Perceptions of farm animal sentience and suffering: evidence from the BRIC countries and the United States

Citation: Mata, F.; Jaeger, B.; Domingues, I. Perceptions of Farm Animal Sentience and Suffering: Evidence from the BRIC Countries and the United States. Animals 2022, 12, 3416. https://doi.org/10.3390/ ani12233416 Academic Editors: Kristine Hill, Michelle Szydlowski, Jes Hooper and Sarah Oxley Heaney Received: 15 October 2022 Accepted: 1 December 2022 Published: 4 December 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). animals Article Perceptions of Farm Animal Sentience and Suffering: Evidence from the BRIC Countries and the United States Fernando Mata 1,* , Bastian Jaeger 2and Ivo Domingues 3,4 1 Centre for Research and Development in Agri-Food Systems and Sustainability, Instituto Politécnico de Viana do Castelo, 4900-367 Viana do Castelo, Portugal 2Department of Social Psychology, Tilburg University, 5037 AB Tilburg, The Netherlands 3Instituto de Ciências Sociais, Universidade do Minho, 4710-057 Braga, Portugal 4Centro de Estudos de Comunicação e Sociedade, Universidade do Minho, 4710-057 Braga, Portugal *Correspondence: [email protected] Simple Summary: The relations between farm animals and humans vary across countries and cultures. It was the aim of this study to understand the position of the population in the BRIC countries (Brazil, Russia, India, and China) and the USA. It was found that perceptions of farm animal sentience and suffering vary a lot with culture, country, gender, and age. This could have important consequences for the globalized trade of animal products does not find common grounds for standardization, and the risk of countries with more advanced animal welfare legislation imposing trade barriers increases. These trade barriers may be precepted as protectionism by exporting countries. Abstract: In this study, we examined how beliefs about farm animal sentience and their suffering vary across culture and demographic characteristics. A total of N = 5027) questionnaires were administered in Brazil, Russia, India, China, and the USA. Brazilians showed higher and Chinese lower levels of perceived animal sentience. In Russia and India, the perception of suffering and sentience increases with age, with similar levels to those observed in the USA. In all the countries, more people agreed than disagreed that animals are sentient. Men in India show higher levels of agreement with the relation between eating meat and animal suffering, followed by women in Brazil and China. Lower levels of agreement are observed in Americans and Chinese. Women show higher levels of compassion than men. In Russia, there is a slightly higher level of agreement between men and in the USA younger men agree more. Young American men show higher levels of agreement, while in India and China age has the opposite effect. For fair trading competition, it is important to standardize procedures and respect the demand for both animal protein and its ethical production. Overall, our results showed that perceptions of farm animal sentience and suffering vary substantially across countries and demographic groups. These differences could have important consequences for the perceived ethicality of meat production and consumption, and for global trade in animal products. Keywords: farm animal sentience; farm animal suffering; BRIC countries; USA; ethics of meat consumption; meat trading standards 1. Introduction Sustainability is a growing concern today. While aiming for agriculture production systems capable of feeding the world, we need to preserve the environment and use natural resources wisely. Under these circumstances, meat production and consumption patterns must also reach sustainable standards. Meat production is the main cause of greenhouse gas emissions and in the consumption of water. Together with sustainable standards, ethical standards are also demanded by today’s informed society. Farm animal welfare (FAW) is part of those standards and is typically defined as “a potentially measurable quality of a living animal at a particular time” [ 1 ]. The first step Animals 2022,12, 3416. https://doi.org/10.3390/ani12233416 https://www.mdpi.com/journal/animals Animals 2022,12, 3416 2 of 18 for FAW is the ability to satisfy a basic need, often referred to as the five freedoms. The term “five freedoms” was coined in the UK by the Farm Animal Welfare Council, is now accepted worldwide, and refers to animals having [2]: 1. “Freedom from hunger and thirst, by ready access to water and a diet to maintain health and vigor. 2. Freedom from discomfort, by providing an appropriate environment. 3. Freedom from pain, injury, and disease, by prevention or rapid diagnosis and treatment. 4. Freedom to express normal behavior, by providing sufficient space, proper facilities, and appropriate company of the animal’s own kind. 5. Freedom from fear and distress, by ensuring conditions and treatment, which avoid mental suffering.” From the perspective of the human relationship with the non-human animal, though, FAW as a construct must also include human obligations towards animals, often referred to as animal rights. It has been suggested that the five freedoms concept needs a broader interpretation [ 3 ] to include the recognition of animal sentience, defined as an animal “having the awareness and cognitive ability necessary to have feelings” [ 4 ]. Despite the existence of historical discourse about animals’ feelings, ranging from the classic Greek thinkers Hippocrates, and Pythagoras to Charles Darwin [ 5 ], most developments in the legal recognition of animal sentience have been made in recent years. European animals gained this official recognition in 1999 through the EU treaty of Amsterdam, which was later complemented with a protocol on their protection and welfare via the 2007 EU Treaty of Lisbon, adopted in 2009, and entering into force with the Directive 2010/63/EU. This event triggered a global reaction, in Western societies (e.g., Canada, Colombia, New Zealand, Switzerland, Turkey, Ukraine, USA) [ 6 ]. More recently, 180 countries adopted the OIE Global FAW Strategy 2017, including the recognition of animal sentience, and up to 46 countries supported the UN’s Universal Declaration on Animal Welfare [7]. The recognition that positive affective experiences are a good indicator of FAW is paramount in connecting animal sentience with more practical considerations of FAW [ 7 ]. In the present, anthropogenic suffering, or the acknowledgement of animal suffering caused by human actions, is seen as the major step towards an ethical relationship between humans and animals [ 8 ]. The role of humans in animal suffering is particularly apparent for farm animals that are kept with the explicit purpose of serving human needs [9]. Humans’ strong demand for animal products and widely held beliefs about the justifiability of meat consumption [ 10 ] makes complete independence from food of animal origin in the near future, unlikely. However, even in the short term, there is room for improvement in the sustainability of animal agriculture to preserve the environment, protect biodiversity, and feed the world’s growing population, while also working towards a more ethical treatment of farm animals, for example, by decreasing negative and promoting positive affective experiences in farm animals [11]. Yet, reducing the suffering of animals by changing the way in which humans treat them in food production may be a challenging task. Most people continue to consume meat even though they care about animals. This observation is often referred to as the meat paradox, defined as the cognitive dissonance or contradiction between the compassion felt by humans towards animals and the suffering imposed on them through the consumption of animal products [ 12 ]. Eating meat can become a particularly salient moral concern when people acknowledge that the animals, they are eating are sentient and capable of suffering. In fact, research has shown that people show increased moral concern for FAW when they are reminded that animals, such as humans, have the capacity to feel and think [ 13 ]. For example, Leach and colleagues [ 14 ] examined to what extent a variety of animal characteristics influence the perceived acceptability of eating the animal. People thought that it was less acceptable to eat animals that have the capacity to experience negative emotions. The relationship between perceived animal sentience and attitudes toward meat consumption seems to be bi-directional. That is, to avoid the negative cognitive consequences of holding Animals 2022,12, 3416 3 of 18 dissonant attitudes (i.e., eating meat is permissible, but making animals suffer is not), people may categorize farm animals as less sentient and therefore less morally relevant when compared to companion animals [ 15 ]. Other strategies used by humans to deal with the cognitive dissonance resulting from meat consumption include endorsing speciesism, or the belief that humans are superior to other animals [ 16 ]; dissociating unpleasant ideas by referring to the bodies of sentient animals as ‘meat’ [ 15 , 17 ]; endorsing consumption as a social norm [ 18 ], and other strategies that are encapsulated in the 4N model: that meat is natural, necessary, normal, and nice [ 10 ]. Additionally, some people may acknowledge that animal farming can cause some animal suffering while asserting that farming procedures can be improved to minimize that suffering [ 12 ]. In short, beliefs about the sentience and suffering of farm animals are psychologically central to how people think about the ethicality of meat production and consumption. Scholars have started to examine how perceptions of farm animal sentience and suffering differ across demographic groups. Some studies suggest that beliefs may differ between men and women [ 19 , 20 ]. One study [ 19 ] with students of different nationalities found higher levels of perceived animal suffering among women, but no difference with regard to perceived animal sentience. Cultural differences between countries were also identified with the authors reaching a generic conclusion that European and American students show stronger beliefs in animal suffering, than Asian students. Another study [ 20 ] reports stronger beliefs in animal sentience among female veterinary students, with similar tendencies across countries and cultures. In both studies individuals agreed on a hierarchy of the “capacity to feel” that places companion animals as more sentient, followed by farm animals and others. The age effect has also been studied as a demographic variable capable of impacting perceived animal suffering and animal sentience. As well, the results obtained are mixed and dependent on the animal setting [ 21 ]. However, the authors cite a study [ 22 ] to justify a tendency to a natural shift in human perception as people become older. Older people tend to change priorities toward family needs and animals tend to be perceived as less important and seen more from a utilitarian point of view. It is the aim of this study to examine beliefs about farm animal sentience and suffering of farm animals in the BRIC countries and the USA, and their variability across ages and gender. These beliefs are crucial for understanding how people think about the ethicality of meat production and consumption, which also has consequences for trade between countries. The global economy and trade are inevitable, but to achieve healthy trading competition it is important to standardize procedures and respect the demand for both animal protein and its ethical production. Nevertheless, religion, cultural differences, age, and gender influence public perceptions and awareness of humans’ relation with non-human animals [ 23 , 24 ]. The emerging economies of the countries known by the acronym BRIC (Brazil, Russia, India, and China) represent 40% plus of the world population and more than 50% of the world’s gross agricultural production in 2018 [ 25 ]. Thus, the present study explores beliefs that are central to how people think about the ethicality of meat production, a sector that is attracting increased attention in discussions about sustainability and climate change due to its important role as a greenhouse gas emitter, and the international trade of animal products. 2. Materials and Methods 2.1. Source of Data The data were collected by Faunalytics as part of an exploratory study on the attitudes and behaviors towards FAW among people in the BRIC countries and the United States (Anderson, 2018b). The data are available in the Open Science Framework repository. The data were collected by YouGov ® in May and June of 2018 from The BRIC countries and the USA. The sample of USA individuals is nationally representative, and it was a direct interview. The samples in Brazil, India, and Russia were collected around urban areas, also via direct interviews. The Chinese sample was collected through the internet. All Animals 2022,12, 3416 4 of 18 the interviewees are above 18 years old. The individuals were randomly chosen from a YouGov®panel of interviewee volunteers. The sample is stratified by gender and age. 2.2. Data Collection Data were collected online in the BRIC countries through questionnaires. These questionnaires were originally written in English and then translated into the local languages using a back-translation process in which the original translation from English to the local language was performed by one individual, while another translator who was blind to the original English version translated it back into English so that discrepancies could be caught. This procedure maximizes equivalence between countries and languages. Recommendations for keeping the wording as simple and direct as possible were also followed, using symmetrical response scales, and using both positively and negatively framed items. The difficulties arriving from cross-cultural research are well studied. In order to overcome these we paid special attention to differences in interpretation, and in the use of response scales. Relatively to differences in interpretation the questions posed to the interviewees were checked against the good question wording [ 26 ]. As such the following was adopted to simplify without loss of meaning: short simple sentences, active voice, nouns instead of pronouns, and use of specific instead of general terminology. The following was avoided: metaphors, subjunctive, possessive form, vague words, and verbs suggesting different actions. An expert consultation phase prior to the finalization of the questionnaire was also included [ 27 ]. Relatively to differences in the use of response scales, we followed the recommendations to use symmetrical, bipolar response scales with a clear middle point [28]. 2.3. Measures Data included demographics (age, gender, country) and nine survey items. Two of these are part of the present study. Interviewees were asked several questions related to FAW, animal sentience, and their diet. In the present paper, we will investigate the responses related to the perceived sentience of animals (“animals used for food have approximately the same ability to feel pain and discomfort as humans”) and the perceived role of meat consumption in contributing to the suffering of animals (“eating meat directly contributes to the suffering of animals”). Participants responded to both items on a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). 2.4. Statistical Analysis The original dataset was explored for the existence of outliers using Tukey’s method, with the production of boxplots. As a result, 28 observations were removed from the analysis. To investigate the agreement/disagreement with the statements listed in the previous section, each response scale was individually entered in a multinomial logistic regression as a function of the demographic variables age, gender, and country. The significance of the models was assessed with the − 2 log likelihood chi-square test. The Akaike’s Information Criterion (AIC) is also shown for each of the models to be adjusted. The parameters were tested for significance using the Wald chi-square test. The statistical package used was the IBM Corp. ® SPSS ® Statistics, Armonk, NY, USA. Version: 28.0.1.1 (15). For the graphic construction, we used Microsoft ® Excel ® for Microsoft 365 MSO (version 2204 Build 16. 0. 15128. 20240) 64-bit. The multinomial logistic models were fit to the data. The main effects were tested all together with a forward stepwise inclusion of interactions. The calculation of the probabilities (P i ) of a national of a country to fall in a particular score while evaluating each of the statements is performed with the generic equation Pi=exp(Xiβi) 1+∑i=2 5exp(Xiβi)(1) Animals 2022,12, 3416 5 of 18 where P i is the probability to score each of the “i” scores (2, 3, 4, 5). The equations’ parameters (βiXi) are arranged in a linear manner adopting the form βmXm+β1X1+β2X2+β3X1Xm+β4X1X2+β5X1X2+β6X1X2Xm(2) where: βm is the parameter associated with “Country” (Brazil, China, India, and Russia), being Xm the dummy associated with this parameter, taking the value one when the respective country is considered in the equation and zero otherwise. β1 is the parameter associated with the covariate “Age”, being X 1 the age. β2 is the parameter associated with “Gender” (Man, Woman), being X2the dummy associated and taking the value of one for men and zero for women. β3 , β4 , and β4 are the parameters associated with the two-way interaction terms (“Age × Country”, “Age × Gender”, and “Country × Gender”), and β6 is the parameter associated with the three-way interaction “Age ×Gender ×Country”. Score 1 (strongly disagree) is used as reference and therefore for the calculation of P 1 in equation (1) the numerator assumes the value 1. The logistic coefficients ( β ) for each predictor variable for each alternative score of the statement are shown in the tables associated with the models representing the respective statements. The coefficients β are the expected amount of change in the logit for each one-unit change in the predictor. The logit is the odds of membership for each of the scores. The closer a logistic coefficient is to zero, the less influence the predictor has in predicting the logit. The exp( β ) is the odds ratio associated with each predictor. Predictors increasing the logit have exp( β ) > 1, those without effect on the logit have exp( β ) = 1 and predictors deceasing the logit have exp( β ) < 1. These correspond respectively to β coefficients above, equal, or below zero. The following sections address each of the adjusted models for the different statements, showing the degree of adjustment, the parameter estimates, and the graphic representation and interpretation. 2.5. Analytical Procedure After adjusting the models with the five categories of the Likert scale, to facilitate the interpretation of the results, and construct simpler graphs, it was decided to aggregate the categories 1 (strongly disagree) with 2 (disagree) and the categories 4 (agree) with 5 (strongly agree). Three new main categories were therefore created: disagree, neutral, and agree. These are the categories herein represented graphically and subject to result interpretation and discussion. 3. Results 3.1. Descriptive Statistics A total of N = 5172 individuals were entered in the analysis (Brazil n = 1027, China n = 966 , India n =1004, Russia n = 1002, USA n = 1173), including n = 2586 men and n = 2586 women . The age distribution within countries is shown in the boxplots in Figure 1. The age distribution for each gender is shown in boxplots in Figure 2. As can be observed data is well balanced for gender, while for age it is slightly skewed towards younger ages in China and India. When considering all the three independent variables together we can observe a slight skewing towards older ages in Chinese men, while for other countries gender is well balanced (Figure 3). 3.2. Fitted Models 3.2.1. Perceived Animal Sentience The model examining the perceived sentience of animals was significant (p< 0.001), − 2 log likelihood 5070, chi-square (859, 40df), and AIC 5158. The parameters found to be significant were ( − 2 log likelihood, chi-square, df, p-value): “country” (5176, 106, 16, p< 0.001), “gender” (5146, 75, 4, p< 0.001), and the interaction “age x country” (5141, 71, 20, p Animals 2022,12, 3416 6 of 18 < 0.001). The percentage of respondents per score and country are given in Table 1. The description of the significant parameters of the model are given in Table 2. Figure 4is the graphical representation of the model. Animals 2022, 12, x FOR PEER REVIEW 6 of 18 observe a slight skewing towards older ages in Chinese men, while for other countries gender is well balanced (Figure 3). Figure 1. Sampled variables distribution. Ages within countries. Figure 2. Sampled variables distribution. Ages within gender. Figure 1. Sampled variables distribution. Ages within countries. Animals 2022, 12, x FOR PEER REVIEW 6 of 18 observe a slight skewing towards older ages in Chinese men, while for other countries gender is well balanced (Figure 3). Figure 1. Sampled variables distribution. Ages within countries. Figure 2. Sampled variables distribution. Ages within gender. Figure 2. Sampled variables distribution. Ages within gender. Animals 2022,12, 3416 7 of 18 Animals 2022, 12, x FOR PEER REVIEW 7 of 18 Figure 3. Sampled variables distribution. Ages within gender and countries. 3.2. Fitted Models 3.2.1. Perceived Animal Sentience The model examining the perceived sentience of animals was significant (p < 0.001), −2 log likelihood 5070, chi-square (859, 40df), and AIC 5158. The parameters found to be significant were (−2 log likelihood, chi-square, df, p-value): “country” (5176, 106, 16, p < 0.001), “gender” (5146, 75, 4, p < 0.001), and the interaction “age x country” (5141, 71, 20, p < 0.001). The percentage of respondents per score and country are given in Table 1. The description of the significant parameters of the model are given in Table 2. Figure 4 is the graphical representation of the model. Table 1. Percentage of respondents to the question in each of the countries in each of the categories disagree, neutral and agree. Question Brazil Russia India China USA Animals used for food have approximately the same ability to feel pain and discomfort as humans Disagree (%) 7 14 14 12 10 Neutral (%) 15 24 18 50 28 Agree (%) 79 63 67 37 62 In general Brazilian participants showed stronger beliefs in animal sentience than other nationalities in the study. Chinese participants showed the lowest belief in animal sentience, and this was particularly true for Chinese participants below the age of 45. Note that the Chinese sample was on average much younger compared to other countries which may have impacted the overall results. Older participants in Russia and India had indicated stronger beliefs in animal sentience than younger participants in these countries. Both have similar levels of perception to those in the USA. China has the highest levels of neutral responses. Women showed stronger beliefs in animal sentience than men across the different countries. The levels of disagreement are all bellow a probability of 0.3 in the different countries, but men from Russia, India and China show the higher levels. Disagreement decreased with age in China, India, and Russia. All the countries showed a higher prevalence of participants who do (vs. do not) believe in animal sentience. Figure 3. Sampled variables distribution. Ages within gender and countries. Table 1. Percentage of respondents to the question in each of the countries in each of the categories disagree, neutral and agree. Question Brazil Russia India China USA Animals used for food have approximately the same ability to feel pain and discomfort as humans Disagree (%) 7 14 14 12 10 Neutral (%) 15 24 18 50 28 Agree (%) 79 63 67 37 62 Animals 2022, 12, x FOR PEER REVIEW 8 of 18 Table 2. Parameters of the multinomial logistic model fitted to the data. The ordinal dependent variable is the statement “animals used for food have approximately the same ability to feel pain and discomfort as humans,” modeled as a function of the independent variables “Country”, Gender” and “Age” together with the interactions between these. Only significant (p < 0.05) parameters are shown. Score 1 is used as reference in the model. Score Parameter β exp(β) 2 Country Brazil 2.336 ** 10.345 Gender Male −0.407 * 0.665 3 Country Brazil 3.902 *** 49.518 India 1.533 *** 4.633 China 2.016 * 7.509 USA 2.541 *** 12.692 Gender Male −0.638 *** 0.529 Country x Age Brazil, Age −0.036 * 0.965 Russia, Age 0.024 * 1.024 4 Country Brazil 4.432 ** 84.058 Russia 1.285 ** 3.614 India 1.738 ** 5.684 USA 2.123 ** 8.359 Gender Male −0.883 *** 0.414 Country x Age China, Age 0.062 * 1.064 5 Country Brazil 4.508 * 90.744 Russia 1.177 * 3.243 India 1.276 ** 3.581 USA 2.376 * 8.435 Gender Male −1.050 *** 0.350 Country x Age China, Age 0.104 *** 1.110 India, Age 0.025 * 0.021 Russia, Age 0.024 * 1.024 Note: * p < 0.05, ** p < 0.01, *** p < 0.001. Figure 4. Graphical representation of the multinomial logistic model fitted to the data. The scores 1, 2 and 4, 5 are aggregated. Probabilities associated with disagreement scores (1+2), neither agree nor disagree score (3), and agreement scores (4+5) given to the statement “animals used for food have approximately the same ability to feel pain and discomfort as humans”. Figure 4. Graphical representation of the multinomial logistic model fitted to the data. The scores 1, 2 and 4, 5 are aggregated. Probabilities associated with disagreement scores (1 + 2), neither agree nor disagree score (3), and agreement scores (4 + 5) given to the statement “animals used for food have approximately the same ability to feel pain and discomfort as humans”. Animals 2022,12, 3416 8 of 18 Table 2. Parameters of the multinomial logistic model fitted to the data. The ordinal dependent variable is the statement “animals used for food have approximately the same ability to feel pain and discomfort as humans,” modeled as a function of the independent variables “Country”, Gender” and “Age” together with the interactions between these. Only significant (p< 0.05) parameters are shown. Score 1 is used as reference in the model. Score Parameter βexp(β) 2Country Brazil 2.336 ** 10.345 Gender Male −0.407 * 0.665 3 Country Brazil 3.902 *** 49.518 India 1.533 *** 4.633 China 2.016 * 7.509 USA 2.541 *** 12.692 Gender Male −0.638 *** 0.529 Country ×Age Brazil, Age −0.036 * 0.965 Russia, Age 0.024 * 1.024 4 Country Brazil 4.432 ** 84.058 Russia 1.285 ** 3.614 India 1.738 ** 5.684 USA 2.123 ** 8.359 Gender Male −0.883 *** 0.414 Country ×Age China, Age 0.062 * 1.064 5 Country Brazil 4.508 * 90.744 Russia 1.177 * 3.243 India 1.276 ** 3.581 USA 2.376 * 8.435 Gender Male −1.050 *** 0.350 Country ×Age China, Age 0.104 *** 1.110 India, Age 0.025 * 0.021 Russia, Age 0.024 * 1.024 Note: * p< 0.05, ** p< 0.01, *** p< 0.001. In general Brazilian participants showed stronger beliefs in animal sentience than other nationalities in the study. Chinese participants showed the lowest belief in animal sentience, and this was particularly true for Chinese participants below the age of 45. Note that the Chinese sample was on average much younger compared to other countries which may have impacted the overall results. Older participants in Russia and India had indicated stronger beliefs in animal sentience than younger participants in these countries. Both have similar levels of perception to those in the USA. China has the highest levels of neutral responses. Women showed stronger beliefs in animal sentience than men across the different countries. The levels of disagreement are all bellow a probability of 0.3 in the different countries, but men from Russia, India and China show the higher levels. Disagreement decreased with age in China, India, and Russia. All the countries showed a higher prevalence of participants who do (vs. do not) believe in animal sentience. 3.2.2. Perceived Animal Suffering For this statement, the model is significant (p< 0.001), − 2 log likelihood 5648, chisquare (1500, 80df), and AIC 5808. The parameters found to be significant were the interactions ( − 2 log likelihood, chi-square, degrees of freedom, p-value): “country x age” (5885, 238, 40df, p< 0.001), and “gender x age x country” (5748, 100, 40df, p< 0.001). The percentage of respondents per score and country is given in Table 3. The description of the significant parameters of the model is given in Table 4. Figure 5is the graphical representation of the model. Animals 2022,12, 3416 9 of 18 Table 3. Percentage of respondents to the question in each of the countries in each of the categories disagree, neutral, and agree. Brazil Russia India China USA Eating meat directly contributes to the suffering of animals Disagree (%) 27 32 21 23 33 Neutral (%) 28 33 28 45 36 Agree (%) 45 35 51 31 30 Table 4. Parameters of the multinomial logistic model fitted to the data. The scores given to the statement “eating meat directly contributes to the suffering of animals,” used as the dependent variable, are modeled function of the independent variables “Country”, Gender” and “Age” together with the interactions between these. Only significant (p< 0.05) parameters are shown. Score 1 is used as a reference in the model. Score Parameter βexp(β) 2 Country ×Gender China, Male 1.686 * 5.396 India, Female 1.851 * 6.365 Russia, Female 2.308 *** 10.050 Country ×Gender ×Age China, Female, Age 0.150 * 1.162 3 Country ×Gender Brazil, Male 1.174 * 3.235 Brazil, Female 1.693 ** 5.435 China, Male 2.586 *** 13.275 India, Female 2.379 *** 10.796 Russia, Male 1200 * 3.322 Russia, Female 2.191 ** 8.947 USA, Male 2.038 *** 7.673 USA, Female 1.571 *** 4.809 Country ×Gender ×Age China, Female, Age 0.147 * 1.159 USA, Male, Age −0.031 *** 0.970 4 Country ×Gender Brazil, Female 1.698 ** 5.464 India, Female 2.387 *** 10.878 Russia, Female 1.730 * 5.643 USA, Male 1.288 ** 3.625 Country ×Gender ×Age China, Female, Age 0.166 ** 1.181 USA, Male, Age −0.025 ** 0.975 5 Country ×Gender Brazil, Female 1.193 * 3.298 China, Male −2.126 * 0.119 China, Female −3.934 * 0.20 India, Female −2.057 ** 7.823 USA, Male 1.097 * 2.996 Country ×Gender ×Age China, Male, Age 0.051 * 1.053 China, Female, Age 0.163 * 1.178 India, Male, Age 0.047 ** 1.048 USA, Male, Age −0.032 *** 0.968 Note: * p< 0.05, ** p< 0.01, *** p< 0.001. 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