Household income (as a marker of socioeconomic position) and neighbourhood fast-food outlet exposure may be related to diet and body weight, which are key risk factors for non-communicable diseases. However, the research evidence is equivocal. Moreover, understanding the double burden of these factors is a matter of public health importance. The purpose of this study was to test associations of neighbourhood fast-food outlet exposure and household income, in relation to frequency of consumption of processed meat and multiple measures of adiposity, and to examine possible interactions.
Our study demonstrated independent associations of neighbourhood fast-food outlet exposure and household income, in relation to diet and multiple objective measures of adiposity, in a large sample of UK adults. Moreover, we provide evidence of the double burden of low income and an unhealthy neighbourhood food environment, furthering our understanding of how these factors contribute jointly to social inequalities in health.
Fast-food access may also contribute to established social inequalities in fast-food consumption and weight . Deprived neighbourhoods have generally greater numbers of fast-food outlets [16, 38], and there is evidence that the influence of neighbourhood environments vary by educational attainment as a marker of SEP . However, there has been little research on the interaction between fast-food access and household income, which may hold implications for diet and weight via different mechanisms. Low-income consumers, in particular, may be disproportionately affected by the presence of fast-food outlets , which serve large portions of energy-dense, calorific foods at low prices .
With mounting evidence of the adverse influence of fast-food outlets on health and the abundance of fast-food outlets in deprived areas, the proliferation of these outlets has become a public health concern. Policies are now in place in many regions of the UK to stem growth in this retail sector , while in the US, a moratorium was placed on the opening of new fast-food outlets in South Los Angeles, for example . At the same time, more empirical research is needed to better understand the magnitude of influence of fast-food outlets on health and their contribution to social inequalities.
Those with the highest proportion of fast-food outlets had 1.28 (95% CI: 1.19, 1.38) greater odds of being frequent processed meat consumers, relative to those with the lowest proportion. Corresponding risk ratios (RRs) for risk of obesity and frequent processed meat consumption related to fast-food proportion were similar in magnitude and again showed evidence of a dose-response association, and are shown in Additional file 5.
In a large UK adult sample, our results showed clear, consistent associations between neighbourhood fast-food outlet proportion and processed meat consumption, as well as proportion in relation to multiple objective measures of adiposity (body mass index and body fat percentage), including odds of obesity, and with some evidence of dose-response observed. We also showed independent associations between household income and each of these outcomes. We found no evidence of multiplicative interaction, suggesting that associations between fast-food proportion and our outcomes were not significantly different across household income groups. However, we demonstrated the magnitude of obesity and frequent processed meat consumption odds within population-subgroups, including the marginally excess odds (evidence of additive interaction) associated with both highest fast-food outlet proportion and lowest income for these outcomes. This double burden of individual-level disadvantage and neighbourhood-level imbalance towards fast-food retail, holds clear implications for public health and understanding the generation and persistence of social inequalities in diet, health and NCD risk [9, 10].
In this sub-group (those most exposed and with lowest incomes) we also observed marginally excess odds of both obesity and frequent processed meat consumption, over and above the additive effects of each risk factor in isolation, which is evidence of additive interaction. We observed no evidence of a differential impact of fast-food exposure across household income levels (multiplicative interaction), in contrast to a US study, which showed that neighbourhood fast-food access was only related to BMI among low income adults . However, the relative merits of assessing interaction on additive vs multiplicative scales has long been the subject of epidemiological debate. While analysis of multiplicative interactions is more commonplace, it has been suggested that additive interaction bears particular relevance to public health, for which a key concern is the risk of disease in the proportion of the population for whom the risk factors occur together [59, 60].
Our study demonstrated independent associations between each of income and neighbourhood fast-food exposure, with diet and two objectively measured adiposity outcomes, in a large sample of UK adults. Moreover, we provide evidence of the double burden of low income and an unhealthy neighbourhood food environment, resulting in an additive interaction and an excess and substantially greater likelihood of unhealthy diet and obesity. Although further work is necessary, these findings support emerging guidelines regarding the regulation of neighbourhood fast-food access for the promotion of population health.
In sum, while other studies using tDCS have found that participants with the lower abilities at baseline gain most from the stimulation (Looi et al. 2016; Sarkar et al. 2014; Tseng et al. 2012), these tRNS results provide further support for the homeostatic set point hypothesis of cortical excitability, in which no further improvement, or even impairments in abilities are found upon the induction of accumulated increases in cortical excitability (Krause and Cohen Kadosh 2014; Krause et al. 2013; Siebner et al. 2004). We were therefore able to provide preliminary evidence that individuals with high expertise in mathematics show calculation impairments under tRNS, when compared to sham. These initial findings add some value to ongoing discussions about the potential consequences of neuroenhancement (Maslen et al. 2014), especially as they might be associated with cognitive costs for the individual (Iuculano and Cohen Kadosh 2013; Sarkar et al. 2014). Ideally, these results will evoke further research to examine whether the NIBS user and the application to different individuals should be considered more carefully than previously assumed.
Misinformation and disinformationFootnote 2 come in endless guises and spread via different mechanisms, including campaigns of persistent inaccurate beliefs and falsehoods, deceptive messages, and engagement echo chambersFootnote 3 [13, 14]. The pandemic has brought a paper tsunami with widespread misinterpretation of both peer-reviewed research and preprints, press releases without scrutinizable data, sensationalized media reporting, and endless conspiracy theories [5, 11, 15, 16]. As a result, finding trustworthy sources of information and guidance on COVID-19 has been difficult for the public. Over the past months, logical fallacies and cognitive biases have relentlessly distracted from critical appraisal and transparent communication of the scientific evidence related to COVID-19 . Confirmation bias, availability bias, motivated reasoning, the Dunning-Kruger effect, black-or-white fallacy (also known as false dilemma, false dichotomy, either/or fallacy, or false choice), straw man fallacy, ad hominem fallacy, appeal to emotion, appeal to ignorance, and appeal to authority fallacies have all run rampant across social media.
The COVID-19 pandemic has been riddled with false dichotomies, which have been used to shut down or polarize debates while oversimplifying complex issues and obfuscating the accompanying nuances. In this review, we aimed to deconstruct six common COVID-19-related false dichotomies (Fig. 2) by reviewing the evidence thoughtfully and thoroughly: 1) Health and lives vs. economy and livelihoods, 2) Indefinite lockdown vs. unlimited reopening, 3) Symptomatic vs. asymptomatic SARS-CoV-2 infection, 4) Droplet vs. aerosol transmission of SARS-CoV-2, 5) Masks for all vs. no masking, and 6) SARS-CoV-2 reinfection vs. no reinfection. At least three trade-offs exist at the interface of science and policy related to this pandemic: clarity-complexity (simple messages vs. conveying uncertainty), speed-quality (timely responses vs. in-depth quality assessment), and data-assumption (data availability vs. required set of assumptions) [22, 23]. Therefore, while exploring challenging and contentious topics, we make the case for a nuanced understanding of COVID-19 science, identify insights relevant to effective pandemic responses, and highlight important research gaps. We also provide examples that echo the importance of interdisciplinary integration, epistemic uncertainty in risk communication, and public health during pandemics [20, 22, 24].
Mass gatheringsFootnote 13 deserve discussion. The risk in mass gatherings is expected to come from unplanned, informal, unregulated, and unmitigated events or activities that lack consideration of risk mitigation measures [40, 139]. Several factors influence transmission in these settings [40, 139, 146, 147]: 1) the environment (i.e., outdoor or indoor), since it contributes ventilation; 2) the geographic scope of the event and the extent to which vulnerable or susceptible individuals may be present (e.g., local vs. international event, attendee ages); 3) event-specific behaviors that influence transmission (e.g., communal travel, indoor congregation in other venues, congregate accommodations, face-to-face vs. side-to-side arrangement, loud conversations, shouting, singing); 4) gathering size, density, duration, and attendee circulation; 5) preparedness to conduct rapid contact tracing in the event of an outbreak; and 6) the multilayered prevention approach adopted. In addition, the underlying transmission levels or infection rates in a community are likely to influence the impact of either permitting or prohibiting mass gatherings. As for outdoor gatherings, upon consideration of crowd density, size, duration, circulation, and preventive interventions, public health officials may balance and mitigate risk across different factors mentioned [40, 139]. That is, an increase in one risk factor may be offset or mitigated by decreasing other risk factors. Therefore, all mass gatherings will not generate equal risks of SARS-CoV-2 transmission and will not need homogenous mitigations . Since mass gatherings may have sociocultural, economic, physical, and mental health implications, it is critical to consider the societal needs. For instance, Black Lives Matter protests in the USA were illustrative of the trade-offs offered by harm reduction. No evidence supported a growth in COVID-19 cases following the protests [66, 68], which may have been due to the outdoor environment and compensating behaviors such as the observed increase in stay-at-home and masking compliance during the protests. 2b1af7f3a8