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The A ustralasian M aternity O utcomes S urveillance S ystem: A n evaluation of stakeholder engagement, usefulness, simplicity, acceptability, data quality and stability
Author(s) -
Halliday Lesley E.,
Peek Michael J.,
Ellwood David A.,
Homer Caroline,
Knight Marion,
Mclintock Claire,
JacksonPulver Lisa,
Sullivan Elizabeth A.
Publication year - 2013
Publication title -
australian and new zealand journal of obstetrics and gynaecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.734
H-Index - 65
eISSN - 1479-828X
pISSN - 0004-8666
DOI - 10.1111/ajo.12020
Subject(s) - stakeholder , medicine , simplicity , family medicine , philosophy , public relations , epistemology , political science
Background The A ustralasian M aternity O utcomes S urveillance S ystem ( AMOSS ) conducts active, prospective surveillance of severe maternal conditions in A ustralia and N ew Z ealand ( ANZ ). AMOSS captures greater than 96% of all births, and utilises an online, active case‐based negative reporting system. Aim To evaluate AMOSS using the U nited S tates C entres for D isease C ontrol ( MMWR 2001; 50 (RR13): 1–35.) surveillance system evaluation framework. Methods Data were gathered using multiple methods, including an anonymous online survey administered to 353 AMOSS data collectors, in addition to review of case data received during 2009–2011, documented records of project board and advisory group meeting minutes, publications, annual reports and the AMOSS database. Results AMOSS is a research system characterised by its simplicity and efficiency. The socio‐demographic, risk factor and severe morbidity clinical data collected on rare conditions are not duplicated in other routine data systems. AMOSS is functioning well and has sustained buy‐in from clinicians, stakeholders and consumers and a high level of acceptability to data collectors in ANZ maternity units. Conclusions AMOSS is the only existing national system of surveillance for rare and severe maternal conditions in ANZ and therefore serves an important function, utilising data collected from reliable sources, in an effective, efficient and timely way.