Bmc Med
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Review Meta Analysis
Gestational weight gain across continents and ethnicity: systematic review and meta-analysis of maternal and infant outcomes in more than one million women.
The association between Institute of Medicine (IOM) guidelines and pregnancy outcomes across ethnicities is uncertain. We evaluated the associations of gestational weight gain (GWG) outside 2009 IOM guidelines, with maternal and infant outcomes across the USA, western Europe and east Asia, with subgroup analyses in Asia. The aim was to explore ethnic differences in maternal prepregnancy body mass index (BMI), GWG and health outcomes across these regions. ⋯ Women in the USA and western Europe have higher prepregnancy BMI and higher rates of GWG above guidelines than women in east Asia. However, when using regional BMI categories in east Asia, rates of GWG above guidelines are similar across the three continents. GWG outside guidelines is associated with adverse outcomes across all regions. If regional BMI categories are used in east Asia, IOM guidelines are applicable in the USA, western Europe and east Asia.
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Mathematical models of transmission dynamics are routinely fitted to epidemiological time series, which must inevitably be aggregated at some spatial scale. Weekly case reports of chikungunya have been made available nationally for numerous countries in the Western Hemisphere since late 2013, and numerous models have made use of this data set for forecasting and inferential purposes. Motivated by an abundance of literature suggesting that the transmission of this mosquito-borne pathogen is localized at scales much finer than nationally, we fitted models at three different spatial scales to weekly case reports from Colombia to explore limitations of analyses of nationally aggregated time series data. ⋯ Our model performed better when posed at finer spatial scales, due to better matching between human populations with locally relevant risk. Confronting spatially aggregated models with spatially aggregated data imposes a serious structural constraint on model behavior by averaging over epidemiologically meaningful spatial variation in drivers of transmission, impairing the ability of models to reproduce empirical patterns.
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The UK, like a number of other countries, has a refugee resettlement programme. External factors, such as higher prevalence of infectious diseases in the country of origin and circumstances of travel, are likely to increase the infectious disease risk of refugees, but published data is scarce. The International Organization for Migration carries out and collates data on standardised pre-entry health assessments (HA), including testing for infectious diseases, on all UK refugee applicants as part of the resettlement programme. From this data, we report the yield of selected infectious diseases (tuberculosis (TB), HIV, syphilis, hepatitis B and hepatitis C) and key risk factors with the aim of informing public health policy. ⋯ Testing refugees in an overseas setting through a systematic HA identified patients with a range of infectious diseases. Our results reflect similar patterns found in other programmes and indicate that the yields for infectious diseases vary by region and nationality. This information may help in designing a more targeted approach to testing, which has already started in the UK programme. Further work is needed to refine how best to identify infections in refugees, taking these factors into account.
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Personalized, precision, P4, or stratified medicine is understood as a medical approach in which patients are stratified based on their disease subtype, risk, prognosis, or treatment response using specialized diagnostic tests. The key idea is to base medical decisions on individual patient characteristics, including molecular and behavioral biomarkers, rather than on population averages. Personalized medicine is deeply connected to and dependent on data science, specifically machine learning (often named Artificial Intelligence in the mainstream media). While during recent years there has been a lot of enthusiasm about the potential of 'big data' and machine learning-based solutions, there exist only few examples that impact current clinical practice. The lack of impact on clinical practice can largely be attributed to insufficient performance of predictive models, difficulties to interpret complex model predictions, and lack of validation via prospective clinical trials that demonstrate a clear benefit compared to the standard of care. In this paper, we review the potential of state-of-the-art data science approaches for personalized medicine, discuss open challenges, and highlight directions that may help to overcome them in the future. ⋯ There is a need for an interdisciplinary effort, including data scientists, physicians, patient advocates, regulatory agencies, and health insurance organizations. Partially unrealistic expectations and concerns about data science-based solutions need to be better managed. In parallel, computational methods must advance more to provide direct benefit to clinical practice.
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Cardiovascular diseases (CVDs) represent the leading cause of death in China. The Chinese government approved the Healthy China 2030 plan (jiànkāng zhōngguó 2030), emphasizing the strategic role of health in China's development. As morbidity and mortality from CVDs are constantly increasing in China, the prevention and treatment of CVDs are vital to achieve this plan. Following the major principles of health priority, science and technology innovation, scientific development, and balanced medical resource allocation outlined in the Healthy China 2030 plan, this Commentary briefly introduces the current status of CVDs in China and marks the important events undertaken to achieve this plan.