In the elderly, 2% to 5% are afflicted with chronic wounds. Chronic wounds account for more than 1 billion dollars of health care cost per year in the U.S. and seven billion dollars per year world wide. The two most common chronic wounds in the elderly population are pressure ulcers and venous leg ulcers. Patients with these wounds have a diminished quality of life, more frequent visits with health care providers, and increased morbidity and mortality. Treatments for these wounds are often unsuccessful, so prevention is important. However, there is very limited clinical information based or good scientific study on how to prevent the occurrence of these wounds. The hypothesis of this application is that individuals with pressure ulcers or venous leg ulcers have identifiable medical profiles that can be used in clinically useful models to determine who is at risk for a venous leg ulcer or pressure ulcer. The incidence rates of venous leg ulcers and pressure ulcers will be determined in cohorts established using data from U.S. Medicaid and the U.K. based General Practice Research Database (GPRD). Both of these databases have been extensively analyzed and used in peer reviewed publications. They differ in that the U.S. Medicaid database is a claims based database and the GPRD is a patient record database. The population of these databases represents only those who are seeking medical care. These are the individuals who are most seriously afflicted with a venous leg ulcer or pressure ulcer, the most likely to suffer morbidity and mortality from the conditions and, since they have contact with health care providers, exactly those who can be evaluated for prevention. A case-cohort study design will be used to examine risk factors for nonspecific chronic wounds and risk factors specific to venous leg ulcers and pressure ulcers, from among those that have been suggested by expert opinion, case reports, non-experimental studies and experimental studies. Fully adjusted explanatory models will be developed utilizing a modified Cox proportional hazards method. Prediction models will then be developed for U.S. Medicaid data, using a modified Cox proportional hazard method, and subsequently validated using the GPRD data.
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