Among a national sample of Medicare beneficiaries older than the age of 65, most prefer treatment focused on palliation rather than life-extension. We did not find a pattern of greater concern about receiving too little medical treatment, less concern about receiving too much medical treatment, preference for spending one’s last days in a hospital, for life-prolonging drugs despite side-effects, and for mechanical ventilation to achieve 1 week’s and 1 month’s life extension across respondents living in regions with progressively greater EOL spending. The observed relationship between respondents’ preferences for avoiding potentially life-shortening palliative drugs and greater spending regions was explained by the confounding effect of race/ethnicity. Taken together, the lack of cross-sectional association between preferences and spending in our study is unsupportive of the hypothesis that differences in preferences explain regional variations in EOL spending.
It is perhaps unsurprising that we did not find a relationship between individual patient preferences and local practice patterns, since the Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatment (SUPPORT) demonstrated that preferences are seldom correctly ascertained,17
let alone heeded.6
However, unlike SUPPORT, we did not correlate actual treatment received with stated preferences. Our findings of lack of cross-sectional association using regional aggregate spending does not refute causality, and further, are potentially subject to the ecological fallacy. That is, although there is an association in aggregate, this association may not exist at the individual level.
Another limitation is the reliance upon a hypothetical scenario. Both the certainty of the prognosis and physicians’ willingness to articulate it may be unrealistic. Furthermore, we ascertained stated, not revealed preferences. It is possible that in the event of an actual life-threatening illness, individuals in higher intensity regions might behave differently than they indicated in response to the hypothetical scenario. Younger, healthier Medicare beneficiaries may not have sufficient experience with EOL decision making to reliably predict their preferences; to address this concern, we restricted the analysis only to respondents 75 or older, and our findings were unchanged. Furthermore, we adjusted all analyses for 3 health status measures, none of which were statistically significant predictors of any of the outcomes in our full models. Nonetheless, these remain important concerns because preferences are not entirely stable over time,18
particularly for those in declining health.19
The particular survey items were intentionally oversimplified to gain insight into broad concerns, goals, and preferences, rather than to anticipate particular treatment choices, and, as such were not as nuanced as required for advance care planning.20
With regard to the question about mechanical ventilation, we did not specify to the respondents the circumstances of their 1 week or 1 month’s life extension; some may have anticipated the reprieve to be lived in good health, while others may have understood the extension to be while still on the ventilator.
There was statistically significant pairwise correlation (with Bonferroni correction) for 10 of 21 outcome pairs. Not surprisingly, similar questions were highly correlated, for example between mechanical ventilation for 1 week and mechanical ventilation for 1 month (r = 0.75, P < 0.001), whereas others were less closely correlated, for example, between between mechanical ventilation for 1 week and worry about “too little” medical treatment (r = −0.11, P < 0.001). We sought a pattern of differences across all 7 outcomes, and indeed found neither a pattern nor significant differences on any single outcome. Some might argue that the lack of a difference on the responses to the questions regarding worry about getting too much or too little treatment indicates that respondents actually are getting just the kind of treatment they want across regions with differing spending levels. (Otherwise they’d be worried about getting too much in the higher spending regions.) Such an inference, however, would be inconsistent with the responses to the questions about specific services, which suggested that preferences and values for EOL care differ little across regions. Perhaps respondents are similar across regions in their values and preferences, but they observe only the intensity of care in their own region, and thus have little basis for judging what is “too much” or “too little.”
The EOL spending measure is based upon hospital and physician services only. It is possible systematic bias exists because of the exclusion of spending from other Medicare benefit cateories, such as hospice, home health, skilled nursing, and long-term acute care, if there is a greater likelihood of substitution of these services for acute care services in lower spending regions. Indeed, as shown by Pritchard and by Virnig, nursing home bed and hospice availability and use are inversely correlated with hospital as the place of death. On the other hand, nursing home bed availability is positively correlated with hospital bed availability and use more generally.8
Ultimately, this systematic bias is unlikely given the high correlation between hospital referral region-level EOL spending and hospital referral region-level overall spending (year 2000 r = 0.82, P
< 0.0001). Indeed, during the last 2 decades, the substitution of these services has displaced the use of hospital acute care services at the end of life, but has not decreased the growth of total EOL Medicare expenditures.21,22
Finally, there was a 35% nonresponse rate to our survey. Although there was not a greater rate of nonresponse in the higher quintiles, it is still theoretically possible that selection bias produced the observed null result. For this to have occurred, beneficiaries preferring more intensive care would have had to be systematically more likely to be nonresponders in the higher intensity regions than in the lower intensity regions, which seems unlikely.
In summary, the results of this survey do not support the hypothesis that observed regional variations in EOL spending are attributable to differences in goals and preferences for care among residents of those regions. Longitudinal study of patients, their preferences, and their health care utilization is a natural next step in disconfirming this hypothesis.