Insights from the 2006 Disease Management Colloquium

This roundtable discussion emanates from the presentations given and issues raised at the 2006 Disease Management Colloquium, which was held May 10–12, 2006 in Philadelphia, Pennsylvania

Value of Laboratory Tests in Employer-Sponsored Health Risk Assessments for Newly Identifying Health Conditions: Analysis of 52,270 Participants

Employer-sponsored health risk assessments (HRA) may include laboratory tests to provide evidence of disease and disease risks for common medical conditions. We evaluated the ability of HRA-laboratory testing to provide new disease-risk information to participants.We performed a cross-sectional analysis of HRA-laboratory results for participating adult employees and their eligible spouses or their domestic partners, focusing on three common health conditions: hyperlipidemia, diabetes mellitus, and chronic kidney disease. HRA with laboratory results of 52,270 first-time participants were analyzed. Nearly all participants had access to health insurance coverage. Twenty-four percent (12,392) self-reported one or more of these medical conditions: 21.1% (11,017) self-identified as having hyperlipidemia, 4.7% (2,479) self-identified as having diabetes, and 0.7% (352) self-identified as having chronic kidney disease. Overall, 36% (n = 18,540) of participants had laboratory evidence of at least one medical condition newly identified: 30.7% (16,032) had laboratory evidence of hyperlipidemia identified, 1.9% (984) had laboratory evidence of diabetes identified, and 5.5% (2,866) had laboratory evidence of chronic kidney disease identified. Of all participants with evidence of hyperlipidemia 59% (16,030 of 27,047), were newly identified through the HRA. Among those with evidence of diabetes 28% (984 of 3,463) were newly identified. The highest rate of newly identified disease risk was for chronic kidney disease: 89% (2,866 of 3,218) of participants with evidence of this condition had not self-reported it. Men (39%) were more likely than women (33%) to have at least one newly identified condition (p<0.0001). Among men, lower levels of educational achievement were associated with modestly higher rates of newly identified disease risk (p<0.0001); the association with educational achievement among women was unclear. Even among the youngest age range (20 to 29 year olds), nearly 1 in 4 participants (24%) had a newly identified risk for disease.These results support the important role of employer-sponsored laboratory testing as an integral element of HRA for identifying evidence of previously undiagnosed common medical conditions in individuals of all working age ranges, regardless of educational level and gender

Patterns of multimorbidity in working Australians

Background: Multimorbidity is becoming more prevalent. Previously-used methods of assessing multimorbidity relied on counting the number of health conditions, often in relation to an index condition (comorbidity), or grouping conditions based on body or organ systems. Recent refinements in statistical approaches have resulted in improved methods to capture patterns of multimorbidity, allowing for the identification of nonrandomly occurring clusters of multimorbid health conditions. This paper aims to identify nonrandom clusters of multimorbidity.Methods: The Australian Work Outcomes Research Cost-benefit (WORC) study cross-sectional screening dataset (approximately 78,000 working Australians) was used to explore patterns of multimorbidity. Exploratory factor analysis was used to identify nonrandomly occurring clusters of multimorbid health conditions.Results: Six clinically-meaningful groups of multimorbid health conditions were identified. These were: factor 1: arthritis, osteoporosis, other chronic pain, bladder problems, and irritable bowel; factor 2: asthma, chronic obstructive pulmonary disease, and allergies; factor 3: back/neck pain, migraine, other chronic pain, and arthritis; factor 4: high blood pressure, high cholesterol, obesity, diabetes, and fatigue; factor 5: cardiovascular disease, diabetes, fatigue, high blood pressure, high cholesterol, and arthritis; and factor 6: irritable bowel, ulcer, heartburn, and other chronic pain. These clusters do not fall neatly into organ or body systems, and some conditions appear in more than one cluster.Conclusions: Considerably more research is needed with large population-based datasets and a comprehensive set of reliable health diagnoses to better understand the complex nature and composition of multimorbid health conditions

A Systematic Review of Cost-of-Illness Studies of Multimorbidity

Objectives: The economic burden of multimorbidity is considerable. This review analyzed the methods of cost-of-illness (COI) studies and summarized the economic outcomes of multimorbidity. Methods: A systematic review (2000–2016) was performed, which was registered with Prospero, reported according to PRISMA, and used a quality checklist adapted for COI studies. The inclusion criteria were peer-reviewed COI studies on multimorbidity, whereas the exclusion criterion was studies focusing on an index disease. Extracted data included the definition, measure, and prevalence of multimorbidity; the number of included health conditions; the age of study population; the variables used in the COI methodology; the percentage of multimorbidity vs. total costs; and the average costs per capita. Results: Among the 26 included articles, 14 defined multimorbidity as a simple count of 2 or more conditions. Methodologies used to derive the costs were markedly different. Given different healthcare systems, OOP payments of multimorbidity varied across countries. In the 17 and 12 studies with cut-offs of ≥2 and ≥3 conditions, respectively, the ratios of multimorbidity to non-multimorbidity costs ranged from 2–16 to 2–10. Among the ten studies that provided cost breakdowns, studies with and without a societal perspective attributed the largest percentage of multimorbidity costs to social care and inpatient care/medicine, respectively. Conclusion: Multimorbidity was associated with considerable economic burden. Synthesising the cost of multimorbidity was challenging due to multiple definitions of multimorbidity and heterogeneity in COI methods. Count method was most popular to define multimorbidity. There is consistent evidence that multimorbidity was associated with higher costs

Impact of the Prevention Plan on Employee Health Risk Reduction

Abstract This study evaluated the impact of The Prevention Plan? on employee health risks after 1 year of integrated primary prevention (wellness and health promotion) and secondary prevention (biometric and lab screening as well as early detection) interventions. The Prevention Plan is an innovative prevention benefit that provides members with the high-tech/high-touch support and encouragement they need to adopt healthy behaviors. Support services include 24/7 nurse hotlines, one-on-one health coaching, contests, group events, and employer incentives. Specifically, we analyzed changes in 15 health risk measures among a cohort of 2606 employees from multiple employer groups who completed a baseline health risk appraisal, blood tests, and biometric screening in 2008 and who were reassessed in 2009. We then compared the data to the Edington Natural Flow of risks. The cohort showed significant reduction in 10 of the health risks measured (9 at P?≤?0.01 and 1 at P?≤?0.05). The most noticeable changes in health risks were a reduction in the proportion of employees with high-risk blood pressure (42.78%), high-risk fasting blood sugar (31.13%), and high-risk stress (24.94%). There was an overall health risk transition among the cohort with net movement from higher risk levels to lower risk levels (P?<?0.01). There was a net increase of 9.40% of people in the low-risk category, a decrease of 3.61% in the moderate-risk category, and a 5.79% decrease in the high-risk category. Compared to Edington's Natural Flow model, 48.70% of individuals in the high-risk category moved from high risk to moderate risk (Natural Flow 31%), 46.35% moved from moderate risk to low risk (Natural Flow 35%), 15.65% moved from high risk to low risk (Natural Flow 6%), and 87.33% remained in the low-risk category (Natural Flow 70%) (P?<?0.001). (Population Health Management 2010;13:275?284)Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85111/1/pop_2010_0027.pd