Repeated Hospitalizations and Self-rated Health among the Elderly: A Multivariate Failure Time Analysis

Byron S. Kennedy1, Stanislav V. Kasl1 and Viola Vaccarino1,2

1 Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT.
2 Present address: Emory University School of Medicine, Center for Outcomes Research, Atlanta, GA.


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
The purpose of this study was to determine to what extent a single measure, self-rated health (SRH), independently predicts long-term hospitalizations due to all causes and to cardiovascular diseases by using both the standard Cox proportional hazards model and a more robust events model. The study cohort consisted of 2,812 elderly subjects residing in New Haven, Connecticut, who were followed from 1982 to 1996 as part of the Established Populations for Epidemiologic Study of the Elderly. After adjustment for baseline risk factors, using the Cox model, a favorable SRH was associated with a significantly lowered risk for a first hospitalization for all causes (risk ratio (RR) = 0.850, 95% confidence interval (CI): 0.774, 0.934) and congestive heart failure (RR = 0.599, 95% CI: 0.426, 0.841) but not for myocardial infarction (RR = 0.882, 95% CI: 0.565, 1.379). With the adjusted robust events model, a positive SRH was associated with a decreased risk in both a first (RR = 0.813, 95% CI: 0.744, 0.889) and a second (RR = 0.870, 95% CI: 0.782, 0.968) hospitalization for any cause. These results indicate that a single measurement of SRH predicts long-term patterns of hospitalization, especially for heart failure, among older adults.

coronary disease; health services for the aged; health status indicators; hospitals; myocardial ischemia; statistical methods; survival analysis

Abbreviations: CI, confidence interval; ICD-9-CM, International Classification of Diseases, Ninth Edition, Clinical Modification; MVFT, multivariate failure time; PWP, Prentice, Williams, and Peterson model; PWP-GT, Prentice, Williams, and Peterson gap time recurrent events model; RR, risk ratio; SRH, self-rated health.


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
Numerous studies have documented the importance that self-ratings play in the overall health assessment of individuals, especially among the elderly (1Go). Indeed, early studies demonstrated that ratings of one's own health were correlated with physicians' assessments (2Go) and with adverse health outcomes (3Go, 4Go). Later on, these self-evaluations were used to predict health outcomes. For example, beginning with the work of Mossey and Shapiro, researchers found that self-rated health (SRH) was an important independent predictor of mortality (5Go). Since this groundbreaking study, a number of papers have been published confirming this association. In fact, in a review of 27 studies, Idler and Benyamini (6Go) found that all but four showed a significant relation between SRH and mortality after controlling for various health covariates. Similarly, in an updated article, the same authors cited 17 (out of 19) additional studies that revealed an effect for SRH (7Go).

More recently, investigators have observed that SRH is also associated with indicators of health based upon health services utilization. In these studies, attention has focused primarily on hospital admissions (8GoGoGoGoGoGoGoGoGoGoGoGoGoGoGoGoGo–25Go). Among these, many subjects may have had their frequencies of hospitalization determined during some fixed, often short, follow-up period. With this approach, however, incomplete covariate information and losses to follow-up may restrict the number of data points available for an appropriate analysis. In other situations, only the risk for a single hospitalization, such as the first one, may have been examined. With this type of study, the main drawback is that a large proportion of the data may be ignored if additional events have occurred after the initial one (26Go).

A more rigorous and informative approach to the question of whether SRH predicts hospitalizations takes into account repeated hospitalizations. Repeated hospital admissions are a common problem among the elderly and drive a large part of the costs associated with chronic conditions such as heart failure and other cardiovascular conditions (27Go, 28Go). The demonstration that perceived health status, easily assessed with one simple question at one point in time, predicts hospitalizations over a long follow-up period should provide a convenient and cost-effective measure for guiding public health efforts. However, because SRH is related to chronic conditions and physical function, the authors sought to demonstrate that the effect of SRH is independent of these well-established predictors of hospitalization.

Accordingly, the aim of this study was to examine to what extent SRH, measured at a single point in time, predicts hospitalizations over a 15-year period, independent of coexisting illness, functional limitations, and cardiovascular disease risk factors, among an urban-dwelling elderly population. To address this objective, this study considered overall as well as cardiovascular disease-specific hospitalizations as endpoints. The latter were chosen since cardiovascular diseases, particularly heart failure, represent the most frequent of cause hospitalization and a major cause of chronic disability among older adults (29Go, 30Go). For our study, the most common primary discharge diagnosis listed was cardiovascular disease, which accounted for 13.4 percent of all hospitalizations.

In comparison with previous studies, this work offers several combined advantages. First, it relies upon a population-based longitudinal dataset that includes a 15-year follow-up period. Second, it utilizes validated outcomes for all cardiovascular disease events. Third, it has extensive information available to consider as potential confounding factors. Fourth, in assessing risk for overall hospitalizations, it accounts for repeated admissions by using the more robust statistical methods of Prentice et al. (31Go), and compares these results with those obtained from the standard Cox (32Go) proportional hazards model.


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
Study sample
The cohort of 2,812 subjects was assembled in 1982 as part of the Yale Health and Aging Project, which was also part of the larger prospective study, the Established Populations for Epidemiologic Study of the Elderly (33Go). For this population-based cohort, noninstitutionalized individuals aged 65 years or more were chosen from the New Haven, Connecticut, area by using a probability sampling scheme that oversampled males and was stratified by housing category (community, public, or private elderly housing). Overall, the baseline response rate was 82 percent. After selection, each subject received a face-to-face interview at home from a trained interviewer. Thereafter, annual telephone interviews as well as personal interviews every 3 years were conducted. Additionally, surveillance of the mortality and hospitalization status was maintained. More specific details of this original sample have been described elsewhere (34Go) and therefore will not be pursued here.

The hospitalization status of all subjects was checked by means of a surveillance of hospital admissions at the two New Haven community hospitals from January 1, 1982 through December 31, 1996. Previous work has demonstrated that this surveillance was able to identify more than 90 percent of the hospitalizations of this cohort (35Go). Vital status was determined through the surveillance of death records, obituaries, and interviews with next of kin.

Study measures
The 1982 baseline interview was the source of all information about candidate predictor variables prior to hospitalization. In this study, the primary predictor variable was SRH. In the questionnaire, subjects were asked, How would you rate your health at the present time? The possible responses were 1) excellent, 2) good, 3) fair, 4) poor, or 5) bad. This variable was then dichotomized, with those who responded that their health was excellent or good as one category and those who responded that it was fair, poor, or bad as another category (the reference category for analysis).

Concerning the potential covariates, age at the baseline interview was trichotomized into ages 65–74 (reference), 75–84, or 85 years or more. For gender, females were considered the reference group. Race was defined as White (reference) or non-White. Education was defined as completing less than 12 years of school (reference) or as finishing 12 years or more. For alcohol intake, subjects were defined as nondrinkers/infrequent drinkers (reference) if their average ethanol consumption per month, weighted by beverage type (36Go), was 6.8 ounces or less or as frequent drinkers if intake was greater than 6.8 ounces. Smoking status was categorized as never smoker (reference), current smoker, or past smoker. The body mass index for subjects was defined as the ratio of their weight (kg)/height (m)2, which was subsequently trichotomized into less than 24, 24–26, or 27 kg or more per meter squared.

Past medical history covariables included the following: 1) hospitalizations within 12 months of baseline interview; 2) previously being a patient in a nursing home; and 3) ever having had a medically confirmed chronic condition such as cardiovascular disease, stroke, cancer, diabetes, liver disease, nonhip fracture, or hypertension. Among other potential predictors, depressive symptoms were measured by utilizing a 20-item self-report scale designed for use within the general population (37Go). Each item was scored on a four-point scale (0–3), allowing a total range of 0–60. The variable for this factor was dichotomized into 0–15 (reference) or 16 or more, which has been shown to distinguish those with sufficient symptoms who resemble depressed patients in treatment (34Go). Presence of functional impairment in the activities of daily living was also considered in the analysis. Subjects were categorized according to whether they reported having 0 (reference) or one or more areas in which they needed at least some help in performing some activity of daily living (38Go).

Study outcomes
The main outcome was the time, in months, to hospitalization. Initially, the time to first hospitalization was considered and was measured from the date of the baseline interview to the date of admission, as indicated by the hospital records. Subsequent hospitalizations were measured in an analogous fashion. Repeated hospitalizations were also measured from the time since the last hospitalization to determine whether the association of the primary predictor persisted beyond a given outcome event number. Hospitalizations beyond the ninth occurrence were not considered in order to ensure a sufficient sample size for analysis in each of the latter event categories. This cutoff accounted for more than 95 percent of the cumulative number of hospitalizations (table 1).


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TABLE 1. Self-rated health status and hospitalization events for New Haven, Connecticut, residents, Yale Health and Aging Project, 1982–1996

 
In defining cardiovascular disease-specific hospitalizations, the International Classification of Diseases, Ninth Edition, Clinical Modification (ICD-9-CM) numeric codes were used to identify potential events of unstable angina (ICD-9-CM codes 411.1 and 411.8), myocardial infarction (ICD-9-CM codes 410.0–410.9), and heart failure (ICD-9-CM codes 428, 402.01, 402.11, 402.91, 404.01, 404.13, 404.91, and 404.93) through the surveillance of primary discharge diagnoses of admitted subjects. Validation of these cases involved further review of the medical records according to predefined diagnostic criteria, which have been previously described by Vaccarino et al. (39Go). In our study, cardiovascular disease-specific hospitalizations were defined as those due to myocardial infarctions and heart failure for the period 1982–1996 because the validated admissions for unstable angina were not available beyond the year 1992. Consequently, separate analyses were conducted for the period 1982–1992, which included hospitalizations for unstable angina among the cardiovascular disease-specific hospitalizations. Since these results were comparable with those for 1982–1996, only the latter are reported here.

Statistical analysis
For the statistical analyses, SAS software, PC Computer Release Version 8 (SAS Institute, Inc., Cary, North Carolina) was used to generate unweighted parameter estimates and associated standard errors for all models. For the weighted analysis, which accounted for the complex sampling design, SUDAAN software, SAS-Callable Release 7.5.2 Individual PC Version (Research Triangle Institute, Research Triangle Park, North Carolina) was used for the final standard Cox proportional hazards model (via PROC SURVIVAL). Since the unweighted and weighted analyses supported the same conclusion, only the unweighted findings are reported (40Go).

For examination of the bivariate associations between the predictors and the time to the first admission, the logrank test (via PROC LIFETEST, SAS Institute, Inc.) and the Cox proportional hazards regression model were used and gave similar results. For the multivariate analysis of the first hospitalization event, Cox proportional hazards regression was also used. With these models, the predictors were checked for departures from the proportional hazards assumption by using the Schoenfeld residuals (41Go). Moreover, a correlational matrix was used to examine possible collinearity between variables (42Go, 43Go). To identify unduly influential observations for exclusion, the residual deviance and likelihood displacement methods were used as previously described (44Go).

In performing the multivariate analysis, an initial model was constructed that, in addition to SRH, included demographic and behavioral factors (age, sex, race, highest level of education achieved, alcohol consumption, smoking history, and body mass index). A subsequent model adjusted for these covariates as well as for medical history and comorbid conditions (depressive symptoms, prior hospitalization within 1 year of baseline interview, ever residing in a nursing home, prior cardiovascular disease, stroke, cancer, diabetes mellitus, liver disease, nonhip fractures, and functional limitations). Inspection of the change in the estimate of SRH between these two models allowed determination of the extent to which medical history and comorbidity factors accounted for the association between self-perceptions of health status and hospitalizations. Similar models were fitted for all-cause hospitalizations, hospitalizations due to heart failure, and hospitalizations due to myocardial infarction, respectively, as dependent variables.

For all-cause hospitalizations, the data were refitted by using the methods described by Andersen and Gill (45Go) as well as those by Prentice, Williams, and Peterson (PWP) (31Go), which account for repeated events. In the area of multivariate failure time (MVFT) analysis, alternative models exist (46Go, 47Go), such as those described by Wei et al. (48Go) (table 2). This model, however, has been described as being less efficient than the PWP approach (49Go). Additionally, it is considered less powerful than both the Andersen-Gill and PWP methods in its ability to detect differences between various exposure categories (47Go).


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TABLE 2. Some comparisons between the major multivariate failure time models

 
In forming the multivariate models, the original data set was appropriately restructured, and then the PROC PHREG (SAS Institute, Inc.) statements were modified according to SAS programming guidelines. For the PWP approach, several models were fitted to reflect the variations in how the data are treated in the analysis based upon differing assumptions. These included the models for an overall association of SRH with all hospitalizations (Common Effect model) and an individual association of SRH with each hospitalization (Uncommon Effect model), which was constructed by using an interaction term between SRH and hospitalization number. Additionally, models were formed based upon measuring the start time of each event from the baseline interview (total time, or PWP-TT) or since the last event (gap time, or PWP-GT). For the sake of brevity, only the findings for the PWP-GT (Uncommon Effect) model are reported, since the other models gave results consistent with the data presented here. Furthermore, this model was refitted after excluding those subjects who had reported being hospitalized within 12 months prior to the baseline interview in order to address the issue of this variable being an intermediate between SRH and multiple hospitalizations. For the analysis of cardiovascular disease-specific hospitalizations, only the standard Cox proportional hazards model was used.

For both the Cox and the PWP regression analyses, death was considered a censoring event. In all multivariate models, the issue of having subjects with missing covariate information was handled by using the method of multiple imputation as previously described by Rubin (50Go) and Schafer (51Go) and implemented by using macro programs in the SAS/IML language based upon the work of Allison (52Go). This method has been shown to provide unbiased parameter and variance estimates even when nonresponse does not occur at random. For this study, the imputed and nonimputed results were comparable. The imputed findings are reported here.


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
Study sample
Of the 2,812 subjects under study, two were excluded because their hospital admission date was missing. Thus, for all-cause hospitalizations, bivariate associations involving the primary predictor variable, SRH, began with 2,810 individuals (1,178 for fair/poor/bad SRH and 1,632 for excellent/good SRH). For the bivariate analysis involving the main outcome, time to hospitalization, the sample size had two fewer observations since the event times of two of the subjects could not be determined. In the multivariate analysis, 685 additional persons had incomplete covariate data; this was handled as described above.

Study outcomes
The maximum number of hospitalizations that any subject experienced was 24 (with a median of two for both SRH groups). Of the 1,178 subjects who had rated their health as fair/poor/bad during the 1982 baseline interview, 912 (77.4 percent) were hospitalized at least once during the follow-up period, while of the 1,632 individuals who had rated their health as excellent/good, 1,195 (73.2 percent) were hospitalized. For any given number of hospitalizations, the proportion of subjects who had rated their health status favorably was consistently less than the proportion of those who had not rated their health status favorably. These results are summarized in table 1. Between 1982 and 1996, 121 subjects (10.3 percent) who had rated their health as fair/poor/bad were hospitalized for cardiovascular disease versus 127 (7.8 percent) of those who had rated their health as excellent/good. During the entire follow-up period, a total of 220 individuals (13.5 percent) who had rated their health favorably at baseline died compared with 184 (15.6 percent) of those who had rated their health unfavorably at baseline.

In examining the bivariate association between SRH and the time to first hospitalization, the log-rank test demonstrated a significant relation, as did the unadjusted Cox proportional hazards model (p < 0.0001 for both) (table 3). In the latter model, the unadjusted all-cause hospitalization risk ratio (RR) for those who had a SRH of excellent/good compared with those who had a SRH of fair/poor/bad was 0.728 with a 95 percent confidence interval (CI) of 0.668, 0.794 (table 4). Among those who had a fair/poor/bad SRH, the median time to the first hospitalization was 38 months; for those who had an excellent/good SRH, the median time was 57 months. Considering hospitalizations due to heart failure, the unadjusted RR for those with a fair/poor/bad SRH was 0.481 (95 percent CI: 0.351, 0.659), while for hospitalizations due to myocardial infarction, the RR was 0.754 (95 percent CI: 0.498, 1.141) (table 4).


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TABLE 3. Sociodemographic and medical characteristics of New Haven, Connecticut, residents, Yale Health and Aging Project, 1982–1996

 

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TABLE 4. Unadjusted and adjusted relative risk with 95 percent confidence intervals for self-rated health (excellent/good vs. fair/poor/bad) and first hospitalization due to all causes, congestive heart failure, and myocardial infarction using the standard Cox proportional hazards model for New Haven, Connecticut, residents, Yale Health and Aging Project, 1982–1996*

 
After adjustment for sociodemographic and behavioral factors, baseline SRH remained a significant (p < 0.0001) independent predictor of time to hospitalization for all causes as well as for heart failure (table 4). The RR for those who rated their health as excellent/good, compared with those who rated their health as fair/poor/bad, was 0.742 (95 percent CI: 0.679, 0.810) for all-cause hospitalizations and 0.513 (95 percent CI: 0.372, 0.707) for hospitalizations due to heart failure. The addition of medical history and comorbid conditions to the model somewhat decreased the association between SRH and hospitalizations for all causes and for heart failure, but did not eliminate it. After all of these factors were added to the model, the RR was 0.850 for all-cause admissions (95 percent CI: 0.774, 0.934) and 0.599 for heart failure admissions (95 percent CI: 0.426, 0.841).

The association of SRH with hospitalizations due to myocardial infarction remained nonsignificant after sociodemographic, behavioral, and medical history factors were added to the model. In addition, health status factors appeared to explain substantially the association between SRH and hospitalizations due to myocardial infarction. After these factors were added to the model, the RR changed from 0.772 (95 percent CI: 0.507, 1.178) to 0.882 (95 percent CI: 0.565, 1.379) (table 4).

With the MVFT approach, the PWP-GT model demonstrated that a significant relation for a favorable SRH existed only with the first (RR = 0.813, 95 percent CI: 0.744, 0.889) and second (RR = 0.870, 95 percent CI: 0.782, 0.968) hospitalizations (table 5). This relation persisted even after exclusion of those subjects who had experienced a hospitalization within 12 months prior to their baseline interview (data not shown). For all remaining admissions, except the seventh (RR = 0.822), the RR was greater than 0.92.


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TABLE 5. Adjusted relative risk with 95 percent confidence intervals for self-rated health (excellent/good vs. fair/poor/bad) and all-cause hospitalization using the standard Cox proportional hazards model and the Prentice, Williams, and Peterson gap time model for New Haven, Connecticut, residents, Yale Health and Aging Project, 1982–1996*

 

    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
There were several important findings in this study. First, the standard Cox proportional hazards model demonstrated that a favorable (compared with an unfavorable) SRH was significantly associated with a delay in the first hospitalization after a subject's 1982 initial interview. This association persisted after baseline adjustment for sociodemographic and behavioral variables, prior hospitalizations and nursing home status, prior chronic diseases, and functional impairment. Additionally, when the hospitalizations were coded as relating to cardiovascular disease, the association with SRH was found to be significant for admissions due to heart failure but not for those due to myocardial infarction. Finally, the PWP-GT model showed that when multiple admissions were considered, the significant relation for SRH persisted through the second hospitalization but not beyond.

These findings are consistent with previous research that has documented that negative self-perceptions of health are correlated with increases in the utilization of hospital services, especially among the elderly (8GoGoGoGoGoGoGoGoGoGoGoGoGoGo–22Go). There are some authors, however, who have published different results. Chi et al. (23Go), for example, observed that SRH was a good predictor of hospital use only among certain subgroups such as the cognitively impaired and elderly males. Their data, though, may simply reflect the rather limited sample size the authors had available in order to detect hospitalization differences.

In another study, Wolinsky et al. (24Go) concluded that poor perceptions of health were associated with greater numbers of hospital episodes among those who had experienced at least one hospitalization after their baseline interview. For the entire cohort, unfavorable self-ratings were associated with a decreased risk in having any hospital episode. To explain this paradox, the authors offered several reasons. First, some individuals may consider any intervention futile if they feel they are at the end of life (53Go, 54Go). Second, if they have functional impairments, they may be more likely to reside in a nursing home and, subsequently, be less likely to be hospitalized (55Go). Alternatively, if they are functionally impaired, they, their families, or their doctors may simply consider hospitalization as inappropriate. Apart from these reasons, it is worth mentioning that the cohort in this study was older (age 70 years or more) than those in other investigations, which may help explain the different results. Older subjects, for example, may have a greater risk for later functional impairment and nursing home placement, which would be consistent with the rationale offered previously by Wolinsky et al.

More recently, in yet another study, Foreman et al. (25Go) similarly reported that a negative SRH was associated with a decrease in inpatient service use among the Chinese elderly living Beijing. In this case, the authors suggested that the observed relation indicated that the very ill were less likely to be hospitalized due to controls in the provision and practice of medical care. Aside from this explanation, their findings may have also been influenced by limitations of the study design. For example, the survey-based investigation could not directly address the temporal relation for their reported findings. Furthermore, the relatively small sample size may not have afforded sufficient power to adequately assess hospitalization patterns. Additionally, the results may not be generalizable to the elderly population in the United States. Finally, it should be noted that Foreman et al. did not model SRH as one variable but as two distinct variables: one for physical health and another for mental health. This disaggregation of SRH may have also contributed to their conclusions, since two variables may have captured information not evaluated by a single global health rating.

Concerning the relation between SRH and cardiovascular disease-specific hospitalizations among an elderly cohort, the literature is relatively sparse. Indeed, a few investigators have published papers supporting an association linking SRH with hospital admissions for cardiovascular disease (21Go, 22Go). In this context, the findings reported here extend this work by demonstrating that a single measurement of SRH, independent of other relevant factors, predicts long-term inpatient use of health services due to heart failure. This inverse relation between SRH and hospital admissions for heart failure may reflect changes in health status that could precipitate admission, given the progressive nature of heart failure (56Go). In contrast, the finding that SRH was less strongly associated with hospitalizations for myocardial infarction could be due to the fact that a myocardial infarction may develop acutely without subjective or clinical evidence of long-standing progression of coronary disease. In addition, it is possible that the association of SRH with hospitalizations due to myocardial infarction is diluted by the fact that individuals with the most severe heart attacks have died before reaching the hospital.

To our knowledge, this study is the first to examine the relation between SRH and repeated hospitalizations, using models that handle recurrent events. Other investigators have begun using some of these techniques in other areas of clinical research (57Go, 58Go). Indeed, it is likely that such statistical methods will find a wider application in epidemiologic studies as more researchers become familiar with the various approaches of MVFT analysis (59GoGo–61Go).

In weighing the public health importance of the findings presented here, attention should be given to what impact early intervention could have on identifying those individuals most at risk based upon their self-perceptions of health. As the PWP-GT model indicated, the effect of SRH upon hospitalizations is most acute for the first hospitalization, and therefore, resources should be targeted appropriately. Furthermore, the median time for the first hospitalization suggests that these events are likely to occur within the first year. Consequently, the interventions should be concentrated in the early phases of any instituted program for older adults. From a clinical perspective, these results also imply that elderly patients with a negative SRH may need further evaluation regardless of their medical presentation (54Go). Such efforts, along with those mentioned above, may serve not only to reduce the future morbidity of older populations but also to address their growing demand for hospital services (62Go), especially those due to cardiovascular disease (28Go).

When the data presented here are interpreted, several potential limitations of the study should be kept in mind. First, in identifying hospitalization events, no attempt was made to develop specification guidelines for distinguishing between readmissions due to treatment failure and rehospitalizations indicative of separate episodes. Wolinski et al. (54Go) considered this issue important in disaggregating hospital resource consumption, especially near the end of life. Applying such specifications to our study, though, would presumably be directed by some understanding of how SRH might influence recovery after an initial hospitalization. These underlying mechanisms, however, are fairly unclear (63Go). In fact, even with mortality studies, a dominant hypothesis about how SRH operates has not yet emerged (64Go, 65Go). Indeed, the unraveling of this variable will require further quantitative as well as qualitative research that explores new questions with different techniques (6Go, 66Go), since health is a multifaceted concept (67Go).

Another possible limitation of this study may have been the inclusion of hospitalization status within the 12 months preceding the baseline interview as a potential covariate for modeling purposes. Indeed, Diehr et al. (68Go) pointed out that it is important not to control for previous health care utilization in regression analysis if such a variable is part of the causal pathway of interest. Since previous utilization is a strong predictor of future utilization, it is possible that prior hospitalization status within a year (or ever) of the baseline interview plays an intermediate role in linking subsequent hospital admissions with SRH. To address this issue, the authors refitted the PWP-GT model after excluding those subjects who had experienced a hospitalization within 1 year prior to their baseline interview and found the results to be consistent with the original model (data not shown).

Concerning the issue of power, which was already mentioned above, it is possible that this study was limited by its sample size to the extent that the associations between SRH with increasing all-cause hospitalizations were insignificant. However, the relative risk estimates for the third through the sixth admissions were close to unity. The use of the MFVT approach actually allowed a much larger data pool in order to make inferences. Moreover, the follow-up period for this longitudinal study spanned more than a decade, making it possible to gather a greater number of endpoints upon which to perform an analysis for both all-cause and cause-specific hospitalizations. Finally, because different models were used that led to the same basic conclusions, the weight of this study's findings is further supported. All of these approaches ensured the overall stability of the findings. However, for the analysis of hospitalizations due to myocardial infarction, the number of hospitalizations was relatively small (92 hospitalizations), and the resulting confidence intervals around the SRH estimate were rather large. Therefore, from our analysis, no firm conclusions should be drawn with reference to this outcome.

Notwithstanding the above-mentioned drawbacks, the data in this study indicate that a single measurement of global self-rated health status is an important and independent predictor of hospitalizations over a prolonged follow-up period among the urban-dwelling elderly. Indeed, those with a favorable self-assessment of their health were at a decreased risk for subsequent hospitalizations independent of self-reported medical conditions and risk factors. This lowered risk was greatest for the first hospitalization and was fairly stable over time. The implications of these findings suggest that more attention should be given to the medical assessment of the elderly who rate their health unfavorably. Additionally, greater efforts should be undertaken to identify these individuals within the community.


    ACKNOWLEDGMENTS
 
Supported in part by contract N01-AG-02105 from the National Institute on Aging.


    NOTES
 
Reprint requests to Dr. Viola Vaccarino, Emory University School of Medicine, Center for Outcomes Research, 1256 Briarcliff Road, Suite 1 North, Atlanta, GA 30306 (e-mail: lvaccar{at}ecor.cardio.emory.edu).


    References
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 

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Received for publication August 9, 1999. Accepted for publication May 5, 2000.