Interventions to reduce meat consumption by appealing to animal welfare: Meta-analysis and evidence-based recommendations

Abstract

Reducing meat consumption may improve human health, curb environmental damage, and limit the large-scale suffering of animals reared in factory farms. Most attention to reducing consumption has focused on restructuring environments where foods are chosen or making health or environmental appeals. However, psychological theory suggests that interventions appealing to animal welfare might operate on distinct, potent pathways. We conducted a systematic review and meta-analysis evaluating the effectiveness of these interventions. We searched eight academic databases and extensively searched grey literature. We meta-analyzed 100 studies assessing interventions designed to reduce meat consumption or purchase by mentioning or portraying farm animals, that measured behavioral or self-reported outcomes related to meat consumption, purchase, or related intentions, and that had a control condition. The interventions consistently reduced meat consumption, purchase, or related intentions at least in the short term with meaningfully large effects (meta-analytic average risk ratio [RR] = 1.22; 95% CI: [1.13, 1.33]). We estimated that a large majority of true population effects (71%; [95% CI: 58%, 80%]) were stronger than RR=1.1 and that few were in the unintended direction. Via meta-regression, we identified some study and intervention characteristics that were associated with effect size. Risk-of-bias assessments identified both methodological strengths and limitations of this literature; however, results did not differ meaningfully in sensitivity analyses retaining only studies at the lowest risk of bias. Evidence of publication bias was not apparent. In conclusion, animal welfare interventions preliminarily appear effective in these typically short-term studies of primarily self-reported outcomes. Future research should use direct behavioral outcomes that minimize the potential for social desirability bias and are measured over long-term follow-up.

Awareness of and reactions to health and environmental harms of red meat among parents in the United States

“…For example, a recent meta-analysis suggested that interventions appealing to animal welfare (e.g. by portraying farm animals) hold promise for reducing meat purchases and consumption (68) . We also did not assess other potentially important aspects of message design, such as message framing (69)(70)(71) .…”

Meta‐regression methods to characterize evidence strength using meaningful‐effect percentages conditional on study characteristics

“…25 Multiplicative measures are also sometimes used in contexts when converting studies' estimates to a common, additive scale would invoke potentially unrealistic distributional assumptions, as was the case in the second applied example. 13 Additive measures are often more relevant to assessing interventions' effects on public health, for example when estimating the "number needed to treat" based on a risk difference or when identifying which subgroup to treat based on an additive interaction measure. 26 When assessing public health effectiveness in this sense, then regardless of the scale on which analyses are conducted, the effect measures should be converted to public healthrelevant measures before one considers whether effects are meaningfully strong.…”

“…Mathur et al 13,34 conducted a meta-analysis to assess the effectiveness of educational behavior interventions that attempt to reduce meat consumption by appealing to animal welfare. They meta-analyzed 100 studies (from 34 articles) of such interventions, all of which measured behavioral or self-reported outcomes related to meat consumption, purchase, or related intentions and had a control condition.…”

“…7,8 In practice, the metrics have been successfully and informatively applied to metaanalyses on a variety of topics. 7,[9][10][11][12][13] Here, we provide extensions to meta-regression that address the two questions above by characterizing, for a chosen level of the meta-regression covariates, the percentage of population effects that are above or below the threshold q. This metric helps characterize evidence strength for meaningfully strong effects in studies with a particular level of the covariates, and it could also be used as a hypothesis-generating method to identify which joint levels of the covariates are associated with the largest estimated percentages of meaningfully strong effects.…”

“…The methods involve first fitting a standard meta-regression (e.g., using semiparametric methods that do not make assumptions on the distribution of population effects [14][15][16] ), using the resulting estimates to appropriately "shrink" studies' point estimates toward the mean, and then estimating the proposed metrics using the empirical distribution of these shrunken estimates. We illustrate by re-analyzing data from two previously published meta-analyses, 13,17 demonstrating that the proposed metrics can provide more information than standard reporting alone (Section 4). We assess the methods' performance in a simulation study that includes a variety of realistic and challenging scenarios (Section 5) and use the results to inform practical reporting guidelines (Section 2.3).…”