Selective IgA Deficiency a Probable Risk of Recurrent Chest Infections in Asthmatics.

BACKGROUND: Selective immunoglobulin A (IgA) deficiency is characterized by a high incidence of both recurrent infections and atopic diseases. Asthma is one of the most common lung diseases affecting around 300 million people worldwide and is associated with risk of serious pneumococcal disease and microbial infections. Multiple studies have attributed this to impaired innate and adaptive immunity in asthmatics. An additional probable hypothesis is the existence of an underlying primary immunodeficiency (PID), such as selective IgA deficiency (sIgAD). AIM: To assess the prevalence of selective IgA deficiency and its correlation to recurrent infections in asthmatic patients. METHODS: A case-control study was conducted on 80 subjects who were divided into 3 groups: 20 Asthmatic patients with recurrent chest infections (Group A), 20 asthmatic patients without recurrent chest infections (Group B) and 40 healthy controls (Group C). RESULTS: On comparing the 3 studied groups, there was a statistically significant difference between the three groups (p = ˂0.001) concerning serum IgA. The mean serum IgA was statistically significantly lower in Group A&B than in Group C. Furthermore, it was significantly lower in Group A than in Group B and C (p1,2 <0.002 and <0.001*, respectively). The percentage of selective IgA deficiency or partial IgA deficiency in asthmatic patients was 56% (26 patients). Group A showed a statistically significant higher percentage of selective/partial IgA deficiency.

A visual analytics approach to understanding spatiotemporal hotspots.

As data sources become larger and more complex, the ability to effectively explore and analyze patterns among varying sources becomes a critical bottleneck in analytic reasoning. Incoming data contain multiple variables, high signal-to-noise ratio, and a degree of uncertainty, all of which hinder exploration, hypothesis generation/exploration, and decision making. To facilitate the exploration of such data, advanced tool sets are needed that allow the user to interact with their data in a visual environment that provides direct analytic capability for finding data aberrations or hotspots. In this paper, we present a suite of tools designed to facilitate the exploration of spatiotemporal data sets. Our system allows users to search for hotspots in both space and time, combining linked views and interactive filtering to provide users with contextual information about their data and allow the user to develop and explore their hypotheses. Statistical data models and alert detection algorithms are provided to help draw user attention to critical areas. Demographic filtering can then be further applied as hypotheses generated become fine tuned. This paper demonstrates the use of such tools on multiple geospatiotemporal data sets.

Syndromic surveillance: STL for modeling, visualizing, and monitoring disease counts.

BACKGROUND: Public health surveillance is the monitoring of data to detect and quantify unusual health events. Monitoring pre-diagnostic data, such as emergency department (ED) patient chief complaints, enables rapid detection of disease outbreaks. There are many sources of variation in such data; statistical methods need to accurately model them as a basis for timely and accurate disease outbreak methods. METHODS: Our new methods for modeling daily chief complaint counts are based on a seasonal-trend decomposition procedure based on loess (STL) and were developed using data from the 76 EDs of the Indiana surveillance program from 2004 to 2008. Square root counts are decomposed into inter-annual, yearly-seasonal, day-of-the-week, and random-error components. Using this decomposition method, we develop a new synoptic-scale (days to weeks) outbreak detection method and carry out a simulation study to compare detection performance to four well-known methods for nine outbreak scenarios. RESULT: The components of the STL decomposition reveal insights into the variability of the Indiana ED data. Day-of-the-week components tend to peak Sunday or Monday, fall steadily to a minimum Thursday or Friday, and then rise to the peak. Yearly-seasonal components show seasonal influenza, some with bimodal peaks.Some inter-annual components increase slightly due to increasing patient populations. A new outbreak detection method based on the decomposition modeling performs well with 90 days or more of data. Control limits were set empirically so that all methods had a specificity of 97%. STL had the largest sensitivity in all nine outbreak scenarios. The STL method also exhibited a well-behaved false positive rate when run on the data with no outbreaks injected. CONCLUSION: The STL decomposition method for chief complaint counts leads to a rapid and accurate detection method for disease outbreaks, and requires only 90 days of historical data to be put into operation. The visualization tools that accompany the decomposition and outbreak methods provide much insight into patterns in the data, which is useful for surveillance operations.