Chronic obstructive pulmonary disease in sub-Saharan Africa.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide and an important cause of death in sub-Saharan Africa (SSA). We conducted a systematic review and meta-analysis on the prevalence of and risk factors for COPD in SSA.METHODS: We conducted a protocol-driven systematic literature search in MEDLINE, EMBASE, CINAHL and Global Health, supplemented by a manual search of the abstracts from thoracic conference proceedings from 2017 to 2020. We did a meta-analysis of COPD prevalence and its association with current smoking.RESULTS: We identified 831 titles, of which 27 were eligible for inclusion in the review and meta-analysis. The population prevalence of COPD ranged from 1.7% to 24.8% (pooled prevalence: 8%, 95% CI 6-11). An increased prevalence of COPD was associated with increasing age, smoking and biomass smoke exposure. The pooled odds ratio for the effect of current smoking (vs. never smoked) on COPD was 2.20 (95% CI 1.62-2.99).CONCLUSION: COPD causes morbidity and mortality in adults in SSA. Smoking is an important risk factor for COPD in SSA, and this exposure needs to be reduced through the combined efforts of clinicians, researchers and policymakers to address this debilitating and preventable lung disease.

Compatibility between livestock databases used for quantitative biosecurity response in New Zealand.

AIM: To characterise New Zealand's livestock biosecurity databases, and investigate their compatibility and capacity to provide a single integrated data source for quantitative outbreak analysis. METHODS: Contemporary snapshots of the data in three national livestock biosecurity databases, AgriBase, FarmsOnLine (FOL) and the National Animal Identification and Tracing Scheme (NAIT), were obtained on 16 September, 1 September and 30 April 2014, respectively, and loaded into a relational database. A frequency table of animal numbers per farm was calculated for the AgriBase and FOL datasets. A two dimensional kernel density estimate was calculated for farms reporting the presence of cattle, pigs, deer, and small ruminants in each database and the ratio of farm densities for AgriBase versus FOL calculated. The extent to which records in the three databases could be matched and linked was quantified, and the level of agreement amongst them for the presence of different species on properties assessed using Cohen's kappa statistic. RESULTS: AgriBase contained fewer records than FOL, but recorded animal numbers present on each farm, whereas FOL contained more records, but captured only presence/absence of animals. The ratio of farm densities in AgriBase relative to FOL for pigs and deer was reasonably homogeneous across New Zealand, with AgriBase having a farm density approximately 80% of FOL. For cattle and small ruminants, there was considerable heterogeneity, with AgriBase showing a density of cattle farms in the Central Otago region that was 20% of FOL, and a density of small ruminant farms in the central West Coast area that was twice that of FOL. Only 37% of records in FOL could be linked to AgriBase, but the level of agreement for the presence of different species between these databases was substantial (kappa>0.6). Both NAIT and FOL shared common farm identifiers which could be used to georeference animal movements, and there was a fair to substantial agreement (kappa 0.32-0.69) between these databases for the presence of cattle and deer on properties. CONCLUSIONS: The three databases broadly agreed with each other, but important differences existed in both species composition and spatial coverage which raises concern over their accuracy. Importantly, they cannot be reliably linked together to provide a single picture of New Zealand's livestock industry, limiting the ability to use advanced quantitative techniques to provide effective decision support during disease outbreaks. We recommend that a single integrated database be developed, with alignment of resources and legislation for its upkeep.

The timing of molecular and morphological changes underlying reproductive transitions in wild tomatoes (Solanum sect. Lycopersicon).

Molecular mechanisms underlying the transition from genetic self-incompatibility to self-compatibility are well documented, but the evolution of other reproductive trait changes that accompany shifts in reproductive strategy (mating system) remains comparatively under-investigated. A notable exception is the transition from exserted styles to styles with recessed positions relative to the anthers in wild tomatoes (Solanum Section Lycopersicon). This phenotypic change has been previously attributed to a specific mutation in the promoter of a gene that influences style length (style2.1); however, whether this specific regulatory mutation arose concurrently with the transition from long to short styles, and whether it is causally responsible for this phenotypic transition, has been poorly investigated across this group. To address this gap, we assessed 74 accessions (populations) from 13 species for quantitative genetic variation in floral and reproductive traits as well as the presence/absence of deletions at two different locations (StyleD1 and StyleD2) within the regulatory region upstream of style2.1. We confirmed that the putatively causal deletion variant (a 450-bp deletion at StyleD1) arose within self-compatible lineages. However, the variation and history of both StyleD1 and StyleD2 was more complex than previously inferred. In particular, although StyleD1 was statistically associated with differences in style length and stigma exsertion across all species, we found no evidence for this association within two species polymorphic for the StyleD1 mutation. We conclude that the previous association detected between phenotypic and molecular differences is most likely due to a phylogenetic association rather than a causal mechanistic relationship. Phenotypic variation in style length must therefore be due to other unexamined linked variants in the style2.1 regulatory region.

A novel approach to real-time risk prediction for emerging infectious diseases: a case study in Avian Influenza H5N1.

Mathematical simulation modelling of epidemic processes has recently become a popular tool in guiding policy decisions for potential disease outbreaks. Such models all rely on various parameters in order to specify quantities such as transmission and detection rates. However, the values of these parameters are peculiar to an individual outbreak, and estimating them in advance of an epidemic has been the major difficulty in the predictive credibility of such approaches. The obstruction to classical approaches in estimating model parameters has been that of missing data: (i) an infected individual is only detected after the onset of clinical signs, we never observe the time of infection directly; (ii) if we wish to make inference on an epidemic while it is in progress (in order to predict how it might unfold in the future), we must take into account the fact that there may be individuals who are infected but not yet detected. In this paper we apply a reversible-jump Markov chain Monte Carlo algorithm to a combined spatial and contact network model constructed in a Bayesian context to provide a real-time risk prediction during an epidemic. Using the example of a potential Avian H5N1 epidemic in the UK poultry industry, we demonstrate how such a technique can be used to give real-time predictions of quantities such as the probability of individual poultry holdings becoming infected, the risk that individual holdings pose to the population if they become infected, and the number and whereabouts of infected, but not yet detected, holdings. Since the methodology generalises easily to many epidemic situations, we anticipate its use as a real-time decision-support tool for targetting disease control to critical transmission processes, and for monitoring the efficacy of current control policy.

Predicting undetected infections during the 2007 foot-and-mouth disease outbreak.

Active disease surveillance during epidemics is of utmost importance in detecting and eliminating new cases quickly, and targeting such surveillance to high-risk individuals is considered more efficient than applying a random strategy. Contact tracing has been used as a form of at-risk targeting, and a variety of mathematical models have indicated that it is likely to be highly efficient. However, for fast-moving epidemics, resource constraints limit the ability of the authorities to perform, and follow up, contact tracing effectively. As an alternative, we present a novel real-time Bayesian statistical methodology to determine currently undetected (occult) infections. For the UK foot-and-mouth disease (FMD) epidemic of 2007, we use real-time epidemic data synthesized with previous knowledge of FMD outbreaks in the UK to predict which premises might have been infected, but remained undetected, at any point during the outbreak. This provides both a framework for targeting surveillance in the face of limited resources and an indicator of the current severity and spatial extent of the epidemic. We anticipate that this methodology will be of substantial benefit in future outbreaks, providing a compromise between targeted manual surveillance and random or spatially targeted strategies.

Engineering RENTA, a DNA prime-MVA boost HIV vaccine tailored for Eastern and Central Africa.

For the development of human immunodeficiency virus type 1 (HIV-1) vaccines, traditional approaches inducing virus-neutralizing antibodies have so far failed. Thus the effort is now focused on elicitation of cellular immunity. We are currently testing in clinical trials in the United Kingdom and East Africa a T-cell vaccine consisting of HIV-1 clade A Gag-derived immunogen HIVA delivered in a prime-boost regimen by a DNA plasmid and modified vaccinia virus Ankara (MVA). Here, we describe engineering and preclinical development of a second immunogen RENTA, which will be used in combination with the present vaccine in a four-component DNA/HIVA-RENTA prime-MVA/HIVA-RENTA boost formulation. RENTA is a fusion protein derived from consensus HIV clade A sequences of Tat, reverse transcriptase, Nef and gp41. We inactivated the natural biological activities of the HIV components and confirmed immunogenicities of the pTHr.RENTA and MVA.RENTA vaccines in mice. Furthermore, we demonstrated in mice and rhesus monkeys broadening of HIVA-elicited T-cell responses by a parallel induction of HIVA- and RENTA-specific responses recognizing multiple HIV epitopes.