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PubMed2004

Trends in the prevalence and ratio of diagnosed to undiagnosed diabetes according to obesity levels in the U.S.

OBJECTIVE: To examine trends in the prevalence of diagnosed and undiagnosed diabetes and the proportion of total cases previously diagnosed, according to obesity status in the U.S. over the past 40 years. RESEARCH DESIGN AND METHODS: We assembled data from five consecutive cross-sectional national surveys: National Health Examination Survey I (1960-1962), National Health and Nutrition Examination Survey (NHANES) I (1971-1974), NHANES II (1976-1980), NHANES III (1988-1994), and NHANES 1999-2000. Diagnosed diabetes was ascertained, and height and weight were measured in adults aged 20-74 years in all surveys. In NHANES II, NHANES III, and NHANES 1999-2000, a fasting glucose level > or =126 mg/dl was used to identify cases among individuals not reporting diabetes. Design-based analyses and Bayesian models estimate the probability that prevalence of diabetes increased within four BMI groups (<25, 25-29, 30-34, and > or =35 kg/m2). RESULTS: In the U.S. population aged 20-74 years between 1976-1980 and 1999-2000, significant increases in the prevalence of diagnosed diabetes (3.3-5.8%, probability >99.9%) were accompanied by nonsignificant increases in undiagnosed diabetes (2.0-2.4%, 66.6%). This resulted in an increase in total diabetes (5.3-8.2%, >99.9%) and a modest nonsignificant increase in the proportion of cases that were diagnosed (62-70%, 62.4%). However, these trends varied considerably by BMI level. In individuals with BMI > or =35 kg/m2, diagnosed diabetes increased markedly (from 4.9% in 1960, to 8.6% during 1976-1980, to 15.1% in 1999-2000; probability >99.9%), whereas undiagnosed diabetes declined considerably (12.5% during 1976-1980 to 3.2% in 1999-2000, probability of increase 4.5%) Therefore, the proportion of total diabetes cases that were diagnosed increased from 41 to 83% (probability 99.9%) among individuals with BMI > or =35 kg/m2. By comparison, changes in prevalence within BMI strata <35 kg/m2 were modest and there was no increase in the percent of total cases that were diagnosed. CONCLUSIONS: National surveys over the last several decades have found large increases in diagnosed diabetes, particularly in overweight and obese individuals, but this has been accompanied by large decreases in undiagnosed diabetes only among individuals with BMI > or =35 kg/m2. This suggests that improvements in diabetes awareness and detection are most prominent among this subgroup.

Adult↗
PubMed2004

Genetic evaluation of calving to first insemination using natural and artificial insemination mating data.

Mating and calving records for 51,084 first-parity heifers in Australian Angus herds were used to examine the relationship between probability of calving to first insemination (CFI) in artificial insemination and natural service (NS) mating data. Calving to first insemination was defined as a binary trait for both sources of data. Two Bayesian models were employed: 1) a bivariate threshold model with CFI in AI data regarded as a trait separate from CFI in NS data and 2) a univariate threshold model with CFI regarded as the same trait for both sources of data. Posterior means (SD) of additive variance in the bivariate analysis were similar: 0.049 (0.013) and 0.075 (0.021) for CFI in AI and NS data, respectively, indicating lack of heterogeneity for this parameter. A similar trend was observed for heritability in the bivariate analysis, with posterior means (SD) of 0.025 (0.007) and 0.048 (0.012) for AI and NS data, respectively. The posterior means (SD) of the additive covariance and corresponding genetic correlation between the traits were 0.048 (0.006) and 0.821 (0.138), respectively. Differences were observed between posterior means for herd-year variance: 0.843 vs. 0.280 for AI and NS data, respectively, which may reflect the higher incidence of 100% conception rates within a herd-year class (extreme category problem) in AI data. Parameter estimates under the univariate model were close to the weighted average of the corresponding parameters under the bivariate model. Posterior means (SD) for additive, herd-year, and service sire variance and heritability under the univariate model were 0.063 (0.007), 0.56 (0.029), 0.131 (0.013), and 0.036 (0.007), respectively. These results indicate that, genetically, cows with a higher probability of CFI when mated using AI also have a high probability of CFI when mated via NS. The high correlation between the two traits, along with the lack of heterogeneity for the additive variance, implies that a common additive variance could be used for AI and NS data. A single-trait analysis of CFI with heterogeneous variances for herd-year and service sire could be implemented. The low estimates of heritability indicate that response to selection for probability of calving to first insemination would be expected to be low.

Animals↗
PubMed2004

Threshold-linear analysis of measures of fertility in artificial insemination data and days to calving in beef cattle.

Mating and calving records for 47,533 first-calf heifers in Australian Angus herds were used to examine the relationship between days to calving (DC) and two measures of fertility in AI data: 1) calving to first insemination (CFI) and 2) calving success (CS). Calving to first insemination and calving success were defined as binary traits. A threshold-linear Bayesian model was employed for both analyses: 1) DC and CFI and 2) DC and CS. Posterior means (SD) of additive covariance and corresponding genetic correlation between the DC and CFI were -0.62 d (0.19 d) and -0.66 (0.12), respectively. The corresponding point estimates between the DC and CS were -0.70 d (0.14 d) and -0.73 (0.06), respectively. These genetic correlations indicate a strong, negative relationship between DC and both measures of fertility in AI data. Selecting for animals with shorter DC intervals genetically will lead to correlated increases in both CS and CFI. Posterior means (SD) for additive and residual variance and heritability for DC for the DC-CFI analysis were 23.5 d2 (4.1 d2), 363.2 d2 (4.8 d2), and 0.06 (0.01), respectively. The corresponding parameter estimates for the DC-CS analysis were very similar. Posterior means (SD) for additive, herd-year and service sire variance and heritability for CFI were 0.04 (0.01), 0.06 (0.06), 0.14 (0.16), and 0.03 (0.01), respectively. Posterior means (SD) for additive, herd-year, and service sire variance and heritability for CS were 0.04 (0.01), 0.07 (0.07), 0.14 (0.16), and 0.03 (0.01), respectively. The similarity of the parameter estimates for CFI and CS suggest that either trait could be used as a measure of fertility in AI data. However, the definition of CFI allows the identification of animals that not only record a calving event, but calve to their first insemination, and the value of this trait would be even greater in a more complete dataset than that used in this study. The magnitude of the correlations between DC and CS-CFI suggest that it may be possible to use a multitrait approach in the evaluation of AI and natural service data, and to report one genetic value that could be used for selection purposes.

Animals↗
PubMed2006

Accounting for outliers and heteroskedasticity in multibreed genetic evaluations of postweaning gain of Nelore-Hereford cattle.

The objectives of this study were to demonstrate the utility of hierarchical Bayesian models combining residual heteroskedasticity with robustness for outlier detection and muting and to evaluate the effects of such joint modeling in multibreed genetic evaluations. A 3 x 2 factorial specification of 6 residual variance models based on several distributional (Gaussian, Student's t, or Slash) and variability (homoskedastic or heteroskedastic) assumptions was used to analyze 22,717 postweaning gain records from a Nelore-Hereford population (40,082 animals in the pedigree). To illustrate the utility of the 2 robust distributional specifications (Student's t and Slash) for outlier detection and muting, 3 records from the same contemporary group (an extreme residual outlier, a mild residual outlier, and a near-zero residual) were chosen for further study. The posterior densities of the corresponding weighting variables of these records were used to assess their degree of Gaussian outlyingness and the ability of the robust models to mute the effects of deviant records. The Student's t heteroskedastic provided the best-fit model among the 6 specifications and was preferred for genetic merit inference. Kendall rank correlations of the posterior means of the additive genetic effects of the animals, used to compare the selection order of the Student's t and Gaussian models, were reasonably high across all animals within the most frequent genotypes, ranging from 0.83 to 0.91 and from 0.89 to 0.95 for the homoskedastic and the heteroskedastic versions, respectively. However, when considering only animals ranked in the top 10% by the customary Gaussian homoskedastic model, these rank correlations were reduced considerably, ranging from 0.29 to 0.57 and from 0.72 to 0.85 between the 2 residual densities within the homoskedastic and heteroskedastic versions, respectively. Rank correlations between the homoskedastic and heteroskedastic versions within each of the Gaussian and Student's t error models tended to be smaller, with a range from 0.68 to 0.90 across all animals and from 0.28 to 0.67 for animals ranked in the top 10%. These results support the implementation of robust models accounting for sources of heteroskedasticity to increase the precision and stability of multibreed genetic evaluations with proper statistical treatment of deviant records.

Analysis of Variance↗
PubMed2026

Shielding the First 24 Postnatal Months of Life: A Proposal for a Prospective Cohort Study of Early-Life Electromagnetic Exposure and Autism Risk.

BACKGROUND: Autism Spectrum Disorder (ASD) involves Mirror Neuron System (MNS) dysfunction, driving core social and imitative impairments. Systemic physiological alterations such as autonomic dysregulation, mitochondrial dysfunction and neuroinflammation are known to impair synchronization and plasticity of neuronal clusters. A less-evident environmental cofactor, coinciding with rising ASD prevalence, is the considerable world-wide increase in electromagnetic radiation (EMR) overall exposure among children. Experimental evidence shows how low-intensity EMR influences cellular processes, via voltage-gated calcium channels (VGCCs), oxidative stress, and mitochondrial metabolism. The Resonant Convergence framework, allow to predict how chronic EMR exposure during the first 24 postnatal months of life can act as a factor in ASD pathogenesis. The best candidate mechanism is chronic Ion Cyclotron Resonance (ICR) detuning the Ca2+-calmodulin pathway, thus disrupting MNS synchronization. METHODS AND ANALYSIS: A prospective observational pilot cohort study (24-month follow-up) proposes to enroll 1000 full-term newborns into two arms: an EMR-reduced cohort (n = 500, rest and sleep-phase Faraday shielding) and a standard exposure cohort (n = 500). Exposure is quantified via radiofrequency (RF)/extremely low frequency(ELF) measurements, proximity analysis, device inventories and wearable dosimetry. The primary endpoint is a continuous neurodevelopmental trajectory score (joint attention, language, electroencephalogram (EEG) mu-rhythm); binary ASD diagnosis (Autism Diagnostic Observation Schedule, Second Edition (ADOS-2), Autism Diagnostic Interview-Revised (ADI-R)) is a secondary, exploratory endpoint. Moreover, an optional genomic screening will evaluate gene-environment interactions within extremely low-frequency electromagnetic field (ELF-EMF) vulnerable pathways, including ASD-associated genes upregulated by RF via bromodomain and extraterminal protein (BET)-mediated epigenetic mechanisms. Analyses will employ risk ratios, Fisher's exact tests and logistic regression adjusted for confounders; mixed-effects and Bayesian modeling will evaluate longitudinal outcomes and exposure reduction effects. Given a 2-3% baseline prevalence, approximately 20-30 ASD cases are expected. The study is therefore powered for exploratory signal detection rather than definitive causal inference, providing the critical baseline data required to justify and design future confirmatory trials. Sex-stratified modeling will address the 4:1 male-to-female prevalence ratio. ETHICS AND DISSEMINATION: Ethics committee approval is not yet sought; full protocol review and approval will be obtained prior to the study initiation, in strict accordance with the Declaration of Helsinki. Written parental informed consent will be mandatory for all participants prior to enrollment. Study findings and methodological milestones will be disseminated through peer-reviewed international scientific publications. This protocol provides a structured methodological framework for the first prospective investigation of sleep-phase EMR reduction as a potential modulator of ASD incidence during early neurodevelopment. Results will inform adequately powered confirmatory trials in electromagnetic neurodevelopmental epidemiology.

autism spectrum disorder↗
PubMed2003

Bivariate analysis of liability to clinical mastitis and to culling in first-lactation cows.

Records taken on 13,070 first-lactation daughters of 250 Norwegian Cattle sires were used to examine associations between susceptibility to clinical mastitis and to culling. Clinical mastitis was defined as a binary trait, whereas culling was treated as either binary (culled or not culled) or continuous (length of opportunity period) for two sampling periods (120 or 300 d of lactation). Two Bayesian models were employed; 1) a bivariate threshold model with both mastitis and culling as binary traits, and 2) a bivariate model with mastitis as a threshold binary variable and time to culling as Gaussian. The heritability of liability to clinical mastitis was not affected by either the length of sampling period (120 vs. 300 d) or by whether culling (binary) or length of opportunity period was the second trait in the bivariate analysis. The posterior mean (standard deviation) of heritability of liability to clinical mastitis was 0.06 to 0.07 (0.02) in all analyses. The heritability estimate of length of opportunity period was less than 0.001. Culling (threshold trait) in first lactation had a low heritability, but a high genetic correlation with clinical mastitis. The posterior means (standard deviation) for heritability of liability to culling were 0.01 (0.006) for 120 d and 0.02 (0.009) for 300 d, and the posterior means (standard deviation) of the genetic correlation between liability to clinical mastitis and to culling were 0.48 (0.24) and 0.53 (0.21) for 120 and 300 d, respectively.

Analysis of Variance↗
PubMed2006

Within-category feature correlations and Bayesian adjustment strategies.

To the extent that categories inform judgments about items, the accuracy with which categories capture the statistical structure of experience should affect judgment accuracy. The authors argue that representations of feature correlations can serve as Bayesian priors, increasing the accuracy of stimulus estimates by decreasing variability. Participants viewed a series of objects that varied on two dimensions that were either uncorrelated or correlated. They estimated each item by manipulating a response object to make it match the presented stimulus. Subsequent classification and feature-inference tasks indicated that the correlation was detected. The pattern of variability in recollections of stimuli suggested that the feature correlation informed estimates as predicted by a Bayesian model of category effects on memory.

Bayes Theorem↗
PubMed2006

Beyond statistical inference: a decision theory for science.

Traditional null hypothesis significance testing does not yield the probability of the null or its alternative and, therefore, cannot logically ground scientific decisions. The decision theory proposed here calculates the expected utility of an effect on the basis of (1) the probability of replicating it and (2) a utility function on its size. It takes significance tests--which place all value on the replicability of an effect and none on its magnitude--as a special case, one in which the cost of a false positive is revealed to be an order of magnitude greater than the value of a true positive. More realistic utility functions credit both replicability and effect size, integrating them for a single index of merit. The analysis incorporates opportunity cost and is consistent with alternate measures of effect size, such as r2 and information transmission, and with Bayesian model selection criteria. An alternate formulation is functionally equivalent to the formal theory, transparent, and easy to compute.

Data Interpretation, Statistical↗
PubMed2006

Salmonella risk in imported fresh beef, beef preparations, and beef products.

Additional guarantees (AGs) for Salmonella in imported defined animal-derived foods were agreed on for Finland when it was admitted to the European Community. The aim of this project was to evaluate the impact of these AGs on the prevalence of Salmonella in the Finnish beef supply and the adequacy of their scope. According to the quantitative Bayesian model, the efficacy of AGs was mainly dependent on the proportions of different beef categories imported and the true prevalence in the countries of origin. According to the model, AGs were able to reach their target in the referred year 1999 and kept the true Salmonella prevalence of beef imports below 1% with quantified uncertainty. The extension of AGs to all imported fresh beef would have reduced the Salmonella prevalence of beef imports from three- to fourfold, whereas expanding the implementation of AGs to all imports of fresh beef, beef preparations, and beef products would have resulted in a sixfold decrease. If current AGs targeting fresh beef intended to be sold as fresh or to be processed by the Finnish industry with processes not achieving 70 degrees C were not implemented, the 95% credible interval of Salmonella prevalence in the Finnish beef supply would be 0.2 to 1.3% (mean, 0.6%) instead of 0.1 to 1.2% (mean, 0.5%). However, if the prevalence in the exporting countries were to rise or the main import countries and/or magnitudes were to change, AGs would be of greater importance.

Animals↗
PubMed1999

Clinical prediction of deep vein thrombosis in patients with leg symptoms.

Symptoms and clinical signs individually are inaccurate for the diagnosis of DVT. However, when assessing patients with leg symptoms, clinicians have access to additional information, such as whether or not DVT risk factors are present that could improve the accuracy of clinical judgment. The purpose of this study was to identify which clinical variables best predict DVT, and to use these variables to create a clinical prediction index for DVT. We studied 271 university hospital patients with a first episode of symptomatic, clinically suspected DVT. The prevalence of DVT was 27%, of which 71% were proximal. At baseline, information was collected on demographic features, comorbidity, and symptoms and signs. A Bayesian model selection strategy was used to estimate the logistic regression model that best predicted DVT. Male sex [OR = 2.8 (1.5, 5.1)], orthopedic surgery [OR = 5.4 (2.2, 13.6)], warmth [OR = 2.1 (1.2, 3.9)] and superficial venous dilation on exam [OR = 2.9 (1.4, 5.7)] were independent predictors of DVT. Using the model, a clinical prediction index that categorized patients into different levels of DVT risk was created, and was useful in a theoretical strategy aimed to limit the need for contrast venography in patients with suspected DVT, such that 96% of study patients could have avoided contrast venography. This index should be evaluated prospectively in other patient populations.

Adult↗
PubMed2000

Decision-support and intelligent tutoring systems in medical education.

One of the challenges in medical education is to teach the decision-making process. This learning process varies according to the experience of the student and can be supported by various tools. In this paper we present several approaches that can strengthen this mechanism, from decision-support tools, such as scoring systems, Bayesian models, neural networks, to cognitive models that can reproduce how the students progressively build their knowledge into memory and foster pedagogic methods.

Artificial Intelligence↗
PubMedDate not supplied

[Cognitive diagnosis of cerebrovascular disease by event related potentials: anatomical sources that generate P300].

INTRODUCTION: Cerebrovascular disease causes different cognitive alterations. There is a need to develop tools that are capable of diagnosing them. One of them could be event related potentials. These provide an indicator of cognitive processing in real time. PATIENTS AND METHODS: A study was conducted of 10 patients with cerebral infarction in the frontal region and 10 paired healthy controls. Evaluation of the patients was performed a week after the stroke. A continuous performance test was applied to both groups together with the recording of the electrical activity in the brain in order to obtain the P300 component. The results were submitted to the non-parametric Student's t test, and the Bayesian model averaging method (BMAM) was employed to calculate the sources generating the electrical activity recorded on the electroencephalogram. RESULTS: Patients displayed significantly poorer performances compared to the healthy controls in the attention test. The BMAM showed that the P300 component was related to the right hand temporal structures in healthy controls, whereas the left temporoparietal regions were also involved in the patients. CONCLUSIONS: These findings indicate the existence of subclinical disorders affecting sustained attention and that they can only be detected by very sensitive tools; furthermore, they also have implications for the brain circuits regulating sustained attention and the P300 component.

Attention↗
PubMed2004

Congenital anomalies and proximity to landfill sites.

The occurrence of congenital anomalies in proximity to municipal landfill sites in the Eastern Region (counties Dublin, Kildare, Wicklow) was examined by small area (district electoral division), distance and clustering tendancies in relation to 83 landfills, five of which were major sites. The study included 2136 cases of congenital anomaly, 37,487 births and 1423 controls between 1986 and 1990. For the more populous areas of the region 50% of the population lived within 2-3 km of a landfill and within 4-5 km for more rural areas. In the area-level analysis, the standardised prevalence ratios, empirical and full Bayesian modelling, and Kulldorff's spatial scan statistic found no association between the residential area of cases and location of landfills. In the case control analysis, the mean distance of cases and controls from the nearest landfill was similar. The odds ratios of cases compared to controls for increasing distances from all landfills and major landfills showed no significant difference from the baseline value of 1. The kernel and K methods showed no tendency of cases to cluster in relationship to landfills. In conclusion, congenital anomalies were not found to occur more commonly in proximity to municipal landfills.

Bayes Theorem↗
PubMed2004

New data on ecological analysis of possible relationship between magnesium in drinking water and liver cancer.

Based on extensive ecological data and conducted Bayesian modeling, this paper reports on epidemiological findings concerning a possible relationship between magnesium in drinking water and carcinogenesis in human liver. Relative contributions of water quality to endemic waterborne disease for particular categories of magnesium concentration and for age groups of patients were ascertained, i.e. the smaller liver cancer morbidity is referred with a greater magnesium concentration in drinking water, both in males and females. The achieved outcomes provide statistically significant evidence of positive health effects of magnesium water supplies on this lethal disease.

Age Factors↗
PubMed2005

Do variations in disease prevalence limit the usefulness of population-based hospitalization rates for studying variations in hospital admissions?

BACKGROUND: Studies of geographic variation in hospitalizations commonly examine age- and gender-adjusted population-based hospitalization rates (ie, the numbers of persons hospitalized relative to what is expected given the age/gender distributions in the area population). OBJECTIVE: To determine whether areas identified as extreme using population-based hospitalization rates remain extreme when ranked by disease-based hospitalization rates (the numbers of persons hospitalized relative to what is expected given the amount of disease in the area). DESIGN: The authors examined 1997 Medicare data on both inpatient admissions and outpatient visits of patients 65 years and older in each of 71 small areas in Massachusetts for 15 medical conditions. For each area, the number of people having each condition was calculated as the sum of those hospitalized plus those treated as outpatients only. The authors used hierarchical Bayesian modeling to estimate area-specific population-based hospitalization rates, disease-based hospitalization rates (DHRs), and disease prevalence. MAIN OUTCOME MEASURE: The extent to which the same areas were identified as extreme based on population-based hospitalization rates versus DHRs. RESULTS: Area-specific population-based hospitalization rates, DHRs, and disease prevalence varied substantially. Areas identified as extreme using population-based hospitalization rates often were not extreme when ranked by DHRs. For 11 of the 15 conditions, 5 or more of the 14 areas ranked in top and bottom deciles by population-based hospitalization rates were more likely than not (ie, with probability > or = 0.50) to be at least 2 deciles less extreme when ranked by DHRs. CONCLUSION: Differences in disease prevalence can limit the usefulness of population-based hospitalization rates for studying variations in hospital admissions.

Aged↗
PubMedDate not supplied

[Evaluation of diagnostic tests in the absence of a gold standard using an Anaplasma marginale field data set].

The evaluation of newly developed diagnostic tests (tests) commonly involves the comparison of the test outcomes (pos/neg.) of a sample of animals to those of a reference test (gold standard) in order to derive sensitivity and specificity estimates. Often, however, new tests have to be evaluated against an imperfect reference test since a true gold standard test is either too expensive or too costly to apply. This results in bias in the test characteristic estimates. To solve this problem, latent class and Bayesian models can be used to estimate sensitivity and specificity when evaluating a diagnostic test in the absence of a gold standard. They require at least two imperfect reference tests applied to all individuals in the study. In our approach we used a two-test two-population scenario. Both the gold standard and these modelling approaches rely on various assumptions. When violated, biased results will be obtained. The analysis of field data from an Anaplasma marginale outbreak in cattle in Switzerland with four diagnostic procedures (detection of the agent, serology, PCR and hematocrit measurements) was used as a practical example to demonstrate and critically discuss the approaches taken. In this relatively small data set (n = 275) the estimates for the test characteristics obtained by the different methods were quite similar. Overall, the bias in the point estimates depended mainly on the chosen estimation approach. All tests showed a non-negligible correlation mainly in the test sensitivities. This emphasizes the importance of taking into account test dependence even if it seems not biologically plausible at first thought.

Anaplasma marginale↗
PubMed2005

Causal Bayesian network for tagging syntactical structure of Croatian sentences.

Paper describes tagging syntactical structure of Croatian language sentences using causal Bayesian network. In the first part of the paper we describe Bayesian model for tagging sentences. Base on this idea, we will test our model on Croatian language sentences on Database of grammatical sentences of Croatian language (http://infoz.ffzg.hr / tepes /). This paper is result of our new research connected with the paper hidden Markov model for tagging of Croatian language texts for project Linguistic Analysis of The European languages and the paper Probability distribution on the parse trees for the project Annotated database and syntactic structure of Croatian languages.

Anthropology↗