To evaluate whether dual use based on inpatient services increases the risk of mortality, data are taken from the Survey on Assets and Health Dynamics among the Oldest Old (AHEAD). Because over-sampling was used to increase the number of African Americans, Hispanics, or Floridians in the AHEAD, the data are weighted to adjust for the unequal probabilities of selection due either to the multi-stage cluster sampling design and/or the over-sampling. The AHEAD data set was selected for three reasons. First, it is a nationally representative probability sample that includes 2,911 men and 4,536 women who were 70 years old or older at their baseline interviews in 1993–94. Because only 57 women (1%) were veterans, these analyses are restricted to men. Among the men, 1,574 (54%) are veterans. This provides a large, nationally representative sample, evenly distributed between veteran and non-veteran men. Second, the AHEAD baseline survey data have been linked to Medicare claims from January 1989 through December 1996. Third, the AHEAD survey and Medicare claims data have also been linked to the National Death Index (NDI) through December 2002. This provides a nine-year window for examining the association of an indirect dual use measure based on inpatient services with mortality, during which 52% (1,524) of these men died.

Unfortunately, the AHEAD is not linked to VHA claims. Thus, an indirect measure of dual use based on inpatient services was constructed. This was done by building on the extant literature addressing differences between self-reports and administrative records of health services use. It has been well established that concordance between the two is not perfect, and that the discordance is not easily predicted 293031323334353637. The magnitude of the discordance is primarily influenced inversely by the salience of the event, and directly by the length of the recall period 333435. Hospital episodes are considered to be the most salient events, and for them a 12-month window provides a reasonable balance between recall abilities and the incidence of hospitalization 2937. Discordance has also been shown to be positively associated with the number of events during the reporting period, and to a lesser extent with demographic, social, and health factors, although these associations have not been consistently observed 37. Elsewhere, we have shown that in the AHEAD, the concordance of self-reports and Medicare claims is high for both any (vs. none; κ = .763) and the precise number of (κ = .663) hospital episodes over a 12-month window 38.

Building on this literature, the indirect measure of dual use based on inpatient services was constructed as follows. At baseline, each AHEAD man was asked whether he was hospitalized overnight during the previous 12 months, and if he had been, how many times this occurred. Using each AHEAD man's baseline interview date, corresponding data were harvested from his Medicare claims. The self-reports were then compared to the claims results. If the AHEAD man reported at least one more hospital episode than was found in his Medicare claims he was considered an over-reporter.

Although straightforward, this approach has four limitations that, although addressable, warrant further mention. First, this approach ignores any dual use that occurs solely in the outpatient setting. As indicated above, however, the veridicality of such measures is substantially less than that obtained by focusing just on the inpatient setting 293031323334353637. Indeed, in the AHEAD, we have shown that the concordance of self-reports and Medicare claims is low for both any (vs. none; κ = .248) and the precise number of (κ = .347) physician visits over a 12-month window 38. Second, this approach ignores the extent of the over-reporting. In these data, however, 79% of those who self-reported more hospital episodes than were found in their Medicare claims over-reported by only one hospital episode. Third, this approach may confound dual use with the proclivity for hospitalization. That is, by definition, all dual users have reported that they were hospitalized. This confound, however, can be addressed by including in the analysis a binary marker for whether the AHEAD man reported being hospitalized. Fourth, this approach does not actually identify dual use, because dual use can only occur among veterans.

Isolation of the dual use effect, however, can be readily achieved by constructing a set of four dummy variables that reflects the cross-classification of over-reporting with veteran status. The analyses reported here include three of these markers – veterans who over-reported (and thus are considered to be dual users of VHA and Medicare), veterans who accurately reported, and non-veterans who over-reported (but could not be dual users of the VHA and Medicare). The omitted or reference group is that of non-veterans who accurately reported their number of hospital episodes.

In this approach, the effect of the dummy variable for veterans who over-reported their number of hospital episodes accurately reflects the mortality risk of dual use of VHA and Medicare based on inpatient services. The hypothesis is that this effect will be statistically significantly greater than unity, reflecting the increased mortality risk associated with dual use of VHA and Medicare. It is further hypothesized that the effect of the dummy variable for non-veterans who over-reported will not be statistically significantly different than unity, because non-veterans have no access to VHA.

Vital status was obtained by linking the AHEAD to the NDI. The NDI files indicate whether each AHEAD man died, and if so, provide the month and year of death. Also provided, but not used in these analyses, are indicators of the probability of the match using standard criteria 39 developed by the National Center for Health Statistics (NCHS), and detailed ICD9-CM codes for the cause of death.

Of the 2,911 AHEAD men, the survey data was provided by a proxy-respondent (usually the spouse) for 391 (13%). Because the literature 29303132333435363738 on reporting discrepancies assumes self-respondents, analyses were restricted to the 2,520 AHEAD men who were self-respondents. Linkage to the Medicare claims was not available for an additional 954 AHEAD men (38%). Of these, 182 refused to consent to having their Medicare claims accessed, and for the remainder (772 men) sufficiently accurate information to facilitate the linkage process was not available. All 954 AHEAD men whose self-reported survey data could not be linked to their Medicare claims were excluded from the analyses. Thus, of the 2,911 AHEAD men, the analyses reported here were restricted to the 1,566 (54%; weighted N = 1,522) who were self-respondents and whose survey data was linked to their Medicare claims.

The exclusion of so many AHEAD men from these analyses raised the potential for selection bias. This potential for selection bias was addressed using propensity score methods. Developed by Rubin, 40 popularized by Rosenbaum and Rubin, 41 and illustrated by D'Agostino, 42 propensity scores are traditionally obtained by using multiple logistic regression to model a binary outcome reflecting group assignment in observational (vs. randomized controlled trial) studies. Here, propensity scores were used to model selection bias. To obtain the selection bias propensity scores, a multivariable logistic regression was conducted using all appropriate baseline covariates, including veterans' status, to predict exclusion of self-respondents from the analytic sample. The fit of the propensity score model was reasonably robust (C-statistic 43 = .638), and there was no evidence of heteroscedastic error (Hosmer-Lemeshow statistic 44 p value = .934). Thus, adding the obtained predicted probabilities of exclusion to the final analytic model among the restricted sample should be an appropriate adjustment for potential selection bias (at least in terms of the data available for analysis). This propensity score regression approach, however, assumed additive linearity in the relationships of interest. Therefore, as an added safeguard, the results were replicated using the more popular stratification approach. In this approach, the final model (excluding the propensity score) was re-estimated separately within strata based on the propensity score. If the results were robust across these strata, greater confidence could be had in the selection bias adjustment process.

Although the main focus of this article was whether dual use of the VHA and Medicare systems, indirectly indexed by veterans' over-reporting their number of hospital episodes, was associated with mortality, a secondary interest involved the etiological mechanism through which this association occurred. It was assumed that (a) dual use decreased the likelihood of receiving continuously coordinated health care, (b) the lack of continuously coordinated health care increased the risk of subsequent hospitalizations for ACSCs, and (c) these three factors (dual use, the lack of continuously coordinated care, and hospitalization for ACSCs) increased the risk of mortality. Left unspecified was whether the effect of dual use was direct, indirect (through its intermediary [e.g., falling domino] effects on the lack of continuously coordinated care and the increased risk of subsequent hospitalization for ACSCs), or a combination of the two. To begin exploring these issues, a final adjustment in the modeling process was made for subsequent hospitalizations for ACSCs. This was done by creating a binary marker indicating whether the subject had one or more hospitalizations for ACSCs after their baseline interview but before January 1, 1997. This marker was coded one for subjects with one or more such hospitalizations based on AHRQ's computerized criteria, 2122 and zero otherwise.

Although a measure of continuity of care based on the Medicare Part B (outpatient) claims has been developed for use in these data, 45 it could not be incorporated into these analyses. The reason was that this measure would have yielded biased estimates of continuity of care among dual users because it would have ignored their VHA outpatient visits, as those data were not available. Not being able to adjust for continuity of care severely constrained the ability to further explore the etiologic mechanism through which dual use was associated with mortality.

In addition to adjusting for potential selection bias, fifteen covariates were included to ensure that the estimated association of dual use with mortality was fully independent from other background factors. These covariates included age, race, education, income, assets, activities of daily living (ADLs), instrumental ADLs (IADLs), self-rated health, five chronic diseases, cognitive ability, and depressive symptoms. Age was measured in years. Race was measured by a set of three dummy variables for Hispanics, African Americans, and other non-Caucasians (with Caucasians as the reference group). Education was measured by a dummy variable contrasting high school graduates (and above) with those having less education. Income was measured by a binary marker for having less than $15,000 in annual income. Household wealth was measured as the sum of all reported assets net of debt, and was coded by a binary marker for having $19,000 or less in total wealth. ADLs were measured by a count of the number of five items (e.g., bathing) that the subject reported having any difficulty performing. Similarly, IADLs were measured by a count of the number of five items (e.g., meal preparation) that the subject reported having any difficulty performing. Self-rated health was measured by a set of four dummy variables for excellent, very good, fair, or poor responses to the standard question asking subjects to rate their health (with a good response as the reference group). Five binary variables (1 = yes, 0 = no) were used to indicate whether the subject reported having ever been told by a physician that he had cancer, diabetes, heart disease, lung disease, or a stroke. Cognitive ability was measured using the 7-item version of the Telephone Index of Cognitive Status, which ranged from 0 (worst) to 15 (best) (TICS-7) 46. Depressive symptoms were measured as the number of symptoms endorsed using an 8-item version of the CES-D 47.

Because the month and year of death are known, proportional hazards models were the appropriate statistical approach for estimating the effect of dual use on mortality 48. A series of proportional hazards models were estimated that initially assessed the crude effect of the set of three dummy variables reflecting the cross-classification of veteran status with over-reporting, and then serially decomposed that effect. The decomposition approach involved four stages that serially introduced (a) the binary marker for reporting any hospital episodes in the year prior to baseline, (b) the binary marker for whether post-baseline hospitalizations for ACSCs occurred, (c) the fifteen covariates, and finally, (d) the propensity score adjustment for potential selection bias. Sensitivity analyses were then conducted in which the final model, excluding the propensity score adjustment for selection bias, was re-estimated within each propensity score strata.

Because the research reported here involved the linkage of public use data files containing the AHEAD survey data with restricted data from the NDI files and Medicare claims, three layers of institutional review and approval were obtained. The first involved review and approval of the research and restricted data protection plans associated with the main NIH grant (R01 AG022913) by the AHEAD's Data Confidentiality Committee (DCC). These were approved by the AHEAD DCC on February 20, 2003 (#2003–006). The second layer of review and approval involved the University of Iowa Institutional Review Board (UI-IRB). The UI-IRB approved the original protocol on March 24, 2003, and has subsequently approved the protocol at all annual reviews (including appropriate modifications to incorporate the second NIH grant – R03 AG027741 – which specifically focused on dual use). The third layer of review and approval involved the Centers for Medicare and Medicaid Services (CMS). CMS approved the Data Use Agreement (DUA 14807) to access the Medicare claims for this research on March 3, 2005.

The analytic sample consists of the 1,566 AHEAD men (weighted N = 1,522) who were self-respondents and whose survey data were linked to their Medicare claims. Table 1 shows the means or percentages for the variables in the final model separately for each of the four distinct groups based on the cross-classification of over-reporting and veteran status. Overall, the mean age of the AHEAD men was 77 years (SD = 6), 7% were Hispanic, 11% were African American, 1% were of another race other than Caucasian, and 82% were Caucasian. Forty-five percent had not graduated from high school, 40% reported $15,000 or less in household income, and 25% indicated that they had less than $19,000 in wealth. The mean number of ADLs was 0.3 (SD = 0.7; 86% had none), and the mean number of IADLs was 0.4 (SD = 0.9; 78% had none). Twelve percent reported their health as excellent, 22% as very good, 32% as good, 22% as fair, and 11% as poor. Cancer was reported by 14%, diabetes was reported by 12%, heart disease was reported by 33%, lung disease was reported by 11%, and stroke was reported by 10%. The mean cognitive status score was 12 (SD = 3; 24% had the maximum score), and the mean number of depressive symptoms was 1.4 (SD = 1.9; 46% had none). Being hospitalized in the year prior to baseline was reported by 23%. Slightly more than half (55%) were veterans. Fourteen percent (226) experienced one or more post-baseline hospitalizations for ACSCs. By December 31, 2002, 817 (52%) of the AHEAD men had died.

Comparisons across columns (within rows) in Table 1 indicated that veterans had significant mortality risk advantages over non-veterans in terms of age, majority status, education, income, wealth, self-rated health, ADLs, IADLs, cognitive status, depressive symptoms, and subsequent hospitalization for ACSCs. In contrast, veterans had significant mortality risk disadvantages compared to non-veterans in terms of cancer and diabetes. Comparisons within veterans or within non-veterans indicated that those who over-reported their number of hospital episodes had considerably greater mortality risk due to their greater morbidity levels, regardless of veteran status.

As indicated in Table 1, the number of self-reported hospital episodes exceeded that found in their linked Medicare claims for 96 (10.8%) of the veterans vs. 60 (9.5%) of the non-veterans. The minimal difference between these crude over-reporting rates (odds ratio = 1.217; p = .261), however, is misleading. Adjusting for age, race, and self-rated health status using multivariable logistic regression revealed that veterans were 50% more likely than non-veterans to over-report (adjusted odds ratio = 1.496; p = .046). Moreover, veterans who over-reported their hospital episodes had the same number of physician visits in their Medicare claims as veterans who did not (79.9% of veterans who over-reported had physician visits vs. 80.9% of veterans who did not; both medians = 5). In contrast, non-veterans who over-reported their hospital episodes had substantially more physician visits in their Medicare claims than non-veterans who did not (87.7% of non-veterans who over-reported had physician visits vs. 84.6% of non-veterans who did not; medians = 8 and 5, respectively). Finally, among those who reported hospital episodes, 48.4% of veterans over-reported the number vs. 41.8% of non-veterans.

Table 2 contains the adjusted hazards ratios (AHRs) obtained from the final proportional hazards regression model. Note that these AHRs were adjusted for the binary marker for reporting any hospital episodes in the year prior to baseline, the binary marker for whether post-baseline hospitalizations for ACSCs occurred, the fifteen covariates, and the propensity score adjustment for potential selection bias. These results support both study hypotheses. That is, compared to non-veterans who accurately reported their number of hospital episodes, veterans who over-reported their number of hospital episodes (i.e., the indirect measure of dual use of VHA and Medicare, based on inpatient services) had an increased relative risk of dying (AHR = 1.561; p = .009). But, non-veterans who over-reported their number of hospital episodes did not have an increased relative risk of dying (AHR = 0.775; p = .191) compared to non-veterans who accurately reported their number of hospital episodes. Similarly, veterans who accurately reported their number of hospital episodes did not have an increased relative risk of dying (AHR = 1.168; p = .079) compared to non-veterans who accurately reported their number of hospital episodes.

The effects of the covariates on mortality in the final model were as expected. The relative risk of mortality significantly increased with age (AHR = 1.099 per year; p < .001), IADLs (AHR = 1.110 for each; p = .019), having poor vs. good self-rated health (AHR = 1.574; p < .001), and having diabetes (AHR = 1.311; p = .015), heart disease (AHR = 1.197; p = .025), lung disease (AHR = 1.458; p < .001), or a history of stroke (AHR = 1.439; p < .001). The relative risk of mortality was significantly reduced (AHR = 0.752; p = .011) among those having very good vs. good self-rated health, and among those having better cognitive status (AHR = 0.945 for each point of improvement on the TICS-7; p < .001). As expected, men with subsequent hospitalizations for ACSCs had increased risk of dying (AHR = 1.874; p < .001). The propensity score adjustment, however, was not associated with mortality (AHR = 0.893; p = .795).

Because the propensity score regression adjustment approach assumed additive linearity in the relationships of interest, sensitivity analyses were conducted. This involved using the propensity score stratification approach, in which the final model (excluding the propensity score) was re-estimated separately within strata based on the propensity score. Although quintiles are used most often 404142, tertiles were used here to balance the distribution of dual users across strata. Those analyses yielded remarkably consistent AHRs (1.465, 1.515, and 1.531 for the lowest to highest propensity score tertiles), indicating that the results shown in Table 2 are not artifacts of the selection bias propensity score adjustment approach.