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Background: Development assistance for health (DAH) to Malawi will likely decrease as a fraction of Gross Domestic Product (GDP) in the next few decades. Given the country's significant reliance on DAH for the delivery of its healthcare services, estimating the impact that this could have on health projections for the country is particularly urgent.
Methods And Findings: We use the Malawi-specific, individual-based "all diseases-whole health-system" Thanzi La Onse model to estimate the impact that declining DAH could have on health system capacities, proxied by the availability of human resources for health, and consequently on population health outcomes, in the period 2019-2040. We estimate that the range of DAH forecasts considered could result in a 7.0% (95% confidence interval (CI) [5.3, 8.3]) to 15.8% (95% CI [14.5,16.7]) increase in disability-adjusted life years compared to a scenario where health spending as a percentage of GDP remains unchanged. This could cause a reversal of gains achieved to date in many areas of health. The burden due to non-communicable diseases, on the other hand, is found to increase irrespective of yearly growth in health expenditure, assuming current reach, and scope of interventions. Finally, we find that greater health expenditure will improve population health outcomes, but at a diminishing rate. The main limitations of this study include the fact that it only considered gradual changes in health expenditure, and did not account for more severe economic shocks or sharp declines in DAH. It also relied on key assumptions about how other factors affecting health beyond healthcare worker numbers -such as consumable availability, range of services available, treatment innovation, and socio-economic and behavioural factors-might evolve.
Conclusions: This analysis reveals the potential risk to population health in Malawi should current forecasts of declining health expenditure as a share of GDP materialise, and underscores the need for both domestic and international authorities to act in response to this anticipated trend.
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http://dx.doi.org/10.1371/journal.pmed.1004488 | DOI Listing |
Thromb Res
September 2025
Center for Thrombosis and Hemostasis, University Medical Center of the Johannes Gutenberg University, Mainz, Germany. Electronic address:
Warfarin is a widely used vitamin K antagonist (VKA) with known pleiotropic effects beyond anticoagulation. Preclinical and case-control evidence suggests that warfarin may affect hematopoiesis, but longitudinal human evidence is lacking. To explore this potential effect, we conducted a post-hoc analysis of participants in the Hokusai-VTE and ENGAGE AF-TIMI 48 trials, which randomized patients to warfarin or the direct oral anticoagulant edoxaban with routine laboratory testing at predefined follow-up visits.
View Article and Find Full Text PDFPublic Health
September 2025
Department of Public Health, Erasmus MC University Medical Center, Rotterdam, the Netherlands.
Objectives: Participation rates in fecal immunochemical test (FIT)-based colorectal cancer (CRC) screening differ across socio-demographic subgroups. The largest health gains could be achieved in subgroups with low participation rates and high risk of CRC. We investigated the CRC risk within different socio-demographic subgroups with low participation in the Dutch CRC screening program.
View Article and Find Full Text PDFDriven by eutrophication and global warming, the occurrence and frequency of harmful cyanobacteria blooms (CyanoHABs) are increasing worldwide, posing a serious threat to human health and biodiversity. Early warning enables precautional control measures of CyanoHABs within water bodies and in water works, and it becomes operational with high frequency in situ data (HFISD) of water quality and forecasting models by machine learning (ML). However, the acceptance of early warning systems by end-users relies significantly on the interpretability and generalizability of underlying models, and their operability.
View Article and Find Full Text PDFAm J Emerg Med
September 2025
University of Toronto, Rotman School of Management, Canada.
Study Objective: Accurately predicting which Emergency Department (ED) patients are at high risk of leaving without being seen (LWBS) could enable targeted interventions aimed at reducing LWBS rates. Machine Learning (ML) models that dynamically update these risk predictions as patients experience more time waiting were developed and validated, in order to improve the prediction accuracy and correctly identify more patients who LWBS.
Methods: The study was deemed quality improvement by the institutional review board, and collected all patient visits to the ED of a large academic medical campus over 24 months.