Impact of morbidity on care need increase and mortality in nursing homes: a retrospective longitudinal study using administrative claims data

Background

A growing number of older people are care dependent and live in nursing homes, which accounts for the majority of long-term-care spending. Specific medical conditions and resident characteristics may serve as risk factors predicting negative health outcomes. We investigated the association between the risk of increasing care need and chronic medical conditions among nursing home residents, allowing for the competing risk of mortality.

Methods

In this retrospective longitudinal study based on health insurance claims data, we investigated 20,485 older adults (≥65 years) admitted to German nursing homes between April 2007 and March 2014 with care need level 1 or 2 (according to the three level classification of the German long-term care insurance). This classification is based on required daily time needed for assistance. The outcome was care level change. Medical conditions were determined according to 31 Charlson and Elixhauser conditions. Competing risks analyses were applied to identify chronic medical conditions associated with risk of care level change and mortality.

Results

The probability for care level change and mortality acted in opposite directions. Dementia was associated with increased probability of care level change compared to other conditions. Patients who had cancer, myocardial infarction, congestive heart failure, cardiac arrhythmias, renal failure, chronic pulmonary disease, weight loss, or recent hospitalization were more likely to die, as well as residents with paralysis and obesity when admitted with care level 2.

Conclusion

This paper identified risk groups of nursing home residents which are particularly prone to increasing care need or mortality. This enables focusing on these risk group to offer prevention or special treatment. Moreover, residents seemed to follow specific trajectories depending on their medical conditions. Some were more prone to increased care need while others had a high risk of mortality instead. Several conditions were neither related to increased care need nor mortality, e.g., valvular, cerebrovascular or liver disease, peripheral vascular disorder, blood loss anemia, depression, drug abuse and psychosis. Knowledge of functional status trajectories of residents over time after nursing home admission can help decision-makers when planning and preparing future care provision strategies (e.g., planning of staffing, physical equipment and financial resources).

Osteoporotic hip fracture prediction from risk factors available in administrative claims data - A machine learning approach

Objective

Hip fractures are among the most frequently occurring fragility fractures in older adults, associated with a loss of quality of life, high mortality, and high use of healthcare resources. The aim was to apply the superlearner method to predict osteoporotic hip fractures using administrative claims data and to compare its performance to established methods.

Methods

We devided claims data of 288,086 individuals aged 65 years and older without care level into a training (80%) and a validation set (20%). Subsequently, we trained a superlearner algorithm that considered both regression and machine learning algorithms (e.g., support vector machines, RUSBoost) on a large set of clinical risk factors. Mean squared error and measures of discrimination and calibration were employed to assess prediction performance.

Results

All algorithms used in the analysis showed similar performance with an AUC ranging from 0.66 to 0.72 in the training and 0.65 to 0.70 in the validation set. Superlearner showed good discrimination in the training set but poorer discrimination and calibration in the validation set.

Conclusions

The superlearner achieved similar predictive performance compared to the individual algorithms included. Nevertheless, in the presence of non-linearity and complex interactions, this method might be a flexible alternative to be considered for risk prediction in large datasets.

Development of a risk assessment tool for osteoporotic fracture prevention: A claims data approach

BACKGROUND

In aging societies osteoporotic fractures are a major health problem with high economic costs. Targeting prevention at individuals at high risk is important to reduce the future burden of fractures. Available risk assessment tools (e.g., FRAX®, QFracture, the algorithm provided by the German Osteology Society (DVO-Tool)) rely on self-reported patient information to predict fracture risk. Time and resource constraints, limited access to clinical data, and (un)willingness to participate may hamper the use of these tools. To overcome such obstacles, the aim is to develop a fracture risk assessment tool based on claims data that may be directly used on an institutional level.

METHODS

Administrative claims data of an elderly (≥65years) population (N=298,530) for the period from 2006 through 2014 was used. Major osteoporotic fractures (MOF) were identified based on hospital diagnoses. We applied Cox proportional hazard regression to determine the association of individual risk factors and fracture risk. Hazard ratios were used to construct a risk score. The discriminative ability of the score was evaluated using C-statistics.

RESULTS

We identified 7864 MOF during follow-up. The median time to first fracture during follow-up was 371.5days. Individuals with a MOF during follow-up had a higher mean and median risk score (mean: 4.53; median: 4) than individuals without MOF (mean: 3.07; median: 3). Adding drug-related risk factors slightly improved discrimination compared to a simple model with age, gender, and prior fracture.

CONCLUSION

We developed a fracture risk score model based on in-hospital treated subjects to predict MOF that can be used on an institutional level. The score included age, sex and prior fracture as risk factors. Adding other risk factors involved very small improvement in discrimination.

The osteoporotic fracture prevention program in rural areas (OFRA): a protocol for a cluster-randomized health care fund driven intervention in a routine health care setting

Background

Fragility fractures are one of the leading causes for disability in old people. The main underlying mechanisms are osteoporosis and falls. Evidence-based measures to prevent either falls or fractures are available. However, coordinated preventive approaches combining bone health and fall prevention are rare.

The objective of the study is to evaluate a health care fund driven program, which encourages insured persons to adhere to national guidelines regarding bone health and physical activity and falls prevention. The health care fund cooperates with the ‘German Association of Rural Women’ and the ‘German Gymnastics Association’. The program consists of mobility and falls prevention classes, the examination of bone health by a DXA scan, and a consultation about ‘safety in the living environment’.

Methods

Cluster-randomized study in 47 intervention and 143 control districts in 5 federal states of Germany. The program is offered to a) community-living women and men aged 70 to <85 years with a prior fragility fracture or b) community-living women aged 75 to <80 years. Within two years more than 10,000 persons will be directly contacted and motivated to make use of the components of the program. The primary outcome is a combined measure of incident osteoporotic fractures. Secondary outcomes include the rate of referrals to a mobility and falls prevention class or a bone mass density measurement. An economic evaluation will be conducted.

Discussion

The study evaluates a complex preventive intervention in a routine health care setting which may serve as model for similar approaches in other areas or countries.

Trial registration

DRKS-ID: DRKS00009000; date of registration: 06.08.2015

Understanding Mortality of Femoral Fractures Following Low-Impact Trauma in Persons With and Without Care Need

OBJECTIVES

Persons with osteoporotic fracture history are subject to an increased risk for subsequent fractures and mortality. The aim of this retrospective study was to investigate the impact of a previous osteoporotic low-impact (fragility) index fracture (eg, forearm, lower leg) on mortality of a subsequent femoral fracture.

DESIGN

Retrospective cohort study.

PARTICIPANTS/MEASUREMENTS

Claims data of a German health insurance agency including >1.2 million insurants aged 65 years or older and observed between 2004 and 2009.

METHODS

A multistate model was developed handling index fractures and care need as time-dependent exposures, while age was chosen as the underlying time scale. Excess risks were expressed as differences in cause-specific hazards. Nelson-Aalen estimates were used for their nonparametric estimation. Time-simultaneous statistical inference was based on confidence bands provided by wild bootstrap resampling.

RESULTS

Excess femoral fracture risk increased with progressive age and was highest in persons with care need. It was observed starting from an age of 79 years in women and 85 years in men onward. A prior index fracture increased mortality after a femoral fracture by increasing femoral fracture risk, while leaving the hazard of death after a subsequent femoral fracture unchanged.

CONCLUSIONS

The results indicated that increased mortality of a subsequent femoral fracture is not triggered by an intrinsically increased mortality hazard but an increased femoral fracture incidence.