Cost-effectiveness analysis of population-based tobacco control strategies in the prevention of cardiovascular diseases in Tanzania

Background: Tobacco consumption contributes significantly to the global burden of disease. The prevalence of smoking is estimated to be increasing in many low-income countries, including Tanzania, especially among women and youth. Even so, the implementation of tobacco control measures has been discouraging in the country. Efforts to foster investment in tobacco control are hindered by lack of evidence on what works and at what cost. Aims: We aim to estimate the cost and cost-effectiveness of population-based tobacco control strategies in the prevention of cardiovascular diseases (CVD) in Tanzania. Materials and methods: A cost-effectiveness analysis was performed using an Excel-based Markov model, from a governmental perspective. We employed an ingredient approach and step-down methodologies in the costing exercise following a government perspective. Epidemiological data and efficacy inputs were derived from the literature. We used disability-adjusted life years (DALYs) averted as the outcome measure. A probabilistic sensitivity analysis was carried out with Ersatz to incorporate uncertainties in the model parameters. Results: Our model results showed that all five tobacco control strategies were very cost-effective since they fell below the ceiling ratio of one GDP per capita suggested by the WHO. Increase in tobacco taxes was the most cost-effective strategy, while a workplace smoking ban was the least cost-effective option, with a cost-effectiveness ratio of USD 5 and USD 267, respectively. Conclusions: Even though all five interventions are deemed very cost-effective in the prevention of CVD in Tanzania, more research on budget impact analysis is required to further assess the government’s ability to implement these interventions.publishedVersio

Attributable deaths and disability-adjusted life-years caused by infections with antibiotic-resistant bacteria in the EU and the European Economic Area in 2015: a population-level modelling analysis

Background: Infections due to antibiotic-resistant bacteria are threatening modern health care. However, estimating their incidence, complications, and attributable mortality is challenging. We aimed to estimate the burden of infections caused by antibiotic-resistant bacteria of public health concern in countries of the EU and European Economic Area (EEA) in 2015, measured in number of cases, attributable deaths, and disability-adjusted life-years (DALYs). Methods: We estimated the incidence of infections with 16 antibiotic resistance–bacterium combinations from European Antimicrobial Resistance Surveillance Network (EARS-Net) 2015 data that was country-corrected for population coverage. We multiplied the number of bloodstream infections (BSIs) by a conversion factor derived from the European Centre for Disease Prevention and Control point prevalence survey of health-care-associated infections in European acute care hospitals in 2011–12 to estimate the number of non-BSIs. We developed disease outcome models for five types of infection on the basis of systematic reviews of the literature. Findings: From EARS-Net data collected between Jan 1, 2015, and Dec 31, 2015, we estimated 671 689 (95% uncertainty interval [UI] 583 148–763 966) infections with antibiotic-resistant bacteria, of which 63·5% (426 277 of 671 689) were associated with health care. These infections accounted for an estimated 33 110 (28 480–38 430) attributable deaths and 874 541 (768 837–989 068) DALYs. The burden for the EU and EEA was highest in infants (aged <1 year) and people aged 65 years or older, had increased since 2007, and was highest in Italy and Greece. Interpretation: Our results present the health burden of five types of infection with antibiotic-resistant bacteria expressed, for the first time, in DALYs. The estimated burden of infections with antibiotic-resistant bacteria in the EU and EEA is substantial compared with that of other infectious diseases, and has increased since 2007. Our burden estimates provide useful information for public health decision-makers prioritising interventions for infectious diseases

ALDH1A2 (RALDH2) genetic variation in human congenital heart disease

Abstract\ud \ud \ud \ud Background\ud \ud Signaling by the vitamin A-derived morphogen retinoic acid (RA) is required at multiple steps of cardiac development. Since conversion of retinaldehyde to RA by retinaldehyde dehydrogenase type II (ALDH1A2, a.k.a RALDH2) is critical for cardiac development, we screened patients with congenital heart disease (CHDs) for genetic variation at the ALDH1A2 locus.\ud \ud \ud \ud Methods\ud \ud One-hundred and thirty-three CHD patients were screened for genetic variation at the ALDH1A2 locus through bi-directional sequencing. In addition, six SNPs (rs2704188, rs1441815, rs3784259, rs1530293, rs1899430) at the same locus were studied using a TDT-based association approach in 101 CHD trios. Observed mutations were modeled through molecular mechanics (MM) simulations using the AMBER 9 package, Sander and Pmemd programs. Sequence conservation of observed mutations was evaluated through phylogenetic tree construction from ungapped alignments containing ALDH8 s, ALDH1Ls, ALDH1 s and ALDH2 s. Trees were generated by the Neighbor Joining method. Variations potentially affecting splicing mechanisms were cloned and functional assays were designed to test splicing alterations using the pSPL3 splicing assay.\ud \ud \ud \ud Results\ud \ud We describe in Tetralogy of Fallot (TOF) the mutations Ala151Ser and Ile157Thr that change non-polar to polar residues at exon 4. Exon 4 encodes part of the highly-conserved tetramerization domain, a structural motif required for ALDH oligomerization. Molecular mechanics simulation studies of the two mutations indicate that they hinder tetramerization. We determined that the SNP rs16939660, previously associated with spina bifida and observed in patients with TOF, does not affect splicing. Moreover, association studies performed with classical models and with the transmission disequilibrium test (TDT) design using single marker genotype, or haplotype information do not show differences between cases and controls.\ud \ud \ud \ud Conclusion\ud \ud In summary, our screen indicates that ALDH1A2 genetic variation is present in TOF patients, suggesting a possible causal role for this gene in rare cases of human CHD, but does not support the hypothesis that variation at the ALDH1A2 locus is a significant modifier of the risk for CHD in humans.Work supported by grants from Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) 01/000090; 00/030722; 01/142381; 02/113402; 03/099982; 04/116068; 04/157044 and Conselho Nacional de Desenvolvimento Científico e Tecnológico 481872/20078. We would like to thank the careful work and thoughtful suggestions of the two reviewers responsible for the reviewing editorial process.Work supported by grants from Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) 01/00009-0; 00/03072-2; 01/14238-1; 02/11340-2; 03/09998-2; 04/11606-8; 04/15704-4 and Conselho Nacional de Desenvolvimento Científico e Tecnológico 481872/2007-8. We would like to thank the careful work and thoughtful suggestions of the two reviewers responsible for the reviewing editorial process

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Accounting for behaviours and context in evaluations of complex health interventions

Health care systems across developed countries face a perfect storm of rising demand and constrained funding. Systems have relied so far on short-term fixes but the time for incremental piecemeal solutions is passing. To achieve transformational change and fundamental service redesign, policy makers are resorting to ever more complex interventions. Evaluating their effects is far from trivial. From public health programmes, to integrated and community care services, to electronic health technologies, complex health interventions typically exhibit a large number of components and interactions among them and other parts of the system; involve numerous intricate behaviours by those delivering and receiving the intervention; engage multiple and diverse groups, organisational levels and populations; result in many outcomes, typically with a high degree of variability; and are extensively tailored to local settings and circumstances. Evaluating such interventions is as much about whether they work, as how and why. In this research, I examine the difficulties in using standard economic evaluation methods to assess complex interventions in the outpatient setting, and develop an approach to evaluation which uses methods and techniques that can explicitly address complexity, incorporate preferences and behaviours of patients and carers, and account for wider contextual influences. I apply the suggested approach to the evaluation of teleconsultation in Alentejo, drawing on insights from previous theoretical and empirical research, new econometric and statistical studies, and simulation modelling. The application makes contributions to extant research on behaviour and decision making, and has implications for the evaluation of teleconsultation, as well as for broader discussions of how to assess complex interventions. Complex health interventions have the potential to deliver a revolution in health care, but to achieve it we must be able to identify those that truly work, how and why. It is hoped the approach suggested here will contribute to that objective.Open Acces

Towards a Post-Implementation Evaluation Framework of Outpatient Electronic Drug Prescribing

The adoption of electronic drug prescribing (ePrescribing) systems has been largely discussed in scientific literature. Yet post-implementation evaluations of these systems are still in short supply. At a time when large investments are being made throughout the world in health information systems and technologies, under pressure for cost-containment, evidence on which systems provide the largest net benefits is required. In this chapter, the authors start by reviewing the literature on the costs and benefits of outpatient ePrescribing systems and find that the evidence is scattered. There is a general consensus that ePrescribing is beneficial, although few studies quantify the net benefits of specific systems. The review also shows that the evaluation of ePrescribing systems is complex and that most studies share limitations associated with the evaluation of other health information technologies and systems. The authors propose an evidence-based framework to inform post-implementation evaluations of outpatient ePrescribing systems and to improve the quality and comparability of studies in the area.</jats:p