Three points from our analysis warrant further discussion, the first of which is by far the most promising. It involves the importance of the dynamic health shock measure, which was indirectly measured in this study using the time-dependent recent hospitalization indicator. There are two aspects of this effect to keep in mind—it was quite large, and it did not mediate the effects of the baseline hip fracture risk factors. What this dynamic health shock measure captures is the transition period when older adults are especially vulnerable to adverse effects associated with both their underlying health shock (i.e., the reasons for their recent hospital admission) and the consequences of their treatments (e.g., delirium, disorientation, and increased risk for falling), especially in fragmented health care delivery systems (35
When calibrated at 14 days, which is when the effect size peaked, the health shock measure increased the risk of hip fracture by 151% and substantially improved the fit of the model (χ2 improvement in the log likelihood ratio = 19.07 at 1 df; p < .005). Although the effect size diminished to a 100% risk increase at 30 days, it then remained stable throughout 120 days. This early (14 day) risk peak, followed by a somewhat reduced risk by 30 days that plateaus for at least 4 months, reflects the initial concentration of risk during the immediate postdischarge period (i.e., short-term risks associated with delirium, disorientation, and increased risk for falling), followed by the enduring elevated risk (i.e., long-term risks associated with reduced systemic and reserve capacities) associated with hospitalization among older adults. As such, this suggests that postdischarge planning and monitoring for at least the first 2–4 weeks following hospitalization for something other than a hip fracture could reduce the risk of subsequent hip fracture.
Clearly, the introduction of the health shock (i.e., recent hospitalization) measure is a very promising development that underscores the need to shift from static to dynamic risk modeling approaches. Further research is needed, however, that explores the health shock measure in order to clarify what the underlying etiologic mechanisms might be. Such research should include whether restrictions to surgical versus medical admissions, shorter versus longer stays, or other decompositions would identify particular hospitalization subsets that pose the greatest risks for subsequent hip fracture.
The second discussion point is that there were no surprises in the identification of either the static baseline risk factors or the magnitudes of their risks. That is, our risk estimates are remarkably consistent with the extant literature (15
). Of the 5,511 AHEAD participants in our analysis, 495 (8.9%) suffered a postbaseline hip fracture. In the static baseline model, hip fracture risk was significantly higher for older adults, whites, women, those who lived in mobile homes, were normal or underweight, current and former smokers, those with diabetes, had poor self-rated health, were fallers, or had lower cognitive status. The greatest of these risks involved age (AHRs of 2.01, 2.82, and 4.91 for 75–79, 80–84, and ≥85 year age groups vs those aged 70–74 years; p
values <.001), sex (AHR = 0.45 for men vs women; p
< .001), race (AHRs of 0.37 and 0.46 for African Americans and Hispanics vs whites; p
values <.001 and <.01), body mass (AHRs of 0.40, 0.77, and 1.73 for obese, overweight, and underweight vs normal weight; p
values <.001, <.05, and <.01), smoking status (AHRs = 1.49 and 1.52 for current and former smokers vs nonsmokers; p
values <.05 and <.001), and diabetes (AHR = 1.99; p
Some might wonder why the risk associated with falling was much smaller by comparison (AHR = 1.35; p
< .01). This is likely due to two reasons. The first is the length of our surveillance period. We had postbaseline Medicare claims for up to 12 years, with a mean of 7.1 years. As the surveillance period lengthens, the relevance of falling in the year prior to baseline diminishes. Sensitivity analyses (not shown) in which we arbitrarily censored the analyses at 2 and 5 years bear this out. A second possible reason for the relatively modest effect of falling is that although it precipitates 95% of all hip fractures among older adults, only a small percentage of falls results in a hip fracture (2
). That is, falling is a necessary but not sufficient condition for hip fracture.
The third discussion point involves the public health policy implications from our static risk factor model. We found that hip fractures were frequent (8.9%) and that the greatest risks for them were either immutable or very hard to change in older adults—age, sex, race, body mass, smoking status, and diabetes. This does not bode well for intervention identification and successful implementation (1
). Nonetheless, these major risk factors could and should be used by health care providers to target older adults for increased monitoring and evaluation, as well as the delivery of applicable preventive interventions.
In concluding this article, we note that our study is not without limitations. Four warrant mention here. First, BMD was not available for inclusion in the analysis. Although it has recently been shown that BMD does not appreciably alter risk estimates obtained for other covariates in multivariable models (34
), and therefore its omission is unlikely to have biased our parameter estimates, we were nonetheless unable to establish either its additive or relative effects. Second, AHEAD did not contain any performance-based tests, such as the Short Portable Physical Battery, which would have allowed us to evaluate the especially relevant roles of balance, lower body strength, and gait speed to hip fracture risk (41
). Third, although the AHEAD is rich in self-reported data and linked to Medicare claims for more than a decade, detailed clinical histories were not available, restricting our study to an epidemiologic versus etiologic analysis. Finally, we relied solely on baseline (i.e., static) risk factors from the AHEAD self-reports, even though several of them (such as ADLs, IADLs, and self-rated health) were repeated at most follow-ups. Although this approach is consistent with much of the hip fracture risk literature (1
), it is suboptimal and creates the potential for the sole dynamic measure (the health shock indicator) to appear to be more important. Inclusion of the repeated self-reports, however, would have created a myriad of complexities associated with missing data, selection bias, and correlated error structures.