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

DISCUSSION

There were three principal findings from the text analysis. First, news coverage of CWF reflects some evidence of a media balance bias. Second, greater reference to children is associated with an increase in non-neutral terms. Third, the extent of negative tone is more strongly associated with CWF defeat as an article’s number of references to children increases.

Our results suggest that the tone in articles is approximately balanced, with a slight favor towards pro-CWF terms than anti-CWF. However, the media balances positive with negative, and pro-CWF with anti-CWF, when children are written about. These results are consistent with complaints by scientists that journalists force a false balance of arguments, and hence generate controversy.^16,17 Coverage of CWF largely appears to conform with media desires to appear balanced to the general audience and sell the news.

Among referendum items, CWF is infamous for its level of polarization despite being an established public health good. Opposition to marginal increases in public water fluoridation arose upon the inception of CWF, with accusations against fluoride focused on the theme of forced medication,¹² dangerous side effects¹⁸ and contamination of natural resources.^4,12,18 Due to public contention, water fluoridation is the most competitive type of referendum and receives the lowest level of support in the event of passage.¹⁹ Among the initial reasons given to CWF’s poor performance is that CWF opponents “[N]eed only to create doubt about fluoridation; they do not need to convince the electorate of all their points” (page 60, italics in original).⁷ The concept of doubt derailing changes to the status quo has since received wider support in research on issue framing and agenda setting; those who seek to change status quo policy need to refute the opposition and present a positive message to make clear that their alternative is superior.^20,21 When anti-fluoride activists raise concerns of fluoride-induced cancer, coupled with the frame of easy opt-in to fluoride via tablets, CWF referendums can easily fail.^12,18,22

In this study, the media gave similar coverage to anti-fluoride arguments and scientific evidence, especially in relation to children. Although we do not know how voters interpreted these conflicting views, the balanced yet biased coverage did not aid pro-fluoride activists.

Where CWF won might offer some insight in CWF messaging. Although mention of children drove the contentious balanced coverage, the rate at which negative and anti-CWF terms were mentioned was significantly less. When people read the news, they read less generally negative and anti-CWF coverage associated with children. Given that people often tend to forget the context of news and associate the tone of coverage with the appeal of the issue in question,^2,14,23 it makes sense that more negative coverage in general might impair passage of CWF. Further, the targeted use of anti-CWF terms in relation to children may very well have associated fluoride with harm to children. Case studies conducted of fluoridation in 1985 found that positive messaging and direct outreach greatly aided in the passage of CWF.²⁴

Given the importance of doubt in derailing CWF referendums, it is necessary to consider the messaging that voters receive from the media. If doubt is all that is needed to prevent pro-fluoridation votes, then even a few hints of anti-fluoride information may be enough to bring down CWF. Fluoride campaigns in California in the 1950s only succeeded in the event of overwhelming support for fluoride without any serious opposition.¹⁸ More recently in Portland, Oregon in 2013, even purportedly balanced coverage did not prevent a lopsided defeat for CWF.¹² Established models of referendum voting demonstrate that voters vote for a change in the status quo only when they are certain that the change will result in a better outcome.²⁰ Given that the average person does not scientifically understand fluoridation or the credibility of information sources,^4,25 mixed messages that introduce even some uncertainty should be enough to lead to a rejection at the polls.

There is no easy way forward to overcome reluctance to accept CWF. One step is to start employing an easy to understand and positive message with direct outreach to voters.²⁴ Handing out accessible information at dental offices and door-to-door would bypass the media filter and resolve confusion. While this will require resources and effort, the status quo does not appear to be working.

Acknowledgments

APPENDIX

Determining Count Model Fit

We determined the best model for tone and sentiment based on first, whether the model converged, and second, the presence of over dispersion in the data. Poisson regression assumes that the count mean and variance are equivalent.¹ When this is not true, the results produced will generally be biased. Negative binomial models account for variances greater than the mean and scale the standard errors relative to the level of over dispersion. However, negative binomial models take up more degrees of freedom and can lead to inefficiency. Given that we account for three different random effects, city, election date, and media type, there is the risk of non-convergence as the model becomes impossible to compute.² Unlike single level models, multilevel models with random effects can reduce the bias that would otherwise afflict Poisson regression models, with observation level effects eliminating all potential bias.³ We therefore seek the best unbiased model that is also computationally possible.

Figure 1 demonstrates the mean relative to the variance for all four models, fitting the data to Poisson and negative binomial lines. As can be seen in the figures, the Poisson models do a sufficient job at explaining the data up until the high mean values. It is at the higher means that the negative binomial lines adjust easier to the higher variances and account for potential bias. It would therefore be ideal to use negative binomial models. However, besides the positive count data, the models do not converge when negative binomial models are used. This is due to the random effects providing too few degrees of freedom. While unfortunate, given that the higher mean values are few in number, the Poisson multilevel models as used for models 2 – 4 offer the best, albeit, imperfect means to run our analyses.

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

Appendix Table 1

Characteristics of referendums and source of articles

	Portland, Oregon					Wichita, Kansas			San Antonio, Texas
Referendum year	1956	1962	1978	1980	2013	1964	1978	2012	1966	1985	2000
Referendum decision	Against	Against	For	Against	Against	Against	Against	Against	Against	Against	For
% voting in favor of fluoridation	42%	45%	51%	46%	39%	37%	46%	40%	32%	48%	53%
Number of votes cast	181,140	144,300	139,373	115,408	164,301	50,997	84,139	129,199	38,855	81,373	292,811
Population size	373,628	372,676	382,619	366,383	733,764	254,698	276,554	356,724	587,718	785,880	1,385,695
News media type
Article	14	13	14	10	12	19	5	23	5	19	26
Editorial	1	2	2	2	12	7	4	2	12	6	18
Advertisement	0	0	0	0	0	1	1	0	4	0	0
Total	15	15	16	12	24	27	10	25	21	25	44U

Open in a separate window

Footnotes

References