Source of health insurance and immunization rates: Findings from the 1996 Texas immunization survey

This study was a further investigation of the 1996 Texas Immunization Survey conducted by the Associateship for Disease Control and Prevention of the Texas Department of Health. The 1996 survey was conducted through 4,599 completed telephone interviews of families with a child between the ages of 3–35 months concerning the immunization status of Texas children. The present study determined differences in immunization rates for children aged 3–35 months for the last shot in the immunization series that should be completed before 2 years of age, a total of four shots, both overall and for different health insurance groups. Life tables were used to determine the percentage and distribution over time of completed vaccination rates for each shot. Emphasis was placed on the proportion of children that were immunized at the end of the recommended range of the immunization schedule, and at 2 years of age. Univariate and multivariate analysis was also performed in order to ascertain which risk factors predict whether or not a child will be immunized. RESULTS: Between 80–90% were immunized for the last shot of Hepatitis B; Measles, Mumps, and Rubella; and Polio at 2 years of age. Approximately 2/3 of the sample was immunized for Diphtheria, Pertussis, and Tetanus. Most of the children were immunized by the end of the recommended range of the immunization schedule except for Measles, Mumps, and Rubella. Children of parents with private indemnity insurance were significantly more likely to have received two of the four shots; children of uninsured parents were significantly less likely to have received three of the four shots. In multivariate analysis, maternal education was the only variable that consistently predicted immunization status for the different shots. Results indicate that a substantial gap exists for immunization rates between children with private insurance and uninsured children, despite recent policy changes to provide immunizations free of charge. Health care providers should pay extra attention to the poor and uninsured to make sure that all children receive timely immunizations. ^

Assessment of the effect on statistical power of regression model misspecification by using techniques of mathematical statistics and simulation study

Objectives. This paper seeks to assess the effect on statistical power of regression model misspecification in a variety of situations. ^ Methods and results. The effect of misspecification in regression can be approximated by evaluating the correlation between the correct specification and the misspecification of the outcome variable (Harris 2010).In this paper, three misspecified models (linear, categorical and fractional polynomial) were considered. In the first section, the mathematical method of calculating the correlation between correct and misspecified models with simple mathematical forms was derived and demonstrated. In the second section, data from the National Health and Nutrition Examination Survey (NHANES 2007-2008) were used to examine such correlations. Our study shows that comparing to linear or categorical models, the fractional polynomial models, with the higher correlations, provided a better approximation of the true relationship, which was illustrated by LOESS regression. In the third section, we present the results of simulation studies that demonstrate overall misspecification in regression can produce marked decreases in power with small sample sizes. However, the categorical model had greatest power, ranging from 0.877 to 0.936 depending on sample size and outcome variable used. The power of fractional polynomial model was close to that of linear model, which ranged from 0.69 to 0.83, and appeared to be affected by the increased degrees of freedom of this model.^ Conclusion. Correlations between alternative model specifications can be used to provide a good approximation of the effect on statistical power of misspecification when the sample size is large. When model specifications have known simple mathematical forms, such correlations can be calculated mathematically. Actual public health data from NHANES 2007-2008 were used as examples to demonstrate the situations with unknown or complex correct model specification. Simulation of power for misspecified models confirmed the results based on correlation methods but also illustrated the effect of model degrees of freedom on power.^

The Effect of Proximity, Explicitness, and Representation of Basic Science Information on Student Clinical Problem-Solving

Problem: Medical and veterinary students memorize facts but then have difficulty applying those facts in clinical problem solving. Cognitive engineering research suggests that the inability of medical and veterinary students to infer concepts from facts may be due in part to specific features of how information is represented and organized in educational materials. First, physical separation of pieces of information may increase the cognitive load on the student. Second, information that is necessary but not explicitly stated may also contribute to the student’s cognitive load. Finally, the types of representations – textual or graphical – may also support or hinder the student’s learning process. This may explain why students have difficulty applying biomedical facts in clinical problem solving. Purpose: To test the hypothesis that three specific aspects of expository text – the patial distance between the facts needed to infer a rule, the explicitness of information, and the format of representation – affected the ability of students to solve clinical problems. Setting: The study was conducted in the parasitology laboratory of a college of veterinary medicine in Texas. Sample: The study subjects were a convenience sample consisting of 132 second-year veterinary students who matriculated in 2007. The age of this class upon admission ranged from 20-52, and the gender makeup of this class consisted of approximately 75% females and 25% males. Results: No statistically significant difference in student ability to solve clinical problems was found when relevant facts were placed in proximity, nor when an explicit rule was stated. Further, no statistically significant difference in student ability to solve clinical problems was found when students were given different representations of material, including tables and concept maps. Findings: The findings from this study indicate that the three properties investigated – proximity, explicitness, and representation – had no statistically significant effect on student learning as it relates to clinical problem-solving ability. However, ad hoc observations as well as findings from other researchers suggest that the subjects were probably using rote learning techniques such as memorization, and therefore were not attempting to infer relationships from the factual material in the interventions, unless they were specifically prompted to look for patterns. A serendipitous finding unrelated to the study hypothesis was that those subjects who correctly answered questions regarding functional (non-morphologic) properties, such as mode of transmission and intermediate host, at the family taxonomic level were significantly more likely to correctly answer clinical case scenarios than were subjects who did not correctly answer questions regarding functional properties. These findings suggest a strong relationship (p < .001) between well-organized knowledge of taxonomic functional properties and clinical problem solving ability. Recommendations: Further study should be undertaken investigating the relationship between knowledge of functional taxonomic properties and clinical problem solving ability. In addition, the effect of prompting students to look for patterns in instructional material, followed by the effect of factors that affect cognitive load such as proximity, explicitness, and representation, should be explored.