A modelling analysis of a new multi-stage pathway for classifying achievement of public health milestones for leprosy.

Several countries have come close to eliminating leprosy, but leprosy cases continue to be detected at low levels. Due to the long, highly variable delay from infection to detection, the relationship between observed cases and transmission is uncertain. The World Health Organization's new technical guidance provides a path for countries to reach elimination. We use a simple probabilistic model to simulate the stochastic dynamics of detected cases as transmission declines, and evaluate progress through the new public health milestones. In simulations where transmission is halted, 5 years of zero incidence in autochthonous children, combined with 3 years of zero incidence in all ages is a flawed indicator that transmission has halted (54% correctly classified). A further 10 years of only occasional sporadic cases is associated with a high probability of having interrupted transmission (99%). If, however, transmission continues at extremely low levels, it is possible that cases could be misidentified as historic cases from the tail of the incubation period distribution, although misleadingly achieving all three milestones is unlikely (less than 1% probability across a 15-year period of ongoing low-level transmission). These results demonstrate the feasibility and challenges of a phased progression of milestones towards interruption of transmission, allowing assessment of programme status. This article is part of the theme issue 'Challenges and opportunities in the fight against neglected tropical diseases: a decade from the London Declaration on NTDs'.

Identifying English Practices that Are High Antibiotic Prescribers Accounting for Comorbidities and Other Legitimate Medical Reasons for Variation.

BACKGROUND: Seeing one's practice as a high antibiotic prescriber compared to general practices with similar patient populations can be one of the best motivators for change. Current comparisons are based on age-sex weighting of the practice population for expected prescribing rates (STAR-PU). Here, we investigate whether there is a need to additionally account for further potentially legitimate medical reasons for higher antibiotic prescribing. METHODS: Publicly available data from 7376 general practices in England between April 2014 and March 2015 were used. We built two different negative binomial regression models to compare observed versus expected antibiotic dispensing levels per practice: one including comorbidities as covariates and another with the addition of smoking prevalence and deprivation. We compared the ranking of practices in terms of items prescribed per STAR-PU according to i) conventional STAR-PU methodology, ii) observed vs expected prescribing levels using the comorbidity model, and iii) observed vs expected prescribing levels using the full model. FINDINGS: The median number of antibiotic items prescribed per practice per STAR-PU was 1.09 (25th-75th percentile, 0.92-1.25). 1133 practices (76.8% of 1476) were consistently identified as being in the top 20% of high antibiotic prescribers. However, some practices that would be classified as high prescribers using the current STAR-PU methodology would not be classified as high prescribers if comorbidity was accounted for (n = 269, 18.2%) and if additionally smoking prevalence and deprivation were accounted for (n = 312, 21.1%). INTERPRETATION: Current age-sex weighted comparisons of antibiotic prescribing rates in England are fair for many, but not all practices. This new metric that accounts for legitimate medical reasons for higher antibiotic prescribing may have more credibility among general practitioners and, thus, more likely to be acted upon. OUTSTANDING QUESTIONS: Findings of this study indicate that the antibiotic prescribing metric by which practices are measured (and need to implement interventions determined) may be inadequate, and therefore raises the question of how they should be measured. Substantial variation between practices remains after accounting for comorbidities, deprivation and smoking. There is a need for a better understanding of why such variation remains and, more importantly, what can be done to reduce it. While antibiotics are more frequently indicated in patients with comorbidities, it is unclear to what extent antibiotic prescribing can be lowered among that patient population and how this could be achieved.

Quantitative analyses and modelling to support achievement of the 2020 goals for nine neglected tropical diseases.

Quantitative analysis and mathematical models are useful tools in informing strategies to control or eliminate disease. Currently, there is an urgent need to develop these tools to inform policy to achieve the 2020 goals for neglected tropical diseases (NTDs). In this paper we give an overview of a collection of novel model-based analyses which aim to address key questions on the dynamics of transmission and control of nine NTDs: Chagas disease, visceral leishmaniasis, human African trypanosomiasis, leprosy, soil-transmitted helminths, schistosomiasis, lymphatic filariasis, onchocerciasis and trachoma. Several common themes resonate throughout these analyses, including: the importance of epidemiological setting on the success of interventions; targeting groups who are at highest risk of infection or re-infection; and reaching populations who are not accessing interventions and may act as a reservoir for infection,. The results also highlight the challenge of maintaining elimination 'as a public health problem' when true elimination is not reached. The models elucidate the factors that may be contributing most to persistence of disease and discuss the requirements for eventually achieving true elimination, if that is possible. Overall this collection presents new analyses to inform current control initiatives. These papers form a base from which further development of the models and more rigorous validation against a variety of datasets can help to give more detailed advice. At the moment, the models' predictions are being considered as the world prepares for a final push towards control or elimination of neglected tropical diseases by 2020.

A comparison of five methods to predict genomic breeding values of dairy bulls from genome-wide SNP markers.

BACKGROUND: Genomic selection (GS) uses molecular breeding values (MBV) derived from dense markers across the entire genome for selection of young animals. The accuracy of MBV prediction is important for a successful application of GS. Recently, several methods have been proposed to estimate MBV. Initial simulation studies have shown that these methods can accurately predict MBV. In this study we compared the accuracies and possible bias of five different regression methods in an empirical application in dairy cattle. METHODS: Genotypes of 7,372 SNP and highly accurate EBV of 1,945 dairy bulls were used to predict MBV for protein percentage (PPT) and a profit index (Australian Selection Index, ASI). Marker effects were estimated by least squares regression (FR-LS), Bayesian regression (Bayes-R), random regression best linear unbiased prediction (RR-BLUP), partial least squares regression (PLSR) and nonparametric support vector regression (SVR) in a training set of 1,239 bulls. Accuracy and bias of MBV prediction were calculated from cross-validation of the training set and tested against a test team of 706 young bulls. RESULTS: For both traits, FR-LS using a subset of SNP was significantly less accurate than all other methods which used all SNP. Accuracies obtained by Bayes-R, RR-BLUP, PLSR and SVR were very similar for ASI (0.39-0.45) and for PPT (0.55-0.61). Overall, SVR gave the highest accuracy.All methods resulted in biased MBV predictions for ASI, for PPT only RR-BLUP and SVR predictions were unbiased. A significant decrease in accuracy of prediction of ASI was seen in young test cohorts of bulls compared to the accuracy derived from cross-validation of the training set. This reduction was not apparent for PPT. Combining MBV predictions with pedigree based predictions gave 1.05 - 1.34 times higher accuracies compared to predictions based on pedigree alone. Some methods have largely different computational requirements, with PLSR and RR-BLUP requiring the least computing time. CONCLUSIONS: The four methods which use information from all SNP namely RR-BLUP, Bayes-R, PLSR and SVR generate similar accuracies of MBV prediction for genomic selection, and their use in the selection of immediate future generations in dairy cattle will be comparable. The use of FR-LS in genomic selection is not recommended.