Search

Estimating the effect of a randomized treatment and the effect that is transmitted through a mediator is often complicated by treatment noncompliance. In literature, an instrumental variable (IV)-based method has been developed to study causal mediation effects in the presence of treatment noncompliance. Existing studies based on the IV-based method focus on identifying the mediated portion of the intention-to-treat effect, which relies on several identification assumptions. However, little attention has been given to assessing the sensitivity of the identification assumptions or mitigating the impact of violating these assumptions. This study proposes a two-stage joint modeling method for conducting causal mediation analysis in the presence of treatment noncompliance, in which modeling assumptions can be employed to decrease the sensitivity to violation of some identification assumptions. The use of a joint modeling method is also conducive to conducting sensitivity analyses to the violation of identification assumptions. We demonstrate our approach using the Jobs II data, in which the effect of job training on job seekers’ mental health is examined.

Being one of the oldest Christian communities in the Middle East, Assyrians have continued to live in various parts of Turkey for thousands of years. Today, the estimates related to the number of Assyrians living in Turkey vary between 4,000–25,000 while they cannot benefit from the rights put forward by the Lausanne Treaty among which schooling is the most important. Assyrian community can be said to be deteriorating in number. This decline in the number of Assyrians living in Turkey raises the question of whether they could maintain their ethnic identity while maintaining their language (Syriac). No studies so far have been carried out to find out the linguistic practices of Assyrian community living in Turkey, as well as their attitudes toward the languages they use. This study aims at shedding light on the present situation of Syriac used among the Assyrian community living in Turkey. The participants are limited to those living in Istanbul due to practical reasons. In this study, language attitudes and language use practices of Assyrian community living in Istanbul are found out through a language attitudes questionnaire. It is hoped that the results of the study will provide the current situation of the Syriac language in terms of its ethnolinguistic vitality as spoken among the community. It is also hoped that the results of the study will provide useful data for those who would like to help protect the ethnolinguistic identities of Assyrian minority in Turkey, as well as all those dispersed around the world, which seems to have become increasingly important for such a country at the gates of the European Union as Turkey.

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., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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., (© 2024. The Author(s).)

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, M age = 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.

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, M age = 66 years, SD age = 6.41) and Study 2 ( N  = 27, M age = 69 years, SD age = 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.

Objectives: The natural learning experience from infancy to emerging adulthood, when considerable cognitive and functional growth is observed, mandates learning multiple real-world skills simultaneously. The present studies investigated whether learning multiple real-world skills simultaneously is possible in older adults and also whether it improves both their cognitive abilities (working memory, episodic memory, and cognitive control) and functional independence., Methods: Over two studies (15 and 27 participants), older adults learned at least three new skills (e.g., Spanish, drawing, music composition) simultaneously for 3 months. Participants completed cognitive and functional assessments before, during, and after the intervention in both studies. Participants were recruited sequentially for an intervention or no-contact control group in Study 1, and Study 2 included only an intervention group, who also completed assessments 4-6 weeks prior to the start of the intervention (i.e., they served as their own control group)., Results: Results from both studies show that simultaneously learning multiple skills is feasible and potentially beneficial for healthy older adults. Learning multiple skills simultaneously increased cognitive abilities in older adults by midpoint of the intervention, to levels similar to performance in a separate sample of middle-aged adults., Discussion: Our findings demonstrate the feasibility and potential of conducting a real-world skill-learning intervention involving learning three novel skills with older adults. Our multiskill intervention may provide broad cognitive gains, akin to the benefits experienced earlier in the life span., (© The Gerontological Society of America 2023.)

Purpose in life (PIL) has been linked with numerous health benefits and adaptive aging, yet it diminishes with age, possibly due to loss of social or familial roles through life transitions. Drawing from the longitudinal surveys of the Midlife in the US study ( n = 3418), we use time-varying coefficient models to investigate how the trajectory of PIL differs across cumulatively (dis)advantaged, upwardly mobile, and downwardly mobile groups and the role of major life events in shaping these trajectories. We found the upwardly mobile group exhibits higher PIL than the cumulatively disadvantaged and downwardly mobile groups. The consistently disadvantaged group experiences more adverse events at non-normative times. Socioeconomic status disparities in PIL during old age decrease after controlling for life events. We discuss how and why well-being changes and the role of structural and social factors in facilitating or impeding the development or maintenance of PIL over the life course.

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.

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., (© 2022 John Wiley & Sons Ltd.)

Stroke is ranked as the fifth leading cause of death and the leading cause of adult disability in the USA. The progression of neuronal damage after stroke is recognized to be a complex integration of glia, neurons, and the surrounding extracellular matrix, therefore potential treatments must target the detrimental effects created by these interactions. In this study, we examined the spatial cellular and neuroinflammatory mechanisms occurring early after ischemic stroke utilizing Nanostring Digital Spatial Profiling (DSP) technology. Male C57bl/6 mice were subjected to photothrombotic middle cerebral artery occlusion (MCAO) and sacrificed at 3 days post-ischemia. Spatial distinction of the ipsilateral hemisphere was studied according to the regions of interest: the ischemic core, peri-infarct tissues, and peri-infarct normal tissue (PiNT) in comparison to the contralateral hemisphere. We demonstrated that the ipsilateral hemisphere initiates distinct spatial regulatory proteomic profiles with DSP technology that can be identified consistently with the immunohistochemical markers, FJB, GFAP, and Iba-1. The core border profile demonstrated an induction of neuronal death, apoptosis, autophagy, immunoreactivity, and early degenerative proteins. Most notably, the core border resulted in a decrease of the neuronal proteins Map2 and NeuN; an increase in the autophagy proteins BAG3 and CTSD; an increase in the microglial and peripheral immune invasion proteins Iba1, CD45, CD11b, and CD39; and an increase in the neurodegenerative proteins BACE1, APP, amyloid β 1-42, ApoE, and hyperphosphorylated tau protein S-199. The peri-infarct region demonstrated increased astrocytic, immunoreactivity, apoptotic, and neurodegenerative proteomic profiles, with an increase in BAG3, GFAP, and hyperphosphorylated tau protein S-199. The PiNT region displayed minimal changes compared to the contralateral cortex with only an increase in GFAP. In this study, we showed that mechanisms known to be associated with stroke, such as apoptosis and inflammation, occur in distinct spatial domains of the injured brain following ischemia. We also demonstrated the dysregulation of specific autophagic pathways that may lead to neurodegeneration in peri-infarct brain tissues. Taken together, these data suggest that identifying post-ischemic mechanisms occurring in a spatiotemporal manner may lead to more precise targets for successful therapeutic interventions to treat stroke., (© 2022. The Author(s).)

Over 785,000 individuals in the U.S. have end-stage renal disease (ESRD) with about 70% of patients on dialysis, a life-sustaining treatment. Dialysis patients experience frequent hospitalizations. In order to identify risk factors of hospitalizations, we utilize data from the large national database, United States Renal Data System (USRDS). To account for the hierarchical structure of the data, with longitudinal hospitalization rates nested in dialysis facilities and dialysis facilities nested in geographic regions across the U.S., we propose a multilevel varying coefficient spatiotemporal model (M-VCSM) where region- and facility-specific random deviations are modeled through a multilevel Karhunen-Loéve (KL) expansion. The proposed M-VCSM includes time-varying effects of multilevel risk factors at the region- (e.g., urbanicity and area deprivation index) and facility-levels (e.g., patient demographic makeup) and incorporates spatial correlations across regions via a conditional autoregressive (CAR) structure. Efficient estimation and inference is achieved through the fusion of functional principal component analysis (FPCA) and Markov Chain Monte Carlo (MCMC). Applications to the USRDS data highlight significant region- and facility-level risk factors of hospitalizations and characterize time periods and spatial locations with elevated hospitalization risk. Finite sample performance of the proposed methodology is studied through simulations.

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 (M age  = 16.02) and 199 Black women (M age  = 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., (© 2021 Society for Research on Adolescence.)

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., (© 2021 John Wiley & Sons Ltd.)

Profiling or evaluation of health care providers, including hospitals or dialysis facilities, involves the application of hierarchical regression models to compare each provider's performance with respect to a patient outcome, such as unplanned 30-day hospital readmission. This is achieved by comparing a specific provider's estimate of unplanned readmission rate, adjusted for patient case-mix, to a normative standard, typically defined as an "average" national readmission rate across all providers. Profiling is of national importance in the United States because the Centers for Medicare and Medicaid Services (CMS) policy for payment to providers is dependent on providers' performance, which is part of a national strategy to improve delivery and quality of patient care. Novel high dimensional fixed effects (FE) models have been proposed for profiling dialysis facilities and are more focused towards inference on the tail of the distribution of provider outcomes, which is well-suited for the objective of identifying sub-standard ("extreme") performance. However, the extent to which estimation and inference procedures for FE profiling models are effective when the outcome is sparse and/or when there are relatively few patients within a provider, referred to as the "low information" context, have not been examined. This scenario is common in practice when the patient outcome of interest is cause-specific 30-day readmissions, such as 30-day readmission due to infections in patients on dialysis, which is only about ~ 8% compared to the > 30% for all-cause 30-day readmission. Thus, we examine the feasibility and effectiveness of profiling models under the low information context in simulation studies and propose a novel correction method to FE profiling models to better handle sparse outcome data.

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., (© 2019 The International Biometric Society.)

Objective: The natural learning experience from infancy to emerging adulthood, when considerable cognitive and functional growth is observed, mandates learning multiple real-world skills simultaneously. The present studies investigated whether learning multiple real-world skills simultaneously is possible in older adults and also whether it improves both their cognitive abilities (working memory, episodic memory, and cognitive control) and functional independence., Method: Over two studies (15 and 27 participants), older adults learned at least three new skills (e.g., Spanish, drawing, music composition) simultaneously for 3 months. Participants completed cognitive and functional assessments before, during, and after the intervention in both studies. Participants were recruited sequentially for an intervention or no-contact control group in Study 1, and Study 2 included only an intervention group, who also completed assessments 4-6 weeks prior to the start of the intervention (i.e., they served as their own control group)., Results: Results from both studies show that simultaneously learning multiple skills is feasible and potentially beneficial for healthy older adults. Learning multiple skills simultaneously increased cognitive abilities in older adults by midpoint of the intervention, to levels similar to performance in a separate sample of middle-aged adults, 30 years younger., Discussion: Our findings demonstrate the feasibility and potential of conducting a real-world skill-learning intervention involving learning three novel skills with older adults. Our multiskill intervention may provide broad cognitive gains, akin to the benefits experienced earlier in the life span., (© The Author(s) 2019. Published by Oxford University Press on behalf of The Gerontological Society of America.)

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., (© 2020 John Wiley & Sons, Ltd.)

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., (© 2018 John Wiley & Sons, Ltd.)

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., (Copyright © 2018 John Wiley & Sons, Ltd.)

We propose a copula-based joint modeling framework for mixed longitudinal responses. Our approach permits all model parameters to vary with time, and thus will enable researchers to reveal dynamic response-predictor relationships and response-response associations. We call the new class of models TIMECOP because we model dependence using a time-varying copula. We develop a one-step estimation procedure for the TIMECOP parameter vector, and also describe how to estimate standard errors. We investigate the finite sample performance of our procedure via three simulation studies, one of which shows that our procedure performs well under ignorable missingness. We also illustrate the applicability of our approach by analyzing binary and continuous responses from the Women's Interagency HIV Study and a smoking cessation program.

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., (© The Author 2017. Published by Oxford University Press for the Infectious Diseases Society of America.)

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.

Competing Interests: RJT and LS have an ownership interest in Sage Analytica, a research consultancy. RJT, LS and CS have received support for other studies from investigator-initiated research grants from Pfizer to Sage Analytica. DMW has received research support for other studies from an investigator-initiated research grant from Pfizer to Yale University, and has received consulting fees from Merck.

Background: Upcoming vaccination efforts against typhoid fever require an assessment of the baseline burden of disease in countries at risk. There are no typhoid incidence data from most low- and middle-income countries (LMICs), so model-based estimates offer insights for decision-makers in the absence of readily available data., Methods: We developed a mixed-effects model fit to data from 32 population-based studies of typhoid incidence in 22 locations in 14 countries. We tested the contribution of economic and environmental indices for predicting typhoid incidence using a stochastic search variable selection algorithm. We performed out-of-sample validation to assess the predictive performance of the model., Results: We estimated that 17.8 million cases of typhoid fever occur each year in LMICs (95% credible interval: 6.9-48.4 million). Central Africa was predicted to experience the highest incidence of typhoid, followed by select countries in Central, South, and Southeast Asia. Incidence typically peaked in the 2-4 year old age group. Models incorporating widely available economic and environmental indicators were found to describe incidence better than null models., Conclusions: Recent estimates of typhoid burden may under-estimate the number of cases and magnitude of uncertainty in typhoid incidence. Our analysis permits prediction of overall as well as age-specific incidence of typhoid fever in LMICs, and incorporates uncertainty around the model structure and estimates of the predictors. Future studies are needed to further validate and refine model predictions and better understand year-to-year variation in cases.

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.

Because the real-world impact of new vaccines cannot be known before they are implemented in national programs, post-implementation studies at the population level are critical. Studies based on analysis of hospitalization rates of vaccine-preventable outcomes are typically used for this purpose. However, estimates of vaccine impact based on hospitalization data are particularly prone to confounding, as hospitalization rates are tightly linked to changes in the quality, access and use of the healthcare system, which often occur simultaneously with introduction of new vaccines. Here we illustrate how changes in healthcare delivery coincident with vaccine introduction can influence estimates of vaccine impact, using as an example reductions in infant pneumonia hospitalizations after introduction of the 10-valent pneumococcal conjugate vaccine (PCV10) in Brazil. To this end, we explore the effect of changes in several metrics of quality and access to public healthcare on trends in hospitalization rates before (2008-09) and after (2011-12) PCV10 introduction in 2010. Changes in infant pneumonia hospitalization rates following vaccine introduction were significantly associated with concomitant changes in hospital capacity and the fraction of the population using public hospitals. Importantly, reduction of pneumonia hospitalization rates after PCV10 were also associated with the expansion of outpatient services in several Brazilian states, falling more sharply where primary care coverage and the number of health units offering basic and emergency care increased more. We show that adjustments for unrelated (non-vaccine) trends commonly employed by impact studies, such as use of single control outcomes, are not always sufficient for accurate impact assessment. We discuss several ways to identify and overcome such biases, including sensitivity analyses using different denominators to calculate hospitalizations rates and methods that track changes in the outpatient setting. Employing these practices can improve the accuracy of vaccine impact estimates, particularly in evolving healthcare settings typical of low- and middle-income countries., (Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Motivated by an empirical analysis of ecological momentary assessment data (EMA) collected in a smoking cessation study, we propose a joint modeling technique for estimating the time-varying association between two intensively measured longitudinal responses: a continuous one and a binary one. A major challenge in joint modeling these responses is the lack of a multivariate distribution. We suggest introducing a normal latent variable underlying the binary response and factorizing the model into two components: a marginal model for the continuous response, and a conditional model for the binary response given the continuous response. We develop a two-stage estimation procedure and establish the asymptotic normality of the resulting estimators. We also derived the standard error formulas for estimated coefficients. We conduct a Monte Carlo simulation study to assess the finite sample performance of our procedure. The proposed method is illustrated by an empirical analysis of smoking cessation data, in which the question of interest is to investigate the association between urge to smoke, continuous response, and the status of alcohol use, the binary response, and how this association varies over time.

Semivarying models extend varying coefficient models by allowing some regression coefficients to be constant with respect to the underlying covariate(s). In this paper we develop a semivarying joint modelling framework for estimating the time-varying association between two intensively measured longitudinal response: a continuous one and a binary one. To overcome the major challenge of jointly modelling these responses, namely, the lack of a natural multivariate distribution, we introduce a Gaussian latent variable underlying the binary response. Then we decompose the model into two components: a marginal model for the continuous response, and a conditional model for the binary response given the continuous response. We develop a two-stage estimation procedure and discuss the asymptotic normality of the resulting estimators. We assess the finite-sample performance of our procedure using a simulation study, and we illustrate our method by analyzing binary and continuous responses from the Women's Interagency HIV Study.

Purpose: The aim of this study was to compare the incidence of ectopic pregnancy in GnRH agonist triggered IVF cycles with intensive luteal support versus hCG triggered IVF cycles., Methods: This study was conducted as a retrospective cohort analysis of women who underwent IVF treatment employing GnRH agonist or recombinant hCG (rhCG) triggers during 2-year period. The medical charts of women who achieved pregnancies were reviewed and their demographic characteristics, infertility reasons and IVF data were recorded. A multiple logistic regression analysis was performed to estimate the association between the triggering medication used to stimulate final oocyte maturation (GnRHa or rhCG) and EP, with adjustment for important confounders: the day of embryo transfer (ETD), the etiology of infertility and estrogen level at the time of triggering., Results: The number of metaphase II oocytes, fertilized oocytes and good quality embryos were significantly higher in the GnRH agonist triggered group compared with the hCG triggered group (p < 0.001 for all). The clinical pregnancy and implantation rates in the hCG triggered cycles were 38.6 and 31.1 %, respectively and 24.7 and 22 %, respectively in the triptorelin triggered cycles. The ectopic pregnancy rates were 5.3 % in the triptorelin triggered group and 1.4 % in the hCG triggered group. The trigger medication and the day of embryo transfer were found to have a significant effect on the probability of developing ectopic pregnancy (p = 0.028, p = 0.046 respectively). However, the estrogen level was not found to have a significant effect on the probability of developing ectopic pregnancy (p = 0.447)., Conclusions: The reasons for higher ectopic pregnancy rates in GnRH agonist triggered cycles relative to hCG triggered cycles may be the decreased receptivity of the endometrium due to insufficient luteal support and higher implantation potential of embryos in correlation with a higher number of good quality embryos obtained in these cycles.

Motivated by a study on factors affecting the level of photosynthetic activity in a natural ecosystem, we propose nonlinear varying coefficient models, in which the relationship between the predictors and the response variable is allowed to be nonlinear. One-step local linear estimators are developed for the nonlinear varying coefficient models and their asymptotic normality is established leading to point-wise asymptotic confidence bands for the coefficient functions. Two-step local linear estimators are also proposed for cases where the varying coefficient functions admit different degrees of smoothness; bootstrap confidence intervals are utilized for inference based on the two-step estimators. We further propose a generalized F test to study whether the coefficient functions vary over a covariate. We illustrate the proposed methodology via an application to an ecology data set and study the finite sample performance by Monte Carlo simulation studies.

Residents with dementia are the least likely to be engaged in the nursing home and often spend most of their time doing nothing at all. However, resident participation in meaningful activities is important to promote both physical and psychological health. Tailoring activities to individual functional abilities and personality preferences improves both the time and level of participation. This pilot study used an analysis of covariance procedure to test the relationship between the personality trait of agreeableness and engagement when activities are ideally tailored to ability and interest. No significant difference was found between the high and low agreeableness groups, indicating that residents were more engaged when activities were individually tailored, regardless of their agreeableness level. Although low agreeableness may pose a challenge when implementing activities for people with dementia, the results of this study suggest that tailoring activities to functional ability and interest may overcome the effects., (Copyright 2010, SLACK Incorporated.)