弥合临床医学与公共卫生的裂痕强化我国的疾病控制工作

阐述临床医学与公共卫生的割裂给中国疾病预防控制工作带来的严重危害;并说明中国卫生系统近年来在弥合这一裂痕方面所做的巨大努力,例如建立了传染病网络直报系统和\"不明原因肺炎\"监测报告体系,加强了结核病控制工作中医疗机构与疾控机构的紧密衔接,以及临床医学逐步接纳现代疾病防控理念,为适应患者需求,在控制重大慢性疾病——脑中风中实施的医疗服务模式的重大转变.虽然这些改革措施已经在调整和加强临床与公共卫生两个子系统的联系与合作中发挥了作用,提高了疾病控制体系的整体效能,但是这些进展仅仅是初步的.卫生系统还需要在制定和实施\"医疗机构公共卫生工作规范\"等方面做出更多的努力,以推动疾病控制效能的进一步提高.     

建立新型国家预防医学体系战略研究

改革开放以来,伴随我国经济发展、劳动生活方式的改变,影响我国人民健康的主要疾病谱以及危险因素发生了巨大变化,现有的临床医学和预防医学教育体系已不再适应疾病预防控制、保障人民健康的实际需求。未来的卫生方针和政策、公共卫生和预防医学工作能否及时调整以应对这些变化和需求,是摆在我国卫生决策者面前的重大挑战。探讨建立适应实际需求的新型国家预防医学体系,并制定相应的卫生战略、战术和相关政策,从而实现有效利用有限的卫生资源,获取最大的健康保障效果。     

属性权重未知情形下基于公众双重评价的多属性大群体决策方法

针对属性权重未知的重大民生决策事项,提出了一种基于公众双重评价的多属性大群体决策方法.首先,根据公众给出的双重评价信息确定各方案的属性值和整体表现情况.然后分别计算各方案属性值与认可度之间、属性值与非认可度之间的关联水平,根据两类关联度测度结果确定属性权重的取值区间.以所有方案属性值的信息离差度最小化为目标构建优化模型确定属性权重.利用TOPSIS法对方案进行排序,确定最优方案.最后通过算例对本文提出的方法进行验证.根据算例验证结果可知,本文提出的方法可以将公众评价信息融入决策活动之中,以此为依据确定相关决策信息和属性权重,使得决策结果能够充分反映民意,确保决策的有效性.     

Inferring pandemic growth rates from sequence data

Using sequence data to infer population dynamics is playing an increasing role in the analysis of outbreaks. The most common methods in use, based on coalescent inference, have been widely used but not extensively tested against simulated epidemics. Here, we use simulated data to test the ability of both parametric and non-parametric methods for inference of effective population size (coded in the popular BEAST package) to reconstruct epidemic dynamics. We consider a range of simulations centred on scenarios considered plausible for pandemic influenza, but our conclusions are generic for any exponentially growing epidemic. We highlight systematic biases in non-parametric effective population size estimation. The most prominent such bias leads to the false inference of slowing of epidemic spread in the recent past even when the real epidemic is growing exponentially. We suggest some sampling strategies that could reduce (but not eliminate) some of the biases. Parametric methods can correct for these biases if the infected population size is large. We also explore how some poor sampling strategies (e.g. that over-represent epidemiologically linked clusters of cases) could dramatically exacerbate bias in an uncontrolled manner. Finally, we present a simple diagnostic indicator, based on coalescent density and which can easily be applied to reconstructed phylogenies, that identifies time-periods for which effective population size estimates are less likely to be biased. We illustrate this with an application to the 2009 H1N1 pandemic.     

Protocols for sampling viral sequences to study epidemic dynamics

With more emphasis being put on global infectious disease monitoring, viral genetic data are being collected at an astounding rate, both within and without the context of a long-term disease surveillance plan. Concurrent with this increase have come improvements to the sophisticated and generalized statistical techniques used for extracting population-level information from genetic sequence data. However, little research has been done on how the collection of these viral sequence data can or does affect the efficacy of the phylogenetic algorithms used to analyse and interpret them. In this study, we use epidemic simulations to consider how the collection of viral sequence data clarifies or distorts the picture, provided by the phylogenetic algorithms, of the underlying population dynamics of the simulated viral infection over many epidemic cycles. We find that sampling protocols purposefully designed to capture sequences at specific points in the epidemic cycle, such as is done for seasonal influenza surveillance, lead to a significantly better view of the underlying population dynamics than do less-focused collection protocols. Our results suggest that the temporal distribution of samples can have a significant effect on what can be inferred from genetic data, and thus highlight the importance of considering this distribution when designing or evaluating protocols and analysing the data collected thereunder.     

Complexity and anisotropy in host morphology make populations less susceptible to epidemic outbreaks

One of the challenges in epidemiology is to account for the complex morphological structure of hosts such as plant roots, crop fields, farms, cells, animal habitats and social networks, when the transmission of infection occurs between contiguous hosts. Morphological complexity brings an inherent heterogeneity in populations and affects the dynamics of pathogen spread in such systems. We have analysed the influence of realistically complex host morphology on the threshold for invasion and epidemic outbreak in an SIR (susceptible–infected–recovered) epidemiological model. We show that disorder expressed in the host morphology and anisotropy reduces the probability of epidemic outbreak and thus makes the system more resistant to epidemic outbreaks. We obtain general analytical estimates for minimally safe bounds for an invasion threshold and then illustrate their validity by considering an example of host data for branching hosts (salamander retinal ganglion cells). Several spatial arrangements of hosts with different degrees of heterogeneity have been considered in order to separately analyse the role of shape complexity and anisotropy in the host population. The estimates for invasion threshold are linked to morphological characteristics of the hosts that can be used for determining the threshold for invasion in practical applications.     

Heterogeneity in susceptible–infected–removed (SIR) epidemics on lattices

The percolation paradigm is widely used in spatially explicit epidemic models where disease spreads between neighbouring hosts. It has been successful in identifying epidemic thresholds for invasion, separating non-invasive regimes, where the disease never invades the system, from invasive regimes where the probability of invasion is positive. However, its power is mainly limited to homogeneous systems. When heterogeneity (environmental stochasticity) is introduced, the value of the epidemic threshold is, in general, not predictable without numerical simulations. Here, we analyse the role of heterogeneity in a stochastic susceptible–infected–removed epidemic model on a two-dimensional lattice. In the homogeneous case, equivalent to bond percolation, the probability of invasion is controlled by a single parameter, the transmissibility of the pathogen between neighbouring hosts. In the heterogeneous model, the transmissibility becomes a random variable drawn from a probability distribution. We investigate how heterogeneity in transmissibility influences the value of the invasion threshold, and find that the resilience of the system to invasion can be suitably described by two control parameters, the mean and variance of the transmissibility. We analyse a two-dimensional phase diagram, where the threshold is represented by a phase boundary separating an invasive regime in the high-mean, low-variance region from a non-invasive regime in the low-mean, high-variance region of the parameter space. We thus show that the percolation paradigm can be extended to the heterogeneous case. Our results have practical implications for the analysis of disease control strategies in realistic heterogeneous epidemic systems.     

The doing and un-doing of public participation during environmental impact assessments in Kenya

Mechanisms that restrict public participation during environmental decision-making in Kenya still exist almost a decade after the inception of the Environmental Management and Co-ordination Act (EMCA) of 1999. This paper analyses the current situation concerning public participation during environmental impact assessment (EIA) and strategic environmental assessment (SEA). It presents the barriers that may impede effective public participation in environmental decision-making in Kenya and draws attention to possible solutions, including the potential of SEA as a bridge to better public participation.     

Health in financial crises: economic recession and tuberculosis in Central and Eastern Europe

The ongoing global financial crisis, which began in 2007, has drawn attention to the effect of declining economic conditions on public health. A quantitative analysis of previous events can offer insights into the potential health effects of economic decline. In the early 1990s, widespread recession across Central and Eastern Europe accompanied the collapse of the Soviet Union. At the same time, despite previously falling tuberculosis (TB) incidence in most countries, there was an upsurge of TB cases and deaths throughout the region. Here, we study the quantitative relationship between the lost economic productivity and excess TB cases and mortality. We use the data of the World Health Organization for TB notifications and deaths from 1980 to 2006, and World Bank data for gross domestic product. Comparing 15 countries for which sufficient data exist, we find strong linear associations between the lost economic productivity over the period of recession for each country and excess numbers of TB cases (r² = 0.94, p < 0.001) and deaths (r² = 0.94, p < 0.001) over the same period. If TB epidemiology and control are linked to economies in 2009 as they were in 1991 then the Baltic states, particularly Latvia, are now vulnerable to another upturn in TB cases and deaths. These projections are in accordance with emerging data on drug consumption, which indicate that these countries have undergone the greatest reductions since the beginning of 2008. We recommend close surveillance and monitoring during the current recession, especially in the Baltic states.     

Stochasticity in staged models of epidemics: quantifying the dynamics of whooping cough

Although many stochastic models can accurately capture the qualitative epidemic patterns of many childhood diseases, there is still considerable discussion concerning the basic mechanisms generating these patterns; much of this stems from the use of deterministic models to try to understand stochastic simulations. We argue that a systematic method of analysing models of the spread of childhood diseases is required in order to consistently separate out the effects of demographic stochasticity, external forcing and modelling choices. Such a technique is provided by formulating the models as master equations and using the van Kampen system-size expansion to provide analytical expressions for quantities of interest. We apply this method to the susceptible–exposed–infected–recovered (SEIR) model with distributed exposed and infectious periods and calculate the form that stochastic oscillations take on in terms of the model parameters. With the use of a suitable approximation, we apply the formalism to analyse a model of whooping cough which includes seasonal forcing. This allows us to more accurately interpret the results of simulations and to make a more quantitative assessment of the predictions of the model. We show that the observed dynamics are a result of a macroscopic limit cycle induced by the external forcing and resonant stochastic oscillations about this cycle.     

生殖健康公共服务信息标准体系框架研究

本文在充分调研分析我国生殖健康领域标准化需求的基础上,构建了生殖健康公共服务信息标准体系,并对每个分体系进行详细说明。加强标准化工作的进程,对于提高生殖健康服务水平具有十分重要的意义,对于夯实各项工作的基础具有无法替代的支撑和保障作用。     

Approximate disease dynamics in household-structured populations.

We argue that the large-dimensional dynamical systems which frequently occur in biological models can sometimes be effectively reduced to much smaller ones. We illustrate this by applying projection operator techniques to a mean-field model of an infectious disease spreading through a population of households. In this way, we are able to accurately approximate the dynamics of the system in terms of a few key quantities greatly reducing the number of equations required. We investigate linear stability in this framework and find a new way of calculating the familiar threshold criterion for household systems.