Name: Community Based Referral and Counter Referral System (en Inglés)
Price: 351.61 PEN
Availability: InStock
Author: Mogere, Dominic M. ; Loum, Constantine ; Mumma, Jane
ISBN: 9786203201499

portada Community Based Referral and Counter Referral System (en Inglés)

Formato

Libro Físico

Autor

Dominic M. Mogere

Constantine Loum

Jane Mumma

Editorial

LAP Lambert Academic Publishing

Idioma

Inglés

N° páginas

228

Encuadernación

Tapa Blanda

Dimensiones

22.9 x 15.2 x 1.3 cm

Peso

0.34 kg.

ISBN13

9786203201499

Categorías

Cuestiones generales de la medicina

Community Based Referral and Counter Referral System (en Inglés)

Dominic M. Mogere (Autor) · Constantine Loum (Autor) · Jane Mumma (Autor) · LAP Lambert Academic Publishing · Tapa Blanda

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S/ 703,22

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Origen: Estados Unidos (Costos de importación incluídos en el precio)

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Reseña del libro "Community Based Referral and Counter Referral System (en Inglés)"

Existing evidence show that every day approximately 810 women die from pregnancy and childbirth related causes. 94 % of these deaths occur in low income countries with 80% occurring at home. Absence of reliable and organized community based referral systems is one of the factors contributing to these deaths. Where there is organized and reliable community referral systems, 70% of the pregnant women deliver in the hospital. The aim of this quasi-experimental study was to develop and test a community based referral and counter referral system where pregnant women, home deliveries and other common household illnesses were identified, counseled and referred to a link hospital. One hundred CHVs were trained on the referral model, issued with referral tools and distributed to cover 100 villages where they regularly visited 2209 HHs households for nine months. Fig 4.3 shows statistically significant increases in postnatal care attendances (166%; p-v = 0.007) and hospital deliveries (135%; p-v=0.001) respectively. It was further observed that CHVs identified and referred up to 38 common ailments to the hospital.The authors recommend scaling of the model in poor settings.