Racial and ethnic minorities in the United States

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1 diabetes case management Evaluation of a Diabetes Case Management Intervention in an Underserved Population: A Retrospective Cohort Study at a Health Disparities Collaborative Site Jackson P. Sekhobo, PhD, MPA, Christopher Wang, MD, MPH, and Pam Ferrari, RN Abstract Objective: To evaluate the impact of case management on glycemic control in a predominantly lowincome, minority patient population receiving care at a multisite community health center. Methods: The case management intervention was implemented at a Health Disparities Collaborative site in A control clinic that that did not have case management but belonged to the same network as the intervention clinic was used for comparison. Type 2 diabetes patients who had 4 or more documented glycosylated hemoglobin (HbA1c) test results from 1 January 2003 through 31 December 2005 comprised the study sample (n = 132). Baseline predictor variables included sociodemographic characteristics, weight status, insurance coverage, hypertension status, medication status, and case management. Results: Regardless of site, participants with poor glycemic control at baseline (HbA1c 9.0%) tended to experience the greatest reductions in HbA1c, with the mean HbA1c declining from 11.0% during the baseline visit to 9.1% during the fourth visit. In repeated-measures multivariate analysis of variance, statistically significant differences were detected between overall mean HbA1c values in the categories of case management status (F = 42.15, p 0.001), baseline glycemic control (F = 6.07, p = ), treatment status (F = 5.87, P = ), and race/ethnicity (F = 3.77, p = ). Tests for within-subject effects did not produce a statistically significant effect of visit (F 3,86 = 0.92, p = ). Conclusion: These results provide preliminary evidence of the effectiveness of a collaborative case management intervention in a predominantly lowincome, minority study population. These findings should be confirmed in larger studies of pooled community health center data. Racial and ethnic minorities in the United States experience higher rates of complications and mortality from diabetes mellitus. Explanations for racial/ ethnic disparities in the occurrence of diabetes complications and mortality include differences in socioeconomic status, health attitudes and behaviors, access to quality medical care, and severity of disease. To date, research studies have demonstrated that racial/ethnic disparities in health care are not only real and widespread but that they cannot be fully explained by differences in insurance status, income, age, and severity of illness [1 8]. Increasingly, therefore, differences in quality of care have been receiving more attention among health disparities researchers and are becoming the focus of national and local efforts to reduce racial/ethnic health disparities [9 13]. Numerous strategies have been used to improve quality of care for type 2 diabetes. These include but are not limited to disease management programs, case management interventions, audit and feedback protocols, team changes, electronic patient registries, provider education, clinician reminders, patient education, diabetes self-management education, patient reminder systems, and continuous quality improvement [14 18]. The effectiveness of many quality improvement strategies on glycemic control remains unclear [15]. To date, studies of the impact of diabetes case management interventions have had mixed results. While many studies have reported improvements in glycemic control in diabetes patients exposed to case management interventions [18], others have failed to observe such effects [17]. Explanations for lack of effects have included use of different definitions for From the Department of Community Health and Social Medicine, Sophie Davis School of Biomedical Education, City College of New York, City University of New York, New York, NY (Dr. Sekhobo); Department of Family Medicine, Columbia University, New York, NY (Dr. Wang); and Quality Improvement Unit, Open Door Family Clinic, Ossining, NY (Ms. Ferrari). 494 JCOM October 2008 Vol. 15, No. 10

2 original Research case management interventions, use of single- versus multifaceted case management interventions, differences across study populations and health care settings, as well as the presence or absence of a collaborative approach between case managers and primary care physicians [17,18]. Despite the promising evidence of effectiveness of case management interventions overall, some important questions remain unanswered. First, very few studies have examined the effects of case management on glycemic control in predominantly low-income and underserved populations [17,18]. Second, most case management interventions have used nurses as case managers; thus, there is a need to examine effects of interventions that use staff other than nurses as case managers. Finally, evaluations of case management interventions have often not been able to isolate the effects of case management from those of other components of diabetes care such as diabetes self-management education and provider education. This study used a retrospective design to examine the impact of case management on glycemic control in a predominantly low-income, minority patient population receiving care at a multisite community health center that participates in the national Health Disparities Collaborative [19,20]. We hypothesized that patients who were assigned to case management would be more likely to attain optimal glycemic control compared with those who were assigned to regular care over the study period regardless of race/ethnicity. Methods Intervention The intervention evaluated in this study is a chronic disease case management program that was implemented at the study clinic in The program consisted of a specially trained case manager who utilized her expertise in chronic disease management and performance improvement to support care teams at the point of care. The case manager was a baccalaureate-level health educator who served as an interface between patients and providers. She provided diabetes education and self-management support directly to patients and linked patients to important resources for ensuring continuity of care within and outside the clinic. Another study clinic that did not have case management but belonged to the same network as the intervention clinic served as the control clinic. Patient Sample Data for this study were obtained from an on-site patient registry that captured information from diabetes-related primary care visits that occurred at the 2 clinics during the period beginning on 1 January 2003 and ending on 31 December In an effort to obtain a study population that had received continuous care at both clinics as well as subjects who had been exposed to the case management intervention for at least 1 year, participants were restricted to type 2 diabetes patients who had visits in 2002 and had at least 3 or more visits during the follow-up period from 1 January 2003 through 31 December Patients were classified into 3 groups based on baseline glycosylated hemoglobin (HbA1c) values: good glycemic control (HbA1c < 7.0%), intermediate control (HbA1c 7.0% but < 9.0%), and poor control (HbA1c 9.0%). These classifications were informed by the American Diabetes Association guidelines [21] and vary slightly from those used in previous studies [17,18,22] because we sought to divide the study population into 3 equal groups. Subjects who had missing information on several key variables including HbA1c, age, sex, and race/ethnicity were excluded from the final study sample. The study protocol was approved by the internal review board of the City College of New York, City University of New York. Study Variables The main outcome measure was improvement in HbA1c level. Improvement in glycemic control was indicated by a decline in the value of HbA1c between test results obtained at 2 consecutive visits. Predictor variables were measured at the beginning of the study period and included sociodemographic and lifestyle characteristics, insurance coverage, and comorbid conditions (ie, hypertension and obesity). In addition to case management status (yes/no), another process of care variable that was considered a potential predictor of glycemic control was medication status (insulin treatment, oral hypoglycemic agents, or diet and exercise). Sociodemographic variables included age, sex, and race/ethnicity, marital status, and household income expressed in relation to the federal poverty level. Lifestyle and health status variables assessed current body weight, cigarette smoking, and hypertensive status. Health insurance coverage was categorized as Medicare, Medicaid, private insurance, and no insurance. Statistical Analyses Descriptive statistics were generated to summarize the characteristics of the study population according to the 3 baseline categories of glycemic control. To determine whether patients differentially experienced changes in HbA1c based on their initial level of glycemic control, HbA1c test results obtained during 4 consecutive diabetes-related visits were used to generate a linear plot of mean HbA1c values for each baseline category of glycemic control. Next, the proportions of patients whose glycemic control could be classified as good, intermediate, or poor were compared across each of the 4 visits to assess overall trends in glycemic control during the study period. Analysis of variance (ANOVA) was Vol. 15, No. 10 October 2008 JCOM 495

3 diabetes case management Table 1. Characteristics of Adults with Type 2 Diabetes According to Baseline Categories of Glycemic Control Good (HbA1c 7.0%) Glycemic Control Intermediate (HbA1c > 7.0% but < 9.0%) Poor (HbA1c 9.0%) Sample size, n Mean age, yr 62.4 ± ± ± 11.2 Mean BMI, kg/m ± ± ± 8.20 Mean HbA1c, % 6.1 ± ± ± 1.60 Interval between visits, mo 1st and 2nd visit 11.1 ± ± ± nd and 3rd visit 5.4 ± ± ± 3.7 3rd and 4th visit 6.6 ± ± ± 2.1 Sex, % Female Male Race/ethnicity, % African American Hispanic White Age > 60 yr, % % of FPL Marital status, % Married Single BMI 30, % Current cigarette smoker, % Hypertensive, % Health insurance coverage, % Medicaid Medicare Private Uninsured Received case management, % Medication status, % Insulin Oral agents Diet/exercise BMI = body mass index; FPL = federal poverty level; HbA1c = glycosylated hemoglobin. used to test for statistically significant differences in mean levels of HbA1c in various subgroups of the study population. Finally, repeated measures multivariate analysis of variance (rmanova) was used to test for between-subject and within-subject effects on HbA1c. All analyses were carried out using SAS (Raleigh, NC). With the exception of race/ethnicity, the multivariate model included only factors that had produced a statistically significant different mean HbA1c at any visit. An alpha level of 0.05 was used to determine the statistical significance of all tests. Results Of 233 patients who had multiple visits from 1 January 2003 through 31 December 2005, 138 met inclusion criteria. Of these 138 patients, 6 were excluded due to missing data on several key variables including age, sex, and race/ethnicity, thus resulting in a final study sample of 132 subjects (50 men and 82 women). A total of 103 men and women received care at the intervention clinic while 29 received care at the control clinic. Forty-three patients had good glycemic control, 46 had intermediate glycemic control, and 43 had poor glycemic control. More than two thirds of the subjects (71.2%) were African American or Hispanic American, and the majority (78.8%) had incomes 100% or less of the federal poverty level (data not shown). Table 1 summarizes the demographic and health characteristics of the study population according to the baseline categories of glycemic control. Patients who had good glycemic control tended to be slightly older than those who had poor glycemic control but did not differ from patients with intermediate and poor glycemic control in terms of mean body mass index and mean interval between return visits. Nearly half of men (46%) had poor glycemic control compared with approximately a quarter of women who had poor control. Equal proportions of African Americans (35.8%) either had good or intermediate glycemic control, while the majority of Hispanic patients (41.5%) had poor glycemic control. Subjects who were aged 60 years or older were less likely to have poor glycemic control (28.8%), while those who were married or obese were more likely to have intermediate (41.9%) or poor (34.9%) glycemic control. Half of all smokers had poor glycemic control. Patients covered by Medicaid were less likely to have good glycemic control (23.1%), while those on Medicare were less likely to have poor glycemic control (27.4%). The majority of uninsured patients (41.2%) had good glycemic control, while the more than a third of privately insured patients had poor glycemic control (36.8%). Patients who were assigned to case management were equally distributed among the 3 categories of baseline glycemic control. With regard to treatment, patients who were taking insulin were less likely to have good glycemic control (18.7%), while the majority of those who were on a diet or exercise regimen were likely to have good glycemic control (60.0%). Figure 1 displays trends in mean HbA1c by baseline categories of glycemic control. The greatest decline in HbA1c was observed among patients who were poorly controlled 496 JCOM October 2008 Vol. 15, No. 10

4 original Research HbA1c, % Good control at baseline Intermediate control at baseline Poor control at baseline Visit 1 Visit 2 Visit 3 Visit 4 Figure 1. Mean glycosylated hemoglobin (HbA1c) levels over time by baseline category. Percent Good control Intermediate control Poor control Visit 1 Visit 2 Visit 3 Visit 4 at baseline. The mean HbA1c at the end of the study period (ie, during the fourth visit) was the same as that at baseline among those who had intermediate glycemic control at baseline and was slightly higher than at baseline among those who initially had good glycemic control. Regardless of baseline category of glycemic control, all 3 groups experienced notable declines in mean HbA1c between the second and third visits, with the greatest rate of decline occurring among those who initially had poor glycemic control. Proportions of patients with good, intermediate, or poor glycemic control at each of the 4 visits are shown in Figure 2. Consistent with the notable declines observed between the second and third visits in mean HbA1c values, the proportion of patients with good glycemic control was greatest during the third visit (51.1%) followed by the proportion observed during the fourth visit (45.5%). The proportion of subjects with poor glycemic control declined gradually between the first and third visit and remained unchanged during the fourth visit. Table 2 shows differences in mean HbA1c values in selected subgroups of the study population. Mean HbA1c during the first and second visits was higher among men than among women. Age was associated with mean HbA1c during the third visit, with subjects younger than 60 years having a higher mean HbA1c than those 60 years or older. There were no statistically significant racial differences in mean HbA1c across the 4 visits. With the exception of mean HbA1c during the first visit, subjects who had been assigned to case management consistently had mean HbA1c values that were lower than those among subjects who had not been assigned to a case manager. Similarly, subjects taking insulin tended to have higher HbA1c values than subjects taking oral agents or on diet or exercise regimens. Results of tests of hypotheses for between-subject effects using rmanova are shown in Table 3. The overall mean Figure 2. Proportions of patients with good, intermediate, and poor glycemic control at baseline and follow-up visits. HbA1c among subjects who had been assigned to case management differed significantly from the overall mean HbA1c among subjects who had not been assigned to a case manager (F = 42.15, P < 0.001). Similarly, statistically significant differences were detected between the overall mean HbA1c values in categories of baseline glycemic control (F = 6.07, P = ), treatment status (F = 5.87, P = ), and race/ethnicity (F = 3.77, P = ). rmanova tests for within-subject effects produced a low F value (F 3,86 = 0.92) and a high P value (P = ) for the null hypothesis of no visit effect on HbA1c (Table 4). However, there were statistically significant effects for interactions of visit with baseline glycemic control (F = 4.19, P < 0.001) and sex (F = 3.36, P = ). The significance of the visit by baseline glycemic control interaction was due to differences in the linear term over time among subjects who initially had poor, intermediate, and good glycemic control (F = 6.84, P = ). In contrast, the significance of the visit by sex interaction was due to differences in the cubic term over time between male and female subjects (F = 3.39, P = ). Discussion The results of this study show that subjects whose baseline HbA1c was 9.0% or more (ie, those with poor glycemic control) tended to experience the greatest declines in HbA1c during the study period. The mean HbA1c among patients with poorly controlled glycemic at baseline declined from 11.0% during the first visit to 9.1% during the fourth visit, whereas it remained roughly the same among those with intermediate or good glycemic control. Most importantly, an additional 11% of the study population attained good glycemic control Vol. 15, No. 10 October 2008 JCOM 497

5 diabetes case management Table 2. Differences in Mean HbA1c Levels for Selected Subgroups Mean Difference in HbA1c Subgroups* Visit 1 Visit 2 Visit 3 Visit 4 Male vs. female < 60 yr vs. 60 yr African American vs. white Hispanic vs. white Medicaid vs. Medicare Medicaid vs. private insurance % of FPL vs. > 100% of FPL Single vs. married Obese vs. nonobese Current smoker vs. nonsmoker Hypertensive vs. normotensive Case management vs. none Insulin vs. oral agents Insulin vs. diet/exercise 2.2* Note: boldface indicates P value < FPL = federal poverty level; HbA1c = glycosylated hemoglobin. *Referent group listed second. by the end of the study period; the proportion of patients with HbA1c values less than 7.0% increased from 32.6% during the first visit to 44.0% during the fourth visit. There is some evidence that these improvements may have occurred as a result of the case management intervention. First, subjects who had been assigned to a case manager consistently had mean HbA1c values that were lower than those of control subjects, with statistically significant differences between the 2 groups observed during the last 3 visits. Second, in rmanova, the overall mean HbA1c among subjects who had been assigned to case management differed significantly from the overall mean HbA1c among control subjects after adjustment for several factors, including baseline glycemic control and treatment status. The independent association of case management with glycemic control after adjustment for medication status in our study is consistent with results of previous studies. A systematic review by Norris et al [18] found that several studies have reported improvement in glycemic control without increased medication treatment intensity, presumably through motivating improvements in self-management. The supposition that case management may improve glycemic control through supporting behavioral modification is consistent with results of a randomized controlled trial that showed that collaborative case management did not appear to increase the intensity of medication, despite being an important component of the intervention [17]. Furthermore, a meta-analysis by Shojania et al [15] found that case management trials that did not involve independent medication changes by a case manager or pharmacist reduced mean HbA1c values by 0.41% more (95% confidence interval, 0.20% 0.62%; P = 0.02) than interventions that did not use case management. Notwithstanding the independent effects of treatment status and race/ethnicity, an important question that needs to be addressed in the context of this study is How or why did the case management intervention work? There are a number of plausible explanations for the positive impact of case management on glycemic control. First, case management may improve patient satisfaction with quality of diabetes care. Patients who are satisfied with their overall diabetes care may, in turn, be more likely to adhere to self-management recommendations, including diet and exercise, thus influencing glycemic control. Previous studies have reported that patients liked case management interventions and were more satisfied with their overall diabetes care [17,23,24]. Alternatively, case management may have improved glycemic control through promoting continuity of care. Much of the published literature has reported on the importance of the continuity of care in the successful treatment and management of a number of chronic diseases, including diabetes [25]. In the context of this study, continuity of care was likely achieved for many, if not all, of the subjects included in the final study sample. However, it is not clear whether continuity of care improves chronic disease outcomes primarily through improved quality of care or serves as a proxy marker for motivated or adherent patients. Within the diabetes research community, there is currently 498 JCOM October 2008 Vol. 15, No. 10

6 original Research Table 3. Repeated Measures Multivariate Analysis of Variance Testing for Between-Subject Effects Over Time Source df F P Case management Baseline glycemic control < category Treatment category Sex Age-group Race/ethnicity Error 88 Table 4. Repeated Measures Multivariate Analysis of Variance Testing for Within-Subject Effects Over Time Source df F P Visit Visit*case management Visit*baseline glycemic control < category Visit*treatment category Visit*sex Visit*age-group Visit*race/ethnicity Error 264 widespread acknowledgment that very few providers are currently meeting recommended standards of diabetes care [26]. While it is still not clear whether the degree to which these standards are met varies by race/ethnicity group, it is possible that case management interventions may influence quality of care through increasing clinician compliance with diabetes clinical guidelines, which in turn improves glycemic control. Recent findings [19,27] have confirmed that Health Disparities Collaboratives improve processes of care for patients with diabetes. Finally, given that the case management intervention in this study included diabetes education and self-management support as well as links to important clinical and community resources, the likelihood that self-management contributed to part or all of the observed effects of case management must be considered. After failing to observe effects of a patient activation intervention on glycemic control in a randomized control of 232 type 2 diabetes patients, Williams et al [28] reasoned that the activation intervention indirectly influenced glycemic control by increasing the active involvement of patients during visits with practitioners, with active involvement subsequently leading to improved glycemic control. Similarly, after reviewing studies of the impact of case management on glycemic control, Shojania et al [15] concluded that an effective case management intervention may be a marker of important patient or organizational attributes that influence glycemic control. Strengths and Limitations To our knowledge, this is the first study to report on the impact of a Health Disparities Collaboratives case management intervention on glycemic control in an underserved study population of predominantly low-income, minority population with type 2 diabetes. Previous studies of the effects of collaboratives interventions in type 2 diabetes have focused on processes of care as outcomes [19,26,27] rather than on glycemic control. With a mean interval of 22 months between the first and fourth visit, the present study overcomes the usual limitation of relatively short periods of follow-up in other studies of the impact of case management on glycemic control; the median follow-up period for studies examining effects of quality improvement strategies HbA1c has been estimated at 12.5 months [18]. Most importantly, the 4 HbA1c values obtained during the study period allowed for the assessment of sustained rather than transient glycemic control among the study participants. Our study was subject to several limitations. First, selection bias cannot be ruled out in this study since only 132 subjects out of a total of 233 subjects with 3 to 5 visits during the study period were included in the analysis. While comparisons of the sociodemographic characteristics of the study population with those of the excluded subjects did not reveal significant differences between the 2 groups, the study participants may have differed from the excluded subjects with regard to important but unknown attributes that influence glycemic control. However, if such selection bias did occur, it is unlikely to explain the observed results in this study given the heterogeneity of the study population with regard to baseline categories of glycemic control (ie, responders, intermediate responders, and poor responders). Another limitation of our study is that HbA1c values were measured at highly variable intervals and thus may not correspond to similar lengths of exposure to the case management intervention at any given return visit (or follow-up HbA1c value). The high variability in intervals between consecutive HbA1c tests could have biased the results of this study either away or towards the null. Bias away from the null would occur if a significant number of return visits among patients who experienced reductions in HbA1c occurred much farther away from the start of case management and after many of the patients had been maximally exposed to the intervention. On the other hand, bias toward the null Vol. 15, No. 10 October 2008 JCOM 499

7 diabetes case management would occur if the majority of return visits among patients who experienced reductions in HbA1c occurred much closer to the start of case management when patients had not had the benefit of maximal exposure to the intervention. Both types of bias, however, are not likely to have occurred in this study because the mean intervals between return visits among poor responders (ie, the subgroup that experienced the greatest reductions in HbA1c) are comparable with those observed among responders and intermediate responders. Similar to previous studies of effects of quality improvement strategies on glycemic control, a major limitation of this study is the inability to isolate effects of case management from other components of diabetes care, including the electronic patient registry, patient education, or selfmanagement. Nevertheless, as suggested elsewhere [29], it is reasonable to assume that case management interventions will generally require more than a case manager to improve glycemic control. Conclusions These results confirm the previously reported impact of case management on glycemic control. Most importantly, the findings provide evidence for the effectiveness of a collaborative case management intervention in a predominantly low-income and predominantly minority study population. These results should be confirmed in larger studies of data pooled from eligible community health centers participating in the Health Disparities Collaboratives. Another conclusion that can be drawn from our findings is that the goal of attaining compliance with the recommended level of glycemic control (ie, HbA1c < 7.0%) is indeed a daunting if not difficult task even in the face of what may qualify as well-coordinated, patient-centered care. The proportion of patients whose HbA1c values declined to levels below 7.0% at the end of the 3-year follow-up period (11%) was modest at best. Future studies should therefore investigate the impact of quality improvement strategies on other physiologic outcomes such as blood pressure, blood lipids, microalbumin, and serum creatinine, all of which are subclinical indicators of diabetic complications. At the time of this study, J. P. S. worked as a research scientist at the New York State Department of Health and C.W. was the medical director of Open Door Family Clinic. Results of a pilot analysis were presented at the 2004 annual conference of the Centers for Disease Control & Prevention s Division of Diabetes Translation in Chicago, IL. Acknowledgment: We are grateful to Dr. Marthe Gold for her review of earlier versions of the manuscript. Corresponding author: Jackson P. Sekhobo, PhD, MPA, Director, Evaluation and Analysis Unit, Div. of Nutrition, Ctr. for Community Health, NYS Dept. of Health, 150 Broadway, Albany, NY 12209, jps04@health. state.ny.us. Financial disclosures: None. References 1. Wexler DJ, Grant RW, Meigs JB, et al. Sex disparities in treatment of cardiac risk factors in patients with diabetes. Diabetes Care 2005;28: Bonds DE, Zaccaro DJ, Karter AJ, et al. Ethnic and racial differences in diabetes care: The Insulin Resistance Atherosclerosis Study (IRAS). Diabetes Care 2003;26: Heisler M, Smith DM, Hayward RA, et al. Racial disparities in diabetes care processes, outcomes, and treatment intensity. Med Care 2003;41: Institute of Medicine. Unequal treatment: confronting racial and ethnic disparities in health care. Washington (DC): National Academy Press; Kanaya AM, Grady D, Barrett-Connor E. Explaining the sex differences in coronary heart disease mortality among patients with type 2 diabetes mellitus: a meta-analysis. 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8 original Research 18. Norris SL, Nichols PJ, Casperson CJ, et al. The effectiveness of disease and case-management for people with diabetes. Am J Prev Med 2002;22(4 Suppl): Landon BE, Hicks LS, O Malley AJ, et al. Improving the management of chronic disease at community health centers. N Engl J Med 2007;356: Health Disparities Collaboratives. Available at www. healthdisparities.net/hdc/html/home.aspx. Accessed 20 May American Diabetes Association. Standards of medical care in diabetes. Diabetes Care 2005;28(Suppl 1):S4 S Spann SJ, Nutting PA, Galliher JM, et al. Management of type 2 diabetes in the primary care setting: a practice-based research network study. Ann Fam Med 2006;4: Aubert RE, Herman WH, Water J, et al. Nurse case management to improve glycemic control in diabetic patients in a health maintenance organization. Ann Intern Med 1998;129: Weinberger M, Kirkman MS, Samsa GP, et al. A nurse- coordinated intervention for primary care patients with noninsulin-dependent diabetes mellitus: impact on glycemic control and health-related quality of life. J Gen Intern Med 1995;10: Cabana MD, Lee SH. Does continuity of care improve patient outcomes? J Fam Pract 2004;53: Chin MH, Cook S, Drum ML, et al. Improving diabetes care in Midwest community health centers with the Health Disparities Collaborative. Diabetes Care 2004;27: Hicks LS, O Malley AJ, Lieu TA, et al. The quality of chronic disease care in U.S. Community Health Centers. Health Aff (Millwood) 2006;25: Williams GC, McGregor H, Zeldman A, et al. Promoting glycemic control through diabetes self-management: evaluating a patient activation intervention. Patient Educ Couns 2003;56: Wagner EH. More than a case manager. Ann Intern Med 1998;128: Copyright 2008 by Turner White Communications Inc., Wayne, PA. All rights reserved. Vol. 15, No. 10 October 2008 JCOM 501