On January 9-11, the 14th annual International Conference on Health Policy Statistics (ICHPS) will be taking place in Scottsdale, Arizona. The theme of this year’s conference is upgrading the pipeline from health data to health policy. Multiple presenters from NORC will attend.

Tuesday, January 10

9:00-10:45 AM MST

Improving Health Policy Decisions by Using Novel Record Linkage Methods

Race and Ethnicity Modeling Applied to Linked Health Data

NORC Presenter: Dean Resnick

Linked data enable robust analyses using variables from more than one source and can be used to validate those on either. The National Center for Health Statistics (NCHS) links survey and administrative data to expand the analytic utility of its surveys. The National Hospital Care Survey collects administrative claims or electronic health record (EHR) data on patients from participating hospitals. Reporting of race/ethnicity in these records is sparse and inconsistent.

Wednesday, January 11

11:30 -11:45 AM MST

Methods and applications in risk prediction

Use NER to Derive Health Policy Insights from Social Media Data

NORC Presenter: Andrew Norris

Having up-to-date information about public opinion and marketing dynamics surrounding tobacco and vaping products is vital to public health policy. Not only do many marketing campaigns target young audiences, but also the spread of misinformation about the health impacts of these products is frequent in certain communities. One central task involved in monitoring marketing and public opinion dynamics around tobacco and vaping products entails identifying specific brands and product flavors. Social media data offer the promise of near real-time monitoring, but, because the data are unstructured and largely text, locating mentions of brands and flavors is a methodologically onerous task. This presentation showcases NORC's efforts using natural language processing (NLP) to address this challenge.

Analysis of Complex Health Survey Data

3:15 -5:15 PM MST

NORC Presenter: Stanislav Kolenikov

Many datasets used to inform health policy decisions come from observational studies such as large scale surveys conducted by federal, state and local governments and agencies. Examples include the National Health Interview Survey (NHIS), National Health and Nutrition Examination Survey (NHANES), Medicare Current Beneficiary Survey (MCBS), Behavioral Risk Factors Surveillance Survey (BRFSS) or National Immunization Survey (NIS). Because these survey data violate the i.i.d. assumptions of standard statistical methods, they require specia