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Previous research has found that music brings back more vivid and emotional autobiographical memories than various other retrieval cues. However, such studies have often been low in ecological validity and constrained by relatively limited cue selection and predominantly young adult samples. Here, we compared music to food as cues for autobiographical memories in everyday life in young and older adults. In two separate four‐day periods, 39 younger (ages 18–34) and 39 older (ages 60–77) adults recorded their music‐ and food‐evoked autobiographical memories in paper diaries. Across both age groups, music triggered more frequent autobiographical memories, a greater proportion of involuntary memories, and memories rated as more personally important in comparison to food cues. Age differences impacted music‐ and food‐evoked memories similarly, with older adults consistently recalling older and less specific memories, which they rated as more positive, vivid, and rehearsed. However, young and older adults did not differ in the number or involuntary nature of their recorded memories. This work represents an important step in understanding the phenomenology of naturally occurring music‐evoked autobiographical memories across adulthood and provides new insights into how and why music may be a more effective trigger for personally valued memories than certain other everyday cues. [ABSTRACT FROM AUTHOR]

Mediation analysis allows the examination of effects of a third variable in the pathway between an exposure and an outcome. The general multiple mediation analysis method, proposed by Yu et al, improves traditional methods (eg, estimation of natural and controlled direct effects) to enable consideration of multiple mediators/confounders simultaneously and the use of linear and nonlinear predictive models for estimating mediation/confounding effects. In this paper, we extend the method for time-to-event outcomes and apply the method to explore the racial disparity in breast cancer survivals. Breast cancer is the most common cancer and the second leading cause of cancer death among women of all races. Despite improvement of survival rates of breast cancer in the US, a significant difference between white and black women remains. Previous studies have found that more advanced and aggressive tumors and less than optimal treatment may explain the lower survival rates for black women as compared to white women. Due to limitations of current analytic methods and the lack of comprehensive data sets, researchers have not been able to differentiate the relative effect each factor contributes to the overall racial disparity. We use the CDC-funded Patterns of Care study to examine the determinants of racial disparities in breast cancer survival using a novel multiple mediation analysis. Using the proposed method, we applied the Cox hazard model and multiple additive regression trees as predictive models and found that all racial disparity in survival among Louisiana breast cancer patients were explained by factors included in the study. [ABSTRACT FROM AUTHOR]

Drug-drug interactions (DDIs) are a common cause of adverse drug events (ADEs). The electronic medical record (EMR) database and the FDA's adverse event reporting system (FAERS) database are the major data sources for mining and testing the ADE associated DDI signals. Most DDI data mining methods focus on pair-wise drug interactions, and methods to detect high-dimensional DDIs in medical databases are lacking. In this paper, we propose 2 novel mixture drug-count response models for detecting high-dimensional drug combinations that induce myopathy. The "count" indicates the number of drugs in a combination. One model is called fixed probability mixture drug-count response model with a maximum risk threshold (FMDRM-MRT). The other model is called count-dependent probability mixture drug-count response model with a maximum risk threshold (CMDRM-MRT), in which the mixture probability is count dependent. Compared with the previous mixture drug-count response model (MDRM) developed by our group, these 2 new models show a better likelihood in detecting high-dimensional drug combinatory effects on myopathy. CMDRM-MRT identified and validated (54; 374; 637; 442; 131) 2-way to 6-way drug interactions, respectively, which induce myopathy in both EMR and FAERS databases. We further demonstrate FAERS data capture much higher maximum myopathy risk than EMR data do. The consistency of 2 mixture models' parameters and local false discovery rate estimates are evaluated through statistical simulation studies. [ABSTRACT FROM AUTHOR]

Objective: To develop a model for identifying clinic performance at fulfilling next-day and walk-in requests after adjusting for patient demographics and risk.Data Source: Using Department of Veterans Affairs (VA) administrative data from 160 VA primary care clinics from 2014 to 2017.Study Design: Using a retrospective cohort design, we applied Bayesian hierarchical regression models to predict provision of timely care, with clinic-level random intercept and slope while adjusting for patient demographics and risk status. Timely care was defined as the provision of an appointment within 48 hours of any patient requesting the clinic's next available appointment or walking in to receive care.Data Collection/extraction Methods: We extracted 1 841 210 timely care requests from 613 263 patients.Principal Findings: Across 160 primary care clinics, requests for timely care were fulfilled 86 percent of the time (range 83 percent-88 percent). Our model of timely care fit the data well, with a Bayesian R2 of .8. Over the four years of observation, we identified 25 clinics (16 percent) that were either struggling or excelling at providing timely care.Conclusion: Statistical models of timely care allow for identification of clinics in need of improvement after adjusting for patient demographics and risk status. VA primary care clinics fulfilled 86 percent of timely care requests. [ABSTRACT FROM AUTHOR]

Biosurveillance for rapid detection of epidemics of diseases is a challenging area of endeavor in many respects. Hence, this area is in need of development of methodology and opens to novel methods of detection. In this study, a new simple statistical early outbreak detection approach is proposed to detect outbreaks of diseases in real time. The new approach is called LWMAT since it is based on linearly weighted moving average. Furthermore, it does not require a long baseline and partly takes into account of likely features of biosurveillance data such as nonstationary and overdispersion to some extent. Moreover, this newly proposed method is easily adapted to automated use in public health surveillance systems to monitor simultaneously large number time series of indicators associated with the relevant diseases. To compare the performance of the new method with those of some well-known outbreak detection methods, semisynthetic data with outbreaks of various magnitudes and durations are simulated by considering the weekly number of outpatient visits for influenza-like illness for the influenza seasons 2014-2015 through 2017-2018 at Centers for Disease Control and Prevention (CDC) in the United States. Under the conditions of the simulation studies, Serfling regression and Farrington flexible seem to be preferable methods for monitoring the weekly influenza data at CDC in terms of early identification of influenza outbreaks with a high probability. In addition, the newly proposed LWMAT-type methods appear to be promising and useful methods in the case of small magnitude outbreaks with a short duration. [ABSTRACT FROM AUTHOR]

Objective: To test whether underwriting modifies the effect of state-based incentives on individuals' purchase of long-term care insurance.Data Source: Health and Retirement Study (HRS), 1996-2012.Study Design: We estimated difference-in-difference regression models with an interaction of state policy indicators with individuals' probabilities of being approved for long-term care insurance.Data Extraction: We imputed probabilities of underwriting approval for respondents in the HRS using a model developed with underwriting decisions from two U.S. insurance firms. We measured the elasticity response to long-term care insurance price using changes in simulated after-tax price as an instrumental variable for premium price.Principal Findings: Tax incentives and Partnership programs increased insurance purchase by 3.62 percentage points and 1.8 percentage points, respectively, among those with the lowest risk (highest approval probability). Neither had any statistically significant effects among the highest risk individuals.Conclusions: We show that ignoring the effects of underwriting may lead to biased estimates of the potential state budget savings of long-term care insurance tax incentives. If the private market is to play a role in financing long-term care, policies need to address the underlying adverse selection problems. [ABSTRACT FROM AUTHOR]

Objective: To examine the relationship between cost and quality in Veterans Health Administration (VA) nursing homes (called Community Living Centers, CLCs) using longitudinal data.Data Sources/study Setting: One hundred and thirty CLCs over 13 quarters (from FY2009 to FY2012) were studied. Costs, resident days, and resident severity (RUGs score) were obtained from the VA Managerial Cost Accounting System. Clinical quality measures were obtained from the Minimum Data Set, and resident-centered care (RCC) was measured using the Artifacts of Culture Change Tool.Study Design: We used a generalized estimating equation model with facilities included as fixed effects to examine the relationship between total cost and quality after controlling for resident days and severity. The model included linear and squared terms for all independent variables and interactions with resident days.Principal Findings: With the exception of RCC, all other variables had a statistically significant relationship with total costs. For most poorer performing smaller facilities (lower size quartile), improvements in quality were associated with higher costs. For most larger facilities, improvements in quality were associated with lower costs.Conclusions: The relationship between cost and quality depends on facility size and current level of performance. [ABSTRACT FROM AUTHOR]

Objective: To estimate the cost of resources required to implement a set of Foundational Public Health Services (FPHS) as recommended by the Institute of Medicine.Study Design: A stochastic simulation model was used to generate probability distributions of input and output costs across 11 FPHS domains. We used an implementation attainment scale to estimate costs of fully implementing FPHS.Data Collection/extraction Methods: We use data collected from a diverse cohort of 19 public health agencies located in three states that implemented the FPHS cost estimation methodology in their agencies during 2014-2015.Principal Findings: The average agency incurred costs of $48 per capita implementing FPHS at their current attainment levels with a coefficient of variation (CV) of 16 percent. Achieving full FPHS implementation would require $82 per capita (CV=19 percent), indicating an estimated resource gap of $34 per capita.Conclusions: Substantial variation in costs exists across communities in resources currently devoted to implementing FPHS, with even larger variation in resources needed for full attainment. Reducing geographic inequities in FPHS may require novel financing mechanisms and delivery models that allow health agencies to have robust roles within the health system and realize a minimum package of public health services for the nation. [ABSTRACT FROM AUTHOR]

Aims: Prognostic models for hospitalized heart failure (HHF) were developed predominantly for patients of European origin in the United States of America; it is unclear whether they perform similarly in other health care systems or for different ethnicities. We sought to validate published prediction models for HHF in the United Kingdom (UK) and Japan.Methods and Results: Patients in the UK (n =894) and Japan (n =3158) were prospectively enrolled and were similar in terms of sex (∼60% men) and median age (∼77 years). Models predicted that British patients would have a higher mortality than Japanese, which was indeed true both for in-hospital (4.8% vs. 2.5%) and 180-day (20.7% vs. 9.5%) mortality. The model c-statistics for the published/derivation (range 0.70-0.76) and Japanese (range 0.75-0.77) cohorts were similar and higher than for the UK (0.62-0.75) but models consistently overestimated mortality in Japan. For in-hospital mortality, the OPTIMIZE-HF model performed best, providing similar discrimination in published/derivation, UK and Japanese cohorts [c-indices: 0.75 (0.74-0.77); 0.75 (0.68-0.81); and 0.77 (0.70-0.83), respectively], and least overestimated mortality in Japan. For 180-day mortality, the c-statistics for the ASCEND-HF model were similar in published/derivation (0.70) and UK [0.69 (0.64-0.74)] cohorts but higher in Japan [0.75 (0.71-0.79)]; calibration was good in the UK but again overestimated mortality in Japan.Conclusion: Calibration of published prediction models appears moderately accurate and unbiased when applied to British patients but consistently overestimates mortality in Japan. Identifying the reason why patients in Japan have a better than predicted prognosis is of great interest. [ABSTRACT FROM AUTHOR]

Objective: (1) To evaluate the operational efficiency of various sampling methods for patient exit interviews; (2) to discuss under what circumstances each method yields an unbiased sample; and (3) to propose a new, operationally efficient, and unbiased sampling method.Study Design: Literature review, mathematical derivation, and Monte Carlo simulations.Principal Findings: Our simulations show that in patient exit interviews it is most operationally efficient if the interviewer, after completing an interview, selects the next patient exiting the clinical consultation. We demonstrate mathematically that this method yields a biased sample: patients who spend a longer time with the clinician are overrepresented. This bias can be removed by selecting the next patient who enters, rather than exits, the consultation room. We show that this sampling method is operationally more efficient than alternative methods (systematic and simple random sampling) in most primary health care settings.Conclusion: Under the assumption that the order in which patients enter the consultation room is unrelated to the length of time spent with the clinician and the interviewer, selecting the next patient entering the consultation room tends to be the operationally most efficient unbiased sampling method for patient exit interviews. [ABSTRACT FROM AUTHOR]

Recent studies found that infection-related hospitalization was associated with increased risk of cardiovascular (CV) events, such as myocardial infarction and stroke in the dialysis population. In this work, we develop time-varying effects modeling tools in order to examine the CV outcome risk trajectories during the time periods before and after an initial infection-related hospitalization. For this, we propose partly conditional and fully conditional partially linear generalized varying coefficient models (PL-GVCMs) for modeling time-varying effects in longitudinal data with substantial follow-up truncation by death. Unconditional models that implicitly target an immortal population is not a relevant target of inference in applications involving a population with high mortality, like the dialysis population. A partly conditional model characterizes the outcome trajectory for the dynamic cohort of survivors, where each point in the longitudinal trajectory represents a snapshot of the population relationships among subjects who are alive at that time point. In contrast, a fully conditional approach models the time-varying effects of the population stratified by the actual time of death, where the mean response characterizes individual trends in each cohort stratum. We compare and contrast partly and fully conditional PL-GVCMs in our aforementioned application using hospitalization data from the United States Renal Data System. For inference, we develop generalized likelihood ratio tests. Simulation studies examine the efficacy of estimation and inference procedures. [ABSTRACT FROM AUTHOR]

Missing data is a common complication in data analysis. In many medical settings missing data can cause difficulties in estimation, precision and inference. Multiple imputation (MI) (Multiple Imputation for Nonresponse in Surveys. Wiley: New York, 1987) is a simulation-based approach to deal with incomplete data. Although there are many different methods to deal with incomplete data, MI has become one of the leading methods. Since the late 1980s we observed a constant increase in the use and publication of MI-related research. This tutorial does not attempt to cover all the material concerning MI, but rather provides an overview and combines together the theory behind MI, the implementation of MI, and discusses increasing possibilities of the use of MI using commercial and free software. We illustrate some of the major points using an example from an Alzheimer disease (AD) study. In this AD study, while clinical data are available for all subjects, postmortem data are only available for the subset of those who died and underwent an autopsy. Analysis of incomplete data requires making unverifiable assumptions. These assumptions are discussed in detail in the text. Relevant S-Plus code is provided. [ABSTRACT FROM AUTHOR]

Objective: To quantify uncertainty in forecasts of health expenditures.Study Design: Stochastic time series models are estimated for historical variations in fertility, mortality, and health spending per capita in the United States, and used to generate stochastic simulations of the growth of Medicare expenditures. Individual health spending is modeled to depend on the number of years until death.Data Sources/study Setting: A simple accounting model is developed for forecasting health expenditures, using the U.S. Medicare system as an example.Principal Findings: Medicare expenditures are projected to rise from 2.2 percent of GDP (gross domestic product) to about 8 percent of GDP by 2075. This increase is due in equal measure to increasing health spending per beneficiary and to population aging. The traditional projection method constructs high, medium, and low scenarios to assess uncertainty, an approach that has many problems. Using stochastic forecasting, we find a 95 percent probability that Medicare spending in 2075 will fall between 4 percent and 18 percent of GDP, indicating a wide band of uncertainty. Although there is substantial uncertainty about future mortality decline, it contributed little to uncertainty about future Medicare spending, since lower mortality both raises the number of elderly, tending to raise spending, and is associated with improved health of the elderly, tending to reduce spending. Uncertainty about fertility, by contrast, leads to great uncertainty about the future size of the labor force, and therefore adds importantly to uncertainty about the health-share of GDP. In the shorter term, the major source of uncertainty is health spending per capita.Conclusions: History is a valuable guide for quantifying our uncertainty about future health expenditures. The probabilistic model we present has several advantages over the high-low scenario approach to forecasting. It indicates great uncertainty about future Medicare expenditures relative to GDP. [ABSTRACT FROM AUTHOR]

An analysis of genetic and environmental contributions to intellectual change was carried out by means of a path model applied to IQ data from the Texas Adoption Project, an adoption study in which children were measured on 2 occasions approximately 10 years apart. Included in the model were assortative mating, selective placement, genotype-environment correlation, a measure of socioeconomic status, and alternative hypotheses about cross-generation environmental transmission and the persistence of a trait over time. Some form of environmental transmission across generations was necessary, but either of the 2 forms tested was sufficient. The data were best fit by considering persistence over time to occur at the level of the developed trait. The effect of both genes and family environment was significant at the time of the first measurement, but only the genes made an additional contribution between the first and the second, suggesting the necessity of revising some popular stereotypes about development. [ABSTRACT FROM AUTHOR]