Population-Level Administrative Data: A Resource to Advance Psychological Science

Abstract

Population-level administrative data—data on individuals’ interactions with administrative systems, such as health-care, social-welfare, criminal-justice, and education systems—are a fruitful resource for research into behavior, development, and well-being. However, administrative data are underutilized in psychological science. Here, we review advantages of population-level administrative data for psychological research and provide examples of advances in psychological theory arising from administrative data studies. We focus on advantages in three areas: the collection and recording of population-level administrative data, the data’s large scale, and unique data linkages. We also describe ethical issues as well as methodological considerations and limitations in population administrative data research and outline future directions to enable psychological scientists to more fully capitalize on administrative data resources.

Keywords

administrative data population registers data linkage causal inference development

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References

Almond

Edlund

Palme

(2009). Chernobyl’s subclinical legacy: Prenatal exposure to radioactive fallout and school outcomes in Sweden. The Quarterly Journal of Economics, 124(4), 1729–1744. https://doi.org/10.1162/qjec.2009.124.4.1729

Andersen

S. H.

(2021). Association of youth age at exposure to household dysfunction with outcomes in early adulthood. JAMA Network Open, 4(1), Article e2032769. https://doi.org/10.1001/jamanetworkopen.2020.32769

Bramson

L. M.

Rickert

M. E.

Class

Q. A.

Sariaslan

Almqvist

Larsson

Lichtenstein

D’Onofrio

B. M.

(2016). The association between childhood relocations and subsequent risk of suicide attempt, psychiatric problems, and low academic achievement. Psychological Medicine, 46(5), 969–979. https://doi.org/10.1017/S0033291715002469

Bronfenbrenner

(1977). Toward an experimental ecology of human development. American Psychologist, 32(7), 513–531. https://doi.org/10.1037/0003-066X.32.7.513

Brown

J. R.

Enos

R. D.

Feigenbaum

Mazumder

(2021). Childhood cross-ethnic exposure predicts political behavior seven decades later: Evidence from linked administrative data. Science Advances, 7(24), Article eabe8432. https://doi.org/10.1126/sciadv.abe8432

D’Mello

S. K.

Tay

Southwell

(2022). Psychological measurement in the information age: Machine-learned computational models. Current Directions in Psychological Science, 31(1), 76–87. https://doi.org/10.1177/09637214211056906

D’Onofrio

B. M.

Lahey

B. B.

Turkheimer

Lichtenstein

(2013). Critical need for family-based, quasi-experimental designs in integrating genetic and social science research. American Journal of Public Health, 103(Suppl. 1), S46–S55. https://doi.org/10.2105/AJPH.2013.301252

Elser

Horváth-Puhó

Gradus

J. L.

Smith

M. L.

Lash

T. L.

Glymour

M. M.

Sørensen

H. T.

Henderson

V. W.

(2023). Association of early-, middle-, and late-life depression with incident dementia in a Danish cohort. JAMA Neurology, 80(9), 949–958. https://doi.org/10.1001/jamaneurol.2023.2309

Engemann

Svenning

J.-C.

Arge

Brandt

Erikstrup

Geels

Hertel

Mortensen

P. B.

Plana-Ripoll

Tsirogiannis

Sabel

C. E.

Sigsgaard

Pedersen

C. B.

(2020). Associations between growing up in natural environments and subsequent psychiatric disorders in Denmark. Environmental Research, 188, Article 109788. https://doi.org/10.1016/j.envres.2020.109788

10.

Foust

Hoonhout

Eastman Andrea

Prindle

Rebbe

Nghiem

Suthar

Cuccaro-Alamin

Mitchell

Dawson

Palmer

Raj

Ahn

Hammond

McNellan

Reddy

Chen

W.-T.

Mayfield

Putnam-Hornstein

McCroskey

(2022). The Children’s Data Network: Harnessing the scientific potential of linked administrative data to inform children’s programs and policies. International Journal of Population Data Science, 6(3), Article 1702. https://doi.org/10.23889/ijpds.v6i3.1702

11.

Goldstone

R. L.

Lupyan

(2016). Discovering psychological principles by mining naturally occurring data sets. Topics in Cognitive Science, 8(3), 548–568. https://doi.org/10.1111/tops.12212

12.

Gordon

(2020). Administrative Data Research UK. Patterns, 1(1), Article 100010. https://doi.org/10.1016/j.patter.2020.100010

13.

Grosz

M. P.

Ayaita

Arslan

R. C.

Buecker

Ebert

Hünermund

Müller

S. R.

Rieger

Zapko-Willmes

Rohrer

J. M.

(2024). Natural experiments: Missed opportunities for causal inference in psychology. Advances in Methods and Practices in Psychological Science, 7(1). https://doi.org/10.1177/25152459231218610

14.

Hyatt

J. M.

Andersen

S. N.

(2019). On the potential of incorporating administrative register data into randomized experiments. Journal of Experimental Criminology, 15(3), 469–497. https://doi.org/10.1007/s11292-019-09354-5

15.

Igumbor

J. O.

Bosire

E. N.

Vicente-Crespo

Igumbor

E. U.

Olalekan

U. A.

Chirwa

T. F.

Kinyanjui

S. M.

Kyobutungi

Fonn

(2021). Considerations for an integrated population health databank in Africa: Lessons from global best practices. Wellcome Open Research, 6, Article 214. https://doi.org/10.12688/wellcomeopenres.17000.1

16.

Jobin

Ienca

Vayena

(2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-019-0088-2

17.

Jutte

D. P.

Roos

L. L.

Brownell

M. D.

(2011). Administrative record linkage as a tool for public health research. Annual Review of Public Health, 32, 91–108. https://doi.org/10.1146/annurev-publhealth-031210-100700

18.

Kalkman

van Delden

Banerjee

Tyl

Mostert

van Thiel

(2022). Patients’ and public views and attitudes towards the sharing of health data for research: A narrative review of the empirical evidence. Journal of Medical Ethics, 48, 3–13. https://doi.org/10.1136/medethics-2019-105651

19.

Larsson

(2021). The importance of selection bias in prospective birth cohort studies. JCPP Advances, 1(3), Article e12043. https://doi.org/10.1002/jcv2.12043

20.

Lessof

(2009). Ethical issues in longitudinal surveys. In Lynn

(Ed.), Methodology of longitudinal surveys (pp. 35–54). John Wiley & Sons. https://doi.org/10.1002/9780470743874.ch3

21.

Light

L. L.

Panicker

Abrams

Huh-Yoo

(2024). Ethical challenges in the use of digital technologies in psychological science: Introduction to the special issue. American Psychologist, 79(1), 1–8. https://doi.org/10.1037/amp0001286

22.

Mekonen

Chan

G. C. K.

Connor

Hall

Hides

Leung

(2021). Treatment rates for alcohol use disorders: A systematic review and meta-analysis. Addiction, 116(10), 2617–2634. https://doi.org/10.1111/add.15357

23.

Milne

B. J.

Atkinson

Blakely

Day

Douwes

Gibb

Nicholson

Shackleton

Sporle

Teng

(2019). Data resource profile: The New Zealand Integrated Data Infrastructure (IDI). International Journal of Epidemiology, 48(3), 677–677e. https://doi.org/10.1093/ije/dyz014

24.

Milne

B. J.

D’Souza

Andersen

S. H.

Richmond-Rakerd

L. S.

(2022). Use of population-level administrative data in developmental science. Annual Review of Developmental Psychology, 4(1), 447–468. https://doi.org/10.1146/annurev-devpsych-120920-023709

25.

National Academies of Sciences, Engineering, and Medicine. (2023). Toward a 21st century national data infrastructure: Enhancing survey programs by using multiple data sources. The National Academies Press. https://doi.org/10.17226/26804

26.

Nead

K. T.

Hinkston

C. L.

Wehner

M. R.

(2022). Cautions when using race and ethnicity in administrative claims data sets. JAMA Health Forum, 3(7), Article e221812. https://doi.org/10.1001/jamahealthforum.2022.1812

27.

Paluck

E. L.

Green

S. A.

Green

D. P.

(2019). The contact hypothesis re-evaluated. Behavioural Public Policy, 3(2), 129–158. https://doi.org/10.1017/bpp.2018.25

28.

Penner

A. M.

Dodge

K. A.

(2019). Using administrative data for social science and policy. RSF: The Russell Sage Foundation Journal of the Social Sciences, 5(2), 1–18. https://doi.org/10.7758/rsf.2019.5.2.01

29.

Richmond-Rakerd

L. S.

Caspi

Arseneault

Baldwin

J. R.

Danese

Houts

R. M.

Matthews

Wertz

Moffitt

T. E.

(2019). Adolescents who self-harm and commit violent crime: Testing early-life predictors of dual harm in a longitudinal cohort study. American Journal of Psychiatry, 176(3), 186–195. https://doi.org/10.1176/appi.ajp.2018.18060740

30.

Richmond-Rakerd

L. S.

D’Souza

Andersen

S. H.

Hogan

Houts

R. M.

Poulton

Ramrakha

Caspi

Milne

B. J.

Moffitt

T. E.

(2020). Clustering of health, crime and social-welfare inequality in 4 million citizens from two nations. Nature Human Behaviour, 4(3), 255–264. https://doi.org/10.1038/s41562-019-0810-4

31.

Richmond-Rakerd

L. S.

D’Souza

Milne

B. J.

Caspi

Moffitt

T. E.

(2022). Longitudinal associations of mental disorders with dementia: 30-year analysis of 1.7 million New Zealand citizens. JAMA Psychiatry, 79(4), 333–340. https://doi.org/10.1001/jamapsychiatry.2021.4377

32.

Sahlin

Kuja-Halkola

Bjureberg

Lichtenstein

Molero

Rydell

Hedman

Runeson

Jokinen

Ljótsson

Hellner

(2017). Association between deliberate self-harm and violent criminality. JAMA Psychiatry, 74(6), 615–621. https://doi.org/10.1001/jamapsychiatry.2017.0338

33.

Schwartz

G. L.

Glymour

M. M.

(2023). Bridging the divide: Tackling tensions between life-course epidemiology and causal inference. Annual Review of Developmental Psychology, 5(1), 355–374. https://doi.org/10.1146/annurev-devpsych-120221-033623

34.

Thalmayer

A. G.

Toscanelli

Arnett

J. J.

(2021). The neglected 95% revisited: Is American psychology becoming less American? American Psychologist, 76(1), 116–129. https://doi.org/10.1037/amp0000622

35.

Wright

A. G. C.

Levinson

C. A.

Crowell

S. E.

(2022). Characterizing and coding psychiatric diagnoses using electronic health record data. JAMA Psychiatry, 79(11), Article 1139. https://doi.org/10.1001/jamapsychiatry.2022.2733