Multivariate spatiotemporal functional principal component analysis for modeling hospitalization and mortality rates in the dialysis population.

Dialysis patients experience frequent hospitalizations and a higher mortality rate compared to other Medicare populations, in whom hospitalizations are a major contributor to morbidity, mortality, and healthcare costs. Patients also typically remain on dialysis for the duration of their lives or until kidney transplantation. Hence, there is growing interest in studying the spatiotemporal trends in the correlated outcomes of hospitalization and mortality among dialysis patients as a function of time starting from transition to dialysis across the United States Utilizing national data from the United States Renal Data System (USRDS), we propose a novel multivariate spatiotemporal functional principal component analysis model to study the joint spatiotemporal patterns of hospitalization and mortality rates among dialysis patients. The proposal is based on a multivariate Karhunen-Loéve expansion that describes leading directions of variation across time and induces spatial correlations among region-specific scores. An efficient estimation procedure is proposed using only univariate principal components decompositions and a Markov Chain Monte Carlo framework for targeting the spatial correlations. The finite sample performance of the proposed method is studied through simulations. Novel applications to the USRDS data highlight hot spots across the United States with higher hospitalization and/or mortality rates and time periods of elevated risk.

Impact of community-based technology training with low-income older adults.

OBJECTIVES: Compared with younger and middle-aged adults, older adults are less likely to adopt new computer technology, potentially limiting access to healthcare and many other important resources available online. This limitation could impact cognitive abilities, well-being, and mental health outcomes of older adults. The aims of the present study were to increase access to online county and healthcare resources, while also assessing the impact of technology access on cognitive functioning and multiple well-being domains. METHODS: A pilot community collaboration provided a two-month tablet training intervention, focused on increasing digital independence via tablet navigation, resources access, and fraud and scam prevention, to 20 low-income older adult participants (75% female, Mage = 70.85). Pre- and post-test phone interviews were conducted to measure any changes in digital independence, cognitive abilities, well-being, mental health, and mindset. RESULTS: Linear mixed effects models revealed no significant changes in outcome measures from pre- to post-test. However, we found effects of digital independence on several well-being measures, providing important information for the impact of technology access and training for low-income older adults. CONCLUSION: This pilot intervention offers limited but promising results, inspiring further investigations that may inform public health and policy services to address barriers to access and potentially improve psychological health.

Spatiotemporal multilevel joint modeling of longitudinal and survival outcomes in end-stage kidney disease.

Individuals with end-stage kidney disease (ESKD) on dialysis experience high mortality and excessive burden of hospitalizations over time relative to comparable Medicare patient cohorts without kidney failure. A key interest in this population is to understand the time-dynamic effects of multilevel risk factors that contribute to the correlated outcomes of longitudinal hospitalization and mortality. For this we utilize multilevel data from the United States Renal Data System (USRDS), a national database that includes nearly all patients with ESKD, where repeated measurements/hospitalizations over time are nested in patients and patients are nested within (health service) regions across the contiguous U.S. We develop a novel spatiotemporal multilevel joint model (STM-JM) that accounts for the aforementioned hierarchical structure of the data while considering the spatiotemporal variations in both outcomes across regions. The proposed STM-JM includes time-varying effects of multilevel (patient- and region-level) risk factors on hospitalization trajectories and mortality and incorporates spatial correlations across the spatial regions via a multivariate conditional autoregressive correlation structure. Efficient estimation and inference are performed via a Bayesian framework, where multilevel varying coefficient functions are targeted via thin-plate splines. The finite sample performance of the proposed method is assessed through simulation studies. An application of the proposed method to the USRDS data highlights significant time-varying effects of patient- and region-level risk factors on hospitalization and mortality and identifies specific time periods on dialysis and spatial locations across the U.S. with elevated hospitalization and mortality risks.

One-year cognitive outcomes from a multiple real-world skill learning intervention with older adults.

OBJECTIVES: Novel skill learning has been shown to have cognitive benefits in the short-term (up to a few months). Two studies expanded on prior research by investigating whether learning multiple novel real-world skills simultaneously (e.g. Spanish, drawing, music composition), for a minimum of six hours a week, would yield 1-year cognitive gains. METHOD: Following a 3-month multi-skill learning intervention, Study 1 (N = 6, Mage = 66 years, SDage = 6.41) and Study 2 (N = 27, Mage = 69 years, SDage = 7.12) participants completed follow-up cognitive assessments 3 months, 6 months, and one year after the intervention period. Cognitive assessments tested executive function (working memory and cognitive control) and verbal episodic memory. RESULTS: Linear mixed-effects models revealed improvements in multiple cognitive outcomes from before the intervention to the follow-up timepoints. Specifically, executive function increased from pre-test to the 1-year follow-up for both studies (an effect driven mostly by cognitive control scores). DISCUSSION: Our findings provide evidence that simultaneously learning real-world skills can lead to long-term improvements in cognition during older adulthood. Future work with diverse samples could investigate individual differences in gains. Overall, our findings promote the benefits of lifelong learning, namely, to improve cognitive abilities in older adulthood.

Long-Term Effects of a Real-World Multi-Skill Intervention on Older Adults' Growth Mindset.

Although there have been interventions to increase growth mindset, little is known about their effectiveness over a longer period, especially for older adults. This study with older adults investigated the long-term effects of a learning intervention that included growth mindset lectures and discussions on growth mindset. In Study 1 (n = 27), participants were tracked for one year after a 12-week intervention. We found that an increased growth mindset did not last beyond the intervention. In Study 2 (n = 71), the COVID-19 pandemic interrupted the intervention after only two months. Participants were followed up for two years, and their growth mindset at one year was greater than at the pretest (Week 0) but declined from the 1- to 2-year follow-up. Taken together, interventions incorporating growth mindset messages can increase growth mindset in the short term but may require booster sessions to retain effects, especially during disruptive life events.

Growth Mindset Predicts Cognitive Gains in an Older Adult Multi-Skill Learning Intervention.

Growth mindset (belief in the malleability of intelligence) is a unique predictor of young learners' increased motivation and learning, and may have broader implications for cognitive functioning. Its role in learning in older adulthood is unclear. As part of a larger longitudinal study, we examined growth mindset and cognitive functioning in older adults engaged in a 3-month multi-skill learning intervention that included growth mindset discussions. Before, during, and after the intervention, participants reported on their growth mindset beliefs and completed a cognitive battery. Study 1 indicated that intervention participants, but not control participants, increased their growth mindset during the intervention. Study 2 replicated these results and found that older adults with higher preexisting growth mindsets showed larger cognitive gains at posttest compared to those with lower preexisting growth mindsets. Our findings highlight the potential role of growth mindset in supporting positive learning cycles for cognitive gains in older adulthood.

A Bayesian multilevel time-varying framework for joint modeling of hospitalization and survival in patients on dialysis.

Over 782 000 individuals in the United States have end-stage kidney disease with about 72% of patients on dialysis, a life-sustaining treatment. Dialysis patients experience high mortality and frequent hospitalizations, at about twice per year. These poor outcomes are exacerbated at key time periods, such as the fragile period after transition to dialysis. In order to study the time-varying effects of modifiable patient and dialysis facility risk factors on hospitalization and mortality, we propose a novel Bayesian multilevel time-varying joint model. Efficient estimation and inference is achieved within the Bayesian framework using Markov chain Monte Carlo, where multilevel (patient- and dialysis facility-level) varying coefficient functions are targeted via Bayesian P-splines. Applications to the United States Renal Data System, a national database which contains data on nearly all patients on dialysis in the United States, highlight significant time-varying effects of patient- and facility-level risk factors on hospitalization risk and mortality. Finite sample performance of the proposed methodology is studied through simulations.

Investigating Longitudinal Associations Between Racial Microaggressions, Coping, Racial/Ethnic Identity, and Mental Health in Black Girls and Women.

Racial microaggressions pose significant risk to health and well-being among Black adolescents and adults. Yet, protective factors (i.e., coping, racial/ethnic identity) can moderate the impact of racial microaggressions over time. Unfortunately, few studies have evaluated the role of these protective factors longitudinally or specifically among Black girls and women. In the current study, we focused on the experiences of Black girls and women and investigated the longitudinal links between racial microaggressions and mental health symptoms over 1 year. We then explored the role of two key protective factors as moderators-coping with racial discrimination and racial/ethnic identity-for mental health. Participants included 199 Black adolescent girls (Mage = 16.02) and 199 Black women (Mage = 42.82) who completed measures on two types of racial microaggressions, three types of coping strategies, racial/ethnic identity, and mental health symptomology. Girls and women completed measures at three time points over 1 year. Results indicated both types of microaggressions predicted increased mental health symptoms in Black women. Among Black girls, assumptions of criminality predicted increased externalizing symptoms only when protective factors were included in the model. Analysis of the protective factors indicated a potential direct benefit rather than a moderating role of coping with racial discrimination through positive thinking for mental health in both Black girls and women. Evidence suggests that coping may have had a direct rather than an indirect effect on Black girls' mental health over time. We conclude with future directions for research and considerations for practice.

Multilevel modeling of spatially nested functional data: Spatiotemporal patterns of hospitalization rates in the US dialysis population.

End-stage renal disease patients on dialysis experience frequent hospitalizations. In addition to known temporal patterns of hospitalizations over the life span on dialysis, where poor outcomes are typically exacerbated during the first year on dialysis, variations in hospitalizations among dialysis facilities across the US contribute to spatial variation. Utilizing national data from the United States Renal Data System (USRDS), we propose a novel multilevel spatiotemporal functional model to study spatiotemporal patterns of hospitalization rates among dialysis facilities. Hospitalization rates of dialysis facilities are considered as spatially nested functional data (FD) with longitudinal hospitalizations nested in dialysis facilities and dialysis facilities nested in geographic regions. A multilevel Karhunen-Loéve expansion is utilized to model the two-level (facility and region) FD, where spatial correlations are induced among region-specific principal component scores accounting for regional variation. A new efficient algorithm based on functional principal component analysis and Markov Chain Monte Carlo is proposed for estimation and inference. We report a novel application using USRDS data to characterize spatiotemporal patterns of hospitalization rates for over 400 health service areas across the US and over the posttransition time on dialysis. Finite sample performance of the proposed method is studied through simulations.

A multilevel mixed effects varying coefficient model with multilevel predictors and random effects for modeling hospitalization risk in patients on dialysis.

For patients on dialysis, hospitalizations remain a major risk factor for mortality and morbidity. We use data from a large national database, United States Renal Data System, to model time-varying effects of hospitalization risk factors as functions of time since initiation of dialysis. To account for the three-level hierarchical structure in the data where hospitalizations are nested in patients and patients are nested in dialysis facilities, we propose a multilevel mixed effects varying coefficient model (MME-VCM) where multilevel (patient- and facility-level) random effects are used to model the dependence structure of the data. The proposed MME-VCM also includes multilevel covariates, where baseline demographics and comorbidities are among the patient-level factors, and staffing composition and facility size are among the facility-level risk factors. To address the challenge of high-dimensional integrals due to the hierarchical structure of the random effects, we propose a novel two-step approximate EM algorithm based on the fully exponential Laplace approximation. Inference for the varying coefficient functions and variance components is achieved via derivation of the standard errors using score contributions. The finite sample performance of the proposed estimation procedure is studied through simulations.

Profiling dialysis facilities for adverse recurrent events.

Profiling analysis aims to evaluate health care providers, such as hospitals, nursing homes, or dialysis facilities, with respect to a patient outcome. Previous profiling methods have considered binary outcomes, such as 30-day hospital readmission or mortality. For the unique population of dialysis patients, regular blood works are required to evaluate effectiveness of treatment and avoid adverse events, including dialysis inadequacy, imbalance mineral levels, and anemia among others. For example, anemic events (when hemoglobin levels exceed normative range) are recurrent and common for patients on dialysis. Thus, we propose high-dimensional Poisson and negative binomial regression models for rate/count outcomes and introduce a standardized event ratio measure to compare the event rate at a specific facility relative to a chosen normative standard, typically defined as an "average" national rate across all facilities. Our proposed estimation and inference procedures overcome the challenge of high-dimensional parameters for thousands of dialysis facilities. Also, we investigate how overdispersion affects inference in the context of profiling analysis. The proposed methods are illustrated with profiling dialysis facilities for recurrent anemia events.

Estimating the population-level impact of vaccines using synthetic controls.

When a new vaccine is introduced, it is critical to monitor trends in disease rates to ensure that the vaccine is effective and to quantify its impact. However, estimates from observational studies can be confounded by unrelated changes in healthcare utilization, changes in the underlying health of the population, or changes in reporting. Other diseases are often used to detect and adjust for these changes, but choosing an appropriate control disease a priori is a major challenge. The "synthetic controls" (causal impact) method, which was originally developed for website analytics and social sciences, provides an appealing solution. With this approach, potential comparison time series are combined into a composite and are used to generate a counterfactual estimate, which can be compared with the time series of interest after the intervention. We sought to estimate changes in hospitalizations for all-cause pneumonia associated with the introduction of pneumococcal conjugate vaccines (PCVs) in five countries in the Americas. Using synthetic controls, we found a substantial decline in hospitalizations for all-cause pneumonia in infants in all five countries (average of 20%), whereas estimates for young and middle-aged adults varied by country and were potentially influenced by the 2009 influenza pandemic. In contrast to previous reports, we did not detect a decline in all-cause pneumonia in older adults in any country. Synthetic controls promise to increase the accuracy of studies of vaccine impact and to increase comparability of results between populations compared with alternative approaches.

Nerve Blocks Prior to Microfocused Ultrasound Treatment are Safe and Reduce Patient Discomfort.

BACKGROUND: Microfocused ultrasound with visualization has become one of the more popular nonsurgical facial rejuvenation therapies available. Although the treatment has gained wide acceptance, providing adequate pain relief during the procedure can be challenging. OBJECTIVES: The aim of this study was to test our hypothesis that nerve blocks prior to treatment would be well tolerated and significantly reduce patient discomfort. METHODS: Subjects undergoing microfocused ultrasound were offered the choice of participating in a split face nerve block, bilateral block, or a control group. Nerves targeted included infraorbital, supratrochlear, supraorbital, zygomaticofrontal, mental, great auricular, and cervical plexus. Pain assessment was based on a 10-point Wong-Backer FACES Pain score. RESULTS: A total of 65 patients were included in the study: 28 in the split face group, 19 in the bilateral block group, and 18 without a block. The mean [standard deviation] pain score of the bilateral block cohort was 3.9 [1.2], and that of the control group was 5.1 [1.7] (P = 0.001). Patients in the split face cohort reported a higher pain score on the unblocked side of the face (7.5 [1.3]) than on the blocked side (2.9 [1.0]) (P < 0.001). The mean pain score for local anesthetic injection was 2.7 and 1.4 for the split face and the bilateral groups, respectively. There were no adverse events. CONCLUSIONS: Nerve blocks are well tolerated and significantly improve patient comfort during microfocused ultrasound treatment without compromising outcomes or increasing adverse events.

Greater magnitude of entheseal microdamage and repair in psoriatic arthritis compared with ankylosing spondylitis on ultrasound.

Objectives: AS and PsA share clinical and immunological features centred on enthesitis. However, a strong association between PsA and preceding injury has been recognized. The aim of this study was to test the hypothesis that the entheseal damage seen by US is commoner in PsA patients than in AS patients. Methods: Seventy-nine AS and 85 PsA patients had US scans of 1640 entheses to calculate entheseal inflammation (hypoechogenicity, thickening and Doppler) and damage scores (calcifications, enthesophytes and erosions). Regression modelling was done to evaluate the effect of diagnoses on outcomes, controlling for age, gender, BMI, clinical enthesitis, HLA-B27, and anti-TNF use. Results: Both inflammation and damage scores on US were correlated with BMI (r = 0.392; r = 0.320) and age (r = 0.308; r = 0.538) (P < 0.001), and men had higher inflammation scores than women [12.3 (7.5) vs 8.9 (7.3), P = 0.001]. In multivariate analysis, despite similar (anti-TNF-treated patients) or slightly less inflammation (anti-TNF-naïve patients) in the PsA group, they had 4.22 times more US damage than their counterparts with AS. The difference was even higher in the anti-TNF-naïve patients (5.6 times). Conclusion: On US assessment, PsA patients have greater entheseal insertion damage scores compared with AS, suggesting potential differences in tissue repair, immunobiology or response to injury at insertions.

Similar subclinical enthesitis in celiac and inflammatory bowel diseases by ultrasound suggests a gut enthesis axis independent of spondyloarthropathy spectrum.

Objective: Higher subclinical enthesitis on US has been reported in IBD and celiac disease, separately. The objective of this study was to compare IBD and celiac disease for enthesitis on US. Higher enthesitis scores in IBD compared with celiac disease would support a shared pathogenic mechanism between IBD and spondyloarthritis, whereas similar scores may suggest a general impact of gut inflammation on the enthesis. Methods: Patients with IBD, celiac disease and healthy controls (HCs) were recruited and 12 entheses were scanned by US, blind to the diagnosis and clinical assessment. Elementary lesions for enthesitis were scored on a scale between 0 and 3, for inflammation, damage and total US scores. Results: A total of 1260 entheses were scanned in 44 patients with celiac disease, 43 patients with IBD and 18 HCs. The three groups were matched for age and BMI. Patients with celiac disease and IBD had higher inflammation scores than HCs [10.4 (6.5), 9.6 (5.4) and 5.6 (5.2), respectively, P = 0.007) whereas damage scores were similar. Both age and BMI had significant effects on the entheseal scores, mostly for inflammation scores but when controlling for these the US enthesopathy scores were still higher in celiac disease and IBD. Conclusion: The magnitude of subclinical enthesopathy scores is similar between celiac disease and IBD in comparison with HCs. These findings suggest that the common factor between both diseases and enthesopathy is abnormal gut permeability, which may be modified by the genetic architecture of IBD leading to clinical arthropathy.

Causal mediation analysis with multiple mediators in the presence of treatment noncompliance.

Randomized experiments are often complicated because of treatment noncompliance. This challenge prevents researchers from identifying the mediated portion of the intention-to-treated (ITT) effect, which is the effect of the assigned treatment that is attributed to a mediator. One solution suggests identifying the mediated ITT effect on the basis of the average causal mediation effect among compliers when there is a single mediator. However, considering the complex nature of the mediating mechanisms, it is natural to assume that there are multiple variables that mediate through the causal path. Motivated by an empirical analysis of a data set collected in a randomized interventional study, we develop a method to estimate the mediated portion of the ITT effect when both multiple dependent mediators and treatment noncompliance exist. This enables researchers to make an informed decision on how to strengthen the intervention effect by identifying relevant mediators despite treatment noncompliance. We propose a nonparametric estimation procedure and provide a sensitivity analysis for key assumptions. We conduct a Monte Carlo simulation study to assess the finite sample performance of the proposed approach. The proposed method is illustrated by an empirical analysis of JOBS II data, in which a job training intervention was used to prevent mental health deterioration among unemployed individuals.

A copula model for joint modeling of longitudinal and time-invariant mixed outcomes.

Motivated by a preclinical study in a mouse model of breast cancer, we suggest a joint modeling framework for outcomes of mixed type and measurement structures (longitudinal versus single time/time-invariant). We present an approach based on the time-varying copula models, which is used to jointly model longitudinal outcomes of mixed types via a time-varying copula, and extend the scope of these models to handle outcomes with mixed measurement structures. Our framework allows the parameters corresponding to the longitudinal outcome to be time varying and thereby enabling researchers to investigate how the response-predictor relationships change with time. We investigate the finite sample performance of this new approach via a Monte Carlo simulation study and illustrate its usefulness by an empirical analysis of the motivating preclinical study, comparing the effect of various treatments on tumor volume (longitudinal continuous response) and the number of days until tumor volume triples (time-invariant count response). Through the real-life application and the simulation study, we demonstrate that, compared with marginal modeling, the joint modeling framework offers more precision in the estimation of model parameters.

Impact of Pneumococcal Conjugate Vaccines on Pneumonia Hospitalizations in High- and Low-Income Subpopulations in Brazil.

BACKGROUND: Pneumococcal conjugate vaccines (PCVs) are being used worldwide. A key question is whether the impact of PCVs on pneumonia is similar in low- and high-income populations. However, most low-income countries, where the burden of disease is greatest, lack reliable data that can be used to evaluate the impact. Data from middle-income countries that have both low- and high-income subpopulations can provide a proxy measure for the impact of the vaccine in low-income countries. METHODS: We evaluated the impact of PCV10 on hospitalizations for all-cause pneumonia in Brazil, a middle-income country with localities that span a broad range of human development index (HDI) levels. We used complementary time series and spatiotemporal methods (synthetic controls and hierarchical Bayesian spatial regression) to test whether the decline in pneumonia hospitalizations associated with vaccine introduction varied across the socioeconomic spectrum. RESULTS: We found that the declines in all-cause pneumonia hospitalizations in children and young and middle-aged adults did not vary substantially across low and high HDI subpopulations. Moreover, the estimated declines seen in infants and young adults were associated with higher levels of uptake of the vaccine at a local level. CONCLUSIONS: These results suggest that PCVs have an important impact on hospitalizations for all-cause pneumonia in both low- and high-income populations.

Bayesian Model Averaging with Change Points to Assess the Impact of Vaccination and Public Health Interventions.

BACKGROUND: Pneumococcal conjugate vaccines (PCVs) prevent invasive pneumococcal disease and pneumonia. However, some low-and middle-income countries have yet to introduce PCV into their immunization programs due, in part, to lack of certainty about the potential impact. Assessing PCV benefits is challenging because specific data on pneumococcal disease are often lacking, and it can be difficult to separate the effects of factors other than the vaccine that could also affect pneumococcal disease rates. METHODS: We assess PCV impact by combining Bayesian model averaging with change-point models to estimate the timing and magnitude of vaccine-associated changes, while controlling for seasonality and other covariates. We applied our approach to monthly time series of age-stratified hospitalizations related to pneumococcal infection in children younger 5 years of age in the United States, Brazil, and Chile. RESULTS: Our method accurately detected changes in data in which we knew true and noteworthy changes occurred, i.e., in simulated data and for invasive pneumococcal disease. Moreover, 24 months after the vaccine introduction, we detected reductions of 14%, 9%, and 9% in the United States, Brazil, and Chile, respectively, in all-cause pneumonia (ACP) hospitalizations for age group 0 to <1 years of age. CONCLUSIONS: Our approach provides a flexible and sensitive method to detect changes in disease incidence that occur after the introduction of a vaccine or other intervention, while avoiding biases that exist in current approaches to time-trend analyses.

Limited reliability of radiographic assessment of spinal progression in ankylosing spondylitis.

Objectives: Conventional radiography is key to assessing AS-related spinal involvement and has become increasingly important given that spinal fusion may continue under biologic therapy. We aimed to compare the reliability of radiographic scoring of the spine by using different approaches to understand how different readers agree on overall scores and on individual findings. Method: Six investigators scored 68 plain radiographs of the cervical and lumbar spine of 34 patients with a 2-year interval, for erosions, sclerosis, squaring, syndesmophytes and ankyloses using the Spondyloarthritis Radiography (SPAR) module. The intraclass correlation coefficients were calculated compared with two gold standards. The reproducibility of each finding in 1632 vertebral corners and new syndesmophytes in each corner was calculated by kappa analysis and positive agreement rates. Results: The intraclass correlation coefficients mostly revealed good to excellent agreement with the gold standards (0.69-0.95). The kappa analysis showed worse agreement, being relatively higher for syndesmophytes (0.163-0.559) and ankylosis (0.48-0.95). Positive agreement rates showed that erosions were never detected at the same vertebral corner by two readers (positive agreement rate: 0%). The mean (range) positive agreement rates were 10.1% (0-27.7%) for sclerosis and 19.2% (0-59.7%) for squaring, and were higher for syndesmophytes [38.8% (21.4-62.5%)] and ankylosis [77.3% (64-95.3%)]. Conclusion: Our results show that there is a poor agreement on the presence of grade 1 lesions included in the Modified Stoke Ankylosing Spondylitis Spine Score-mostly for erosions and sclerosis-which may increase the measurement error. The currently used definitions of reliability have a risk of overestimating reproducibility.