The recently published ADVERSE CHILDHOOD EXPERIENCES DATA REPORT: Behavioral Risk Factor Surveillance System (BRFSS), 2011-2017: An Overview of Adverse Childhood Experiences in California (https://osg.ca.gov/sg-report/ ) illustrates the ways that presentations of ACE data can mislead as well as inform us. After acknowledging very briefly the reality of “additional childhood adversities” not captured in the traditional ACE measure, the Report highlights the frequency of each of the traditional ten ACE indicators (retrospectively reported by adults) and their unequal distribution in the population.
The research uses bar graphs to show the relative frequency of various adult health issues by the number of self-reported ACEs. One such bar graphs show, for example, that 9.5 % of those with no ACEs report fair or poor health compared to 17.6% of those with four or more ACEs. The bolded interpretation of that bar graph is that those with 4+ ACEs are “2.0 times as likely to report fair/poor self-rated health” as those with no ACEs (ratio adjusted to match California demographic distribution).
That, of course, is a profoundly important difference in the likelihood of fair to poor health. But roughly 83% of those with 4+ ACEs do not report experiencing fair to poor health. That means that if we use the ACE score of four to predict that an individual’s health status is fair to poor, we would be wrong about 83% of the time. Clearly, many more factors shape adult health status than ACEs.
Another example shows that 1.3% of those with 4 or more ACEs experience heart disease compared to only .9% of those with no ACEs. The bolded interpretation emphasizes that those with four or more ACEs are “1.5 times as likely to have heart disease,” despite the very low likelihood for all BRFSS respondents (ratio adjusted to match California demographic distribution). Predicting an individual adult would have heart disease by their high ACE score would be in error almost 99% of the time.
The emphasis on the ratios of likelihood of adult health issues for those with four or more ACEs compared to those with none disguises what are often relatively low probabilities of these outcomes. This proves to be highly deceptive when the apparent power of prediction provided by a retrospective ACE score is applied at an individual level. Indeed, ACE scores do not predict individual outcomes well. Not only does using ACE scores falsely identify many individuals as high risk, but it also could lead to ignoring other individuals at high risk despite low ACE scores.
What use then are the ACE data? They provide important information about the presence of risk in a population, not individuals. Thus, the entire population should be the focus of the response. The population predictions coming from these ratios should help guide public health strategies that reduce adversity for large populations where these odds hold. Strategies that lowered the number of people experiencing adversities should reduce the frequency of fair/poor health (as compared to good/excellent health) and of heart disease Dr. Robert Anda made this same case strongly: “The ACE score is a powerful tool for describing the population impact of the cumulative effect of childhood stress and provides a framework for understanding how prevention of ACEs can reduce the burden of many public health problems and concerns. However, the ACE score is neither a diagnostic tool nor is it predictive at the individual level.” (https://www.ajpmonline.org/art...(20)30058-1/fulltext at P. 294).
Public health and policy measures would focus on reducing levels of adversity by changing the resources and social circumstances of families – such as respecting and supporting caregivers, reducing child poverty, making high quality day care available at no or low cost, and expanding mental health services. Such interventions would not only enrich lives but would also diminish the burden of many adult behavioral and physical health issues.
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