Linking death registration and survey data: Procedures and cohort profile for The Irish Longitudinal Study on Ageing (TILDA)

Formal health care costs among older people in Ireland: methods and estimates using The Irish Longitudinal Study on Ageing (TILDA)

Background: Reliable data on health care costs in Ireland are essential to support planning and evaluation of services. New unit costs and high-quality utilisation data offer the opportunity to estimate individual-level costs for research and policy. Methods: Our main dataset was The Irish Longitudinal Study on Ageing (TILDA). We used participant interviews with those aged 55+ years in Wave 5 (2018) and all end-of-life interviews (EOLI) to February 2020. We weighted observations by age, sex and last year of life at the population level. We estimated total formal health care costs by combining reported usage in TILDA with unit costs (non-acute care) and public payer reimbursement data (acute hospital admissions, medications). All costs were adjusted for inflation to 2022, the year of analysis. We examined distribution of estimates across the population, and the composition of costs across categories of care, using descriptive statistics. We identified factors associated with total costs using generalised linear models. Results: There were 5,105 Wave 5 observations, equivalent at the population level to 1,207,660 people aged 55+ years and not in the last year of life, and 763 EOLI observations, equivalent to 28,466 people aged 55+ years in the last year of life. Mean formal health care costs in the weighted sample were EUR 8,053; EUR 6,624 not in the last year of life and EUR 68,654 in the last year of life. Overall, 90% of health care costs were accounted for by 20% of users. Multiple functional limitations and proximity to death were the largest predictors of costs. Other factors that were associated with outcome included educational attainment, entitlements to subsidised care and serious chronic diseases. Conclusions: Understanding the patterns of costs, and the factors associated with very high costs for some individuals, can inform efforts to improve patient experiences and optimise resource allocation.

Overprescribing among older people near end of life in Ireland: Evidence of prevalence and determinants from The Irish Longitudinal Study on Ageing (TILDA)

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.

Do Mini-Mental State Examination and Montreal Cognitive Assessment predict high-cost health care users? A competing risks analysis in The Irish Longitudinal Study on Ageing

OBJECTIVES

Policymakers want to better identify in advance the 10% of people who account for approximately 75% of health care costs. We evaluated how well Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) predicted high costs in Ireland.

METHODS/DESIGN

We used five waves from The Irish Longitudinal Study on Ageing, a biennial population-representative survey of people aged 50+ (2010-2018). We used competing risks analysis where our outcome of interest was "high costs" (top 10% at any wave) and the competing outcome was dying or loss to follow-up without first having the high-cost outcome. Our binary predictors of interest were a 'low score' (bottom 10% in the sample) in MMSE (≤25 pts) and MoCA (≤19 pts) at baseline, and we calculated sub-hazard ratios after controlling for sociodemographic, clinical and functional factors.

RESULTS

Of 5856 participants, 1427 (24%) had the 'high cost' outcome; 1463 (25%) had a competing outcome; and 2966 (51%) completed eight years of follow-up without either outcome. In multivariable regressions a low MoCA score was associated with high costs (SHR: 1.38 (95% CI: 1.2-1.6) but a low MMSE score was not. Low MoCA score at baseline had a higher true positive rate (40%) than did low MMSE score (35%). The scores had similar association with exit from the study.

CONCLUSIONS

MoCA had superior predictive accuracy for high costs than MMSE but the two scores identify somewhat different types of high-cost user. Combining the approaches may improve efforts to identify in advance high-cost users.

Projecting future health and service use among older people in Ireland: an overview of a dynamic microsimulation model in The Irish Longitudinal Study on Ageing (TILDA)

Background: Demographic ageing is a population health success story but poses unprecedented policy challenges in the 21st century. Policymakers must prepare health systems, economies and societies for these challenges. Policy choices can be usefully informed by models that evaluate outcomes and trade-offs in advance under different scenarios. Methods: We developed a dynamic demographic-economic microsimulation model for the population aged 50 and over in Ireland: the Irish Future Older Adults Model (IFOAM).  Our principal dataset was The Irish Longitudinal Study on Ageing (TILDA). We employed first-order Markovian competing risks models to estimate transition probabilities of TILDA participants to different outcomes: diagnosis of serious diseases, functional limitations, risk-modifying behaviours, health care use and mortality. We combined transition probabilities with the characteristics of the stock population to estimate biennial changes in outcome state.   Results: IFOAM projections estimated large annual increases in total deaths, in the number of people living and dying with serious illness and functional impairment, and in demand for hospital care between 2018 and 2040.  The most important driver of these increases is the rising absolute number of older people in Ireland as the population ages. The increasing proportion of older old and oldest old citizens is projected to increase the average prevalence of chronic conditions and functional limitations. We deemed internal validity to be good but lacked external benchmarks for validation and corroboration of most outcomes. Conclusion: We have developed and validated a microsimulation model that projects health and related outcomes among older people in Ireland.  Future research should address identified policy questions. The model enhances the capacity of researchers and policymakers to quantitatively forecast health and economic dynamics among older people in Ireland, to evaluate ex ante policy responses to these dynamics, and to collaborate internationally on global challenges associated with demographic ageing.

Variability and agreement of frailty measures and risk of falls, hospital admissions and mortality in TILDA

Little is known about the within-person variability of different frailty instruments, their agreement over time, and whether use of repeat assessments could improve the strength of associations with adverse health outcomes. Repeat measurements recorded in 2010–2011 (Wave 1) and 2012 (Wave 2) from The Irish Longitudinal Study on Ageing (TILDA) were used to classify individuals with frailty using the frailty phenotype (FP) and frailty index (FI). Within-person variability and agreement of frailty classifications were assessed using ANOVA and kappa (K) statistics, respectively. Associations of each frailty measure (wave 1, wave 2, or mean of both waves) with risk of falls, hospitalisations and all-cause mortality were assessed using logistic regression. Among 7455 individuals (mean age 64.7 [SD 9.9] years), within-person SD was 0.664 units (95% CI 0.654–0.671) for FP and 2 health deficits (SD 0.050 [0.048–0.051]) for FI. Agreement of frailty was modest for both measures, but higher for FI (K 0.600 [0.584–0.615]) than FP (K 0.370 [0.348–0.401]). The odds ratios (ORs) for all-cause mortality were higher for frailty assessed using the mean of two versus single measurements for FI (ORs for mortality 3.5 [2.6–4.9] vs. 2.7 [1.9–3.4], respectively) and FP (ORs for mortality 6.9 [4.6–10.3] vs. 4.0 [2.8–5.635], respectively). Frailty scores based on single measurements had substantial within-person variability, but the agreement in classification of frailty was higher for FI than FP. Frailty assessed using the mean of two or more measurements recorded at separate visits was more strongly associated with adverse health outcomes than those recorded at a single visit.

Projecting future health and service use among older people in Ireland: an overview of a dynamic microsimulation model in The Irish Longitudinal Study on Ageing (TILDA)

Background: Demographic ageing is a population health success story but poses unprecedented policy challenges in the 21st century. Policymakers must prepare health systems, economies and societies for these challenges. Policy choices can be usefully informed by models that evaluate outcomes and trade-offs in advance under different scenarios.

Methods: We developed a dynamic demographic-economic microsimulation model for the population aged 50 and over in Ireland: the Irish Future Older Adults Model (IFOAM).  Our principal dataset was The Irish Longitudinal Study on Ageing (TILDA). We employed first-order Markovian competing risks models to estimate transition probabilities of TILDA participants to different outcomes: diagnosis of serious diseases, functional limitations, risk-modifying behaviours, health care use and mortality. We combined transition probabilities with the characteristics of the stock population to estimate biennial changes in outcome state.  

Results: IFOAM projections estimated large annual increases in total deaths, in the number of people living and dying with serious illness and functional impairment, and in demand for hospital care between 2018 and 2040.  The most important driver of these increases is the rising absolute number of older people in Ireland as the population ages. The increasing proportion of older old and oldest old citizens is projected to increase the average prevalence of chronic conditions and functional limitations. We deemed internal validity to be good but lacked external benchmarks for validation and corroboration of most outcomes.

Conclusion: We have developed and validated a microsimulation model that predicts future health and related outcomes among older people in Ireland.  Future research should address identified policy questions. The model enhances the capacity of researchers and policymakers to quantitatively forecast future health and economic dynamics among older people in Ireland, to evaluate ex ante policy responses to these dynamics, and to collaborate internationally on global challenges associated with demographic ageing.

Frail by different measures: a comparison of 8-year mortality in The Irish Longitudinal Study on Ageing (TILDA)

PURPOSE

We compared the ability of four frailty identification tools (frailty phenotype: FP; FRAIL scale; 32-item Frailty Index: FI; and Clinical Frailty Scale: CFS) to predict 8-year mortality in TILDA.

METHODS

We included wave 1 (2010) participants with data for all four tools. Mortality was ascertained at wave 5 (2018). Age, sex and education-adjusted binary logistic regression models were computed.

RESULTS

At baseline, there were 5700 participants (mean age 63, range 50-98, 54% women). Frailty prevalences were 2.3% by FRAIL, 3.8% by FP, 10.9% by CFS, and 12.8% by FI. Mortality was 41.2%, 44.9%, 25.3% and 27.0%, respectively. The highest adjusted OR for mortality was for FRAIL (OR 4.48, 95% CI 2.93-6.85, P < 0.001), followed by FP (OR 3.55, 95% CI 2.52-5.00, P < 0.001), FI (OR 2.10, 95% CI 1.68-2.62, P < 0.001), and CFS (OR 1.88, 95% CI 1.48-2.38, P < 0.001).

CONCLUSIONS

All tools significantly predicted mortality, but FRAIL and FP seemed more specific.

Frailty index transitions over eight years were frequent in The Irish Longitudinal Study on Ageing

Background: The frailty index (FI) is based on accumulation of health deficits. FI cut-offs define non-frail, prefrail and frail states. We described transitions of FI states in The Irish Longitudinal Study on Ageing (TILDA).

Methods: Participants aged ≥50 years with information for a 31-deficit FI at wave 1 (2010) were followed-up over four waves (2012, 2014, 2016, 2018). Transitions were visualized with alluvial plots and probabilities estimated with multi-state Markov models, investigating the effects of age, sex and education.

Results: 8174 wave 1 participants were included (3744 men and 4430 women; mean age 63.8 years). Probabilities from non-frail to prefrail, and non-frail to frail were 18% and 2%, respectively. Prefrail had a 19% probability of reversal to non-frail, and a 15% risk of progression to frail. Frail had a 21% probability of reversal to prefrail and 14% risk of death. Being older and female increased the risk of adverse FI state transitions, but being female reduced the risk of transition from frail to death. Higher level of education was associated with improvement from prefrail to non-frail.

Conclusions: FI states are characterized by dynamic longitudinal transitions and frequent improvement. Opportunities exist for reducing the probability of adverse transitions.

The Importance of Age in the Prediction of Mortality by a Frailty Index: A Machine Learning Approach in the Irish Longitudinal Study on Ageing

The quantification of biological age in humans is an important scientific endeavor in the face of ageing populations. The frailty index (FI) methodology is based on the accumulation of health deficits and captures variations in health status within individuals of the same age. The aims of this study were to assess whether the addition of age to an FI improves its mortality prediction and whether the associations of the individual FI items differ in strength. We utilized data from The Irish Longitudinal Study on Ageing to conduct, by sex, machine learning analyses of the ability of a 32-item FI to predict 8-year mortality in 8174 wave 1 participants aged 50 or more years. By wave 5, 559 men and 492 women had died. In the absence of age, the FI was an acceptable predictor of mortality with AUCs of 0.7. When age was included, AUCs improved to 0.8 in men and 0.9 in women. After age, deficits related to physical function and self-rated health tended to have higher importance scores. Not all FI variables seemed equally relevant to predict mortality, and age was by far the most relevant feature. Chronological age should remain an important consideration when interpreting the prognostic significance of an FI.

Using administrative health data for palliative and end of life care research in Ireland: potential and challenges

Background: This study aims to examine the potential of currently available administrative health and social care data for palliative and end-of-life care (PEoLC) research in Ireland. Objectives include to i) identify data sources for PEoLC research ii) describe the challenges and opportunities of using these and iii) evaluate the impact of recent health system reforms and changes to data protection laws.

Methods: The 2017 Health Information and Quality Authority catalogue of health and social care datasets was cross-referenced with a recognised list of diseases with associated palliative care needs. Criteria to assess the datasets included population coverage, data collected, data dictionary and data model availability, and mechanisms for data access.

Results: Nine datasets with potential for PEoLC research were identified, including death certificate data, hospital episode data, pharmacy claims data,  one national survey, four disease registries (cancer, cystic fibrosis, motor neurone and interstitial lung disease) and a national renal transplant registry.  The ad hoc development of the health system in Ireland has resulted in i) a fragmented information infrastructure resulting in gaps in data collections particularly in the primary and community care sector where much palliative care is delivered, ii) ill-defined data governance arrangements across service providers, many of whom are not part of the publically funded health service and iii) systemic and temporal issues that affect data quality. Initiatives to improve data collections include introduction of i) patient unique identifiers, ii) health entity identifiers and iii) integration of the Eircode postcodes. Recently enacted general data protection and health research regulations will clarify legal and ethical requirements for data use.

Conclusions: Ongoing reform initiatives and recent changes to data privacy laws combined with detailed knowledge of the datasets, appropriate permissions, and good study design will facilitate future use of administrative health and social care data for PEoLC research in Ireland.

Informing patterns of health and social care utilisation in Irish older people according to the Clinical Frailty Scale

Background: There is increasing policy interest in the consideration of frailty measures (rather than chronological age alone) to inform more equitable allocation of health and social care resources. In this study the Clinical Frailty Scale (CFS) classification tree was applied to data from The Irish Longitudinal Study on Ageing (TILDA) and correlated with health and social care utilisation. CFS transitions over time were also explored. Methods: Applying the CFS classification tree algorithm, secondary analyses of TILDA data were performed to examine distributions of health and social care by CFS categories using descriptive statistics weighted to the population of Ireland aged ≥65 years at Wave 5 (n=3,441; mean age 74.5 (SD ±7.0) years, 54.7% female). CFS transitions over 8 years and (Waves 1-5) were investigated using multi-state Markov models and alluvial charts. Results: The prevalence of CFS categories at Wave 5 were: 6% 'very fit', 36% 'fit', 31% 'managing well', 16% 'vulnerable', 6% 'mildly frail', 4% 'moderately frail' and 1% 'severely frail'. No participants were 'very severely frail' or 'terminally ill'. Increasing CFS categories were associated with increasing hospital and community health services use and increasing hours of formal and informal social care provision. The transitions analyses suggested CFS transitions are dynamic, with 2-year probability of transitioning from 'fit' (CFS1-3) to 'vulnerable' (CFS4), and 'fit' to 'frail' (CFS5+) at 34% and 6%, respectively. 'Vulnerable' and 'frail' had a 22% and 17% probability of reversal to 'fit' and 'vulnerable', respectively. Conclusions: Our results suggest that the CFS classification tree stratified the TILDA population aged ≥65 years into subgroups with increasing health and social care needs. The CFS could be used to aid the allocation of health and social care resources in older people in Ireland. We recommend that CFS status in individuals is reviewed at least every 2 years.

Eight Orthostatic Haemodynamic Patterns in The Irish Longitudinal Study on Ageing (TILDA): Stability and Clinical Associations after 4 Years

Previous research cross-sectionally characterised eight morphological systolic blood pressure (SBP) active stand (AS) patterns using a clinical clustering approach at Wave 1 (W1) of the Irish Longitudinal Study on Ageing. We explored the longitudinal stability and clinical associations of these groupings at Wave 3 (W3), four years later. Eight AS groups had their clinical characteristics and AS patterns at W3 compared to W1. We explored longitudinal associations (new cognitive decline, falls, syncope, disability, and mortality) using multivariate logistic regression models. In total, 2938 participants (60% of Wave 1 sample) had adequate AS data from both W1 and 3 for analysis. We found no longitudinal stability of the eight AS groups or their morphological patterns between the waves. A pattern of impaired stabilisation and late deficit seemed more preserved and was seen in association with new cognitive decline (OR 1.63, 95% CI: 1.12–2.36, p = 0.011). An increase in antihypertensive usage seemed associated with reduced immediate SBP drops, improved AS patterns, and reduced orthostatic intolerance (OI). In pure longitudinal groups, AS patterns were not preserved after 4 years. AS patterns are longitudinally dynamic, and improvements after 4 years are possible even in the presence of higher antihypertensive burden.

Using linked administrative health data for palliative and end of life care research in Ireland: potential and challenges

Background: This study aims to examine the potential of currently available administrative health data for palliative and end-of-life care (PEoLC) research in Ireland. Objectives include to i) identify administrative health data sources for PEoLC research ii) describe the challenges and opportunities of using these and iii) estimate  the impact of recent health system reforms and changes to data protection laws.  Methods: The 2017 Health Information and Quality Authority catalogue of health and social care datasets was cross-referenced with a recognised list of diseases with associated palliative care needs. Criteria to assess the datasets included population coverage, data collected, data dictionary and data model availability and mechanisms for data access. Results: Eight datasets with potential for PEoLC research were identified, including four disease registries, (cancer, cystic fibrosis, motor neurone and interstitial lung disease), death certificate data, hospital episode data, community prescription data and one national survey. The ad hoc development of the health system in Ireland has resulted in i) a fragmented information infrastructure resulting in gaps in data collections particularly in the primary and community care sector where much palliative care is delivered, ii) ill-defined data governance arrangements across service providers, many of whom are not part of the publically funded health service and iii) systemic and temporal issues that affect data quality. Initiatives to improve data collections include introduction of i) patient unique identifiers, ii) health entity identifiers and iii) integration of the eircode postcodes. Recently enacted general data protection and health research regulations will clarify legal and ethical requirements for data  use. Conclusions: With appropriate permissions, detailed knowledge of the datasets and good study design currently available administrative health data can be used for PEoLC research. Ongoing reform initiatives and recent changes to data privacy laws will facilitate future use of administrative health data for PEoLC research.