Simple counts of ADL dependencies do not adequately reflect older adults' preferences toward states of functional impairment

Analyzing health outcomes measured as bounded counts

This paper assesses analytical strategies that respect the bounded-count nature of health outcomes encountered often in empirical applications. Absent in the literature is a comprehensive discussion and critique of strategies for analyzing and understanding such data. The paper's goal is to provide an in-depth consideration of prominent issues arising in and strategies for undertaking such analyses, emphasizing the merits and limitations of various analytical tools empirical researchers may contemplate. Three main topics are covered. First, bounded-count health outcomes' measurement properties are reviewed and their implications assessed. Second, issues arising when bounded-count outcomes are the objects of concern in evaluations are described. Third, the (conditional) probability and moment structures of bounded-count outcomes are derived and corresponding specification and estimation strategies presented with particular attention to partial effects. Many questions may be asked of such data in health research and a researcher's choice of analytical method is often consequential.

Informing a home time measure reflective of quality of life: A data driven investigation of time frames and settings of health care utilization

OBJECTIVE

To evaluate short- and long-term measures of health care utilization-days in the emergency department (ED), inpatient (IP) care, and rehabilitation in a post-acute care (PAC) facility-to understand how home time (i.e., days alive and not in an acute or PAC setting) corresponds to quality of life (QoL).

DATA SOURCES

Survey data on community-residing veterans combined with multipayer administrative data on health care utilization.

STUDY DESIGN

VA or Medicare health care utilization, quantified as days of care received in the ED, IP, and PAC in the 6 and 18 months preceding survey completion, were used to predict seven QoL-related measures collected during the survey. Elastic net machine learning was used to construct models, with resulting regression coefficients used to develop a weighted utilization variable. This was then compared with an unweighted count of days with any utilization.

PRINCIPAL FINDINGS

In the short term (6 months), PAC utilization emerged as the most salient predictor of decreased QoL, whereas no setting predominated in the long term (18 months). Results varied by outcome and time frame, with some protective effects observed. In the 6-month time frame, each weighted day of utilization was associated with a greater likelihood of activity of daily living deficits (0.5%, 95% CI: 0.1%-0.9%), as was the case with each unweighted day of utilization (0.6%, 95% CI: 0.3%-1.0%). The same was true in the 18-month time frame (for both weighted and unweighted, 0.1%, 95% CI: 0.0%-0.3%). Days of utilization were also significantly associated with greater rates of instrumental ADL deficits and fair/poor health, albeit not consistently across all models. Neither measure outperformed the other in direct comparisons.

CONCLUSIONS

These results can provide guidance on how to measure home time using multipayer administrative data. While no setting predominated in the long term, all settings were significant predictors of QoL measures.

Interpretable classifiers for prediction of disability trajectories using a nationwide longitudinal database

OBJECTIVES

To explore the heterogeneous disability trajectories and construct explainable machine learning models for effective prediction of long-term disability trajectories and understanding the mechanisms of predictions among the elderly Chinese at community level.

METHODS

This study retrospectively collected data from the Chinese Longitudinal Healthy Longevity and Happy Family Study between 2002 and 2018. A total of 4149 subjects aged 65 + in 2002 with completed activities of daily living (ADL) information for at least three waves were included. The mixed growth model was used to identify disability trajectories, and five machine learning models were further established to predict disability trajectories using epidemiological variables. An explainable approach was deployed to understand the model's decisions.

RESULTS

Three distinct disability trajectories, including normal class (77.3%), progressive class (15.5%), and high-onset class (7.2%), were identified for three-class prediction. The latter two were further merged into abnormal class, accompanied by normal class for two-class prediction. Machine learning, especially random forest and extreme gradient boosting achieved good performance in both two tasks. ADL, age, leisure activity, cognitive function, and blood pressure were key predictors.

CONCLUSION

The findings suggest that machine learning showed good performance and maybe of additional value in analyzing quality indicators in predicting disability trajectories, thereby providing basis to personalize intervention measures.

Heterogeneous Long-Term Trajectories of Dependency in Older Adults: The PAQUID Cohort, a Population-Based Study over 22 years

BACKGROUND

A critical step toward successful aging is to identify opportunities for prevention of functional decline. Our aim was to describe the heterogeneity in trajectories of dependency preceding death in elders and to identify factors associated with this heterogeneity.

METHODS

The study relied on 3,238 participants of the prospective population-based PAQUID cohort aged 65+ at baseline in 1988. Dependency was defined from an 11-item scale of basic and instrumental activities of daily living (ADL: bathing, dressing, toileting, continence, eating, and transferring; instrumental activities of daily living (IADL): telephoning, shopping, using transport, handling medication, and managing finances) collected over 22 years. Heterogeneous trajectories were estimated using a longitudinal item response theory model including latent classes.

RESULTS

Five distinct profiles of functional dependency were identified over the two last decades of life: persistently high (12%), moderate (26%), persistently low (40%), and accelerated high dependency (15%), and no dependency (8%). Main factors associated with heterogeneity included age at death, sex, education, initial cognition (Mini-Mental State Examination [MMSE] score and dementia), initial disability, and poly-medication.

CONCLUSIONS

In the two last decades of life, more than 9 elders in 10 were characterized as functional decliners. On average, around half of the elders died with no or mild dependency, while 27% live several years with a high level of limitations and would need assistance in activities of daily living, at least for 2-4 years preceding death. The identified factors associated with these trajectories are important to understand functional heterogeneity in elders and to propose interventions to postpone or prevent "chronic" disability.

The DEP-6D, a new preference-based measure to assess health states of dependency

In medical literature there are numerous multidimensional scales to measure health states for dependence in activities of daily living. However, these scales are not preference-based and are not able to yield QALYs. On the contrary, the generic preference-based measures are not sensitive enough to measure changes in dependence states. The objective of this paper is to propose a new dependency health state classification system, called DEP-6D, and to estimate its value set in such a way that it can be used in QALY calculations. DEP-6D states are described as a combination of 6 attributes (eat, incontinence, personal care, mobility, housework and cognition problems), with 3-4 levels each. A sample of 312 Spanish citizens was surveyed in 2011 to estimate the DEP-6D preference-scoring algorithm. Each respondent valued six out of the 24 states using time trade-off questions. After excluding those respondents who made two or more inconsistencies (6% out of the sample), each state was valued between 66 and 77 times. The responses present a high internal and external consistency. A random effect model accounting for main effects was the preferred model to estimate the scoring algorithm. The DEP-6D describes, in general, more severe problems than those usually described by means of generic preference-based measures. The minimum score predicted by the DEP-6D algorithm is -0.84, which is considerably lower than the minimum value predicted by the EQ-5D and SF-6D algorithms. The DEP-6D value set is based on community preferences. Therefore it is consistent with the so-called 'societal perspective'. Moreover, DEP-6D preference weights can be used in QALY calculations and cost-utility analysis.

Natural History of Dependency in the Elderly: A 24-Year Population-Based Study Using a Longitudinal Item Response Theory Model

We aimed to describe the hierarchical structure of Instrumental Activities of Daily Living (IADL) and basic Activities of Daily Living (ADL) and trajectories of dependency before death in an elderly population using item response theory methodology. Data were obtained from a population-based French cohort study, the Personnes Agées QUID (PAQUID) Study, of persons aged ≥65 years at baseline in 1988 who were recruited from 75 randomly selected areas in Gironde and Dordogne. We evaluated IADL and ADL data collected at home every 2-3 years over a 24-year period (1988-2012) for 3,238 deceased participants (43.9% men). We used a longitudinal item response theory model to investigate the item sequence of 11 IADL and ADL combined into a single scale and functional trajectories adjusted for education, sex, and age at death. The findings confirmed the earliest losses in IADL (shopping, transporting, finances) at the partial limitation level, and then an overlapping of concomitant IADL and ADL, with bathing and dressing being the earliest ADL losses, and finally total losses for toileting, continence, eating, and transferring. Functional trajectories were sex-specific, with a benefit of high education that persisted until death in men but was only transient in women. An in-depth understanding of this sequence provides an early warning of functional decline for better adaptation of medical and social care in the elderly.

Quantifying Long-Term Care Preferences

Background. Current policies redirecting long-term care (LTC) delivery away from institutional care to home- and community-based services are being made in the absence of crucial evidence on preferences. Studies indicate that the shift to home care is generally not cost-saving; thus, an empirical assessment of effectiveness is needed to evaluate policies incenting home care investment. This study quantifies LTC preferences between different delivery modes. Design. This study extended the time tradeoff method to elicit utilities and LTC preferences associated with the receipt of different modes of LTC services, conditional on health states defined by varying levels of functional and cognitive impairment. Users’ LTC preferences are measured as differential utilities between alternative LTC options for each health state. Results. For the same health state, respondents ( n = 81) significantly preferred home care over institutional care, except for the most impaired health state. The preference for home care over institutional care is quantified as 0.30 quality-of-life (QOL) weight when people need help with only 1 activity of daily living (ADL). The preference for home care depends significantly on levels of disability and was weaker once the need for help became greater. Under the most severe health state of having moderate to severe dementia and needing help with 6 ADLs, the quantified home care preference was only 0.03 QOL weight and was not statistically significant. Limitations. Because the sample is mostly composed of African Americans, the results may not be generalizable to other racial and ethnic groups. Conclusions. People do not always strongly prefer home care over institutional care, as is often assumed. The costs of expanding home- and community-based care should be weighed against these preferences.

Using time trade-off methods to assess preferences over health care delivery options: a feasibility study

OBJECTIVES

Time trade-off (TTO) methods are often used for utility assessments of different health states to measure quality of life (QOL). They have not generally been used to assess social preferences with respect to options for health care delivery, although the need for quantifying these preferences is arguably just as important. Policymakers are increasingly faced with decisions about how much to invest in, and how much to incentivize, particular modes of health care delivery, generally with little evidence about user preferences.

METHODS

This study draws on long-term care (LTC) delivery modes as an example. Focus groups were conducted to approach this issue both qualitatively and quantitatively. In a qualitative pilot study, two focus groups discussed issues of the LTC decision-making process and preferences among different LTC options. The TTO was then used to assess QOL for each LTC option, conditional on a specific health state, and then quantified user's LTC preferences by differential QOL between the two options.

RESULTS

This study found that the TTO-elicited utilities and their differences are consistent with the LTC preferences revealed from focus group discussions. These preferences depend on levels of disability and education.

CONCLUSIONS

The modified TTO technique seems a feasible method to quantify preferences over LTC delivery options. These methods may be applicable to various health care alternatives in which better evidence is needed to guide funding policy.