In summer 2024, the Children’s Bureau revised the rules that govern data suppression in datasets from the National Child Abuse and Neglect Data System (NCANDS). To minimize the risk of re-identification, researchers had previously been unable to identify counties with fewer than 1000 annual maltreatment cases. Under the new data suppression rule, county identifiers will only be suppressed for counties with fewer than 700 cases. In this report, we document the consequences of this shift for research data access and re-identification risks, showing that reducing the data suppression threshold increased the number of identifiable counties from 835 to 1096 (a 31.3% increase) and doubled the number of identifiable rural counties. The percentage of reported children who face a particularly elevated re-identification risk due to having unique demographic characteristics increased from 0.7% to 1.0%.
BelangerK.Price-MayoB.EspinosaD. (2008). The plight of rural child welfare: Meeting standards without services. Journal of Public Child Welfare, 1(4), 1–19. https://doi.org/10.1080/15548730802118181
2.
EiermannM. (2024a). The impact of data suppression rules on data access and re-identification risk in adoption and foster care analysis and reporting system annual files. Child Maltreatment. Advanced Online Publication. https://doi.org/10.1177/10775595241270042
3.
EiermannM. (2024b). Algorithmic risk scoring and welfare state contact among U.S. children. Sociological Science, 11, 707–742. https://doi.org/10.15195/v11.a26
4.
FlukeJ. D.YuanY.-Y. T.EdwardsM. (1999). Recurrence of maltreatment: An application of the national child abuse and neglect data system (NCANDS). Child Abuse & Neglect, 23(7), 633–650. https://doi.org/10.1016/s0145-2134(99)00039-3
5.
HotzV. J.BollingerC. R.KomarovaT.ManskiC. F.MoffittR. A.NekipelovD.SojournerA.SpencerB. D. (2022). Balancing data privacy and usability in the federal statistical system. Proceedings of the National Academy of Sciences, 119(31), e2104906119. https://doi.org/10.1073/pnas.2104906119
6.
KimH.DrakeB. (2019). Cumulative prevalence of onset and recurrence of child maltreatment reports. Journal of the American Academy of Child & Adolescent Psychiatry, 58(12), 1175–1183. https://doi.org/10.1016/j.jaac.2019.02.015
7.
KimH.WildemanC.Jonson-ReidM.DrakeB. (2017). Lifetime prevalence of investigating child maltreatment among U.S. children. American Journal of Public Health, 107(2), 274–280. https://doi.org/10.2105/AJPH.2016.303545
8.
LaBrenzC. A.OhriF.KimY.BaidenP. (2024). Adolescents with substance use referred to child protective services: Does substance use treatment prevent foster care entry?Child & Family Social Work, 29(2), 363–373. https://doi.org/10.1111/cfs.13089
9.
Maguire-JackK.JespersenB.KorbinJ. E.SpilsburyJ. C. (2021). Rural child maltreatment: A scoping literature review. Trauma, Violence, & Abuse, 22(5), 1316–1325. https://doi.org/10.1177/1524838020915592
10.
Maguire-JackK.KimH. (2021). Rural differences in child maltreatment reports, reporters, and service responses. Children and Youth Services Review, 120, 105792. https://doi.org/10.1016/j.childyouth.2020.105792
11.
Maguire-JackK.ParkY.FeelyM.SchneiderW.PaceG. T.KlikaJ.ThibodeauE. (2024). Childcare subsidy employment and copayment requirements and child maltreatment. Child Maltreatment, 29(4), 612–624. https://doi.org/10.1177/10775595231218174
12.
NadonM.ParkK.LeeJ. Y.WrightW. (2023). Who makes the call? Examining the relationship between child maltreatment referral sources and case outcomes in the United States, 2008-2018. Child Abuse & Neglect, 145, 106404. https://doi.org/10.1016/j.chiabu.2023.106404
13.
PiñaG.MooreK.Mihalec-AdkinsB.DarlingK.AbdiF.LiehrA. (2024). State policy levers for reducing early childhood maltreatment: The importance of family planning and economic support policies. Child Maltreatment, Advanced Online Publication.
14.
RamachandranA.SinghL.PorterE.NagleF. (2012). Exploring re-identification risks in public domains. In 2012 tenth annual international conference on privacy, security and trust (pp. 35–42). https://doi.org/10.1109/pst.2012.6297917
15.
RocherL.HendrickxJ. M.de MontjoyeY. A. (2019). Estimating the success of re-identifications in incomplete datasets using generative models. Nature Communications, 10(1), 3069. https://doi.org/10.1038/s41467-019-10933-3
16.
SilovskyJ. F.BardD.ChaffinM.HechtD.BurrisL.OworaA.BeasleyL.DoughtyD.LutzkerJ. (2011). Prevention of child maltreatment in high-risk rural families: A randomized clinical trial with child welfare outcomes. Children and Youth Services Review, 33(8), 1435–1444. https://doi.org/10.1016/j.childyouth.2011.04.023
17.
SmithB. D.LiQ.WangK.SmithA. M. (2021). A national study of child maltreatment reporting at the county level: Interactions among race/ethnicity, rurality and poverty. Children and Youth Services Review, 122, 105925. https://doi.org/10.1016/j.childyouth.2021.105925
18.
SmithB. D.PressleyT. D. (2019). Do surprisingly low child maltreatment rates in rural southern counties reflect lower rates of substantiation?Children and Youth Services Review, 107, 104493. https://doi.org/10.1016/j.childyouth.2019.104493
19.
WildemanC.EmanuelN.LeventhalJ. M.Putnam-HornsteinE.WaldfogelJ.LeeH. (2014). The prevalence of confirmed maltreatment among U.S. children, 2004 to 2011. JAMA Pediatrics, 168(8), 706–713. https://doi.org/10.1001/jamapediatrics.2014.410
