Multiple Data Analyses and Statistical Approaches for Analyzing Data from Metagenomic Studies and Clinical Trials

Metagenomics, also known as environmental genomics, is the study of the genomic content of a sample of organisms (microbes) obtained from a common habitat. Metagenomics and other “omics” disciplines have captured the attention of researchers for several decades. The effect of microbes in our body is a relevant concern for health studies. There are plenty of studies using metagenomics which examine microorganisms that inhabit niches in the human body, sometimes causing disease, and are often correlated with multiple treatment conditions. No matter from which environment it comes, the analyses are often aimed at determining either the presence or absence of specific species of interest in a given metagenome or comparing the biological diversity and the functional activity of a wider range of microorganisms within their communities. The importance increases for comparison within different environments such as multiple patients with different conditions, multiple drugs, and multiple time points of same treatment or same patient. Thus, no matter how many hypotheses we have, we need a good understanding of genomics, bioinformatics, and statistics to work together to analyze and interpret these datasets in a meaningful way. This chapter provides an overview of different data analyses and statistical approaches (with example scenarios) to analyze metagenomics samples from different medical projects or clinical trials

Reconstructing continuity of care in mental health services:a multilevel conceptual framework

Continuity of mental health care is a key issue in the organization and evaluation of services for patients with disabling chronic conditions. Over many years, health services researchers have been exploring the conceptual boundaries between continuity of care and other service characteristics. On the basis of papers published over the past decade, we argue that while conceptual consensus is growing, there is room to improve continuity measures, and the development of practical interventions is still at an early stage. There is growing consensus that continuity of care is a multidimensional concept. We identified four core elements: continuous care; care of an individual patient; cross-boundary care; and care recorded objectively. These elements help clarify conceptual boundaries, and incorporate measurement guidelines. With reference to these core elements, we define types of continuity of care, including informational continuity, management continuity, relational continuity and contact continuity. In order to improve continuity of care, better understanding is needed of the complex inter-relationship of core elements and types of continuity. A multilevel perspective on continuity of care can guide research to develop and evaluate new interventions. Achieving continuity of care is hindered by the lack of standard measures and administrative data appropriate to assessing continuity. Account should be taken not only of the nature of the patient population, but also of local conditions. To address these topics and identify best practices, research should be multidisciplinary and take a comparative, naturalistic form