The societal benefits of reducing six behavioural risk factors : an economic modelling study from Australia

Background
A large proportion of disease burden is attributed to behavioural risk factors. However, funding for public health programs in Australia remains limited. Government and non-government organisations are interested in the productivity effects on society from reducing chronic diseases. We aimed to estimate the potential health status and economic benefits to society following a feasible reduction in the prevalence of six behavioural risk factors: tobacco smoking; inadequate fruit and vegetable consumption; high risk alcohol consumption; high body mass index; physical inactivity; and intimate partner violence.
Methods
Simulation models were developed for the 2008 Australian population. A realistic reduction in current risk factor prevalence using best available evidence with expert consensus was determined. Avoidable disease, deaths, Disability Adjusted Life Years (DALYs) and health sector costs were estimated. Productivity gains included workforce (friction cost method), household production and leisure time. Multivariable uncertainty analyses and correction for the joint effects of risk factors on health status were undertaken. Consistent methods and data sources were used.
Results
Over the lifetime of the 2008 Australian adult population, total opportunity cost savings of AUD2,334 million (95% Uncertainty Interval AUD1,395 to AUD3,347; 64% in the health sector) were found if feasible reductions in the risk factors were achieved. There would be 95,000 fewer DALYs (a reduction of about 3.6% in total DALYs for Australia); 161,000 less new cases of disease; 6,000 fewer deaths; a reduction of 5 million days in workforce absenteeism; and 529,000 increased days of leisure time.
Conclusions
Reductions in common behavioural risk factors may provide substantial benefits to society. For example, the total potential annual cost savings in the health sector represent approximately 2% of total annual health expenditure in Australia. Our findings contribute important new knowledge about productivity effects, including the potential for increased household and leisure activities, associated with chronic disease prevention. The selection of targets for risk factor prevalence reduction is an important policy decision and a useful approach for future analyses. Similar approaches could be applied in other countries if the data are available.

Spectral element modelling of wave propagation with boundary and structural discontinuity reflections

Spectral element method is very efficient in modelling high-frequency stress wave propagation because it works in the frequency domain. It does not need to use very fine meshes in order to capture high frequency wave energy as the time domain methods do, such as finite element method. However, the conventional spectral element method requires a throw-off element to be added to the structural boundaries to act as a conduit for energy to transmit out of the system. This makes the method difficult to model wave reflection at boundaries. To overcome this limitation, imaginary spectral elements are proposed in this study, which are combined with the real structural elements to model wave reflections at structural boundaries. The efficiency and accuracy of this proposed approach is verified by comparing the numerical simulation results with measured results of one dimensional stress wave propagation in a steel bar. The method is also applied to model wave propagation in a steel bar with not only boundary reflection, but also reflections from single and multiple cracks. The reflection and transmission coefficients, which are obtained from the discrete spring model, are adopted to quantify the discontinuities. Experimental tests of wave propagation in a steel bar with one crack of different depths are also carried out. Numerical simulations and experimental results show that the proposed method is effective and reliable in modelling wave propagation in one-dimensional waveguides with reflections from boundary and structural discontinuities. The proposed method can be applied to effectively model stress wave propagation for structural damage detection.

A lightwave 3d plug-in for modelling long hair on virtual humans

Multimedia applications today make use of virtual humans. Generating realistic virtual humans is a challenging problem owing to a number of factors, one being the simulation of realistic hair. The difficulty in simulating hair is due to the physical properties of hair. The average human head holds thousands of hairs, with the width of each hair often smaller than the size of a pixel. There are also complex lighting effects that occur within hair. This paper presents a LightWave 3D plug-in for modeling thousands of individual hairs on virtual humans. The plug-in allows the user to specify the length, thickness and distribution of the hair, as well as the number of segments a hair is made up of. The plug-in is able to add hairs to a head model, which the user then modifies to define a hairstyle. The hairs are then multiplied by the plug-in to produce many hairs. By providing a plug-in that does most of the work and produces realistic results, the user is able to produce a hairstyle without modeling each individual strand of hair. This greatly reduces the time spent on hair modeling, and makes the possibility of adding realistic long hair to virtual humans reasonable.

Mathematical modelling of deep bed filtration

Numerous mathematical models have been developed to evaluate both initial and transient stage removal efficiency of deep bed filters. Microscopic models either using trajectory analysis or convective-diffusion equations were used to compute the initial removal efficiency. These models predicted the removal efficiency under favorable filtration conditions quantitatively, but failed to predict the removal efficiency under unfavorable conditions. They underestimated the removal efficiency under unfavorable conditions. Thus, semi-empirical formulations were developed to compute initial removal efficiencies under unfavorable conditions. Also, correction for the adhesion of particles onto filter grains improved the results obtained for removal efficiency from the trajectory analysis. Macroscopic models were used to predict the transient stage removal efficiency of deep bed filters. O’Melia and Ali’s model assumed that the particle removal is due to filter grains as well as the particles that are already deposited onto the filter grain. Thus, semi-empirical models were used to predict the ripening of filtration. Several modifications were made to the model developed by O’Melia and Ali to predict the deterioration of particle removal during the transient stages of filtration. Models considering the removal of particles under favorable conditions and the accumulation of charges on the filter grains during the transient stages were also developed. This paper evaluates those models and their applicability under different operating conditions of filtration.

Water quality modelling for drinking water distribution systems

A dynamic water quality model for drinking water distribution systems has been developed in this study, to include processes that occur in the bulk water, as well as those occurring in the biofilm of a distribution system. The model has been validated against water quality data obtained from extensive experimental studies conducted with biofilm reactors. Protein and carbohydrate densities in the biofilm represent biofilm biomass. This model is able to predict the disinfectant decay due to organic matter in the bulk water, as well as that due to biofilm. It simultaneously predicts the growth of biofilm in terms of carbohydrate and protein densities. While this model is complex enough to describe the water quality changes in a distribution system, it is also simple enough to be incorporated into a hydraulic model in order to describe the interaction between disinfectant and microbiological quality throughout a drinking water distribution system.

Multi-scale material modelling platform

The data includes material models suitable for modelling and simulation of multi-scale heterogeneous materials, as well as simulation results and experimental observations for verification and validation of simulated results.

Backward modelling and look-ahead fuzzy energy management controller for a parallel hybrid vehicle

This paper focuses on a parallel hybrid electric vehicle. It first develops a model for the vehicle using the backward-looking approach where the flow of energy starts from wheels and spreads towards engine and electric motor. Next, a fuzzy logic-based strategy is developed to control the operation of the vehicle. The objectives of the controller include managing the energy flow from engine and electric motor, controlling transmission ratio, adjusting speed, and sustaining battery's state of charge. The controller examines current vehicle speed, demand torque, slope difference, state of charge of battery, and engine and electric motor rotation speeds. Then, it determines the best values for continuous variable transmission ratio, speed, and torque. A slope window scheme is also developed to take into account the look-ahead slope information and determine the best vehicle speed for better fuel economy. The developed model and control strategy are simulated. The simulation results are presented and discussed. It is shown that the use of the proposed fuzzy controller reduces fuel consumption.

Modelling of potential food policy interventions in Fiji and Tonga and their impacts on noncommunicable disease mortality

Background: To compare the likely costs and benefits of a range of potential policy interventions in Fiji and Tonga targeted at diet-related noncommunicable diseases (NCDs), in order to support more evidence-based decision-making.

Method: A relatively simple and quick macro-simulation methodology was developed. Logic models were developed by local stakeholders and used to identify costs and dietary impacts of policy changes. Costs were confined to government costs, and excluded cost offsets. The best available evidence was combined with local data to model impacts on deaths from noncommunicable diseases over the lifetime of the target population. Given that the modelling necessarily entailed assumptions to compensate for gaps in data and evidence, use was made of probabilistic uncertainty analysis.

Results: Costs of implementing policy changes were generally low, with the exception of some requiring additional long-term staffing or construction activities. The most effective policy options in Fiji and Tonga targeted access to local produce and high-fat meats respectively, and were estimated to avert approximately 3% of diet-related NCD deaths in each population. Many policies had substantially lower benefits. Cost-effectiveness was higher for the low-cost policies. Similar policies produced markedly different results in the two countries.

Conclusion: Despite the crudeness of the method, the consistent modelling approach used across all the options, allowed reasonable comparisons to be made between the potential policy costs and impacts. This type of modelling can be used to support more evidence-based and informed decision-making about policy interventions and facilitate greater use of policy to achieve a reduction in NCDs.

Thermodynamic and kinetic modelling hydrogenation of titanium particles and sheets

Use of hydrogen as a temporary alloying element in Ti alloys is an attractive approach to improve the mechanical properties of the materials, enhance processability and thereby reduce manufacturing costs. In this paper, the hydrogen diffusion process and the phase transformation both between Ti particles and in Ti sheets were simulated to analyze the mechanism of hydrogen diffusion in different phases (α-Ti, β-Ti and TiHx). With the simulation based on the kinetics and thermodynamics, quantitative behaviors of the hydrogen diffusion and the phase transformation were analyzed. The simulation results provide an insight into the diffusion process and improve the fundamental understanding of the mechanism of diffusion and phase transformation.

What is the optimal level of population alcohol consumption for chronic disease prevention in England? Modelling the impact of changes in average consumption levels

Objective To estimate the impact of achieving alternative average population alcohol consumption levels on chronic disease mortality in England.

Design A macro-simulation model was built to simultaneously estimate the number of deaths from coronary heart disease, stroke, hypertensive disease, diabetes, liver cirrhosis, epilepsy and five cancers that would be averted or delayed annually as a result of changes in alcohol consumption among English adults. Counterfactual scenarios assessed the impact on alcohol-related mortalities of changing (1) the median alcohol consumption of drinkers and (2) the percentage of non-drinkers.

Data sources Risk relationships were drawn from published meta-analyses. Age- and sex-specific distributions of alcohol consumption (grams per day) for the English population in 2006 were drawn from the General Household Survey 2006, and age-, sex- and cause-specific mortality data for 2006 were provided by the Office for National Statistics.

Results The optimum median consumption level for drinkers in the model was 5 g/day (about half a unit), which would avert or delay 4579 (2544 to 6590) deaths per year. Approximately equal numbers of deaths from cancers and liver disease would be delayed or averted (∼2800 for each), while there was a small increase in cardiovascular mortality. The model showed no benefit in terms of reduced mortality when the proportion of non-drinkers in the population was increased.

Conclusions Current government recommendations for alcohol consumption are well above the level likely to minimise chronic disease. Public health targets should aim for a reduction in population alcohol consumption in order to reduce chronic disease mortality.

Modelling the health impact of environmentally sustainable dietary scenarios in the UK

BACKGROUND/OBJECTIVES:


Food is responsible for around one-fifth of all greenhouse gas (GHG) emissions from products consumed in the UK, the largest contributor of which is meat and dairy. The Committee on Climate Change have modelled the impact on GHG emissions of three dietary scenarios for food consumption in the UK. This paper models the impact of the three scenarios on mortality from cardiovascular disease and cancer.
SUBJECTS/METHODS:


A previously published model (DIETRON) was used. The three scenarios were parameterised by fruit and vegetables, fibre, total fat, saturated fat, monounsaturated fatty acids, polyunsaturated fatty acids, cholesterol and salt using the 2008 Family Food Survey. A Monte Carlo simulation generated 95% credible intervals.
RESULTS:


Scenario 1 (50% reduction in meat and dairy replaced by fruit, vegetables and cereals: 19% reduction in GHG emissions) resulted in 36 910 (30 192 to 43 592) deaths delayed or averted per year. Scenario 2 (75% reduction in cow and sheep meat replaced by pigs and poultry: 9% reduction in GHG emissions) resulted in 1999 (1739 to 2389) deaths delayed or averted. Scenario 3 (50% reduction in pigs and poultry replaced with fruit, vegetables and cereals: 3% reduction in GHG emissions) resulted in 9297 (7288 to 11 301) deaths delayed or averted.
CONCLUSION:


Modelled results suggest that public health and climate change dietary goals are in broad alignment with the largest results in both domains occurring when consumption of all meat and dairy products are reduced. Further work in real-life settings is needed to confirm these results.

Discrete event simulation enabled high level emulation of a distribution centre

Emulation facilitates the testing of control systems through the use of a simulation model. Typically emulation has focused on low level control, to ensure that resources within a system are commissioned correctly. Higher level control that deals with complex issues such as throughput, in-system time and stacking, has not received as much attention. In this paper, a higher level agent-based emulation framework was proposed. Then an emulation model for a distribution centre is described that can test distribution centre level algorithms directly. This methodology also allows playback of real world operations, making it an ideal tool to analyse problems with performance of commissioned systems.

Video driven traffic modelling in ParamicsTM

With urbanization and vehicle availability, there exist many traffic problems including congestion, environmental impact and safety. In order to address these problems, we propose a video driven traffic modelling system in this paper. The system can simulate real-world traffic activities in a computer, based on traffic data recorded in videos. Video processing is employed to estimate metrics such as traffic volumes. These metrics are used to update the traffic system model, which is then simulated using the Paramics™ traffic simulation platform. Video driven traffic modelling has widespread potential application in traffic systems, due to the convenience and reduced costs of model development and maintenance. Experiments are conducted in this paper to demonstrate the effectiveness of the proposed system.

Video driven traffic modelling

We propose Video Driven Traffic Modelling (VDTM) for accurate simulation of real-world traffic behaviours with detailed information and low-cost model development and maintenance. Computer vision techniques are employed to estimate traffic parameters. These parameters are used to build and update a traffic system model. The model is simulated using the Paramics traffic simulation platform. Based on the simulation techniques, effects of traffic interventions can be evaluated in order to achieve better decision makings for traffic management authorities. In this paper, traffic parameters such as vehicle types, times of starting trips and corresponding origin-destinations are extracted from a video. A road network is manually defined according to the traffic composition in the video, and individual vehicles associated with extracted properties are modelled and simulated within the defined road network using Paramics. VDTM has widespread potential applications in supporting traffic decision-makings. To demonstrate the effectiveness, we apply it in optimizing a traffic signal control system, which adaptively adjusts green times of signals at an intersection to reduce traffic congestion.