A zombie statistic is a false or misleading statistic,
often reanimated from studies conducted many years ago and now printed as truth
Well-constructed prevalence studies on health conditions require large populations to produce statistically relevant results. The rarer the disease, the bigger the required population. The expense and effort to accurately assess the prevalence of a rare disease is beyond the reach of most study budgets. As a result, zombie-like rare disease prevalence estimates may not have a basis in reality. In addition, publications rarely segment prevalence rates by U.S. health insurance market (commercial, Medicaid, Medicare, individual) even though sometimes significant differences in prevalence may exist among different covered populations. The United States incentivizes the development of drugs to treat rare diseases through the Orphan Drug Act of 1983, which defines a rare disease as having fewer than 200,000 individuals affected.
In this article, Milliman’s Donna Wix and David Williams used real-world data to calculate the age-adjusted prevalence rate for three diseases: rheumatoid arthritis, which is uncommon but not a rare disease by the Orphan Drug Act definition; ulcerative colitis, which could be considered a rare disease depending on which source is cited; and hemophilia, for which an antihemophilic factor was approved in 2010 by the U.S. Food and Drug Administration (FDA) under the 1983 Orphan Drug Act. They then compared these results to zombie statistics commonly cited.
The availability of real-world data (RWD) and the recognition of its value are on the rise, both in the UK and overseas. RWD is described as data relating to patient health status or the delivery of healthcare collected during the course of clinical care and captured in a variety of data sources, such as administrative claims, electronic health records and product and disease registries.
Real-world evidence (RWE) is generated through the analysis and/or synthesis of RWD and can identify the effects of healthcare interventions, such as benefits, risk or resource use, that are not routinely collected during randomised control trials (RCTs). RWE may enable research that is not possible to model using RCTs but that may be possible through RWE and pragmatic trials.
When considering the use of RWD, a number of questions
should be kept in mind from the outset and initial decision-making phase on
whether or not to use RWD right through to the analysis and producing results.
This paper by Milliman’s Joanne Buckle and Tanya Hayward outlines some of these key considerations associated with using RWD to widen the evidence base in economic evaluations. These considerations are discussed with a focus on the English National Health Service (NHS) but apply more generally to any healthcare system considering the use of RWD.
Real-world evidence (RWE) is key to understanding health-related experience in everyday settings. Stakeholders seek to use RWE for specific reasons related to their roles in healthcare innovation and decision-making. For example, health insurers may use it to make determinations about coverage and benefit design for specific medical products or services. Healthcare providers may use RWE to develop evidence-based clinical guidelines and to develop tools to guide a patient’s clinical care.
RWE relies on dependable real-world data (RWD). Administrative claims data, a type of RWD, offer a valuable combination of costs and information on patients’ diagnoses and service usage that can be leveraged for population estimates of important clinical and economic measures of healthcare.
As RWE plays an increasingly important role in healthcare decision-making, the translation of RWD into actionable and meaningful evidence requires the use of high-quality data and rigorous, meticulously developed analytic methodologies in order to establish confidence in the findings.
In this paper, Milliman consultants discuss using administrative claims data to expand the use of RWE in healthcare decision-making.