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Associations of hospital-treated infections with subsequent dementia: nationwide 30-year analysis

Nature Aging (2024)Cite this article

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Abstract

Infections, which can prompt neuroinflammation, may be a risk factor for dementia1,2,3,4,5. More information is needed concerning associations across different infections and different dementias, and from longitudinal studies with long follow-ups. This New Zealand-based population register study tested whether infections antedate dementia across three decades. We identified individuals born between 1929 and 1968 and followed them from 1989 to 2019 (n = 1,742,406, baseline age = 21–60 years). Infection diagnoses were ascertained from public hospital records. Dementia diagnoses were ascertained from public hospital, mortality and pharmaceutical records. Relative to individuals without an infection, those with an infection were at increased risk of dementia (hazard ratio 2.93, 95% confidence interval 2.68–3.20). Associations were evident for dementia diagnoses made up to 25–30 years after infection diagnoses. Associations held after accounting for preexisting physical diseases, mental disorders and socioeconomic deprivation. Associations were evident for viral, bacterial, parasitic and other infections, and for Alzheimer’s disease and other dementias, including vascular dementia. Preventing infections might reduce the burden of neurodegenerative conditions.

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Fig. 1: Distributions of age-at-first-diagnosis for infection and dementia.
Fig. 2: Overrepresentation of dementia among individuals with an infection.
Fig. 3: Specificity of associations.

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Data availability

The following administrative databases from the IDI were used in the study: publicly funded hospital discharges, pharmaceutical data, mortality data, address notification, person overseas spell, personal details, resident population table, births and deaths. Information about the databases is located at www.stats.govt.nz/integrated-data/integrated-data-infrastructure/data-in-the-idi. The IDI register data used in this article are stored on Statistics New Zealand servers and cannot be shared by the authors. Access to the IDI is by application to Statistics New Zealand. Information about the application process is available at www.stats.govt.nz/integrated-data/apply-to-use-microdata-for-research/.

Code availability

The statistical code has been archived with GitHub at https://github.com/leahrr-umich/Infection-Dementia.git and is available upon request from the corresponding author.

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Acknowledgements

This research was supported by grant no. P30AG066582 from the National Institute on Aging (NIA) through the Center to Accelerate Population Research in Alzheimer’s (to L.S.R.-R. and T.E.M.) and grant no. P30AG066589 from the NIA through the Center for Advancing Sociodemographic and Economic Study of Alzheimer’s Disease and Related Dementias (to L.S.R.-R.). Additional support was provided by grant nos. R01AG032282 and R01AG049789 from the NIA (to A.C. and T.E.M.) and grant no. MR/P005918 from the UK Medical Research Council (to A.C. and T.E.M.). We thank Statistics New Zealand and their staff for access to the IDI data and timely ethics review of the output data. We thank the Public Policy Institute at the University of Auckland for access to their Statistics New Zealand data laboratory. We also thank H. Jamieson, A. Kvalsvig and Alzheimers New Zealand for helpful comments on earlier drafts of this manuscript. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Department of Psychology, University of Michigan, Ann Arbor, MI, USA

    Leah S. Richmond-Rakerd, Monica T. Iyer & Lara Khalifeh

  2. Aetion,Inc, New York, NY, USA

    Monica T. Iyer

  3. Centre of Methods and Policy Application in the Social Sciences, University of Auckland, Auckland, New Zealand

    Stephanie D’Souza & Barry J. Milne

  4. School of Social Sciences, University of Auckland, Auckland, New Zealand

    Stephanie D’Souza & Barry J. Milne

  5. Department of Psychology and Neuroscience, Duke University, Durham, NC, USA

    Avshalom Caspi & Terrie E. Moffitt

  6. Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA

    Avshalom Caspi & Terrie E. Moffitt

  7. Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, UK

    Avshalom Caspi & Terrie E. Moffitt

  8. Promenta Center, University of Oslo, Oslo, Norway

    Avshalom Caspi & Terrie E. Moffitt

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Contributions

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

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Correspondence to Leah S. Richmond-Rakerd.

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Nature Aging thanks David Bennett and Mika Kivimaki for their contribution to the peer review of this work.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Statistics New Zealand disclaimer: These results are not official statistics. They have been created for research purposes from the IDI which is carefully managed by Statistics New Zealand. Statistics New Zealand approved the use of the IDI for this project (ref. MAA2022-15). For more information about the IDI please visit https://www.stats.govt.nz/integrated-data/.

Extended data

Extended Data Fig. 1 Associations of infections with dementia across varying follow-up intervals.

To address the potential for ascertainment bias as well as reverse-causation related to dementia’s long pre-diagnosis phase, we estimated associations across varying follow-up intervals from the index infection (0–1, 1–5, 5–10, 10–15, 15–20, 20–25, and 25–30 years). Increased risk of dementia was observed across varying follow-up intervals from infection, from 0–1 years to 25–30 years. Analyses comprised 1,732,080 individuals; individuals with dementia diagnoses that predated infection diagnoses were excluded. Estimates are hazard ratios. Error bars indicate 95% confidence intervals. Follow-up intervals were non-overlapping, such that dementia risk estimated for a particular time interval considered only dementia diagnoses made in the specified interval, and did not include risk in the prior years.

Extended Data Fig. 2 Tests of a dose-response pattern.

In secondary analyses to test for a dose-response pattern, we counted infection-related hospital admissions and infection types across a 20-year exposure period (July 1989-June 2009), and as outcome ascertained dementia diagnoses across a 10-year follow-up period (July 2009-June 2019). We used Poisson regression models with relative risks to estimate the associations between number of infection-related hospital admissions and number of infection diagnoses of different types with subsequent dementia, controlling for mental disorders and physical diseases diagnosed before the index infection. Individuals who accumulated more infection-related hospital admissions (a) and more infection diagnoses of different types (b), across a 20-year exposure period, were more likely to be diagnosed with dementia in the 10-year follow-up period. Analyses excluded the 169,983 individuals who received a dementia diagnosis, died, or left the country during the exposure period. Error bars indicate 95% confidence intervals.

Supplementary information

Supplementary Information

Supplementary Results 1 and 2, Tables 1–9 and References.

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Richmond-Rakerd, L.S., Iyer, M.T., D’Souza, S. et al. Associations of hospital-treated infections with subsequent dementia: nationwide 30-year analysis. Nat Aging (2024). https://doi.org/10.1038/s43587-024-00621-3

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