Medical Students Circadian Sleep Rhythms and Academic Performance

Objective: to evaluate, with a preliminary study, the distribution of circadian rhythms, sleep schedule patterns and their relationship with academic performance on medical students. Methodology: in this descriptive study, a 10 item original questionnaire about sleep rhythms and academic performance was applied to medical students from different semesters. Week (class time) and weekend schedules, preferences, daytime somnolence and academic performance were asked. Three chronotypes (morningness, intermediate and eveningness) were defined among waking-sleeping preference, difficulty to sleep early, exam preparation preference hour and real sleep schedule. The sleep hour deficit per week night was also calculated. Results: Of the 318 medical students that answered the questionnaire, 62.6% corresponded to intermediate chronotypes, 8.8% to evening-type and 28.7% to morning-type. Significant difference was found among the two chronotype tails (p=0.000, Chi-square 31.13). No correlation was found between academic performance and age, sex, chronotype, week sleep deficit and sleep hours in week and weekends. A 71.1% of the students slept 6 or fewer hours during class time and 78% had a sleep deficit (more frequent in the evening chronotype). Conclusions: No relation was found between sleep chronotype and academic performance. Students tend to morningness. Few studies have been made on equatorial zones or without seasons

Small-area variation in health care affecting the choice of cesarean delivery: the case of a Colombian health insurer

In the midst of health care reform, Colombia has succeeded in increasing health insurance coverage and the quality of health care. In spite of this, efficiency continues to be a matter of concern, and small-area variations in health care are one of the plausible causes of such inefficiencies. In order to understand this issue, we use individual data of all births from a Contributory-Regimen insurer in Colombia. We perform two different specifications of a multilevel logistic regression model. Our results reveal that hospitals account for 20% of variation on the probability of performing cesarean sections. Geographic area only explains 1/3 of the variance attributable to the hospital. Furthermore, some variables from both demand and supply sides are found to be also relevant on the probability of undergoing cesarean sections. This paper contributes to previous research by using a hierarchical model and by defining hospitals as cluster. Moreover, we also include clinical and supply induced demand variables.