The aging Canadian population and hospitalizations for acute myocardial infarction: projection to 2020

<p>Abstract</p> <p>Background</p> <p>The risk of experiencing an acute myocardial infarction (AMI) increases with age and Canada's population is aging. The objective of this analysis was to examine trends in the AMI hospitalization rate in Canada between 2002 and 2009 and to estimate the potential increase in the number of AMI hospitalizations over the next decade.</p> <p>Methods</p> <p>Aggregated data on annual AMI hospitalizations were obtained from the Canadian Institute for Health Information for all provinces and territories, except Quebec, for 2002/03 and 2009/10. Using these data in a Poisson regression model to control for age, gender and year, the rate of AMI hospitalizations was extrapolated between 2010 and 2020. The extrapolated rate and Statistics Canada population projections were used to estimate the number of AMI hospitalizations in 2020.</p> <p>Results</p> <p>The rates of AMI hospitalizations by gender and age group showed a decrease between 2002 and 2009 in patients aged ≥ 65 years and relatively stable rates in those aged < 64 years in both males and females. However, the total number of AMI hospitalizations in Canada (excluding Quebec) is projected to increase by 4667 from 51847 in 2009 to 56514 in 2020, a 9.0% increase. Inflating this number to account for the unavailable Quebec data results in an increase of approximately 6200 for the whole of Canada. This would amount to an additional cost of between $46 and$54 million and sensitivity analyses indicate that it could be between $36 and$65 million.</p> <p>Conclusions</p> <p>Despite projected decreasing or stable rates of AMI hospitalization, the number of hospitalizations is expected to increase substantially as a result of the aging of the Canadian population. The cost of these hospitalizations will be substantial. An increase of this extent in the number of AMI hospitalizations and the ensuing costs would significantly impact the already over-stretched Canadian healthcare system.</p

How many people have had a myocardial infarction? Prevalence estimated using historical hospital data

<p>Abstract</p> <p>Background</p> <p>Health administrative data are increasingly used to examine disease occurrence. However, health administrative data are typically available for a limited number of years – posing challenges for estimating disease prevalence and incidence. The objective of this study is to estimate the prevalence of people previously hospitalized with an acute myocardial infarction (AMI) using 17 years of hospital data and to create a registry of people with myocardial infarction.</p> <p>Methods</p> <p>Myocardial infarction prevalence in Ontario 2004 was estimated using four methods: 1) observed hospital admissions from 1988 to 2004; 2) observed (1988 to 2004) and extrapolated unobserved events (prior to 1988) using a "back tracing" method using Poisson models; 3) DisMod incidence-prevalence-mortality model; 4) self-reported heart disease from the population-based Canadian Community Health Survey (CCHS) in 2000/2001. Individual respondents of the CCHS were individually linked to hospital discharge records to examine the agreement between self-report and hospital AMI admission.</p> <p>Results</p> <p>170,061 Ontario residents who were alive on March 31, 2004, and over age 20 years survived an AMI hospital admission between 1988 to 2004 (cumulative incidence 1.8%). This estimate increased to 2.03% (95% CI 2.01 to 2.05) after adding extrapolated cases that likely occurred before 1988. The estimated prevalence appeared stable with 5 to 10 years of historic hospital data. All 17 years of data were needed to create a reasonably complete registry (90% of estimated prevalent cases). The estimated prevalence using both DisMod and self-reported "heart attack" was higher (2.5% and 2.7% respectively). There was poor agreement between self-reported "heart attack" and the likelihood of having an observed AMI admission (sensitivity = 63.5%, positive predictive value = 54.3%).</p> <p>Conclusion</p> <p>Estimating myocardial infarction prevalence using a limited number of years of hospital data is feasible, and validity increases when unobserved events are added to observed events. The "back tracing" method is simple, reliable, and produces a myocardial infarction registry with high estimated "completeness" for jurisdictions with linked hospital data.</p

Behavior of a Metabolic Cycling Population at the Single Cell Level as Visualized by Fluorescent Gene Expression Reporters

BACKGROUND: During continuous growth in specific chemostat cultures, budding yeast undergo robust oscillations in oxygen consumption that are accompanied by highly periodic changes in transcript abundance of a majority of genes, in a phenomenon called the Yeast Metabolic Cycle (YMC). This study uses fluorescent reporters of genes specific to different YMC phases in order to visualize this phenomenon and understand the temporal regulation of gene expression at the level of individual cells within the cycling population. METHODOLOGY: Fluorescent gene expression reporters for different phases of the YMC were constructed and stably integrated into the yeast genome. Subsequently, these reporter-expressing yeast were used to visualize YMC dynamics at the individual cell level in cultures grown in a chemostat or in a microfluidics platform under varying glucose concentrations, using fluorescence microscopy and quantitative Western blots. CONCLUSIONS: The behavior of single cells within a metabolic cycling population was visualized using phase-specific fluorescent reporters. The reporters largely recapitulated genome-specified mRNA expression profiles. A significant fraction of the cell population appeared to exhibit basal expression of the reporters, supporting the hypothesis that there are at least two distinct subpopulations of cells within the cycling population. Although approximately half of the cycling population initiated cell division in each permissive window of the YMC, metabolic synchrony of the population was maintained. Using a microfluidics platform we observed that low glucose concentrations appear to be necessary for metabolic cycling. Lastly, we propose that there is a temporal window in the oxidative growth phase of the YMC where the cycling population segregates into at least two subpopulations, one which will enter the cell cycle and one which does not

Record linkage research and informed consent: who consents?

BACKGROUND: Linking computerized health insurance records with routinely collected survey data is becoming increasingly popular in health services research. However, if consent is not universal, the requirement of written informed consent may introduce a number of research biases. The participants of a national health survey in Taiwan were asked to have their questionnaire results linked to their national health insurance records. This study compares those who consented with those who refused. METHODS: A national representative sample (n = 14,611 adults) of the general adult population aged 20 years or older who participated in the Taiwan National Health Interview Survey (NHIS) and who provided complete survey information were used in this study. At the end of the survey, the respondents were asked if they would give permission to access their National Health Insurance records. Information given by the interviewees in the survey was used to analyze who was more likely to consent to linkage and who wasn't. RESULTS: Of the 14,611 NHIS participants, 12,911 (88%) gave consent, and 1,700 (12%) denied consent. The elderly, the illiterate, those with a lower income, and the suburban area residents were significantly more likely to deny consent. The aborigines were significantly less likely to refuse. No discrepancy in gender and self-reported health was found between individuals who consented and those who refused. CONCLUSION: This study is the first population-based study in assessing the consent pattern in a general Asian population. Consistent with people in Western societies, in Taiwan, a typical Asian society, a high percentage of adults gave consent for their health insurance records and questionnaire results to be linked. Consenters differed significantly from non-consenters in important aspects such as age, ethnicity, and educational background. Consequently, having a high consent rate (88%) may not fully eliminate the possibility of selection bias. Researchers should take this source of bias into consideration in their study design and investigate any potential impact of this source of bias on their results

Building robust prediction models for defective sensor data using Artificial Neural Networks

Predicting the health of components in complex dynamic systems such as an automobile poses numerous challenges. The primary aim of such predictive systems is to use the high-dimensional data acquired from different sensors and predict the state-of-health of a particular component, e.g., brake pad. The classical approach involves selecting a smaller set of relevant sensor signals using feature selection and using them to train a machine learning algorithm. However, this fails to address two prominent problems: (1) sensors are susceptible to failure when exposed to extreme conditions over a long periods of time; (2) sensors are electrical devices that can be affected by noise or electrical interference. Using the failed and noisy sensor signals as inputs largely reduce the prediction accuracy. To tackle this problem, it is advantageous to use the information from all sensor signals, so that the failure of one sensor can be compensated by another. In this work, we propose an Artificial Neural Network (ANN) based framework to exploit the information from a large number of signals. Secondly, our framework introduces a data augmentation approach to perform accurate predictions in spite of noisy signals. The plausibility of our framework is validated on real life industrial application from Robert Bosch GmbH.Comment: 16 pages, 7 figures. Currently under review. This research has obtained funding from the Electronic Components and Systems for European Leadership (ECSEL) Joint Undertaking, the framework programme for research and innovation Horizon 2020 (2014-2020) under grant agreement number 662189-MANTIS-2014-

Hypothalamic arcuate nucleus glucokinase regulates insulin secretion and glucose homeostasis

Aims Glucokinase (GK) serves as a glucose sensor in several tissues including glucose‐sensitive neurons of the arcuate nucleus within the hypothalamus. We have previously demonstrated a role for arcuate GK in the regulation of food and glucose intake. However, its role in the regulation of glucose homeostasis is less clear. We therefore sought to investigate the role of arcuate GK in the regulation of glucose homeostasis. Materials and Methods Recombinant adeno‐associated virus expressing either GK or an antisense GK construct was used to alter GK activity specifically in the hypothalamic arcuate nucleus. GK activity in this nucleus was also increased by stereotactic injection of the GK activator, compound A. The effect of altered arcuate nucleus GK activity on glucose homeostasis was subsequently investigated using glucose and insulin tolerance tests. Results Increased GK activity specifically within the arcuate nucleus increased insulin secretion and improved glucose tolerance in rats during oral glucose tolerance tests. Decreased GK activity in this nucleus reduced insulin secretion and increased glucose levels during the same tests. Insulin sensitivity was not affected in either case. The effect of arcuate nucleus glucokinase was maintained in a model of type 2 diabetes. Conclusions These results demonstrate a role for arcuate nucleus GK in systemic glucose homeostasis

Comparison of artificial neural network and logistic regression models for prediction of mortality in head trauma based on initial clinical data

BACKGROUND: In recent years, outcome prediction models using artificial neural network and multivariable logistic regression analysis have been developed in many areas of health care research. Both these methods have advantages and disadvantages. In this study we have compared the performance of artificial neural network and multivariable logistic regression models, in prediction of outcomes in head trauma and studied the reproducibility of the findings. METHODS: 1000 Logistic regression and ANN models based on initial clinical data related to the GCS, tracheal intubation status, age, systolic blood pressure, respiratory rate, pulse rate, injury severity score and the outcome of 1271 mainly head injured patients were compared in this study. For each of one thousand pairs of ANN and logistic models, the area under the receiver operating characteristic (ROC) curves, Hosmer-Lemeshow (HL) statistics and accuracy rate were calculated and compared using paired T-tests. RESULTS: ANN significantly outperformed logistic models in both fields of discrimination and calibration but under performed in accuracy. In 77.8% of cases the area under the ROC curves and in 56.4% of cases the HL statistics for the neural network model were superior to that for the logistic model. In 68% of cases the accuracy of the logistic model was superior to the neural network model. CONCLUSIONS: ANN significantly outperformed the logistic models in both fields of discrimination and calibration but lagged behind in accuracy. This study clearly showed that any single comparison between these two models might not reliably represent the true end results. External validation of the designed models, using larger databases with different rates of outcomes is necessary to get an accurate measure of performance outside the development population