Abstract:Objectives
We aimed to replicate existing international (US and UK) mortality indices using Irish data. We developed and validated a four-year mortality index for adults aged 50 + in Ireland and compared performance with these international indices. We then extended this model by including additional predictors (self-report and healthcare utilization) and compared its performance to our replication model.
Methods
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“…2 and Supplementary Material s, Table S1 ). Therefore, k-means clustering was applied separately for males and females and, within each sex group, separately for three distinct age groups (50–64, 65–74 and 75 + years, with the selection of these age groups based on previous research involving the TILDA sample; e.g., [4] , [39] ). Fig.…”
“…2 and Supplementary Material s, Table S1 ). Therefore, k-means clustering was applied separately for males and females and, within each sex group, separately for three distinct age groups (50–64, 65–74 and 75 + years, with the selection of these age groups based on previous research involving the TILDA sample; e.g., [4] , [39] ). Fig.…”
“…It is worth noting that other previously reported mortality-specific predictor models outperform both the 3-item HI and the frailty measures concerning all-cause mortality. Notably, reported AUCs of 0.774 (TILDA; Ireland) [32], 0.859 (UK; English Longitudinal Study of Ageing (ELSA)) [33], and 0.82 (USA; Health and Retirement Study (HRS)) [34] have been achieved by these indices. However, it should be acknowledged that these indices rely on 10 to 14 self-reported variables, which were specifically derived from a wide pool of self-report variables (41 to 67) in two of the studies to optimize mortality prediction [32,34].…”
Section: Discussionmentioning
confidence: 99%
“…Notably, reported AUCs of 0.774 (TILDA; Ireland) [32], 0.859 (UK; English Longitudinal Study of Ageing (ELSA)) [33], and 0.82 (USA; Health and Retirement Study (HRS)) [34] have been achieved by these indices. However, it should be acknowledged that these indices rely on 10 to 14 self-reported variables, which were specifically derived from a wide pool of self-report variables (41 to 67) in two of the studies to optimize mortality prediction [32,34]. Consequently, direct comparisons to the present study are challenging, as our aim was to develop a more objective, data-driven measure.…”
Section: Discussionmentioning
confidence: 99%
“…Unlike many existing frailty measures, which have limitations related to the count-based, largely self-reported approach, and predefined criteria [3,4,31], the HI relies on objective and continuous health measures and a data-driven quantification of their combined dysregulation. Notably, other researchers previously proposed mortality-specific indices, employing models based on 10 to 14 predictor variables, which significantly predicted mortality [32][33][34]. Yet, these models continue to rely on self-reported data, and we therefore hypothesized that the new 3-item HI would predict mortality more accurately than existing measures.…”
This study was carried out using a large cohort (N = 4265; 416 deceased) of older, community-dwelling adults from The Irish Longitudinal Study on Ageing (TILDA). The study compared the performance of a new 3-item health index (HI) with two existing measures, the 32-item frailty index (FI) and the frailty phenotype (FP), in predicting mortality risk. The HI was based on the objective measurement of resting-state systolic blood pressure sample entropy, sustained attention reaction time performance, and usual gait speed. Mortality data from a 12-year follow up period were analyzed using Cox proportional regression. All data processing was performed using MATLAB and statistical analysis using STATA 15.1. The HI showed good discriminatory power (AUC = 0.68) for all-cause mortality, similar to FI (AUC = 0.68) and superior to FP (AUC = 0.60). The HI classified participants into Low-Risk (84%), Medium-Risk (15%), and High-Risk (1%) groups, with the High-Risk group showing a significant hazard ratio (HR) of 5.91 in the unadjusted model and 2.06 in the fully adjusted model. The HI also exhibited superior predictive performance for cardiovascular and respiratory deaths (AUC = 0.74), compared with FI (AUC = 0.70) and FP (AUC = 0.64). The HI High-Risk group had the highest HR (15.10 in the unadjusted and 5.61 in the fully adjusted models) for cardiovascular and respiratory mortality. The HI remained a significant predictor of mortality even after comprehensively adjusting for confounding variables. These findings demonstrate the effectiveness of the 3-item HI in predicting 12-year mortality risk across different causes of death. The HI performed similarly to FI and FP for all-cause mortality but outperformed them in predicting cardiovascular and respiratory deaths. Its ability to classify individuals into risk groups offers a practical approach for clinicians and researchers. Additionally, the development of a user-friendly MATLAB App facilitates its implementation in clinical settings. Subject to external validation in clinical research settings, the HI can be more useful than existing frailty measures in the prediction of cardio-respiratory risk.
“…We derived an index following prior work in the HRS family [ 4 , 29 ]; this created a weighted index of demographic, health, functional, risk factors and prior service use that has high predictive power for all-cause mortality (see S1 File for summary based on sample employed in this study). The development of our full TILDA mortality risk index is discussed in detail elsewhere [ 30 ]. Participants were included in the analysis only once–at the first wave for which they exceeded the high-mortality risk threshold (11 points or above).…”
International evidence shows that people approaching end of life (EOL) have high prevalence of polypharmacy, including overprescribing. Overprescribing may have adverse side effects for mental and physical health and represents wasteful spending. Little is known about prescribing near EOL in Ireland. We aimed to describe the prevalence of two undesirable outcomes, and to identify factors associated with these outcomes: potentially questionable prescribing, and potentially inadequate prescribing, in the last year of life (LYOL). We used The Irish Longitudinal Study on Ageing, a biennial nationally representative dataset on people aged 50+ in Ireland. We analysed a sub-sample of participants with high mortality risk and categorised their self-reported medication use as potentially questionable or potentially inadequate based on previous research. We identified mortality through the national death registry (died in <365 days versus not). We used descriptive statistics to quantify prevalence of our outcomes, and we used multivariable logistic regression to identify factors associated with these outcomes. Of 525 observations, 401 (76%) had potentially inadequate and 294 (56%) potentially questionable medications. Of the 401 participants with potentially inadequate medications, 42 were in their LYOL. OF the 294 participants with potentially questionable medications, 26 were in their LYOL. One factor was significantly associated with potentially inadequate medications in LYOL: male (odds ratio (OR) 4.40, p = .004) Three factors were associated with potentially questionable medications in LYOL: male (OR 3.37, p = .002); three or more activities of daily living (ADLs) (OR 3.97, p = .003); and outpatient hospital visits (OR 1.03, p = .02). Thousands of older people die annually in Ireland with potentially inappropriate or questionable prescribing patterns. Gender differences for these outcomes are very large. Further work is needed to identify and reduce overprescribing near EOL in Ireland, particularly among men.
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