Cultural Biases and Clinical Variation

<b>Background</b>: This study investigates the possible role of cultural biases in clinical variation. <b>Methods</b>: An observational, analytical, and retrospective cohort design, using a Spanish primary care database, was used, on which a cross-sectional analysis was performed. Diseases were classified into the following: generic conditions with established protocols (control group), specialist-diagnosed diseases, women's diseases, and psychiatric diseases. Diagnostic incidence was calculated across 25 geographical clusters, and variability was measured using the coefficient of variation (CV). To test systematic differences, multivariate linear regression models were adjusted for socio-demographics and healthcare system proxies. <b>Results</b>: The final sample for analysis consisted of 617,222 women aged 15 to 65, with a mean age of 43. The results did not reveal significant variability between generic and specialized diseases, but women's and psychiatric diseases showed consistently higher CVs in all models (<i>p</i> < 0.05), indicating that approximately 20% to 30% of the clinical variation observed in these groups was unexplained. <b>Conclusions</b>: Although we cannot establish causality, these findings may suggest that cultural taboos and gender biases likely contribute to unjustified clinical variation, highlighting cultural bias as a plausible explanatory factor.

Authors

Institutions

Publication Details

Journal
Clinics and Practice
Published
2025-12-05
DOI
https://doi.org/10.3390/clinpract15120229
Primary Topic
Cultural Competency in Health Care
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Cultural Biases and Clinical Variation

Cielo García‐Montero, Miguel Á. Ortega, Manuel F. Carrillo-Rodríguez, Antonio Sarría‐Santamera et al.
Clinics and Practice
Cultural Competency in Health Care
article

Cultural Biases and Clinical Variation

Cielo García‐Montero, Miguel Á. Ortega, Manuel F. Carrillo-Rodríguez, Antonio Sarría‐Santamera, Óscar Gasulla, Óscar Fraile-Martínez, Melchor Álvarez‐Mon, Ferran A. Mazaira‐Font, Ángel Asúnsolo, Diego Cantalapiedra, Belen Gómez-Valcárcel
article en

Abstract

<b>Background</b>: This study investigates the possible role of cultural biases in clinical variation. <b>Methods</b>: An observational, analytical, and retrospective cohort design, using a Spanish primary care database, was used, on which a cross-sectional analysis was performed. Diseases were classified into the following: generic conditions with established protocols (control group), specialist-diagnosed diseases, women's diseases, and psychiatric diseases. Diagnostic incidence was calculated across 25 geographical clusters, and variability was measured using the coefficient of variation (CV). To test systematic differences, multivariate linear regression models were adjusted for socio-demographics and healthcare system proxies. <b>Results</b>: The final sample for analysis consisted of 617,222 women aged 15 to 65, with a mean age of 43. The results did not reveal significant variability between generic and specialized diseases, but women's and psychiatric diseases showed consistently higher CVs in all models (<i>p</i> < 0.05), indicating that approximately 20% to 30% of the clinical variation observed in these groups was unexplained. <b>Conclusions</b>: Although we cannot establish causality, these findings may suggest that cultural taboos and gender biases likely contribute to unjustified clinical variation, highlighting cultural bias as a plausible explanatory factor.

Clinics and PracticeVol. 15(12)
Universidad de Alcalá (ES), Instituto Cajal (ES), Bellvitge University Hospital (ES), Centre for Biomedical Network Research on Rare Diseases (ES), Instituto Ramón y Cajal de Investigación Sanitaria (ES), Nazarbayev University (KZ), Universitat de Barcelona (ES), Universidad Europea de Madrid (ES)
Openalex Percentile: Top 52%
Cultural Competency in Health Care
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.