Longitudinal dietary trajectories from preconception to mid-childhood in women and children in the Southampton Women’s Survey and their relation to offspring adiposity: a group-based trajectory modelling approach

Background Rates of childhood obesity are increasing globally, with poor dietary quality an important contributory factor. Evaluation of longitudinal diet quality across early life could identify timepoints and subgroups for nutritional interventions as part of effective public health strategies. Objective This research aimed to: (1) define latent classes of mother-offspring diet quality trajectories from pre-pregnancy to child age 8–9 years, (2) identify early life factors associated with these trajectories, and (3) describe the association between the trajectories and childhood adiposity outcomes. Design Dietary data from 2963 UK Southampton Women’s Survey mother-offspring dyads were analysed using group-based trajectory modelling of a diet quality index (DQI). Maternal diet was assessed pre-pregnancy and at 11- and 34-weeks’ gestation, and offspring diet at ages 6 and 12 months, 3, 6-7- and 8–9-years using interviewer-administered food frequency questionnaires. At each timepoint, a standardised DQI was derived using principal component analysis. Adiposity age 8–9 years was assessed using dual-energy X-ray absorptiometry (DXA) and BMI z-scores. Results A five-trajectory group model was identified as optimal. The diet quality trajectories were characterised as stable, horizontal lines and were categorised as poor (n = 142), poor-medium (n = 667), medium (n = 1146), medium-better (n = 818) and best (n = 163). A poorer dietary trajectory was associated with higher maternal pre-pregnancy BMI, smoking, multiparity, lower maternal age and lower educational attainment. Using linear regression adjusted for confounders, a 1-category decrease in the dietary trajectory was associated with higher DXA percentage body fat (0.08 SD (95% confidence interval 0.01, 0.15) and BMI z-score (0.08 SD (0.00, 0.16) in the 1216 children followed up at age 8–9 years. Conclusion Mother-offspring dietary trajectories are stable across early life, with poorer diet quality associated with maternal socio-demographic and other factors and childhood adiposity. The preconception period may be an important window to promote positive maternal dietary changes in order to improve childhood outcomes.

Breakfast Quality Index Trajectories and Association With Obesity Outcomes Among Australian Children Under 5 Years of Age

Background: Breakfast quality in early childhood remains understudied. This study described the changes in breakfast quality index (BQI) (i.e. trajectory) and assessed the association between BQI trajectories and obesity outcomes in early childhood.Methods: Data of children who participated in the Melbourne InFANT Program were used (n=328). Dietary intakes were assessed at ages 1.5, 3.5 and 5.0 years using three 24-hour recalls. BQI was calculated using a revised 9-item BQI tool based on Australian dietary recommendations for young children. Group-based trajectory modelling identified BQI trajectory groups. Multivariable linear and logistic regression examined the associations between identified BQI trajectory groups and obesity outcomes at age 5 years. Results: Mean BQI at ages 1.5, 3.5 and 5.0 years was 4.8, 4.8, 2.7 points, respectively. Two BQI trajectory groups were identified, and both showed a decline in BQI. The mean BQI of most children (74%) decreased from 5.0 to 4.0 points from ages 1.5 to 5.0 years (referred as “High BQI” group). The remaining children (26%) had a mean BQI of 4.8 and 1.2 points at age 1.5 and 5.0 years, respectively (referred as “Low BQI” group). The “Low BQI” group appeared to show higher risk of overweight (OR:1.39, 95%CI: 0.67, 2.88) at age 5 years than the “High BQI” group.Conclusions: Two BQI trajectory groups were identified. Both groups showed a decline in breakfast quality from ages 1.5 to 5.0 years. Our study highlights the need for early health promotion interventions and strategies to improve and maintain breakfast quality across early childhood.

Trajectories of sickness absence and disability pension days among people with multiple sclerosis by type of occupation

Background: Multiple sclerosis (MS) can impact working life, sickness absence (SA) and disability pension (DP). Different types of occupations involve different demands, which may be associated with trajectories of SA/DP among people with MS (PwMS). Objectives: To explore, among PwMS and references, if SA/DP differ according to type of occupation. Furthermore, to examine how trajectories of SA/DP days are associated with type of occupation among PwMS. Methods: A longitudinal nationwide Swedish register-based cohort study was conducted, including 6100 individuals with prevalent MS and 38,641 matched references from the population. Trajectories of SA/DP were identified with group-based trajectory modelling. Multinomial logistic regressions were estimated for associations between identified trajectories and occupations. Results: Increase of SA/DP over time was observed in all occupational groups, in both PwMS and references, with higher levels of SA/DP among PwMS. The lowest levels of SA/DP were observed among managers. Three trajectory groups of SA/DP were identified: Persistently Low (55.2%), Moderate Increasing (31.9%) and High Increasing (12.8%). Managers and those working in Science & Technology, and Economics, Social & Cultural were more likely to belong to the Persistently Low group. Conclusion: Results suggest that type of occupation plays a role in the level and course of SA/DP.

Group-Based Trajectory Modelling (GBTM) To Assess the Effect of Medication Adherence on Health-Related Outcomes: A Protocol for A Systematic Review

Background: The Group-based trajectory modelling (GBTM) method is increasingly used in pharmacoepidemiologic studies to describe medication adherence trajectories over time. However, assessing the effects of these medication adherence trajectories on health-related outcomes remains challenging. The purpose of this review is to describe studies assessing the effects of medication adherence trajectories estimated by the GBTM method on health-related outcomes. Methods: We will conduct a systematic review according to the recommendations of the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) guidelines. We will search in the following databases: PubMed, Embase, PsycINFO, Web of Science, CINAHL, and Cochrane database up to April 1st, 2021. Two reviewers will independently select articles and extract data. Discrepancies at every step will be resolved through discussion, and consensus will be reached for all disagreed articles. A third reviewer will act as a referee if needed. We will use tables to synthesize the modalities used to estimate medication adherence trajectories and the effect of adherence trajectories on health-related outcomes. We will identify the types of health-related outcomes studied and how they are defined, the statistical models used, the effect measure yield, and how medication adherence trajectories have been incorporated in the model. We will also review the limitations and biases reported by the authors and their attempts to mitigate them. We will provide a narrative synthesis.Discussion: This review will provide a clear view of the strategies and methods used in medication adherence research to estimate the effects of adherence trajectories on different health-related outcomes. A thorough exploration of how GBTM is used for this specific purpose could represent the first crucial steps towards optimizing the utilization of this method in adherence studies. Systematic review registration: Prospero CRD42021213503.

Young Adulthood Outcomes of Joint Mental Health Trajectories: A Group-Based Trajectory Model Analysis of a 13-Year Longitudinal Cohort Study

Developmental trajectories of common mental health issues such as ADHD symptoms, internalising problems, and externalising problems can often be usefully summarised in terms of a small number of ‘developmental subtypes’ (e.g., ‘childhood onset’, ‘adolescent onset’) that may differ in their profiles or levels of clinically meaningful variables such as etiological risk factors. However, given the strong tendency for symptoms in these domains to co-occur, it is important to consider not only developmental subtypes in each domain individually, but also the joint developmental subtypes defined by symptoms trajectories in all three domains together (e.g., ‘late onset multimorbid’, ‘pure internalising’, ‘early onset multimorbid’). Previous research has illuminated the joint developmental subtypes of ADHD symptoms, internalising problems, and externalising problems that emerge from normative longitudinal data using methods such as group-based trajectory modelling, as well as predictors of membership in these developmental subtypes. However, information on the long-term outcomes of developmental subtype membership is critical to illuminate the likely nature and intensity of support needs required for individuals whose trajectories fit different developmental subtypes. We, therefore, evaluated the relations between developmental subtypes previously derived using group-based trajectory modelling in the z-proso study (n = 1620 with trajectory data at ages 7, 8, 9, 10, 11, 12, 13, 15) and early adulthood outcomes. Individuals with multimorbid trajectories but not ‘pure’ internalising problem elevations showed higher levels of social exclusion and delinquency at age 20. These associations held irrespective of the specific developmental course of symptoms (e.g., early versus late onset versus remitting). There was also some evidence that intimate partner violence acts as a form of heterotypic continuity for earlier externalising problems. Results underline the need for early intervention to address the pathways that lead to social exclusion and delinquency among young people with multiple co-occurring mental health issues.

Process Mining of Disease Trajectories: A Literature Review

Disease trajectories model patterns of disease over time and can be mined by extracting diagnosis codes from electronic health records (EHR). Process mining provides a mature set of methods and tools that has been used to mine care pathways using event data from EHRs and could be applied to disease trajectories. This paper presents a literature review on process mining related to mining disease trajectories using EHRs. Our review identified 156 papers of potential interest but only four papers which directly applied process mining to disease trajectory modelling. These four papers are presented in detail covering data source, size, selection criteria, selections of the process mining algorithms, trajectory definition strategies, model visualisations, and the methods of evaluation. The literature review lays the foundations for further research leveraging the established benefits of process mining for the emerging data mining of disease trajectories.