Globally, congenital toxoplasmosis remains a significant cause of morbidity and mortality, and outbreaks of infection with T. gondii represent a significant, emerging public health burden, especially in the developing world. This parasite is a threat to public health. Disease often is not recognized and is inadequately managed. Herein, we analyze the status of congenital toxoplasmosis in Morocco, Colombia, the United States, and France. We identify the unique challenges faced by each nation in the implementation of optimal approaches to congenital toxoplasmosis as a public health problem. We suggest that developed and developing countries use a multipronged approach, modeling their public health management protocols after those in France. We conclude that education, screening, appropriate treatment, and the development of novel modalities will be required to intervene successfully in caring for individuals with this infection. Gestational screening has been demonstrated to be cost-effective, morbidity-sparing, and life-saving. Recognition of the value and promise of public health interventions to prevent human suffering from this emerging infection will facilitate better patient and societal outcomes.
BackgroundStreet imagery is a promising and growing big data source providing current and historical images in more than 100 countries. Studies have reported using this data to audit road infrastructure and other built environment features. Here we explore a novel application, using Google Street View (GSV) to predict travel patterns at the city level.MethodsWe sampled 34 cities in Great Britain. In each city, we accessed 2000 GSV images from 1000 random locations. We selected archived images from time periods overlapping with the 2011 Census and the 2011–2013 Active People Survey (APS). We manually annotated the images into seven categories of road users. We developed regression models with the counts of images of road users as predictors. The outcomes included Census-reported commute shares of four modes (combined walking plus public transport, cycling, motorcycle, and car), as well as APS-reported past-month participation in walking and cycling.ResultsWe found high correlations between GSV counts of cyclists (‘GSV-cyclists’) and cycle commute mode share (r = 0.92)/past-month cycling (r = 0.90). Likewise, GSV-pedestrians was moderately correlated with past-month walking for transport (r = 0.46), GSV-motorcycles was moderately correlated with commute share of motorcycles (r = 0.44), and GSV-buses was highly correlated with commute share of walking plus public transport (r = 0.81). GSV-car was not correlated with car commute mode share (r = –0.12). However, in multivariable regression models, all outcomes were predicted well, except past-month walking. The prediction performance was measured using cross-validation analyses. GSV-buses and GSV-cyclists are the strongest predictors for most outcomes.ConclusionsGSV images are a promising new big data source to predict urban mobility patterns. Predictive power was the greatest for those modes that varied the most (cycle and bus). With its ability to identify mode of travel and capture street activity often excluded in routinely carried out surveys, GSV has the potential to be complementary to new and traditional data. With half the world’s population covered by street imagery, and with up to 10 years historical data available in GSV, further testing across multiple settings is warranted both for cross-sectional and longitudinal assessments.
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