Methodologic issues in the study of frequent and recurrent health problems falls in the elderly

Methodologic Issues in the Study of Frequent and Recurrent Health Problems Falls in the Elderly Robert G. Cumming, MBS, MPH, Jennifer and Michael C. Nevitt, PhD

L. Kelsey, PhD,

In this paper the example offulls in the elderly is used to demonstrate methodologic difficulties that arise in the epidemiologic study offr eq uenr and recurrent health problems. Issues discussed include whether the relevant outcome is the state of being u failer or the rate at which fulls occur, misclassification of self-reported outcome data, the inadequacies of current terminology for describing certain study types and measures of frequency and effect from studies of recurrent events, the potential for outcome to influence exposure status in cohort studies of recurrent health problems, and the question of controlEing for fulls occurring prior to the study period. It is concluded that epidemiology needs to develop a framework for studying frequent and recuTTent health problems.

Ann Epidemiol 1990; 1~49-56. KEY WORDS:

Accidental falls, epidemiologic methods, geriatrics.

INTRODUCTION

Much epidemiologic research to date has been concerned with the cause of nonrecurrent health problems that affect only a small proportion of the population over the study period. The focus of research is usually risk factors for the first occurrence of some health problem of interest. Because most diseases are statistically rare over the period of study, various estimators of relative risk (odds ratios, incidence density ratios, and cumulative incidence ratios) will have similar values (1) and so different research designs and types of statistical models will produce essentially the same results (assuming the same degree of selection, measurement, and confounding bias). Furthermore, from a practical perspective, much epidemiologic research is concerned with diseases that normally come to the attention of health services and so case ascertainment is not a major problem. Many health problems, however, are common, recurrent, or difficult to ascertain. Examples include spontaneous abortion, motor vehicle accidents, childhood injuries, asthma attacks, and epileptic seizures. The study of these types of conditions requires consideration of a number of methodologic issues that do not appear to have received sufficient attention in the epidemiologic literature. Falls in the elderly occur with great frequency and individuals tend to suffer multiple falls. Approximately 30% of those aged 65 years and older fall at least once each year (2). The most obvious consequence of falls is osteoporotic fractures, most

From the Division of Epidemiology, School of Public Health, Columbia University, New York, (R.G.C., J.L.K.); Department of Community Medicine, Westmead Hospital, Westmead, Australia (R.G.C.); and Departments of Medicine and Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA (M.C.N.). Address for reprints: Robert G. Cumming, Department of Community Medicine, Westmead Hospital, Westmead 2145, Australia. Received May 1, 1990; revised May 9, 1990. 0

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importantly fractures of the hip. Even if no fracture occurs, frequent falls can themselves result in a postfall syndrome of anxiety, immobilization, and nursing home admission (2). Most falls, however, are not medically treated, so that identification of falls is problematic. Falls merit the attention of epidemiologists. In recognition of the clinical and public health importance of falls, a number of studies over the past dozen years inquired into the frequency and cause of falls. As noted by Perry in his 1982 review (3), many of these studies were methodologically poor from an epidemiologic perspective, but several, more sophisticated reports have been published recently (4-10). The purpose of this paper is to stimulate those doing epidemiologic research into the cause of falls and other common, recurrent events to consider more carefully the methods they use to conduct and analyze their studies. Although this paper focuses on falls in the elderly, many of the issues we discuss are generally relevant to the epidemiologic study of frequent and recurrent health problems.

DEFINING

A FALL AND

ASCERTAINING

ITS OCCURRENCE

An initial step in any epidemiologic investigation is to develop a clear case definition. A recent consensus report suggested that a fall be defined as “an event which results in a person coming to rest inadvertently on the ground or other lower level and other than as a consequence of the following: sustaining a violent blow, loss of consciousness, sudden onset of paralysis, as in a stroke, or an epileptic seizure” (2). It is preferable to use a broad definition of falls in epidemiologic studies, especially if the concern is with prediction of falls. If the main interest is in etiology, in the analysis, falls of widely different causes can be considered separately, thus increasing the specificity of risk relationships and potentially enhancing our understanding of causality. For example, falls resulting from loss of consciousness, stroke, or overwhelming external violence should be analyzed separately from typical falls in older people associated with neuromuscular impairment, since causes may differ substantially. Since falls are often unwitnessed events and much important information about the fall can only be obtained from the person who falls, self-report is a critical source of data on falls. However, recurring or mundane health events like falls are often recalled inaccurately (11). In a study in which falls were ascertained weekly and each fall was followed up by an interview with a nurse, 13% of people who reported a fall during weekly follow-up did not recall any falls when questioned at the end of the 12-month study period (12). These data probably underestimate the tendency to forget falls, since they derive from a sample undergoing intensive surveillance for falls. In addition, 10% of events reported as falls did not meet the study definition of a fall, most often because no part of the body actually hit the ground or floor. If it is assumed that the error in measurement of falls by self-report is nondifferential with respect to exposure, then the measures of effect in these studies will be biased somewhat toward the null (13). Because falls are so common, this bias will not be as great as it would be for a rarer disorder, given the same misclassification probabilities (13). Of course, in many cases, misclassification of falls may be related to risk factors for falls. In the study cited earlier (12), misclassification of fall status was associated with decreased mental status, neuromuscular disabilities, alcohol consumption, and past stroke. Hence, for example, this misclassification would lead to an underestimation of the magnitude of the effect of dementia on the risk of falling.

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Reliance on a self-reported measure of the fall outcome as well as self-reported exposures has the potential to lead to the finding of spurious associations because of differential misclassification. It is possible that those individuals who spend most time thinking about their health will report more falls, more illnesses, and more of some other exposures than those who pay less attention to health matters. In this case, the observed relationship between falls and, for example, self-reported arthritis will be exaggerated. A similar problem is a tendency for some subjects to answer “yes” to both exposure and outcome questions. During the conduct of the study this problem could be partly resolved by ensuring verification of reported events with detailed follow-up probes, thereby reducing false-positive reports of falls. Another approach is to use a sensitivity analysis, as part of the overall data analysis; feasible values for fall measurement error in the exposed and unexposed could be assessed for their effect on the magnitude of measures of effect.

THE OUTCOME OF INTEREST IN FALLS RESEARCH: THE RATE OF FALLS OR FALLERS? It is important to decide whether the focus of research will be the rate off& or falkrs. Most published research on falls has compared risk factor prevalence in a group of people who fell during the study period with risk factor prevalence in a group who did not fall. The outcome of interest here is the state of “being a faller.” Although rarely made explicit, the objective of this type of research is to determine why some elderly people are fallers and why some are not. An alternative strategy is to study the rate of falls. The aim of the study would then be to compare the rate of falls in those exposed to some potential risk factor with the rate of falls in those not exposed. One problem with using fallers as the outcome variable is that its definition is arbitrary. Subjects are usually classified as fallers if they fall at least once during a certain time period. If this time period is short, then the probability of having a fall, and so being judged a faller, is low. Further, to some extent, the particular period chosen for study will determine the faller status of an individual. Consider two subjects: One has never fallen before but falls once during the year of study. The other has had an average of two falls per year over the past 5 years but, by chance, had no falls during this particular year. Who is the true faller? A more fundamental problem with studying fallers is that it is really the rate of falls that is of most public health importance. A small risk of a fracture is associated with each fall. Thus, someone who falls frequently has a greater chance of suffering a fracture than does someone who falls only once during a certain time period (14). Choosing to study fallers or the rate of falls could also affect the conclusion as to whether or not a particular exposure is a risk factor for falling. Suppose that there are ten individuals, of whom five are exposed and five are not. Of the exposed subjects, one has three falls during the study period, one has one fall, and the remainder have no falls. In the unexposed group, two subjects have one fall each and the other three have no falls. There are two fallers (defined as one or more falls in the study period) in each group, suggesting that there is no association between exposure and falling. However, there were twice as many falls in the exposed group as in the nonexposed group. Thus, using the rate of falls indicates that the exposure may be a risk factor for falls. It is conceivable that some exposures are risk factors for falls but not for being a faller. Host factors (such as poor eyesight and balance disorders) tend to be stable over

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time and are likely to be related to the state of being a faller. However, there are many exposures that vary over time for an individual and could lead to many falls among many different people but might not result in any particular person becoming a recurrent faller. Examples of time-varying exposures include environmental hazards (such as an uneven sidewalk), alcohol drinking, and medication use.

CHOICE AND CLASSIFICATION OF STUDY TYPES IN FALL RESEARCH The widely used classification of observational epidemiologic studies into cross-sectional, case-control, and cohort can be difficult to apply to studies of falls and fallers. Furthermore, some of the generally accepted advantages of cohort studies compared with the other two study types are lost when doing research in this area. Most studies of fallers published to date have had the following design: Exposures have been measured as they exist at the time of the interview and examination, and the fall outcome has been defined in terms of whether or not the subject reported one or more falls in the past year. What type of study is this? It is not really a crosssectional study because falls are ascertained retrospectively, nor is it a case-control study because sampling is not based on presence of outcome. Moreover, it is a very poor design from a causality perspective because the outcome often precedes the exposures in time. Rather than attempting to categorize this type of study, the investigators should just describe how the study was done. The problem of exposure following outcome in the type of study described in the previous paragraph may not be overcome by using a cohort design. It is not possible to exclude people who have fallen in the past from cohort studies of falls because people suffer numerous falls during their lifetime. However, including past fallers could cause difficulties in the interpretation of study findings. Suppose that a cohort study finds that the use of a particular drug is associated with an increased likelihood of being a faller. Because of the cohort design, we know that the drug was not prescribed to treat the falls that occurred during the study. However, it might be difficult to make certain that it was not given because of falls that happened in the past. Another related problem occurs because cohort studies of fallers necessarily include subjects who have fallen prior to commencement of follow-up. Investigators and subjects may be aware of subjects’ fall susceptibility; this could result in the unusual situation of outcome influencing exposure in a cohort study, because of the correlation between past and future falls. An extreme example of this is present in studying the relationship between balance and gait (as exposure variables) and subsequent falls. It is likely that those who have fallen in the past will be less willing to stand with their eyes closed for any length of time (a measure of balance) or to walk very rapidly along a set distance (a measure of gait) than those who have not fallen. Thus, falls, to some degree at least, cause low scores on measures of balance and gait. One way to deal with the problem of past falls affecting exposure in cohort studies of falls is to analyze the data in strata according to the history of falls. We will return to this point in the section of this paper on control of confounders.

NAMING OF MEASURES OF FREQUENCY AND EFFECT IN STUDIES IN WHICH FALLERS ARE THE OUTCOME OF INTEREST If the rate of falls is the outcome measure, then this measure can simply be called the rate of falls. However, what name should be given to the measure of frequency resulting from studies of people who fall (fallers)? Many studies of fallers report the

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percentage of subjects that fall in 1 year, typically about 30%. Is this a prevalence or an incidence measure? This figure might be thought of as describing the proportion of people who fell in a defined population over a particular time; in other words, it is the period prevalence rate of fallers. However, because falls themselves are point events it could be confusing to talk about the that cannot logically have a prevalence, “prevalence of fallers. ” It is probably most accurate to avoid using any term and simply state the results as above: The proportion of subjects who fall at least once over the study period. It is misleading to call this measure the cumulative incidence of fallers because this is generally understood to mean the proportion of new cases occurring in a population initially freeof the disease (15). A further difficulty is naming the measure of association that is commonly used in studies of fallers. These studies compare the proportion of fallers in an exposed group with the proportion in an unexposed group. One option is to call this a prevalence rate ratio, based on the definition of a prevalence rate given in A Dictionary of Epidemiology (16). However, this is misleading, as it implies that the result is a true rate ratio. If rate of falling is used as the measure of frequency, then a rate ratio can be computed; although A Dictionary of E@demiology refers to the rate ratio as a ratio of incidence rates, that definition seems unnecessarily restrictive, since rates at which recurrent events occur also meet the formal definition of a rate. A similar problem with epidemiologic terminology was noted recently by Martin and colleagues, in relation to recurrent episodes of myocardial infarction (17). They reported difficulty in finding an unambiguous term to describe the number of myocardial infarctions (first and recurrent) occurring over time in a defined population. It appears that epidemiologists have yet to develop a terminology for describing recurrent event data.

ESTIMATION

OF MEASURES

OF EFFECT

IN FALL RESEARCH

About 30% of those aged 65 years and over fall each year (2). The high rate at which falls occur does not affect the calculation of rate ratios in studies comparing the rate of falls in exposed and nonexposed groups. However, the high proportion of fallers in elderly populations does have important implications for the choice of effect measure in studies that focus on comparing fallers with nonfallers. If a disorder is “rare,” then the odds ratio will be a close estimate of the cumulative incidence ratio (1); this is not the case with falls. A recent paper by Tinetti and colleagues clearly demonstrates this (4). They reported that the crude “relative risk” for falls associated with sedative use in a cohort of 336 elderly people was 3.1. After adjusting for confounders in a logistic regression model, they found the adjusted odds ratio to be 28.3. This difference in the magnitude of the association was a consequence of the use of different measures of effect, since the crude odds ratio for sedative use was 31.1. This extraordinarily large difference between the two crude effect measures is the result of the very high proportion of fallers in those exposed to sedatives (93% of whom fell at least once in the lyear follow-up) and in those not exposed to sedatives (30% of whom fell). There are several possible solutions to this problem. The state of being a faller could be defined as two or more falls during the study period, leading to reduced frequency of outcome by definition. Another approach is to express all results, both crude and adjusted, as odds ratios. The danger with this is that readers might interpret the odds ratio as a close approximation of the rate ratio and so gain a falsely high impression of the strength of association between an exposure and falling. Altematively, investigators should explicitly state the measure of effect they use in each

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HEALTH PROBLEMS

analysis. A recent paper on risk factors for osteoarthritis (which affected about 4% of the study population), for example, gives “relative prevalence rates” for the crude results and odds ratios for the adjusted (18). In their table displaying the adjusted results, the authors also provide the crude odds ratios for comparison. Finally, a multivariate model that could directly determine the proportion of fallers in exposed and nonexposed groups could be used (19, 20). Risch and associates used this method in a study of risk factors for spontaneous abortion, which occurred in over 10% of the pregnancies they studied (2 1). As suggested already in this paper, the outcome of most interest in falls research is the rate of falling in the exposed compared to the rate of falling in the unexposed. However, it seems that epidemiologists have given little thought as to the best statistical approach to use to obtain this measure of effect. Several different classes of analytic approaches that might be applied to the analysis of recurrent outcome events in the same individual have been discussed in the literature on statistics. Examples include the analysis of longitudinal data using generalized linear models (22, 23), the use of overdispersed exponential families (24, 25), and adaptations of the proportional hazards model (26). Comparative studies of the properties of these various techniques are currently being undertaken, and it is planned that the results of these studies be described in a separate paper.

CONTROLLING FOR PAST FALL RESEARCH

FALLS AND

OTHER

VARIABLES

IN

The inevitable inclusion of subjects who have fallen in the past in any study of falls means that investigators have to decide whether or not to control for past falls in their analyses. One of the major predictors of falling is having fallen in the past (4, 5) and so previous falls might be considered a potential confounder in any study on causes of falls. More correctly, previous falls are, for the most part, a proxy for risk factors for falls. Ideally, the variables for which a history of falls is a proxy should be controlled for directly; however, since most risk factors remain to be identified, this is not realistic. Instead, we suggest conducting a stratified analysis, with strata defined by fall history. In the baseline interview in cohort studies of falls, subjects should be asked about falls in the past year. Three strata can then sensibly be formed: those who report no falls, those who report just one fall, and those who report two or more falls in the year prior to interview. If the size of the effect measure is constant across strata, then a single result, adjusted for past falls, can be reported. (Note that the group with no reported falls in the past year comes closest to a study of new [incident] fallers). Lubin used this type of strategy in a study of the relation of benign breast disease and a family history of breast cancer (27). He presented his results in strata defined by the number of past episodes of benign breast disease. A practical difficulty in controlling for past falls is that inaccurate recall will lead to misclassification of subjects across past fall strata, resulting in incomplete control for this variable. It could also be argued that controlling for past falls is really overadjustment that will bias estimates of effect toward the null. This is because one of the many variables for which past falls is a proxy is likely to be the very exposure under study. A recent cohort study illustrates this for the exposure variable “activities of daily living” (ADL). Need for help with ADL was found to be associated with two or more falls during the year of follow-up in a model that did not control for past falls, but it was no

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longer important when past falls were included in the model. Now, poor ADL performance is itself a proxy for a number of (largely unknown) variables, many of which are probably shared by the past falls variable. It seems to us that the likely small bias toward a diminished association because of controlling for past falls is preferable to the bias of unknown magnitude away from the null that results when past falls are not controlled. Neuromuscular disturbances, such as impaired balance and gait, have repeatedly been shown to be associated with an increased risk of falling and so are at least potential confounders of any other exposure-falls relationship. If, in the study sample, some measure of neuromuscular impairment is correlated with the exposure of interest, should it be controlled for? Many such impairments will be the result, at least in part, of the exposure; they will be intermediate variables. For example, a drug such as diazepam (Valium) results in slowed reaction time. Controlling for reaction time in a study of diazepam and falls would be inappropriate unless the research question concerned the effect of diazepam on falls other than that mediated through impaired reactions. Of course, neuromuscular abnormalities are likely to have multiple causes apart from the exposure under study. Hence, these variables might be simultaneously intermediate and confounding variables and so failure to adjust for them could result in incomplete control of confounding. New analytic techniques are currently being developed to control for such variables (28). However, until these methods are available, we suggest that researchers should be wary of controlling for variables such as balance and gait in etiologic studies of falls unless they are confident that these are not on the causal pathway of interest. In studies of exposures where the causal mechanism is unclear, a sensible approach might be to conduct a secondary analysis controlling for neuromuscular variables. Note that if the objective of research is to develop an index that can be used clinically to predict who will and who will not fall in the future, then any variable that is strongly associated with falls in the data should definitely be included in models for prediction of falls.

CONCLUSION In this paper we used the example of falls in the elderly to illustrate a number of issues that arise in the epidemiologic study of health problems that are frequent or recurrent, or both. Epidemiology has not yet developed a framework or terminology for studying these types of health problems. We hope that this paper will help stimulate further development. Dr. Cumming is a National Health and Medical Research Council of Australia Public Health Research Fellow. We wish to thank Sander Greenland and Diane Mundt for reviewing drafts of this paper.

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3. Perry BC. Falls among the elderly: A review of the methods and conclusions of epidemiologic studies, Am J Geriatr Sot. 1982;30:367-7 1, 4. Tinetti ME, Speechly M, Ginter SF. Risk factors for falls among elderly people living in the community, N Engl ] Med. 1988;319:1701-7. 5. Nevitt MC, Cummings SR, Kidd S, Black D. Risk factors for recurrent nonsyncopal falls. A prospective study, JAMA. 1989;261:2663-8. 6. Sorock GS, Shimkin EE. Use of benzodiazepine sedatives and risk of falling in a community-dwelling elderly cohort, Arch Intern Med. 1988;148:2441-4. 7. Buchner DM, Larson EB. Falls and fractures in patients with Alzheimer-type dementia, JAMA. 1987;257:1492-5. 8. Blake AJ, Morgan K, Bendall MJ, et al. Falls by elderly people at home: Prevalence and associated factors, Age Ageing. 1988;17:365-72. 9. Campbell AJ, Borrie MJ, Spears GF. Risk factors for falls in a community-based prospective study of people 70 years and older, J Gerontol. 1989;44:M112-7. 10. Wickham C, Cooper C, Margetts BM, Barker DJP. Muscle strength, activity, housing and the risk of falls in elderly people, Age Ageing. 1989;18:47-51. memory for health-related 11. Means B, Nigam A, Zarrow M, et al. Autobiographical events. National Center for Health Statistics, Vital Health Stat [2]. 1989;6(2). 12. Cummings SR, Nevitt MC, Kidd S. Forgetting falls. The limited accuracy of recall in the elderly, J Am Geriatr Sot. 1988;36:613-6. 13. Copeland KT, Checkoway H, McMichael A], Holbrook RH. Bias due to misclassification in the estimation of relative risk, Am J Epidemiol. 1977;105:488-95. 14. Cummings SR, Nevitt MC. Risk factors for fall-related injury and long lies: Findings from a prospective study of the older persons living in the community. In: Weindruch R, Ory M, eds. Frailty Reconsidered: Reducing Fall-Related Injury in the Elderly. Springfield, Illinois: Charles C. Thomas; 1990 (In press). 15. Morgenstern H, Kleinbaum DG, Kupper LL. Measures of disease frequency used in epidemiologic research, Int J Epidemiol. 1980;9:97-104. 16. Last JM, ed. A Dictionary of Epidemiology. 2nd ed. New York: Oxford University Press; 1988:103. 17. Martin CA, Jamrozik K, Armstrong BK, De Klerk NH, English DR, Hobbs MST. An unjustified attack on “incidence !” Am J Epidemiol. 1989;129:653-4. 18. Davis MA, Ettinger WH, Neuhaus JM, Hauck WW. Sex differences in osteoarthritis of the knee, Am J Epidemiol. 1988;127:1019-30. 19. Flanders WD, Rhodes PH. Large sample confidence intervals for regression standardized risks, risk ratios and risk differences, J Chronic Dis. 1987;40:697-704. 20. Wacholder S. Binomial regression in GLIM: Estimating risk ratios and risk differences, Am J Epidemiol. 1986;123:174-84. 21. Risch HA, Weiss NS, Clarke EA, Miller AB. Risk factors for spontaneous abortion and its recurrence, Am J Epidemiol. 1988;128:420-30. 22. Zeger SL, Liang K-Y. Longitudinal data analysis for discrete and continuous outcomes, Biometrics. 1986;42:121-30. 23. Liang K-Y, Zeger SL. Longitudinal data analysis using generalized linear models, Biometrika. 1986;73:13-22. 24. Efron B. Double exponential families and their use in generalized linear regression, J Am Stat Assoc. 1986;81:709-21. 25. Gelfand AE, Dalal SR. A note on overdispersed families, Biometrika. 1990;77:5564. 26. Prentice RI, Williams BJ, Peterson AV. On the regression analysis of multivariate failure time data, Biometrika. 1981;68:373-9. 27. Lubin JH. Case-control methods in the presence of multiple failure times and competing risks, Biometrics. 1985;41:49-54. 28. Robins JM. The control of confounding by intermediate variables, Stat Med. 1989;8:679-701.