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Review
. 2023 Jul 3;13(1):38.
doi: 10.1186/s13561-023-00451-w.

Sick and depressed? The causal impact of a diabetes diagnosis on depression

Alessio Gaggero  1 , Joan Gil  2 , Dolores Jiménez-Rubio  1 , Eugenio Zucchelli  3   4   5
Affiliations
Review

Sick and depressed? The causal impact of a diabetes diagnosis on depression

Alessio Gaggero et al. Health Econ Rev. .

Abstract

Background: There is sparse evidence on the impact of health information on mental health as well as on the mechanisms governing this relationship. We estimate the causal impact of health information on mental health via the effect of a diabetes diagnosis on depression.

Methods: We employ a fuzzy regression discontinuity design (RDD) exploiting the exogenous cut-off value of a biomarker used to diagnose type-2 diabetes (glycated haemoglobin, HbA1c) and information on psycometrically validated measures of diagnosed clinical depression drawn from rich administrative longitudinal individual-level data from a large municipality in Spain. This approach allows estimating the causal impact of a type-2 diabetes diagnosis on clinica ldepression.

Results: We find that overall a type-2 diabetes diagnosis increases the probability of becoming depressed, however this effect appears to be driven mostly by women, and in particular those who are relatively younger and obese. Results also appear to differ by changes in lifestyle induced by the diabetes diagnosis: while women who did not lose weight are more likely to develop depression, men who did lose weight present a reduced probability of being depressed. Results are robust to alternative parametric and non-parametric specifications and placebo tests.

Conclusions: The study provides novel empirical evidence on the causal impact of health information on mental health, shedding light on gender-based differences in such effects and potential mechanisms through changes in lifestyle behaviours.

Keywords: Administrative longitudinal data; Depression; Diabetes; Fuzzy regression discontinuity design; Lifestyle changes.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Continuity Test
Fig. 2
Fig. 2
Density of the Running variable. Note: The Figure shows evidence of no manipulation of the running variable. Bin size = 0.1. The bin size has been selected by means of the McCrary test Stata routine, i.e. DCdensity
Fig. 3
Fig. 3
RD Graphical Evidence. Note: A shows local polynomial estimates of the probability of being diagnosed with T2DM as a function of the (normalised) HbA1c, our rst stage. Similarly, b shows local polynomial estimates of the probability of being diagnosed with depression as a function of the (normalised) HbA1c
See this image and copyright information in PMC

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