Browsing by Author "Department of Biostatistics, Fairbanks School of Public Health"

Now showing 1 - 3 of 3
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    Nonadherence to Oral Antihyperglycemic Agents: Subsequent Hospitalization and Mortality among Patients with Type 2 Diabetes in Clinical Practice
    (IOS, 2015) Zhu, Vivienne J.; Tu, Wanzhu; Rosenman, Marc B.; Overhage, J. Marc; Department of Biostatistics, Fairbanks School of Public Health
    Using real-world clinical data from the Indiana Network for Patient Care, we analyzed the associations between non-adherence to oral antihyperglycemic agents (OHA) and subsequent diabetes-related hospitalization and all-cause mortality for patients with type 2 diabetes. OHA adherence was measured by the annual proportion of days covered (PDC) for 2008 and 2009. Among 24,067 eligible patients, 35,507 annual PDCs were formed. Over 90% (n=21,798) of the patients had a PDC less than 80%. In generalized linear mixed model analyses, OHA non-adherence is significantly associated with diabetes related hospitalizations (OR: 1.2; 95% CI [1.1,1.3]; p<0.0001). Older patients, white patients, or patients who had ischemic heart disease, stroke, or renal disease had higher odds of hospitalization. Similarly, OHA non-adherence increased subsequent mortality (OR: 1.3; 95% CI [1.02, 1.61]; p<0.0001). Patient age, male gender, income and presence of ischemic heart diseases, stroke, and renal disease were also significantly associated with subsequent all-cause death.
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    Pitfalls of practicing cancer epidemiology in resource-limited settings: the case of survival and loss to follow-up after a diagnosis of Kaposi’s sarcoma in five countries across sub-Saharan Africa.
    (BMC, 2016) Freeman, Esther; Semeere, Aggrey; Wenger, Megan; Bwana, Mwebesa; Asirwa, F. Chite; Busakhala, Naftali; Oga, Emmanuel; Jedy-Agba, Elima; Kwaghe, Vivian; Iregbu, Kenneth; Jaquet, Antoine; Dabis, Francois; Yumo, Habakkuk Azinyui; Dusingize, Jean Claude; Bangsberg, David; Anastos, Kathryn; Phiri, Sam; Bohlius, Julia; Egger, Matthias; Yiannoutsos, Constantin; Wools-Kaloustian, Kara; Martin, Jeffrey; Department of Biostatistics, Fairbanks School of Public Health
    Survival after diagnosis is a fundamental concern in cancer epidemiology. In resource-rich settings, ambient clinical databases, municipal data and cancer registries make survival estimation in real-world populations relatively straightforward. In resource-poor settings, given the deficiencies in a variety of health-related data systems, it is less clear how well we can determine cancer survival from ambient data.
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    A practical method for predicting frequent use of emergency department care using routinely available electronic registration data.
    (BMC, 2016) Wu, Jianmin; Grannis, Shaun J.; Xu, Huiping; Finnell, John T.; Department of Biostatistics, Fairbanks School of Public Health
    Accurately predicting future frequent emergency department (ED) utilization can support a case management approach and ultimately reduce health care costs. This study assesses the feasibility of using routinely collected registration data to predict future frequent ED visits.