Exploring the longitudinal dynamics of herd BVD antibody test results using model-based clustering

A novel shape-based approach to identify gestational age-adjusted growth patterns from birth to 11 years of age

Child growth patterns assessment is critical to design public health interventions. However, current analytical approaches may overlook population heterogeneity. To overcome this limitation, we developed a growth trajectories clustering pipeline that incorporates a shape-respecting distance, baseline centering (i.e., birth-size normalized trajectories) and Gestational Age (GA)-correction to characterize shape-based child growth patterns. We used data from 3945 children (461 preterm) in the 2004 Pelotas Birth Cohort with at least 3 measurements between birth (included) and 11 years of age. Sex-adjusted weight-, length/height- and body mass index-for-age z-scores were derived at birth, 3 months, and at 1, 2, 4, 6 and 11 years of age (INTERGROWTH-21st and WHO growth standards). Growth trajectories clustering was conducted for each anthropometric index using k-means and a shape-respecting distance, accounting or not for birth size and/or GA-correction. We identified 3 trajectory patterns for each anthropometric index: increasing (High), stable (Middle) and decreasing (Low). Baseline centering resulted in pattern classification that considered early life growth traits. GA-correction increased the intercepts of preterm-born children trajectories, impacting their pattern classification. Incorporating shape-based clustering, baseline centering and GA-correction in growth patterns analysis improves the identification of subgroups meaningful for public health interventions.

Lessons learned for animal health governance from bovine viral diarrhea eradication schemes in Scotland and Ireland

This paper explores lessons learned for animal health governance from bovine viral diarrhea (BVD) eradication schemes in Scotland and Ireland, drawing on qualitative key stakeholder interviews. Bovine viral diarrhea is an endemic cattle disease that causes animal health and welfare problems, as well as financial losses to farmers. Initial voluntary industry-led schemes to eradicate BVD were introduced in both countries in the 2010s, followed by compulsory phases involving legislation. The paper uses a theoretical framework of co-productive governance to analyze stakeholder views on how well the design and execution of the eradication schemes worked and what can be learned to inform future directions of animal health governance. The term “co-productive governance” comes from the field of environmental governance and was developed to describe how science and politics influence each other in a context where governance is carried out by multiple actors working collaboratively. The results of key stakeholder interviews are analyzed using the concepts of vision, context, knowledge, and process. In relation to vision, the results show the importance of creating a clear narrative about the goal of disease eradication schemes, which may incorporate or replace existing vet or farmer “narratives” about a disease. With regard to context, it is difficult to engage all actors in biosecurity governance, when initiatives are developed with the legacy of existing relationships and tensions. In relation to knowledge, the results showed the importance but political complexity of basing decisions on scientific research. One of the lessons learned was the benefit of involving industry stakeholders in setting scientific questions to inform the design of the scheme. Additionally, with reference to the process, while interviewees were enthusiastic about future prospects for industry and government working together to achieve biosecurity goals co-productive governance is not a panacea for enrolling all actors in biosecurity goals. The results also highlighted that farmers and other actors might object to an eradication scheme, whether it is run by government or private industry. Thus, it is useful to keep questions about who benefits in what way from biosecurity governance open.

Multi-cohort and longitudinal Bayesian clustering study of stage and subtype in Alzheimer's disease

Understanding Alzheimer's disease (AD) heterogeneity is important for understanding the underlying pathophysiological mechanisms of AD. However, AD atrophy subtypes may reflect different disease stages or biologically distinct subtypes. Here we use longitudinal magnetic resonance imaging data (891 participants with AD dementia, 305 healthy control participants) from four international cohorts, and longitudinal clustering to estimate differential atrophy trajectories from the age of clinical disease onset. Our findings (in amyloid-β positive AD patients) show five distinct longitudinal patterns of atrophy with different demographical and cognitive characteristics. Some previously reported atrophy subtypes may reflect disease stages rather than distinct subtypes. The heterogeneity in atrophy rates and cognitive decline within the five longitudinal atrophy patterns, potentially expresses a complex combination of protective/risk factors and concomitant non-AD pathologies. By alternating between the cross-sectional and longitudinal understanding of AD subtypes these analyses may allow better understanding of disease heterogeneity.