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From knowledge to planning considerations: a matrix to assess health needs for the perinatal network in eastern Paris
Background: Regionalization of perinatal care is required throughout networks to reduce perinatal morbidity and mortality and to organize access to health services for high-risk new borns, such as in the eastern Paris in 2007. Our study sought to design a matrix to build a perinatal knowledge base f...
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Published in: | European journal of public health 2011-08, Vol.21 (4), p.504-511 |
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Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Request full text |
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Summary: | Background: Regionalization of perinatal care is required throughout networks to reduce perinatal morbidity and mortality and to organize access to health services for high-risk new borns, such as in the eastern Paris in 2007. Our study sought to design a matrix to build a perinatal knowledge base for assessing health needs and facilitating public health planning process for the perinatal network in eastern Paris. Methods: Our matrix listed as its columns 'perinatal stages' from conception through the age of 6 years, whereas the rows covered components related to public health planning (i.e. target population, perinatal risk factors and health services). For each situation, the matrix lists require information and potential data sources to measure health status and health services. Results: Our matrix structures the cyclical process for building knowledge for action. The eastern Paris has a population of 670 000. Its nine maternity units are distributed into three levels of care, a level-3 unit opened up in June 2007. A total of 16 400 deliveries occur every year in the nine units; 2500 women living in the eastern area deliver outside the area. Conclusion: Our matrix is useful for building a comprehensive perinatal knowledge base depending upon perinatal stages and health-care dimensions. It should imply stakeholders in collecting, synthesizing and analysing massive amounts of data. It can be adapted to any health network or local health policy. |
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ISSN: | 1101-1262 1464-360X |
DOI: | 10.1093/eurpub/ckq101 |