Factors Influencing Immunization Status in Primary Care Clinics

Background and Objectives: National standards and goals for childhood immunization rates are well established. Yet, despite clear standards and goals, physicians do not achieve the desired rate (90%) for immunization coverage. This study examined factors related to immunization status for 2-year-old children in pediatric and family practice settings. Methods: Specially trained personnel used computer software to audit 2,552 records from 42 practices in Northeast Florida throughout 1997–1999. Immunization records were judged as either complete or incomplete, and factors related to immunization status were studied. Clinic type and 18 immunization practice standards were reviewed for effect on immunization status. Results: The probability of complete immunization status for children in pediatric clinics was greater than for those in family practice clinics. Multivariate logistic regression revealed that use of semiannual audits (odds ratio [OR]=2.00, confidence interval [CI]=1.65–2.42) was the most important factor for immunization completion. This was followed by availability of discounted immunizations (OR=.44, CI=.27–.73) and the use of an immunization tracking system (OR=1.48, CI=1.18–1.70). Factors that were not found to contribute included clinic type and the remaining 15 practice standards. Conclusions: Considering the significant factors, immunization status was not affected by the type of clinic providing immunizations. Based on this analysis, family physicians should implement tracking systems and should perform semiannual audits to match the success of pediatricians in immunizing children. Neither group met nationally established goals for administration of immunizations for 2-year-old children

The magnitude distribution of earthquakes near Southern California faults

We investigate seismicity near faults in the Southern California Earthquake Center Community Fault Model. We search for anomalously large events that might be signs of a characteristic earthquake distribution. We find that seismicity near major fault zones in Southern California is well modeled by a Gutenberg-Richter distribution, with no evidence of characteristic earthquakes within the resolution limits of the modern instrumental catalog. However, the b value of the locally observed magnitude distribution is found to depend on distance to the nearest mapped fault segment, which suggests that earthquakes nucleating near major faults are likely to have larger magnitudes relative to earthquakes nucleating far from major faults

The Segment Ontology: Bridging Music-generic and Domain-specific

Existing semantic representations of music analysis encapsulate narrow sub-domain concepts and are frequently scoped by the context of a particular MIR task. Segmentation is a crucial abstraction in the investigation of phenomena which unfold over time; we present a Segment Ontology as the backbone of an approach that models properties from the musicological domain independently from MIR implementations and their signal processing foundations, whilst maintaining an accurate and complete description of the relationships that link them. This framework provides two principal advantages which are explored through several examples: a layered separation of concerns that aligns the model with the needs of the users and systems that consume and produce the data; and the ability to link multiple analyses of differing types through transforms to and from the Segment axis

Unachievable Region in Precision-Recall Space and Its Effect on Empirical Evaluation

Precision-recall (PR) curves and the areas under them are widely used to summarize machine learning results, especially for data sets exhibiting class skew. They are often used analogously to ROC curves and the area under ROC curves. It is known that PR curves vary as class skew changes. What was not recognized before this paper is that there is a region of PR space that is completely unachievable, and the size of this region depends only on the skew. This paper precisely characterizes the size of that region and discusses its implications for empirical evaluation methodology in machine learning.Comment: ICML2012, fixed citations to use correct tech report numbe