State and regional estimates using seven cycles of pooled nationally representative HINTS data. (March 2022)

• Record Type: Journal Article
• Authors: Harding, Lee; Iachan, Ronaldo; Martin, Kelly; Deng, Yangyang; Middleton, Deirdre; Moser, Richard; Blake, Kelly
• Is Part Of: Social science & medicine. Volume 297(2022)
• Publication Date: 2022-03
• Subjects: Regional estimates -- Survey weights -- Pooled data -- Cancer epidemiology; Social medicine -- Periodicals; Medical anthropology -- Periodicals; Public health -- Periodicals; Psychology -- Periodicals; Medicine -- Periodicals; Medicine -- Periodicals; Médecine sociale -- Périodiques; Anthropologie médicale -- Périodiques; Santé publique -- Périodiques; Psychologie -- Périodiques; Médecine -- Périodiques; Electronic journals; 362.105
• DOI: 10.1016/j.socscimed.2022.114724
• Languages: English
• ISSNs: 0277-9536

Abstract

Abstract: Background: The Health Information National Trends Survey (HINTS) is a probability-based, nationally representative survey conducted routinely to gather information about the American public's cancer-related beliefs and behaviors, including the use of cancer-related information. HINTS was created to produce national estimates and has lacked the ability to create accurate and precise state and regional estimates. The motivation for this current work was to create state- and regional-level estimates using a national sample (HINTS) through standard calibration methods. Health estimates at a local level can inform policy decisions that better target the cancer needs within a community. Local-level data allow researchers an opportunity to examine local populations in finer detail without additional costly data collection. Methods: By combining seven cycles of HINTS data from 2012 to 2018 and then raking the previously created person-level weights, we were able to create tables and maps of HINTS subnational survey estimates for key outcomes that have small variances and little potential bias. Results and conclusion: This paper describes the methods used to harmonize and aggregate data across cycles, create state- and regional-level estimates from the pooled data, and produce survey weights for the pooled datasets. It demonstrates both the opportunities and the challenges of pooled data analysis. Highlights: State- and regional-level estimates through standard calibration methods. Data harmonized and aggregated across cycles to produce a weighted dataset. Pooling national cancer survey data to create state and regional estimates. Subnational estimates can help drive cancer related health policy decisions. Aggregate data to increase the statistical power of estimates for small areas.