Referendum opposition to fluoridation and health literacy: a cross-sectional analysis conducted in three large US cities.

OBJECTIVE: To explore health literacy as a marker of voter confusion in order to understand the basis for public opposition to community water fluoridation. DESIGN: A cross-sectional study. SETTING: Conducted in three large US cities of San Antonio, Texas (602 voting precincts); Wichita, Kansas (171 voting precincts); and Portland, Oregon (132 voting precincts). Precinct-level voting data were compiled from community water fluoridation referendums conducted in San Antonio in 2002, Wichita in 2012 and Portland in 2013. PARTICIPANTS: Voter turnout expressed as a percentage of registered voters was 38% in San Antonio (n=2 92 811), 47% in Wichita (n=129 199) and 38% in Portland (n=164 301). MAIN OUTCOME MEASURES: The dependent variable was the percentage of votes in favour of fluoridating drinking water. Precinct-level voting data were mapped to precinct scores of health literacy, and to US Census and American Community Survey characteristics of race/ethnicity, age, income and educational attainment. Multilevel regression with post-stratification predicted the precinct mean health literacy scores, with weights generated from the National Association of Adult Literacy health literacy survey, with item response theory computed scoring for health literacy. Predictive models on voter support of community water fluoridation were compared using robust linear regression to determine how precinct-level characteristics influenced voter support in order to determine whether health literacy explained more variance in voting preference than sociodemographic characteristics. RESULTS: Precinct-level health literacy was positively associated with voter turnout, although sociodemographic characteristics were better predictors of turnout. Approximately 60% of voters opposed community water fluoridation in Wichita and Portland, whereas in San Antonio, a small majority (53%) voted in favour of it. Models suggest that a one SD increase in health literacy scores predicted a 12 percentage point increase support for community water fluoridation. CONCLUSION: Educational attainment and health literacy are modifiable characteristics associated with voting precincts' support for community water fluoridation.

Fluoridation advocacy in referenda where media coverage is balanced yet biased.

BACKGROUND: Despite supporting scientific evidence, community water fluoridation (CWF) often fails in public referenda. To understand why, the authors quantitatively analyzed text from news media coverage of CWF referenda. METHODS: The authors analyzed text from 234 articles covering 11 CWF referenda conducted in 3 US cities from 1956 through 2013. The authors used cluster analysis to identify each article's core rhetoric and classified it according to sentiment and tone. The authors used multilevel count regression models to measure the use of positive and negative words regarding CWF. RESULTS: Media coverage more closely resembled core rhetoric used by fluoridation opponents than the rhetoric used by fluoridation proponents. Despite the scientific evidence, the media reports were balanced in tone and sentiment for and against CWF. However, in articles emphasizing children, greater negative sentiment was associated with CWF rejection. CONCLUSIONS: Media coverage depicted an artificial balance of evidence and tone in favor of and against CWF. The focus on children was associated with more negative tone in cities where voters rejected CWF. PRACTICAL IMPLICATIONS: When speaking to the media, advocates for CWF should emphasize benefits for children and use positive terms about dental health rather than negative terms about dental disease.

Scientific Training in the Era of Big Data: A New Pedagogy for Graduate Education.

The era of "big data" has radically altered the way scientific research is conducted and new knowledge is discovered. Indeed, the scientific method is rapidly being complemented and even replaced in some fields by data-driven approaches to knowledge discovery. This paradigm shift is sometimes referred to as the "fourth paradigm" of data-intensive and data-enabled scientific discovery. Interdisciplinary research with a hard emphasis on translational outcomes is becoming the norm in all large-scale scientific endeavors. Yet, graduate education remains largely focused on individual achievement within a single scientific domain, with little training in team-based, interdisciplinary data-oriented approaches designed to translate scientific data into new solutions to today's critical challenges. In this article, we propose a new pedagogy for graduate education: data-centered learning for the domain-data scientist. Our approach is based on four tenets: (1) Graduate training must incorporate interdisciplinary training that couples the domain sciences with data science. (2) Graduate training must prepare students for work in data-enabled research teams. (3) Graduate training must include education in teaming and leadership skills for the data scientist. (4) Graduate training must provide experiential training through academic/industry practicums and internships. We emphasize that this approach is distinct from today's graduate training, which offers training in either data science or a domain science (e.g., biology, sociology, political science, economics, and medicine), but does not integrate the two within a single curriculum designed to prepare the next generation of domain-data scientists. We are in the process of implementing the proposed pedagogy through the development of a new graduate curriculum based on the above four tenets, and we describe herein our strategy, progress, and lessons learned. While our pedagogy was developed in the context of graduate education, the general approach of data-centered learning can and should be applied to students and professionals at any stage of their education, including at the K-12, undergraduate, graduate, and professional levels. We believe that the time is right to embed data-centered learning within our educational system and, thus, generate the talent required to fully harness the potential of big data.