Effects of estimated community-level health literacy on treatment initiation and preventive care among older adults with newly diagnosed diabetes

Purpose: Individual measures of health literacy are not feasible for administration on a large scale, yet estimates of community-level health literacy in the US recently became available. We sought to investigate whether community-level health literacy estimates are associated with the initiation of oral antihyperglycemic agents (OHA) and the use of standard preventive care services among older adults with newly diagnosed diabetes. Patients and methods: We conducted a retrospective cohort study of 169,758 patients, ≥65 years old with hypertension and newly diagnosed type 2 diabetes using 2007–2011 data from the Center for Medicare and Medicaid Services Chronic Conditions Warehouse. We examined the relationship between community-level health literacy estimates and initiation of OHA, receipt of flu shots, eye exams, Hemoglobin A1c tests, and lipid tests within 12 months post diabetes diagnosis. Results: Patients living in communities with above basic health literacy (vs. basic/below basic) were 15% more likely to initiate OHA (Hazard Ratio=1.15; 95% CI 1.12 to 1.18). After classifying the health literacy distribution as quintiles, the analysis revealed a dose– response relationship with OHA initiation that plateaued at the third and fourth quintiles and declined at the fifth quintile. Individuals residing in communities with higher health literacy were more likely to participate in preventive care services (relative risk ranged from 1.09 for lipid test [95% CI 1.07–1.11] to 1.43 for flu shot [95% CI 1.41–1.46]). Conclusion: Community-level health literacy estimates were associated with the initiation of OHA and uptake of standard preventive care services in older adults. Community-level health literacy may help to inform targeted diabetes education and support efforts

MINING HYBRID SEQUENTIAL PATTERNS BY HIERARCHICAL MINING TECHNIQUE

Unlike sequential patterns, hybrid sequential patterns display not only the path but also the relationship among transaction items. The information provided by the collection of hybrid sequential patterns is useful in improving the analysis of marketing strategies, such as, browsing web pages, discovering customers' behaviors and so on. The process of mining hybrid sequential patterns in a database, however, becomes Complicated by the huge number of candidate patterns. In this paper, we propose a hierarchical mining technique to deal with this complexity. The unique features of this new technique include: counting hybrid sequential patterns by class, and examining database transactions in a top-down manner. This results in scanning a database, at most, twice. Using the technique, we develop an efficient mining algorithm, and conduct a simulation to study its performance. There are three major contributions in this paper. First, our proposed pattern-class concept provides a new way to count a group of patterns simultaneously. Second, we propose a novel decomposition model to lower the I/O cost in counting patterns from a large database. And third, we prove the correctness of counting patterns in the pattern decomposition model in this paper