180482 Imputation strategies for child poverty level

Wednesday, October 29, 2008: 11:30 AM

Kai Ya Tsai, MSPH , Center for Community Health Studies, University of Southern California, Alhambra, CA
Trevor A. Pickering, MS , Center for Community Health Studies, University of Southern California, Alhambra, CA
Gregory D. Stevens, PhD , Center for Community Health Studies, University of Southern California, Alhambra, CA
Background: Most researchers using survey data encounter incomplete observations. The nature of missing values may generate unwanted bias and diminish power, affecting the standard error and model estimates.

Objective: All survey data have missing information on socio-demographic variables— especially poverty level. Because most of the national children's health surveys are available for public use and poverty level is a key demographic variable, it is important to develop imputation strategies for child poverty level.

Methods: Poverty status was grouped as more or less than 200% of the federal poverty level and single imputation (multivariable logistic regression) was performed for complete cases. Variables associated with poverty level, but not related to other outcomes of interest in our hypotheses (to avoid potential co-linearity) were chosen for the model. Since the actual values for complete cases are known, this provided a basis for assessing the imputation method.

Results: The imputation produced a correct classification rate of 80% for all complete cases. We then predict values for the 8.4% of cases with incomplete poverty level data. Among other variables, parents' health insurance status, parents' health condition, and family structure were used. After the imputation procedure, 25% and 75% of the incomplete cases assigned to less than and more than 200% of poverty level, respectively.

Conclusion: Choosing the most suitable imputation method depends on the purpose and structure of each specific dataset. Since it is almost impossible to avoid missing values with survey data, correctly using statistical imputation can be useful to overcome this problem.

Learning Objectives:
1. Develop imputation strategies for child poverty level in public used survey data. 2. Analyze incomplete data using imputed values.

Keywords: Children and Adolescents, Survey

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I have Master of Science degree in Public Health in Biostatistics and I am the statistician for the center and have been work on children's health dataset for 2 years.
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.

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