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Education and medication use later in life and the role of intelligence

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Abstract

We investigate the association between education and disease-specific medications in old age, prescribed by medical doctors, accounting for confounders and how this association is shaped by intelligence. We use administrative data on men including prescribed medication records. To account for endogeneity of education we estimate a structural model, consisting of (i) an ordered probit for educational attainment, (ii) a Gompertz mortality model for survival up to old age, (iii) a probit model for prescribed medications in old age, (iv) a measurement system using IQ tests to identify latent intelligence. The results suggest a strong effect of education on prescribed medications for most medications, except for prescribed medication for cardiac diseases and for depression and anxiety.

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Data Availability

Our analyses were carried out under specific confidentiality agreement with Statistics Netherlands to access non-public national mortality data linked by Statistics Netherlands to our military examination records. For additional analyses by bona-fide researchers, we can provide guidance on how they could also obtain privileged access to these data under special confidentiality agreements. We are also willing to share all scripts that were used in producing our results. Data requests can be submitted to the Centre for policy statistics of Statistics Netherlands: Centrum voor Beleidsstatistiek: microdata@CBS.nl. Data requests should refer to project 7012.

Notes

  1. We cannot distinguish between type-1 and type-2 diabetes. As type-1 diabetes usually occurs early in life there may be simultaneous impacts between diabetes and education. However, this issue should be negligible as type-2 diabetes is much more common than type-1 in adults.

  2. Note that for identification we need at least three different intelligence tests and to restrict one of the intelligence parameters in the IQ equations, \(\zeta\) in (4), to one.

  3. Gaussian quadrature is a numerical integration method based on Hermite polynomials. It provides an efficient approximation for evaluating indefinite integrals based on normal distributions. The STATA estimation programs are available upon request.

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Funding

Financial supports were received from U.S. National Institutes of Health, Grant RO1-AG028593, and from the signature area Public Health of the Faculty of Economic and Business, University of Groningen.

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Authors and Affiliations

  1. VIDAS, Milan, Italy

    Gianmaria Niccodemi

  2. Netherlands Interdisciplinary Demographic Institute (NIDI-KNAW)/University of Groningen, The Hague, The Netherlands

    Govert Bijwaard

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  1. Gianmaria Niccodemi
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All authors have contributed equally to the research. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Gianmaria Niccodemi.

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Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Anonymity, study-approval and informed consent

The anonymity of the included individuals is guaranteed by Statistics Netherlands. The data can only be analyzed at Statistics Netherlands or through Remote Access. Access to the data is only possible with fingerprint ID and the personal smartcard. The study was reviewed by the Institutional Review Board of the Columbia University Medical Center in New York, NY. The Board determined that studies on this study population do no meet the DHSS definition of ‘human subject research’ and are exempt from IRB approval. In the Netherlands, the study does not need approval by Ethical Review Boards or by the National Data Protection Authority as all study procedures are in compliance with Dutch privacy legislation and do not need the consent of the data subjects concerned or of their relatives. The study is based on population wide administrative records and not on patient records.

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Appendices

Appendix A: Additional tables

See Tables 5, 6, 7, 8 and 9

Table 5 Distribution of IQ scores by education level, 36,882 individuals (incl. those who died before 2006)
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Table 6 Sample characteristics by education level, 36,882 individuals
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Table 7 Diseases and relative medications
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Table 8 Prescribed medications in subsample (Sub) and the Netherlands (NL), in 2007 and 2014.
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Table 9 Percentage with medication use 2006 by characteristic, 33,428 individuals
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Appendix B: Additional figures

(See Figs. 5 and 6)

Fig. 5
figure 5

ATEs Low to Medium education on medication probability by intelligence, age 59–62, from 9-equation and 3-equation measurement system

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Fig. 6
figure 6

ATEs Medium to High education on medication probability by intelligence, age 59–62,, from 9-equation and 3-equation measurement system

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Appendix C: Tables with parameter estimates

(See Table 10)

Table 10 Ordered probit estimates for educational attainment
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Table 11 Ordered probit estimates for IQ measurement system, by education level, in structural model: Raven Test
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Table 12 Distribution of latent intelligence
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Table 13 Ordered probit estimates for IQ measurement system, by education level, in structural model: Arithmetic Test
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Table 14 Ordered probit estimates for IQ measurement system, by education level, in structural model: Language Test
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Table 15 Gompertz proportional mortality rate estimates, by education level
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Table 16 Probit estimates for diabetes medications, by education level
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Table 17 Probit estimates for blood pressure medications, by education level
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Table 18 Probit estimates for lipid medications, by education level
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Table 19 Probit estimates for cardiac medications, by education level
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Table 20 Probit estimates for COPD medications, by education level
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Table 21 Probit estimates for depression medications, by education level
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Niccodemi, G., Bijwaard, G. Education and medication use later in life and the role of intelligence. Eur J Health Econ 25, 333–361 (2024). https://doi.org/10.1007/s10198-023-01586-7

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