Economic modelling of antenatal screening and ultrasound scanning programmes for identification of fetal abnormalities

Objective Within the framework of a health technology assessment and using an economic model, to determine the most clinically and cost effective policy of scanning and screening for fetal abnormalities in early pregnancy. Design A discrete event simulation model of 50,000 singleton pregnancies. Setting Maternity services in Scotland. Population Women during the first 24 weeks of their pregnancy. Methods The mathematical model was populated with data on uptake of screening, prevalence, detection and false positive rates for eight fetal abnormalities and with costs for ultrasound scanning and serum screening. Inclusion of abnormalities was based on the relative prevalence and clinical importance of conditions and the availability of data. Six strategies for the identification of abnormalities prenatally including combinations of first and second trimester ultrasound scanning and first and second trimester screening for chromosomal abnormalities were compared. Main outcome measures The number of abnormalities detected and missed, the number of iatrogenic losses resulting from invasive tests, the total cost of strategies and the cost per abnormality detected were compared between strategies. Results First trimester screening for chromosomal abnormalities costs more than second trimester screening but results in fewer iatrogenic losses. Strategies which include a second trimester ultrasound scan result in more abnormalities being detected and have lower costs per anomaly detected. Conclusions The preferred strategy includes both first and second trimester ultrasound scans and a first trimester screening test for chromosomal abnormalities. It has been recommended that this policy is offered to all women in Scotland.

Bridging top down and bottom up: Modelling community preferences for a dispersed rural settlement pattern

This paper explores the scope to bridge top-down and bottom-up perspectives on spatial planning by drawing on EU-funded action research in relation to rural settlement planning in Northern Ireland. The empirical work is located within a review of planning theory that exposes a long running tension between the technocratic stances of government planners and the aspirations of engaged citizens. It demonstrates the operation of a large group planning methodology that delivers community preference with environmental responsibility as a participatory input into planning policy formulation. Transferable insights into the dynamics of spatial planning are identified.

A Holistic vs. an Attribute-based Approach to Agri-Environmental Policy Valuation: Do Welfare Estimates Differ?

Different economic valuation methodologies can be used to value the non-market benefits of an agri-environmental scheme. In particular, the non-market value can be examined by assessing the public's willingness to pay for the policy outputs as a whole or by modelling the preferences of society for the component attributes of the rural landscape that result from the implementation of the policy. In this article we examine whether the welfare values estimated for an agri-environmental policy are significantly different between an holistic valuation methodology (using contingent valuation) and an attribute-based valuation methodology (choice experiment). It is argued that the valuation methodology chosen should be based on whether or not the overall objective is the valuation of the agri-environment policy package in its entirety or the valuation of each of the policy's distinct environmental outputs.

Incorporating uncertainty in predictive species distribution modelling

Motivated by the need to solve ecological problems (climate change, habitat fragmentation and biological invasions), there has been increasing interest in species distribution models (SDMs). Predictions from these models inform conservation policy, invasive species management and disease-control measures. However, predictions are subject to uncertainty, the degree and source of which is often unrecognized. Here, we review the SDM literature in the context of uncertainty, focusing on three main classes of SDM: niche-based models, demographic models and process-based models. We identify sources of uncertainty for each class and discuss how uncertainty can be minimized or included in the modelling process to give realistic measures of confidence around predictions. Because this has typically not been performed, we conclude that uncertainty in SDMs has often been underestimated and a false precision assigned to predictions of geographical distribution. We identify areas where development of new statistical tools will improve predictions from distribution models, notably the development of hierarchical models that link different types of distribution model and their attendant uncertainties across spatial scales. Finally, we discuss the need to develop more defensible methods for assessing predictive performance, quantifying model goodness-of-fit and for assessing the significance of model covariates.

Applying Health Locus of Control and Latent Class Modelling to food and physical activity choices affecting CVD risk

Health Locus of Control (HLC) classifies our beliefs about the connection between our actions and health outcomes (Skinner, 1996) into three categories: “internal control”, corresponding to health being the result of an individual's effort and habits; “control by powerful others”, whereby health depends on others, such as doctors; and “chance control”, according to which health depends on fate and chance. Using Choice Experiments we investigate the relationship between HLC and willingness to change lifestyle, in terms of eating habits, physical activity and associated cardiovascular disease risk, in a 384 person sample representative of the 40–65 aged population of Northern Ireland administered between February and July 2011. Using latent class analysis we identify three discrete classes of people based on their HLC: the first class is sceptical about their capacity to control their health and certain unhealthy habits. Despite being unsatisfied with their situation, they are reluctant to accept behaviour changes. The second is a group of individuals unhappy with their current situation but willing to change through exercise and diet. Finally, a group of healthy optimists is identified, who are satisfied with their current situation but happy to take more physical activity and improve their diet. Our findings show that any policy designed to modify people's health related behaviour should consider the needs of this sceptical class which represents a considerable proportion of the population in the region.

Modelling the effectiveness of grass buffer strips in managing muddy floods under a changing climate

Muddy floods occur when rainfall generates runoff on agricultural land, detaching and transporting sediment into the surrounding natural and built environment. In the Belgian Loess Belt, muddy floods occur regularly and lead to considerable economic costs associated with damage to property and infrastructure. Mitigation measures designed to manage the problem have been tested in a pilot area within Flanders and were found to be cost-effective within three years. This study assesses whether these mitigation measures will remain effective under a changing climate. To test this, the Water Erosion Prediction Project (WEPP) model was used to examine muddy flooding diagnostics (precipitation, runoff, soil loss and sediment yield) for a case study hillslope in Flanders where grass buffer strips are currently used as a mitigation measure. The model was run for present day conditions and then under 33 future site-specific climate scenarios. These future scenarios were generated from three earth system models driven by four representative concentration pathways and downscaled using quantile mapping and the weather generator CLIGEN. Results reveal that under the majority of future scenarios, muddy flooding diagnostics are projected to increase, mostly as a consequence of large scale precipitation events rather than mean changes. The magnitude of muddy flood events for a given return period is also generally projected to increase. These findings indicate that present day mitigation measures may have a reduced capacity to manage muddy flooding given the changes imposed by a warming climate with an enhanced hydrological cycle. Revisions to the design of existing mitigation measures within existing policy frameworks are considered the most effective way to account for the impacts of climate change in future mitigation planning.