Abstract: Disability is associated with lower quality of life and premature death in older people. Therefore, prevention and intervention targeting older people living with a disability is important. Frailty can be considered a major predictor of disability. In this study, we aimed to develop nomograms with items of the Tilburg Frailty Indicator (TFI) as predictors by using cross-sectional and longitudinal data (follow-up of five and nine years), focusing on the prediction of total disability, disability in activities of daily living (ADL), and disability in instrumental activities of daily living (IADL). At baseline, 479 Dutch community-dwelling people aged 75 years participated. They completed a questionnaire that included the TFI and the Groningen Activity Restriction Scale to assess the three disability variables. We showed that the TFI items scored different points, especially over time. Therefore, not every item was equally important in predicting disability. ‘Difficulty in walking’ and ‘unexplained weight loss’ appeared to be important predictors of disability. Healthcare professionals need to focus on these two items to prevent disability. We also conclude that the points given to frailty items differed between total, ADL, and IADL disability and also differed regarding years of follow-up. Creating one monogram that does justice to this seems impossible.
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Background: Burden of disease estimates are an important resource in public health. Currently, robust estimates are not available for the burn population. Our objectives are to adapt a refined methodology (INTEGRIS method) to burns and to apply this new INTEGRIS-burns method to estimate, and compare, the burden of disease of burn injuries in Australia, New Zealand and the Netherlands. Methods: Existing European and Western-Australian health-related quality of life (HRQL) datasets were combined to derive disability weights for three homogenous burn injury groups based on percentage total body surface area (%TBSA) burned. Subsequently, incidence data from Australia, New Zealand, and the Netherlands from 2010 to 2017 were used to compute annual non-fatal burden of disease estimates for each of these three countries. Non-fatal burden of disease was measured by years lived with disability (YLD). Results: The combined dataset included 7159 HRQL (EQ-5D-3 L) outcomes from 3401 patients. Disability weights ranged from 0.046 (subgroup <5% TBSA burned > 24 months post-burn) to 0.497 (subgroup > 20% TBSA burned 0-1 months post-burn). In 2017 the non-fatal burden of disease of burns for the three countries (YLDs/100,000 inhabitants) was 281 for Australia, 279 for New Zealand and 133 for the Netherlands. Conclusions: This project established a method for more precise estimates of the YLDs of burns, as it is the only method adapted to the nature of burn injuries and their recovery. Compared to previous used methods, the INTEGRIS-burns method includes improved disability weights based on severity categorization of burn patients; a better substantiated proportion of patients with lifelong disability based; and, the application of burn specific recovery timeframes. Information derived from the adapted method can be used as input for health decision making at both the national and international level. Future studies should investigate whether the application is valid in low- and middle- income countries.
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Background: Advanced statistical modeling techniques may help predict health outcomes. However, it is not the case that these modeling techniques always outperform traditional techniques such as regression techniques. In this study, external validation was carried out for five modeling strategies for the prediction of the disability of community-dwelling older people in the Netherlands. Methods: We analyzed data from five studies consisting of community-dwelling older people in the Netherlands. For the prediction of the total disability score as measured with the Groningen Activity Restriction Scale (GARS), we used fourteen predictors as measured with the Tilburg Frailty Indicator (TFI). Both the TFI and the GARS are self-report questionnaires. For the modeling, five statistical modeling techniques were evaluated: general linear model (GLM), support vector machine (SVM), neural net (NN), recursive partitioning (RP), and random forest (RF). Each model was developed on one of the five data sets and then applied to each of the four remaining data sets. We assessed the performance of the models with calibration characteristics, the correlation coefficient, and the root of the mean squared error. Results: The models GLM, SVM, RP, and RF showed satisfactory performance characteristics when validated on the validation data sets. All models showed poor performance characteristics for the deviating data set both for development and validation due to the deviating baseline characteristics compared to those of the other data sets. Conclusion: The performance of four models (GLM, SVM, RP, RF) on the development data sets was satisfactory. This was also the case for the validation data sets, except when these models were developed on the deviating data set. The NN models showed a much worse performance on the validation data sets than on the development data sets.
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