Daily counts of computer records of hospital emergency department arrivals grouped according to diagnosis (called here syndrome groupings) can be monitored by epidemiologists for changes in frequency that could provide early warning of bioterrorism events or naturally occurring disease outbreaks and epidemics. This type of public health surveillance is sometimes called syndromic surveillance. We used transitional Poisson regression models to obtain one-day-ahead arrival forecasts. Regression parameter estimates and forecasts were updated for each day using the latest 365 days of data. The resulting time series of recursive estimates of parameters such as the amplitude and location of the seasonal peaks as well as the one-day-ahead forecasts and forecast errors can be monitored to understand changes in epidemiology of each syndrome grouping.The counts for each syndrome grouping were autocorrelated and non-homogeneous Poisson. As such, the main methodological contribution of the article is the adaptation of Cumulative Sum (CUSUM) and Exponentially Weighted Moving Average (EWMA) plans for monitoring non-homogeneous counts. These plans were valid for small counts where the assumption of normally distributed one-day-ahead forecasts errors, typically used in other papers, breaks down. In addition, these adaptive plans have the advantage that control limits do not have to be trained for different syndrome groupings or aggregations of emergency departments.Conventional methods for signaling increases in syndrome grouping counts, Shewhart, CUSUM, and EWMA control charts of the standardized forecast errors were also examined. Shewhart charts were, at times, insensitive to shifts of interest. CUSUM and EWMA charts were only reasonable for large counts. We illustrate our methods with respiratory, influenza, diarrhea, and abdominal pain syndrome groupings.
Background: MNS blood group system genes GYPA and GYPB share a high degree of sequence homology and gene structure. Homologous exchanges between GYPA and GYPB form hybrid genes encoding hybrid glycophorins GP(A-B-A) and GP(B-A-B). Over 20 hybrid glycophorins have been characterised. Each has a distinct phenotype defined by the profile of antigens expressed including Mi a. Seven hybrid glycophorins carry Mi a and have been reported in Caucasian and Asian population groups. In Australia, the population is diverse; however, the prevalence of hybrid glycophorins in the population has never been determined. The aims of this study were to determine the frequency of Mi a and to classify Mi a-positive hybrid glycophorins in an Australian blood donor population. Method: Blood samples from 5,098 Australian blood donors were randomly selected and screened for Mi a using anti-Mi a monoclonal antibody (CBC-172) by standard haemagglutination technique. Mi a-positive red blood cells (RBCs) were further characterised using a panel of phenotyping reagents. Genotyping by high-resolution melting analysis and DNA sequencing were used to confirm serology. Result: RBCs from 11/5,098 samples were Mi a-positive, representing a frequency of 0.22%. Serological and molecular typing identified four types of Mi a-positive hybrid glycophorins: GP.Hut (n = 2), GP.Vw (n = 3), GP.Mur (n = 5), and 1 GP.Bun (n = 1). GP.Mur was the most common. Conclusion: This is the first comprehensive study on the frequency of Mi a and types of hybrid glycophorins present in an Australian blood donor population. The demographics of Australia are diverse and ever-changing. Knowing the blood group profile in a population is essential to manage transfusion needs.
Deletion mutations bordered by repeat sequences are a hallmark of slipped-strand mispairing (SSM) event. We propose this genetic mechanism generated the germline deletion in the Caucasian donor. Extensive studies show that the RHD*1227A is the most prevalent DEL allele in East Asian populations and may have confounded the initial molecular studies. Review of the literature revealed that the SSM model explains some of the extreme polymorphisms observed in the clinically significant RhD blood group antigen.
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