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307326
Quantifying Geographic Risk for Poor Birth Outcomes: A Composite Indicator Analysis
Monday, November 17, 2014
Jana Goins, MHS
,
Bureau of Maternal and Child Health, Baltimore City Health Department, Baltimore, MD
Rebecca S. Dineen, MS
,
Maternal and Child Health, Baltimore City Health Department, Baltimore, MD
Aruna Chandran, MD
,
Baltimore City Health Department, Baltimore, MD
Baltimore City has one of the highest infant mortality rates among large US cities, averaging 11.5 per 1000 live births from 2006-2012. Birth outcomes are driven by a variety of factors such as individual health behaviors, community awareness, and access to primary health care services. Historically Baltimore City has used specific birth outcome data, such as infant mortality rates, to target high-risk areas for points of intervention, but this strategy may not adequately capture social determinants or other geographic risk factors. In 2013, the City opted to use a composite indicator analysis to incorporate multiple data points into a single measure to better assess such complex risk. This method, frequently used to measure performance for economic development, was applied in a novel way to help maternal and child health programs in Baltimore City identify neighborhoods at highest risk for poor birth outcomes. The first step identified five overarching domains that best framed the risk. Next, several indicators were identified under each domain, based on their data availability and relationship to the outcome. Co-linearity was determined between indicators within each domain, and only non-collinear indicators were kept. The remaining indicators were compiled into a single indicator of measurement and used to quantify and rank risk for each Baltimore City community statistical area (CSA). The final CSA rankings were mapped using geo-spatial software (ArcGIS) to show risk of poor birth outcomes by Baltimore City neighborhood. This composite indicator analysis allowed local programs to assess risk for poor birth outcomes in a more nuanced manner that accounted for multiple risk factors that varied by geographic location. This may be a relevant approach for maternal and child health programs to take when determining where to concentrate efforts and resources.
Learning Areas:
Epidemiology
Learning Objectives:
Demonstrate the geographic variation of risk using both individual and neighborhood level data points in a composite indicator analysis
Keyword(s): MCH Epidemiology
Presenting author's disclosure statement:Qualified on the content I am responsible for because: I am the principal epidemiologist for the Bureau of Maternal & Child Health at the Baltimore City Health Department. A large majority of my work is focused on identifying risk for poor birth outcomes among low-income Baltimore City residents. My research interests include identifying geographic areas and sub-populations of high need through the systematic analysis of data and ultimately informing community-based interventions to improve the health of women and infants in the City.
Any relevant financial relationships? No
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.
