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Abstract
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The coronavirus disease 2019 (COVID-19) pandemic has exposed the frailty of the just-in-time medical system currently in place in the United States. Large gaps in access to care, unequal distribution of testing, and disparities in mortality rates in many ways reflect the greater inequalities that many communities and families were confronting daily before COVID-19. These inequities now may mean life or death. COVID-19 is not the great equalizer it is often referenced to be. It does not affect all communities, all families, or all children equally. Some neighborhoods are ravaged by food insecurity, loss of hourly wage jobs, and threats of evictions, while for others, COVID-19 is disrupting and troubling but not a true existential threat.
As is often the case, inequalities affect children most harshly.1 Built and unbuilt power structures in communities are often indifferent to the needs of children. While the current decrease in calls to child welfare services as reported in many states may be because of school closures (in that teachers are the most common reporters to Child Protective Services [CPS]), there remains a growing concern that the family and community disruption caused by COVID-19 may result in an increase in violence toward vulnerable children and/or parents.2
There is a natural urge to protect children who may be at heightened risk as a result of social isolation, financial stress, or physical harm brought on by the pandemic. Being able to reliably separate children who are at risk of maltreatment from those who are not remains frustratingly elusive. The growth of big data and greater analytic sophistication have contributed to the exploration of predictive risk modeling (PRM) in child welfare work--the goal being to use large data sets, usually from child welfare systems, to assess which child, caregiver, or community characteristics were associated with unfavorable outcomes for the children. These criteria would be applied to future children to define their risk potential. In this issue of JAMA Pediatrics, Vaithianathan et al3 report their work in validating a PRM derived from a database of children reported to CPS in Allegheny County, Pennsylvania. The authors then validated the algorithm's ability to predict subsequent emergency department visits for injury to a child. They demonstrated that their algorithm was able to classify children at risk for subsequent emergency department visits for injuries but did not demonstrate an association with their control condition (emergency department visits for cancer).
While this is a promising advance, some meaningful cautions need to be explored. Much harm can be done under the umbrella of good intentions, because big data is a big weapon. As noted by...
Language: en |