ObjectivesTo develop predictive models for blood culture (BC) outcomes in an emergency department (ED) setting.DesignRetrospective observational study.SettingED of a large teaching hospital in the Netherlands between 1 September 2018 and 24 June 2020.ParticipantsAdult patients from whom BCs were collected in the ED. Data of demographic information, vital signs, administered medications in the ED and laboratory and radiology results were extracted from the electronic health record, if available at the end of the ED visits.Main outcome measuresThe primary outcome was the performance of two models (logistic regression and gradient boosted trees) to predict bacteraemia in ED patients, defined as at least one true positive BC collected at the ED.ResultsIn 4885 out of 51 399 ED visits (9.5%), BCs were collected. In 598/4885 (12.2%) visits, at least one of the BCs was true positive. Both a gradient boosted tree model and a logistic regression model showed good performance in predicting BC results with area under curve of the receiver operating characteristics of 0.77 (95% CI 0.73 to 0.82) and 0.78 (95% CI 0.73 to 0.82) in the test sets, respectively. In the gradient boosted tree model, the optimal threshold would predict 69% of BCs in the test set to be negative, with a negative predictive value of over 94%.ConclusionsBoth models can accurately identify patients with low risk of bacteraemia at the ED in this single-centre setting and may be useful to reduce unnecessary BCs and associated healthcare costs. Further studies are necessary for validation and to investigate the potential clinical benefits and possible risks after implementation.

Background Local policymakers require information about public health, housing and well-being at small geographical areas. A municipality can for example use this information to organize targeted activities with the aim of improving the well-being of their residents. Surveys are often used to gather data, but many neighborhoods can have only few or even zero respondents. In that case, estimating the status of the local population directly from survey responses is prone to be unreliable. Methods Small Area Estimation (SAE) is a technique to provide estimates at small geographical levels with only few or even zero respondents. In classical individual-level SAE, a complex statistical regression model is fitted to the survey responses by using auxiliary administrative data for the population as predictors, the missing responses are then predicted and aggregated to the desired geographical level. In this paper we compare gradient boosted trees (XGBoost), a well-known machine learning technique, to a structured additive regression model (STAR) designed for the specific problem of estimating public health and well-being in the whole population of the Netherlands. Results We compare the accuracy and performance of these models using out-of-sample predictions with five-fold Cross Validation (5CV). We do this for three data sets of different sample sizes and outcome types. Compared to the STAR model, gradient boosted trees are able to improve both the accuracy of the predictions and the total time taken to get these predictions. Even though the models appear quite similar in overall accuracy, the small area predictions at neighborhood level sometimes differ significantly. It may therefore make sense to pursue slightly more accurate models for better predictions into small areas. However, one of the biggest benefits is that XGBoost does not require prior knowledge or model specification. Data preparation and modelling is much easier, since the method automatically handles missing data, non-linear responses, interactions and accounts for spatial correlation structures. Conclusions In this paper we provide new nationwide estimates of health, housing and well-being indicators at neighborhood level in the Netherlands, see ’Online materials’. We demonstrate that machine learning provides a good alternative to complex statistical regression modelling for small area estimation in terms of accuracy, robustness, speed and data preparation. These results can be used to make appropriate policy decisions at a local level and make recommendations about which estimation methods are beneficial in terms of accuracy, time and budget constraints.

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Decentralizations of governmental tasks in the field of public health and well being, make analysis of Life Expectancy (LE) data at the municipality levelmore important for obtaining insight into local health trends.On the basis of 4-year moving average Chiang II type LE determinations from 355 Dutch municipalities over the period 1996 - 2019,the characteristics of their LE growth trajectories were investigatedby a mixed four parameter logistic regression model with random parameters for municipalities.For almost all municipalities it was found that their LE values in time fitted an S-shape type of growth trajectory very well,this included municipalities with only 10,000 inhabitants.Within the study period the LE increase varies between 2.0 and 5.9 years and the end LE levels vary between 78.8 and 86.0 years over all municipalities.The maximal LE growth rate of 0.38 LE years was attained at February 2007.However, the LE growth rate drastically decreased to 0.02 increase per year in 2019, strongly suggesting stagnation in LE growth.There are large differences observed between municipalities on several aspects of LE growth.The estimated growth curves represent the differences and similarities in trends of life expectancy over the period 1996-2019 of Dutch municipalities quite well.The results contribute to a better understanding of local life expectancy trends in time.