Assessment of image analysis methods and computer software used in (99m) Tc-MAG3 dynamic renography is important to ensure reliable study results and ultimately the best possible care for patients. In this work, we present a national multicentre study of the quantification accuracy in (99m) Tc-MAG3 renography, utilizing virtual dynamic scintigraphic data obtained by Monte Carlo-simulated scintillation camera imaging of digital phantoms with time-varying activity distributions. Three digital phantom studies were distributed to the participating departments, and quantitative evaluation was performed with standard clinical software according to local routines. The differential renal function (DRF) and time to maximum renal activity (Tmax ) were reported by 21 of the 28 Swedish departments performing (99m) Tc-MAG3 studies as of 2012. The reported DRF estimates showed a significantly lower precision for the phantom with impaired renal uptake than for the phantom with normal uptake. The Tmax estimates showed a similar trend, but the difference was only significant for the right kidney. There was a significant bias in the measured DRF for all phantoms caused by different positions of the left and right kidney in the anterior-posterior direction. In conclusion, this study shows that virtual scintigraphic studies are applicable for quality assurance and that there is a considerable uncertainty associated with standard quantitative parameters in dynamic (99m) Tc-MAG3 renography, especially for patients with impaired renal function.
Case mix methods such as diagnosis related groups have become a basis of payment for inpatient hospitalizations in many countries. Specifying cost weight values for case mix system payment has important consequences; recent evidence suggests case mix cost weight inaccuracies influence the supply of some hospital-based services. To begin to address the question of case mix cost weight accuracy, this paper is motivated by the objective of improving the accuracy of cost weight values due to inaccurate or incomplete comorbidity data. The methods are suitable to case mix methods that incorporate disease severity or comorbidity adjustments. The methods are based on the availability of detailed clinical and cost information linked at the patient level and leverage recent results from clinical data audits. A Bayesian framework is used to synthesize clinical data audit information regarding misclassification probabilities into cost weight value calculations. The models are implemented through Markov chain Monte Carlo methods. An example used to demonstrate the methods finds that inaccurate comorbidity data affects cost weight values by biasing cost weight values (and payments) downward. The implications for hospital payments are discussed and the generalizability of the approach is explored.
To examine the incidence, mortality and case fatality of acute coronary syndrome (ACS) in Finland during 1993-2007 and to create forecasts of the absolute numbers of ACS cases in the future, taking into account the aging of the population.
Community surveillance study and modelled forecasts of the future.
Two sets of population-based coronary event register data from Finland (FINAMI and the National Cardiovascular Disease Register (CVDR)). Bayesian age-period-cohort (APC) modelling.
24 905 observed ACS events in the FINAMI register and 364 137 in CVDR.
Observed trends of ACS events during 1993-2007, forecasted numbers of ACS cases, and the prevalence of ACS survivors until the year 2050.
In the FINAMI register, the average annual declines in age-standardised incidence of ACS were 1.6% (p
Compared with no alcohol consumption, heavy alcohol intake is associated with a higher rate of heart failure (HF) whereas light-to-moderate intake may be associated with a lower rate. However, several prior studies did not exclude former drinkers, who may have changed alcohol consumption in response to diagnosis. This study aimed to investigate the association between alcohol intake and incident HF.
We conducted a prospective cohort study of 33 760 men aged 45 to 79 years with no HF, diabetes mellitus, or myocardial infarction at baseline participating in the Cohort of Swedish Men Study. We excluded former drinkers. At baseline, participants completed a food frequency questionnaire and reported other characteristics. HF was defined as hospitalization for or death from HF, ascertained by Swedish inpatient and cause-of-death records from January 1, 1998, through December 31, 2011. We constructed Cox proportional hazards models to estimate multivariable-adjusted incidence rate ratios. During follow-up, 2916 men were hospitalized for (n=2139) or died (n=777) of incident HF. There was a U-shaped relationship between total alcohol intake and incident HF (P=0.0004). There was a nadir at light-to-moderate alcohol intake: consuming 7 to
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Campylobacteriosis is the most frequently reported zoonosis in the EU and the epidemiology of sporadic campylobacteriosis, especially the routes of transmission, is to a great extent unclear. Poultry easily become colonised with Campylobacter spp., being symptom-less intestinal carriers. Earlier it was estimated that internationally between 50% and 80% of the cases could be attributed to chicken as a reservoir. In a Norwegian surveillance programme all broiler flocks under 50 days of age were tested for Campylobacter spp. The aim of the current study was to identify simultaneous local space-time clusters each year from 2002 to 2007 for human cases of campylobacteriosis and for broiler flocks testing positive for Campylobacter spp. using a multivariate spatial scan statistic method. A cluster occurring simultaneously in humans and broilers could indicate the presence of common factors associated with the dissemination of Campylobacter spp. for both humans and broilers.
Local space-time clusters of humans and broilers positive for Campylobacter spp. occurring simultaneously were identified in all investigated years. All clusters but one were identified from May to August. Some municipalities were included in clusters all years.
The simultaneous occurrence of clusters of humans and broilers positive for Campylobacter spp. combined with the knowledge that poultry meat has a nation-wide distribution indicates that campylobacteriosis cases might also be caused by other risk factors than consumption and handling of poultry meat.Broiler farms that are positive could contaminate the environment with further spread to new broiler farms or to humans living in the area and local environmental factors, such as climate, might influence the spread of Campylobacter spp. in an area. Further studies to clarify the role of such factors are needed.
Cites: Appl Environ Microbiol. 2004 Dec;70(12):7474-8015574950
Most jurisdictions in North America have some version of graduated driver licensing (GDL). A sound body of evidence documenting the effectiveness of GDL programs in reducing collisions, fatalities and injuries among novice drivers is available. However, information about the relative importance of individual components of GDL is lacking. The objectives of this study are to calculate a summary statistic of GDL effectiveness and to identify the most effective components of GDL programs using a meta-analytic approach. Data from 46 American States, the District of Columbia and 11 Canadian jurisdictions are used and were obtained from the Fatality Analysis Reporting System (FARS) for the U.S. and from Transport Canada's Traffic Accident Information Database (TRAID) for Canada. The timeframe of this evaluation is 1992 through 2006, inclusive. Relative fatality risks and their 95% confidence intervals were calculated using fatality counts and population data for target and comparison groups, both in a pre-implementation and post-implementation period in each jurisdiction. The target groups were 16-, 17-, 18- and 19-year-old drivers. The comparison group was 25-54-year-old drivers. The relative fatality risks of all jurisdictions were summarized using the random effects DerSimonian and Laird model. Meta-regression using Restricted Maximum Likelihood (REML) and Markov Chain Monte Carlo (MCMC) Gibbs sampling was also conducted. Strong evidence in support of GDL was found. GDL had a positive and significant impact on the relative fatality risk of 16-year-old drivers (reduction of 19.1%). Significant effects were found for meta-regression models with 16-, 18- and 19-year-old drivers. These effects include length of night restriction in the learner stage, country, driver education in the learner stage and in the intermediate stage, whether night restrictions are lifted in the intermediate stage for work purposes, passenger restriction in the intermediate stage, whether passenger restrictions in the intermediate stage are lifted if passengers are family members, and whether there is an exit test in the intermediate stage. In conclusion, several GDL program components have an important effect on the relative fatality risk of novice drivers. These results help understand how such effects are achieved.
This paper presents a risk index model that can be used for assessing the safety effect of countermeasures. The model estimates risk in a multiplicative way, which makes it possible to analyze the impact of different factors separately. Expert judgments are incorporated through a Bayesian error model. The variance of the risk estimate is determined by Monte-Carlo simulation. The model was applied to assess the safety effect of a new design of a bicycle crossing. The intent was to gain safety by raising the crossings to reduce vehicle speeds and by making the crossings more visible by painting them in a bright color. Before the implementations, bicyclists were riding on bicycle crossings of conventional Swedish type, i.e. similar to crosswalks but delineated by white squares rather than solid lines or zebra markings. Automobile speeds were reduced as anticipated. However, it seems as if the positive effect of this was more or less canceled out by increased bicycle speeds. The safety per bicyclist was still improved by approximately 20%. This improvement was primarily caused by an increase in bicycle flow, since the data show that more bicyclists at a given location seem to benefit their safety. The increase in bicycle flow was probably caused by the new layout of the crossings since bicyclists perceived them as safer and causing less delay. Some future development work is suggested. Pros and cons with the used methodology are discussed. The most crucial parameter to be added is probably a model describing the interaction between motorists and bicyclists, for example, how risk is influenced by the lateral position of the bicyclist in relation to the motorist. It is concluded that the interaction seems to be optimal when both groups share the roadway.
A Canadian specialty nursing association identified the necessity to examine the role and impact of enterostomal (ET) nursing in Canada. We completed a retrospective analysis of the cost-effectiveness and benefits of ET nurse-driven resources for the treatment of acute and chronic wounds in the community.
This was a multicenter retrospective pragmatic chart audit of 3 models of nursing care utilizing 4 community nursing agencies and 1 specialty company owned and operated by ET nurses. An analysis was completed using quantitative methods to evaluate healing outcomes, nursing costs, and cost-effectiveness.
Kaplan-Meier estimates were calculated to determine the average time to 100% healing of acute and chronic wounds and total nursing visit costs for treatment in a community setting. Average direct nursing costs related to management of each wound were determined by number of nursing visits and related reimbursement for each visit. A Monte Carlo simulation method was used to help account for costs and benefits in determination of cost-effectiveness between caring groups and the uncertainty from variation between patients and wounds.
Three hundred sixty chronic wounds and 54 acute surgical wound charts were audited. Involvement of a registered nurse (RN) with ET or advanced wound ostomy skills (AWOS) in community-level chronic and acute wound care was associated with lower overall costs mainly due to reduced time to 100% closure of the wound and reduced number of nursing visits. The differences in health benefits and total costs of nursing care between the ET/AWOS and a hybrid group that includes interventions developed by an ET nurse and followed by general visiting nurses that could include both RNs and registered practical nurses is an expected reduction in healing times of 45 days and an expected cost difference of $5927.00 per chronic wound treated. When outcomes were broken into ET/AWOS involvement categories for treatment of chronic wounds, there was a significantly faster time to 100% closure at a lower mean cost as the ET/AWOS involvement increased in the case. For acute wound treatment, the differences in health benefits and total costs between the ET/AWOS and a hybrid nursing care model were an expected reduction in healing times of 95 days and an expected cost difference of $9578.00 per acute wound treated. Again, there was a significant difference in healing times and reduced mean cost as the ET/AWOS became more involved in the treatment. The financial benefit to the Ontario Ministry of Health and Long-Term Care is estimated to increase as the involvement of nurses with ET/AWOS specialty training increases.
The greater the involvement both directly and indirectly of an ET/AWOS nurse in the management of wounds, the greater the savings and the shorter the healing times.
INTRODUCTION: Populations eligible for public health programs are often narrowly defined and, therefore, difficult to describe quantitatively, particularly at the local level, because of lack of data. This information, however, is vital for program planning and evaluation. We demonstrate the application of a statistical method using multiple sources of data to generate county estimates of women eligible for free breast cancer screening and diagnostic services through California's Cancer Detection Programs: Every Woman Counts. METHODS: We used the small-area estimation method to determine the proportion of eligible women by county and racial/ethnic group. To do so, we included individual and community data in a generalized, linear, mixed-effect model. RESULTS: Our method yielded widely varied estimated proportions of service-eligible women at the county level. In all counties, the estimated proportion of eligible women was higher for Hispanics than for whites, blacks, Asian/Pacific Islanders, or American Indian/Alaska Natives. Across counties, the estimated proportions of eligible Hispanic women varied more than did those of women of other races. CONCLUSION: The small-area estimation method is a powerful tool for approximating narrowly defined eligible or target populations that are not represented fully in any one data source. The variability and reliability of the estimates are measurable and meaningful. Public health programs can use this method to estimate the size of local populations eligible for, or in need of, preventive health services and interventions.
Gaussian process (GP) models are widely used in disease mapping as they provide a natural framework for modeling spatial correlations. Their challenges, however, lie in computational burden and memory requirements. In disease mapping models, the other difficulty is inference, which is analytically intractable due to the non-Gaussian observation model. In this paper, we address both these challenges. We show how to efficiently build fully and partially independent conditional (FIC/PIC) sparse approximations for the GP in two-dimensional surface, and how to conduct approximate inference using expectation propagation (EP) algorithm and Laplace approximation (LA). We also propose to combine FIC with a compactly supported covariance function to construct a computationally efficient additive model that can model long and short length-scale spatial correlations simultaneously. The benefit of these approximations is computational. The sparse GPs speed up the computations and reduce the memory requirements. The posterior inference via EP and Laplace approximation is much faster and is practically as accurate as via Markov chain Monte Carlo.