Calculating the global burden of disease: time for a strategic reappraisal?

Improved data clustering using multi-trial vector-based differential evolution with Gaussian crossover

“…In this section, the performance of the proposed IMTDE clustering algorithm has been compared with twelve well-known algorithms that have been reported in existing literature, including K-Means [39], Affinity propagation [11], Mean-shift [5], Agglomerative clustering [20], DBSCAN [10], OPTICS [2], Gaussian mixtures model [31], BIRCH [44], Particle swarm optimisation (PSO) and Hybrid PSO and K-Means (HPSOkmeans) [10] Based on chosen strategy 𝑥, use Eq. ( 5),( 6) or ( 7) accordingly for producing a trial vector The Scikit-learn machine learning library has been used to implement other methods [29].…”

Setting priorities for the health care sector in Zimbabwe using cost-effectiveness analysis and estimates of the burden of disease

“…Perhaps the most serious limitation was the scarcity of evidence on effectiveness of health interventions and of health systems research in Zimbabwe. The observations made by several authors [ 14 , 61 , 62 ] that we desperately lack data on these aspects are therefore also very relevant for the Zimbabwean situation. Research on factors influencing how health interventions work in the real world was extremely limited.…”

Assessing Cost-Effectiveness in Mental Health: Helping Policy-Makers Prioritize and Plan Health Services

“…The brief analysis of the impact of our recommendations for more efficient services for depression and schizophrenia shows the value of combining information on cost-effectiveness with the size of health problems, thus refuting the criticism of some health economists that burden of disease studies are a waste of valuable resources [38][39][40]. Clearly, the quantification of the potential impact on mental health budgets of implementing change is essential to policy-makers and this requires basic burden of disease information on prevalence, incidence and duration of disease.…”