Factores asociados a la demora prehospitalaria en hombres y mujeres con síndrome coronario agudo

  1. Daponte Codina, A. 1
  2. Bolivar Muñoz, J.; Área de Salud Pública, Escuela Andaluza de Salud Pública, Granada; Centros de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP).
  3. Sánchez-Cantalejo, E.; Registro de Cáncer de Granada, Escuela Andaluza de Salud Pública, Granada; Centros de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP).
  4. Mateo-Rodríguez, I.; Área de Salud Pública, Escuela Andaluza de Salud Pública, Granada; Centros de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP).
  5. Babio, G.; Subsecretaría de Salud del Ministerio de Salud de la Provincia de Tierra del Fuego, Antártida e Islas del Atlántico Sur, Argentina; Centros de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP)
  6. Romo-Avilés, N. 3
  7. Rosell-Ortiz, F. 2
  1. 1 Observatorio de Salud y Medio Ambiente de Andalucía, Escuela Andaluza de Salud Pública
  2. 2 Servicio Provincial 061 Almería, Empresa Pública de Emergencias Sanitarias (EPES), Almería
  3. 3 Instituto de Estudios de la Mujer, Universidad de Granada, Granada
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Journal:
Anales del sistema sanitario de Navarra

ISSN: 1137-6627

Year of publication: 2016

Volume: 39

Issue: 1

Pages: 47-58

Type: Article

DOI: 10.4321/S1137-6627/2016000100006 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Anales del sistema sanitario de Navarra

Abstract

Objective. To identify factors associated with prehospital delay in people who have had an acute coronary syndrome.            Methods. Using a survey we studied patients admitted due to acute coronary syndrome in the 33 Andalusian public hospitals, obtaining information about different types of variables: socio-demographic, contextual, clinical, perception, action, and transportation. Multivariate logistic regression models were applied to calculate the odds ratios for the delay.            Results. Of the 1,416 patients studied, more than half had a delay of more than an hour. This is associated to distance to the hospital and means of transport: when the event occurs in the same city, using the patient’s own means of transport increases the delay, odds ratio = 1.51 (1.02 to 2.23); if the distance is 1 to25 kilometers from the hospital, there is no difference between the patient’s own means of transport and an ambulance, odds ratio = 1.41 and odds ratio =1.43 respectively; and when the distance exceeds25 kilometers transport by ambulance means more delay, odds ratio = 3.13 and odds ratio = 2.20 respectively. Also, typical symptoms reduce delay amongst men but increase amongst women. Also, not caring and waiting for the resolution of symptoms, seeking health care other than a hospital or emergency services, previous clinical history, being away from home, and having an income under 1,500 euros, all increase delay.            Conclusions. Prehospital delay times do not meet health recommendations. The physical and social environment, in addition to clinical, perceptual and attitudinal factors, are associated with this delay.            Keywords. Acute coronary syndrome. Pre-hospital delay. Gender. Emergency medical services. Inequalities.

Data source: Dialnet