Our projects

Our research projects are all about data analytics for social impact, with most projects focusing on impact evaluation, predictive risk modelling or wellbeing surveys.  Projects are listed from most recent to least recent.

Ethnic Disparities in Child Protection

Location: New Zealand
Timeframe: 2016-2017

Dedicated project page

This study followed the child protection interactions of almost 60,000 New Zealand children from birth until the age of 18. The study is New Zealand’s first cumulative count of child protection encounters by ethnicity.

Rouland, B., Vaithianathan, R., Wilson, D., & Putnam-Hornstein, E. (2019). Ethnic Disparities in Childhood Prevalence of Maltreatment: Evidence from a New Zealand Birth Cohort. Am J Public Health.

This publication co-authored by Rhema Vaithianathan and Benedicte Rouland (CSDA research fellow 2016-2018) aims to document ethnic disparities in childhood abuse and neglect among New Zealand children. By following the 1998 New Zealand birth cohort of 56,904 children through 2016, the authors identify substantial ethnic differences in child maltreatment and child protection involvement. They find that despite long-standing child support policies and reparation for breaches of Indigenous people’s rights, significant child maltreatment disparities persist.

Adverse Childhood Experiences, Protective Factors and School Readiness

Location: New Zealand
Partners: Children and Families Research Fund, New Zealand Ministry of Social Development
Timeframe: 2018-2019

Dedicated project page

This research, funded by the New Zealand’s Ministry of Social Development’s Children and Families Research Fund, looked at the prevalence, school readiness outcomes and protective factors around adverse childhood experiences (ACEs) in New Zealand children. The researchers identified protective factors that may allow some children to experience no ACEs, despite being at heightened risk of experiencing multiple ACEs.  They also found that a child’s performance in cognitive tests at four-and-a-half years of age declined in direct correlation with the number of ACEs they had experienced.

Walsh, M. C., Joyce, S., Maloney, T., & Vaithianathan, R. (2019). Exploring the Protective Factors of Children and Families Identified at Highest Risk of Adverse Childhood Experiences by a Predictive Risk Model: An Analysis of the Growing up in New Zealand Cohort. Children and Youth Services Review, 104556.

The authors explore what protective factors might exist for the families of children identified by a predictive risk model as at high risk of experiencing adverse childhood experiences. Identifying protective factors is an important step in designing preventive services for families as well as helping social workers take a strengths-based approach to these families.  The authors identify 56 factors associated with protective effects against adversities and find that a positive mother-partner relationship helps children at risk of adversities.

Walsh, M.  C., Maloney, T., Vaithianathan,  R., &  Joyce, S.  (2019). Adverse  childhood experiences and school readiness outcomes Results from the Growing up in New Zealand study, Wellington: Ministry of Social Development.

This report describes using Growing Up in New Zealand (GUiNZ) survey instruments to create a measurement of adverse child experiences (ACEs) and correlates this measurement with school readiness outcomes. Statistically significant associations were found between a child’s experience of ACEs and their performance in cognitive tests administered at 54 months. This study was authored by Matthew Walsh (CSDA Senior Research Fellow 2017-2019), Sophie Joyce, Tim Maloney (CSDA Co-Director 2016-2019) and Rhema Vaithianathan and funded by the Children and Families Research Fund (Ministry of Social Development, NZ).

Walsh,  M.  C., Maloney,  T.,  Vaithianathan,  R., &  Joyce,  S. (2019).  Protective  factors of  children  and families at highest risk of adverse childhood experiences:  An analysis of children and families in the Growing up in New Zealand data who “beat the odds” Wellington: Ministry of Social Development.

With increasing access to integrated administrative data, it is easy to identify infants who are likely to suffer childhood adversities. However, many infants who appear “at risk” end up thriving - experiencing few of the adversities that beset other children with similar risk factors. Understanding what helps children “beat the odds” is important for policymakers and frontline services that want to help families at risk. This report authored by Matthew Walsh (CSDA Senior Research Fellow 2017-2019), Tim Maloney (CSDA co-director 2016-2019), Rhema Vaithianathan and Sophie Joyce analyses the Growing Up in New Zealand (GUiNZ) birth cohort to identify protective factors for at-risk children who “beat the odds”.

Walsh, M. C., Joyce, S., Maloney, T., & Vaithianathan, R.  (2019). Adverse childhood experiences and school readiness outcomes:  results from the Growing Up in New Zealand study. The New Zealand medical journal, 132(1493), 15‐24

The Center for Disease Control’s (CDC) Adverse Childhood Experiences (ACEs) have been associated with adverse health consequences in adults and children, but less is known about any association between ACEs and early learning skills. Researchers Matthew Walsh (CSDA Senior Research Fellow 2017-2019), Sophie Joyce, Tim Maloney (CSDA co-director 2016-2019) and Rhema Vaithianathan investigated the relationship between ACEs and objective preschool measures of skills using the Growing up in New Zealand (GUiNZ) cohort study.

Community Perspectives on the Use of Algorithms by Government

Location: New Zealand and United States
Partners: Toi Āria, Massey University (NZ), Carnegie Mellon University (PA, United States), University of Southern California (CA, United States)
Timeframe: 2018-2019

Dedicated project page

A pilot project exploring whether it is possible to create guidelines for trusted use of algorithms informed by the community. Researchers systematically gathered and analysed community concerns and views about the use of algorithms.  The study relied on structured participatory design workshops with professionals and families who have experience of the child welfare systems in New Zealand and the United States.  Seed funding for this project was provided through the Association of Computing Machinery’s Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD) Impact Program for 2018.

Brown, A., Chouldechova, A., Putnam‐Hornstein, E., Tobin, A., & Vaithianathan, R. (2019, April). Toward Algorithmic Accountability in Public Services: A Qualitative Study of Affected Community Perspectives on Algorithmic Decision‐making in Child Welfare Services. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (p. 41). ACM.

Researchers, policy experts, and civil rights groups have all voiced concerns that algorithmic decision-making systems are being deployed without adequate consideration of potential harms, disparate impacts, and public accountability practices. Yet little is known about the concerns of those most likely to be affected by these systems. The authors, including Rhema Vaithianathan report and discuss the findings of workshops conducted to learn about the concerns of affected communities in the context of child welfare services.

Allegheny Family Screening Tool

Location: Allegheny County – PA, United States
Partner/s: Children’s Data Network (University of Southern California), Carnegie Mellon University, Allegheny County Department of Human Services
Timeframe: 2014-ongoing

Dedicated project page

Researchers led by Rhema Vaithianathan modelled, designed and supported implementation of this world-first child welfare predictive analytics tool.  The Allegheny Family Screening Tool (AFST) uses rich administrative data to generate a screening score for incoming calls alleging child maltreatment and neglect. The score is an additional piece of information that helps call screeners as they decide whether to open an investigation.  Allegheny County introduced this decision support tool with the aim of improving accuracy and consistency of call screening decisions. An independent impact evaluation of the tool was completed by researchers at Stanford University in March 2019.  The evaluators’ findings included that use of the tool improved the accurate identification of children in need of services and was associated with a modest reduction in racial disparities in case openings.

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.

The authors, including Rhema Vaithianathan, review the literature and provide a context for predictive risk modelling in the current risk assessment paradigm in child protective services. They describe how predictive analytics or predictive risk modelling using linked administrative data may provide a useful complement to current approaches.

Vaithianathan, R., Jiang, N., Maloney, T., Nand, P., & Putnam-Hornstein, E. (2017). Developing predictive risk models to support child maltreatment hotline screening decisions: Allegheny County methodology and implementation. Auckland: Centre for Social Data Analytics.

This methodology document traces the development and implementation of the Allegheny Family Screening Tool (AFST), a child welfare predictive risk-modelling tool built by a research team led by Rhema Vaithianathan. The AFST is intended to support call screening of child maltreatment referrals by Allegheny County Department of Human Services.  The methodology document includes an overview of existing practice, methodology and performance metrics of the model and details of the external validation and next steps for the model rebuild.

Chouldechova, A., Putnam-Hornstein, E., Benavides-Prado, D., Fialko, O., & Vaithianathan, R. (2018). A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions. Proceedings of Machine Learning Research, 1-15.

Diana Bendavides-Prado, Rhema Vaithianathan, Oleksandr Fialko (CSDA research fellow 2017-2018), Alexandra Chouldechova and Emily Putnam-Hornstein describe their work on developing, validating, fairness auditing, and deploying a risk prediction model in Allegheny County, PA, USA. They discuss the results of their analysis to-date and highlight critical problems and data bias issues that present challenges for model evaluation and deployment.

Vaithianathan, R., Kulick, E,  Putnam-Hornstein, E & Benavides Prado, D. (2019). Allegheny Family Screening Tool:  Methodology, Version 2. Centre for Social Data Analytics. Auckland: Centre for Social Data Analytics.

This methodology report describes changes to the Allegheny Family Screening Tool (AFST), building upon and updating the original methodology report, Developing Predictive Risk Models to Support Child Maltreatment Hotline Screening Decisions (March 2017). Modifications implemented include changes to specific predictor fields used in the model itself, the modelling methodology, and County policies concerning the tool’s use,upholding Allegheny County’s ongoing commitment to transparency by continuing to inform the community about changes to the tool and the County’s policies.

Vaithianathan R, Putnam-Hornstein E, Chouldechova A, Benavides-Prado D, Berger R. Hospital Injury Encounters of Children Identified by a Predictive Risk Model for Screening Child Maltreatment Referrals: Evidence From the Allegheny Family Screening Tool. JAMA Pediatr. Published online August 03, 2020. doi:10.1001/jamapediatrics.2020.2770

This paper confirms that children identified as at risk by the Allegheny Family Screening Tool (AFST), a predictive risk modelling tool that supports child protection decisions in Allegheny County (PA, United States), are also at considerably heightened risk of hospitalisation injury.  The authors, including Rhema Vaithianathan and Diana Benavides Prado, used a one-off linkage between the child protection system and Pittsburgh Children’s Hospital data to show that children identified as at risk by the AFST are indeed at considerably heightened risk of injury, abuse, and self-harm hospitalisation.  The authors also identified a sharply heightened hospitalisation risk for children receiving the highest AFST scores and found that the AFST is particularly sensitive to the risk facing White children, which means the child protection system is potentially under protecting those children.

Key AFST documents including Methodology and Methodology V2, Ethics Report, Frequently Asked Questions and Impact Evaluation (March 2019) along with press coverage are available on the Allegheny Analytics site.

Family Start Impact Evaluation

Location: New Zealand
Partner/s: Ministry of Social Development (NZ)
Timeframe: 2016

Dedicated project page

Researchers completed the first ever impact evaluation of Family Start,  a New Zealand home visiting programme for vulnerable families.  This quasi experimental study estimated that the Family Start  programme: reduced post neonatal sudden unexpected infant death (SUDI), increased the use of health services; increased engagement with early childhood education, increased use of mental health services by mothers; and increased the likelihood of immunisation.

Vaithianathan, R., Wilson, M., Maloney, T., & Baird, S. (2016). The Impact of the Family Start Home Visiting Programme on Outcomes for Mothers and Children: A Quasi-Experimental Study. Ministry of Social Development.

Family Start workers make regular home visits and, using a structured program, seek to improve parenting capability and practice. Researchers including Rhema Vaithianathan and Tim Maloney (CSDA co-director 2016-2019)) evaluate the impact of the Family Start programme in improving outcomes for participating families

Vaithianathan, R., Wilson, M., Maloney, T., & Baird, S. (2017) Family Start Impact Study Selected Extensions Wellington:  Ministry of Social Development.

This report by authors including Rhema Vaithianathan and Tim Maloney (CSDA Co-Director 2016-2019) provides selected extensions to a 2016 quasi-experimental evaluation of the Family Start Home Visiting programme.  The extensions explore the efficacy of Family Start for additional sub-populations of participant families, and when delivered by sub-populations of providers.  Results indicate that Family Start was effective in reducing some measures of post-neonatal infant mortality across sub-groups studied, including teen and non-teen mothers, children in families with and without past contact with Child Youth and Family, and Maori children receiving Family Start from Maori and mainstream providers.

Cram, F., Vette, M., Wilson, M., Vaithianathan, R., Maloney, T., & Baird, S. (2018). He awa whiria—braided rivers: Understanding the outcomes from Family Start for Maori. Evaluation Matters, 165-207.

In Aotearoa, New Zealand, the “braided rivers—he awa whiria” metaphor is facilitating conversations between Māori (indigenous peoples) and non-Māori researchers about the integration of knowledge systems. Rhema Vaithianathan and her co-authors explore how an approach based on he awa whiria can work in practice, in the examination of the efficacy for Māori whānau (families) of the government’s intensive home-visiting programme; Family Start. Published in Evaluation Matters—He Take Tō Te Aromatawai Online First.

Study and Related Documents

Douglas County Decision Aide

Location: Douglas County - CO, United States
Partner/s: Douglas County Department of Human Services, Children’s Data Network (University of Southern California)
Timeframe: 2017-2020

Dedicated project page

In early 2017 researchers developed a prototype child welfare (maltreatment) predictive risk model for Douglas County, Colorado.  The Douglas County Decision Aide is a decision support tool designed to help triage incoming calls alleging child maltreatment and neglect.  The tool uses data from child welfare and public welfare eligibility systems.  The DCDA was implemented by Douglas County leadership as a year-long randomised controlled trial in February 2019, with results on the impact of the tool expected in mid-2020.

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.

The authors, including Rhema Vaithianathan, review the literature and provide a context for predictive risk modelling in the current risk assessment paradigm in child protective services. They describe how predictive analytics or predictive risk modelling using linked administrative data may provide a useful complement to current approaches.

Vaithianathan, R., Jiang, N., Maloney, T., Nand, P., & Putnam-Hornstein, E. (2017). Develping predictive risk models to support child maltreatment hotline screening decisions: Allegheny County methodology and implementation. Auckland: Centre for Social Data Analytics.

This methodology document traces the development and implementation of the Allegheny Family Screening Tool (AFST), a child welfare predictive risk-modelling tool built by a research team led by Rhema Vaithianathan. The AFST is intended to support call screening of child maltreatment referrals by Allegheny County Department of Human Services.  The methodology document includes an overview of existing practice, methodology and performance metrics of the model and details of the external validation and next steps for the model rebuild.

Chouldechova, A., Putnam-Hornstein, E., Benavides-Prado, D., Fialko, O., & Vaithianathan, R. (2018, January). A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions. In Conference on Fairness, Accountability and Transparency (pp. 134-148).

Diana Bendavides-Prado, Rhema Vaithianathan, Oleksandr Fialko (CSDA research fellow 2017-2018), Alexandra Chouldechova and Emily Putnam-Hornstein describe their work on developing, validating, fairness auditing, and deploying a risk prediction model in Allegheny County, PA, USA. They discuss the results of their analysis to-date and highlight critical problems and data bias issues that present challenges for model evaluation and deployment.

Vaithianathan, R., Dinh, H., Kalisher, A., Kithulgoda, C. I., Kulick, E., Mayur, M., … Putnam-Hornstein, E. (December 2019). Implementing a Child Welfare Decision Aide in Douglas County: Methodology Report. Centre for Social Data Analytics.

This report summarises the development and implementation of the Douglas County Decision Aide (DCDA), a child welfare predictive risk-modelling tool built and deployed by the Centre for Social Data Analytics. The DCDA is intended to support call screening of child maltreatment referrals made to the Douglas County Department of Human Services (CO, United States). The methodology outlines how Douglas County was triaging referrals prior to the deployment of the DCDA and how the DCDA has changed that, as well as details of how the DCDA tool was built and validated and how ethical concerns were identified and responded to.

California Child Welfare Predictive Risk Model Proof of Concept

Location: California, United States
Partner/s: Led by Children’s Data Network together with California Child Welfare Indicators Project, California Department of Social Services, California Department of Public Health, County Child Welfare departments in Los Angeles, Monterey and San Francisco, CSDA
Timeframe: 2016-June 2018

Dedicated project page

Modelled closely on the Allegheny Family Screening Tool, in 2016 Vaithianathan and Putnam-Hornstein also began PRM work in California. Given that the state does not have an integrated data system, the goal of this project was to assess whether a PRM built exclusively from child protection records could approach the accuracy of the AFST. This proof-of-concept also included comparisons between scores generated through a PRM and risk levels assigned through the use of the SDM® Family Risk Assessment tool (i.e., “SDM® risk tool”) which has been in use in California since 1998. Findings indicated that the PRM was more accurate than the SDM® risk tool in identifying children who would have chronic or intensive involvement with the child protection system. The state is currently developing implementation plans with county stakeholders.

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(3), 354-359.

This paper co-authored by Rhema Vaithianathan and Tim Maloney (CSDA co-director 2016-2019) outlines the method of using an automated predictive risk model to identify children at high risk of maltreatment, as well as discussing the strategic targeting of prevention activities toward these individuals.

Cuccaro-Alamin, S., Foust, R., Vaithianathan, R., & Putnam-Hornstein, E. (2017). “Risk assessment and decision making in child protective services: Predictive risk modelling in context”. Children and Youth Services Review 79

The authors, including Rhema Vaithianathan, review the literature and provide a context for predictive risk modelling in the current risk assessment paradigm in child protective services. They describe how predictive analytics or predictive risk modelling using linked administrative data may provide a useful complement to current approaches.

Chouldechova, A., Putnam-Hornstein, E., Benavides-Prado, D., Fialko, O., & Vaithianathan, R. (2018). A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions. Proceedings of Machine Learning Research, 1-15.

Diana Bendavides-Prado, Rhema Vaithianathan, Oleksandr Fialko (CSDA research fellow 2017-2018), Alexandra Chouldechova and Emily Putnam-Hornstein describe their work on developing, validating, fairness auditing, and deploying a risk prediction model in Allegheny County, PA, USA. They discuss the results of their analysis to-date and highlight critical problems and data bias issues that present challenges for model evaluation and deployment.

Predictive Risk Modeling: Practical Considerations, Children’s Data Network

Teen Parent Units Impact Evaluation

Location: New Zealand
Partner/s: Ministry of Social Development (NZ)
Timeframe: 2016-2017

Dedicated project page

This study evaluated a unique intervention in New Zealand where some teenage mothers have the opportunity to receive their schooling at a Teen Parent Unit (TPU). These TPUs are specifically designed for teenagers who are pregnant or already parents.  Evaluation of this intervention helps to establish whether poor schooling outcomes for teen mothers can be offset through schooling opportunities that are designed around the needs of young mothers.  Researchers found that teen mothers with access to TPUs had better educational outcomes than those with no access. This research was conducted through the linkage of social sector administrative data.

Vaithianathan, R., Maloney, T., Wilson, M., Staneva, A., & Jiang, N. (2017). Impact of School-based Support on Educational Outcomes of Teen-mothers: Evidence from New Zealand’s “teen Parent Units”. Ministry of Social Development.

Researchers including Rhema Vaithianathan and Tim Maloney (CSDA Co-Director 2016-2019) found New Zealand teenage mothers with access to Teen Parent Units (TPUs) had better educational outcomes than those with no access. This research was conducted through the linkage of social sector administrative data.

Social Workers in Schools Impact Evaluation

Location: New Zealand
Partner/s: Ministry of Social Development (NZ)
Timeframe: 2016-2018

Dedicated project page

This study was a preliminary investigation of the impact of the government-funded Social Workers in Schools service (SWiS) which is available in selected New Zealand primary and intermediate schools. The investigation used the Integrated Child Dataset(ICD)-linked data that foreshadowed the child-level data linkages now available through the Statistics New Zealand Integrated Data Infrastructure(IDI). Researchers identified positive effects for some student groups from the SWiS programme. This research was supported by funding from the Ministry of Social Development (MSD) and in-kind contributions from Auckland University of Technology (AUT) and MSD.

Jiang, N., Maloney, T., Staneva, A., Wilson, M., & Vaithianathan, R. (2018). The impact of Social Workers in Schools: A preliminary investigation using linked administrative data.

This paper co-authored by Rhema Vaithianathan and Tim Maloney (CSDA co-director 2016-2019) reports on a preliminary investigation of the impact of the government-funded Social Workers in Schools service (SWiS) which is available in selected primary and intermediate schools. The aim of SWiS is to see safe, healthy and socialised children with a strong sense of identity, who are fully engaged in school. This analysis compares outcomes for children who attended SWiS schools with similar children who attended similar schools that at the time were not part of the SWiS programme. The study finds evidence of a reduction in non-enrolment days, a 5% increase for girls attaining NCEA level 1 by their 16th birthday, and a reduction in CYF youth justice referrals for the first three years of high school.

Alerta Ninez  Child Welfare Predictive Risk Model (Proof of Concept)

Location: Chile
Partner/s: Ministry of Social Development and Family (Chile), Adolfo Ibáñez University
Timeframe:
2018-2019

Dedicated project page

In November 2018, a consortium of researchers from the Adolfo Ibáñez University and CSDA began a three-month contract with Chile’s Ministry of Social Development (now the Ministry of Social Development and Family). This study explored the feasibility of a predictive risk modelling tool to provide an early alert system to help identify and provide services to children who are at heightened risk of adverse outcomes.  The consortium delivered three reports including a proof of concept predictive risk model.

Hello Baby Program - Proactive Child Welfare Predictive Risk Model

Location: Allegheny County – PA, United States
Partner/s: Allegheny County Department of Human Services, Children’s Data Network (University of Southern California)
Timeframe: 2018-

Dedicated project page

Allegheny County’s Hello Baby Program is intended to provide every family of a new-born universal access to information and resources and differentiated and intensive support for families with complex challenges and needs. The CSDA research team was engaged by Allegheny County Department of Human Services to develop a screening tool that would allow for identification of families with highest needs.  The agency will contract externally for programmatic intervention and independent evaluation.

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(3), 354-359.

This paper co-authored by Rhema Vaithianathan and Tim Maloney (CSDA co-director 2016-2019) outlines the method of using an automated predictive risk model to identify children at high risk of maltreatment, as well as discussing the strategic targeting of prevention activities toward these individuals.

Vaithianathan, R., Rouland, B., & Putnam-Hornstein, E. (2018). Injury and mortality among children identified as at high risk of maltreatment. Pediatrics, 141(2), e20172882.

This study by Rhema Vaithianathan, Bénédicte Rouland (CSDA research fellow 2016-2018) and Emily Putnam-Hornstein explored a model which assigned risk scores for the risk of a substantiated finding of maltreatment to children born in New Zealand in 2010, to see whether children at the highest 10% and 20% of risk would have an elevated chance of injury or death in early childhood. The study found that children assessed as being “very high risk” (highest 10%) and “high-risk” (highest 20%) had 4.8 times and 4.2 times respectively higher post-neonatal mortality rates than other children.

Vaithianathan, Rhema; Diana Benavides-Prado and Emily Putnam-Hornstein. Implementing the Hello Baby Prevention Program in Allegheny County. Centre for Social Data Analytics. Auckland, New Zealand. September 2020.

This report summarises the development and implementation of the Hello Baby program which is supported by a predictive risk modelling (PRM) tool, built and deployed by the Centre for Social Data Analytics. Hello Baby is a new universal tiered program designed to prevent harm and protect children, introduced as a pilot program by the Allegheny County Department of Human Services in September 2020.  The County will use the PRM tool to establish eligibility for higher intensity services, alongside self-referral and professional referral. The methodology provides background to the introduction of Hello Baby, explains the selection of the PRM tool as an eligibility pathway, describes how historical data was used to train and validate the PRM tool and explains how the tool will be used as part of the implemented Hello Baby program. Additional documentation about the Hello Baby program including ethics reports and FAQs is found on the Allegheny County website.

MyDay Survey with Health Education England, Working Across Wessex

Location: Wessex, England
Partner/s: Health Education England, Working Across Wessex, University of Southampton, City, University of London, University of Zurich
Timeframe: 2017-2018

Dedicated project page

Researchers used MyDay, a purpose-built web-based survey tool, to collect anonymous data from up to 2100 junior doctors about their wellbeing at work.  Participants listed tasks they did in five short windows of time, and how they felt doing each task.  The research set out to provide a rich and useful picture of the experienced wellbeing of this key workforce.

MyDay Survey with Warren and Mahoney

Location: Auckland, New Zealand
Partner/s: Warren and Mahoney
Timeframe: 2018

Dedicated project page

CSDA researchers partnered with an Australasian architectural practice to explore the feasibility of combining a survey on workplace wellbeing with location data, to understand the impact of physical spaces on wellbeing at work.

An Ethical Framework for Social Policy Applications of Predictive Analytics

Location: Auckland, New Zealand
Partner/s: University of Auckland (NZ)
Timeframe: 2018-2020

Dedicated project page

The objective of this research is to identify, analyse, and respond to ethical issues generated by social policy uses of predictive analytics.   Outputs will include a set of practical guidelines for the ethical use of predictive analytics in social policy contexts, based on the researchers’ experience in policy applications of predictive analytics and detailed research including consultation with policy makers.  This research is funded by a 2017 NZ Royal Society Marsden Fund Grant.  Rhema Vaithianathan is an associate investigator on this project - the principal investigator is Professor Tim Dare (University of Auckland, New Zealand).

Homelessness Predictive Risk Model for Allegheny County, US

Location: Allegheny County, PA, United States
Partner/s: Allegheny County Department of Human Services
Timeframe: 2017-

Dedicated project page

CSDA researchers used rich administrative data to build a predictive risk modelling tool to support the prioritisation of clients for longer term housing programs in Allegheny County, Pennsylvania (United States). The Allegheny Housing Assessment (AHA) was deployed by Allegheny County in September 2020. The CSDA research team continues to work with the County and partners to fine tune the model and the County is planning to issue a solicitation for an independent evaluation of the AHA in 2021.

Documentation for the AHA includes the Methodology, Frequently Asked Questions, Report on Client Focus Groups, Independent Ethical/Data Science Review and Allegheny County’s Response to the Ethical/Data Science Review.

  • The Allegheny Homelessness Tool (August 2019) - a poster presented at the Bloomberg Data for Good Exchange (D4GX) in New York, September 2019.
  • Using Predictive Risk Modeling to Prioritize Services for People Experiencing Homelessness in Allegheny County. Centre for Social Data Analytics. Auckland, New Zealand. September 2020.
    This report summarises the development and implementation of the Allegheny Housing Assessment, a predictive risk modelling tool, built and deployed by the Centre for Social Data Analytics for the Allegheny County Department of Human Services, Pennsylvania, United States. The AHA will be used to support the prioritisation of people for longer term housing programs. A prioritisation tool is needed because the demand for housing exceeds supply. The AHA replaces the VI-SPDAT, a prioritisation system without local validation. The methodology provides background to the development of the AHA including a comparison between the AHA and the previously used VI-SPDAT. The methodology provides background on homeless services in Allegheny County and the decision to explore the use of a predictive risk modelling tool, before discussing elements including community engagement, the methodology used to develop the tool, accuracy and the business process it will be used within.

Understanding the factors influencing wellbeing among Indigenous youth in the Northern Territory

Location: Australia
Partner/s: University of Sydney, Melbourne Institute of Applied Economic and Social Research, University of Western Australia, Menzies School of Health Research, University of Melbourne, Danila Dilba Health Service (Darwin), Centre for Remote Health, Massey University (NZ)
Timeframe: 2018-2023

Dedicated project page

This mixed-methods project studies the factors that influence social and emotional wellbeing (SEWB) of Indigenous youth in the Northern Territory. It uses administrative data and econometric techniques to identify communities where youth have higher or lower SEWB than expected. These locations are then visited to ask Indigenous youth about their perspective on SEWB (giving them a voice). Research-to-practice translation will focus on cultural safety, respect and benefits for Indigenous youth. Rhema Vaithianathan is an associate investigator on this project.  The chief investigator is Associate Professor Stephanie Schurer (University of Sydney).  This five-year project is funded via the National Health and Medical Research Council (NHMRC) Targeted Call for Research.

Our publications

Read our latest reports and academic papers.

List of publications