{"id":"AIBD-1131","title":"Evaluators in the developers' program find that a risk score shown to Colorado child protection teams cut child injury admissions by 21 percent","summary":"In a Colorado county's randomized trial with 4,681 children, those whose referrals were screened with an algorithm's risk score had 21 percent fewer injury admissions.","date_observed":"2022-03-29","date_basis":"event","date_precision":"day","benefit_status":"reported","source_collection":null,"source_entry":null,"account":"Rhema Vaithianathan and colleagues at the Centre for Social Data Analytics built a decision aid for child protection teams in a large Colorado county. It is a LASSO logistic regression trained on about 500,000 past child referrals. It scores each child from 1 to 20 for the risk of removal to foster care within two years. From 1 November 2020 to 29 March 2022, teams saw the score for randomly chosen households and screened the others as usual. Marie-Pascale Grimon and Christopher Mills linked 4,681 children to statewide hospital records. They report that access to the score reduced a hospital-based harm index by 0.05 standard deviations and injury admissions by 21 percent. The injury result was statistically significant only at the 10 percent level. Workers shifted investigations toward children more likely to be harmed without changing the rate of investigation. The authors also report that workers with the score investigated fewer low-risk Black children. The study belongs to a research program led by the tool's developers and is a working paper. Its registry entry names the county as Larimer County.","category":"aggregate result","unexpected":false,"purpose":"Rhema Vaithianathan and colleagues at the Centre for Social Data Analytics built the decision aid to predict a child's likelihood of removal from home within two years of a referral. 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Workers kept full discretion and recorded the score in at least 73 percent of decisions. The model is a LASSO logistic regression trained on about 500,000 past child referrals."},"evidence":[{"kind":"report","citation":"Grimon M-P, Mills C. Better Together? A Field Experiment on Human-Algorithm Interaction in Child Protection. Working paper, arXiv:2502.08501v3 [econ.GN], 18 February 2026, abstract page. The paper states that the study is part of a research program on risk modeling in human services led by Rhema Vaithianathan and Emily Putnam-Hornstein.","title":"Better Together? A Field Experiment on Human-Algorithm Interaction in Child Protection","url":"https://arxiv.org/abs/2502.08501","date":"2026-02-18","written_by":"evaluator","quote":"Algorithm access reduced maltreatment-related hospitalizations, especially among disadvantaged groups, while reducing CPS surveillance of Black children. Notably, child injury admissions decreased by 21 percent."},{"kind":"other","citation":"Grimon M-P, Mills C. The Effect of Algorithmic Tools on Child Welfare Decision-Making and Outcomes. AEA RCT Registry, AEARCTR-0006311, first published 21 August 2020, last updated 22 September 2022. The entry lists the region as Larimer County in Colorado and reports no results.","title":"The Effect of Algorithmic Tools on Child Welfare Decision-Making and Outcomes","url":"https://www.socialscienceregistry.org/trials/6311","date":"2020-08-21","written_by":"evaluator","quote":"The tool uses machine learning methods trained using several years of historic administrative data and is highly predictive future home removal (e.g., of any child to foster care), re-referral to child protective services (CPS), and child maltreatment death.","supports_benefit":false}],"replication":{"produced_by":["The model was trained on about 500,000 past child referrals from the trial county and other Colorado counties.","Teams of about five workers saw each child's score from 1 to 20 in the morning meetings where they decided which referrals to investigate.","Workers were trained with examples so that they did not decide on the score alone."],"must_hold":["Workers keep information that the algorithm does not use, such as the current allegation.","Teams use the score within a group discussion and keep the final decision."],"echoes":[]},"paired_incident":null,"headline":"Child injury admissions fall with a Colorado *decision aid*","metric":"21 percent fewer injury admissions","comparator":{"against":"Children in households randomly assigned to screening without the score","before":"a mean of 0.17 injury admissions per child in the control group","after":"0.03 fewer injury admissions per child, a 21 percent reduction","basis":"measured","quote":"In particular, the algorithmic tool reduced the average number of future injury admissions by 21 percent, or 0.03 admissions relative to a mean of 0.17.","url":"https://arxiv.org/pdf/2502.08501v3"},"later":{"status":"scaled","date":"2026-02-18","note":"After the trial the tool became available for all referred children in the county. 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