Patterns of Multimorbidity

Background: With multimorbidity becoming the norm rather than the exception, the management of multiple chronic diseases is a major challenge facing healthcare systems worldwide. Methods: Using a large, nationally representative database of electronic medical records from the United Kingdom spanning the years 2005 to 2016 and consisting over 4.5 million patients, we apply statistical methods and network analysis to identify comorbid pairs and triads of diseases and identify clusters of chronic conditions across different demographic groups. Unlike many previous studies, which generally adopt cross-sectional designs, we examine temporal changes in the patterns of multimorbidity. In addition, we perform survival analysis to examine the impact of multimorbidity on mortality. Results: The proportion of the population with multimorbidity has increased by approximately 2.5 percentage points over the last decade, with more than 17% having at least two chronic morbidities. We find that the prevalence and the severity of multimorbidity increase progressively with age. Stratifying by socioeconomic status, we find that people living in more deprived areas are more likely to be multimorbid compared to those living in more affluent areas at all ages. The same trend holds consistently for all years in our data. In addition to a number of strongly associated comorbid pairs (e.g., cardiac-vascular and cardiac-metabolic disorders), we identify three principal clusters: a respiratory cluster, a cardiovascular cluster, and a mixed cardiovascular-renal-metabolic cluster. These are supported by established pathophysiological mechanisms and shared risk factors, and are largely consistent with existing studies in the medical literature. Conclusions: In this paper, we use data-driven methods to characterize multimorbidity patterns in different demographic groups and their evolution over the past decade. Our findings contribute to the better understanding of the epidemiology of multimorbidity that is needed to develop more effective primary care for multimorbid patients.

Testing the Effect of UNIV1000 on Retention in a Regional University in the US

Student retention is a major concern for many universities. We use observational data from a regional university located in Alabama to test whether taking a first-year seminar improves student retention rates. Using a linear probit model, we find that taking a first-year seminar course is negatively correlated with retention rates, after controlling for several confounding effects. We perform survival analysis and find that the students who take first year seminar courses have a better survival rate for retention than those that do not take the course. We also find that other macro and micro economic factors are equally important in improving student retention rates, such as labor market opportunities and competition from similar universities.