IMPORTANCE: The clinical management of BRCA1 and BRCA2 mutation carriers requires accurate, prospective cancer risk estimates. OBJECTIVES: To estimate age-specific risks of breast, ovarian, and ...contralateral breast cancer for mutation carriers and to evaluate risk modification by family cancer history and mutation location. DESIGN, SETTING, AND PARTICIPANTS: Prospective cohort study of 6036 BRCA1 and 3820 BRCA2 female carriers (5046 unaffected and 4810 with breast or ovarian cancer or both at baseline) recruited in 1997-2011 through the International BRCA1/2 Carrier Cohort Study, the Breast Cancer Family Registry and the Kathleen Cuningham Foundation Consortium for Research into Familial Breast Cancer, with ascertainment through family clinics (94%) and population-based studies (6%). The majority were from large national studies in the United Kingdom (EMBRACE), the Netherlands (HEBON), and France (GENEPSO). Follow-up ended December 2013; median follow-up was 5 years. EXPOSURES: BRCA1/2 mutations, family cancer history, and mutation location. MAIN OUTCOMES AND MEASURES: Annual incidences, standardized incidence ratios, and cumulative risks of breast, ovarian, and contralateral breast cancer. RESULTS: Among 3886 women (median age, 38 years; interquartile range IQR, 30-46 years) eligible for the breast cancer analysis, 5066 women (median age, 38 years; IQR, 31-47 years) eligible for the ovarian cancer analysis, and 2213 women (median age, 47 years; IQR, 40-55 years) eligible for the contralateral breast cancer analysis, 426 were diagnosed with breast cancer, 109 with ovarian cancer, and 245 with contralateral breast cancer during follow-up. The cumulative breast cancer risk to age 80 years was 72% (95% CI, 65%-79%) for BRCA1 and 69% (95% CI, 61%-77%) for BRCA2 carriers. Breast cancer incidences increased rapidly in early adulthood until ages 30 to 40 years for BRCA1 and until ages 40 to 50 years for BRCA2 carriers, then remained at a similar, constant incidence (20-30 per 1000 person-years) until age 80 years. The cumulative ovarian cancer risk to age 80 years was 44% (95% CI, 36%-53%) for BRCA1 and 17% (95% CI, 11%-25%) for BRCA2 carriers. For contralateral breast cancer, the cumulative risk 20 years after breast cancer diagnosis was 40% (95% CI, 35%-45%) for BRCA1 and 26% (95% CI, 20%-33%) for BRCA2 carriers (hazard ratio HR for comparing BRCA2 vs BRCA1, 0.62; 95% CI, 0.47-0.82; P=.001 for difference). Breast cancer risk increased with increasing number of first- and second-degree relatives diagnosed as having breast cancer for both BRCA1 (HR for ≥2 vs 0 affected relatives, 1.99; 95% CI, 1.41-2.82; P<.001 for trend) and BRCA2 carriers (HR, 1.91; 95% CI, 1.08-3.37; P=.02 for trend). Breast cancer risk was higher if mutations were located outside vs within the regions bounded by positions c.2282-c.4071 in BRCA1 (HR, 1.46; 95% CI, 1.11-1.93; P=.007) and c.2831-c.6401 in BRCA2 (HR, 1.93; 95% CI, 1.36-2.74; P<.001). CONCLUSIONS AND RELEVANCE: These findings provide estimates of cancer risk based on BRCA1 and BRCA2 mutation carrier status using prospective data collection and demonstrate the potential importance of family history and mutation location in risk assessment.
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.
Visceral leishmaniasis (VL) has been targeted by the WHO for elimination as a public health problem (< 1 case/10,000 people/year) in the Indian sub-continent (ISC) by 2020. Bihar State in India, ...which accounts for the majority of cases in the ISC, remains a major target for this elimination effort. However, there is considerable spatial, temporal and sub-population variation in occurrence of the disease and the pathway to care, which is largely unexplored and a threat to achieving the target.
Data from 6081 suspected VL patients who reported being clinically diagnosed during 2012-2013 across eight districts in Bihar were analysed. Graphical comparisons and Chi-square tests were used to determine differences in the burden of identified cases by season, district, age and sex. Log-linear regression models were fitted to onset (of symptoms)-to-diagnosis and onset-to-treatment waiting times to estimate their associations with age, sex, district and various socio-economic factors (SEFs). Logistic regression models were used to identify factors associated with mortality.
Comparisons of VL caseloads suggested an annual cycle peaking in January-March. A 17-fold variation in the burden of identified cases across districts and under-representation of young children (0-5 years) relative to age-specific populations in Bihar were observed. Women accounted for a significantly lower proportion of the reported cases than men (41 vs 59%, P < 0.0001). Age, district of residence, house wall materials, caste, treatment cost, travelling for diagnosis and the number of treatments for symptoms before diagnosis were identified as correlates of waiting times. Mortality was associated with age, district of residence, onset-to-treatment waiting time, treatment duration, cattle ownership and cost of diagnosis.
The distribution of VL in Bihar is highly heterogeneous, and reported caseloads and associated mortality vary significantly across different districts, posing different challenges to the elimination campaign. Socio-economic factors are important correlates of these differences, suggesting that elimination will require tailoring to population and sub-population circumstances.
Abstract We present three transmission models of visceral leishmaniasis (VL) in the Indian subcontinent (ISC) with structural differences regarding the disease stage that provides the main ...contribution to transmission, including models with a prominent role of asymptomatic infection, and fit them to recent case data from 8 endemic districts in Bihar, India. Following a geographical cross-validation of the models, we compare their predictions for achieving the WHO VL elimination targets with ongoing treatment and vector control strategies. All the transmission models suggest that the WHO elimination target (<1 new VL case per 10,000 capita per year at sub-district level) is likely to be met in Bihar, India, before or close to 2020 in sub-districts with a pre-control incidence of 10 VL cases per 10,000 people per year or less, when current intervention levels (60% coverage of indoor residual spraying (IRS) of insecticide and a delay of 40 days from onset of symptoms to treatment (OT)) are maintained, given the accuracy and generalizability of the existing data regarding incidence and IRS coverage. In settings with a pre-control endemicity level of 5/10,000, increasing the effective IRS coverage from 60 to 80% is predicted to lead to elimination of VL 1–3 years earlier (depending on the particular model), and decreasing OT from 40 to 20 days to bring elimination forward by approximately 1 year. However, in all instances the models suggest that L. donovani transmission will continue after 2020 and thus that surveillance and control measures need to remain in place until the longer-term aim of breaking transmission is achieved.
Abstract Some bovine enteric caliciviruses form a new genus in the family Caliciviridae . In this study, Bayesian phylogenetic analysis of 31 full length capsid sequences from Europe, North America ...and Asia revealed that this new genus had four currently circulating lineages that showed both temporal and geographical distribution. These groupings were supported by the distribution of the frequency of pair-wise distances. However, the nucleotide and amino acid heterogeneity was low, with a maximum nucleotide and amino acid divergence of 16.7% and 8.4%, respectively. Most variability was found between amino acid residues 288 and 420 of the capsid protein and the sequence motifs observed in this region supported the division of the four lineages. Homology modelling using the structure of the San Miguel sea lion capsid indicated that most variation occurred in the predicted P2 domain and thus, may affect antigenic sites on the surface of the capsid of this newly described genus.
Although BRCA1 and BRCA2 mutations account for only ∼27% of the familial aggregation of ovarian cancer (OvC), no OvC risk prediction model currently exists that considers the effects of BRCA1, BRCA2 ...and other familial factors. Therefore, a currently unresolved problem in clinical genetics is how to counsel women with family history of OvC but no identifiable BRCA1/2 mutations.
We used data from 1548 patients with OvC and their relatives from a population-based study, with known BRCA1/2 mutation status, to investigate OvC genetic susceptibility models, using segregation analysis methods.
The most parsimonious model included the effects of BRCA1/2 mutations, and the residual familial aggregation was accounted for by a polygenic component (SD 1.43, 95% CI 1.10 to 1.86), reflecting the multiplicative effects of a large number of genes with small contributions to the familial risk. We estimated that 1 in 630 individuals carries a BRCA1 mutation and 1 in 195 carries a BRCA2 mutation. We extended this model to incorporate the explicit effects of 17 common alleles that are associated with OvC risk. Based on our models, assuming all of the susceptibility genes could be identified we estimate that the half of the female population at highest genetic risk will account for 92% of all OvCs.
The resulting model can be used to obtain the risk of developing OvC on the basis of BRCA1/2, explicit family history and common alleles. This is the first model that accounts for all OvC familial aggregation and would be useful in the OvC genetic counselling process.
Family history is one of the most important risk factors for epithelial ovarian cancer (EOC). Little is known, however, on how EOC familial relative risks (FRRs) vary by factors such as tumour ...subtype or the combined effects of common EOC susceptibility alleles. In addition, no data currently exist on the FRRs associated with EOC after exclusion of BRCA1 or BRCA2 mutation carriers.
EOC FRRs were computed from observed EOCs in relatives of 1548 patients with EOC recruited between 1999 and 2010 from a population-based cohort study with known BRCA1 and BRCA2 mutation status and tumour subtype, compared with the number expected in the general population.
The EOC FRR to all first-degree relatives was estimated to be 2.96 (95% CI 2.35 to 3.72) but there was no evidence of difference in the FRRs for mothers, sisters and daughters. There was significant evidence that the FRR for relatives of patients with EOC diagnosed under age 50 years is higher than that for older patients (4.72 (95% CI 3.21 to 6.95) and 2.53 (95% CI 1.91 to 3.35), p-diff=0.0052) and a suggestion that the FRR in relatives of patients with serous disease is higher than that for non-serous tumours (3.64 (95% CI 2.72 to 4.87) and 2.25 (95% CI 1.56 to 3.26), p-diff=0.0023). The FRR to relatives of cases without a deleterious mutation in BRCA1 or BRCA2 was estimated to be over twice that of the general population (2.24 (95% CI 1.71 to 2.94)). BRCA1 and BRCA2 mutations were estimated to account for about 24% of the EOC FRR to first-degree relatives. FRRs were found to increase with increasing polygenic risk score of the index patient, although the trend was not significant.
These estimates could be useful in the counselling of relatives of patients with ovarian cancer.
Visceral Leishmaniasis (VL) is a neglected vector-borne disease. In India, it is transmitted to humans by Leishmania donovani-infected Phlebotomus argentipes sand flies. In 2005, VL was targeted for ...elimination by the governments of India, Nepal and Bangladesh by 2015. The elimination strategy consists of rapid case detection, treatment of VL cases and vector control using indoor residual spraying (IRS). However, to achieve sustained elimination of VL, an appropriate post elimination surveillance programme should be designed, and crucial knowledge gaps in vector bionomics, human infection and transmission need to be addressed. This review examines the outstanding knowledge gaps, specifically in the context of Bihar State, India.The knowledge gaps in vector bionomics that will be of immediate benefit to current control operations include better estimates of human biting rates and natural infection rates of P. argentipes, with L. donovani, and how these vary spatially, temporally and in response to IRS. The relative importance of indoor and outdoor transmission, and how P. argentipes disperse, are also unknown. With respect to human transmission it is important to use a range of diagnostic tools to distinguish individuals in endemic communities into those who: 1) are to going to progress to clinical VL, 2) are immune/refractory to infection and 3) have had past exposure to sand flies.It is crucial to keep in mind that close to elimination, and post-elimination, VL cases will become infrequent, so it is vital to define what the surveillance programme should target and how it should be designed to prevent resurgence. Therefore, a better understanding of the transmission dynamics of VL, in particular of how rates of infection in humans and sand flies vary as functions of each other, is required to guide VL elimination efforts and ensure sustained elimination in the Indian subcontinent. By collecting contemporary entomological and human data in the same geographical locations, more precise epidemiological models can be produced. The suite of data collected can also be used to inform the national programme if supplementary vector control tools, in addition to IRS, are required to address the issues of people sleeping outside.
There are well-established methods for estimating and removing seasonality from a stationary Gaussian time series and in recent years, models for integer-valued, nonstationary time series, such as ...count data, have been developed. However, little research has been done into the analysis of time series which are both non-stationary integer-valued data sets and display strong periodic behaviour. Sunspot numbers and case counts of endemic diseases are just two examples of data sets dependent upon one or more periods of unknown length, illustrating the need for models which can simultaneously capture non-stationarity and unknown periodicity. Conditional on a latent process εt, let Y be a T-element time series of counts with distribution given by yt|εt ~ Po (exp (xT t θ),Ft) where {εt} is an unobserved stationary process. This is known as a serially correlated error model or latent variable model. In Chapter 1, we give examples of count data time series with strong periodicity and evaluate the unconditional mean and covariance structure of {yt} in the case where k = 1. In Chapter 2, we study several previously suggested methods for estimating a single unknown frequency from the data, including least-squares parameter estimation for non-linear models and an autocovariance estimator for the latent process as mentioned in Zeger (1988). In Chapter 3, we introduce a GLM-based mean parameter estimator and a Discrete Fourier transform (DFT) based estimator for w, then establish consistency of both and asymptotic normality of the former. In Chapter 4, extensions of the single-period model to cover data sets with extra short-term dependence or several periods are introduced and the parameter estimators and their properties are extended to cover these models. Finally, in Chapter 5, we study a data set of twenty-three years of measles case counts using results established in previous chapters and consider the wide applications for extensions of the latent variable model to analyse longitudinal, multivariate or spatial-temporal data.<?xml:namespace prefix = o ns = "urn:schemas-microsoft-com:office:office" />
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