Adult-onset asthma morbidity and related economic costs in middle age due to intentional chronic absenteeism in high school: An epidemiologic study using the national longitudinal survey of youth 1979 data.

OBJECTIVE: Many authors examined the individual and societal impact of school absenteeism. Nevertheless, no empirical study has looked at the potential direct correlation between deliberate school absences and chronic illnesses in mid-adulthood. Our goal is to investigate any potential direct links between purposeful school absences and adult-onset asthma in middle age, as well as measure any associated costs of asthma. METHODS: Data were sourced from the National Longitudinal Survey of Youth 1979, a nationally representative survey. The outcome measure was self-reported asthma in mid-adulthood. School records of absenteeism from grades nine through twelve were the key explanatory variables. Logistic regressions were performed with controls for demographic, economic and health variables. Predicted probabilities from the regressions were used to quantify costs of adult-onset asthma in middle age due to intentional high school absenteeism. RESULTS: More years of chronic absenteeism in high school were associated with higher risk of adult-onset asthma in middle age. Four years of chronic absenteeism in high school during the late 1970s through the early 1980s could potentially have incurred between $817 million to $1 billion of asthma related costs in 2002, when these students were in their mid-adulthood. These potential asthma related costs due to high school absenteeism are sizeable considering that this high school cohort only accounted for six percent of the U.S. population. CONCLUSIONS: Reducing high school absenteeism could lower the incidence of adult-onset asthma in middle age, and its associated future economic burden.

Mixture prior for sparse signals with dependent covariance structure.

In this study, we propose an estimation method for normal mean problem that can have unknown sparsity as well as correlations in the signals. Our proposed method first decomposes arbitrary dependent covariance matrix of the observed signals into two parts: common dependence and weakly dependent error terms. By subtracting common dependence, the correlations among the signals are significantly weakened. It is practical for doing this because of the existence of sparsity. Then the sparsity is estimated using an empirical Bayesian method based on the likelihood of the signals with the common dependence removed. Using simulated examples that have moderate to high degrees of sparsity and different dependent structures in the signals, we demonstrate that the performance of our proposed algorithm is favorable compared to the existing method which assumes the signals are independent identically distributed. Furthermore, our approach is applied on the widely used "Hapmap" gene expressions data, and our results are consistent with the findings in other studies.

Growth in basic science knowledge in first-year medical school and USMLE Step 1 results: a longitudinal investigation at one school.

PURPOSE: Our US medical school uses National Board of Medical Examiners (NBME) tests as progress tests during the pre-clerkship curriculum to assess students. In this study, we examined students' growth patterns using progress tests in the first year of medical school to identify students at risk for failing United States Medical Licensing Examination (USMLE) Step 1. METHOD: Growth Mixture Modeling (GMM) was used to examine the growth trajectories based on NBME progress test scores in the first year of medical school. Achieving a passing score on the USMLE Step 1 at the end of the second year of medical school was used as the distal outcome, controlling for Medical College Admissions Test (MCAT) scores and underrepresented in medicine (URiM) status. RESULTS: A total of 518 students from a US medical school were included in the analysis. Five different growth patterns were identified based on students' NBME test results. Seventy-eight students identified in Group 1 had the lowest starting NBME test score (mean = 33.6, 95% CI 32.0-35.2) and lowest growth rate (mean = 2.30, 95% CI 2.06-2.53). All 26 students who failed Step 1 at the end of the second year were in Group 1 (failing rate = 33%). Meanwhile Group 4 (n = 65 students) had moderate starting NBME test scores (mean = 37.9, 95% CI 36.3-39.0) but the highest growth rate with mean slope at 6.07 (95% CI 5.40-6.73). This group of students achieved significant higher USMLE Step1 scores comparing with the 3 other groups of students (P < 0.05). CONCLUSIONS: Our study found students had heterogeneous growth patterns in progress test results in their first year of medical school. Growth patterns were highly predictive of USMLE step 1 results. This study can provide performance benchmarks for our future students to assess their progress and for medical educators to identify students who need support and guidance.