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PubMed2002

Public sector risk management: a specific model.

Risk management programs for state mental health authorities are generally limited in scope and reactive in nature. Recent changes in how mental health care is provided render it necessary to redirect the risk management focus from its present institutional basis to a statewide, network-based paradigm that is integrated across public and private inpatient and community programs alike. These changes include treating an increasing number of individuals in less-secure settings and contracting for an increasing number of public mental health services with private providers. The model proposed here is closely linked to the Quality Management Process.

Continuity of Patient Care↗
PubMed2000

Validated risk stratification model accurately predicts low risk in patients with unstable angina.

BACKGROUND: In the mid 1990s, two unstable angina risk prediction models were proposed but neither has been validated on separate population or compared. OBJECTIVES: The purpose of this study was to compare patient outcome among high, medium and low risk unstable angina patients defined by the Agency for Health Care Policy and Research (AHCPR) guideline to similar risk groups defined by a validated model from our institution (RUSH). METHODS: Four hundred sixteen patients consecutively admitted to the hospital with unstable angina between January 1, 1995, and December 31, 1997, were prospectively evaluated for risk factors. The presence of major adverse events such as myocardial infarction (MI), death and heart failure was assessed for each patient by chart review. RESULTS: The composite end point of heart failure, MI or death occurred in 3% and 5% of the RUSH and AHCPR low risk categories, respectively, and in 8% and 10% of AHCPR and RUSH high risk categories, respectively. Recurrent ischemic events were best predicted by the RUSH model (high: 24% vs. medium: 12% and low: 10%, p = 0.029), but not by the AHCPR model (high: 14% vs. medium: 13% and low: 9%, p = 0.876). The RUSH model identified five times more low risk patients than the AHCPR model. CONCLUSIONS: Both models identify patients with low and high event rates of MI, death or heart failure. However, the RUSH model allowed for five times more patients to be candidates for outpatient evaluation (low risk) with a similar observed event rate to the AHCPR model; also, the RUSH model more successfully predicted ischemic complications. We conclude that the RUSH model can be used clinically to identify patients for early noninvasive evaluation, thereby improving cost effectiveness of care.

Aged↗
PubMed2005

On criteria for evaluating models of absolute risk.

Absolute risk is the probability that an individual who is free of a given disease at an initial age, a, will develop that disease in the subsequent interval (a, t]. Absolute risk is reduced by mortality from competing risks. Models of absolute risk that depend on covariates have been used to design intervention studies, to counsel patients regarding their risks of disease and to inform clinical decisions, such as whether or not to take tamoxifen to prevent breast cancer. Several general criteria have been used to evaluate models of absolute risk, including how well the model predicts the observed numbers of events in subsets of the population ("calibration"), and "discriminatory power," measured by the concordance statistic. In this paper we review some general criteria and develop specific loss function-based criteria for two applications, namely whether or not to screen a population to select subjects for further evaluation or treatment and whether or not to use a preventive intervention that has both beneficial and adverse effects. We find that high discriminatory power is much more crucial in the screening application than in the preventive intervention application. These examples indicate that the usefulness of a general criterion such as concordance depends on the application, and that using specific loss functions can lead to more appropriate assessments.

Antineoplastic Agents, Phytogenic↗
PubMed2003

Description of the use of a risk estimation model to assess the increased risk of non-melanoma skin cancer among outdoor workers in Central Queensland, Australia.

BACKGROUND: The aim was to use the measured data on annual exposure to ultraviolet (UV) radiation in the risk estimation model to estimate the increase in risk of Non-Melanomic Skin Cancer (NMSC) among outdoor workers compared to indoor workers in Rockhampton (lat. 23 degrees S), Central Queensland, Australia. METHODS: Results on annual occupational exposure measured on two occupational groups namely Australia Post Mail Delivery Personnel (APMDP) and Physical Education Teachers (PE) using film badge dosimeters was used in the risk estimation model to determine the increase in risk of NMSC with years of outdoor occupational exposure compared to indoor workers. The sensitivity of the model was tested for variations in recreational and childhood exposure of both groups, as well as occupational exposure of indoor workers. RESULTS: For APMDP the increase in risk for Basal cell carcinoma (BCC) varied from 1.1 to 3.6 for 5-20 years of exposure and for Squamous cell carcinoma (SCC) the risk varied from 1.2 to 5.5 for the same periods of exposure. For the PE teachers the risk for BCC varied from 1.1 to 1.8 and for SCC the range was 1.1-2.3 for similar exposure periods. CONCLUSION: The increased risk estimates did not show any significant changes for variations in occupational and recreational exposure. A maximum change of 20% was computed for 25% variation in childhood exposure, which was mainly for the APMDP with high occupational exposure levels and more than 10 years of occupational exposure. The increased risk estimates are useful to identify high risk groups at an early age and implement long-term protective measures against NMSC.

Adult↗
PubMed2002

The spatial epidemiology of cholera in an endemic area of Bangladesh.

This paper defines high-risk areas of cholera based on environmental risk factors of the disease in an endemic area of Bangladesh. The risk factors include proximity to surface water, high population density, and low educational status, which were identified in an earlier study by the authors. Cholera data were analyzed by spatially referenced extended household units for two time periods, 1983-1987 and 1992-1996. These periods were chosen because they had different dominant cholera agents. From 1983-1987 classical cholera was dominant and from 1992-1996 El Tor was dominant. By defining high-risk areas based on risk factors, this study builds a spatial risk model for cholera. The model is then evaluated based on the locations of observed cholera cases. The study also identifies the determinants of death due to cholera for the two different time periods dominated by the different cholera agents. The modeled risk areas that were based on the risk factors were found to correspond with actual distributions of cholera morbidity and mortality. The high-risk areas of the dominant cholera agents are relatively stable over time. However, from 1983-1987 El Tor cholera, which was not the dominant agent during that period, was not associated with high-risk areas, suggesting that the El Tor habitat may have changed over time. The case fatality rate for cholera was related to proximity to a diarrhea treatment hospital in the study area.

Bangladesh↗
PubMed1995

Cigarette use and the estimation of lung cancer attributable to radon in the United States.

Residential exposure to radioactive radon and its decay products has been estimated to account for 10-12% of all lung cancer deaths in the U.S. It has been difficult to evaluate fully the impact of cigarette smoking, the most important cause of lung cancer, on this estimate, because factors for patterns of tobacco use have not been included in the risk models, since risk models are derived from studies of underground miners exposed to radon and detailed data on smoking are limited. Lung cancer risk estimates for exposure to radon progeny in smoker and non-smoker populations are obtained by applying the same risk model to each population group, thereby assuming the joint effects of smoking and exposure to radon progeny are multiplicative. However, in miners, joint relative risks (RR) for the two exposures are most consistent with an intermediate relationship between multiplicative and additive, so that the present approach likely results in an overestimate of risk in smokers and an underestimate of risk in nonsmokers. Lubin et al. (National Institutes of Health Publication No. 94-3644, 1994) present an ad hoc approach for adjusting risk models to incorporate smoking status. The approach is based on the relative magnitude of the effects of radon progeny in smokers and nonsmokers and therefore may not be applicable to non-miner populations if the proportion of smokers and the RR for smoking differ. We show that the modification can be derived explicitly by assuming an arithmetic mixture model for the joint RR for smoking and exposure to radon progeny. In this way, smoking parameters in the population of interest (the proportion of smokers and the RR of smoking) can be used directly to adjust radon progeny risk models and obtain risk estimates that are specific for smokers and nonsmokers. With an intermediate RR relationship for smoking and radon progeny, the attributable percentage of lung cancer deaths from residential radon may be twofold greater in non-smokers than in smokers.

Air Pollution, Indoor↗
PubMed2001

[Model of mortality risk in stage I non-small cell bronchogenic carcinoma].

OBJECTIVE: To develop and validate a mortality risk model for patients with resected stage I non-small cell bronchogenic carcinoma (NSCBC). PATIENTS AND METHOD: Tumors from 798 patients with diagnoses of NSCBC were resected and classified in stage I. The Kaplan-Meier method and Cox's proportional hazard model were used to analyze the influence of clinical and pathologic variables on survival. RESULTS: Univariate analysis revealed that age (p = 0.0461), symptoms (p = 0.0383), histology (p = 0.0489) and tumor size (p = 0.0002) and invasion (p = 0.0010) affected survival. Size (p = 0.0000) and age (p = 0.0269) were entered into multivariate analysis. Each patient's risk was estimated by applying the regression equation derived from multivariate analysis; the mean was 1.47 +/- 0.31 (range 0.68 to 2.92). The series was divided into three groups by degree of risk (low, intermediate and high), establishing the cutoff points at 1.16 and 1.78 (standard deviation of the mean). Five-year survival rates were 85%, 62% and 46%, respectively (p = 0.0000). To validate the model's predictive capacity, the series was divided randomly into two groups: the study group with 403 patients and the validation group with 395. Age (p = 0.0295), symptoms (p = 0.0396), tumor size (p = 0.0010) and invasion (p = 0.0010) affected survival in the univariate analysis. Size (p = 0.0000) and age (p = 0.0358) were entered into Cox's model. Mean risk was 1.94 +/- 0.36 (range 0.98 to 3.32). The series was divided into three risk groups, with cut-off points established at 1.58 and 2.30. Five year survival rates were 90%, 62% and 46% for the low, intermediate and high risk groups, respectively (p = 0.0000). The same model proved able to identify risk when applied to the validation group, in which five-year survival rates were 78%, 61% and 48%, respectively (p = 0.0000). CONCLUSIONS: Risk models can identify patient subgroups, potentially influenced by co-adjuvant treatment, as well as facilitate comparison of patient series.

Adult↗
PubMed2002

Value of electronic data for model validation and refinement: bacteremia risk in children with fever and neutropenia.

BACKGROUND: Validating published risk models in a different time and setting can be a labor-intensive process. Data in electronic format provide the potential to test the validity of risk models without labor-intensive chart reviews and data capture. The authors attempted to use readily available electronic data to find appropriate cases and to validate and refine a previously developed risk model for predicting bacteremia in children with cancer who had fever and neutropenia. PATIENTS AND METHODS: By applying a largely automated case-finding algorithm to linked, electronic clinical and administrative data systems, the authors identified and acquired data regarding 157 episodes of fever and neutropenia in children with cancer admitted to a children's hospital during an 11-month period in 1997. The authors applied a previously developed and validated risk model for bacteremia to this 1997 cohort by assessing the odds ratios among risk groups. The model assigns encounters with absolute monocyte count of 100 cells or more/mm3 to a low-risk group and encounters with an absolute monocyte count of less than 100 cells/mm3 to intermediate-risk (temperature <39.0 degrees C) or high-risk (> or = 39.0 degrees C) groups. In addition, the authors explored whether the new data would have generated the same model. Univariate and multivariable analyses were performed to determine whether there were additional independent predictors of bacteremia. Recursive partitioning of admission absolute monocyte count and temperature was used to assess whether similar cutpoints would be found. RESULTS: There were 12 episodes of bacteremia (7.6%) among the 157 encounters. The previously developed model correctly predicted increasing rates of bacteremia in this 1997 cohort, ranging from 2.5% in the low-risk group (one episode in a child with an infected central line) to 24% in the high-risk group. The odds ratio for the high-risk versus intermediate-risk group was 4.09 (95% confidence interval 1.05-15.91), comparable to the odds ratio of 3.96 in the previously published derivation cohort (95% confidence interval 1.4-11.1). Multivariate analysis of the new data revealed no independent risk factors for bacteremia other than admission absolute monocyte count and temperature. Recursive partitioning of absolute monocyte count and temperature generated risk categories that were somewhat different from those of the original model. The new data yielded three categories: low risk (temperature < or = 39.5 degrees C and absolute monocyte count >10/mm3), intermediate risk (temperature < or = 39.5 degrees C and absolute monocyte count < or = 10/mm3), and high risk (temperature >39.5 degrees C). CONCLUSIONS: Existing electronic data provide an efficient means for case-finding and model validation and refinement. The previously developed bacteremia model had good but not optimal predictive performance in the new data set. Admission absolute monocyte count and temperature remain significant risk factors for bacteremia. Redefining the risk categories, including a much lower cutpoint for admission absolute monocyte count, improved the model's discrimination, which suggests that predictive models need periodic updating.

Adolescent↗
PubMed2005

Fuzzy-probabilistic model for risk assessment of radioactive material railway transportation.

Transportation of radioactive materials is obviously accompanied by a certain risk. A model for risk assessment of emergency situations and terrorist attacks may be useful for choosing possible routes and for comparing the various defence strategies. In particular, risk assessment is crucial for safe transportation of excess weapons-grade plutonium arising from the removal of plutonium from military employment. A fuzzy-probabilistic model for risk assessment of railway transportation has been developed taking into account the different natures of risk-affecting parameters (probabilistic and not probabilistic but fuzzy). Fuzzy set theory methods as well as standard methods of probability theory have been used for quantitative risk assessment. Information-preserving transformations are applied to realise the correct aggregation of probabilistic and fuzzy parameters. Estimations have also been made of the inhalation doses resulting from possible accidents during plutonium transportation. The obtained data show the scale of possible consequences that may arise from plutonium transportation accidents.

Body Burden↗
PubMed2005

A linear model for managing the risk of antimicrobial resistance originating in food animals.

A linear population risk model used by the U.S. Food and Drug Administration (FDA) Center for Veterinary Medicine (CVM) estimates the risk of human cases of campylobacteriosis caused by fluoroquinolone-resistant Campylobacter. Among the cases of campylobacteriosis attributed to domestically produced chicken, the fluoroquinolone resistance is assumed to result from the use of fluoroquinolones in poultry in the United States. Properties of the linear population risk model are contrasted with those of a farm-to-fork model commonly used for microbial risk assessments. The utility of the linear population model for the purpose for which it was used by CVM is discussed.

Animals↗
PubMed2003

Risk management models in netball.

Risk management strategies have been used in a wide range of sports. Many issues involved in the implementation of risk management models in sport have not been investigated especially the sport specific and location specific nature of such models. The aim of this study was to ascertain the usefulness of Occupational Health & Safety risk management (RM) models in a mainly voluntary sporting organisation and to investigate the requirements of a netball population in developing "user friendly" risk management plans. The industry RM plans were trialled in a rural setting at various netball associations over a six week period. After these trials focus groups and interviews were conducted and the trials evaluated. The requirements of the netball population for a RM plan were investigated using focus groups from regional and rural areas. The industry RM models presented were not generally accepted by the trial participants. They found that the level of expertise required did not exist in netball at the club/team level. The focus groups on the needs of netball for a user friendly model brought out a number of issues including the specific needs of rural football/netball leagues, the need to develop strategies for all levels of netball (coach, player, umpire, administrator). Barriers to the uptake of risk management models were discussed. An effective RM plan can be developed for voluntary sports associations but they must be specific to the sport and flexible. The RM plan must also take into account the barriers to implementation for each sporting culture.

Athletic Injuries↗
PubMed1996

[The Brodsky & Brodsky risk factor model of schizophrenia--an empirical contribution].

In the year 1981 the Canadian psychologists Patricia & Marvin Brodsky published an article about a model integrating risk variables involved in the development of Schizophrenia which is very important but not very well known in the German speaking countries. This model which describes essentially the interaction of four risk variables, such as mental health of the mother, neonatal status, temperament and mothering style, shall be published for the interested reader on the one hand. On the other hand the consequences of the risk variable "mothering style" should be empirically examined in a retrospective study. With a standardized interview containing all essential variables of Brodsky's risk model 73 patients suffering in schizophrenia and being inpatient in the Rheinische Landesklinik Bonn were examined and the interview data were evaluated with regard to the contents in contrast to a control group of 26 healthy persons. With regard to the "schizophrenia spectrum" the portion of so called "superphrenics" (defined as "artistically and muscially gifted offspring with a schziophrenic mother" was not significantly increased in the schizophrenic sample (17.8% vs 7.7%) but the comparison of mothering style (empathic contingent vs. non-empathic and non-contingent) revealed a statistically significant difference between the groups. Finally the data of the risk variable "mothering style" are discussed in the light of the Brodsky-model and it is pointed at the special problems in reliable and valid opertionalisation and assessment of the risk variables.

Adult↗
PubMed1999

Applying risk assessment models in general surgery: effective risk stratification.

The risk of venous thromboembolism (VTE) in general surgery patients varies from 10-50% depending on the type of surgery and underlying patient risk factors. Despite the availability of effective antithrombotic therapies, thromboprophylaxis is substantially underused in the general surgical field. The development of risk assessment models (RAMs) that can accurately and prospectively categorize patients according to their thromboembolic risk level may lead to improved use of thromboprophylaxis. Several evidence-based RAMs are now available that include general surgery in their range of clinical categories, allowing patients to be stratified rapidly for thromboembolic risk. Therapeutic recommendations are included in some cases. Tailoring anticoagulant therapy according to risk of VTE should maximize the clinical benefit and cost-effectiveness of prophylaxis, and minimize the risk of bleeding complications. Further studies are required to validate novel RAMs in clinical practice, and to extend their scope to include patient groups with particular needs, such as those undergoing neurosurgical procedures.

Humans↗
PubMed1995

Achieving credibility in risk assessment models.

Validation of a mathematical model requires demonstrating that a model is free of mathematical errors (internal consistency), is sensitive to large but not small errors or uncertainties in parameter values (verifiability and robustness), reproduces experimental observations on the system being modeled (external consistency), and leads to testable predictions of the system's biological properties. To be heuristically valid, a model also must be a realistic representation of the actual biological system. Only then would the model's predictions be credible to the wider community of biological scientists who would use the model for risk assessment and dose or species extrapolation. Owing to incomplete data, most current dosimetric models are insufficiently realistic to pass this test of credibility. Enhancements to such models that would help achieve credibility are presented, and suggestions are offered for institutionalizing realistic modeling practices in risk assessment.

Animals↗
PubMed2001

Modeling preclinical cardiovascular risk for use in epidemiologic studies: Miami community health study.

To develop a method for assessing preclinical cardiovascular disease risk, models of resting cardiovascular regulation and of insulin metabolic syndrome were derived from information collected from 1991 to 1996 in a culturally heterogeneous sample of 319 healthy men and women (aged 25-44 years) from Miami-Dade County, Florida. The model of resting cardiovascular regulation used 8 noninvasive measures of autonomic and cardiovascular function. Three factors were derived: 1) parasympathetic, 2) inotropy, and 3) systemic vascular resistance. The model of insulin metabolic syndrome used 12 measures assessing body mass, insulin, glucose, and lipid metabolism. Four factors were derived: 1) body mass and fat distribution, 2) glucose level and regulation, 3) insulin level and regulation, and 4) plasma lipid levels. Analyses of the association of the two models revealed that subjects with lower cardiac contractility had greater body mass, higher fasting and postload insulin and glucose levels, and lower insulin sensitivity. Subjects with greater vascular resistance had greater body mass, higher total cholesterol and triglyceride levels, and lower high density lipoprotein cholesterol levels. These findings indicate that preclinical cardiovascular disease risk may involve pathophysiologic processes in which cardiac inotropic and vasodilatory functions are linked to specific aspects of insulin metabolic syndrome.

Adult↗
PubMed2006

An integrated physical dispersion and behavioral response model for risk assessment of radiological dispersion device (RDD) events.

A radiological dispersion device (RDD) or "dirty" bomb is a conventional explosive wrapped in radiological material. Terrorists may use an RDD to disperse radioactive material across a populated area, causing casualties and/or economic damage. Nearly all risk assessment models for RDDs make unrealistic assumptions about public behavior in their health assessments, including assumptions that the public would stand outside in a single location indefinitely. In this article, we describe an approach for assessing the risks of RDD events incorporating both physical dispersion and behavioral response variables. The general approach is tested using the City of Pittsburgh, Pennsylvania as a case study. Atmospheric models simulate an RDD attack and its likely fallout, while radiation exposure models assess fatal cancer risk. We model different geographical distributions of the population based on time of day. We evaluate aggregate health impacts for different public responses (i.e., sheltering-in-place, evacuating). We find that current RDD models in use can be improved with the integration of behavioral components. Using the results from the model, we show how risk varies across several behavioral and physical variables. We show that the best policy to recommend to the public depends on many different variables, such as the amount of trauma at ground zero, the capability of emergency responders to get trauma victims to local hospitals quickly and efficiently, how quickly evacuations can take place in the city, and the amount of shielding available for shelterers. Using a parametric analysis, we develop behaviorally realistic risk assessments, we identify variables that can affect an optimal risk reduction policy, and we find that decision making can be improved by evaluating the tradeoff between trauma and cancer fatalities for various RDD scenarios before they occur.

Humans↗
PubMed1992

Risk assessment for aflatoxin: III. Modeling the relative risk of hepatocellular carcinoma.

Estimates have been made of the cancer potency of aflatoxin exposure among the U.S. population. Risk modeling is used to assess the dose-response relationship between aflatoxin exposure and primary liver cancer, controlling for hepatitis B virus (HBV), based on data provided by the Yeh et al. study in China. A relative risk model is proposed as a more appropriate alternative to the additive ("absolute" risk) model for transportation of risk coefficients between populations with different baseline rates. Several general relative risk models were examined; the exponential model provided the best fit. The Poisson regression method was used to fit the relative risk model to the grouped data. The effects of exposure to aflatoxin (AFB1) and hepatitis B infection were both found to be statistically significant. The risk of death from liver cancer for those exposed to AFB1 relative to the unexposed population, increases by 0.05% per ng/kg/day exposure of AFB1 (p less than 0.001). The results also indicated a 25-fold increase in the risk of death from liver cancer among those infected with hepatitis B virus, relative to noncarriers (p less than 0.0001). With a hepatitis prevalence rate of 1%, the aflatoxin intake level associated with liver cancer lifetime excess risk of 1 x 10(-5) for the U.S. population was estimated as 253 ng/day, based on a liver cancer baseline rate of 3.4/100,000/yr.

Aflatoxins↗
PubMed1997

Comparative Analysis of Coronary Surgery Risk Stratification Models.

BACKGROUND: Preoperative risk assessment models for coronary bypass surgery (CABG) have been proposed, but comparison of them using independent databases needs to be done. METHODS: Models of CABG hospital mortality were tested on a set of 3,443 patients who underwent CABG including a subset of 3,237 patients who had isolated CABG (no valve procedures), in our database since 1991. Four models previously described were designated as Parsonnet (PS), Cleveland (CL), and Society of Thoracic Surgeons version 1 (ST1) and version 2 (ST2). We developed our own Bayesian (BA) and logistic regression (LR) models and calibrated the PS and CL models on 2,842 patients operated on prior to 1991. Models were compared with respect to 1) mean predicted mortality, 2) correlation of predicted to observed mortality, 3) Brier mean probability score, 4) descriptive statistics, 4) the C-Index (area beneath the receiver operating characteristic curve), and 5) predictive efficiency. Since the ST1 and ST2 models were developed for use only with isolated CABG patients, these models were compared with the others using an isolated CABG subset. RESULTS: Observed mortality for all 3,443 CABG patients was 4.0%. For this group, the mean mortality predicted by PS, CL, BA, LR, was 9.0 +/- 8.0, 6.0 +/- 6.0, 7.6 +/- 15.6, and 5.1 +/- 7.7 (mean +/- standard deviation) respectively. C-Indexes were.80 +/-.02,.80 +/-.02,.83 +/-.02, and.80 +/-.02 (C-Index +/- standard error) respectively. Observed mortality for 3,237 isolated CABG patients was 3.7%. For this subgroup, the mean mortality predicted by PS, CL, BA, LR, ST1, and ST2 was 8.4 +/- 7.4, 5.7 +/- 5.9, 6.5 +/- 13.9, 4.5 +/- 6.5, 9.6 +/- 9.1, and 3.0 +/- 3.3 respectively. C-Indexes were.80 +/-.03,.80 +/-.03,.83 +/-.02,.79 +/-.03,.77 +/-.03, and.81 +/-.02 respectively. CONCLUSIONS: Existing CABG models can accurately discriminate outcome about 80 percent of the time. Models developed on a national database and those from non-local databases appear to have validity for our local data set. Predictions can vary widely between models and existing methods for comparing models appear to be inadequate. The methodology presented here is applicable for use with patients undergoing interventions in the cardiac catheterization laboratory.

Journal Article↗