PLoS medicine
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Multimorbidity, characterised by the coexistence of multiple chronic conditions in an individual, is a rising public health concern. While much of the existing research has focused on cross-sectional patterns of multimorbidity, there remains a need to better understand the longitudinal accumulation of diseases. This includes examining the associations between important sociodemographic characteristics and the rate of progression of chronic conditions. ⋯ Our results indicate that early phases of multimorbidity development could warrant increased attention. The potential importance of earlier detection and intervention of chronic conditions is underscored, particularly for MH conditions and higher-risk populations. These insights may have important implications for the management of multimorbidity.
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Despite universal healthcare, socioeconomic differences in healthcare utilization (HCU) persist in modern welfare states. However, little is known of how HCU inequalities has developed over time. The aim of this study is to assess time trends of differences in utilization of primary and specialized care for the lowest (Q1) and highest (Q5) income quantiles and compare these to mortality. ⋯ Income-related differences in the utilization of primary and specialized outpatient care were considerably smaller than for mortality, and this discrepancy widened with time. Facilitating motivated use of primary and outpatient care among low-income groups could help mitigate the growing health inequalities.
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In this Policy Forum article, James A. Watson and colleagues discuss recent guidelines relating to pre-referral treatment of suspected severe malaria with rectal artesunate suppositories in remote areas.
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[This corrects the article DOI: 10.1371/journal.pmed.1002368.].
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Risk-based screening for lung cancer is currently being considered in several countries; however, the optimal approach to determine eligibility remains unclear. Ensemble machine learning could support the development of highly parsimonious prediction models that maintain the performance of more complex models while maximising simplicity and generalisability, supporting the widespread adoption of personalised screening. In this work, we aimed to develop and validate ensemble machine learning models to determine eligibility for risk-based lung cancer screening. ⋯ We present parsimonious ensemble machine learning models to predict the risk of lung cancer in ever-smokers, demonstrating a novel approach that could simplify the implementation of risk-based lung cancer screening in multiple settings.