In this study of the organizational predictors of hospital end-of-life treatment intensity in 164 acute care Pennsylvania hospitals, we found that administratively-derived structural and market characteristics, including larger bed size, greater proportion of admissions among black patients, and higher area-level hospital market competition, were more powerful predictors of end-of-life treatment intensity than specific hospital programs, policies, or practices, with the exception of an ICU long length of stay review committee. Despite our effort to capture a comprehensive set of explanatory variables, almost half of hospital-level variation in end-of-life treatment intensity remained unexplained.
We found that hospitals with most of the studied programs, policies, and practices had higher mean hospital end-of-life treatment intensity indices than hospitals without such organizational initiatives, although most of these did not reach statistical significance. This paradoxical relationship between organizational initiatives that we hypothesized would be inversely related to intensity may reflect “reverse causation” in our cross-sectional analysis. Specifically, these initiatives may have been implemented in response to perceptions of higher than average use of ICU and life-sustaining treatments among patients at the end of life. Indeed, a longitudinal—not cross-sectional—design that could assess utilization changes associated with policy implementation would have been optimal. The finding that most programs, policies, and practices significantly associated with intensity in univariable tests were no longer significant in the multivariable model that included structural and market characteristics suggests that our study could have been underpowered or the associations could simply have been spurious due to confounding by structural characteristics. However, a recent national study demonstrated an inverse cross-sectional relationship between state-level penetration of one kind of program—hospital-based palliative care—and total end-of-life Medicare spending14
; national hospital-level analyses are lacking.
The association between the concentration of black patients in a region,15
or nursing home18
and greater end-of-life treatment intensity is not new. It is not entirely clear whether this observation reflects black patients' preferences for these life-sustaining treatments19–23
or some other phenomenon. For example, the higher observed end-of-life ICU use among blacks is attributable to black patients' disproportionate use of hospitals with greater end-of-life ICU use for both black and white patients.16,17
Our study also revealed a significant positive relationship between larger hospitals and end-of-life treatment intensity. This relationship was not attributable independently to tertiary care status (measured by transfers) or to teaching status (measured by the resident-to-bed ratio). It is possible that local practice norms play a significant role in this relationship, in addition to the ease of access to beds and other technologies in larger facilities. Area-level bed supply, as distinct from institution-specific supply, has a well-documented association with utilization and spending.24–28
Variation in hospice use, on the other hand, is not explained by area-level market characteristics.29
This may be because hospice use is more dependent on patient preferences, demographics, and local social norms, and may have very low levels of penetration in certain counties.
In our study, higher market competition was associated with higher end-of-life intensity. This relationship existed even after controlling for bed size, a proxy for technological resources. It is possible that more competitive environments lead to higher intensity treatments because of the incentive to acquire and offer more technology and resources to compete for highly remunerated cases—such as transplant and coronary-artery bypass grafting. This may, in turn, result in greater use of those resources for patients at a high risk for dying.
There are several limitations to our study. The validity and reliability of the survey responses provided by a single informant, the CNO, was not assessed and is likely imperfect. Although the CNO is likely the single best informant, she/he may not have been knowledgeable about all aspects of hospital and ICU policy. Reassuringly, 84% reported that they felt “confident” or “very confident” in their answers to the survey questions.
Our study is unable to establish cause-effect relationships between organizational initiatives and end-of-life treatment intensity in this cross-sectional study. We did attempt to ascertain the direction of the relationship by asking CNOs in our survey when each initiative was implemented. However, many of the responses to timing of implementation were “don't know” (on average 50%–75% of responses), leading to inadequate sample sizes for analysis.
Likewise, the timing of the survey did not coincide exactly with the timing of the selected PHC4 discharge data. The dates of the hospital discharge data used to develop estimates of each hospital's treatment intensity (2001–2005) abutted, but did not overlap, the dates of the survey (2005–2006). Each hospital's practice pattern was stable over the 4 years of available data, but we are nonetheless unable to attribute temporal associations or direction of effect.
Although it was a strength to compare hospitals in 1 state with a uniform regulatory, legal, and financing environment, our study may have been underpowered and is not generalizable to other states. The response rate to our survey was impressive at 76%, but nonetheless reflects only 124 hospitals. And, based on the comparison of observable characteristics, survey nonrespondent hospitals were more likely to be in higher competition markets, confirming a likely response bias.
Unlike outcomes such as end-of-life treatment costs, our end-of-life treatment intensity index itself does not have a natural metric, given that it is a factor score based on 6 underlying standardized utilization ratios. It is not strictly possible to explain that a difference in the index of, for example, 1 point is directly related to X% fewer intubations or ICU admissions. Thus, it cannot be used to compare utilization of individual life-sustaining treatments across hospitals.
Another potential weakness is that our end-of-life treatment intensity index was calculated for all admissions, rather than just among ICU admissions, which may have diluted the “true” contribution of ICU-specific practices on intensity. However, because the treatments included in the index were ICU use (admission and length of stay) and those commonly delivered only in the ICU (mechanical ventilation), we believe that our hospital-specific measure is closely tied to ICU-specific treatments. However, among respondent hospitals with more than 1 ICU (N = 105), we asked the CNO to give us information about programs, policies, and practices implemented in the ICU that takes care of the “majority of chronically ill elders” in their hospitals (typically the MICU or a mixed medical-surgical ICU). We did not ask about the programs, policies, and practices in the other ICUs. For these 105 hospitals, there may be some misspecification—the hospital's overall intensity index is affected by practice in an ICU that we have no information about and which may have different practices than the “sampled” ICU.
Finally, it is always possible that unmeasured case-mix variation contributes to the higher end-of-life treatment intensity seen at larger hospitals. However, we did account for as much case-mix variation as possible by including the mean “predicted risk of death on admission” among the elderly (mean illness severity)—derived from key clinical factors on admission—as an independent variable. We did not find a relationship between the hospital's mean predicted risk of death and intensity in univariable analysis. In addition to this variable, each hospital's transfer-in rates and resident-to-bed ratio were eligible for model inclusion—both of which may measure staffing/equipment/capacity for taking care of sick patients. Although each variable was significantly associated with intensity in univariable analyses, neither was retained in the parsimonious multivariable model. However, hospital bed size and share of black patients may be capturing some of this “resource” construct.
In summary, the presence of the 29 hospital programs, policies, and practices that we assessed provide only minimal additional explanation of the large variation in hospital end-of-life treatment intensity in Pennsylvania acute care hospitals. Although several structural and market characteristics were associated with end-of-life treatment intensity, the mechanism of these effects remains opaque, and almost half of the variation remains unexplained. Future studies should explore the contribution of more difficult-to-measure social norms in shaping hospital practice patterns.