Background In 2015, the Netherlands implemented long-term care (LTC) reforms to promote aging-in-place, potentially impacting nursing home (NH) access for older individuals with dementia. This study examines how the reform affected NH admission rates and waiting list prevalence for this population.Methods We performed interrupted time series analyses to evaluate trends in NH admissions (2011-2019, Statistics Netherlands) and waiting list prevalence (2013-2018, National Healthcare Institute) before and after the 2015 LTC reform. Incidence rate ratios (IRR) were calculated for monthly NH admission rates and waiting list prevalence.Results Among 270,706 older people with dementia, the reform was negatively associated with NH admission rates (IRR 0.610 [0.547-0.681]), halting the pre-reform decline and stabilizing the post-reform trend (IRR 1.001 [0.999-1.002]). The reform was positively associated with NH waiting list prevalence (IRR 1.159 [1.048-1.282]).Conclusion Among older Dutch people with dementia, the 2015 Dutch LTC reform was associated with fewer NH admissions and longer waiting lists. While stabilization of the NH admissions may reflect prioritization of persons with dementia within stricter eligibility criteria, the concurrent rise in waiting list prevalence suggests that institutional capacity did not keep pace with persistent need. As a result, many older people with dementia remain longer in the community, raising concerns regarding their health and safety as well as the burden on their informal caregivers.

OBJECTIVES:Older adults hospitalized to the ICU are at risk for functional decline. In-hospital rehabilitation can mitigate functional decline; however, its association with long-term outcomes is unknown. Our objective was to describe days alive and at home (DAAH) in the 100 days (DAAH 100 ) after ICU hospitalization among older adults and evaluate whether in-hospital rehabilitation is associated with improved DAAH 100 . DESIGN:Retrospective cohort study. SETTING:National Health and Aging Trends Study linked with Medicare claims (2011-2019). PATIENTS:Community-dwelling Medicare beneficiaries 65 years old or older who survived ICU hospitalization. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:The outcome DAAH 100 was calculated by subtracting all post-discharge days in any of emergency department, observation unit, inpatient medical, psychiatric, rehabilitation unit, skilled nursing or hospice facility, and post-death from 100. The exposure was units of in-hospital rehabilitation, that is, physical and/or occupational therapy. We constructed a proportional odds logistic regression model of DAAH 100 (ordinal) adjusted for demographics, pre-hospitalization frailty and functional status, and hospitalization characteristics. We identified 884 ICU hospitalizations (weighted n = 5,330,486) of older adults discharged alive (age, median [interquartile range (IQR)]: 81 yr [75-86]; 50.5% female). Median DAAH 100 was 95 (IQR: 58.4-100) with median of 4 units (~1 hr) of in-hospital rehabilitation delivered over 6 days. After adjustment, each hour of in-hospital rehabilitation was associated with 8% higher odds of experiencing any of the three highest levels of DAAH 100 after discharge (adjusted odds ratio [95% CI], 1.08 [1.04-1.08]). CONCLUSIONS:In this nationally representative study of older ICU survivors, the average patient spent 95 of the first 100 post-discharge DAAH; delivery of greater amounts of in-hospital rehabilitation was associated with increased DAAH 100 after discharge. These findings highlight the substantial heterogeneity in time spent at home by older ICU survivors and the potential for in-hospital rehabilitation to improve this important patient-centered outcome.

Persistent symptoms following COVID-19 disproportionately affect older adults and may be exacerbated by neighborhood socioeconomic deprivation. We evaluated the association between neighborhood deprivation and symptom burden after COVID-19 hospitalization among 298 older adults from five Connecticut hospitals (June 2020-June 2021). Symptom burden was measured using the Edmonton Symptom Assessment System at baseline and 1, 3, and 6 months post-discharge, while neighborhood deprivation was assessed with the Area Deprivation Index. Using Bayesian linear mixed models adjusted for demographic and clinical factors, we found that residing in high-deprivation neighborhoods (ADI > 9/10; n = 28) was associated with higher symptom burden over the 6-month follow-up period. Adjusted analyses estimated a 2.2-point greater mean symptom burden (95% CI: 0.1-4.2). These findings suggest that neighborhood socioeconomic factors may significantly contribute to the persistence of COVID-19 symptoms in older adults, underscoring the need for targeted post-discharge care strategies.

Objectives Among older persons hospitalized in the intensive care unit (ICU) for critical illness, little is known about the health-related social needs (HRSNs) of food insecurity, social isolation, and transportation disadvantage in the year after discharge. This study aims to ascertain the prevalence of food insecurity, social isolation, and transportation disadvantage in the years preceding and following critical illness and to evaluate factors associated with each post-ICU HRSN.Methods Data from community-living participants in Rounds 2-9 of the National Health and Aging Trends Study (NHATS) 2011 cohort were linked to Medicare claims to identify ICU hospitalizations. HRSNs, demographics, preadmission, and in-hospital factors were drawn from NHATS and claims data. The prevalence of each HRSN was determined before and after critical illness. Factors associated with each HRSN in the year after discharge were evaluated using population-weighted multivariable logistic regression.Results Among 450 participants, the mean age was 80.1 (SD 7.1), 50.9% were women, and 110 (24.7%) were non-Hispanic Black individuals. All three HRSNs increased in the year after critical illness (food insecurity, 4.9%-7.8%, social isolation, 31.9%-39.4%, and transportation disadvantage, 10.5%-15.6%). Socioeconomic disadvantage was associated with greater odds of social isolation after critical illness (adjusted odds ratio [aOR], 3.26; 95% CI, 1.38-7.70). Mechanical ventilation was associated with greater odds of post-ICU transportation disadvantage (aOR, 2.69; 95% CI, 1.03-7.01). No factors were significantly associated with post-ICU food insecurity.Discussion These findings emphasize the need for screening and interventions to address HRSNs among older survivors of critical illness.

Background The Netherlands introduced abrupt, large-scale, long-term care (LTC) reforms in 2015 that promoted ageing-in-place. However, there has been no comprehensive population-level study evaluating how these reforms have impacted nursing home (NH) utilisation. This study examines the association between the 2015 reforms with national monthly rates of NH admissions and survival time amongst newly admitted older adults.Methods We analysed population data from Statistics Netherlands (2011-2019), conducting an interrupted time-series analysis to compare monthly NH admission rates before and after the 2015 reforms amongst adults aged 65 and older (N = 402 350). A Cox proportional hazards model was used to assess the reform's impact on mortality risk amongst newly admitted residents.Results The adjusted NH admission rate before the reform was 88.80 per 100 000 older adults (95% CI (confidence interval): 82.36-95.83), compared to 69.82 per 100 000 after the reform (95% CI: 65.91-73.78), indicating a significant reduction (incident rate ratio: 0.80, 95% CI: 0.74-0.86). Over a 3-year follow-up, the average survival time for those admitted after the reform was 608 days (95% CI: 608.72-610.74), compared to 622.52 days (95% CI: 620.59-624.45) for those admitted before the reform. The reform was associated with a slightly increased mortality risk (hazard ratio: 1.05, 95% CI: 1.02-1.07).Conclusions The 2015 Dutch LTC reform is associated with a reduction in national NH admissions and a decrease in average survival time of 2 weeks.

BACKGROUND:Little is known about functional trajectories among older adults who survive hospitalization for coronavirus disease 2019 (COVID-19). We characterized these trajectories over 6 months following discharge and evaluated the associations of potential risk factors with trajectory membership. METHODS:Participants were community-dwelling adults ≥ 60 years of age hospitalized for COVID-19 from June 2020 to June 2021. Interviews completed at 1, 3, and 6 months after discharge included assessments for disability in 15 functional activities. Functional trajectories were identified using latent class analysis. Factors associated with trajectory membership were evaluated using multinomial regression. RESULTS:311 participants (mean age 71.3 years) were included. Four different functional trajectories were identified: no (43%), mild (16%), moderate (23%), and severe (18%) disability. The pre-admission count of disabilities was independently associated with membership in each non-reference trajectory. Additional factors independently associated with the moderate trajectory included in-hospital delirium (OR 4.12 [95% CI 1.11-15.4]), frailty (OR 1.67 [95% CI 1.12-2.50]) and number of comorbidities (OR 1.41 [95% CI 1.12-1.79]) and with the severe trajectory included in-hospital delirium (OR 12.4 [95% CI 1.93-79.4]), frailty (OR 2.01 [95% CI 1.11-3.62]), number of comorbidities (OR 1.59 [95% 1.11-2.28]), severity of illness (OR 1.46 [95% CI 1.09-1.95]), and age (OR 1.10 [95% CI 1.02-1.18]). CONCLUSIONS:Older survivors of COVID-19 hospitalization experience distinct functional trajectories. Our findings may help inform shared medical decision-making during and after hospitalization and stimulate further research into modifiable risk factors.

Objective: To develop and test an NLP algorithm that accurately detects the presence of information reported from DXA scans containing femoral neck T-scores of the patients scanned. Methods: A rule-based NLP algorithm that iteratively built a collection of regular expressions in testing data consisting of 889 snippets of text pulled from DXA reports. This was manually checked by clinical experts to determine the proportion of manually verified annotations that contained T-score information detected by this algorithm called ‘BoneScore’. Testing of 30- and 50-word lengths on each side of the key term ‘femoral’ were pursued until achievement of adequate accuracy. A separate clinical validation regressed the extracted T-score values on five risk factors with established associations. Results: BoneScore built a set of 20 regular expressions that in concert with a width of 50 words on each side of the key term yielded an accuracy of 98% in the testing data. The extracted T-scores, when modeled with multivariable linear regression, consistently exhibited associations supported by the literature. Conclusion: BoneScore uses regular expressions to accurately extract annotations of T-score values of bone mineral density with a width of 50 words on each side of the key term. The extracted T-scores exhibit clinical face validity.

Background: A recent international consensus conference called for the development of risk prediction models to identify ICU survivors at increased risk of each of the post-ICU syndrome domains. We previously developed and validated a risk prediction tool for functional impairment after ICU admission among older adults. Research Question: In this pilot study, we assessed the feasibility of administering the risk prediction tool in the hospital to older adults who had just survived critical illness. An exploratory objective was to evaluate whether augmentation of the model with additional hospital-related factors improved discrimination. Study Design and Methods: Between January and October 2020, 50 adults aged 65 years and older underwent in-hospital administration of the risk prediction tool. Survivors were called monthly for 6 months after discharge. Feasibility was defined as completion of all tool components by ≥ 70% of enrolled participants. Persistent functional impairment was defined as failure to return to the functional baseline from before the ICU stay at the 6-month interview based on seven daily activities. The model was sequentially refit after adding three in-hospital factors as predictors, one at a time and then all together. Model discrimination was assessed with receiver operating characteristic curves. Results: The tool met the a priori feasibility threshold, with 92.0% of enrolled participants completing all eight components. In the exploratory analysis, the addition of Acute Physiology and Chronic Health Evaluation II score, presence of delirium, and maximum in-hospital mobility resulted in a 5% gain in discrimination that did not achieve statistical significance (area under the receiver operating characteristic curve, 0.75; 95% CI, 0.68-0.82; P = .09). Interpretation: Our results indicate that the risk prediction tool is feasible for use in the hospital setting, enabling the identification of ICU survivors at high risk of persistent functional impairment at 6 months after discharge. Augmentation with hospital-related factors improved model discrimination, but did not achieve statistical significance in this pilot study. Future studies should evaluate the augmented model in larger cohorts.

Background: Introduced in 2010, the sub-discipline of gerontologic biostatistics (GBS) was conceptualized to address the specific challenges in analyzing data from research studies involving older adults. However, the evolving technological landscape has catalyzed data science and statistical advancements since the original GBS publication, greatly expanding the scope of gerontologic research. There is a need to describe how these advancements enhance the analysis of multi-modal data and complex phenotypes that are hallmarks of gerontologic research. Methods: This paper introduces GBS 2.0, an updated and expanded set of analytical methods reflective of the practice of gerontologic biostatistics in contemporary and future research. Results: GBS 2.0 topics and relevant software resources include cutting-edge methods in experimental design; analytical techniques that include adaptations of machine learning, quantifying deep phenotypic measurements, high-dimensional -omics analysis; the integration of information from multiple studies, and strategies to foster reproducibility, replicability, and open science. Discussion: The methodological topics presented here seek to update and expand GBS. By facilitating the synthesis of biostatistics and data science in gerontology, we aim to foster the next generation of gerontologic researchers.

Introduced in 2010, the subdiscipline of gerontologic biostatistics was conceptualized to address the specific challenges of analyzing data from clinical research studies involving older adults. Since then, the evolving technological landscape has led to a proliferation of advancements in biostatistics and other data sciences that have significantly influenced the practice of gerontologic research, including studies beyond the clinic. Data science is the field at the intersection of statistics and computer science, and although the term "data science" was not widely used in 2010, the field has quickly made palpable effects on gerontologic research. In this Review in Depth, we describe multiple advancements of biostatistics and data science that have been particularly impactful. Moreover, we propose the subdiscipline of "gerontologic biostatistics and data science," which subsumes gerontologic biostatistics into a more encompassing practice. Prominent gerontologic biostatistics and data science advancements that we discuss herein include cutting-edge methods in experimental design and causal inference, adaptations of machine learning, the rigorous quantification of deep phenotypic measurement, and analysis of high-dimensional -omics data. We additionally describe the need for integration of information from multiple studies and propose strategies to foster reproducibility, replicability, and open science. Lastly, we provide information on software resources for gerontologic biostatistics and data science practitioners to apply these approaches to their own work and propose areas where further advancement is needed. The methodological topics reviewed here aim to enhance data-rich research on aging and foster the next generation of gerontologic researchers.

Trans arterial chemoembolization (TACE) is the most frequently utilized locoregional therapy for patients with hepatocellular carcinoma (HCC). The reported evidence has been mixed regarding outcomes in patients with decompensated cirrhosis who undergo TACE. The aim of our study was to evaluate the clinical outcomes of patients with cirrhosis and HCC that underwent TACE procedures while awaiting liver transplantation. This was a retrospective cohort study of patients listed for transplant between February 2018 and April 2022. We analyzed 74 patients that had a total of 171 TACE procedures, and defined outcomes within 90 days of TACE in four categorical levels as follows: clinical stability/improvement (1), worsening of liver functioning (2), hospitalization (3), and death or delisting (4). The primary statistical analysis was based on multinomial modeling of this categorical outcome. Patients with decompensated liver function at the time of TACE had odds of being hospitalized within 90 days of the TACE procedure that were 8 times higher than those with compensated liver function (p=0.007). Patients with albumin <3 g/dL or bilirubin >3mg/dL were more likely to experience poor outcomes within 90 days following TACE. There was no statistically significant difference in death and delisting after TACE between patients with compensated and decompensated liver function, though the sample size in this outcome was small.

OBJECTIVE:Fragility fractures (fractures) are a critical outcome for persons aging with HIV (PAH). Research suggests that the fracture risk assessment tool (FRAX) only modestly estimates fracture risk among PAH. We provide an updated evaluation of how well a 'modified FRAX' identifies PAH at risk for fractures in a contemporary HIV cohort. DESIGN:Cohort study. METHODS:We used data from the Veterans Aging Cohort Study to evaluate veterans living with HIV, aged 50+ years, for the occurrence of fractures from 1 January 2010 through 31 December 2019. Data from 2009 were used to evaluate the eight FRAX predictors available to us: age, sex, BMI, history of previous fracture, glucocorticoid use, rheumatoid arthritis, alcohol use, and smoking status. These predictor values were then used to estimate participant risk for each of two types of fractures (major osteoporotic and hip) over the subsequent 10 years in strata defined by race/ethnicity using multivariable logistic regression. RESULTS:Discrimination for major osteoporotic fracture was modest [Blacks: area under the curve (AUC) 0.62; 95% confidence interval (CI) 0.62, 0.63; Whites: AUC 0.61; 95% CI 0.60, 0.61; Hispanic: AUC 0.63; 95% CI 0.62, 0.65]. For hip fractures, discrimination was modest to good (Blacks: AUC 0.70; 95% CI 0.69, 0.71; Whites: AUC 0.68; 95% CI 0.67, 0.69]. Calibration was good in all models across all racial/ethnic groups. CONCLUSION:Our 'modified FRAX' exhibited modest discrimination for predicting major osteoporotic fracture and slightly better discrimination for hip fracture. Future studies should explore whether augmentation of this subset of FRAX predictors results in enhanced prediction of fractures among PAH.

Background Critical illness often leads to persistent functional impairment among older Intensive Care Unit (ICU) survivors. Identification of high-risk survivors prior to discharge from their ICU hospitalization can facilitate targeting for restorative interventions after discharge, potentially improving the likelihood of functional recovery. Our objective was to develop and validate a prediction model for persistent functional impairment among older adults in the year after an ICU hospitalization. Methods The analytic sample included community-living participants enrolled in the National Health and Aging Trends Study 2011 cohort who survived an ICU hospitalization through December 2017 and had a follow-up interview within 1 year. Persistent functional impairment was defined as failure to recover to the pre-ICU level of function within 12 months of discharge from an ICU hospitalization. We used Bayesian model averaging to identify the final predictors from a comprehensive set of 17 factors. Discrimination and calibration were assessed using area-under-the-curve (AUC) and calibration plots. Results The development cohort included 456 ICU admissions (2,654,685 survey-weighted admissions) and the validation cohort included 227 ICU admissions (1,350,082 survey-weighted admissions). In the development cohort, the median age was 81.0 years (interquartile range [IQR] 76.0, 86.0) and 231 (50.7%) participants were women; demographic characteristics were comparable in the validation cohort. The rates of persistent functional impairment were 49.3% (development) and 50.2% (validation). The final model included age, pre-ICU disability, probable dementia, frailty, prior hospitalizations, vision impairment, depressive symptoms, and hospital length of stay. The model demonstrated good discrimination (AUC 71%, 95% confidence interval [CI] 0.66-0.76) and good calibration. When applied to the validation cohort, the model demonstrated comparable discrimination (AUC 72%, 95% CI 0.66-0.78) and good calibration. Conclusions Application of the model prior to discharge from an ICU hospitalization may identify older adults at the highest risk of persistent functional impairment in the subsequent year, thereby facilitating targeted interventions and follow-up.

Background: Although young women ( aged <= 55 years) are at higher risk than similarly aged men for hospital readmission within 1 year after an acute myocardial infarction (AMI), no risk prediction models have been developed for them. The present study developed and internally validated a risk prediction model of 1-year post-AMI hospital readmission among young women that considered demographic, clinical, and gender-related variables.Methods: We used data from the US Variation in Recovery: Role of Gender on Outcomes of Young AMI Patients (VIRGO) study (n = 2007 women), a prospective observational study of young patients hospitalized with AMI. Bayesian model averaging was used for model selection and bootstrapping for internal validation. Model calibration and discrimination were respectively assessed with calibration plots and area under the curve.Results: Within 1-year post-AMI, 684 women (34.1%) were readmitted to the hospital at least once. The final model predictors included: any in-hospital complication, baseline perceived physical health, obstructive coronary artery disease, diabetes, history of congestive heart failure, low income ( < $30,000 US), depressive symptoms, length of hospital stay, and race (White vs Black). Of the 9 retained predictors, 3 were gender-related. The model was well calibrated and exhibited modest discrimination (area under the curve = 0.66).Conclusions: Our female-specific risk model was developed and internally validated in a cohort of young female patients hospitalized with AMI and can be used to predict risk of readmission. Whereas clinical factors were the strongest predictors, the model included several gender-related variables (ie, perceived physical health, depression, income level). However, discrimination was modest, indicating that other unmeasured factors contribute to variability in hospital readmission risk among younger women.

Purpose: A substantial proportion of global deaths is attributed to unhealthy diets, which can be assessed at baseline or longitudinally. We demonstrated how to simultaneously correct for random measurement error, correlations, and skewness in the estimation of associations between dietary intake and all-cause mortality. Methods: We applied a multivariate joint model (MJM) that simultaneously corrected for random measurement error, skewness, and correlation among longitudinally measured intake levels of cholesterol, total fat, dietary fiber, and energy with all-cause mortality using US National Health and Nutrition Examination Survey linked to the National Death Index mortality data. We compared MJM with the mean method that assessed intake levels as the mean of a person's intake. Results: The estimates from MJM were larger than those from the mean method. For instance, the logarithm of hazard ratio for dietary fiber intake increased by 14 times (from -0.04 to -0.60) with the MJM method. This translated into a relative hazard of death of 0.55 (95% credible interval: 0.45, 0.65) with the MJM and 0.96 (95% credible interval: 0.95, 0.97) with the mean method. Conclusions: MJM adjusts for random measurement error and flexibly addresses correlations and skewness among longitudinal measures of dietary intake when estimating their associations with death. (c) 2023 Elsevier Inc. All rights reserved.