A Real-World Analysis of Migraine: A Cross-Sectional Study of Disease Burden and Treatment Patterns

To describe the prevalence, sociodemographic profile, and the burden of migraine in the United States in 1999 and to compare results with the original American Migraine Study, a 1989 population-based study employing identical methods.

The Panel on Cost-Effectiveness in Health and Medicine has called for an "off-the-shelf" catalogue of nationally representative, community-based preference scores for health states, illnesses, and conditions. A previous review of cost-effectiveness analyses found that 77% did not incorporate community-based preferences, and 33% used arbitrary expert or author judgment. These results highlight the necessity of making a wide array of appropriate, community-based estimates more accessible to cost-effectiveness researchers. To provide nationally representative EQ-5D index scores for chronic ICD-9 codes. The nationally representative Medical Expenditure Panel Survey (MEPS) was pooled (2000-2002) to create a data set of 38,678 adults. Ordinary least squares (OLS), Tobit, and censored least absolute deviations (CLAD) regression methods were used to estimate the marginal disutility of each condition, controlling for age, comorbidity, gender, race, ethnicity, income, and education. Most chronic conditions, age, comorbidity, income, and education were highly statistically significant predictors of EQ-5D index scores. Homoskedasticity and normality assumptions were rejected, suggesting only CLAD estimates are theoretically unbiased. The magnitude and statistical significance of coefficients varied by analytic method. OLS and Tobit coefficients were on average 60% and 143% greater than CLAD, respectively. The marginal disutility of 95 chronic ICD-9 codes as well as unadjusted mean, median, and 25th and 75th percentiles are reported. This research provides nationally representative, community-based EQ-5D index scores associated with a wide variety of chronic ICD-9 codes that can be used to estimate quality-adjusted life-years in cost-effectiveness analyses.

Treatment guidelines and strategies are often based on data from randomized controlled trials and observational clinical studies. These sources drive treatment decisions, yet the data they provide may have limited relevance to the wider population in real-world clinical practice due to the narrow selection criteria applied to patients in trials. Information used to inform clinical practice and improve patient outcomes can, therefore, be unreflective of real-world clinical situations. The purpose of this article is to assess the value of Adelphi Disease Specific Programmes (DSPs) as sources of real world data. DSPs are large, multinational, observational studies of clinical practice for a range of common chronic diseases. Treatment practice data are collected by physicians (n = 700) who are asked to provide information for the next 10 patients consulting for a specific condition. These patients (n = 7000) are also invited to fill out a self-completion form providing their own assessment of symptoms, expectations and quality of life. This article provides examples of the statistical techniques that have been employed to analyse the data in terms of cost/burden of illness, quality of life, disease severity and progression, compliance and adherence to therapy, impact of treatment guidelines and analyses of unmet need. DSPs can support clinical understanding of how diseases are managed including rationale for doctor decision-making and patient attitudes to their condition. Comparisons with other data sources and limitations of the programmes are discussed (including the fact that, unlike claims databases and registries, the DSPs are cross-sectional and not longitudinal).