Clustering of health, crime and social-welfare inequality in 4 million citizens from two nations

Abstract

Health and social scientists have documented the hospital revolving-door problem, the concentration of crime, and long-term welfare dependence. Have these distinct fields identified the same citizens? Using administrative databases linked to 1.7 million New Zealanders, we quantified and monetized inequality in distributions of health and social problems and tested whether they aggregate within individuals. Marked inequality was observed: Gini coefficients equalled 0.96 for criminal convictions, 0.91 for public-hospital nights, 0.86 for welfare benefits, 0.74 for prescription-drug fills and 0.54 for injury-insurance claims. Marked aggregation was uncovered: a small population segment accounted for a disproportionate share of use-events and costs across multiple sectors. These findings were replicated in 2.3 million Danes. We then integrated the New Zealand databases with the four-decade-long Dunedin Study. The high-need/high-cost population segment experienced early-life factors that reduce workforce readiness, including low education and poor mental health. In midlife they reported low life satisfaction. Investing in young people’s education and training potential could reduce health and social inequalities and enhance population wellbeing.

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Fig. 1: Nationwide data capture of poor health, crime and social-welfare dependency in 1.7 million New Zealanders.
Fig. 2: Inequality in the distributions of poor health, crime and social welfare.
Fig. 3: Impact of high-need/high-cost users.
Fig. 4: Aggregation of poor health, crime and social-welfare dependency.
Fig. 5: Replication in Danish nationwide registers linked to 2.3 million citizens.
Fig. 6: Characterizing high-need users.

Data availability

The NZIDI and Danish register data cannot be shared by the authors. Researchers who wish to use the NZIDI data must submit an application through Statistics New Zealand. Researchers who wish to use the Danish register data must request permission through the Danish Data Protection Agency. The Dunedin Study data are not publicly available as informed consent and ethical approval for public data-sharing were not obtained from participants. The data are available on request by qualified scientists. Requests require a concept paper describing the purpose of data access, ethical approval at the applicant’s institution and provision for secure data access. We offer secure access on the Duke University, Otago University and King’s College London campuses.

Code availability

Custom code that supports the findings of this study in the NZIDI is provided in the supplementary information. Custom code that supports the findings of this study in the Danish nationwide registers and the Dunedin Longitudinal Study is available from the corresponding author on request.

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Acknowledgements

Supported by grants from the National Institute on Aging (Nos. AG032282, AG049789 and P30AG034424), the National Institute of Child Health and Human Development (NICHD; No. HD077482), the UK Medical Research Council (Nos. P005918 and G1002190), the Jacobs Foundation and the Avielle Foundation. The Dunedin Multidisciplinary Health and Development Research Unit is supported by the New Zealand Health Research Council and the New Zealand Ministry of Business, Innovation and Employment (MBIE). L.S.R.-R. was supported by a postdoctoral fellowship from the NICHD (T32-HD007376) through the Frank Porter Graham Child Development Institute at the University of North Carolina at Chapel Hill. S.H.A. was supported by the Rockwool Foundation. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank A. O’Rand. We thank the Statistics New Zealand Methods Team for their assistance and the Public Policy Institute at the University of Auckland for access to their Statistics New Zealand Data Lab. The results in this paper are not official statistics. They have been created for research purposes from the Integrated Data Infrastructure (IDI) managed by Statistics New Zealand. The opinions, findings, recommendations and conclusions expressed in this paper are those of the authors, not Statistics NZ. Access to the anonymized data used in this study was provided by Statistics NZ under the security and confidentiality provisions of the Statistics Act 1975. Only people authorized by the Statistics Act 1975 are allowed to see data about a particular person, household, business or organization and the results in this paper have been confidentialized to protect these groups from identification and to keep their data safe. Careful consideration has been given to the privacy, security and confidentiality issues associated with using administrative and survey data in the IDI. Further detail can be found in the privacy impact assessment for the IDI available from www.stats.govt.nz.

Author information

L.S.R.-R., A.C., B.J.M. and T.E.M. designed the research. L.S.R.-R., S.H.A., S.H., R.P., S.R., A.C., B.J.M. and T.E.M. performed research, L.S.R.-R., S.D.S., S.H.A., R.M.H. and B.J.M. analysed data and L.S.R.-R., A.C. and T.E.M. wrote the paper. All authors reviewed drafts, provided critical feedback and approved the final manuscript.

Correspondence to Leah S. Richmond-Rakerd.

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Supplementary information

Supplementary Information

Supplementary Methods, Supplementary Results, Supplementary Tables 1–11 and Supplementary References.

Reporting Summary

Supplementary Software

Statistical code used for analyses of the New Zealand nationwide administrative data.

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Richmond-Rakerd, L.S., D’Souza, S., Andersen, S.H. et al. Clustering of health, crime and social-welfare inequality in 4 million citizens from two nations. Nat Hum Behav (2020). https://doi.org/10.1038/s41562-019-0810-4

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