. Biomechanics. 3. Human Engineering. 4. Musculoskeletal Diseases—prevention & control. 5. Occupational Diseases—etiology, WE 140 M9854 2006] RC925.5M8783 2006 616.7—dc22 2006043830 Acquisitions Editor: Rolla Couchman Project Manager: Bryan Hayward Printed in United States of America Last digit is the print number: 9 8 7 6 5 4 3 2 1 Foreword Workers’ health priorities are now driven by demographics. to his or her previous level of performance, accomplishing All of the First World Nations are facing a future with a rapidly a particular task within a reasonable time frame. Although diminishing work force, an aging population, and a growing the average orthopaedic surgeon may be well-versed with the number of pensioners. The prevention and efficient man- conditions of the gridiron, he or she may not be familiar with agement of work place injuries and disabilities has become the requirements and limitations of the industrial playing a priority. field. In industrial medicine, it is not only necessary to "fix" Medical professionals work in an increasingly specialized the worker; one must have an idea about how to fix the work- world brought on by an explosion of knowledge, the demand place to prevent further injury. Like sports medicine, the from society for "the very best" in services, and the need for management and prevention of industrial injury demands expertise to keep pace with technological change and innova- a dedicated and knowledgeable cadre of physicians, surgeons, tion. Modern medical history, in particular, is replete with and therapists who are able to apply modern knowledge and examples of sudden bursts of information that challenged the expertise to a successful medical program. growth of new domains and abilities. The period of World This volume brings together chapters authored by the War II saw an explosion of medical knowledge, rapidly divid- most knowledgeable group of surgeons, physicians, scientists, ing General Surgery into numerous subspecialties. Similarly, ergonomists, and therapists currently addressing the preven- the 1970s was a critical period in orthopaedic surgical practice tion and management of workplace injury. The editors have as many subspecialties developed that allowed greater expert assembled a most versatile and practical tool for the many use of modern technology. allied-health professionals involved with work-related injuries. Occupational orthopaedics is a relatively young specialty This updated text will have a strong impact within industry that is still evolving rapidly. As in sports medicine, we have and on the management of patients well into the 21st century. learned that it is not sufficient to examine and treat injuries alone. After recovery, an injured football player is expected to Victor H. Frankel KNO, MD, PhD return to the game and perform at his previous level of athletic Professor of Orthopaedic Surgery, NYU ability. The injured industrial worker is also expected to return President Emeritus, Hospital for Joint Diseases v Contributors K. N. An, Ph.D. Amit Bhattacharya, Ph.D., C.P.E. Craig J. Della Valle, M.D. John and Posy Krehbiel Professor of Professor Assistant Professor of Orthopaedic Surgery Orthopaedics Biomechanics-Ergonomics Research Laboratories Rush-Presbyterian-St. Luke's Medical Center Maylo Clinic College of Medicine Department of Environmental Health Chicago, IL 60612 Orthopaedics Biomechanics Lab University of Cincinnati Medical College Rochester, MN 55905 Cincinnati, OH 45267-0056 James A. Dewees, M.S., C.P.E., C.E.E.S. ERGO Accommodations Inc Gunnar B. J. Andersson, M.D., Ph.D. Anthony M. Buoncristiani, M.D., L.T. P O Box 499 Professor and Chairman Orthopaedics Department Union, KY 41091-0499 Department of Orthopaedic Surgery Naval Medical Center Rush-Presbyterian- San Diego, CA 92134 Jiri Dvorak, M.D., Ph.D. Department of Neurology St. Luke’s Medical Center Linda Carroll, M.D. Schulthess Hospital Chicago, IL 60612 Associate Professor 8008 Zurich Thomas J. Armstrong, Ph.D. Department of Public Health Sciences Switzerland Professor University of Alberta Ulf Eklund M.D. Industrial and Operations Engineering Edmonton, Alberta T6G 2E1 Orthopedic Surgeon Director Canada Department of Orthopedics Center for Ergonomics J. David Cassidy, M.D. Molndal Hospital University of Michigan Department of Public Health Sciences Molndal, Sweden Ann Arbor, MI 48109-2117 University of Alberta Freddie H. Fu, M.D. Federico Balagué Edmonton, Alberta T6G 2E1 Department of Orthopaedic Surgery Médecin Chef Adjoint Canada University of Pittsburgh Division of Rheumatology, Physical Medicine Christine Cedraschi Pittsburgh, PA 15203 and Rehabilitation Division of General Medical Rehabilitation & Hôpital Cantonal Multidisciplinary Pain Center Douglass Gross, Ph.D. 1708 Fribourg Division of Clinical Pharmacology and Assistant Professor Switzerland Toxicology Department of Physical Therapy and Geneva University Hospitals University of Alberta Adjunct Associate Professor 1211 Geneva 14 Edmonton, Alberta T6G 2G4 Department of Orthopaedic Surgery Switzerland Canada New York University School of Medicine Robert Gunzburg, M.D., Ph.D. New York, NY 10014 Mark S. Cohen, M.D. Senior Consultant Department of Orthopaedic Surgery Department of Orthopaedics Michele Crites Battié, Ph.D. Rush-Presbyterian- Centenary Clinic Professor St. Luke’s Medical Center 2018 Antwerp Department of Physical Therapy Chicago, IL 60612 Belgium University of Alberta Pierre Côté, D.C., Ph.D. Edmonton, Alberta T6G 2G4 Daniel J. Habes, M.S.E., C.P.E. Scientist Canada Industrial Engineer Institute for Work and Health Industrial Hygiene Section Jane Bear-Lehman, Ph.D., OTR, FAOTA Toronto, Ontario M5G 2E9 Hazard Evaluations and Technical Assistance Associate Professor Canada Branch New York University Benjamin Crane, M.D. Division of Surveillance, Hazard Evaluations, Steinhardt School of Education Resident and Field Studies Occupational Therapy Department Department of Orthopaedic Surgery National Institute for Occupational Safety and New York, NY 10012 Rush University Medical Center Health Chicago, IL 60612 Cincinnati, OH David P. Beason, M.S. Research Engineer James N. DeBritz, M.D. Robert H. Haralson, III, M.D., M.B.A. Laboratory Manager Assistant Instructor Executive Director of Medical Affairs McKay Orthopaedic Research Laboratory Department of Orthopaedics American Association of Orthopaedic University of Pennsylvania Georgetown University Hospital Surgeons Philadelphia, PA 19104 Washington, DC 20007 Rosemont, IL 60018 vii viii Contributors Rudi Hiebert, B.S. Margareta Nordin, Dr.Sci. David Rempel, M.D., M.P.H. Interim Director Professor Professor Musculoskeletal Epidemiology Unit Departments of Orthopaedics and School of Medicine –Ergonomics Program Occupational & Industrial Orthopaedic Center Environmental Medicine Division of Occupational and Environmental NYU Hospital for Joint Diseases School of Medicine Medicine New York, NY 10014 New York University Program Director University of California, San Francisco Program of Ergonomics and Richmond, CA 94804 Beat Hintermann, M.D. Biomechanics Chief Orthopaedic Clinic Graduate School of Arts and Science Michiel Reneman, Ph.D, P.T. University of Basel New York University Center for Rehabilitation Kantonsspital Director University Medical Center Groningen CH-4410 Liestal Occupational and Industrial University of Groningen Switzerland Orthopaedic Center (OIOC) P.O. Box 30002, 9750 RA Haren David M. Kalainov, M.D. NYU Hospital for Joint Diseases The Netherlands Clinical Assistant Professor New York University Medical Center Department of Orthopaedic Surgery New York, NY 10014 Per A.F.H. Renström, M.D., Ph.D. Northwestern University Mooyeon Oh-Park, M.D. Professor Chicago, IL 60611 Clinical Associate Professor Department of Molecular Medicine and Surgery Department of Rehabilitation Medicine Dennis D.J. Kim, M.D. Section of Orthopaedics and Sports Montefiore Medical Center Associate Professor Medicine Bronx, NY 10467 Department of Physical Medicine and Rehabilitation Karolinska Hospital Montefiore Medical Center Rita M. Patterson, Ph.D. SE-171 76 Stockholm Bronx, NY 10467 Associate Professor and Deputy Director Sweden Stephan Konz, Ph.D., P.E. Orthopaedics Biomechanics Laboratory Professor Division of Research Mana Rezai, H.B.Sc., D.C., M.H.Sc. Candidate Department of Industrial Engineering Department of Orthopaedic Surgery and Research Associate Kansas State University Rehabilitation Institute for Work & Health Manhattan, KS 66506 University of Texas Medical Branch University of Toronto Galveston, TX 77555 Toronto, Ontario M5G 2E9 Vicki Kristman, B.Sc., M.Sc. Canada David I. Pedowitz, M.S., M.D. Ph.D. Candidate, Epidemiology Department of Public Health Sciences Chief Resident Tonu Saartok, M.D., Ph.D. University of Toronto Department of Orthopaedic Surgery Department of Surgical Sciences Research Associate University of Pennsylvania Section of Sports Medicine Institute for Work & Health Philadelphia, PA 19004 Karolinska Institute Toronto, Ontario M5F 2E9 SE-171 76 Anthony Petrizzo, M.D Canada Stockholm, Sweden c/o Ronald Moskovich, M.D. 301 East 17th Street Shrawan Kumar, Ph.D., D.Sc., F.Erg.S., F.R.S.C. G. James Sammarco, M.D. New York, New York 10003 Professor The Center for Orthopaedic Care, Inc. Department of Physical Therapy Derek Plausinis, M.D. Cincinnati, OH 45219-2906 Faculty of Rehabilitation Medicine Shoulder & Elbow Surgery Fellow University of Alberta Department of Orthopaedic Surgery Peter Sheehan, M.D. Edmonton, Alberta T6G 2G4 NYU Hospital for Joint Diseases Director Canada New York, NY 10003 Diabetes Center of Greater New York Cabrini Medical Center Marianne Magnusson, R.P.T., Dr.Med.Sci. Malcolm H. Pope, Dr.Med.Sci., Ph.D. New York, NY 10003 Senior Lecturer Professor Liberty Safe Work Research Centre Liberty Safework Research Centre Ali Sheikhzadeh, Ph.D., C.I.E. Department of Economy and Technology Department of Environmental & Occupational Research Assistant Professor Halmstad University Health Departments of Orthopaedic Surgery and SE-301 18 Halmstad Foresterhill Environmental Medicine Sweden Aberdeen, Scotland AB 25 2ZD New York University School of Medicine United Kingdom Associate Director of Research Paul H. Marks, M.D. Occupational and Industrial Orthopaedic Associate Professor Laura Punnett, Sc.D. Center Department of Surgery Professor NYU Hospital for Joint Diseases University of Toronto Department of Work Environment New York, NY 10014 Toronto, ON M4Y 1H1 University of Massachusetts Lowell Canada Lowell, MA 01854 Mary-Louise Skovron, Dr. PH. Ronald Moskovich, M.D. Robert G. Radwin, Ph.D. Group Director, Pharmaco-epidemiology Assistant Professor Professor and Chair Global Epidemiology Department of Orthopaedic Surgery Department of Biomedical Engineering Bristol–Myers Squibb NYU Hospital for Joint Diseases University of Wisconsin 311 Pennington-Rocky Hill Road New York, NY 10003 Madison, WI 53706 Pennington, NJ 09534 Contributors ix Louis J. Soslowsky, Ph.D. Ross Taylor, M.D. Sam W. Wiesel, M.D. Professor of Orthopaedic Surgery and Coastal Orthopaedic Associates Professor and Chair Bioengineering Conway, SC 29526 Department of Orthopaedic Surgery Vice Chair for Research Georgetown University Medical Center Victor Valderrabano, M.D., Ph.D. Director, McKay Orthopaedic Research Washington, D.C. 20007 Human Performance Laboratory and Laboratory Orthopaedic Department Harriët Wittink, Ph.D., M.S., P.T. University of Pennsylvania University of Calgary Head Philadelphia, PA 19104 Calgary, Alberta T2N1N4 Physical Therapy Professional Master Program Dan M. Spengler, M.D. Canada Hogeschool Utrecht Professor and Chair also 3508 AD Utrecht Department of Orthopaedics and Orthopaedic Department The Netherlands Rehabilitation University Hospital of Basel Joseph D. Zuckerman, M.D. Vanderbilt Orthopaedics Institute 4031 Basel Professor and Chair Nashville, TN 37232 Switzerland Department of Orthopaedic Surgery Marek Szpalski, M.D. Tapio Videman, M.D., D.Med.Sci. NYU Hospital for Joint Diseases Associate Professor and Chair Professor New York, NY 10003 Department of Orthopaedics Faculty of Rehabilitation Medicine IRIS South Teaching Hospitals University of Alberta Free University of Brussels Edmonton, Alberta T6G 2G4 1190 Brussels Canada Belgium Sherri Weiser, Ph.D. James B. Talmage M.D. Research Assistant Professor of Environmental Occupational Health Center Medicine Cookeville, TN 38501 Occupational & Industrial Orthopaedic Center NYU Hospital for Joint Diseases New York, NY 10014 1 CHAPTER Introduction to Epidemiologic Concepts in Musculoskeletal Disorders Mary Louise Skovron and Rudi Hiebert The literature on the epidemiology of occupational musculoskele- Health Administration (OSHA) by employers, workers’ compen- tal disorders is often confusing because of conflicting evidence on sation records, records of visits to the workplace health facility, the importance of various potential risk or causal factors. This and surveys of the work force.11 In clinical practice, the simple chapter describes basic epidemiologic methods so the reader can case count is usually derived by chart review (retrospectively) or evaluate critically the published literature on occupational mus- by enrollment of patients seen during a given period (prospec- culoskeletal disorders. Most examples are drawn from the litera- tively). The frequency of the disorder can also be expressed as a ture on occupational low back pain, but the reader should be proportionate ratio, a ratio of cases of a particular disorder to aware that similar methodologic standards must be applied to cases of all disorders in the population of interest. In 1985 for the literature on upper extremity disorders. example, occupational back injuries accounted for 26% of all Epidemiology is the study of the distribution and determi- closed compensation cases in a sample of nine states. nants of diseases and injuries in human populations. It consists By itself, numerator data cannot provide useful information of a developed methodology for testing scientific hypotheses in regarding the risk or probability of acquiring the disorder. The groups of individuals rather than in a laboratory setting. With case frequency has to be related to the underlying population knowledge of the intrinsic strengths and limitations of the design that could have potentially developed the disorder. For example, and execution of studies reported in the literature, it is possible to the U.S. Bureau of Labor Statistics estimated 303,750 OSHA- evaluate the strength of the evidence derived from these studies reportable occupational injuries involving the back in 2003.4 and even to make sense of conflicting results from different studies Without reference to the number of people at risk, it is not pos- on the same topic. In this chapter we present an overview of the sible to estimate the risk of back injury in the population or to test basic terminology used in epidemiology and the characteristics and hypotheses regarding risk factors for occupational back injury. For generic strengths and limitations of analytic (hypothesis testing) this reason, rates are used when the objective is to assess the risk study designs, with an emphasis on observational study designs. of the disorder or determinants of disorders or their outcomes. There are several types of epidemiologic studies. Descriptive epidemiology is a means of monitoring the health of a population, identifying health problems, and compiling information that can Rates and ratios be used for the development of causal hypotheses. Analytic epi- demiology is a set of epidemiologic study methods used to test Rates describe the frequency of a disorder or disorder per unit specific hypotheses. size of the population per unit time of observation. The rates commonly used in epidemiology are morbidity and mortality rates. The general form of a morbidity or mortality rate is MEASURES OF DISORDER FREQUENCY Numberofcases The fundamental strategy of epidemiology is the analysis of rel- ×1000 (1000, etc.)perunittime Numberofpersons atrisk ative and absolute measures of frequency and a comparison of the characteristics of individuals with and without disorder. The most obvious measures of frequency are case counts and their The most frequently used morbidity rates in epidemiologic variations, which are often referred to as numerator data. They research are the incidence rate and the prevalence rate. The inci- describe the frequency of the disorder without reference to the dence rate is based on new cases of a disorder or disorders (or new underlying population at risk. Examples of sources of case count disorder events), whereas the prevalence rate is based on existing data include back injury reports to the Occupational Safety and cases. Because they are based on new versus existing cases, 4 Chapter 1 ● Introduction to epidemiologic concepts in musculoskeletal disorders incidence and prevalence rates have different uses and different stable and the duration of a disorder is also stable, it is possible limitations. to estimate prevalence from incidence and vice versa according In a sense, the incidence rate is a rate of change, the frequency to the following approximation: with which people change from healthy to injured, sick, or dis- Prevalence ~~incidence ×duration abled. Therefore the appropriate denominator is the population at risk of acquiring the disorder (i.e., those who are free of the Thus a change in prevalence may reflect changes in the inci- disorder at the start of the time interval). The incidence rate may dence rate, duration, or both. For example, the prevalence of low be quantified in a number of ways, for example, as the number back pain in a population may change because of alterations in of new events per 1000 persons per year, when the population is individual, work-related, or other environmental risk factors stable and the number of new events is counted each year. affecting incidence rate or because treatment changes alter the Alternatively, it may be quantified as the number of new events duration of back pain episodes and risk of chronicity. It is occa- per 1000 person-years, as is done in prospective studies where a sionally the case that improved treatment extends the duration fixed population is followed until the disorder, the end of the of a disorder, with the result that the prevalence increases in the study, or loss to follow-up occurs. In practice, although the best face of a decreasing incidence, as occurred some decades ago denominator for incidence rates is the number of people free of with Down syndrome. The survival of infants with Down syn- the disorder at the start of the time interval, surveillance incidence drome improved because of improved medical and surgical man- rates (and prevalence rates) that are based on case reports often use agement of their associated disorders. The prevalence of Down the total population derived from census data or from work-force syndrome increased, although the incidence declined as a result estimates. The U.S. Bureau of Labor Statistics’ estimate of 303,750 of prenatal screening programs. OSHA-reportable occupational injuries involvingthe back repre- sents an incidence of 3.46 new cases per 1000 workers.4 DESCRIPTIVE EPIDEMIOLOGY The prevalence rate is the number of existing cases of a disor- der in a given population in a given time period. For example, the 1-year prevalence of disabling back pain is as high as 25%.14 The first step often undertaken in epidemiology is development Point prevalence is the number of cases per unit population of the descriptive epidemiology of a disorder or disorders. at one moment of counting, for example, all persons receiving Descriptive epidemiology supports the development of causal disability because of back pain in the work force of a metropoli- hypotheses but does not in itself support conclusions about disor- tan electrical utility company on January 1, 2005, expressed per der causality or about any hypotheses. In descriptive epidemiol- 1000 population. For point prevalence, the unit of time is often ogythe frequency of a disorder in the population is characterized not expressed because the period of time is effectively instanta- in terms of person (e.g., age, sex, ethnicity-specific incidence rates, neous. Period prevalence is the number of cases existing at one economic, behavioral, occupational, and other factors), place time or another during a definable time interval such as 1-year, (rural versus urban, type of housing, national variations, type of 5-year, or lifetime prevalence. Some epidemiologists do not industry, job requirements), and time (long-term trend, seasonal- express prevalence as a rate because in practice it is often derived ity, occasionally day of the week or time of day). from surveys that are difficult to assign to a specific time interval. The need to explain variation in descriptive studies drives the A number of factors other than the risk factor under study formulation of causal hypotheses. Drawing on current available may affect the incidence and prevalence rates. These include information from various fields (such as anatomy, physiology, demographic characteristics of the underlying population, most psychology, behavioral science, etc.), specific hypotheses are obviously age distribution6because age is known to be associated developed by inductive reasoning to explain observed patterns of with the onset of almost all disorders. Gender and ethnicity dis- variation and then evaluated using specific study designs to test tributions must also be taken into account when incidence rates these hypotheses. Studies that test specific hypotheses are called are interpreted. Other influences can distort the apparent inci- analytic. As the results of hypothesis-testing (analytic) studies are dence rate, including certain company policies, workers’ com- accrued, they are added to the basis for causal inference, depend- pensation claims, and health care system influences that affect ing on their strengths and generalizability, and hypotheses are the likelihood of seeking medical attention, of being diagnosed supported, modified, or negated. with a given disorder or disorders, or of having the disorder In interpreting the evidence from all scientific sources, the reported. These factors should be considered when measures of rules of causal inference are applied.7 Briefly, the hypothesized disorder frequency are evaluated, particularly when changes are cause must be demonstrated to have preceded the disorder by a assessed over time or different populations are compared. length of time sufficient to allow disorder development and To eliminate the effects of differences in these factors, the rates expression (time sequence of events). The disorder should be may be adjusted or standardized algebraically. The adjusted rates more common in those with the hypothesized cause than in express the risk of acquiring the disorder in the populations being those without it (increased risk in those exposed to the hypothe- compared as if they had the same age, sex, and ethnicity distribu- sized cause), and as the intensity or duration of exposure to the tions. Alternatively, if it is not necessary to have a single summary hypothesized cause increases, the frequency of the disorder index of disorder risk, the morbidity rates within population should increase (dose-response relationship). The association strata defined by age, sex, and ethnicity may be compared. between the hypothesized causal factor and the disorder should The number of existing cases of a disorder or disorders at any be consistently demonstrated in methodologically sound studies time is a function of both the rate of new cases (incidence) and and should be biologically plausible. In addition, the specificity the duration of that disorder. Therefore, when a population is of an association (i.e., the extent to which the hypothesized Chapter 1 ● Analytic epidemiology 5 causal factor is associated with only one disease or disorder) adds bias because the health behavior and health status of people who weight to a causal hypothesis, but it is not necessary for causal volunteer for research are well documented to be better than inference; for example, cigarette smoking is accepted as a cause those of refusers. No characteristics of the individuals should of lung cancer, although the association is not specific. Cigarette affect the likelihood of selection for the study, including their smoking is also associated with a number of other cancers, knowledge of the question at issue; their beliefs about the risk obstructive pulmonary disorder, heart disorder, and a variety of factors or about the cause of the disorder being studied; or any disorders, including osteoporosis, low back pain, and, in particu- characteristic such as age, sex, or education that could be inde- lar, herniated intervertebral disks. pendently associated with both the disorder and the hypothe- sized causal factor. It is important for the internal validity of the study results ANALYTIC EPIDEMIOLOGY that the information collected is accurate and complete. If there is inaccuracy (measurement error) in the information collected, Analytic, or hypothesis-testing, epidemiology relies on two types the ability to detect the association of interest is reduced. If the of study designs: observational and experimental. In observa- accuracy of the information is worse for one exposure group tional studies, exposure to the hypothesized causal factor and than for another, the effect on the study results may not be development of the disorder in the population under study predictable. For this reason, an evaluation of the accuracy (or occur in the natural course of events; the investigator does not validity) of measurements is necessary for any study. Research cause them to occur. The study is designed and executed to max- reports should describe the validity of the sources of information. imize the extent to which it can be seen as a natural experiment, Questionnaires or reporting methods that have been validated in that is, the extent to which all extraneous sources of variation are the study population or in similar populations or circumstances eliminated and only the exposure to the putative cause and the should be used. The problem of validity of information is partic- frequency of disorder vary between populations being com- ularly important in research on occupational musculoskeletal pared. It is often the case that once substantial observational disorders because the methods of both case diagnosis13 and evidence has accrued, causality is widely accepted. However, it is measurement of work exposure17have substantial limitations. desirable in etiologic epidemiology and almost universally Before specific study designs can be discussed, the term con- required in evaluations of treatment that the final test of the founding must be defined. Confounding occurs when the study hypothesis is in interventional or experimental studies. results can be explained by a factor extraneous to the hypothesis In experimental studies, the investigator causes individuals or being tested. A potential confounding factor must be associated groups of individuals in the population to receive the treatment with both the disorder in question and the hypothesized causal in question. To demonstrate ethically the causal role of a risk fac- factor. That is, the proportion of persons with the disorder hav- tor for which there is only observational evidence, the investiga- ing the confounding exposure must be different from the pro- tor would prevent exposure to the risk factor for a group of portion of persons without the disorder with the confounding people. In both types of interventional design strategies, a com- exposure. It is also necessary that the proportion of those with parison group that does not receive the intervention is necessary. the hypothesized causal factor who have the confounding expo- All other factors that might influence the outcome of the study sure are different from the proportion of those not exposed to (potential confounding factors) can be eliminated or controlled the hypothesized causal factor who have the confounding factor. by the investigator. Because the conditions of the study are much For example, a study that found an association between job sat- more under control of the investigator, interventional studies can isfaction and the risk of occupational back injury could be con- more closely approximate true experiments than can observa- founded by the physical requirements of work if heavy work was tional studies. When such studies are well designed and executed, a risk factor for back injury and was also associated with lack of they provide very strong support (or negation) for a hypothesis. job satisfaction in the studied population. Potential confounding All analytic study designs have potential problems of internal factors can be eliminated in the design of the study by restricted and external validity that must be solved by the investigator or matched sampling or, in the data analysis phase, by stratified either in the study design or in the data analysis. Internal validity or multivariate analysis, for example. If in the study just is the extent to which a study is a true test of the specific hypoth- described the statistical analyses controlled for physical require- esis, that is, the extent to which all possible biases of measure- ments of work or if the researchers conducted an exploratory ment or information and all possible confounding variables are analysis and found no association between job satisfaction and eliminated as explaining the observed study result. External the physical requirements of work, the potential for confound- validity is the extent to which the study results can be general- ing would be eliminated. In experimental studies, potential con- ized to the population of interest, namely, whether the study founding should be successfully eliminated by truly random subjects are representative of the population at risk. If the poten- blind assignment of subjects to the different treatments under tial validity problems have been solved in either the design or study. Comparability of the treatment groups should be con- analysis of the study, the study evidence is strengthened. firmed by presentation of the baseline characteristics of each Because it is not possible to study the entire universe of group on entry to the study. potentially eligible subjects, epidemiologic studies are conducted Confounding invalidates a study as a test of the hypothesis. on samples of the population of interest. Even a study of an entire The study’s results cannot be taken as evidence of causality or city or the work force of a company constitutes a sample. The efficacy of treatment. Lack of generalizability, as opposed to con- method of sampling should not introduce selection biases. For founding, does not invalidate a study’s results but merely example, a volunteer study is potentially susceptible to selection restricts inference to populations similar to those under study. 6 Chapter 1 ● Introduction to epidemiologic concepts in musculoskeletal disorders Observational study designs are applicable in both clinical Disease and etiologic epidemiology. In etiologic epidemiology the or researcher tests whether a hypothesized factor is a determinant or outcome occurs Risk factor cause of disorder in previously healthy people, whereas in clini- present cal epidemiology one tests whether particular characteristics, risk Disease or outcome factors, or clinical interventions are determinants of the progno- does not occur sis or outcome. The classic observational analytic study designs Target are the cohort study, the case-control study, and the cross-sec- population Sample tional study. Disease or outcome occurs Risk factor Cohort study (Prognostic study) absent Disease or outcome The cohort study is the observational design that, when well does not occur designed and executed, produces the soundest results in terms of incidence rates and disorder etiology or prognostic determinants Figure 1.1 Cohort study. of all the observational study designs. The hallmark of a cohort study is that a population initially free of the outcome of interest is identified and characterized with respect to the hypothesized risk factor, important covariates, and potential confounders. The population is observed for a period of time adequate for devel- In this case, the observed relative risk would need to be very large opment of the disorder, and the new cases (incident cases) are to support the causal hypothesis. For example, consider a cohort recorded. Rates of disorder development are compared between study examining the causal role of occupational repetitive those exposed and those not exposed to the hypothesized risk motion in carpal tunnel syndrome. New workers hired in 1985 factor. through 1990 are enrolled and followed forward for 10 years, A study of prognostic factors related to return to work after with information on new cases of carpal tunnel syndrome com- episodes of absence due to work-related low-back-pain sickness is ing from the company medical department records. If 30% of an example of a cohort study. The cohort consisted of all those the workers retire, take disability pensions, die, get another job, first presenting to an occupational health clinic at a large munic- or leave the company for other reasons, there is a substantial loss ipal transportation agency for medical clearance for sick leave to follow-up. A bias in loss to follow-up occurs if the workers from work because of a complaint of work-related low back pain. who leave the company are those with the highest exposure to These individuals were asked to complete a questionnaire on repetitive work movements and those who leave because upper function, pain, satisfaction with work, and beliefs about pain. The extremity problems consistent with preclinical carpal tunnel syn- occupational physicians conducting the sickness absence clear- drome are making it more difficult for them to do the job. The ance examinations included assessment of gain, posture, and dis- observed relative risk is an underestimate of the true relative risk tribution of painful symptoms specific for back pain. Participants because the detected incidence of carpal tunnel syndrome among in the study were followed for 3 months, at which time the those with repetitive-motion jobs is lower than the true incidence participant’s return to work status was determined. To identify and the detected incidence among those not exposed is not which factors best predicted return to work, rates of return to affected. Biased loss to follow-up leading to underestimates of work were compared between those with high and low scores on incidence in the unexposed would produce an inflated observed clinical signs and symptoms, function, pain, work satisfaction, relative risk. High proportions lost to follow-up or higher propor- and pain beliefs. Predictors that showed large differences in rates tions lost in one exposure category than another (selective loss to of return to work were interpreted as being strongly predictive.12 follow-up) leave open the possibility of biased loss to follow-up Cohort studies can be prospective in nature, meaning that a with consequent distortion of the study findings. disorder-free population or group is initially identified and then Another form of selection bias can occur. This bias, called subsequently tracked over time (Fig. 1.1). This same model can selective survival or selective attrition, occurs when people who also be used with historical records. Employment records, for have both the exposure and the disorder have a different proba- example, can be used to identify a group of new employees at a bility of dropping out of the population available to be included company. Job status and medical records can then be linked to in the study than do people who are not exposed and get the these employment records to identify work exposures and the disorder. This type of bias can easily occur in cross-sectional and development of the disorder of interest. Studies that use histori- case-control studies. It can also occur in a particular variant of cal records are called retrospective. the cohort study called the prevalent cohort study. For example, Loss to follow-up is a potential problem in cohort studies. If a prevalent cohort study examining occupational repetitive a substantial proportion of subjects are lost to the study for any motion as a risk factor for carpal tunnel syndrome that enrolled reason, for example, having moved out of the region, it would be workers who were first employed between 1985 and 1990 and expected that fewer cases of the disorder in question would arise were still actively employed in 2005 could be affected by selec- in the study than originally planned. The number of study cases tive attrition if carpal tunnel syndrome by and large developed may ultimately be too small to yield stable estimates of the within 15 years of employment and workers tended to leave the incidence rates and, consequently, estimates of the relative risk. company when carpal tunnel syndrome developed.
Description:The list of books you might like
