Part I of this two-part article series presents a simple but effective approach to understanding the relationship between two conditions. However, complex cases often call for a deeper level of analysis.
The more detailed the data analysis, the more important clear communication, and collaboration become. Actuaries and data analysts should consult domain experts, including underwriters and medical directors, to shape the analysis and help find alternative ways to assess the problem. For example, age is often a significant factor when exploring the relative severity of conditions and comorbidities. In RGA’s analysis of diabetes and coronary artery disease (CAD), for example, the adverse synergy outlined in Part I persists across all ages, but the intensity of the synergy varies. Younger ages exhibit greater excess mortality for those with comorbidity relative to those with just one of the conditions.
Disease severity is another key factor. Determining the degree of severity requires risk assessment expertise to guide the analysis and interpret results. With diabetes and CAD, RGA underwriters and medical directors advised that the subset of CAD with a more severe manifestation, myocardial infarction, may have a different relationship with diabetes. Indeed, those with a myocardial infarction diagnosis proved to have a much more heightened adverse synergy than those with all other CAD diagnoses.
Bringing together multiple pieces of data-based evidence can help clarify the risk profile. With diabetes, cross-referencing medical claims data with information from prescription histories can help indicate the severity or type of diabetes. Even something as simple as separating insulin users from non-insulin users can paint a clearer picture and influence the overall conclusion. RGA’s analysis found that those with a diabetic diagnosis and insulin use exhibit a more intense adverse synergy with CAD. Conversely, the synergistic relationship with CAD is almost non-existent when looking at those taking medications associated with diabetes other than insulin.
Figure 1: CAD and Diabetes by Diabetic Medication
Leveraging multiple pieces of data-based evidence can help address a variety of complex cases. For example, RGA underwriters wanted to know: Does a chronic pain diagnosis alongside the presence of opioid prescriptions result in an adverse synergy that requires additional debits when combined (i.e., is 2 + 2 > 4)? At first glance, the answer was yes, with the combination exhibiting significant additional mortality beyond each condition’s independent contribution (see Figure 2).
Figure 2: Chronic Pain and Opioids
The team was curious whether the extent of the exposure to opioids made a difference. When examining the results in this light, the adverse synergy essentially disappears. Whether exposure to opioids is low, high, or somewhere in between, the extra mortality from the combination no longer presents (see Figure 3).
Figure 3: Chronic Pain and Opioids by Opioid Exposure
So what was driving the initial observation? It turns out, as one might expect, that chronic pain and opioid use are highly correlated. Those with chronic pain are much more likely than the average person to be prescribed opioids, and to be prescribed greater amounts over a longer period of time. Conversely, the opioids-only group is shifted to low opioid use (e.g., one or two short-term prescriptions are common). Such an imbalance at the aggregate level provides a misleading comparison, one which is not quite “apples to apples.” The interaction between chronic pain and opioids is less about a complex synergistic relationship than it is about a significant overlap between the two. This is a great example of Simpson’s Paradox (see article addendum below), and how the way the data is structured and aggregated can influence conclusions.