We sought to identify simple ADL dependency prediction models. Although the predictive validity of the simple models is lower than that of the benchmark model, the results do indicate a very good fit for the simple models. A scoring system developed from the odds ratios of the simple models retained a very good fit to the data. About 30 percent of persons scoring eight or more on the simple model with disease or on the model without disease went on to develop ADL dependency; whereas 10%-14% of those scoring six or less did so. Twenty-nine percent of those scoring eight or more on the benchmark model experienced incident ADL. Although the percent identified as high risk at a cutoff of eight is similar in the benchmark and simple model with disease, the Kappa statistic showed only fair agreement(13
) between these models. Future work might explore alternative cut points, particularly identification
of optimal cut points for subgroups.
Replication of the benchmark model in the 2006-2008 data shows clinical utility that is very good and very similar to that found in its original development based on data from 1993-1995. The benchmark model has a c-statistic that is better than the best simple model (0.79 vs. 0.74, respectively) but it requires multiple measures of functional status that are not available in common medical records.
Although all of the models shown in this report were developed using survey data, having eliminated certain variables from the benchmark interview model, we have created simple models for which scores could be created from medical records data. Although standardization of electronic medical records (EMR) has not yet been achieved
) EMRs are expanding and will be increasingly used for health care decisions.(15
) In fact, the Centers for Medicare and Medicaid have distributed $73 billion dollars in incentive payments to be made from 2011 to 2015 for organizations that meet EMR usage criteria.(16
) EMR data are not generally based on self-report so replication using actual EMR data on clinical populations need to be done and may produce different results.
For an individual clinician, the value of these brief instruments may be in raising awareness of the relative importance of certain states and conditions in risk for dependency (compared to other potential risk factors). However, for health care systems, administrators, policy makers, and researchers, these brief models are likely efficient, valuable screening tools to identify a high risk subset of older adults within a population. For example, applying a score of four or more as a positive screen to the simple model that includes chronic disease would identify 36% of the population as elevated risk and capture 67% of the incident ADL cases (calculated from data shown in
). Within the 36% elevated risk subgroup, additional evaluation via interview,(5
) brief assessment,(17
) or comprehensive geriatric assessment(18
) might be used to tailor interventions to high risk individuals. Comprehensive geriatric assessments of those at elevated risk may be the ideal approach given evidence that collaborative care interventions can be cost saving in high risk elders.(19
In discussion above, we have used the cutoff examples of both 4 and 8; someone aged 90 or over would have a minimum score of 15 or above in the two simple models. Thus, it may be most efficient to assume that persons 90 years of age or over are a high risk group. Other indicators in the scoring system are helpful in older adults less than 90 years of age and our sensitivity analyses provided supporting evidence for the validity of the model in the 65 to 90 year age group.
Although self-report hospitalizations have been shown to correlate well with actual hospitalization records,(20
) there are other limitations to the measure of hospitalizations available in the HRS. It is an all-cause indicator and the effect of hospitalization on risk for dependency may vary by type of hospitalization. Some hospitalizations are for procedures that can improve ADL capacity (e.g., joint replacements), while others are for acute deteriorations in health. Hospitalization rates also vary by region, urban versus rural communities, and community socioeconomic level.(22
) Thus, the role of hospitalization in incident ADL may differ by region or community due to differing admission criteria.
The baseline data we have used to inform the predictor variables are based on self-report data. Validation of self-reported chronic disease diagnoses against medical records data has shown good to excellent agreement (23
) but confirming this in the context of these models would be important. Also, we were only able to evaluate chronic disease diagnoses contained in the HRS and this did not include chronic renal failure. It is unclear whether this would have a significant effect on results. Gill et al, 2010 showed that dependency trajectories are highly heterogeneous for persons with organ failure.(10
) ADL dependency at two year follow-up was based on a proxy report for 7.5% of the sample. Proxy respondents have been found to overestimate the amount of hours given to ADL assistance but overestimates of need for ADL assistance are less apparent.(26
) The intraclass correlation between older adults and their proxies on reports of need for assistance in seven instrumental ADLs was found to be 0.85 in a study of hip fracture patients.(27
For some of the reasons noted above, validation in clinical populations using clinical data is needed but these analyses suggest that a simple approach to identifying elders at elevated risk for ADL dependency may be possible, particularly as EMRs become widely available. Identifying such high risk elders is a necessary step in implementing models of care that have been shown to improve outcomes and reduce costs of care for vulnerable older adults.(18
) Thus, in the context of further assessment and interventions, ADL risk assessment tools could contribute to the goals of reducing Medicare expenditures and maintaining independence at home.