Overview of Disease Management Approaches

Corporations have engaged in sponsorship of health management programs and, more recently, disease management programs to facilitate healthy and productive work environments. The purpose of this review is to examine the health and financial outcomes from these corporate-sponsored disease management programs. This article focuses on seven diseases or chronic conditions (arthritis, asthma, cancer, depression, diabetes mellitus, heart disease, and migraine) that potentially impact employee productivity (both in time away from work and in loss of effectiveness at work) and health status including medical and pharmaceutical utilization and costs.Corporate-sponsored disease management programs typically focus on education and screening for selected diseases or chronic conditions. Partnerships have been formed with health plans and third-party program providers to reach employees with interventions and treatment. The typical outcome measures from these programs have primarily been clinical indicators and medical utilization. Measures of productivity need to be incorporated as important outcome measures for disease management programs.The estimated financial opportunity for the corporation is a reflection of the cost differential for a given disease and the prevalence of that disease within the employee population. Primary diseases, chronic conditions, and health risks contribute to increased medical utilization and decreased productivity within the corporation. Promoting programs that focus on the whole person, including health risks, chronic conditions, and diseases, will likely increase the possibility of success in helping the employee to better self-manage their health conditions and consequently provide gains for both the individual and the corporation.     

Seminested polymerase chain reaction (PCR) for detecting Helicobacter pylori DNA in carotid atheromas.

A polymerase chain reaction (PCR) method for the detection of the glmM gene, selected as Helicobacter pylori target sequence, was improved. While performing pathogenicity island cagA gene detection to discriminate pathogenic strains in atherosclerotic carotid samples, several cagA-positive but glmM-negative samples were found. Polymorphisms present in the region amplified in the nested PCR reaction could explain this result; primers were therefore designed to perform a seminested reaction; this modification optimized sensitivity while maintaining specificity. A real-time PCR for Helicobacter DNA detection was also setup. The combination of all 4 PCR reactions detected 83% of H. pylori DNA-positive samples in atherosclerotic carotid tissue, 64% of which were cagA gene positive.     

Sarcoid Myelopathy

A 45-year-old woman with history of iritis, uveitis, and sarcoidosis of the skin presented with a subacute cervical myelopathy. Magnetic resonance imaging (MRI) ;howed patchy, multifocal, gadoliniumenhancing intramedullary lesions of the spinal cord, and extramedullary lesions of the basal cisterns and fourth ventricle. Symptoms and MRI abnormalities were improved within 1 month of corticosteroid therapy.     

Biochemical analysis of acute and chronic wound environments

The process of wound healing involves a complex interaction between numerous cell types, extracellular matrix molecules, and soluble mediators including growth factors and cytokines. This complex milieu is under active investigation for the purposes of beginning to understand how this environment regulates tissue repair. Quantitation of growth factors, cytokines, and matrix metalloproteinases within surgical wound fluids may help to elucidate this regulatory network, not only in noncomplicated wound healing but also in pathologic lesions such as chronic ulcers.     

Evaluation of an Algorithm To Identify Women with Carcinoma of the Breast

Although claims data are increasingly being used to measure and manage the cost and quality of health care, few studies have evaluated algorithms developed for such analyses. Therefore, the present study was performed to evaluate prospectively a previously published algorithm used to identify women with the new diagnosis of carcinoma of the breast. This algorithm had been developed from the patterns of claims that suggested common clinical presentations of carcinoma of the breast. In the present study, this algorithm was used to identify 177 potential cases of women with newly diagnosed carcinoma of the breast from the claims database of a large health maintenance organization (HMO). The algorithm's positive predictive value for cases identified in the present study was 83% (147/177). To attempt to improve upon the positive predictive value, multiple modifications of the algorithm were performed. The previously defined best modification of the initial algorithm yielded a positive predictive value of 84% (147/174) in the present study with the loss of none of the true positive cases. These results demonstrate that logic-based algorithms can be used as a valid and efficient method of identifying large numbers of new breast cancer cases from claims data. This algorithm provides a powerful tool to perform health care analysis and research for women with newly diagnosed carcinoma of the breast.