
==== Front
Lancet Reg Health Eur
Lancet Reg Health Eur
The Lancet Regional Health - Europe
2666-7762
Elsevier

S2666-7762(24)00225-4
10.1016/j.lanepe.2024.101058
101058
Correspondence
Beyond one size fits all: tailoring healthcare to the realities of migration
Ceccarelli Giancarlo giancarlo.ceccarelli@uniroma1.it
abc∗
Branda Francesco d
Giovanetti Marta efg
d’Ettorre Gabriella a
Scarpa Fabio h
Ciccozzi Massimo cd
a Department of Public Health and Infectious Diseases, University of Rome Sapienza, Rome, Italy
b Azienda Ospedaliero Universitaria Umberto I, Rome, Italy
c Migrant and Global Health Research Organization (Mi-HeRO), Rome, Italy
d Medical Statistics and Molecular Epidemiology, University Campus Bio-Medico, Rome, Italy
e Climate Amplified Diseases and Epidemics (CLIMADE), Brazil, Brazil
f Instituto Rene Rachou, Fundação Oswaldo Cruz, Minas Gerais, Brazil
g Sciences and Technologies for Sustainable Development and One Health, Università Campus Bio-Medico di Roma, Italy
h Department of Biomedical Sciences, University of Sassari, Sassari, Italy
∗ Corresponding author. Department of Public Health and Infectious Diseases, University of Rome Sapienza, Viale del Policlinico 155, 00161, Rome, Italy. giancarlo.ceccarelli@uniroma1.it
31 8 2024
10 2024
31 8 2024
45 10105819 8 2024
21 8 2024
© 2024 The Author(s)
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
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pmcDespite current healthcare policies striving to improve access and engage migrant populations, a significant methodological barrier to achieving equitable healthcare delivery remains unaddressed.1 The foundations of Western medicine are largely built upon research conducted on predominantly Caucasian populations. However, the direct application of this evidence base to non-Western migrant populations, which exhibit significant heterogeneity in geographic origin, is not inherently generalizable and necessitates careful scrutiny. This extrapolation of findings from one population group to another can introduce significant bias.

Specifically, laboratory reference intervals and derived diagnostic algorithms developed for Caucasian populations are applied universally, despite the well-established fact that ethnic subpopulations exhibit unique distributions in laboratory test results.2,3 Furthermore, the underrepresentation of non-Caucasian populations in clinical trials limits the generalizability of findings related to disease presentation, drug metabolism, and treatment efficacy.4 This is particularly concerning given the well-documented ethnic variations in drug responses and pharmacogenomics.5 Exacerbating these disparities, existing healthcare inequalities, reflected in data used to train AI algorithms, can perpetuate biases in diagnostic and therapeutic models, further disadvantaging non-Caucasian populations. Additionally, epidemiological studies and healthcare policies often overlook the inherent heterogeneity of migrant populations, neglecting crucial variations in genetic predispositions, microbiomes, and prior exposure to health risks.

Addressing these biases necessitates a paradigm shift in global health, moving beyond access to developing and validating inclusive medical knowledge. This shift requires a critical reassessment of existing methodologies, increased research diversity, and a commitment to culturally sensitive healthcare practices that reflect the unique needs of diverse migrant populations.

Contributors

GC and FB contributed to the design and writing the original draft of the manuscript. MG, GdE, and FS contributed to the investigation, methodology and implementation of the research, to the analysis of the data and to the writing of the manuscript. FS and MC contributed to supervision, validation and critically revised the manuscript. All authors provided critical feedback and helped shape the research.

Declaration of interests

The authors declare that they have no competing interests. This work received no financial support.
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References

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