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. 2018 Apr;149(4):273-280.e3.
doi: 10.1016/j.adaj.2017.10.016. Epub 2018 Feb 14.

Fluoridation advocacy in referenda where media coverage is balanced yet biased

John A Curiel, Anne E Sanders, Thu-Mai L Christian, Sophia Lafferty-Hess, Thomas M Carsey, Lewis N Lampiris, Gary D Slade

Fluoridation advocacy in referenda where media coverage is balanced yet biased

John A Curiel et al. J Am Dent Assoc. 2018 Apr.

Abstract

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.

Keywords: Fluoridation; drinking water; health promotion; public health and community dentistry; public opinion.

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Figures

Figure 1
Figure 1. Count Model Dispersion Plots
Plot (a) is model 1 of the positive word count, plot (b) is model 2 of the negative word count, plot (c) is model 3 of the pro-CWF word count, and plot (d) is model 4 of the anti-CWF word count. All plots demonstrate the best fit lines of the multilevel Poisson models compared to the negative binomial models. For all models, the Poisson model explains most of the data up until the high mean values. Higher means are better explained by negative binomial models. However, in the data the random effects largely explained by the high mean values, and the lack of degrees of freedom lead to a failure of multilevel negative binomial models to converge. Therefore, the Poisson multilevel models explaining the data sufficiently
Figure 1
Figure 1
Figure 2
Figure 2
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