UNASSIGNED: This study aimed to examine the association between different dimensions of bi-directional intergenerational support and the social participation patterns of rural older adults, while also exploring the heterogeneity of these older adults by gender and age.
UNASSIGNED: Based on longitudinal survey data from the \'Well-Being of Older People in Anhui Province (WESAP)\' in 2018 and 2021, this study used latent class analysis to identify social participation patterns and used multinomial logistic regressions to explore the relationship between intergenerational support and social participation patterns among rural older adults.
UNASSIGNED: First, the social participation patterns of rural older persons can be divided into four categories: leisure type (9%), work type (11%), housekeeping type (57.9%) and family labor type (22.1%). Second, there is heterogeneity in the relationship between intergenerational support and social participation patterns in older people, among them, receiving financial support decreased the likelihood of older adults being categorized as work type and family labor type by 14 and 7.7%, respectively, while providing financial support increased the likelihood of older adults belonging to the family labor type by 7.5%; receiving caring support increased the likelihood of older parents being categorized as leisure type by 6%, while providing caring support decreased the likelihood of older parents being categorized as leisure type by 10%; emotional support is related to all patterns of social participation among older adults. Third, the effects of intergenerational support vary across subgroups based on gender and age.
UNASSIGNED: Two-way intergenerational support was significantly associated with social participation among rural older adults, and there was significant heterogeneity in this association. Children and older persons should be encouraged to strengthen two-way intergenerational mobility to give full play to the positive impact of different dimensions of intergenerational support on the social participation of older persons in rural areas, ultimately enhancing the quality of life for rural older adults.

Dietary assessments provide the snapshots of population-based dietary habits. Questions remain about how generalisable those snapshots are in national survey data, where certain subgroups are sampled disproportionately. We propose a Bayesian overfitted latent class model to derive dietary patterns, accounting for survey design and sampling variability. Compared to standard approaches, our model showed improved identifiability of the true population pattern and prevalence in simulation. We focus application of this model to identify the intake patterns of adults living at or below the 130% poverty income level. Five dietary patterns were identified and characterised by reproducible code/data made available to encourage further research.

This study aimed to estimate the prevalence of poultry aspergillosis and evaluate the accuracy of histopathology (test under evaluation) and mycological culture (an imperfect reference test). Farms raising layer and breeder or broiler birds, with suspected aspergillosis cases, clinical or subclinical, were eligible and visited for sampling. After necropsy, histopathology and mycological culture examinations were conducted by two evaluators. A Bayesian latent class model was used to estimate the accuracy of histopathology when compared to the imperfect reference test, mycological culture. A total of 142 chicken farms, 96 laying and breeding hen farms, and 46 broiler farms were used for the study. True aspergillosis median prevalence was estimated at 63.7% (95% credibility intervals, CrI: 53.8%, 73.0%) in layers and breeders and at 65.2% (95% CrI: 50.2%, 78.3%) in the broiler farms\' population. The median diagnostic sensitivity of histopathology and culture were estimated at, respectively, 98.8% (95% CrI: 94.6%, 100.0%) and 90.4% (95% CrI: 83.6%, 95.3%). Tests\' diagnostic specificity was estimated at, respectively, 97.3% (95% CrI: 87.7%, 99.9%) and 95.7% (95% CrI: 91.8%, 98.2%). Both tests had very high and comparable positive predictive values, but, in a population where disease prevalence was 25%, histopathology had a higher negative predictive value than culture.

BACKGROUND: Diagnosis of tuberculous meningitis (TBM) is hampered by the lack of a gold standard. Current microbiological tests lack sensitivity and clinical diagnostic approaches are subjective. We therefore built a diagnostic model that can be used before microbiological test results are known.
METHODS: We included 659 individuals aged [Formula: see text] years with suspected brain infections from a prospective observational study conducted in Vietnam. We fitted a logistic regression diagnostic model for TBM status, with unknown values estimated via a latent class model on three mycobacterial tests: Ziehl-Neelsen smear, Mycobacterial culture, and GeneXpert. We additionally re-evaluated mycobacterial test performance, estimated individual mycobacillary burden, and quantified the reduction in TBM risk after confirmatory tests were negative. We also fitted a simplified model and developed a scoring table for early screening. All models were compared and validated internally.
RESULTS: Participants with HIV, miliary TB, long symptom duration, and high cerebrospinal fluid (CSF) lymphocyte count were more likely to have TBM. HIV and higher CSF protein were associated with higher mycobacillary burden. In the simplified model, HIV infection, clinical symptoms with long duration, and clinical or radiological evidence of extra-neural TB were associated with TBM At the cutpoints based on Youden\'s Index, the sensitivity and specificity in diagnosing TBM for our full and simplified models were 86.0% and 79.0%, and 88.0% and 75.0% respectively.
CONCLUSIONS: Our diagnostic model shows reliable performance and can be developed as a decision assistant for clinicians to detect patients at high risk of TBM. Diagnosis of tuberculous meningitis is hampered by the lack of gold standard. We developed a diagnostic model using latent class analysis, combining confirmatory test results and risk factors. Models were accurate, well-calibrated, and can support both clinical practice and research.

Clustering longitudinal features is a common goal in medical studies to identify distinct disease developmental trajectories. Compared to clustering a single longitudinal feature, integrating multiple longitudinal features allows additional information to be incorporated into the clustering process, which may reveal co-existing longitudinal patterns and generate deeper biological insight. Despite its increasing importance and popularity, there is limited practical guidance for implementing cluster analysis approaches for multiple longitudinal features and evaluating their comparative performance in medical datasets. In this paper, we provide an overview of several commonly used approaches to clustering multiple longitudinal features, with an emphasis on application and implementation through R software. These methods can be broadly categorized into two categories, namely model-based (including frequentist and Bayesian) approaches and algorithm-based approaches. To evaluate their performance, we compare these approaches using real-life and simulated datasets. These results provide practical guidance to applied researchers who are interested in applying these approaches for clustering multiple longitudinal features. Recommendations for applied researchers and suggestions for future research in this area are also discussed.

Understanding the relationships between exposure and disease incidence is an important problem in environmental epidemiology. Typically, a large number of these exposures are measured, and it is found either that a few exposures transmit risk or that each exposure transmits a small amount of risk, but, taken together, these may pose a substantial disease risk. Further, these exposure effects can be nonlinear. We develop a latent functional approach, which assumes that the individual effect of each exposure can be characterized as one of a series of unobserved functions, where the number of latent functions is less than or equal to the number of exposures. We propose Bayesian methodology to fit models with a large number of exposures and show that existing Bayesian group LASSO approaches are a special case of the proposed model. An efficient Markov chain Monte Carlo sampling algorithm is developed for carrying out Bayesian inference. The deviance information criterion is used to choose an appropriate number of nonlinear latent functions. We demonstrate the good properties of the approach using simulation studies. Further, we show that complex exposure relationships can be represented with only a few latent functional curves. The proposed methodology is illustrated with an analysis of the effect of cumulative pesticide exposure on cancer risk in a large cohort of farmers.

Cancer is a major contributor to global disease burden. Many countries experienced or are experiencing the transition that non-infection-related cancers replace infection-related cancers. We aimed to characterise burden changes for major types of cancers and identify global transition patterns. We focused on 10 most common cancers worldwide and extracted age-standardised incidence and mortality in 204 countries and territories from 1990 to 2019 through the Global Burden of Disease Study. Two-stage modelling design was used. First, we applied growth mixture models (GMMs) to identify distinct trajectories for incidence and mortality of each cancer type. Next, we performed latent class analysis to detect cancer transition patterns based on the categorisation results from GMMs. Kruskal-Wallis H tests were conducted to evaluate associations between transition patterns and socioeconomic indicators. Three distinct patterns were identified as unfavourable, intermediate and favourable stages. Trajectories of lung and breast cancers had the strongest association with transition patterns among men and women. The unfavourable stage was characterised by rapid increases in lung, breast and colorectal cancers alongside stable or decreasing burden of gastric, cervical, oesophageal and liver cancers. In contrast, the favourable stage exhibited rapid declines in most cancers. The unfavourable stage was associated with lower sociodemographic index, health expenditure, gross domestic product per capita and higher maternal mortality ratio (P < .001 for all associations). Our findings suggest that unfavourable, intermediate and favourable transition patterns exist. Countries and territories in the unfavourable stage tend to be socioeconomically disadvantaged, and tailored intervention strategies are needed in these resource-limited settings.

Radionuclide imaging plays a critical role in the diagnosis and management of kidney obstruction. However, most practicing radiologists in US hospitals have insufficient time and resources to acquire training and experience needed to interpret radionuclide images, leading to increased diagnostic errors. To tackle this problem, Emory University embarked on a study that aims to develop a computer-assisted diagnostic (CAD) tool for kidney obstruction by mining and analyzing patient data comprised of renogram curves, ordinal expert ratings on the obstruction status, pharmacokinetic variables, and demographic information. The major challenges here are the heterogeneity in data modes and the lack of gold standard for determining kidney obstruction. In this article, we develop a statistically principled CAD tool based on an integrative latent class model that leverages heterogeneous data modalities available for each patient to provide accurate prediction of kidney obstruction. Our integrative model consists of three sub-models (multilevel functional latent factor regression model, probit scalar-on-function regression model, and Gaussian mixture model), each of which is tailored to the specific data mode and depends on the unknown obstruction status (latent class). An efficient MCMC algorithm is developed to train the model and predict kidney obstruction with associated uncertainty. Extensive simulations are conducted to evaluate the performance of the proposed method. An application to an Emory renal study demonstrates the usefulness of our model as a CAD tool for kidney obstruction.

This study explores the factors associated with passenger satisfaction on the UK railways. To uncover taste variation, the data was segmented into three homogeneous groups of passengers through a latent class ordered logit model, whereby the class allocation was based on observed personal and trip characteristics. The findings suggest that there is significant variation in the impact of service attributes on overall satisfaction across the segments, \'class a\', \'class b\' and \'class c\'. Class a (15% of the sample) consists of moderately dissatisfied to highly dissatisfied passengers, for whom \'punctuality/reliability\' is most impactful on overall satisfaction. Respondents in this class are much more likely to experience adverse service conditions such as delays or crowding conditions. Class b (32% of the sample) consists of passenger who are quite critical and moderately satisfied, for whom \'hedonic\' factors such as \'upkeep and repair of the train\' and \'seat comfort\' were most impactful. Finally, class c (53% of the sample) consists of passengers that are generally satisfied, and for whom the \'value for money of the ticket price\' is most impactful on overall satisfaction. Interestingly, for both \'class b\' and \'class c\', \'punctuality/reliability\' plays a more limited role in determining overall satisfaction compared to \'class a\'. This suggests that the role of \'punctuality/reliability\' in determining overall satisfaction is more complex than presented in the literature thus far. Finally, unobserved taste variation plays an important role in the model, as the class allocation is not always easily linked to observed groups in the data. This paper thus highlights the importance of accounting for unobserved and systematic sources of heterogeneity in the data and could provide useful insights for analysts, policy makers and practitioners, to provide more targeted strategies to improve passenger satisfaction.

Researchers in biology and medicine have increasingly focused on characterizing circadian rhythms and their potential impact on disease. Understanding circadian variation in metabolomics, the study of chemical processes involving metabolites may provide insight into important aspects of biological mechanism. Of scientific importance is developing a statistical rigorous approach for characterizing different types of 24-hour patterns among high dimensional longitudinal metabolites. We develop a latent class approach to incorporate variation in 24-hour patterns across metabolites where profiles are modeled with finite mixtures of distinct shape-invariant circadian curves that themselves incorporate variation in amplitude and phase across metabolites. An efficient Markov chain Monte Carlo sampling is used to carry out Bayesian posterior computation. When the model was fit separately by individual to the data from a small group of participants, two distinct 24-hour rhythms were identified, with one being sinusoidal and the other being more complex with multiple peaks. Interestingly, the latent pattern associated with circadian variation (simple sinusoidal curve) had a similar phase across the three participants, while the more complex latent pattern reflecting diurnal variation differed across individual. The results suggested that this modeling framework can be used to separate 24-hour rhythms into an endogenous circadian and one or more exogenous diurnal patterns in describing human metabolism.