Phylogeographical analyses of shellfish viruses: inferring a geographical origin for ostreid herpesviruses OsHV-1 (Malacoherpesviridae)

Mortality episodes have regularly been affecting the shellfish industry throughout its history. Some of these mortalities, especially in the oyster industry, have been attributed to herpesviruses. Purification of viral particles and molecular characterization have led to the development of routine monitoring, as well as improved taxonomic classification. Ostreid herpesviruses (Malacoherpesviridae), mostly affecting Pacific oysters Crassostrea gigas, have been sporadically recorded in the French oyster industry since the early 1990s (OsHV-1 'reference'). From 2008, a new variant of ostreid herpesvirus (OsHV-1 mu Var) has emerged and seriously impacted oyster production in France and other European countries. Consequently, the presence of ostreid herpesviruses has been monitored in different oyster producing areas around the world. The present study compiles molecular data that are available from survey efforts and takes a biogeographical approach, in order to infer an origin for ostreid herpesviruses. The highest genotype diversity was found in East Asia, despite a lower survey effort in that area than in Europe. Genotype network analyses show that both populations of ostreid herpesviruses present in Europe (OsHV-1 'reference' and OsHV-1 mu Var) are closely related to genotypes recorded in Asia. Moreover, ostreid herpesviruses have been detected in wild and symptom-free populations of various Asian native Crassostrea species. In the rest of the world, ostreid herpesvirus genotypes were recorded from cultivated C. gigas, and mostly associated with mortality episodes. Results of this study are therefore highly suggestive of an Asian origin for these viruses, which can be pathogenic under farming conditions. It also highlights the risks of European stock improvements, by means of overseas shellfish imports.

A sting in the spit: widespread cross-infection of multiple RNA viruses across wild and managed bees

Declining populations of bee pollinators are a cause of concern, with major repercussions for biodiversity loss and food security. RNA viruses associated with honeybees represent a potential threat to other insect pollinators, but the extent of this threat is poorly understood. This study aims to attain a detailed understanding of the current and ongoing risk of emerging infectious disease (EID) transmission between managed and wild pollinator species across a wide range of RNA viruses. Within a structured large-scale national survey across 26 independent sites, we quantify the prevalence and pathogen loads of multiple RNA viruses in co-occurring managed honeybee (Apis mellifera) and wild bumblebee (Bombus spp.) populations. We then construct models that compare virus prevalence between wild and managed pollinators. Multiple RNA viruses associated with honeybees are widespread in sympatric wild bumblebee populations. Virus prevalence in honeybees is a significant predictor of virus prevalence in bumblebees, but we remain cautious in speculating over the principle direction of pathogen transmission. We demonstrate species-specific differences in prevalence, indicating significant variation in disease susceptibility or tolerance. Pathogen loads within individual bumblebees may be high and in the case of at least one RNA virus, prevalence is higher in wild bumblebees than in managed honeybee populations. Our findings indicate widespread transmission of RNA viruses between managed and wild bee pollinators, pointing to an interconnected network of potential disease pressures within and among pollinator species. In the context of the biodiversity crisis, our study emphasizes the importance of targeting a wide range of pathogens and defining host associations when considering potential drivers of population decline.

Using interviewer random effects to remove selection bias from HIV prevalence estimates

Background: Selection bias in HIV prevalence estimates occurs if non-participation in testing is correlated with HIV status. Longitudinal data suggests that individuals who know or suspect they are HIV positive are less likely to participate in testing in HIV surveys, in which case methods to correct for missing data which are based on imputation and observed characteristics will produce biased results. Methods: The identity of the HIV survey interviewer is typically associated with HIV testing participation, but is unlikely to be correlated with HIV status. Interviewer identity can thus be used as a selection variable allowing estimation of Heckman-type selection models. These models produce asymptotically unbiased HIV prevalence estimates, even when non-participation is correlated with unobserved characteristics, such as knowledge of HIV status. We introduce a new random effects method to these selection models which overcomes non-convergence caused by collinearity, small sample bias, and incorrect inference in existing approaches. Our method is easy to implement in standard statistical software, and allows the construction of bootstrapped standard errors which adjust for the fact that the relationship between testing and HIV status is uncertain and needs to be estimated. Results: Using nationally representative data from the Demographic and Health Surveys, we illustrate our approach with new point estimates and confidence intervals (CI) for HIV prevalence among men in Ghana (2003) and Zambia (2007). In Ghana, we find little evidence of selection bias as our selection model gives an HIV prevalence estimate of 1.4% (95% CI 1.2% – 1.6%), compared to 1.6% among those with a valid HIV test. In Zambia, our selection model gives an HIV prevalence estimate of 16.3% (95% CI 11.0% - 18.4%), compared to 12.1% among those with a valid HIV test. Therefore, those who decline to test in Zambia are found to be more likely to be HIV positive. Conclusions: Our approach corrects for selection bias in HIV prevalence estimates, is possible to implement even when HIV prevalence or non-participation is very high or very low, and provides a practical solution to account for both sampling and parameter uncertainty in the estimation of confidence intervals. The wide confidence intervals estimated in an example with high HIV prevalence indicate that it is difficult to correct statistically for the bias that may occur when a large proportion of people refuse to test.

On the Assumption of Bivariate Normality in Selection Models: A Copula Approach Applied to Estimating HIV Prevalence

Background: Heckman-type selection models have been used to control HIV prevalence estimates for selection bias when participation in HIV testing and HIV status are associated after controlling for observed variables. These models typically rely on the strong assumption that the error terms in the participation and the outcome equations that comprise the model are distributed as bivariate normal.
Methods: We introduce a novel approach for relaxing the bivariate normality assumption in selection models using copula functions. We apply this method to estimating HIV prevalence and new confidence intervals (CI) in the 2007 Zambia Demographic and Health Survey (DHS) by using interviewer identity as the selection variable that predicts participation (consent to test) but not the outcome (HIV status).
Results: We show in a simulation study that selection models can generate biased results when the bivariate normality assumption is violated. In the 2007 Zambia DHS, HIV prevalence estimates are similar irrespective of the structure of the association assumed between participation and outcome. For men, we estimate a population HIV prevalence of 21% (95% CI = 16%–25%) compared with 12% (11%–13%) among those who consented to be tested; for women, the corresponding figures are 19% (13%–24%) and 16% (15%–17%).
Conclusions: Copula approaches to Heckman-type selection models are a useful addition to the methodological toolkit of HIV epidemiology and of epidemiology in general. We develop the use of this approach to systematically evaluate the robustness of HIV prevalence estimates based on selection models, both empirically and in a simulation study.

Adjusting HIV Prevalence Estimates for Non-participation: an Application to Demographic Surveillance

Introduction: HIV testing is a cornerstone of efforts to combat the HIV epidemic, and testing conducted as part of surveillance provides invaluable data on the spread of infection and the effectiveness of campaigns to reduce the transmission of HIV. However, participation in HIV testing can be low, and if respondents systematically select not to be tested because they know or suspect they are HIV positive (and fear disclosure), standard approaches to deal with missing data will fail to remove selection bias. We implemented Heckman-type selection models, which can be used to adjust for missing data that are not missing at random, and established the extent of selection bias in a population-based HIV survey in an HIV hyperendemic community in rural South Africa.

Methods: We used data from a population-based HIV survey carried out in 2009 in rural KwaZulu-Natal, South Africa. In this survey, 5565 women (35%) and 2567 men (27%) provided blood for an HIV test. We accounted for missing data using interviewer identity as a selection variable which predicted consent to HIV testing but was unlikely to be independently associated with HIV status. Our approach involved using this selection variable to examine the HIV status of residents who would ordinarily refuse to test, except that they were allocated a persuasive interviewer. Our copula model allows for flexibility when modelling the dependence structure between HIV survey participation and HIV status.

Results: For women, our selection model generated an HIV prevalence estimate of 33% (95% CI 27–40) for all people eligible to consent to HIV testing in the survey. This estimate is higher than the estimate of 24% generated when only information from respondents who participated in testing is used in the analysis, and the estimate of 27% when imputation analysis is used to predict missing data on HIV status. For men, we found an HIV prevalence of 25% (95% CI 15–35) using the selection model, compared to 16% among those who participated in testing, and 18% estimated with imputation. We provide new confidence intervals that correct for the fact that the relationship between testing and HIV status is unknown and requires estimation.

Conclusions: We confirm the feasibility and value of adopting selection models to account for missing data in population-based HIV surveys and surveillance systems. Elements of survey design, such as interviewer identity, present the opportunity to adopt this approach in routine applications. Where non-participation is high, true confidence intervals are much wider than those generated by standard approaches to dealing with missing data suggest.

Identification of Peptide Inhibitors of Enveloped Viruses Using Support Vector Machine

The peptides derived from envelope proteins have been shown to inhibit the protein-protein interactions in the virus membrane fusion process and thus have a great potential to be developed into effective antiviral therapies. There are three types of envelope proteins each exhibiting distinct structure folds. Although the exact fusion mechanism remains elusive, it was suggested that the three classes of viral fusion proteins share a similar mechanism of membrane fusion. The common mechanism of action makes it possible to correlate the properties of self-derived peptide inhibitors with their activities. Here we developed a support vector machine model using sequence-based statistical scores of self-derived peptide inhibitors as input features to correlate with their activities. The model displayed 92% prediction accuracy with the Matthew’s correlation coefficient of 0.84, obviously superior to those using physicochemical properties and amino acid decomposition as input. The predictive support vector machine model for self- derived peptides of envelope proteins would be useful in development of antiviral peptide inhibitors targeting the virus fusion process.

Supra-treatment threshold neonatal jaundice: Incidence in HIV-exposed compared to non-exposed neonates at Queen Elizabeth Central Hospital in Blantyre, Malawi

INTRODUCTION: Jaundice is the yellowish pigmentation of the skin, sclera, and mucous membranes resulting from bilirubin deposition. Children born to mothers with HIV are more likely to be born premature, with low birth weight, and to become septic-all risk factors for neonatal jaundice. Further, there has been a change in the prevention of mother-to-child transmission (PMTCT) of HIV guidelines from single-dose nevirapine to a six-week course, all of which theoretically put HIV-exposed newborns at greater risk of developing neonatal jaundice.

AIM: We carried out a study to determine the incidence of severe and clinical neonatal jaundice in HIV-exposed neonates admitted to the Chatinkha Nursery (CN) neonatal unit at Queen Elizabeth Central Hospital (QECH) in Blantyre.

METHODS: Over a period of four weeks, the incidence among non-exposed neonates was also determined for comparison between the two groups of infants. Clinical jaundice was defined as transcutaneous bilirubin levels greater than 5 mg/dL and severe jaundice as bilirubin levels above the age-specific treatment threshold according the QECH guidelines. Case notes of babies admitted were retrieved and information on birth date, gestational age, birth weight, HIV status of mother, type of feeding, mode of delivery, VDRL status of mother, serum bilirubin, duration of stay in CN, and outcome were extracted.

RESULTS: Of the 149 neonates who were recruited, 17 (11.4%) were HIV-exposed. One (5.88%) of the 17 HIV-exposed and 19 (14.4%) of 132 HIV-non-exposed infants developed severe jaundice requiring therapeutic intervention (p = 0.378). Eight (47%) of the HIV-exposed and 107 (81%) of the non-exposed neonates had clinical jaundice of bilirubin levels greater than 5 mg/dL (p < 0.001).

CONCLUSIONS: The study showed a significant difference in the incidence of clinical jaundice between the HIV-exposed and HIV-non-exposed neonates. Contrary to our hypothesis, however, the incidence was greater in HIV-non-exposed than in HIV-exposed infants.

Explanations for child sexual abuse given by convicted offenders in Malawi:no evidence for "HIV cleansing"

OBJECTIVE: A commonly cited, but unproven reason given for the rise in reported cases of child sexual abuse in Sub-Saharan Africa is the "HIV cleansing myth"-the belief that an HIV infected individual can be cured by having sex with a child virgin. The purpose of this study was to explore in Malawi the reasons given by convicted sex offenders for child sexual abuse and to determine if a desire to cure HIV infection motivated their offence.

METHODS: Offenders convicted of sexual crimes against victims under the age of 18 were interviewed in confidence in Malawi's two largest prisons. During the interview the circumstances of the crime were explored and the offenders were asked what had influenced them to commit it. Each participant was asked the closed question "Did you think that having sex with your victim would cure or cleanse you from HIV?"

RESULTS: 58 offenders agreed to participate. The median (range) age of offenders and victims was 30 (16-66) years and 14 (2-17) years, respectively. Twenty one respondents (36.2%) denied that an offence had occurred. Twenty seven (46.6%) admitted that they were motivated by a desire to satisfy their sexual desires. Six (10.3%) stated they committed the crime only because they were under the influence of drugs or alcohol. None of the participants said that a desire to cure or avoid HIV infection motivated the abuse.

CONCLUSION: This study suggests that offenders convicted of a sexual crime against children in Malawi were not motivated by a desire to be cured or "cleansed" from HIV infection. A need to fulfil their sexual urges or the disinhibiting effect of drugs or alcohol was offered by the majority of participants as excuses for their behaviour.