A Longitudinal Analysis of Total 3‐Year Healthcare Costs for Older Adults Who Experience a Fall Requiring Medical Care

Health Promotion Research Center, University of Washington, Seattle, Washington 98105, USA.
Journal of the American Geriatrics Society (Impact Factor: 4.57). 05/2010; 58(5):853-60. DOI: 10.1111/j.1532-5415.2010.02816.x
Source: PubMed


To compare longitudinal changes in healthcare costs between fallers admitted to the hospital at the time of the fall (admitted), those not admitted to the hospital (nonadmitted), and nonfaller controls; test hypotheses related to differences in mean costs between and within these groups over time; and estimate the costs attributable to falling.
Longitudinal cohort.
Group Health Cooperative of Puget Sound.
Seven thousand nine hundred ninety-three nonadmitted fallers, 976 admitted fallers, and 8,956 nonfallers aged 67 and older enrolled in an integrated healthcare delivery system. Fallers were identified according to fall-related E-Codes and International Classification of Diseases, Ninth Revision codes recorded between January 1, 2004, and December 31, 2006. Nonfallers were frequency matched on age group and sex.
Quarterly costs during a 3-year period were modeled using generalized estimating equations. Covariates included index age, sex, RxRisk (a comorbidity adjuster), fall status, time, and interactions between fall status and time.
Cost differences between the faller cohorts and nonfallers were greatest in quarters closest to the fall (all P<.01) and persisted throughout the entire year of follow-up. Although nonfaller costs increased with time, faller cohort costs increased more quickly (all P<.01). For admitted fallers, 92% of costs incurred in the quarter of the fall were estimated to be attributable to falling ($27,745 of $30,038, P<.001).
Falls for which medical attention are sought resulted in higher costs than for nonfallers for up to 12 months after a fall, particularly for falls requiring hospitalization. Prevention efforts should focus on reducing fall-related injuries requiring hospitalization because they produce the highest excess costs and have a higher likelihood of 1-year mortality.

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    • "The costs arising from falls, particularly hip fractures, skull fractures and leg injuries, represent a large proportion of healthcare spending. It is estimated that 92% of the costs of health care for patients who have suffered a fall are attributable to this factor [6], although it is difficult to obtain an accurate figure because most studies only include the costs of patients admitted following an injury, and do not take into account those who fall within the hospital itself [7]. An estimate by the British National Health Service estimated that about £15 million a year are incurred in hospital costs as a result of falls (£92,000 per year for an 800-bed hospital) [8]. "
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    ABSTRACT: Background Falls are a serious problem for hospitalized patients, reducing the duration and quality of life. It is estimated that over 84% of all adverse events in hospitalized patients are related to falls. Some fall risk assessment tools have been developed and tested in environments other than those for which they were developed with serious validity discrepancies. The aim of this review is to determine the accuracy of instruments for detecting fall risk and predicting falls in acute hospitalized patients. Methods Systematic review and meta-analysis. Main databases, related websites and grey literature were searched. Two blinded reviewers evaluated title and abstracts of the selected articles and, if they met inclusion criteria, methodological quality was assessed in a new blinded process. Meta-analyses of diagnostic ORs (DOR) and likelihood (LH) coefficients were performed with the random effects method. Forest plots were calculated for sensitivity and specificity, DOR and LH. Additionally, summary ROC (SROC) curves were calculated for every analysis. Results Fourteen studies were selected for the review. The meta-analysis was performed with the Morse (MFS), STRATIFY and Hendrich II Fall Risk Model scales. The STRATIFY tool provided greater diagnostic validity, with a DOR value of 7.64 (4.86 - 12.00). A meta-regression was performed to assess the effect of average patient age over 65 years and the performance or otherwise of risk reassessments during the patient’s stay. The reassessment showed a significant reduction in the DOR on the MFS (rDOR 0.75, 95% CI: 0.64 - 0.89, p = 0.017). Conclusions The STRATIFY scale was found to be the best tool for assessing the risk of falls by hospitalized acutely-ill adults. However, the behaviour of these instruments varies considerably depending on the population and the environment, and so their operation should be tested prior to implementation. Further studies are needed to investigate the effect of the reassessment of these instruments with respect to hospitalized adult patients, and to consider the real compliance by healthcare personnel with procedures related to patient safety, and in particular concerning the prevention of falls.
    BMC Health Services Research 04/2013; 13(1):122. DOI:10.1186/1472-6963-13-122 · 1.71 Impact Factor
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    • "Falls are an important problem in elderly patients causing a significant number of unplanned hospitalizations, operations (mainly hip replacement) and invalidity, and eventually nursing-home care [13–15]. Accordingly, the costs of falls are considerable and their causes are numerous [16]. "
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    ABSTRACT: Background. Falls and fractures in the elderly are among the leading causes of disability. We investigated whether pacemaker implantation prevents falls in patients with SND in a large cohort of patients. Methods. Patient demographics and medical history were collected prospectively. Fall history was retrospectively reconstituted from available medical records. The 10-year probability for major osteoporotic fractures was calculated retrospectively from available medical records using the Swiss fracture risk assessment tool FRAX-Switzerland. Results. During a mean observation period of 2.3 years after implantation, the rates of fallers and injured fallers with fracture were reduced to 15% and 6%, respectively. This corresponds to a relative reduction in the number of fallers of 75% (P < 0.001) and of injured fallers of 63% (P = 0.014) after pacemaker implantation. Similarly, the number of falls was reduced from 60 (48%) before pacemaker implantation to 22 (18%) thereafter (relative reduction 63%, P = 0.035) and the number of falls with injury from 22 (18%) to 7 (6%), which corresponds to a relative reduction of 67%, P = 0.013. Conclusion. In patients with SND, pacemaker implantation significantly reduces the number of patients experiencing falls, the total number of falls, and the risk for osteoporotic fractures.
    Cardiology Research and Practice 10/2012; 2012:498102. DOI:10.1155/2012/498102
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    ABSTRACT: Generalized additive models for location, scale, and shape (GAMLSS) are a class of semi-parametric models with potential applicability to health care cost data. We compared the bias, accuracy, and coverage of GAMLSS estimators with two distributions [gamma and generalized inverse gaussian (GIG)] using a log link to the generalized linear model (GLM) with log link and gamma family and the log-transformed OLS. The evaluation using simulated gamma data showed that the GAMLSS and GLM gamma model had similar bias, accuracy, and coverage and outperformed the GAMLSS GIG. When applied to simulated GIG data, the GLM gamma was similar or improved in bias, accuracy, and coverage compared to the GAMLSS GIG and gamma; furthermore, the GAMLSS estimators produced wildly inaccurate or overly-precise results in certain circumstances. Applying all models to empirical data on health care costs after a fall-related injury, all estimators produced similar coefficient estimates, but GAMLSS estimators produced spuriously smaller standard errors. Although no single alternative was best for all simulations, the GLM gamma was the most consistent, so we recommend against using GAMLSS estimators using GIG or gamma to test for differences in mean health care costs. Since GAMLSS offers many other flexible distributions, future work should evaluate whether GAMLSS is useful when predicting health care costs.
    Health Services and Outcomes Research Methodology 03/2012; 13(1). DOI:10.1007/s10742-012-0086-x
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