Using family network data in child protection services
Autoři:
Alex James aff001; Jeanette McLeod aff001; Shaun Hendy aff002; Kip Marks aff004; Delia Rusu aff004; Syen Nik aff004; Michael J. Plank aff001
Působiště autorů:
School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand
aff001; Te Pūnaha Matatini, Auckland, New Zealand
aff002; Department of Physics, University of Auckland, Auckland, New Zealand
aff003; Ministry of Social Development, Wellington, New Zealand
aff004; Inland Revenue Department, Wellington, New Zealand
aff005
Vyšlo v časopise:
PLoS ONE 14(10)
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pone.0224554
Souhrn
Preventing child abuse is a unifying goal. Making decisions that affect the lives of children is an unenviable task assigned to social services in countries around the world. The consequences of incorrectly labelling children as being at risk of abuse or missing signs that children are unsafe are well-documented. Evidence-based decision-making tools are increasingly common in social services provision but few, if any, have used social network data. We analyse a child protection services dataset that includes a network of approximately 5 million social relationships collected by social workers between 1996 and 2016 in New Zealand. We test the potential of information about family networks to improve accuracy of models used to predict the risk of child maltreatment. We simulate integration of the dataset with birth records to construct more complete family network information by including information that would be available earlier if these databases were integrated. Including family network data can improve the performance of models relative to using individual demographic data alone. The best models are those that contain the integrated birth records rather than just the recorded data. Having access to this information at the time a child’s case is first notified to child protection services leads to a particularly marked improvement. Our results quantify the importance of a child’s family network and show that a better understanding of risk can be achieved by linking other commonly available datasets with child protection records to provide the most up-to-date information possible.
Klíčová slova:
Human families – Children – Ethnicities – New Zealand – Decision making – Child abuse – Social welfare
Zdroje
1. Vaithianathan R, Maloney T, Putnam-Hornstein E, Jiang N (2013) Children in the public benefit system at risk of maltreatment: Identification via predictive modeling. American journal of preventive medicine 45: 354–359. doi: 10.1016/j.amepre.2013.04.022 23953364
2. Wilson ML, Tumen S, Ota R, Simmers AG (2015) Predictive modeling: potential application in prevention services. American journal of preventive medicine 48: 509–519. doi: 10.1016/j.amepre.2014.12.003 25794472
3. Maloney T, Jiang N, Putnam-Hornstein E, Dalton E, Vaithianathan R (2017) Black–White differences in child maltreatment reports and foster care placements: A statistical decomposition using linked administrative data. Maternal and child health journal 21: 414–420. doi: 10.1007/s10995-016-2242-3 28124189
4. Vaithianathan R, Putnam-Hornstein E, Jiang N, Nand P, Maloney T (2017) Developing predictive models to support child maltreatment hotline screening decisions: Allegheny County methodology and implementation. Center for Social data Analytics.
5. Chouldechova A, Benavides-Prado D, Fialko O, Vaithianathan R. A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions; 2018. pp. 134–148.
6. Eckerd(2014) Rapid Safety Feedback: Blue Ribbon Commission on Child Projection.
7. Pisano ED, Gatsonis C, Hendrick E, Yaffe M, Baum JK, Acharyya S et al. (2005) Diagnostic performance of digital versus film mammography for breast-cancer screening. New England Journal of Medicine 353: 1773–1783. doi: 10.1056/NEJMoa052911 16169887
8. Billings J, Dixon J, Mijanovich T, Wennberg D (2006) Case finding for patients at risk of readmission to hospital: development of algorithm to identify high risk patients. Bmj 333: 327. doi: 10.1136/bmj.38870.657917.AE 16815882
9. Panattoni LE, Vaithianathan R, Ashton T, Lewis GH (2011) Predictive risk modelling in health: options for New Zealand and Australia. Australian Health Review 35: 45–51. doi: 10.1071/AH09845 21367330
10. Fazel S, Chang Z, Fanshawe T, Långström N, Lichtenstein P, Larsson H et al. (2016) Prediction of violent reoffending on release from prison: derivation and external validation of a scalable tool. The Lancet Psychiatry 3: 535–543. doi: 10.1016/S2215-0366(16)00103-6 27086134
11. Mossman E (2011) Research to validate the New Zealand Police Youth Offending Risk Screening Tool (YORST) Phase II: Predictive ability analysis. Wellington: New Zealand Police. 2011.
12. Bakker L, Riley D, O'Malley J (1999) Risk of reconviction: Statistical models which predict four types of re-offending. Christchurch: Department of Corrections.
13. Macchione N, Wooten W, Yphantides N, Howell JR (2013) Integrated health and human services information systems to enhance population-based and person-centered service. American journal of preventive medicine 45: 373–374. doi: 10.1016/j.amepre.2013.06.001 23953367
14. Keddell E (2014) Current debates on variability in child welfare decision-making: A selected literature review. Social Sciences 3: 916–940.
15. Blank A, Cram F, Dare T, de Haan I, Smith B, Vaithianathan R (2015) Ethical issues for Māori in predictive risk modelling to identify new-born children who are at high risk of future maltreatment.
16. Gillingham P (2006) Risk assessment in child protection: Problem rather than solution? Australian Social Work 59: 86–98.
17. Braverman DW, Doernberg SN, Runge CP, Howard DS (2016) OxRec model for assessing risk of recidivism: ethics. The Lancet Psychiatry 3: 808–809.
18. Gillingham P (2017) Predictive Risk Modelling to Prevent Child Maltreatment: Insights and Implications from Aotearoa/New Zealand. Journal of Public Child Welfare 11: 150–165.
19. Dare T (2015) The ethics of predictive risk modeling. Challenging child protection: New directions in safeguarding children: 64–76.
20. Fluke J, Harden BJ, Jenkins M, Reuhrdanz A (2011) Disparities and disproportionality in child welfare: Analysis of the research.
21. Grove WM, Zald DH, Lebow BS, Snitz BE, Nelson C (2000) Clinical versus mechanical prediction: a meta-analysis. Psychological assessment 12: 19. 10752360
22. Kleinberg J, Lakkaraju H, Leskovec J, Ludwig J, Mullainathan S (2017) Human decisions and machine predictions. The quarterly journal of economics 133: 237–293. doi: 10.1093/qje/qjx032 29755141
23. Keddell E (2015) The ethics of predictive risk modelling in the Aotearoa/New Zealand child welfare context: Child abuse prevention or neo-liberal tool? Critical Social Policy 35: 69–88.
24. de Haan I, Connolly M (2014) Another Pandora's box? Some pros and cons of predictive risk modeling. Children and Youth Services Review 47: 86–91.
25. Shroff R (2017) Predictive Analytics for City Agencies: Lessons from Children's Services. Big data 5: 189–196. doi: 10.1089/big.2016.0052 28829624
26. Fluke JD, Chabot M, Fallon B, MacLaurin B, Blackstock C (2010) Placement decisions and disparities among aboriginal groups: An application of the decision making ecology through multi-level analysis. Child Abuse & Neglect 34: 57–69.
27. Dettlaff AJ, Rivaux SL, Baumann DJ, Fluke JD, Rycraft JR, James J (2011) Disentangling substantiation: The influence of race, income, and risk on the substantiation decision in child welfare. Children and Youth Services Review 33: 1630–1637.
28. Tilbury C (2009) The over‐representation of indigenous children in the Australian child welfare system. International Journal of Social Welfare 18: 57–64.
29. Williams T, Ruru J, Irwin-Easthope H, Quince K, Gifford H (2019) Care and protection of tamariki Māori in the family court system. Te Arotahi Series Paper May 2019–01 Auckland: Ngā Pae o te Māramatanga.
30. Chouldechova A (2017) Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data 5: 153–163. doi: 10.1089/big.2016.0047 28632438
31. Eckhouse L, Lum K, Conti-Cook C, Ciccolini J (2019) Layers of bias: A unified approach for understanding problems with risk assessment. Criminal Justice and Behavior 46: 185–209.
32. Berk R, Heidari H, Jabbari S, Kearns M, Roth A (2018) Fairness in criminal justice risk assessments: The state of the art. Sociological Methods & Research: 0049124118782533.
33. New Zealand Government (2017) Children, Young Persons, and Their Families (Oranga Tamariki) Legislation Act. Wellington, New Zealand.
34. Schwartz DR, Kaufman AB, Schwartz IM (2004) Computational intelligence techniques for risk assessment and decision support. Children and Youth Services Review 26: 1081–1095.
35. Putnam-Hornstein E, Wood JN, Fluke J, Yoshioka-Maxwell A, Berger RP (2013) Preventing severe and fatal child maltreatment: making the case for the expanded use and integration of data. Child welfare 92.
36. Cuccaro-Alamin S, Foust R, Vaithianathan R, Putnam-Hornstein E (2017) Risk assessment and decision making in child protective services: Predictive risk modeling in context. Children and Youth Services Review 79: 291–298.
37. Kinley L, Doolan M (1997) Patterns and reflections: Mehemea. Wellington, NZ: Children, Young Persons and their Families Service.
38. Vaithianathan R, Maloney T, Jiang N, De Haan I, Dale C, Putnam-Hornstein E et al. (2012) Vulnerable children: Can administrative data be used to identify children at risk of adverse outcomes. Report Prepared for the Ministry of Social Development Auckland: Centre for Applied Research in Economics (CARE), Department of Economics, University of Auckland.
39. Gillingham P (2015) Predictive risk modelling to prevent child maltreatment and other adverse outcomes for service users: Inside the ‘black box’of machine learning. The British Journal of Social Work 46: 1044–1058. doi: 10.1093/bjsw/bcv031 27559213
40. Rea D, Erasmus R (2017) Report of the enhancing decision making intake project. Wellington, New Zealand: Ministry of Social Development. 142 p.
41. Stats NZ(2019) Integrated data infrastrucure.
42. Drake B, Jolley JM, Lanier P, Fluke J, Barth RP, Jonson-Reid M (2011) Racial bias in child protection? A comparison of competing explanations using national data. Pediatrics 127: 471–478. doi: 10.1542/peds.2010-1710 21300678
43. Skeem JL, Lowenkamp CT (2016) Risk, race, and recidivism: Predictive bias and disparate impact. Criminology 54: 680–712.
44. Fawcett T (2006) An introduction to ROC analysis. Pattern recognition letters 27: 861–874.
45. Anderson D, Burnham K (2004) Model selection and multi-model inference. Second NY: Springer-Verlag.
46. Milne BJ, Atkinson J, Blakely T, Day H, Douwes J, Gibb S et al. (2019) Data Resource Profile: The New Zealand Integrated Data Infrastructure (IDI). International Journal of Epidemiology.
Článok vyšiel v časopise
PLOS One
2019 Číslo 10
- Metamizol jako analgetikum první volby: kdy, pro koho, jak a proč?
- Nejasný stín na plicích – kazuistika
- Masturbační chování žen v ČR − dotazníková studie
- Těžké menstruační krvácení může značit poruchu krevní srážlivosti. Jaký management vyšetření a léčby je v takovém případě vhodný?
- Fixní kombinace paracetamol/kodein nabízí synergické analgetické účinky
Najčítanejšie v tomto čísle
- Correction: Low dose naltrexone: Effects on medication in rheumatoid and seropositive arthritis. A nationwide register-based controlled quasi-experimental before-after study
- Combining CDK4/6 inhibitors ribociclib and palbociclib with cytotoxic agents does not enhance cytotoxicity
- Experimentally validated simulation of coronary stents considering different dogboning ratios and asymmetric stent positioning
- Risk factors associated with IgA vasculitis with nephritis (Henoch–Schönlein purpura nephritis) progressing to unfavorable outcomes: A meta-analysis