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30.11.2017 | Original Contribution

Linking Time-Use Data to Explore Health Outcomes: Choosing to Vaccinate Against Influenza

verfasst von: Kevin Berry, Julia E. Anderson, Jude Bayham, Eli P. Fenichel

Erschienen in: EcoHealth | Ausgabe 2/2018

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Abstract

To inform public health and medical decision makers concerning vaccination interventions, a methodology for merging and analyzing detailed activity data and health outcomes is presented. The objective is to investigate relationships between individual’s activity choices and their decision to receive an influenza vaccination. Data from the Behavioral Risk Factor Surveillance System (BRFSS) are used to predict vaccination rates in the American Time Use Survey (ATUS) data between 2003 and 2013 by using combined socioeconomic and demographic characteristics. The correlations between the extensive (do or not do) and intensive (how much) decisions to perform activities and influenza vaccination are further explored. Significant positive and negative correlations were found between several activities and vaccination. For some activities, the sign of the correlation flips when considering either the intensive or the extensive decision. This flip occurs with highly studied activities, like smoking. Correlations between activities and vaccination can provide an additional metric for targeting those least likely to vaccinate. The methodology outlined in this paper can be replicated to explore correlation among actions and other health outcomes.

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Charts and tables of time spent on various activities by demographic characteristics are available online at the ATUS website, https://www.bls.gov/tus/home.htm/. The website also includes coding lexicons, data dictionaries, and examples of the questionnaires used. Average time spent on various activities is available on the ATUS Tables subpage. A full categorized activity codebook is available in Table S2 in the online appendix.

BRFSS data are available at https://www.cdc.gov/brfss/about/index.htm. This site includes survey data, documentation, and examples of the questionnaires used.

Omitting variables from the regression may bias specific parameter estimates. However, our goal is to predict the likelihood of vaccination and not study the determinants of vaccination. Vaccination determinants such as health care/insurance are correlated with income and employment status, which bias the specific coefficient estimates on income and employment status when omitted but should not affect the out-of-sample prediction for the ATUS respondents of interest.

This is analogous to the problem of underestimating the standard error of an imputed variable.

We chose 100 estimates due to computing time constraints, currently the intensive decision model takes 4.37 h to estimate, and the extensive decision takes 22.75 h to estimate.

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Titel: Linking Time-Use Data to Explore Health Outcomes: Choosing to Vaccinate Against Influenza
verfasst von: Kevin Berry
Julia E. Anderson
Jude Bayham
Eli P. Fenichel
Publikationsdatum: 30.11.2017
Verlag: Springer US
Erschienen in: EcoHealth / Ausgabe 2/2018
Print ISSN: 1612-9202
Elektronische ISSN: 1612-9210
DOI: https://doi.org/10.1007/s10393-017-1296-z

Live-Webinar: Aktuelle Leitlinien bei Herz-Kreislauf-Erkrankungen

Springer Medizin

Linking Time-Use Data to Explore Health Outcomes: Choosing to Vaccinate Against Influenza

Abstract

Live-Webinar: Aktuelle Leitlinien bei Herz-Kreislauf-Erkrankungen

Springer Medizin

Abstract

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