A systematic review of the impact of including both waist and hip circumference in risk models for cardiovascular diseases, diabetes and mortality

Both a larger waist and narrow hips are associated with heightened risk of diabetes, cardiovascular diseases and premature mortality. We review the risk of these outcomes for levels of waist and hip circumferences when terms for both anthropometric measures were included in regression models. MEDLINE and EMBASE were searched (last updated July 2012) for studies reporting the association with the outcomes mentioned earlier for both waist and hip circumferences (unadjusted and with both terms included in the model). Ten studies reported the association between hip circumference and death and/or disease outcomes both unadjusted and adjusted for waist circumference. Five studies reported the risk associated with waist circumference both unadjusted and adjusted for hip circumference. With the exception of one study of venous thromboembolism, the full strength of the association between either waist circumference or hip circumference with morbidity and/or mortality was only apparent when terms for both anthropometric measures were included in regression models. Without accounting for the protective effect of hip circumference, the effect of obesity on risk of death and disease may be seriously underestimated. Considered together (but not as a ratio measure), waist and hip circumference may improve risk prediction models for cardiovascular disease and other outcomes.

Transition, school, friends and obesity risk models (the TranSFORM study)

The TranSFORM study demonstrated the impact that a change of school environment has on physical activity, sedentary and dietary behaviour in late childhood. Results provide strong evidence of the influence of friendship networks, and the potential for changing and reinforcing positive weight related behaviour in early adolescence.

Laboratory and non-laboratory-based risk prevention models for secondary prevention of cardiovascular disease : the LIPID study

Aims : The aims of this study were to examine whether risk prediction models for recurrent cardiovascular disease (CVD) events have prognostic value, and to particularly examine the performance of those models based on non-laboratory data. We also aimed to construct a risk chart based on the risk factors that showed the strongest relationship with CVD.

Methods and results : Cox proportional hazards models were used to calculate a risk score for each recurrent event in a CVD patient who was enrolled in a very large randomized controlled clinical trial. Patients were then classified into groups according to quintiles of their risk score. These risk models were validated by calibration and discrimination analyses based on data from patients recruited in New Zealand for the same study. Non-laboratory-based risk factors, such as age, sex, body mass index, smoking status, angina grade, history of myocardial infarction, revascularization, stroke, diabetes or hypertension and treatment with pravastatin, were found to be significantly associated with the risk of developing a recurrent CVD event. Patients who were classified into the medium and high-risk groups had two-fold and four-fold the risk of developing a CVD event compared with those in the low-risk group, respectively. The risk prediction models also fitted New Zealand data well after recalibration.

Conclusion : A simpler non-laboratory-based risk prediction model performed equally as well as the more comprehensive laboratory-based risk prediction models. The risk chart based on the further simplified Score Model may provide a useful tool for clinical cardiologists to assess an individual patient's risk for recurrent CVD events.

Wastewater irrigation: the state of play

As demand for fresh water intensifies, wastewater is frequently being seen as a valuable resource. Furthermore, wise reuse of wastewater alleviates concerns attendant with its discharge to the environment. Globally, around 20 million ha of land are irrigated with wastewater, and this is likely to increase markedly during the next few decades as water stress intensifies. In 1995, around 2.3 billion people lived in water-stressed river basins and this could increase to 3.5 billion by 2025. We review the current status of wastewater irrigation by providing an overview of the extent of the practice throughout the world and through synthesizing the current understanding of factors influencing sustainable wastewater irrigation. A theme that emerges is that wastewater irrigation is not only more common in water-stressed regions such as the Near East, but the rationale for the practice also tends to differ between the developing and developed worlds. In developing nations, the prime drivers are livelihood dependence and food security, whereas environmental agendas appear to hold greater sway in the developed world. The following were identified as areas requiring greater understanding for the long-term sustainability of wastewater irrigation: (i) accumulation of bioavailable forms of heavy metals in soils, (ii) environmental fate of organics in wastewater-irrigated soils, (iii) influence of reuse schemes on catchment hydrology, including transport of salt loads, (iv) risk models for helminth infections (pertinent to developing nations), (v) microbiological contamination risks for aquifers and surface waters, (vi) transfer efficiencies of chemical contaminants from soil to plants, (vii) health effects of chronic exposure to chemical contaminants, and (viii) strategies for engaging the public.

Back to basics : empirical support for the importance of release planning in reducing sex offender recidivism

This article reports findings from a series of empirical studies investigating whether poor release planning might contribute to sex offender recidivism. A coding protocol was developed to measure the comprehensiveness of release planning which included items relating to accommodation, employment, pro-social support, community based treatment, and the Good Lives Model (T. Ward & C.A. Stewart, 2003) secondary goods. The protocol was retrospectively applied to groups of recidivist and non recidivist child molesters, matched on static risk level and time since release. As predicted, overall release planning was significantly poorer for recidivists compared to non recidivists. The accommodation, employment, and social support items combined to best predict recidivism, with predictive accuracy comparable to that obtained using static risk models. Results highlighted the importance of release planning in efforts to reduce sex offender recidivism. Implications for policy makers and community members are briefly discussed.

Assessment of community reintegration planning for sex offenders

Poor planning for reintegrating child molesters from prison to the community is a likely risk factor for sexual recidivism. The quality of reintegration planning was retrospectively measured for groups of recidivist (n = 30) and nonrecidivist (n = 30) child molesters who were individually matched on static risk level and time since release. Recidivists had significantly poorer reintegration planning scores than nonrecidivists, consistent with a previous study by the authors. Data from both studies were combined (total N = 141), and survival analyses showed that poor reintegration planning predicted an increased rate of recidivism. Accommodation, employment, and social support planning combined to predict recidivism, with predictive validity comparable to static risk models (area under the curve = .71). Summing these items yielded a scale of reintegration planning quality that differentiated well between recidivists and nonrecidivists and may have practical utility for risk assessment as an adjunct to static models.

Effects of infection-induced migration delays on the epidemiology of avian influenza in wild mallard populations

Wild waterfowl populations form a natural reservoir of Avian Influenza (AI) virus, and fears exist that these birds may contribute to an AI pandemic by spreading the virus along their migratory flyways. Observational studies suggest that individuals infected with AI virus may delay departure from migratory staging sites. Here, we explore the epidemiological dynamics of avian influenza virus in a migrating mallard (Anas platyrhynchos) population with a specific view to understanding the role of infection-induced migration delays on the spread of virus strains of differing transmissibility. We develop a host-pathogen model that combines the transmission dynamics of influenza with the migration, reproduction and mortality of the host bird species. Our modeling predicts that delayed migration of individuals influences both the timing and size of outbreaks of AI virus. We find that (1) delayed migration leads to a lower total number of cases of infection each year than in the absence of migration delay, (2) when the transmission rate of a strain is high, the outbreak starts at the staging sites at which birds arrive in the early part of the fall migration, (3) when the transmission rate is low, infection predominantly occurs later in the season, which is further delayed when there is a migration delay. As such, the rise of more virulent AI strains in waterfowl could lead to a higher prevalence of infection later in the year, which could change the exposure risk for farmed poultry. A sensitivity analysis shows the importance of generation time and loss of immunity for the effect of migration delays. Thus, we demonstrate, in contrast to many current transmission risk models solely using empirical information on bird movements to assess the potential for transmission, that a consideration of infection-induced delays is critical to understanding the dynamics of AI infection along the entire flyway.

Credit cycle dependent spread determinants in emerging sovereign debt markets

We address credit cycle dependent sovereign credit risk determinants. In our model, the spread determinants' magnitude is conditional on an unobservable endogenous sovereign credit cycle as represented by the underlying state of a Markov regime switching process. Our explanatory variables are motivated in the tradition of structural credit risk models and include changes in asset prices, interest rates, implied market volatility, gold price changes and foreign exchange rates. We examine daily frequency variations of U.S. dollar denominated Eurobond credit spreads of four major Latin American sovereign bond issuers (Brazil, Colombia, Mexico and Venezuela) with liquid bond markets during March 2000 to June 2011. We find that spread determinants are statistically significant and consistent with theory, while their magnitude remarkably varies with the state of the credit cycle. Crisis states are characterized by high spread change uncertainty and high sensitivities with respect to the spread change determinants. We further document that not only changes of local currencies, but also changes of the Euro with respect to the U.S. dollar are significant spread drivers and argue that this is consistent with the sovereigns' ability to pay.

Quantitative microbial risk assessment models for consumption of raw vegetables irrigated with reclaimed water

Quantitative microbial risk assessment models for estimating the annual risk of enteric virus infection associated with consuming raw vegetables that have been overhead irrigated with nondisinfected secondary treated reclaimed water were constructed. We ran models for several different scenarios of crop type, viral concentration in effluent, and time since last irrigation event. The mean annual risk of infection was always less for cucumber than for broccoli, cabbage, or lettuce. Across the various crops, effluent qualities, and viral decay rates considered, the annual risk of infection ranged from 10–3 to 10–1 when reclaimed-water irrigation ceased 1 day before harvest and from 10–9 to 10–3 when it ceased 2 weeks before harvest. Two previously published decay coefficients were used to describe the die-off of viruses in the environment. For all combinations of crop type and effluent quality, application of the more aggressive decay coefficient led to annual risks of infection that satisfied the commonly propounded benchmark of ≤10–4, i.e., one infection or less per 10,000 people per year, providing that 14 days had elapsed since irrigation with reclaimed water. Conversely, this benchmark was not attained for any combination of crop and water quality when this withholding period was 1 day. The lower decay rate conferred markedly less protection, with broccoli and cucumber being the only crops satisfying the 10–4 standard for all water qualities after a 14-day withholding period. Sensitivity analyses on the models revealed that in nearly all cases, variation in the amount of produce consumed had the most significant effect on the total uncertainty surrounding the estimate of annual infection risk. The models presented cover what would generally be considered to be worst-case scenarios: overhead irrigation and consumption of vegetables raw. Practices such as subsurface, furrow, or drip irrigation and postharvest washing/disinfection and food preparation could substantially lower risks and need to be considered in future models, particularly for developed nations where these extra risk reduction measures are more common.

The conceptualisation of operational risk management models

Operational risk is evolving as a specialist field of risk management that must be practiced within all organisations, but currently has a particular relevance to banks. The Basel Committee on Banking Supervision has circulated a consultative paper which, if adopted by nation-state bank supervisors, will impose an operational risk capital charge on banks as part of a new Capital Accord. The definition of operational risk is wide-ranging and creates some unique issues related to the development of appropriate risk management models. This paper conceptualises two distinct operational risk management models; being a predictive model that will result in a known outcome upon its implementation, and a pre-emptive operational risk management model which prepares an organisation in the event that a future risk occurrence results in a disruption to critical business operations.

The conceptualisation of operational risk management models

Operational risk is evolving as a specialist field of risk management that must be practiced within all organisations, but currently has a particular relevance to banks. The Basel Committee on Banking Supervision has circulated a consultative paper which, if adopted by nation-state bank supervisors, will impose an operational risk capital charge on banks as part of a new Capital Accord. The definition of operational risk is wide-ranging and creates some unique issues related to the development of appropriate risk management models. This paper conceptualises two distinct operational risk management models; being a predictive model that will result in a known outcome upon its implementation, and a pre-emptive operational risk management model which prepares an organisation in the event that a future risk occurrence results in a disruption to critical business operations.

Deterministic versus stochastic quantitative microbial Risk Assessment models for wastewater irrigation of food crops

Reuse of wastewater to irrigate food crops is being practiced in many parts of the world and is becoming more commonplace as the competition for, and stresses on, freshwater resources intensify. But there are risks associated with wastewater irrigation, including the possibility of transmission of pathogens causing infectious disease, to both workers in the field and to consumers buying and eating produce irrigated with wastewater. To manage these risks appropriately we need objective and quantitative estimates of them. This is typically achieved through one of two modelling approaches: deterministic or stochastic. Each parameter in a deterministic model is represented by a single value, whereas in stochastic models probability functions are used. Stochastic models are theoretically superior because they account for variability and uncertainty, but they are computationally demanding and not readily accessible to water resource and public health managers. We constructed models to estimate risk of enteric virus infection arising from the consumption of wastewater-irrigated horticultural crops (broccoli, cucumber and lettuce), and compared the resultant levels of risk between the deterministic and stochastic approaches. Several scenarios were tested for each crop, accounting for different concentrations of enteric viruses and different lengths of environmental exposure (i.e. the time between the last irrigation event and harvest, when the viruses are liable to decay or inactivation). In most situations modelled the two approaches yielded similar estimates of risk (within 1 order-of-magnitude). The two methods diverged most markedly, up to around 2 orders-of-magnitude, when there was large uncertainty associated with the estimate of virus concentration and the exposure period was short (1 day). Therefore, in some circumstances deterministic modelling may offer water resource managers a pragmatic alternative to stochastic modelling, but its usefulness as a surrogate will depend upon the level of uncertainty in the model parameters.

Overview of risk prediction models in cardiovascular disease research

Many risk prediction models have been developed for cardiovascular diseases in different countries during the past three decades. However, there has not been consistent agreement regarding how to appropriately assess a risk prediction model, especially when new markers are added to an established risk prediction model. Researchers often use the area under the receiver operating characteristic curve (ROC) to assess the discriminatory ability of a risk prediction model. However, recent studies suggest that this method has serious limitations and cannot be the sole approach to evaluate the usefulness of a new marker in clinical and epidemiological studies. To overcome the shortcomings of this traditional method, new assessment methods have been proposed. The aim of this article is to overview various risk prediction models for cardiovascular diseases, to describe the receiver operating characteristic curve method and discuss some new assessment methods proposed recently. Some of the methods were illustrated with figures from a cardiovascular disease study in Australia.