Health Preference Research in Europe: A Review of Its Use in Marketing Authorization, Reimbursement, and Pricing Decisions-Report of the ISPOR Stated Preference Research Special Interest Group.

OBJECTIVE: This study examines European decision makers' consideration and use of quantitative preference data. METHODS: The study reviewed quantitative preference data usage in 31 European countries to support marketing authorization, reimbursement, or pricing decisions. Use was defined as: agency guidance on preference data use, sponsor submission of preference data, or decision-maker collection of preference data. The data could be collected from any stakeholder using any method that generated quantitative estimates of preferences. Data were collected through: (1) documentary evidence identified through a literature and regulatory websites review, and via key opinion leader outreach; and (2) a survey of staff working for agencies that support or make healthcare technology decisions. RESULTS: Preference data utilization was identified in 22 countries and at a European level. The most prevalent use (19 countries) was citizen preferences, collected using time-trade off or standard gamble methods to inform health state utility estimation. Preference data was also used to: (1) value other impact on patients, (2) incorporate non-health factors into reimbursement decisions, and (3) estimate opportunity cost. Pilot projects were identified (6 countries and at a European level), with a focus on multi-criteria decision analysis methods and choice-based methods to elicit patient preferences. CONCLUSION: While quantitative preference data support reimbursement and pricing decisions in most European countries, there was no utilization evidence in European-level marketing authorization decisions. While there are commonalities, a diversity of usage was identified between jurisdictions. Pilots suggest the potential for greater use of preference data, and for alignment between decision makers.

Prevalence of obesity, type II diabetes mellitus, hyperlipidemia, and hypertension in the United States: findings from the GE Centricity Electronic Medical Record database.

This study analyzed GE Centricity Electronic Medical Record (EMR) data to examine the effects of body mass index (BMI) and obesity, key risk factor components of metabolic syndrome, on the prevalence of 3 chronic diseases: type II diabetes mellitus, hyperlipidemia, and hypertension. These chronic diseases occur with high prevalence and impose high disease burdens. The rationale for using Centricity EMR data is 2-fold. First, EMRs may be a good source of BMI/obesity data, which are often underreported in surveys and administrative databases. Second, EMRs provide an ideal means to track variables over time and, thus, allow longitudinal analyses of relationships between risk factors and disease prevalence and progression. Analysis of Centricity EMR data showed associations of age, sex, race/ethnicity, and BMI with diagnosed prevalence of the 3 conditions. Results include uniform direct correlations between age and BMI and prevalence of each disease; uniformly greater disease prevalence for males than females; varying differences by race/ethnicity (ie, African Americans have the highest prevalence of diagnosed type II diabetes and hypertension, while whites have the highest prevalence of diagnosed hypertension); and adverse effects of comorbidities. The direct associations between BMI and disease prevalence are consistent for males and females and across all racial/ethnic groups. The results reported herein contribute to the growing literature about the adverse effects of obesity on chronic disease prevalence and about the potential value of EMR data to elucidate trends in disease prevalence and facilitate longitudinal analyses.

Comparison of GE Centricity Electronic Medical Record database and National Ambulatory Medical Care Survey findings on the prevalence of major conditions in the United States.

The study objective was to facilitate investigations by assessing the external validity and generalizability of the Centricity Electronic Medical Record (EMR) database and analytical results to the US population using the National Ambulatory Medical Care Survey (NAMCS) data and results as an appropriate validation resource. Demographic and diagnostic data from the NAMCS were compared to similar data from the Centricity EMR database, and the impact of the different methods of data collection was analyzed. Compared to NAMCS survey data on visits, Centricity EMR data shows higher proportions of visits by younger patients and by females. Other comparisons suggest more acute visits in Centricity and more chronic visits in NAMCS. The key finding from the Centricity EMR is more visits for the 13 chronic conditions highlighted in the NAMCS survey, with virtually all comparisons showing higher proportions in Centricity. Although data and results from Centricity and NAMCS are not perfectly comparable, once techniques are employed to deal with limitations, Centricity data appear more sensitive in capturing diagnoses, especially chronic diagnoses. Likely explanations include differences in data collection using the EMR versus the survey, particularly more comprehensive medical documentation requirements for the Centricity EMR and its inclusion of laboratory results and medication data collected over time, compared to the survey, which focused on the primary reason for that visit. It is likely that Centricity data reflect medical problems more accurately and provide a more accurate estimate of the distribution of diagnoses in ambulatory visits in the United States. Further research should address potential methodological approaches to maximize the validity and utility of EMR databases.

The association of diabetes with job absenteeism costs among obese and morbidly obese workers.

OBJECTIVE: To determine the extent to which absenteeism costs associated with obesity and morbid obesity are traceable to diabetes, and whether obesity and morbid obesity remain predictors of absenteeism costs after controlling for diabetes. METHODS: Data from the Medical Expenditure Panel Survey for 2000-2004 are examined. Outcomes are probability of missing work in the previous year and number of workdays missed. Predictors include diabetes, obesity and morbid obesity, age, education, occupation category, and race. Models are estimated by gender. RESULTS: Probability of missing work in the past year, number of days missed, and absenteeism costs rise significantly with diabetes among the obese and morbidly obese, with costs higher for the morbidly obese, after controlling for diabetes. CONCLUSIONS: Diabetes is strongly predictive of absenteeism among obese and morbidly obese workers. Employer efforts to reduce absenteeism should include consideration of anti-obesity interventions and diabetes prevention.

Occupation-specific absenteeism costs associated with obesity and morbid obesity.

OBJECTIVE: To document the absenteeism costs associated with obesity and morbid obesity by occupation. METHODS: Data from the Medical Expenditure Panel Survey for 2000-2004 are examined. The outcomes are probability of missing any work in the previous year and number of days of work missed in the previous year. Predictors include clinical weight classification, age, education, and race. Models are estimated separately by gender and occupation category. RESULTS: The probability of missing work in the past year, number of days missed, and costs of absenteeism rise with clinical weight classification for both women and men, and vary across occupation. Absenteeism costs associated with obesity total $4.3 billion annually in the United States. CONCLUSION: Substantial absenteeism costs are associated with obesity and morbid obesity. Employers should explore workplace interventions and health insurance expansions to reduce these costs.