What we are studying
IBM Watson Health’s (formerly Truven Health Analytics) MarketScan Research Databases contains longitudinal, real-world treatment patterns for over 230 million US patients, the largest such database in the world. These data have been used for more than 60 published analyses of opioid- related health outcomes. However, the assessment of fatalities is limited without linkage to death data since insurance claims only capture in-hospital deaths.
We will leverage our partnership with IBM Watson Health to develop new algorithms which differentiate disenrollment (due to change in insurance provider) from disenrollment as a result of death, including out-of-hospital deaths. In preliminary analyses, we found that 88% of deaths in the Marketscan population under the age of 65 (Commercial Claims and Encounters [CCAE]) and 89.6% of deaths among those 65 and older (Medicare Supplemental [MDS]) occurred out-of-hospital.
Research using insurance claims data allow for a comprehensive nationwide assessment of opioid prescribing patterns and downstream effects. However without linkage to death data, disenrollment from the database is treated as a censoring event, as patients are lost to follow-up. Our preliminary analyses show that 27.6% of individuals who “disenroll” from the Medicare Supplemental plan have a death date within 30 days of disenrollment. Death proximal to disenrollment is rare in those <65 (1.1% of disenrollment), but this is also a population where overdose-related death is a more frequent cause-of-death, making this a population of particular interest. When evaluating the association between opioid use and mortality, treating disenrollment as a censoring event will result in misclassifying outcome events of interest. Under special circumstances, unbiased relative effect measures can be estimated despite this. Specifically, deaths that are recognized (in-hospital) must be correctly classified (perfect specificity), and the proportion of deaths that is not captured must be the same across exposure groups and independent of other risk factors for the outcome. If this is not the case, the estimated risk ratio will be biased (either toward or away from the null). Currently, there is no way to know the extent to which this assumption holds without analyzing linked claims-death data. With sensitivity for mortality at ~10% using only in-hospital deaths, there is a substantial risk that modest differences in the location of deaths (in- vs. out-of-hospital) due to underlying differences in the exposure groups would result in bias. Moreover, all estimates of absolute effect measures (risk difference, incidence rate difference, cumulative incidence) will be biased, and these effect measures are critical for understanding public health impact and communicating risks to patients and their healthcare providers.
There are also study designs for which mortality is not specifically an outcome of interest (e.g., addiction treatment outcomes, long-term opioid use, tampering and injection consequences such as hepatitis and HIV infection) but nonetheless can introduce bias when death is mistakenly handled as a source of administrative censoring. When individuals are followed for differing amounts of follow-up time (as is often the case in analyses of administrative claims data), the date of disenrollment is typically considered a censoring event. The methods that are routinely used for time-to-event data (e.g. Kaplan-Meier, Cox proportional hazards) assume that those patients remain at risk after being censored, and outcomes that they are assumed to experience at the same rate as the uncensored patients are reflected in the cumulative incidence by having those who remain under observation stand-in for them. This will result in estimates of cumulative incidence that are higher than they should be. Differences between the comparison groups in the proportion of disenrollment due to death would lead to bias (either upward or downward). This is a serious concern in older populations (e.g., those >65 years of age) in whom death accounts for a substantial proportion of disenrollment. At present, accurate ascertainment of mortality in commercially insured patients under the age of 65 requires linkage to either state-specific death data or National Death Index (NDI). Given the complexity of requesting death data from all 50 states for any national study of health outcomes, National Death Index data are more straightforward, but they are also costly – often prohibitively so.