IntroductionHyperglycemia in pregnancy (HIP, including gestational diabetes and pre-existing type 1 and type 2 diabetes) is increasing, with associated risks to the health of women and their babies. Strategies to manage and prevent this condition are contested. Dynamic simulation models (DSM) can test policy and program scenarios before implementation in the real world. This paper reports the development and use of an advanced DSM exploring the impact of maternal weight status interventions on incidence of HIP.MethodsA consortium of experts collaboratively developed a hybrid DSM of HIP, comprising system dynamics, agent-based and discrete event model components. The structure and parameterization drew on a range of evidence and data sources. Scenarios comparing population-level and targeted prevention interventions were simulated from 2018 to identify the intervention combination that would deliver the greatest impact.ResultsPopulation interventions promoting weight loss in early adulthood were found to be effective, reducing the population incidence of HIP by 17.3% by 2030 (baseline (‘business as usual’ scenario)=16.1%, 95% CI 15.8 to 16.4; population intervention=13.3%, 95% CI 13.0 to 13.6), more than targeted prepregnancy (5.2% reduction; incidence=15.3%, 95% CI 15.0 to 15.6) and interpregnancy (4.2% reduction; incidence=15.5%, 95% CI 15.2 to 15.8) interventions. Combining targeted interventions for high-risk groups with population interventions promoting healthy weight was most effective in reducing HIP incidence (28.8% reduction by 2030; incidence=11.5, 95% CI 11.2 to 11.8). Scenarios exploring the effect of childhood weight status on entry to adulthood demonstrated significant impact in the selected outcome measure for glycemic regulation, insulin sensitivity in the short term and HIP in the long term.DiscussionPopulation-level weight reduction interventions will be necessary to ‘turn the tide’ on HIP. Weight reduction interventions targeting high-risk individuals, while beneficial for those individuals, did not significantly impact forecasted HIP incidence rates. The importance of maintaining interventions promoting healthy weight in childhood was demonstrated.
IntroductionCaregivers of persons with dementia and mild cognitive impairment (MCI) are at risk of decreased well-being. While many interventions for caregivers exist, evidence is sparse regarding intervention timing and effectiveness at an early stage of cognitive decline. Our systematic review aims to answer the following questions: (1) Do interventions for caregivers of persons with early stage dementia or MCI affect their well-being and ability to provide care? (2) Are particular types of caregiver interventions most effective during early stage cognitive decline? (3) How does effectiveness differ when early and later interventions are directly compared? (4) Do effects of early stage caregiver intervention vary based on care recipient and caregiver characteristics (eg, sex, type of dementia)?Methods and analysisThe databases MEDLINE, EMBASE, PSYCINFO and CINAHL, as well as grey literature databases, will be searched for English language studies using search terms related to caregiver interventions and dementia/MCI. Abstracts and full texts will be screened by two independent reviewers; included studies must assess the effects of an intervention for caregivers of persons with early stage dementia or MCI on caregiver well-being or ability to provide care. Intervention, study and participant characteristics will be extracted by two independent reviewers, along with outcome data. Risk of bias will be assessed using the Cochrane risk of bias tool (for controlled trials with and without randomisation). Interventions will be grouped by type (eg, psychoeducational) and a narrative synthesis is planned due to expected heterogeneity, but a meta-analysis will be performed where possible. The Grading of Recommendations, Assessment, Development and Evaluations approach will be used to inform conclusions regarding the quality of evidence for each type of intervention.Ethics and disseminationFindings from this review will be disseminated via conferences and peer-reviewed publication, and a summary will be provided to the Alzheimer Society.PROSPERO registration numberCRD42018114960.
BackgroundThe objective of the current study was to develop a stochastic agent-based model using empirical data from Ontario (Canada) swine sites in order to evaluate different surveillance strategies for detection of emerging porcine reproductive and respiratory syndrome virus (PRRSV) strains at the regional level. Four strategies were evaluated, including (i) random sampling of fixed numbers of swine sites monthly; (ii) risk-based sampling of fixed numbers, specifically of breeding sites (high-consequence sites); (iii) risk-based sampling of fixed numbers of low biosecurity sites (high-risk); and (iv) risk-based sampling of breeding sites that are characterized as low biosecurity sites (high-risk/high-consequence). The model simulated transmission of a hypothetical emerging PRRSV strain between swine sites through three important industry networks (production system, truck and feed networks) while considering sites’ underlying immunity due to past or recent exposure to heterologous PRRSV strains, as well as demographic, geographic and biosecurity-related PRRS risk factors. Outcomes of interest included surveillance system sensitivity and time to detection of the three first cases over a period of approximately three years.ResultsSurveillance system sensitivities were low and time to detection of three first cases was long across all examined scenarios.ConclusionTraditional modes of implementing high-risk and high-consequence risk-based surveillance based on site’s static characteristics do not appear to substantially improve surveillance system sensitivity. Novel strategies need to be developed and considered for rapid detection of this and other emerging swine infectious diseases. None of the four strategies compared herein appeared optimal for early detection of an emerging PPRSV strain at the regional level considering model assumptions, the underlying population of interest, and absence of other forms of surveillance.
Persuasive health technology (PHT) is any technology purposely designed to influence, reinforce, change, or shape health-related attitudes or behaviors. Behavioral interventions can be developed for the purpose of maintaining or improving a person’s health status. Delivering behavioral interventions via PHTs is a promising approach for encouraging healthy behaviors among individuals and populations. Important attributes of all PHTs include their functionalities. A functionality refers to any useful features, functions, capabilities, or technologies associated with computer hardware or software. Creating effective PHTs requires a deliberate selection of appropriate functionalities for supporting specific behavioral interventions. The number and types of functionalities necessary to create an effective PHT will be specific to the context of each project, influenced by project objectives, stakeholder goals, behavioral interventions, and a variety of real-world constraints. Selecting appropriate functionalities can be challenging. Fortunately, there are frameworks and models developed specifically for guiding the design of PHTs. The Persuasive Systems Design model describes 4 categories, and 28 design principles for creating effective persuasive interventions. These same design principles could also be useful for guiding the selection of appropriate functionalities.
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