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Population Impact (population + impact)

Distribution by Scientific Domains

Life Sciences	50%

Selected Abstracts

Psychological problems of partnership and profit or: who has the higher population impact?

ADDICTION, Issue 9 2000
Ulrich John
First page of article [source]

Use of longitudinal data in genetic studies in the genome-wide association studies era: summary of Group 14

GENETIC EPIDEMIOLOGY, Issue S1 2009
Berit Kerner
Abstract Participants analyzed actual and simulated longitudinal data from the Framingham Heart Study for various metabolic and cardiovascular traits. The genetic information incorporated into these investigations ranged from selected single-nucleotide polymorphisms to genome-wide association arrays. Genotypes were incorporated using a broad range of methodological approaches including conditional logistic regression, linear mixed models, generalized estimating equations, linear growth curve estimation, growth modeling, growth mixture modeling, population attributable risk fraction based on survival functions under the proportional hazards models, and multivariate adaptive splines for the analysis of longitudinal data. The specific scientific questions addressed by these different approaches also varied, ranging from a more precise definition of the phenotype, bias reduction in control selection, estimation of effect sizes and genotype associated risk, to direct incorporation of genetic data into longitudinal modeling approaches and the exploration of population heterogeneity with regard to longitudinal trajectories. The group reached several overall conclusions: (1) The additional information provided by longitudinal data may be useful in genetic analyses. (2) The precision of the phenotype definition as well as control selection in nested designs may be improved, especially if traits demonstrate a trend over time or have strong age-of-onset effects. (3) Analyzing genetic data stratified for high-risk subgroups defined by a unique development over time could be useful for the detection of rare mutations in common multifactorial diseases. (4) Estimation of the population impact of genomic risk variants could be more precise. The challenges and computational complexity demanded by genome-wide single-nucleotide polymorphism data were also discussed. Genet. Epidemiol. 33 (Suppl. 1):S93,S98, 2009. © 2009 Wiley-Liss, Inc. [source]

Community intervention and public policy in the prevention of antisocial behavior

THE JOURNAL OF CHILD PSYCHOLOGY AND PSYCHIATRY AND ALLIED DISCIPLINES, Issue 1-2 2009
Kenneth A. Dodge
As academic clinical science moves to community intervention to achieve impact on population prevalence of antisocial behavior disorders, exciting potential is tempered by realistic caution. Three kinds of efforts are noted. First, individual evidence-based therapies are being implemented at scale. Difficulties in high-fidelity implementation are noted, and the unlikelihood of population impact is highlighted. Second, communities are receiving new resources to support individuals, although connecting community resources to highest-risk individuals is difficult. Third, community factors are being targeted for change through policy reform, with mixed results. As the field moves in this direction, the importance of adhering to principles of scientific rigor and empirical evidence is emphasized, to keep scientist-practitioners from overstepping their bounds. [source]

Host culling as an adaptive management tool for chronic wasting disease in white-tailed deer: a modelling study

JOURNAL OF APPLIED ECOLOGY, Issue 2 2009
Gideon Wasserberg
Summary 1Emerging wildlife diseases pose a significant threat to natural and human systems. Because of real or perceived risks of delayed actions, disease management strategies such as culling are often implemented before thorough scientific knowledge of disease dynamics is available. Adaptive management is a valuable approach in addressing the uncertainty and complexity associated with wildlife disease problems and can be facilitated by using a formal model. 2We developed a multi-state computer simulation model using age, sex, infection-stage, and seasonality as a tool for scientific learning and managing chronic wasting disease (CWD) in white-tailed deer Odocoileus virginianus. Our matrix model used disease transmission parameters based on data collected through disease management activities. We used this model to evaluate management issues on density- (DD) and frequency-dependent (FD) transmission, time since disease introduction, and deer culling on the demographics, epizootiology, and management of CWD. 3Both DD and FD models fit the Wisconsin data for a harvested white-tailed deer population, but FD was slightly better. Time since disease introduction was estimated as 36 (95% CI, 24,50) and 188 (41,>200) years for DD and FD transmission, respectively. Deer harvest using intermediate to high non-selective rates can be used to reduce uncertainty between DD and FD transmission and improve our prediction of long-term epidemic patterns and host population impacts. A higher harvest rate allows earlier detection of these differences, but substantially reduces deer abundance. 4Results showed that CWD has spread slowly within Wisconsin deer populations, and therefore, epidemics and disease management are expected to last for decades. Non-hunted deer populations can develop and sustain a high level of infection, generating a substantial risk of disease spread. In contrast, CWD prevalence remains lower in hunted deer populations, but at a higher prevalence the disease competes with recreational hunting to reduce deer abundance. 5Synthesis and applications. Uncertainty about density- or frequency-dependent transmission hinders predictions about the long-term impacts of chronic wasting disease on cervid populations and the development of appropriate management strategies. An adaptive management strategy using computer modelling coupled with experimental management and monitoring can be used to test model predictions, identify the likely mode of disease transmission, and evaluate the risks of alternative management responses. [source]