Back to Annual Meeting

Back to Annual Meeting

Abstract #160327

• Slides (pdf) or Handout

Richard Summerhayes, MPH, Northern Rivers University Department of Rural Health, Southern Cross University, PO Box 3074, Lismore, Australia, Geoff Morgan, PhD, Northern Rivers University Department of Rural Health, University of Sydney; Southern Cross University; North Coast Area Health Service, PO Box 3074, Lismore, Australia, Arul Earnest, MSc, Northern Rivers University department of Rural Health, University of Sydney; Southern Cross University, PO Box 3074, Lismore, 2480, Australia, Therese M. Dunn, B Health Sci, Division of Population Health, North Coast Area Health Service, 31 Uralba St, Lismore, 2480, Australia, Danielle Taylor, MPH, National Centre for Social Applications of GIS, University of Adelaide, Level 7 Napier Building, University of Adelaide, Adelaide, 5005, Australia, and John R. Beard, MBBS PhD, FAFPHM, Centre for Urban Epidemiologic Studies, New York Acadamy of Medicine, 1216 Fifth Avenue, New York, NY 10029, 212-822-7378, jbeard@nyam.org.

The association between socioeconomic disadvantage and low birth weight is well known. We explored the impact of disadvantage on birth weight by undertaking spatial analysis of all births occurring to residents of New South Wales, Australia, from 1990-2003. We also investigated the influence of other covariates including smoking and remoteness. We geocoded the place of residence for 1,210,858 of 1,221,079 births, and assigned each to one of 587 postcodes. We determined the socioeconomic status of each postcode using the Australian Index of Relative Social Disadvantage, a composite Census derived index that combines variables relating to education, occupation, non-English speaking background, Indigenous origin and the economic resources of households. We examined and mapped the spatial distribution of this data, using Conditional Autoregressive (CAR) modeling to determine the relationship between the relative disadvantage of each area and low birth weight (<2.5 kg). Mean birth weight was 3.37 kg with a standard deviation of 600g. 93,723 births were of 2.5 kg or less, and CAR analysis suggested that 56% of the variation in risk of low birth weight between areas was spatial. An interquartile range increase in disadvantage was associated with a 15% increase in the risk of low birth weight, 7% increase in the risk of a pre term birth and 20% increase in the risk of a small for gestational age baby. The provision of maternity and antenatal care needs to take account of the increased birth risks associated with neighborhood disadvantage.

Learning Objectives:

• Recognize that risk of low birth weight is spatially distributed and may be influenced by a range of social factors.
• Describe the association between area level socio-economic status and low birth weight.
• Apply the findings of spatial analysis of birth weight to service planning.

Keywords: Birth Outcomes, Epidemiology

Presenting author's disclosure statement:

Any relevant financial relationships? No
Any institutionally-contracted trials related to this submission?

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.

• Slides (pdf) or Handout