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¿Refleja la historia clínica electrónica los determinantes sociales de la salud desde Atención Primaria?

OBJECTIVE: Analyze whether the use of Z codes in the Electronic Health Record (EHR) correlates with the socioeconomic reality of the population attended. DESIGN: Observational, descriptive, cross-sectional, ecological study. LOCATION: 90 health centres of two Primary Health Care (PHC) Departments of...

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Detalles Bibliográficos
Autores principales: Jiménez Carrillo, Marta, Fernández Rodker, Joanna, Sastre Paz, Marta, Alberquilla Menendez-Asenjo, Ángel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7752986/
https://www.ncbi.nlm.nih.gov/pubmed/32417165
http://dx.doi.org/10.1016/j.aprim.2020.01.007
Descripción
Sumario:OBJECTIVE: Analyze whether the use of Z codes in the Electronic Health Record (EHR) correlates with the socioeconomic reality of the population attended. DESIGN: Observational, descriptive, cross-sectional, ecological study. LOCATION: 90 health centres of two Primary Health Care (PHC) Departments of the Community of Madrid. PARTICIPANTS: The total number of patients treated during 2016: 1,920,124 (54.33% women, 45.67% men). The 7.15% received some Z code (67.29% women, 32.71% men). MAIN MEASUREMENTS: As a dependent variable, the proportion of patients with Z code records in their EHRs was established. As independent variable, two socioeconomic indicators were selected that objectively reflect the differences between Basic Health Areas: Average Income Available per capita and Proportion of Economic Immigrants. To evaluate the correlation between dependent and independent variables, a multivariate correlation-regression analysis was used. RESULTS: It was observed that the higher the disposable income, the lower the proportion of Z code records in the EHRs (Pearson correlation coefficient: −0.56). However, there is a great variability in the registration of Z codes and the coding fails to make visible the socio-economic realities of the populations covered (Diagnostic Odds Ratio: 0.12. CI: 0.05-0.32). CONCLUSIONS: The use of different tools that facilitate the visualization of the health impact of social inequalities, as well as their evaluation through various research methodologies, is relevant for a community orientation of the PHC. The Z codes do not make visible in the studied area the social determinants of health of the population attended.