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  • Participants, inclusion criteria, and study design: Data on participants from the English Longitudinal Study of Ageing (ELSA) were used under data-sharing project number 82538. ELSA is an ongoing longitudinal cohort study based on a representative sample of middle-aged and elderly general population 50 years and over living in England [14]. ELSA has extensive subjective and objective information collected in biennial surveys (waves). All waves gathered information concerning physical, cognitive, and psychological health, disability, lifestyle factors, comorbidities, social participation, and social support. Also, even-numbered waves have objective measures: physical functioning assessment and biological sampling [15]. Ethical approval was obtained from the Multicentre Research and Ethics Committee and all participants provided written informed consent [16]. Participants aged 60 or over (because not all frailty-related variables were measured in participants younger than 60 years) who gave permission to link their data with a national mortality register and had a nurse visit in wave 2 were included. The outcomes were measured up to 2012, when mortality data were assessed. This is a longitudinal secondary data analysis of ELSA and no formal written analysis plan exists. The analysis was planned in November 2015 during meetings with coauthors. We used the second wave (2004–2005) as baseline because this was the first wave with a clinical examination and laboratory samples. The exposure was the frailty state measured with 35 different frailty scores at baseline, and the follow-up time was from 2004–2005 to 2012. A structured search was performed to identify all published original frailty scores. The search strategy has previously been described in detail [8]. The original scores that could be calculated with the ELSA wave 2 data (i.e., those for which at least 80% of the necessary variables were measured) were selected. Multiple imputation was used to deal with missing data in the underlying measured study variables necessary to calculate the frailty scores. In order to obtain optimally plausible values for the scores, imputation was applied to the original underlying variables, and frailty scores were calculated a posteriori using imputed values. For preparing an analysis in one single continuous scale, frailty scores were rescaled from 0 (non-frail) to 1 (maximum frail) by dividing the output of each frailty score by the maximum possible value. If the frailty score was defined with a score that gave different weight to some variables, the output was accorded this weight and then rescaled. In addition, some frailty scores had to be inverted to convert the result, according the definition of 0 as non-frail and 1 as maximum frail. Scores were classified into 4 groups depending on their underlying frailty approach: phenotype of frailty (mainly physical functioning variables), multidimensional (at least 2 different dimensions and less than 30 variables), accumulation of deficits (at least 30 variables), and disability (mainly disability variables). A total of 67 original frailty scores were found in the literature search and 35 had at least 80% of variables possible to calculate with the data of ELSA wave 2, and in consequence, they were selected (Table 1). Out of them, 19 had binary cutoffs identifying frail and non-frail individuals, and 10 had categorical cutoffs, additionally identifying an intermediate pre-frail group [8]. Table data removed from full text. Table identifier and caption: 10.1371/journal.pmed.1002543.t001 Frailty scores calculated in participants of ELSA wave 2 (2004–2005). Abbreviation: ELSA, English Longitudinal Study of Ageing. Missing data of some needed variables to calculate frailty scores were observed in 1 (<1.0%) to 3,037 (57.4%) participants. The mechanism of missing data was assumed to be missing at random because the underlying values necessary to calculate frailty scores that were missing for some individuals are likely to depend on observed data in the ELSA data. In other words, missing data did not depend on any unobserved data, but only upon observed data. Each variable was defined as being of numerical, binary, or categorical type, which defined the appropriate method for imputation. The chained equations approach was chosen because it is a very effective, flexible, and straightforward method to impute data. This method is based on a set of models adapted to the type of missing value; the values are filled first with random sampling, based only on the observed data, and then also based on already imputed data [49,50]. The imputation model was built by selecting the best missing data predictors among the available variables. The imputation model incorporated strong predictors of missing data (cognition, disability) and confounders (age, sex, education, physical activity). Moreover, outcomes were included in the imputation model (mortality, cancer, CVD), but they were not imputed. To optimise the imputed values, the data were ordered from lower to higher percentage of missing data before running the imputation, and a seed was set to allow reproducibility. We performed 30 imputations to create 30 different data sets. Then, we ran 20 iterations by each of these 30 imputations, sufficient to achieve convergence of the Gibbs sampler. The imputations were assessed by hand (plausible values for imputed data compared to completed data) and by using graphical methods. We assessed 3 main outcomes: all-cause mortality, CVD, and cancer events. Mortality data linked to ELSA participants was provided by the National Health Service’s Central Registry, Southport, UK. For 68 participants, mortality was obtained from other sources (found during ELSA fieldwork or from participants’ relatives). Main causes of death were registered as CVD, cancer, diseases of the respiratory system, and other causes. CVD or cancer events were defined by self-report in waves 3–5. A CVD event could be myocardial infarction, heart failure, stroke, or CVD death. A cancer event could be cancer of any type, including cancer death. For each outcome separately, participants’ exposure time was calculated from the participant’s age at entry (wave 2 clinical examination: 2004–2005) to participant’s age at first event or final censoring (date of mortality assessment: February 2012). Participants lost to follow-up were right-censored at the midpoint between their last visit and the next one. For analysis of CVD and cancer incidence, respective prevalent cases at baseline were excluded. Smoker status was defined as never, previous, or current smoker. The maximum alcohol consumption per day was defined as 0, 1, 2, and >2 units/day. Body mass index (BMI) was defined as a continuous variable calculated as weight (kg)/height (m)2. Self-reported physical activity was defined as time spent in vigorous, moderate, low, and sedentary activity. Diabetes was defined through self-reported medical diagnosis or fasting glucose ≥7.0 mmol/L or glycated haemoglobin ≥6.5% [51]. Hypertension was defined from systolic or diastolic blood pressure ≥140 or ≥90 mm Hg, respectively, or self-reported high blood pressure medication [52]. Anaemia was defined as a measured haemoglobin level <13 g/dL (men) and <12 g/dL (women) [53]. Arthritis was self-reported diagnosis. Neuropsychiatric problems were self-reported diagnoses of: Alzheimer or Parkinson disease, dementia, or psychiatric problems. Cognition was evaluated with a total continuous cognitive index (memory and executive functions) [54]. Self-rated health was defined as excellent, very good, good, fair, or poor. Quality of life was evaluated with the 19-item scale control, autonomy, pleasure, and self-realization (CASP-19) questionnaire [55]. Depression symptoms were assessed with the 8-item Centre for Epidemiologic Study Depression Scale, with cutoff ≥4 points [56]. We performed two parallel statistical analyses. The first was a continuous analysis with frailty scores rescaled to the range 0 (no frailty) to 1 (frailty). The second was a categorical analysis of frailty scores using cutoffs when they were defined. All data analyses were carried out in R version 3.3.0 using packages ‘Mice’, ‘lattice’, ‘Survival’, mitml’, and ‘survC1’. A p-value of less than 0.05 was considered statistically significant. Cox proportional hazards models were fitted for each outcome and independently for each frailty score as a continuous variable. Where a published cutoff level to define frailty was available, an additional model was run on the binary or categorical frailty classification. For each outcome (all-cause mortality, CVD, and cancer events), 4 models were fitted with progressive levels of adjustment (0–3): model 0: frailty score; model 1: model 0 + sex; model 2: model 1 + smoking status and alcohol consumption; and model 3: model 2 + physical activity, BMI, diabetes, hypertension, CVD, cancer, anaemia, chronic obstructive pulmonary disease (COPD), arthritis, neuropsychiatric problems, depression, cognition, and self-rated health and quality of life. The covariates in each model were chosen because all of them could potentially be confounders, affecting the outcome and/or the exposure. To avoid collinearity issues, the covariates of model 3 were tailored to each frailty score, excluding covariates that were an underlying variable of the score or a highly correlated variable. For CVD and cancer models, CVD and cancer were excluded as covariates (see S1 Table). The proportional hazards assumption was checked by adding a time–covariate interaction in the model. The interaction term was retained in the model if significant [57]. The Cox models were fitted in 30 imputed data sets and the results, including calculated 95% confidence intervals, were pooled according to Rubin’s rules [58]. The discrimination ability was assessed with Harrell’s C statistic [9] using a calendar time to event scale. Three basic adjusted models: model 1 = age and sex; model 2 = model 1 + age, sex, smoking status, and alcohol; model 3 = model 2 + physical activity, BMI, diabetes, hypertension, CVD, cancer, anaemia, COPD, arthritis, neuropsychiatric problems, depression, cognition, and self-rated health and quality of life were calculated for each outcome. Each frailty score was added to each of these models and improvement of the predictive ability was assessed by evaluating whether the C statistic of the model with the score was significantly higher than in the respective base model. Results are expressed as the difference in C statistics (delta C with 95% confidence intervals) of each model, including a score and its respective base model. We performed a sensitivity analysis by excluding all events that occurred during the first year of follow-up with the objective of assessing if pre-existing disease near the date of enrolling could bias the results. For all-cause mortality, all analyses were also performed stratified by sex and age (>70/≤70 years). This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines (S1 Text).
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