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Individual-level longitudinal data on biological, behavioural, and social dimensions are becoming increasingly available. Typically, these data are analysed using mixed effects models, with the result summarised in terms of an average trajectory plus measures of the individual variations around this average. However, public health investigations would benefit from finer modelling of these individual variations which identify not just one average trajectory, but several typical trajectories. If evidence of heterogeneity in the development of these variables is found, the role played by temporally preceding (explanatory) variables as well as the potential impact of differential trajectories may have on later outcomes is often of interest. A wide choice of methods for uncovering typical trajectories and relating them to precursors and later outcomes exists. However, despite their increasing use, no practical overview of these methods targeted at epidemiological applications exists. Hence we provide: (a) a review of the three most commonly used methods for the identification of latent trajectories (growth mixture models, latent class growth analysis, and longitudinal latent class analysis); and (b) recommendations for the identification and interpretation of these trajectories and of their relationship with other variables. For illustration, we use longitudinal data on childhood body mass index and parental reports of fussy eating, collected in the Avon Longitudinal Study of Parents and Children.

Chronic musculoskeletal pain is common and associated with more general healthcare-seeking. However, musculoskeletal-related healthcare utilization is under-explored. This study aimed to explore, describe and profile trajectories of long-term musculoskeletal healthcare for people reporting chronic musculoskeletal pain. This exploratory prognostic cohort study combined survey and national health register data from a representative group of adult Danes reporting chronic musculoskeletal pain (N = 2929). Trajectories of long-term musculoskeletal healthcare use were generated using latent class growth analysis. Types of healthcare-seeking, individual, sociodemographic, health, belief and work-related factors were used to describe and profile identified trajectories. We identified five distinct trajectories of long-term musculoskeletal healthcare utilization (low stable, low ascending, low descending, medium stable and high stable). The low stable trajectory group (no or almost no annual contacts) represented 39% of the sample, whereas the high stable trajectory group (consistent high number of annual contacts) represented 8%. Most healthcare-seeking was in primary healthcare settings (GP/physiotherapy/chiropractor). Opioid consumption was primarily in the high stable trajectory group, and surgery was rare. There were statistically significant differences across the five trajectory groups in individual, sociodemographic, health, belief and work-related profiles. Long-term use of musculoskeletal healthcare services varied in this chronic musculoskeletal pain population. Almost 40% coped without seeking care, whereas 8% had consistent high use of healthcare services. Chronic musculoskeletal pain was mostly managed in primary care settings, which aligns with musculoskeletal guidelines, as did the use of pain medication and surgery. People with different musculoskeletal healthcare trajectories had different individual, sociodemographic, health, belief and work-related profiles.

This study aims at combining time and quantity of carbohydrate (CH) intake in the definition of eating patterns in UK adults and investigating the association of the derived patterns with type 2 diabetes (T2D). The National Diet and Nutrition Survey (NDNS) Rolling Program included 6155 adults in the UK. Time of the day was categorized into 7 pre-defined time slots: 6–9 am, 9–12 noon, 12–2 pm, 2–5 pm, 5–8 pm, 8–10 pm, and 10 pm–6 am. Responses for CH intake were categorized into: no energy intake, CH <50% or ≥50% of total energy. Non-parametric multilevel latent class analysis (MLCA) was applied to identify eating patterns of CH consumption across day-time, as a novel method accounting for the repeated measurements of intake over 3–4 days nested within individuals. Survey-designed multivariable regression was used to assess the associations of CH eating patterns with T2D. Three CH eating day patterns (low, high CH percentage and regular meal CH intake day) emerged from 24,483 observation days; based on which three classes of CH eaters were identified and characterized as: low (28.1%), moderate (28.8%) and high (43.1%) CH eaters. On average, low-CH eaters consumed the highest amount of total energy intake (7985.8 kJ) and had higher percentages of energy contributed by fat and alcohol, especially after 8 pm. Moderate-CH eaters consumed the lowest amount of total energy (7341.8 kJ) while they tended to have their meals later in the day. High-CH eaters consumed most of their carbohydrates and energy earlier in the day and within the time slots of 6–9 am, 12–2 p.m. and 5–8 pm, which correspond to traditional mealtimes. The high-CH eaters profile had the highest daily intake of CH and fiber and the lowest intake of protein and fat. Low-CH eaters had greater odds than high-CH eaters of having T2D in self-reported but not in previously undiagnosed diabetics. Further research using prospective longitudinal studies is warranted to ascertain the direction of causality in the association of CH patterns with type 2 diabetes.

As new cannabis products and administration methods proliferate, patterns of use are becoming increasingly heterogeneous. However, few studies have explored different profiles of cannabis use and their association with problematic use. Latent class analysis (LCA) was used to identify subgroups of past-year cannabis users endorsing distinct patterns of use from a large international sample (n = 55 240). Past-12-months use of six different cannabis types (sinsemilla, herbal, hashish, concentrates, kief, edibles) were used as latent class indicators. Participants also reported the frequency and amount of cannabis used, whether they had ever received a mental health disorder diagnosis and their cannabis dependence severity via the Severity of Dependence Scale (SDS). LCA identified seven distinct classes of cannabis use, characterised by high probabilities of using: sinsemilla & herbal (30.3% of the sample); sinsemilla, herbal & hashish (20.4%); herbal (18.4%); hashish & herbal (18.8%); all types (5.7%); edibles & herbal (4.6%) and concentrates & sinsemilla (1.7%). Relative to the herbal class, classes characterised by sinsemilla and/or hashish use had increased dependence severity. By contrast, the classes characterised by concentrates use did not show strong associations with cannabis dependence but reported greater rates of ever receiving a mental health disorder diagnosis. The identification of these distinct classes underscores heterogeneity among cannabis use behaviours and provides novel insight into their different associations with addiction and mental health.

Background The Naive Bayes (NB) classifier is a powerful supervised algorithm widely used in Machine Learning (ML). However, its effectiveness relies on a strict assumption of conditional independence, which is often violated in real-world scenarios. To address this limitation, various studies have explored extensions of NB that tackle the issue of non-conditional independence in the data. These approaches can be broadly categorized into two main categories: feature selection and structure expansion. In this particular study, we propose a novel approach to enhancing NB by introducing a latent variable as the parent of the attributes. We define this latent variable using a flexible technique called Bayesian Latent Class Analysis (BLCA). As a result, our final model combines the strengths of NB and BLCA, giving rise to what we refer to as NB-BLCA. By incorporating the latent variable, we aim to capture complex dependencies among the attributes and improve the overall performance of the classifier. Methods Both Expectation-Maximization (EM) algorithm and the Gibbs sampling approach were offered for parameter learning. A simulation study was conducted to evaluate the classification of the model in comparison with the ordinary NB model. In addition, real-world data related to 976 Gastric Cancer (GC) and 1189 Non-ulcer dyspepsia (NUD) patients was used to show the model's performance in an actual application. The validity of models was evaluated using the 10-fold cross-validation. Results The presented model was superior to ordinary NB in all the simulation scenarios according to higher classification sensitivity and specificity in test data. The NB-BLCA model using Gibbs sampling accuracy was 87.77 (95% CI: 84.87-90.29). This index was estimated at 77.22 (95% CI: 73.64-80.53) and 74.71 (95% CI: 71.02-78.15) for the NB-BLCA model using the EM algorithm and ordinary NB classifier, respectively. Conclusions When considering the modification of the NB classifier, incorporating a latent component into the model offers numerous advantages, particularly within medical and health-related contexts. By doing so, the researchers can bypass the extensive search algorithm and structure learning required in the local learning and structure extension approach. The inclusion of latent class variables allows for the integration of all attributes during model construction. Consequently, the NB-BLCA model serves as a suitable alternative to conventional NB classifiers when the assumption of independence is violated, especially in domains pertaining to health and medicine.

Background Epidemiological studies showed that Covid-19 patients with underlying diseases had higher rates of severe Covid-19. Previous studies focused on the presence of a single chronic disease but this study investigated the prevalence and patterns of multi-morbidities in patients with Covid-19 and its relationship with the severity of Covid-19. Methods This retrospective study focused on patients age 30 years and older with positive polymerase chain reaction (PCR) results in 24 hospitals of Mashhad in northeastern Iran from 20-3-2020 to 21-1-2022. The number of studied confirmed patients was 318,502. The underlying diseases were identified according to the International Classification of Diseases, and the severity of Covid-19, including death, need for ventilation, and need for treatment in the intensive care unit (ICU). The pattern of multi-morbidities in these confirmed cases was investigated using latent class analysis (LCA), and the relationship between this pattern and the severity of Covid-19 was determined by multivariate logistic regression. Results The most common coexisting diseases were hypertension in 30,100 patients (9.5%), metabolic disorders in 23,798 (7.5%) and hyperlipidemia in 22,454 (7%). Different comorbidities were grouped into three classes by the LCA model. Class 1 was patients without multi-morbidities, or 83% people., Class 2, which included 9% patients, was patients with hypertension, diabetes, respiratory diseases, and mental behavioral disorders (HRMD class). Class 3, which included patients with metabolic diseases, for whom the probability of developing hypertension, hyperlipidemia, diabetes, and metabolic disorders was high, included 7% patients. The results of multivariate logistic regression showed that having HRMD and metabolic diseases compared to no multi-morbidity adjusted for some risk factors increased the odds of developing severe Covid-19 by 81% and 55%, respectively. Conclusions The classes identified in this study provided a clear view of different groups of Covid-19 patients with certain multi-morbidities and underscore the importance of considering these patterns, rather than individual comorbidities, in risk assessment and management of COVID-19 patients. This approach will guide clinical decision-making and resource allocation in the ongoing management of the COVID-19 pandemic.

Tuberculin skin tests (TST), the primary diagnostic tool for bovine tuberculosis (bTB), cross-react with BCG vaccine. Recently developed defined antigen skin tests (DSTs) aim to differentiate infected amongst vaccinated animals. We evaluated the field performance of different interpretations of the TST and DSTs relative to IGRA and IDEXX M. bovis antibody tests. This panel of tests was assessed in 446 unvaccinated cattle across 22 Ethiopian dairy herds using Bayesian latent class models. We extended the standard Walter-Hui model to include age-related effects to explore evidence of the presence of diagnostic anergy. The latent class models estimate sensitivity and specificity of the DSTs to be between 84–88% and 79–85% respectively. The DSTs perform intermediately between the comparative intradermal test (CIT, sensitivity 77%, specificity 100%) and single intradermal test (SIT, sensitivity 99%, specificity 76%). We observed significant age-related declines in test sensitivity, most notably for CIT (declining from 75 to 52% over 9 years) and DST10 (83% to 68%), while other tests showed more stable sensitivity across age groups. This variable pattern across tests suggests mechanisms beyond simple age-related anergy. Together, these findings demonstrate that DSTs’ superior sensitivity to CIT and comparable or better specificity than SIT, combined with their ability to distinguish vaccinated animals, creates a viable pathway for implementing BCG vaccination programs. Given the absence of any gold standard definition of infection with bTB, latent class analyses are essential to assess the relative performance of different diagnostic tests. While our results provide encouraging news for the sensitivity of the new DST tests, the high prevalence of bTB within our study population makes our design underpowered to assess the specificity of the DSTs. Future research, including assessment of the specificity of DSTs in disease-free populations and optimization of test formulation and validation through large-scale field trials is essential to fully establish the case for use in vaccination and surveillance programs.

We propose a two-step estimator for multilevel latent class analysis (LCA) with covariates. The measurement model for observed items is estimated in its first step, and in the second step covariates are added in the model, keeping the measurement model parameters fixed. We discuss model identification, and derive an Expectation Maximization algorithm for efficient implementation of the estimator. By means of an extensive simulation study we show that (1) this approach performs similarly to existing stepwise estimators for multilevel LCA but with much reduced computing time, and (2) it yields approximately unbiased parameter estimates with a negligible loss of efficiency compared to the one-step estimator. The proposal is illustrated with a cross-national analysis of predictors of citizenship norms.

Latent class models are powerful statistical modeling tools widely used in psychological, behavioral, and social sciences. In the modern era of data science, researchers often have access to response data collected from large-scale surveys or assessments, featuring many items (large J ) and many subjects (large N ). This is in contrary to the traditional regime with fixed J and large N . To analyze such large-scale data, it is important to develop methods that are both computationally efficient and theoretically valid. In terms of computation, the conventional EM algorithm for latent class models tends to have a slow algorithmic convergence rate for large-scale data and may converge to some local optima instead of the maximum likelihood estimator (MLE). Motivated by this, we introduce the tensor decomposition perspective into latent class analysis with binary responses. Methodologically, we propose to use a moment-based tensor power method in the first step and then use the obtained estimates as initialization for the EM algorithm in the second step. Theoretically, we establish the clustering consistency of the MLE in assigning subjects into latent classes when N and J both go to infinity. Simulation studies suggest that the proposed tensor-EM pipeline enjoys both good accuracy and computational efficiency for large-scale data with binary responses. We also apply the proposed method to an educational assessment dataset as an illustration.

Background & Aims The significance of a reciprocal link between public opinion and policy making is widely acknowledged in literature. The aim of this study is to explore public opinion on gambling policy by identifying distinct latent classes of attitudes towards policy goals. The findings can provide insights into future gambling regulation and policy making. Methods Data were derived from a 2023 population-based Finnish Gambling Study ( n  = 5977) representing the population aged 15-74-year-olds. A set of questions regarding gambling policy attitudes were measured. By using a latent class analysis (LCA) we identified classes of individuals with regard to their respond patterns towards gambling policy questions, considering age, gender, gambling frequency and problem gambling status. Logistic regression was then used to predict class membership by demographic background, gambling behavior, and attitude towards gambling. Results Harm prevention, crime reduction, and control of unauthorized operators were the most endorsed gambling policy goals. Women, non-gamblers, and those affected by others’ gambling more often supported harm reduction and marketing restrictions, while men and recent gamblers prioritized consumer protection, player choice, and competition. Latent class analysis identified three groups: Traditionalists (54.8%), Harm-reducers (32.5%), and Libertarians (12.7%). Compared to Traditionalists, Harm-reducers were more likely to be younger and non-gamblers, while Libertarians were typically male, offshore, with at-risk/problem gambling, and held more pro-gambling attitudes. Conclusions Most respondents supported harm-reduction or even stricter gambling policies, while only a minority – mainly younger males, offshore gamblers, and those with at-risk or problem gambling behavior – favored liberalization and increased consumer choice. This reveals a clear mismatch between public preferences and current policy trends in Finland, which are moving toward more liberal gambling policy. Attitudes were closely tied to personal gambling behavior, with younger non-gamblers supporting gambling reduction and older gamblers without harms backing stronger regulation.