1932

Abstract

Population-level administrative data—data on individuals’ interactions with administrative systems (e.g., health, criminal justice, and education)—have substantially advanced our understanding of life-course development. In this review, we focus on five areas where research using these data has made significant contributions to developmental science: () understanding small or difficult-to-study populations, () evaluating intergenerational and family influences, () enabling estimation of causal effects through natural experiments and regional comparisons, () identifying individuals at risk for negative developmental outcomes, and () assessing neighborhood and environmental influences. Further advances will be made by linking prospective surveys to administrative data to expand the range of developmental questions that can be tested; supporting efforts to establish new linked administrative data resources, including in developing countries; and conducting cross-national comparisons to test findings’ generalizability. New administrative data initiatives should involve consultation with population subgroups including vulnerable groups, efforts to obtain social license, and strong ethical oversight and governance arrangements.

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2022-12-09
2024-04-26
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Literature Cited

  1. Adelman M, Haimovich F, Ham A, Vazquez E. 2018. Predicting school dropout with administrative data: new evidence from Guatemala and Honduras. Educ. Econ. 26:4356–72
    [Google Scholar]
  2. Agerbo E, Nordentoft M, Mortensen PB. 2002. Familial, psychiatric, and socioeconomic risk factors for suicide in young people: nested case-control study. BMJ 325:735574
    [Google Scholar]
  3. Ahn E, Gil Y, Putnam-Hornstein E. 2021. Predicting youth at high risk of aging out of foster care using machine learning methods. Child Abuse Negl 117:105059
    [Google Scholar]
  4. Airaksinen J, Aaltonen M, Tarkiainen L, Martikainen P, Latvala A. 2021. Associations of neighborhood disadvantage and offender concentration with criminal behavior: between-within analysis in Finnish registry data. J. Crim. Justice 74:101813
    [Google Scholar]
  5. al-Haddad BJS, Jacobsson B, Chabra S, Modzelewska D, Olson EM et al. 2019. Long-term risk of neuropsychiatric disease after exposure to infection in utero. JAMA Psychiatry 76:6594–602
    [Google Scholar]
  6. Allen J, Adams C, Flack F. 2019. The role of data custodians in establishing and maintaining social licence for health research. Bioethics 33:4502–10
    [Google Scholar]
  7. Almond D, Edlund L, Palme R. 2009. Chernobyl's subclinical legacy: prenatal exposure to radioactive fallout and school outcomes in Sweden. Q. J. Econ. 124:41729–72
    [Google Scholar]
  8. Andersen SH. 2021. Association of youth age at exposure to household dysfunction with outcomes in early adulthood. JAMA Netw. Open 4:1e2032769
    [Google Scholar]
  9. Andersen SH, Richmond-Rakerd LS, Moffitt TE, Caspi A. 2021. Nationwide evidence that education disrupts the intergenerational transmission of disadvantage. PNAS 118:31e2103896118
    [Google Scholar]
  10. Antonsen S, Mok PLH, Webb RT, Mortensen PB, McGrath JJ et al. 2020. Exposure to air pollution during childhood and risk of developing schizophrenia: a national cohort study. Lancet Planet. Health 4:2E64–73
    [Google Scholar]
  11. Asher S, Novosad P, Rafkin C. 2021. Intergenerational mobility in India: new methods and estimates across time, space, and communities. Work. Pap. 66 Gend. Growth Labour Mark. Low Income Ctries. Programme Bonn, Ger.: https://g2lm-lic.iza.org/publications/wp/intergenerational-mobility-in-india-new-methods-and-estimates-across-time-space-and-communities/
  12. Atatoa-Carr P, Paine S-J, Prickett K. 2021. Ethical considerations of the use of child data in the IDI Ethics Note, Health Res. Counc. N. Z. Grafton, New Zealand: https://mcusercontent.com/57af16fa15f95ed83e0b434a9/files/f44c70c4-f187-2423-bed6-9e5fc8826157/Ethics_Notes_Atatoa_Carr_et_el_ed.pdf
  13. Ballantyne A, Stewart C. 2019. Big data and public-private partnerships in healthcare and research: the application of an ethics framework for big data in health and research. Asian Bioeth. Rev. 11:3315–26
    [Google Scholar]
  14. Belsher BE, Smolenski DJ, Pruitt LD, Bush NE, Beech EH et al. 2019. Prediction models for suicide attempts and deaths: a systematic review and simulation. JAMA Psychiatry 76:6642–51
    [Google Scholar]
  15. Bennedsen BE, Mortensen PB, Olesen AV, Henriksen TB, Frydenberg M. 2001. Obstetric complications in women with schizophrenia. Schizophr. Res. 47:2–3167–75
    [Google Scholar]
  16. Berg V, Kuja-Halkola R, D'Onofrio BM, Lichtenstein P, Latvala A 2021. Parental substance misuse and reproductive timing in offspring: a genetically informed study. Evol. Hum. Behav. 42:2157–64
    [Google Scholar]
  17. Bertrand M, Mogstad M, Mountjoy J. 2021. Improving educational pathways to social mobility: evidence from Norway's Reform 94. J. Labor Econ. 39:4965–1010
    [Google Scholar]
  18. Bowden N, Gibb S, Thabrew H, Kokaua J, Audas R et al. 2020. Case identification of mental health and related problems in children and young people using the New Zealand Integrated Data Infrastructure. BMC Med. Inform. Decis. Mak. 20:142
    [Google Scholar]
  19. Bu F, Zaninotto P, Fancourt D. 2020. Longitudinal associations between loneliness, social isolation and cardiovascular events. Heart 106:181394–99
    [Google Scholar]
  20. Cannon TD, Cornblatt B, McGorry P. 2007. Editor's introduction: the empirical status of the ultra high-risk (prodromal) research paradigm. Schizophr. Bull. 33:3661–64
    [Google Scholar]
  21. Cantor-Graae E, Pedersen CB. 2013. Full spectrum of psychiatric disorders related to foreign migration: a Danish population-based cohort study. JAMA Psychiatry 70:4427–35
    [Google Scholar]
  22. Cantor-Graae E, Pedersen CB, McNeil TF, Mortensen PB. 2003. Migration as a risk factor for schizophrenia: a Danish population-based cohort study. Br. J. Psychiatry 182:117–22
    [Google Scholar]
  23. Carroll SR, Akee R, Chung P, Cormack D, Kukutai T et al. 2021. Indigenous peoples’ data during COVID-19: from external to internal. Front. Sociol. 6:617895
    [Google Scholar]
  24. Carter P, Laurie GT, Dixon-Woods M. 2015. The social licence for research: why care.data ran into trouble. J. Med. Ethics 41:5404–9
    [Google Scholar]
  25. Cesarini D, Lindqvist E, Östling R, Wallace B 2016. Wealth, health, and child development: evidence from administrative data on Swedish lottery players. Q. J. Econ. 131:2687–738
    [Google Scholar]
  26. Cesta CE, Öberg AS, Ibrahimson A, Yusuf I, Larsson H et al. 2020. Maternal polycystic ovary syndrome and risk of neuropsychiatric disorders in offspring: prenatal androgen exposure or genetic confounding?. Psychol. Med. 50:4616–24
    [Google Scholar]
  27. Class QA, Abel KM, Khashan AS, Rickert ME, Dalman C et al. 2014. Offspring psychopathology following preconception, prenatal and postnatal maternal bereavement stress. Psychol. Med. 44:171–84
    [Google Scholar]
  28. Coley RY, Johnson E, Simon GE, Cruz M, Shortreed SM 2021. Racial/ethnic disparities in the performance of prediction models for death by suicide after mental health visits. JAMA Psychiatry 78:7726–34
    [Google Scholar]
  29. Coyne CA, Fontaine NMG, Långström N, Lichtenstein P, D'Onofrio BM 2013. Teenage childbirth and young adult criminal convictions: a quasi-experimental study of criminal outcomes for teenage mothers. J. Crim. Justice 41:5318–23
    [Google Scholar]
  30. Damm AP, Dustmann C. 2014. Does growing up in a high crime neighborhood affect youth criminal behavior?. Am. Econ. Rev. 104:61806–32
    [Google Scholar]
  31. De Wolf R, Vanden Abeele MMP. 2020. Editorial: children's voices on privacy management and data responsibilization. Media Commun 8:4158–62
    [Google Scholar]
  32. Desai T, Ritchie F, Welpton R 2016. Five Safes: Designing Data Access for Research Bristol, UK: Univ. West Engl.
  33. D'Onofrio BM, Rickert ME, Frans E, Kuja-Halkola R, Almqvist C et al. 2014. Paternal age at childbearing and offspring psychiatric and academic morbidity. JAMA Psychiatry 71:4432–38
    [Google Scholar]
  34. D'Onofrio BM, Sjölander A, Lahey BB, Lichtenstein P, Öberg AS. 2020. Accounting for confounding in observational studies. Annu. Rev. Clin. Psychol. 16:25–48
    [Google Scholar]
  35. D'Onofrio BM, Sujan AC. 2017. Maternal antidepressant use and pregnancy outcomes. JAMA 318:7666–67
    [Google Scholar]
  36. Donovan GH, Michael YL, Gatziolis D, ’t Mannetje A, Douwes J. 2019. Association between exposure to the natural environment, rurality, and attention-deficit hyperactivity disorder in children in New Zealand: a linkage study. Lancet Planet. Health 3:5E226–34
    [Google Scholar]
  37. Drew C. 2018. Design for data ethics: using service design approaches to operationalize ethical principles on four projects. Philos. Trans. R. Soc. A 376:212820170353
    [Google Scholar]
  38. D'Souza S, Bowden N, Gibb S, Shackleton N, Audas R et al. 2020. Medication dispensing for attention-deficit/hyperactivity disorder to New Zealand youth. N. Z. Med. J. 133:152284–95
    [Google Scholar]
  39. Dunning T. 2012. Natural Experiments in the Social Sciences: A Design-Based Approach Cambridge, UK: Cambridge Univ. Press
  40. Ejlskov L, Wulff JN, Kalkbrenner A, Ladd-Acosta C, Fallin MD et al. 2021. Prediction of autism risk from family medical history data using machine learning: a national cohort study from Denmark. Biol. Psychiatry Glob. Open Sci. 1:2156–64
    [Google Scholar]
  41. Engemann K, Pedersen CB, Arge L, Tsirogiannis C, Mortensen PB, Svenning J-C. 2018. Childhood exposure to green space—a novel risk-decreasing mechanism for schizophrenia?. Schizophr. Res. 199:142–48
    [Google Scholar]
  42. Engemann K, Pedersen CB, Arge L, Tsirogiannis C, Mortensen PB, Svenning J-C. 2019. Residential green space in childhood is associated with lower risk of psychiatric disorders from adolescence into adulthood. PNAS 116:115188–93
    [Google Scholar]
  43. Engemann K, Svenning J-C, Arge L, Brandt J, Erikstrup C et al. 2020a. Associations between growing up in natural environments and subsequent psychiatric disorders in Denmark. Environ. Res. 188:109788
    [Google Scholar]
  44. Engemann K, Svenning J-C, Arge L, Brandt J, Geels C et al. 2020b. Natural surroundings in childhood are associated with lower schizophrenia rates. Schizophr. Res. 216:488–95
    [Google Scholar]
  45. Erlangsen A, Drefahl S, Haas A, Bjorkenstam C, Nordentoft M, Andersson G. 2020. Suicide among persons who entered same-sex and opposite-sex marriage in Denmark and Sweden, 1989–2016: a binational, register-based cohort study. J. Epidemiol. Commun. Health 74:178–83
    [Google Scholar]
  46. Erlangsen A, Eaton WW, Mortensen PB, Conwell Y. 2012. Schizophrenia—a predictor of suicide during the second half of life?. Schizophr. Res. 134:2–3111–17
    [Google Scholar]
  47. Falcão IR, Ribeiro-Silva RdC, de Almeida MF, Fiaccone RL, Silva NJ et al. 2021. Factors associated with small- and large-for-gestational-age in socioeconomically vulnerable individuals in the 100 Million Brazilian Cohort. Am. J. Clin. Nutr. 114:1109–16
    [Google Scholar]
  48. Fallesen P, Wildeman C. 2015. The effect of medical treatment of attention deficit hyperactivity disorder (ADHD) on foster care caseloads. J. Health Soc. Behav. 56:3398–414
    [Google Scholar]
  49. Ford E, Rooney P, Oliver S, Hoile R, Hurley P et al. 2019. Identifying undetected dementia in UK primary care patients: a retrospective case-control study comparing machine-learning and standard epidemiological approaches. BMC Med. Inform. Decis. Mak. 19:1248
    [Google Scholar]
  50. Frans EM, McGrath JJ, Sandin S, Lichtenstein P, Reichenberg A et al. 2011. Advanced paternal and grandpaternal age and schizophrenia: a three-generation perspective. Schizophr. Res. 133:1–3120–24
    [Google Scholar]
  51. Frans EM, Sandin S, Reichenberg A, Långström N, Lichtenstein P et al. 2013. Autism risk across generations: a population-based study of advancing grandpaternal and paternal age. JAMA Psychiatry 70:5516–21
    [Google Scholar]
  52. Frei P, Poulsen AH, Mezei G, Pedersen C, Cronberg Salem L et al. 2013. Residential distance to high-voltage power lines and risk of neurodegenerative diseases: a Danish population-based case-control study. Am. J. Epidemiol. 177:9970–78
    [Google Scholar]
  53. Funk MJ, Landi SN. 2014. Misclassification in administrative claims data: quantifying the impact on treatment effect estimates. Curr. Epidemiol. Rep. 1:4175–85
    [Google Scholar]
  54. Gibb S, Milne B, Shackleton N, Taylor BJ, Audas R. 2019. How universal are universal preschool health checks? An observational study using routine data from New Zealand's B4 School Check. BMJ Open. 9:4e025535
    [Google Scholar]
  55. Ginsberg Y, D'Onofrio BM, Rickert ME, Class QA, Rosenqvist MA et al. 2019. Maternal infection requiring hospitalization during pregnancy and attention-deficit hyperactivity disorder in offspring: a quasi-experimental family-based study. J. Child Psychol. Psychiatry 60:2160–68
    [Google Scholar]
  56. Gradus JL, Rosellini AJ, Horváth-Puhó E, Street AE, Galatzer-Levy I et al. 2020. Prediction of sex-specific suicide risk using machine learning and single-payer health care registry data from Denmark. JAMA Psychiatry 77:12534
    [Google Scholar]
  57. Grusky DB, Hout M, Smeeding TM, Snipp CM. 2019. The American Opportunity Study: a new infrastructure for monitoring outcomes, evaluating policy, and advancing basic science. RSF Russell Sage Found. . J. Soc. Sci. 5:220–39
    [Google Scholar]
  58. Hakulinen C, McGrath JJ, Timmerman A, Skipper N, Mortensen PB et al. 2019. The association between early-onset schizophrenia with employment, income, education, and cohabitation status: nationwide study with 35 years of follow-up. Soc. Psychiatry Psychiatr. Epidemiol. 54:111343–51
    [Google Scholar]
  59. Hamad AF, Walld R, Lix LM, Urquia ML, Roos LL, Wall-Wieler E. 2022. Data resource profile: The Manitoba Multigenerational Cohort. Int. J. Epidemiol. 51:3E65–72
    [Google Scholar]
  60. Hamm NC, Hamad AF, Wall-Wieler E, Roos LL, Plana-Ripoll O, Lix LM. 2021. Multigenerational health research using population-based linked databases: an international review. Int. J. Popul. Data Sci. 6:11686
    [Google Scholar]
  61. Hand DJ. 2018. Aspects of data ethics in a changing world: where are we now?. Big Data 6:3176–90
    [Google Scholar]
  62. Harden KP, Domingue BW, Belsky DW, Boardman JD, Crosnoe R et al. 2020. Genetic associations with mathematics tracking and persistence in secondary school. npj Sci. Learn. 5:11
    [Google Scholar]
  63. Harron K, Dibben C, Boyd J, Hjern A, Azimaee M et al. 2017a. Challenges in administrative data linkage for research. Big Data Soc 4:2 https://doi.org/10.1177/2053951717745678
    [Crossref] [Google Scholar]
  64. Harron K, Doidge JC, Goldstein H. 2020. Assessing data linkage quality in cohort studies. Ann. Hum. Biol. 47:2218–26
    [Google Scholar]
  65. Harron KL, Doidge JC, Knight HE, Gilbert RE, Goldstein H et al. 2017b. A guide to evaluating linkage quality for the analysis of linked data. Int. J. Epidemiol. 46:51699–1710
    [Google Scholar]
  66. Hirvikoski T, Boman M, Chen Q, D'Onofrio BM, Mittendorfer-Rutz E et al. 2020. Individual risk and familial liability for suicide attempt and suicide in autism: a population-based study. Psychol. Med. 50:91463–74
    [Google Scholar]
  67. Hjort J, Sølvsten M, Wüst M. 2017. Universal investment in infants and long-run health: evidence from Denmark's 1937 home visiting program. Am. Econ. J. Appl. Econ. 9:478–104
    [Google Scholar]
  68. Hobbs M, Kingham S, Wiki J, Marek L, Campbell M. 2021. Unhealthy environments are associated with adverse mental health and psychological distress: cross-sectional evidence from nationally representative data in New Zealand. Prev. Med. 145:106416
    [Google Scholar]
  69. Hooker S. 2021. Moving beyond “algorithmic bias is a data problem. .” Patterns 2:4100241
    [Google Scholar]
  70. Igumbor JO, Bosire EN, Vicente-Crespo M, Igumbor EU, Olalekan UA et al. 2021. Considerations for an integrated population health databank in Africa: lessons from global best practices. Wellcome Open Res 6:214
    [Google Scholar]
  71. Jacobucci R, Grimm KJ. 2020. Machine learning and psychological research: the unexplored effect of measurement. Perspect. Psychol. Sci. 15:3809–16
    [Google Scholar]
  72. Johnson DS, Massey C, O'Hara A. 2015. The opportunities and challenges of using administrative data linkages to evaluate mobility. Ann. Am. Acad. Pol. Soc. Sci. 657:1247–64
    [Google Scholar]
  73. Jones NL, Gilman SE, Cheng TL, Drury SS, Hill CV, Geronimus AT. 2019. Life course approaches to the causes of health disparities. Am. J. Public Health 109:S1S48–55
    [Google Scholar]
  74. Jutte DP, Roos LL, Brownell MD. 2011. Administrative record linkage as a tool for public health research. Annu. Rev. Public Health 32:91–108
    [Google Scholar]
  75. Kalkman S, van Delden J, Banerjee A, Tyl B, Mostert M, van Thiel G. 2022. Patients’ and public views and attitudes towards the sharing of health data for research: a narrative review of the empirical evidence. J. Med. Ethics 48:3–13
    [Google Scholar]
  76. Kessing LV, Gerds TA, Knudsen NN, Jørgensen LF, Kristiansen SM et al. 2017. Association of lithium in drinking water with the incidence of dementia. JAMA Psychiatry 74:101005–10
    [Google Scholar]
  77. Kharbanda EO, Vazquez-Benitez G, DeSilva MB, Naleway AL, Klein NP et al. 2021. Association of inadvertent 9-valent human papillomavirus vaccine in pregnancy with spontaneous abortion and adverse birth outcomes. JAMA Netw. Open 4:4e214340
    [Google Scholar]
  78. Khashan AS, Abel KM, McNamee R, Pedersen MG, Webb RT et al. 2008. Higher risk of offspring schizophrenia following antenatal maternal exposure to severe adverse life events. Arch. Gen. Psychiatry 65:2146–52
    [Google Scholar]
  79. Köhler O, Petersen L, Mors O, Mortensen PB, Yolken RH et al. 2017. Infections and exposure to anti-infective agents and the risk of severe mental disorders: a nationwide study. Acta Psychiatr. Scand. 135:297–105
    [Google Scholar]
  80. Köhler-Forsberg O, Petersen L, Gasse C, Mortensen PB, Dalsgaard S et al. 2019. A nationwide study in Denmark of the association between treated infections and the subsequent risk of treated mental disorders in children and adolescents. JAMA Psychiatry 76:3271–79
    [Google Scholar]
  81. Landersø R, Fallesen P. 2021. Psychiatric hospital admission and later crime, mental health, and labor market outcomes. Health Econ 30:1165–79
    [Google Scholar]
  82. Larsen JT, Munk-Olsen T, Bulik CM, Thornton LM, Koch SV et al. 2017. Early childhood adversities and risk of eating disorders in women: a Danish register-based cohort study. Int. J. Eat. Disord. 50:121404–12
    [Google Scholar]
  83. Larsson HJ, Eaton WW, Madsen KM, Vestergaard M, Olesen AV et al. 2005. Risk factors for autism: perinatal factors, parental psychiatric history, and socioeconomic status. Am. J. Epidemiol. 161:10916–25
    [Google Scholar]
  84. Latvala A, Kuja-Halkola R, D'Onofrio BM, Jayaram-Lindström N, Larsson H, Lichtenstein P 2022. Association of parental substance misuse with offspring substance misuse and criminality: a genetically informed register-based study. Psychol. Med. 52:3495–505
    [Google Scholar]
  85. Latvala A, Kuja-Halkola R, D'Onofrio BM, Larsson H, Lichtenstein P 2016. Cognitive ability and risk for substance misuse in men: genetic and environmental correlations in a longitudinal nation-wide family study. Addiction 111:101814–22
    [Google Scholar]
  86. Lee SC, DelPozo-Banos M, Lloyd K, Jones I, Walters JTR et al. 2020. Area deprivation, urbanicity, severe mental illness and social drift—a population-based linkage study using routinely collected primary and secondary care data. Schizophr. Res. 220:130–40
    [Google Scholar]
  87. Leong KSW, McLay J, Derraik JGB, Gibb S, Shackleton N et al. 2020. Associations of prenatal and childhood antibiotic exposure with obesity at age 4 years. JAMA Netw. Open 3:1e1919681
    [Google Scholar]
  88. Leyenaar JK, Schaefer AP, Wasserman JR, Moen EL, O'Malley AJ, Goodman DC 2021. Infant mortality associated with prenatal opioid exposure. JAMA Pediatr. 175:7706–14
    [Google Scholar]
  89. Li J, Hansen D, Mortensen PB, Olsen J. 2002. Myocardial infarction in parents who lost a child: a nationwide prospective cohort study in Denmark. Circulation 106:131634–39
    [Google Scholar]
  90. Li J, Laursen TM, Precht DH, Olsen J, Mortensen PB. 2005. Hospitalization for mental illness among parents after the death of a child. N. Engl. J. Med. 352:121190–96
    [Google Scholar]
  91. Li J, Precht DH, Mortensen PB, Olsen J. 2003. Mortality in parents after death of a child in Denmark: a nationwide follow-up study. Lancet 361:9355363–67
    [Google Scholar]
  92. Ljung T, Lichtenstein P, Sandin S, D'Onofrio B, Runeson B et al. 2013. Parental schizophrenia and increased offspring suicide risk: exploring the causal hypothesis using cousin comparisons. Psychol. Med. 43:3581–90
    [Google Scholar]
  93. Manski CF. 1995. Identification Problems in the Social Sciences Cambridge, MA: Harvard Univ. Press
  94. Marek L, Hobbs M, Wiki J, Kingham S, Campbell M. 2021. The good, the bad, and the environment: developing an area-based measure of access to health-promoting and health-constraining environments in New Zealand. Int. J. Health Geogr. 20:116
    [Google Scholar]
  95. McCoy BM, Rickert ME, Class QA, Larsson H, Lichtenstein P, D'Onofrio BM 2014. Mediators of the association between parental severe mental illness and offspring neurodevelopmental problems. Ann. Epidemiol. 24:9629–34.e1
    [Google Scholar]
  96. Meier SM, Bulik CM, Thornton LM, Mattheisen M, Mortensen PB, Petersen L. 2015. Diagnosed anxiety disorders and the risk of subsequent anorexia nervosa: a Danish population register study. Eur. Eat. Disord. Rev. 23:6524–30
    [Google Scholar]
  97. Milne BJ, Atkinson J, Blakely T, Day H, Douwes J et al. 2019. Data resource profile: The New Zealand Integrated Data Infrastructure (IDI). Int. J. Epidemiol. 48:3677 Correction 2019. Int. J. Epidemiol. 48:31027
    [Google Scholar]
  98. Milne BJ, Moffitt TE, Crump R, Poulton R, Rutter M et al. 2008. How should we construct psychiatric family history scores? A comparison of alternative approaches from the Dunedin Family Health History Study. Psychol. Med. 38:121793–1802
    [Google Scholar]
  99. Moffitt TE, Arseneault L, Belsky D, Dickson N, Hancox RJ et al. 2011. A gradient of childhood self-control predicts health, wealth, and public safety. PNAS 108:72693–98
    [Google Scholar]
  100. Mustelin L, Hedman AM, Thornton LM, Kuja-Halkola R, Keski-Rahkonen A et al. 2017. Risk of eating disorders in immigrant populations. Acta Psychiatr. Scand. 136:2156–65
    [Google Scholar]
  101. Nori VS, Hane CA, Crown WH, Au R, Burke WJ et al. 2019a. Machine learning models to predict onset of dementia: a label learning approach. Alzheimers Dement. Transl. Res. Clin. Interv. 5:918–25
    [Google Scholar]
  102. Nori VS, Hane CA, Martin DC, Kravetz AD, Sanghavi DM. 2019b. Identifying incident dementia by applying machine learning to a very large administrative claims dataset. PLOS ONE 14:7e0203246
    [Google Scholar]
  103. Olesen K, Rugulies R, Rod NH, Bonde JP. 2014. Does retirement reduce the risk of myocardial infarction? A prospective registry linkage study of 617 511 Danish workers. Int. J. Epidemiol. 43:1160–67
    [Google Scholar]
  104. Østergaard SD, Larsen JT, Dalsgaard S, Wilens TE, Mortensen PB et al. 2016. Predicting ADHD by assessment of Rutter's indicators of adversity in infancy. PLOS ONE 11:6e0157352
    [Google Scholar]
  105. Paksarian D, Eaton WW, Mortensen PB, Pedersen CB. 2015. Childhood residential mobility, schizophrenia, and bipolar disorder: a population-based study in Denmark. Schizophr. Bull. 41:2346–54
    [Google Scholar]
  106. Paprica PA, de Melo MN, Schull MJ. 2019. Social licence and the general public's attitudes toward research based on linked administrative health data: a qualitative study. CMAJ Open 7:1E40–46
    [Google Scholar]
  107. Pedersen CB, Mortensen PB. 2004. Sibship characteristics during upbringing and schizophrenia risk. Am. J. Epidemiol. 160:7652–60
    [Google Scholar]
  108. Pedersen CB, Mortensen PB. 2006. Urbanization and traffic related exposures as risk factors for schizophrenia. BMC Psychiatry 6:2
    [Google Scholar]
  109. Penner AM, Dodge KA. 2019. Using administrative data for social science and policy. RSF Russell Sage Found. . J. Soc. Sci. 5:21–18
    [Google Scholar]
  110. Petrović-van der Deen FS, Cunningham R, Manuel J, Gibb S, Porter RJ et al. 2020. Exploring indigenous ethnic inequities in first episode psychosis in New Zealand—a national cohort study. Schizophr. Res. 223:311–18
    [Google Scholar]
  111. Präg P, Mills MC. 2017. Cultural determinants influence assisted reproduction usage in Europe more than economic and demographic factors. Hum. Reprod. 32:112305–14
    [Google Scholar]
  112. Putnam-Hornstein E, Ghaly M, Wilkening M. 2020. Integrating data to advance research, operations, and client-centered services in California. Health Aff. 39:4655–61
    [Google Scholar]
  113. Quinn PD, Fine KL, Rickert ME, Sujan AC, Boersma K et al. 2020. Association of opioid prescription initiation during adolescence and young adulthood with subsequent substance-related morbidity. JAMA Pediatr 174:111048–55
    [Google Scholar]
  114. Ranning A, Benros ME, Thorup AAE, Davidsen KA, Hjorthøj C et al. 2020. Morbidity and mortality in the children and young adult offspring of parents with schizophrenia or affective disorders—a nationwide register-based cohort study in 2 million individuals. Schizophr. Bull. 46:1130–39
    [Google Scholar]
  115. Richmond-Rakerd LS, D'Souza S, Andersen SH, Hogan S, Houts RM et al. 2020. Clustering of health, crime and social-welfare inequality in 4 million citizens from two nations. Nat. Hum. Behav. 4:3255–64
    [Google Scholar]
  116. Richmond-Rakerd LS, D'Souza S, Milne BJ, Caspi A, Moffitt TE 2021. Longitudinal associations of mental disorders with physical diseases and mortality among 2.3 million New Zealand citizens. JAMA Netw. Open 4:1e2033448
    [Google Scholar]
  117. Richmond-Rakerd LS, D'Souza S, Milne BJ, Caspi A, Moffitt TE 2022. Longitudinal associations of mental disorders with dementia: 30-year analysis of 1.7 million New Zealand citizens. JAMA Psychiatry 79:4333–40
    [Google Scholar]
  118. Rossin-Slater M, Wüst M. 2020. What is the added value of preschool for poor children? Long-term and intergenerational impacts and interactions with an infant health intervention. Am. Econ. J. Appl. Econ. 12:3255–86
    [Google Scholar]
  119. Rossman H, Shilo S, Barbash-Hazan S, Artzi NS, Hadar E et al. 2021. Prediction of childhood obesity from nationwide health records. J. Pediatr. 233:132–40.E1
    [Google Scholar]
  120. Rowe RK, Carroll SR, Healy C, Rodriguez-Lonebear D, Walker JD. 2021. The SEEDS of Indigenous population health data linkage. Int. J. Popul. Data Sci. 6:11417
    [Google Scholar]
  121. Rutter M, Cox A, Tupling C, Berger M, Yule W 1975. Attainment and adjustment in two geographical areas. I—The prevalence of psychiatric disorder. Br. J. Psychiatry 126:493–509
    [Google Scholar]
  122. Rutter M, Quinton D 1977. Psychiatric disorder: ecological factors and concepts of causation. Ecological Factors in Human Development H McGurk 173–87 Amsterdam: North Holland
    [Google Scholar]
  123. Sariaslan A, Långström N, D'Onofrio B, Hallqvist J, Franck J, Lichtenstein P 2013. The impact of neighbourhood deprivation on adolescent violent criminality and substance misuse: a longitudinal, quasi-experimental study of the total Swedish population. Int. J. Epidemiol. 42:41057–66
    [Google Scholar]
  124. Schoeni RF, Stafford F, McGonagle KA, Andreski P. 2013. Response rates in national panel surveys. Ann. Am. Acad. Pol. Soc. Sci. 645:60–87
    [Google Scholar]
  125. Segal L, Armfield JM, Gnanamanickam ES, Preen DB, Brown DS et al. 2021. Child maltreatment and mortality in young adults. Pediatrics 147:1e2020023416
    [Google Scholar]
  126. Shackleton N, Broadbent JM, Thornley S, Milne BJ, Crengle S, Exeter DJ. 2018. Inequalities in dental caries experience among 4-year-old New Zealand children. Community Dent. Oral Epidemiol. 46:3288–96
    [Google Scholar]
  127. Skoglund C, Chen Q, D'Onofrio BM, Lichtenstein P, Larsson H 2014. Familial confounding of the association between maternal smoking during pregnancy and ADHD in offspring. J. Child Psychol. Psychiatry 55:161–68
    [Google Scholar]
  128. Slykerman RF, Li E, Shackleton N, Milne BJ. 2021. Birth by caesarean section and educational achievement in adolescents. Aust. N. Z. J. Obstet. Gynaecol. 61:3386–93
    [Google Scholar]
  129. Smith M, Lix LM, Azimaee M, Enns JE, Orr J et al. 2018. Assessing the quality of administrative data for research: a framework from the Manitoba Centre for Health Policy. J. Am. Med. Inform. Assoc. 25:3224–29
    [Google Scholar]
  130. Sørensen HJ, Nielsen PR, Pedersen CB, Benros ME, Nordentoft M, Mortensen PB. 2014. Population impact of familial and environmental risk factors for schizophrenia: a nationwide study. Schizophr. Res. 153:1–3214–19
    [Google Scholar]
  131. Sorensen LC. 2019.. “ Big data” in educational administration: an application for predicting school dropout risk. Educ. Adm. Q. 55:3404–46
    [Google Scholar]
  132. Sporle A, Hudson M, West K 2021. Indigenous data and policy in Aotearoa New Zealand. Indigenous Data Sovereignty and Policy M Walter, T Kukutai, SR Carroll, D Rodriguez-Lonebear 62–80 New York, NY: Routledge. , 1st ed..
    [Google Scholar]
  133. Sujan AC, Rickert ME, Quinn PD, Ludema C, Wiggs KK et al. 2021. A population-based study of concurrent prescriptions of opioid analgesic and selective serotonin reuptake inhibitor medications during pregnancy and risk for adverse birth outcomes. Paediatr. Perinat. Epidemiol. 35:2184–93
    [Google Scholar]
  134. Svardal CA, Waldie K, Milne B, Morton SM, D'Souza S 2022. Prevalence of antidepressant use and unmedicated depression in pregnant New Zealand women. Aust. N. Z. J. Psychiatry 56:5489–99
    [Google Scholar]
  135. Taquet M, Luciano S, Geddes JR, Harrison PJ. 2021. Bidirectional associations between COVID-19 and psychiatric disorder: retrospective cohort studies of 62 354 COVID-19 cases in the USA. Lancet Psychiatry 8:2130–40
    [Google Scholar]
  136. Taylor MJ, Rosenqvist MA, Larsson H, Gillberg C, D'Onofrio BM et al. 2020. Etiology of autism spectrum disorders and autistic traits over time. JAMA Psychiatry 77:9936–43
    [Google Scholar]
  137. van Walraven C, Austin P 2012. Administrative database research has unique characteristics that can risk biased results. J. Clin. Epidemiol. 65:2126–31
    [Google Scholar]
  138. Walter M, Lovett R, Maher B, Williamson B, Prehn J et al. 2021. Indigenous data sovereignty in the era of big data and open data. Aust. J. Soc. Issues. 56:2143–56
    [Google Scholar]
  139. Webb RT, Qin P, Stevens H, Mortensen PB, Appleby L, Shaw J. 2011. National study of suicide in all people with a criminal justice history. Arch. Gen. Psychiatry 68:6591–99
    [Google Scholar]
  140. Xafis V, Schaefer GO, Labude MK, Brassington I, Ballantyne A et al. 2019. An ethics framework for big data in health and research. Asian Bioeth. Rev. 11:3227–54
    [Google Scholar]
  141. Yang W, Li X, Pan K-Y, Yang R, Song R et al. 2021. Association of life-course depression with the risk of dementia in late life: a nationwide twin study. Alzheimers Dement 17:81383–90
    [Google Scholar]
  142. Yarkoni T, Westfall J. 2017. Choosing prediction over explanation in psychology: lessons from machine learning. Perspect. Psychol. Sci. 12:61100–22
    [Google Scholar]
  143. Zerwas S, Larsen JT, Petersen L, Thornton LM, Quaranta M et al. 2017. Eating disorders, autoimmune, and autoinflammatory disease. Pediatrics 140:6e20162089
    [Google Scholar]
  144. Zhang T, Brander G, Mantel Ä, Kuja-Halkola R, Stephansson O et al. 2021. Assessment of cesarean delivery and neurodevelopmental and psychiatric disorders in the children of a population-based Swedish birth cohort. JAMA Netw. Open 4:3e210837
    [Google Scholar]
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