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Mycobacterium kansasii, a slow-growing nontuberculous mycobacteria (NTM), can cause destructive pulmonary diseases in humans that cause similar clinical manifestations to those of M. tuberculosis pulmonary disease (1). In recent years, [...]

A major question in clinical science is how to study the natural course of a chronic disease from inception to end, which is challenging because it is impractical to follow patients over decades. Here, we developed BETR (Bayesian entry time realignment), a hierarchical Bayesian method for investigating the long-term natural history of diseases using data from patients followed over short durations. A simulation study shows that BETR outperforms an existing method that ignores patient-level variation in progression rates. BETR, when combined with a common Bayesian model comparison tool, can identify the correct disease progression function nearly 100% of the time, with high accuracy in estimating the individual disease durations and progression rates. Application of BETR in patients with geographic atrophy, a disease with a known natural history model, shows that it can identify the correct disease progression model. Applying BETR in patients with Huntington's disease demonstrates that the progression of motor symptoms follows a second order function over approximately 20 years.

Although research indicates health and well-being benefits of greenspace, little is known regarding how greenspace may influence adaptation to health risks from heat, particularly how these risks change over time. Using daily hospitalization rates of Medicare beneficiaries ≥65 years for 2000-2016 in 40 U.S. Northeastern urban counties, we assessed how temperature-related hospitalizations from cardiovascular causes (CVD) and heat stroke (HS) changed over time. We analyzed effect modification of those temporal changes by Enhanced Vegetation Index (EVI), approximating greenspace. We used a two-stage analysis including a generalized additive model and meta-analysis. Results showed that relative risk (RR) (per 1 °C increase in lag0-3 temperature) for temperature-HS hospitalization was higher in counties with the lowest quartile EVI (RR = 2.7, 95% CI: 2.0, 3.4) compared to counties with the highest quartile EVI (RR = 0.40, 95% CI: 0.14, 1.13) in the early part of the study period (2000-2004). RR of HS decreased to 0.88 (95% CI: 0.31, 2.53) in 2013-2016 in counties with the lowest quartile EVI. RR for HS changed over time in counties in the highest quartile EVI, with RRs of 0.4 (95% CI: -0.7, 1.4) in 2000-2004 and 2.4 (95% CI: 1.6, 3.2) in 2013-2016. Findings suggest that adaptation to heat-health associations vary by greenness. Greenspace may help lower risks from heat but such health risks warrant continuous local efforts such as heat-health plans.

Social and decision-making deficits are often the first symptoms of neuropsychiatric disorders. In recent years, economic games, together with computational models of strategic learning, have been increasingly applied to the characterization of individual differences in social behavior, as well as their changes across time due to disease progression, treatment, or other factors. At the same time, the high dimensionality of these data poses an important challenge to statistical estimation of these models, potentially limiting the adoption of such approaches in patients and special populations. We introduce a hierarchical Bayesian implementation of a class of strategic learning models, experience-weighted attraction (EWA), that is widely used in behavioral game theory. Importantly, this approach provides a unified framework for capturing between- and within-participant variation, including changes associated with disease progression, comorbidity, and treatment status. We show using simulated data that our hierarchical Bayesian approach outperforms representative agent and individual-level estimation methods that are commonly used in extant literature, with respect to parameter estimation and uncertainty quantification. Furthermore, using an empirical dataset, we demonstrate the value of our approach over competing methods with respect to balancing model fit and complexity. Consistent with the success of hierarchical Bayesian approaches in other areas of behavioral science, our hierarchical Bayesian EWA model represents a powerful and flexible tool to apply to a wide range of behavioral paradigms for studying the interplay between complex human behavior and biological factors.

The association between a single interpregnancy interval (IPI) and birth outcomes has not yet been explored using matched methods. We modeled the odds of preterm birth, being small for gestational age, and having low birth weight in a second, live-born infant in a cohort of 192,041 sibling pairs born in Western Australia between 1980 and 2010. The association between IPI and birth outcomes was estimated from the interaction between birth order and IPI (with 18-23 months as the reference category), using conditional logistic regression. Matched analysis showed the odds of preterm birth were higher for siblings born following an IPI of

Background: Heterogeneous outcome correlations across treatment arms and clusters have been increasingly acknowledged in cluster randomized trials with binary endpoints, where analytical methods have been developed to study such heterogeneity. However, cluster-specific outcome variances and correlations have yet to be studied for cluster randomized trials with continuous outcomes. Methods: This article proposes models fitted in the Bayesian setting with hierarchical variance structure to quantify heterogeneous variances across clusters and explain it with cluster-level covariates when the outcome is continuous. The models can also be extended to analyzing heterogeneous variances in individually randomized group treatment trials, with arm-specific cluster-level covariates, or in partially nested designs. Simulation studies are carried out to validate the performance of the newly introduced models across different settings. Results: Simulations showed that overall the newly introduced models have good performance, reporting low bias and approximately 95% coverage for the intraclass correlation coefficients and regression parameters in the variance model. When variances are heterogeneous, our proposed models had improved model fit over models with homogeneous variances. When used to analyze data from the Kerala Diabetes Prevention Program study, our models identified heterogeneous variances and intraclass correlation coefficients across clusters and examined cluster-level characteristics associated with such heterogeneity. Conclusion: We proposed new hierarchical Bayesian variance models to accommodate cluster-specific variances in cluster randomized trials. The newly developed methods inform the understanding of how an intervention strategy is implemented and disseminated differently across clusters and can help improve future trial design. [ABSTRACT FROM AUTHOR]

PurposeTo independently validate the performance of the University of North Carolina Optical Coherence Tomography (UNC OCT) Index in diagnosing and predicting early glaucoma.MethodsData of 118 normal subjects (118 eyes) and 96 subjects (96 eyes) with early glaucoma defined as visual field mean deviation (MD) greater than -4 decibels (dB), aged 40 to 80 years, and who were enrolled in the Full-Threshold Testing Size III, V, VI comparison study were used in this study. CIRRUS OCT average and quadrants' retinal nerve fiber layer (RNFL); optic disc vertical cup-to-disc ratio (VCDR), cup-to-disc area ratio, and rim area; and average, minimum, and six sectoral ganglion cell-inner plexiform layer (GCIPL) measurements were run through the UNC OCT Index algorithm. Area under the receiver operating characteristic curve (AUC) and sensitivities at 95% and 99% specificity were calculated and compared between single parameters and the UNC OCT Index.ResultsMean age was 60.1 ± 11.0 years for normal subjects and 66.5 ± 8.1 years for glaucoma patients (P < 0.001). MD was 0.29 ± 1.04 dB and -1.30 ± 1.35 dB in normal and glaucomatous eyes (P < 0.001), respectively. The AUC of the UNC OCT Index was 0.96. The best single metrics when compared to the UNC OCT Index were VCDR (0.93, P = 0.054), average RNFL (0.92, P = 0.014), and minimum GCIPL (0.91, P = 0.009). The sensitivities at 95% and 99% specificity were 85.4% and 76.0% (UNC OCT Index), 71.9% and 62.5% (VCDR, all P < 0.001), 64.6% and 53.1% (average RNFL, all P < 0.001), and 66.7% and 58.3% (minimum GCIPL, all P < 0.001), respectively.ConclusionsThe findings confirm that the UNC OCT Index may provide improved diagnostic perforce over that of single OCT parameters and may be a good tool for detection of early glaucoma.Translational relevanceThe UNC OCT Index algorithm may be incorporated easily into routine clinical practice and be useful for detecting early glaucoma.

BackgroundTo the authors' knowledge, limited data exist regarding long-term quality of life (QOL) for patients diagnosed with intracranial meningioma.MethodsThe data in the current study concerned 1722 meningioma cases diagnosed among residents of Connecticut, Massachusetts, California, Texas, and North Carolina from May 1, 2006 through March 14, 2013, and 1622 controls who were frequency matched to the cases by age, sex, and geography. These individuals were participants in a large, population-based, case-control study. Telephone interviews were used to collect data regarding QOL at the time of initial diagnosis or contact, using the Medical Outcomes Study Short-Form 36 Health Survey. QOL outcomes were compared by case/control status.ResultsPatients diagnosed with meningioma reported levels of physical, emotional, and mental health functioning below those reported in a general healthy population. Case participants and controls differed most significantly with regard to the domains of Physical and Social Functioning, Role-Physical, Role-Emotional, and Vitality.ConclusionsIn the current study, patients with meningioma experienced statistically significant decreases in QOL compared with healthy controls of a similar demographic breakdown, although these differences were found to vary in clinical significance. Cancer 2018;124:161-6. © 2017 American Cancer Society.

BackgroundPneumococcal 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.MethodsWe 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.ResultsWe 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.ConclusionsThese results suggest that PCVs have an important impact on hospitalizations for all-cause pneumonia in both low- and high-income populations.

BackgroundPneumococcal 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.MethodsWe 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.ResultsOur 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

BackgroundUpcoming 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.MethodsWe 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.ResultsWe 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.ConclusionsRecent 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.