title: Geospatial analyses to identify clusters of adverse antenatal factors for targeted interventions.
abstract: BACKGROUND
  Late antenatal care and smoking during pregnancy are two important factors that are amenable to intervention. Despite the adverse health impacts of smoking during pregnancy and the health benefits of early first antenatal visit on both the mother and the unborn child, substantial proportions of women still smoke during pregnancy or have their first antenatal visit after 10 weeks gestation. This study was undertaken to assess the usefulness of geospatial methods in identifying communities at high risk of smoking during pregnancy and timing of the first antenatal visit, for which targeted interventions may be warranted, and more importantly, feasible.
  
  
  METHODS
  The Perinatal Data Collection, from 1999 to 2008 for south-western Sydney, were obtained from the New South Wales Ministry of Health. Maternal addresses at the time of delivery were georeferenced. A spatial scan statistic implemented in SaTScan was then used to identify statistically significant spatial clusters of women who smoked during pregnancy or women whose first antenatal care visit occurred at or after 10 weeks of pregnancy.
  
  
  RESULTS
  Four spatial clusters of maternal smoking during pregnancy and four spatial clusters of first antenatal visit occurring at or after 10 weeks were identified in our analyses. In the maternal smoking during pregnancy clusters, higher proportions of mothers, were aged less than 35 years, had their first antenatal visit at or after 10 weeks and a lower proportion of mothers were primiparous. For the clusters of increased risk of late first antenatal visit at or after 10 weeks of gestation, a higher proportion of mothers lived in the most disadvantaged areas and a lower proportion of mothers were primiparous.
  
  
  CONCLUSIONS
  The application of spatial analyses provides a means to identify spatial clusters of antenatal risk factors and to investigate the associated socio-demographic characteristics of the clusters.
authors.affiliation: Centre for Research, Evidence Management and Surveillance, South Western Sydney Local Health Districts, Sydney, Australia. Shanley.chong@sswahs.nsw.gov.au.
authors.forename: Shanley
authors.initials: S
authors.lastname: Chong
authors.forename: Michael
authors.initials: M
authors.lastname: Nelson
authors.forename: Roy
authors.initials: R
authors.lastname: Byun
authors.forename: Liz
authors.initials: L
authors.lastname: Harris
authors.forename: John
authors.initials: J
authors.lastname: Eastwood
authors.forename: Bin
authors.initials: B
authors.lastname: Jalaludin
mesh_terms: D000293:Adolescent; D000328:Adult; D016000:Cluster Analysis; D061345:Early Medical Intervention / Q000379:methods*; D005260:Female; D040362:Geographic Information Systems; D062305:Geographic Mapping; D006801:Humans; D009517:New South Wales / Q000453:epidemiology; D011159:Population Surveillance / Q000379:methods*; D011247:Pregnancy; D011295:Prenatal Care / Q000379:methods*; D012907:Smoking / Q000453:epidemiology*; D000074606:Smoking Prevention; D055815:Young Adult