Quantitative Methods to Detect Suicide and Self-Harm Clusters: A Systematic Review.

Suicide and self-harm clusters exist in various forms, including point, mass, and echo clusters. The early identification of clusters is important to mitigate contagion and allocate timely interventions. A systematic review was conducted to synthesize existing evidence of quantitative analyses of suicide and self-harm clusters. Electronic databases including Medline, Embase, Web of Science, and Scopus were searched from date of inception to December 2020 for studies that statistically analyzed the presence of suicide or self-harm clusters. Extracted data were narratively synthesized due to heterogeneity among the statistical methods applied. Of 7268 identified studies, 79 were eligible for narrative synthesis. Most studies quantitatively verified the presence of suicide and self-harm clusters based on the scale of the data and type of cluster. A Poisson-based scan statistical model was found to be effective in accurately detecting point and echo clusters. Mass clusters are typically detected by a time-series regression model, although limitations exist. Recently, the statistical analysis of suicide and self-harm clusters has progressed due to advances in quantitative methods and geospatial analytical techniques, most notably spatial scanning software. The application of such techniques to real-time surveillance data could effectively detect emerging clusters and provide timely intervention.

Children's unscheduled primary and emergency care in Ireland: a multimethod approach to understanding decision making, trends, outcomes and parental perspectives (CUPID): project protocol.

INTRODUCTION: The aim of this project is to determine the patterns, decision-making processes and parental preferences associated with unscheduled paediatric healthcare utilisation in Ireland. Unscheduled paediatric healthcare is outpatient care provided within primary care settings by general practitioners (GPs), emergency departments (EDs) located in paediatric and general hospitals, and out-of-hours services provided by cooperatives of GPs operating on a regional basis. This project will take a multimethod approach to analysing the utilisation of unscheduled paediatric healthcare nationally within the context of a significant change to the provision of healthcare for young children in Ireland-the introduction of free at the point of delivery GP care for all children aged under 6. METHODS AND ANALYSIS: A multimethod approach consisting of three work packages will be employed. Using patient-level data, work package 1 will describe patterns of attendance at primary care, out-of-hours medical services and at EDs. Applying a difference-in-difference methodology, the impact of the introduction of free GP care for children under 6 on attendance will be assessed. Work package 2 will explore geospatial trends of attendance at EDs, identifying disparities in ED attendance by local area and demographic characteristics. Work package 3 will employ two discrete choice experiments to examine parental preferences for unscheduled paediatric healthcare and GP decision making when referring a child to the ED. The insights gained by each of the work packages individually and collectively will inform evidence-based health policy for the organisation of paediatric care and resource allocation. ETHICS AND DISSEMINATION: Ethical approval for this research has been granted by University College Dublin, The Irish College of General Practitioners and the five participating hospitals. Results will be disseminated via publication in peer-reviewed journals, national and international conferences, and to relevant stakeholders and interest groups.

Factors that influence family and parental preferences and decision making for unscheduled paediatric healthcare: a systematic review protocol.

There is a plethora of factors that dictate where parents and families choose to seek unscheduled healthcare for their child; and the complexity of these decisions can present a challenge for policy makers and healthcare planners as these behaviours can have a significant impact on resources in the health system. The systematic review will seek to identify the factors that influence parents' and families' preferences and decision making when seeking unscheduled paediatric healthcare.  Five databases will be searched for published studies (CINAHL, PubMed, SCOPUS, PsycInfo, EconLit) and grey literature will also be searched. Inclusion and exclusion criteria will be applied and articles assessed for quality. A narrative approach will be used to synthesise the evidence that emerges from the review. By collating the factors that influence decision-making and attendance at these services, the review can inform future health policies and strategies seeking to expand primary care to support the provision of accessible and responsive care. The systematic review will also inform the design of a discrete choice experiment (DCE) which will seek to determine parental and family preferences for unscheduled paediatric healthcare. Policies such as Sláintecare that seek to expand primary care and reduce hospital admissions from emergency departments need to be cognisant of the nuanced and complex factors that govern patients' behaviour.

The importance of regional models in assessing canine cancer incidences in Switzerland.

Fitting canine cancer incidences through a conventional regression model assumes constant statistical relationships across the study area in estimating the model coefficients. However, it is often more realistic to consider that these relationships may vary over space. Such a condition, known as spatial non-stationarity, implies that the model coefficients need to be estimated locally. In these kinds of local models, the geographic scale, or spatial extent, employed for coefficient estimation may also have a pervasive influence. This is because important variations in the local model coefficients across geographic scales may impact the understanding of local relationships. In this study, we fitted canine cancer incidences across Swiss municipal units through multiple regional models. We computed diagnostic summaries across the different regional models, and contrasted them with the diagnostics of the conventional regression model, using value-by-alpha maps and scalograms. The results of this comparative assessment enabled us to identify variations in the goodness-of-fit and coefficient estimates. We detected spatially non-stationary relationships, in particular, for the variables related to biological risk factors. These variations in the model coefficients were more important at small geographic scales, making a case for the need to model canine cancer incidences locally in contrast to more conventional global approaches. However, we contend that prior to undertaking local modeling efforts, a deeper understanding of the effects of geographic scale is needed to better characterize and identify local model relationships.