Yann le strat investing

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yann le strat investing

of new health professionals - all for a reasonable financial investment. Le Roux E, Mari Muro M, Novic M, et al. Yann Le Strat. Global burden and investment for neglected diseases? Nicola Dimitri Stéphane Le Vu, Yann Le Strat, Francis Barin, Josiane Pillonel. Build Your Portfolio Your Way. Choose Investments Using $0 Online Stock and ETF Trades. BITCOIN PRIC3

The identification of wild maintenance hosts and their effective management is therefore a key determinant of the efficacy of control measures [ 3 , 6 , 7 ], despite the lack of a requirement for mandatory bTB surveillance in wildlife in current EU legislation.

The surveillance of bTB is challenging, due to its underlying complex epidemiology and the multiple hosts involved in domestic and wild populations, particularly in wildlife, due to practical constraints imposed. We used the stochastic scenario tree modelling approach described by Martin et al. This approach can be used to identify and develop efficient surveillance strategies and it enhances communication and collaboration between stakeholders, decision makers and scientists.

It provides a transparent and structured approach for decision-making and opportunities for improving the framework, with the acquisition of more accurate data. However, one disadvantage of this method is that it does not take temporal aspects into account the time interval between infection and TBL development, for example [ 31 ]. With this method, it is possible to include multiple sources of information in the same tree. This is a considerable advantage when not all the data required are available and expert opinion must be sought.

In our case, it is difficult to obtain data for bTB prevalence and the densities of susceptible species within a given geographic area, or information about the behaviour of field stakeholders in terms of their participation in the surveillance system and disease awareness. When quantitative information about the key parameters affecting the detection capacity of the SSC was not available, expert advice was sought to reach a consensus, and parameter estimates were described by distributions, relatively wide in some cases, to account for uncertainty.

When no consensus between experts could be reached, uniform distributions were used, to take into account the variability or uncertainty in the parameter estimates, which may be considerable for wildlife. The assumptions underlying variable simulations may be biased due to the preconceived opinions of experts, but these expert opinions were nevertheless useful in the absence of epidemiological field survey data. Furthermore, ignoring the effects of parameters that are difficult to quantify such as disease awareness would have yielded misleading estimates for the effectiveness of the surveillance system [ 32 ].

As information about relative risks of infection and proportion of the population in each risk group were not available for wild populations, it is not possible to use straightforwardly the method described by Martin et al. A two-step approach was so used: first, the relative risks by risk-level area were estimated from cattle data as there is a good correlation between outbreaks in cattle and in wildlife and secondly, the Sylvatub data were used to estimate the probabilities of infection according to the species and age class through a standardization procedure.

These probabilities may not provide an accurate reflection of the real epidemiological situation in wildlife, but they are nevertheless relevant, as the geographic risk levels for bTB surveillance in wildlife are based, in part, on bTB prevalence in cattle and there is a good correlation between infection rates in wild animals and outbreaks in cattle [ 9 ].

The risks of infection for each risk level were then fitted to the Sylvatub data for each species and age. However, only a few infected red and roe deer were detected in France, accounting for the low sensitivity of the surveillance system for these species: the results are thus valid only for the model calculation and assumptions about the risk of infection.

The unit sensitivity for an infected animal is available in Supplementary material S2 File as it could be useful for adaptation of the results of the model to another epidemiological situation i. Furthermore, fewer animals than planned for this model may be processed in reality for this surveillance component.

This component remains relevant, as it is the only surveillance component applied in summer no hunting is generally allowed in the summer for wild boar and deer, so the probability of detection by hunting is zero during this period : the seasonality of surveillance should therefore also be taken into account when assessing the overall effectiveness of the surveillance system. Post-mortem examination ability to detect TBLs has a low sensitivity, but training to increase the awareness of hunters and being in a high risk area increased the sensitivity of the EC-SSC.

The experts considered the efficacy of post-mortem inspection to be greater for deer than for wild boar, because the TBLs are often located on cervical lymph nodes in wild boars and are, therefore, difficult for hunters to detect during their examination of the carcass Table 4. These assumptions are consistent with the findings of several studies showing the sensitivity of post-mortem inspection to be low [ 33 , 34 , 35 ].

However, to globally assess the performance of a surveillance system, the measure of its sensitivity is not sufficient, as other factors such as economic or socio-economic factors could influence the effectiveness. We report here an estimation of the costs of the surveillance activities of the Sylvatub system, and of the cost-effectiveness of each surveillance component, by specie and risk-level, based on scenario tree modelling with the same tree structure as used for the sensitivity evaluation.

The cost-effectiveness of the Sylvatub surveillance is better in higher-risk departments, due in particular to the higher probability of detecting the infection sensitivity. The calculation of the cost-effectiveness ratio shows that PSURV-SSC remains the most cost-effective surveillance component of the Sylvatub system, despite its high cost in terms of coordination, sample collection and laboratory analysis.

This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Interested researchers may contact bsa. Funding: The authors have no support or funding to report. Competing interests: The authors have declared that no competing interests exist. Introduction Bovine tuberculosis bTB is a chronic disease caused by Mycobacterium bovis or less frequently by M.

The European Commission has considered France to be bTB-free since , but infected herds and cases in wildlife red deer—Cervus elaphus—, roe deer—Capreolus capreolus—, Eurasian wild boar—Sus scrofa—and badgers—Meles meles— are still detected each year in some areas [ 2 ]. Wild species, and particularly maintenance hosts, represent an obstacle to the eradication of bTB in cattle, as potentially source of re-infection [ 3 , 4 , 5 , 6 ].

Furthermore, the bTB transmission between wildlife species has a potential impact on biodiversity, ecotourism and commercial game farming [ 7 ] and the control measures of bTB in wildlife could be difficult to implement and maintain in such multi-host systems especially when species share habitat with cattle e.

A national surveillance system for bTB in wildlife, the Sylvatub system, was launched in France since Its aims are the early detection of cases and the monitoring of infection levels in affected areas. The Sylvatub system relies on three independent surveillance system components SSCs : passive scanning surveillance on hunted wild species, passive surveillance on dead or dying wild animals and planned active surveillance on hunted or trapped wild animals.

As bTB surveillance in wildlife species is subject to several constraints, the effectiveness of the three SSC of the Sylvatub system was assessed previously, for each SSC, specie and risk-level [ 10 ]. However, the evaluation of surveillance activities cannot be based alone on their ability to detect cases effectiveness. The costs of surveillance must also be considered, due to limited financial resources facing an increasing need for surveillance and control programmes in animal health.

It is therefore essential to develop tools to identify optimal strategies in terms of efficiency [ 11 , 12 , 13 ]. In developed countries, bTB induces major economic losses in the livestock sector, with costs to the cattle industry and government due to surveillance expenses testing costs , and control measures movement restrictions on the international trade of animals and their products and compensation for slaughtered cattle.

While the cost of bTB in cattle has been widely studied in recent years [ 14 ] the economic aspects of the bTB infection in wildlife have rarely been investigated. However, economic analyses are essential to justify the allocation of funds for surveillance, eradication and control programmes of animal diseases, and constitute a helpful tool to support decision in the choice of various strategies.

The objective of this study was the assessment, for the first time in France, of the costs and the cost-effectiveness of the bTB Sylvatub surveillance system in free-ranging wildlife in France. We used the scenario tree modelling approach, with the same model structure as for the efficiency assessment [ 10 ]. This method presents the advantage of combining several data sources based on non-probabilistic sampling, evaluating separately or together the effectiveness and the costs of several components in order to estimate the efficiency of surveillance strategies, all within the French context in terms of geography, resources and infrastructure [ 18 , 19 ].

Materials and methods 2. Ethics statement This study did not involve the deliberate killing of animals for the sole purpose of the study, as all samples were collected from animals trapped or hunted legally during the hunting season with appropriate permits, shot legally because of severe debilitation or found dead. All the samples included in this study were obtained from animals analyzed within an official context relating to bTB surveillance in free-ranging wildlife.

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Although the major issue of representativeness for surveillance systems is to compare the monitored population to the general population, comparisons are often limited to that of participating GPs to others [ 10 ]. Here, in the French Sentinelles network, participating SGPs were similar to other GPs in a number of ways age, practice of complementary medicine , but differed in some respects: they were more frequently males, were not equally spread over the territory, and they saw more patients each week.

Bias should only incur if the probability of a GP reporting a case of disease is related to these characteristics. For example, having more male SGPs could be an issue, as some conditions are more likely to be reported to a female GP than to a male GP [ 21 ]. The conditions monitored here are unlikely candidates for such differential reporting, making this sex imbalance irrelevant for population representativeness. French female GPs also more often work part-time than male GPs [ 22 ], but as this directly leads to variation in the number of consultations, incidence estimates weighted using the volume of consultations must correct for this imbalance.

A last issue is that participation in a research or surveillance network could lead to systematic differences between participating GPs and others. But, if different patterns of prescription have been reported between GPs participating in research and others [ 23 ],[ 24 ], fewer differences concerned the case-mix of patients. For example, the prevalence of 11 common chronic diseases was almost the same in GPs who were taking part in surveillance and those who were not [ 25 ].

We assumed that this would be the case for common acute conditions like ILI, AD, or varicella, especially as a detailed case definition was used. A systematic identification of all characteristics that would lead to differences in reporting between SGPs and GPs is difficult in practice, as information on potential but non-participating providers is seldom available. A final caveat is that repeat consultations with GPs by the same patient for the same disease episode could bias incidence estimates.

However, SGPs would only report patients once per episode, and consultations with several GPs by the same patient is rare in the French system as it leads to lower reimbursements by the social security system. Apart from systematic differences due to the characteristics of the participating GPs, a further problem in computing incidence is the lack of a proper denominator.

In health systems based on registration of patients with a practice, it may be possible to use the size of the patient list [ 2 ],[ 10 ]. In the French health insurance system, free choice of the GP and absence of registration makes this approach infeasible. This very large coverage means that administrative data on reimbursements for consultations charged to patients provide a very good picture of the activity of all French GPs.

Moreover, it makes the whole population a sensible denominator in the end, as very little primary care medical activity is excluded from the national health insurance system data. Improving estimates for surveillance networks, especially to provide better spatial estimates, may resort to different solutions. These approaches are difficult to apply when participants are voluntary GPs, whose reasons to participate and survival in the system are poorly characterized [ 29 ].

Identifying and collecting relevant external reference to compute weights is a first required step. Examining the prevalence of individuals with D m enrolled in clinical trials for MDD is required, and may help estimating the potential impact on internal validity as well as guiding eligibility criteria operationalization for future clinical trials in major depressive disorder. Our aims were 1 to estimate the proportion of individuals with D m that would have been included in RCTs for MDD using classical eligibility criteria, and 2 examine the potential impact of including these patients on internal validity of RCTs for MDD.

We first determined the prevalence of D m i. We applied a standard set of exclusion criteria commonly used in clinical trials for MDD, using a method previously described by Blanco and colleagues in clinical trials for major depression [3]. We then examined the proportion of all participants with a current diagnosis of D m and pure MDD in the NESARC that would have been eligible if the traditional eligibility criteria were applied to these samples, and compared it with that of individuals with bipolar 2 disorder if they were applied the same set of eligibility criteria.

Because no consensus subthreshold bipolar-specifier diagnosis is available to date [11] , [19] , we defined four models including different subthreshold bipolar-specifier diagnoses. We hypothesized that 1 a significant proportion of subjects that would have been eligible for clinical trials for MDD present with a lifetime history of subthreshold hypomania, and 2 a substantial proportion of individuals with D m significantly differ from those with pure MDD but not from those with bipolar 2 disorder in overall eligibility rate, assuming that they share a similar pattern of exclusion rates.

Because individuals who seek treatment for a disorder may differ from those who do not [3] , [20] , [21] , we applied the exclusion criteria first to all participants with a current diagnosis of D m and pure MDD, and then to the subsamples of participants who sought treatment. By employing this large representative sample, we sought to stress the consequences of including participants with D m in clinical trials for MDD, resulting in a potential selection bias, within a broad public health context.

The NESARC target population was the civilian noninstitutionalized population, aged 18 years and older, residing in households and group quarters in the 50 states and the District of Columbia. Data collection was conducted via face-to-face computer assisted personal interviews under the supervision of the NIAAA staff. African Americans, Hispanics, and young adults aged 18—24 were oversampled.

Once weighted, the data were adjusted to be representative of the U. The research protocol, including informed consent procedures, received full ethical review and approval from the U. Census Bureau and the Office of Management and Budget [22]. Mood Disorders Assessment Lifetime and twelve-month mood disorders were diagnosed following the DSM-IV criteria, except for the requirement of symptoms assessing a mixed episode criterion B for major depressive disorder and criterion C for hypomania.

Major depressive disorder MDD was defined as having a lifetime history of at least 1 MDE, without a lifetime history of mania or hypomania. Participants reporting a major depressive episode occurring during the year preceding the interview without any lifetime history of mania or hypomania were considered as having a current major depressive disorder MDD.

Participants who endorsed either of these questions were then asked an extensive list of symptom questions that operationalize DSM-4 criterion B for hypomania. Because no consensus subthreshold bipolar-specifier diagnosis is available to date [11] , [19] , we defined 4 models including different definitions of subthreshold hypomania.

Among participants with current MDD, those who endorsed at least 1, 2, 3 or 4 lifetime concomitant hypomanic probes screening criterion A or B for hypomania were successively defined as having a current diagnosis of MDD plus a lifetime history of subthreshold hypomania D m. By contrast, those without a lifetime history of subthreshold hypomania were successively classified as having a current diagnosis of pure MDD across the 4 models used.

In each of the four models, participants with a current diagnosis of MDD were divided in 2 mutually exclusive subgroups as follows: 1 current pure MDD without a lifetime history of subthreshold hypomania, hypomania or mania , 2 current MDD plus a lifetime history of subthreshold hypomania D m.

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