Abstract Given the potential for genetic modification (GM) to impact human health, via food and health mechanisms, a greater understanding of the social acceptance of GM is necessary to facilitate improved health outcomes. This analysis sought to quantify U.S. residents’ acceptance of GM across five potential uses (grain production, fruit or vegetable production, livestock production, human medicine, and human health, i.e. disease vector control) and provides an in-depth analysis of a timely case study–the Zika virus (ZIKV). The two categories with the highest levels of acceptance for GM use were human medicine (62% acceptance) and human health (68% acceptance); the proportions agreeing with the use of GM for these two categories were statistically different from all other categories. Acceptance of GM in food uses revealed 44% of the sample accepted the use of GM in livestock production while grain production and fruit and vegetable production showed similar levels of agreement with 49% and 48% of responses, respectively. Two variables were significant in all five models predicting GM acceptance; namely, being male and GM awareness. Being male was significant and positive for all models; respondents who reported being male were more likely (than those who reported female) to agree with all five of the uses of GM studied. Those who were reportedly aware of GM mosquito technology were also more likely to agree with all uses of GM technology investigated. The potential relationship between awareness of GM technology uses and acceptance of other uses could help inform rates of acceptance of new technologies by various population segments.

Citation: Olynk Widmar NJ, Dominick SR, Tyner WE, Ruple A (2017) When is genetic modification socially acceptable? When used to advance human health through avenues other than food. PLoS ONE 12(6): e0178227. https://doi.org/10.1371/journal.pone.0178227 Editor: Zach N. Adelman, Texas A&M University College Station, UNITED STATES Received: December 3, 2016; Accepted: May 10, 2017; Published: June 7, 2017 Copyright: © 2017 Olynk Widmar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: All relevant data are within the paper and its Supporting Information files. Funding: Discretionary internal university funds were used to fund this research. Competing interests: The authors have declared that no competing interests exist.

Introduction Genetic modification (GM) of plants and animals by humans has occurred for centuries through the process of domestication and conventional breeding. However, GM of field crops, particularly insect resistant corn and herbicide tolerant soybeans, has recently been the source of controversy in the public sphere. Some public intellectuals outside of agriculture have taken sides in the debate on GM crops including the economist Nassim Taleb [1], biologist Richard Dawkins [2], and philosopher Peter Singer [3]. Some lines drawn were that the health risks are unpredictable [1], the real risk is in the combination and use [2] and that risks of one form of use (agriculture) should be evenly distributed to (or also ignored in) other uses (pharmaceuticals) [3]. Even then, scientific evidence suggests that GM crops are not dangerous, and the evidence from economics shows that GM crops are associated with positive economic outcomes, including for the poorest people [4]. Nonetheless, many consumers demonstrate a preference for non-GM crops. Certainly the public perception or acceptance of technologies, including GM technologies, has the potential to shape public policy, impact investments in research and development, and ultimately influence the development and use of technologies in society. In general, public perceptions of the use of GM within the context of medicine have been largely favorable [5]. Technological advances in the 1970s allowed an expedited process of genetic engineering through direct manipulation of the genome, which rapidly led to development of synthetic hormones, such as somatostatin, and medications, like insulin [6][7]. Use of genetic engineering also had a dramatic effect on vaccine development, and recombinant vaccines were created for diseases like hepatitis B, Lyme disease, cytomegalovirus, and pertussis [8][9][10][11]. While advances in the development of medications and vaccines may be enabled by the use of genetic engineering the degree to which the public, or specific members of the public, recognize genetic engineering in these advances remains elusive in many cases. For example, even if someone is well-informed with regard to medical advances and aware of the need for advances in vaccine development, do they necessarily recognize the role that genetic engineering has in those advances? The recent outbreak of Zika virus (ZIKV) in the Americas and Caribbean was declared a Public Health Emergency of International Concern by the World Health Organization [12]. The location of the 2016 Summer Olympics in a region that was highly impacted by ZIKV, Brazil fueled what was already significant coverage of the outbreak. Symptoms associated with the ZIKV epidemic in Brazil range from microcephaly in fetuses and newborns [13] and death in some patients [14], in addition to the constellation of other milder symptoms previously reported, such as rash, fever, and headache [15]. There currently exists the option of using a GM mosquito to mitigate the spread of ZIKV. The GM mosquito (Aedes Aegypti) which mates to bear terminal offspring could be employed to fight the spread of ZIKV by being released to mate, thereby baring terminal offspring and significantly reducing the mosquito population. It is not currently known if the U.S. public will consider the use of GM of mosquitoes ethical in order to mitigate the spread of ZIKV in various regions of the World (including within the U.S.). According to a 2016 report, Florida residents favor the use of GM mosquitos, 60% compared to 50% general favor by the remaining U.S. [16]. Understanding acceptance of the use of GM mosquitoes to combat the ZIKV outbreak may help provide insight into likely acceptance of GM technologies to combat other, potentially unforeseen, health crises. This analysis sought to quantify U.S. resident’s acceptance of GM across five potential categories or uses, including grain production, fruit or vegetable production, livestock production, human medicine, and human health reasons (i.e. disease vector control such as the GM mosquito to control ZIKV). The five GM uses studied in this analysis were selected to allow comparisons between direct impacts on human health (through human medicine and human health factors i.e. disease vector control) and the more indirect impacts on human well-being through food production (including grain production, fruit or vegetable production, and livestock production). Given the press and media attention on some aspects of GM use (particularly outside of medicine), it is hypothesized that significantly larger proportions of survey respondents will accept GM in medical uses than the other uses studied. The timing of the outbreak relative to data collection for this study facilitates ZIKV as a case study or specific example for study in this analysis. Currently, health officials and leaders worldwide are struggling to address ZIKV and deal with the devastating human health impacts being realized. Given the ZIKV challenges being faced, this analysis delves specifically into the acceptance of GM mosquitoes to help combat ZIKV. The fundamental contribution of this analysis is the identification of significant factors in predicting acceptance of GM across the five uses, which included both food and non-food uses impacting human health.

Materials and methods Survey development and administration On February 10th, 2016 a web-based survey, hosted on Qualtrics, was launched in order to understand respondent’s acceptance of GM uses. The survey instrument, in its entirety, is provided as a supplementary file S2 File. Data collection concluded on the 12th of February. Lightspeed GMI provided a panel of opt-in respondents, and 964 completed surveys were collected from U.S. residents (data provided in S1 File). Quotas were set within Qualtrics to facilitate the collection of a sample of respondents which was targeted to be representative of the U.S. population in terms of age, gender, income, education, and region of residence in accordance with the 2014 U.S. Census Bureau estimates [17]. Respondents were asked basic demographic questions, including questions about recent travel (focusing on the Caribbean) and general ZIKV awareness. Central to the objective of this analysis, inquiries were made into respondent’s perceptions and beliefs about acceptable uses for GM technology. Specifically, the question read Please indicate whether you agree or disagree with the following uses of GM and respondents were provided the following list: grain production, fruit or vegetable production, livestock production, human medicine, and human health reasons (i.e. disease vector control). Respondents were asked to select “strongly disagree”, “disagree”, “agree”, or “strongly agree” in response to each use provided. Given the timing of the data collection and intention to incorporate respondent’s perceptions of ZIKV as an example of a potential GM-based solution to a human health challenge, questions regarding mosquito control mechanisms were asked. Questions asked gathered information on respondent’s knowledge and perceptions of mosquito borne illnesses, preferred mosquito control methods in the Caribbean and U.S., and awareness and acceptance of GM mosquitoes in the U.S. and Caribbean. Basic summary statistics were calculated for all demographics collected and for all GM acceptance and understanding questions. The Fisher’s Exact test, which directly calculates a p-value, was used to compare the proportions of the sample reporting acceptance of GM uses and was conducted in STATA 14.0. The Fisher’s Exact test tests against the hypothesis that the proportions of individuals who accept different uses of GM technology are not statistically different. Cross tabulations were used to understand relationships between demographics, GM acceptance and knowledge levels about GM. Further, pairwise correlations were used to understand the relationship between agreement in one category of GM use and agreement in another. Ordered logit models Five maximum likelihood ordered logit models were estimated with respect to the respondent’s agreement with the use of GM across the five categories (uses) of grain production, fruit or vegetable production, livestock production, human medicine, and human health reasons (i.e. disease vector control). The five GM uses studied were provided in the survey without definition and interpretation was left up to the respondent. This analysis focused on the public perceptions of GM uses, and asked specifically about acceptance of specific mosquito control mechanisms. Often residents are asked to weigh in on issues, such as the GM mosquito, with limited background or supplementary information readily available. Furthermore, residents often form perceptions or opinions with limited, or varying, interpretation of various uses or technologies. Thus, respondents were presented with each of the five GM uses, but not provided with background information or details surrounding those uses. The nature of the debate surrounding GM uses and the rather discrete paths forward surrounding GM uses, of either allow or not allow usage, make a discrete response surrounding GM use most easily/directly interpretable. In order to facilitate interpretation of responses with regard to acceptance of GM uses, the responses were used as discrete ranks for ordered logit estimation. For each of the five uses a discrete dependent variable was created with (1) being “strongly disagree”, (2) “disagree”, (3) “agree”, and (4) “strongly agree” The probability of respondent(s) increasingly agreeing with the use of GM technology for a particular category can be estimated and is represented using ordered logits. Ordered logits are an appropriate analysis tool for this dependent variable. The nature of the variable is categorical or discrete and the numerical ranks are superficial and only establish that (1) or “strongly disagree” is below (2) “disagree” which is below (3) “agree”, and (4) “strongly agree”, when estimating how likely respondents are to agreeing with the presented uses of GMO technology. That is to say, agreement of 1.5 or 3.76 is meaningless because no discrete category was assigned to those values. Wooldridge [18] explains that dependent variables can be limited to a small number of values and are discrete (the fractions are meaningless or inappropriate) and, there for, continuous estimators can have limitations. The ranked nature of the dependent variable also makes linear or cardinal regressions inappropriate but discrete ordered dependent variables, such as Likert scale values, can be regressed using ordered logits [19]. Other studies have used ordered logits to analyze Likert scale developed dependent variables [20][21]. Using the ranked categorical dependent variables thresholds are generated. The ordered logit estimates the likelihood an outcome would fall between or beyond the threshold [19]. With y representing the dependent variable; y = 1, 2, 3, 4, and using k to represent thresholds, j, a rank would be established in the following way where y* is a latent variable [19]. The variable, y* represents the respondent’s agreement to compare against the thresholds. For each survey respondent i, y* can be explained by a set of variables X i , The probability of each rank outcome j can then be estimated depending on the outcome of the regression falling between k j and k j-1 [19]. The coefficients, while representing the direction of change, cannot be interpreted in terms of magnitude of change, so marginal effects were estimated [19]. The regressions were performed using STATA statistical software [22]. All explanatory variables employed in the models were discrete binary variables based on the demographic information provided by respondents. Being male (Male) and having a college degree (Degree) were represented as dummy variables with (1) indicating that this demographic was present. Age and income were each represented by two binary variables, Age18 to 34 and Age55 to 88, and Inclow (less than $50,000 in annual household income) and Inchigh: ($75,001 or more household income), respectively (with bases, or omitted categories of Age 35 to 54, and middle income $50,001 to $75,000). Geographic region of residence for respondents was represented in the model through three binary variables, namely Northeast, South, and Midwest (West served at the base or omitted region). Zika was a dummy variable with (1) representing an affirmative answer to the question Were you aware of the current Zika virus outbreak before participating in this survey? Similarly, GM was a dummy variable with (1) representing a response of yes to the question Were you aware that a biotechnology company has developed genetically modified mosquitoes (specifically the Aedes Aegypti mosquito) which produce offspring that do not survive to adulthood when they mate? Logit models The nature of the debate surrounding GM uses and the rather discrete paths forward surrounding GM uses, of either allow or not allow usage, make a discrete response surrounding GM easily/directly interpretable. In order to facilitate interpretation of responses with regard to acceptance of GM uses, the responses were discretized into the general categories of agree and disagree. For each of the five uses a discrete dependent variable was created with (0) being the combined “strongly disagree” and “disagree” to form “strongly/disagree” and (1) being the combined “strongly agree” and “agree” to form “strongly/agree.” In the logit models employed, the probability of respondent(s) agreeing with the use of GM technology for a particular category can be estimated and is represented by where i is the individual and X i is the vector of variables and β is a vector of coefficients [18]. The coefficients, while representing the direction of change, cannot be interpreted in terms of magnitude of change [19], so marginal effects were estimated.

Discussion The results of this study align with past studies that suggest people are more willing to accept the use of GM technology for human medicine and human health reasons (62% and 68% respectively) than for livestock production, grain production, or fruit and vegetable production (44%, 49% and 48% respectively.) Notably, the proportion of survey respondent acceptance of food production uses (grain, fruit and vegetable, and livestock production) differed significantly from the proportion which accepted GM for both human health reasons and human medicine. Perhaps, less obvious in terms of distinction amongst respondents is the difference in acceptance of GM for use in livestock versus grain production (at the 5% level) and fruit or vegetable production (at the 10% level). A potential hypothesis surrounding this difference in acceptance of GM amongst food production uses is the association with animals (livestock) versus plants (crops) and the perceived relationship to human beings. Animals are more human-like than plants and it is conceivable that GM in animals is perceived quite differently than when used in plant production. Regardless of the minor differences in acceptance between livestock and other food production uses, the significant differences, and notably higher acceptance of GM for human medicine and human health reasons, are important results which offer some insight into the potential for accepted GM uses to improve the human condition. Various willingness-to-pay and willingness-to-accept analyses of consumer preference in various countries have been conducted regarding GM and non-GM food products. Fernandez-Cornejo et al. [24] provide an overview of much of the research done in this area and conclude that while many consumers are willing to pay a premium for non-GM foods, willingness depends on where the study is being performed. Lusk et al. [25] performed a meta-analysis of studies focused on GM vs. non-GM valuation and Europeans appear willing to pay a 42% premium for non-GM over GM, but in Asia the premium is only 16%. Chiang et al. [26] reported that a substantial percentage of consumers across the world believe that GM crops are dangerous for human consumption. This analysis supports previous findings that people have a higher rate of acceptance of GM for human medicine and human health uses than other potential uses (such as food production). Being male, younger, of higher income, and college educated generally contributed to higher willingness to accept GM technology, which could be related to the access of information. Costa-Font and Mossialos [27] suggested that what they term “dread of GM crops” is at least partially explained by lack of information. Increasing resources, such as income and education, could improve access to information about GM technology, and eventually, understanding. Conversations about GM technology have been rising over time and younger people are avid users of quick information portals, i.e. the internet, and this increased exposure could account for increase GM acceptance. While, specific awareness of ZIKV was an insignificant variable in all five of the logit models, GM mosquito awareness was statistically significant and positive in contributing to acceptance of all five GM uses. GM mosquito technology could find some support in ZIKV impacted regions and may even be preferred to some other method of control. This finding could suggest information about one aspect of GM technology that could impact the acceptance of another. Thus, promotion of awareness of GM technologies used in health and medicine may be an important component of attempts to gain acceptance (in the realm of public perception) of GM technologies for use in other aspects of improving the human condition (such as through food and nutrition). Admittedly the measurement of GM awareness in this analysis is limited; specifically only awareness of the GM mosquito was measured (or self-reported) and then used in the models. Additional measures of awareness across the five uses may further inform related analyses. Furthermore, logit models predicting acceptance of GM across five uses employed basic demographics (sex, age, income, region of residence, education) ZIKV awareness, and GM awareness (measured by GM mosquito awareness). Certainly one might consider the potential contribution of factors beyond the scope of this study, including awareness of or experience with GM in medical procedures, occupation, or more specific knowledge on food production. Furthermore, future studies may wish to consider both more specific GM uses, beyond the five broad categories studied here, and perhaps the study of GM acceptance concurrently (rather than each use analyzed separately) to account for within person impacts on responses. The development of a GM mosquito as a means of control for mosquito borne illnesses was a key area of focus around the globe in 2016 relating to human health. Most obviously related in terms of this study were the stated preferences for mosquito control strategies. Respondents were distributed nearly in thirds for all levels of preference for all methods (fogging and spraying in public places, release of GM mosquitoes to reduce populations, and personal use of insect repellents, bug sprays, and protective clothing), for both the U.S. and the Caribbean. In other words, no single method was chosen as the most preferred for any majority of respondents in the sample for either the location. However, for both locations, the largest share of respondents (although not a majority of respondents) found fogging and pesticide spraying to be the least preferred method. It is possible that recent press related to illegal use of pesticides in the U.S. Virgin Islands may have fed fears of public spraying, in particular in resort locations where multiple offenses have been admitted to and families left permanently disabled [28][29]. This finding surrounding acceptability of fogging and spraying leaves room for the further acceptance of GM mosquitoes or GM-derived control methods in the future. Notably, this study found 72.5% of respondents would support the use of GM mosquitos for illness control in the Caribbean and 78.0% showed support for use in the U.S. Interestingly, region of residence did not significantly explain acceptance of any of the five GM uses investigated. The lack of significance of region is important to consider for GM mosquitos because different regions face different impacts from mosquito populations. Populations in highly impacted regions have expressed interest in preventing the spread of diseases using GM technology. For example, research conducted in Mali showed that most study participants were pragmatic about use of GM mosquitoes as part of the vector control strategy for malaria when they were properly informed about the purposes of the program [30]. Specifically to the U.S. according to a 2016 report, Florida residents favor the use of GM mosquitos 60%, compared to 50% general favor by the remaining U.S. [16]. While not explored here, one reason for the insignificance of region could be national and global coverage of ZIKV in the media, making all more aware, not just those in regions of higher risk. Election day 2016 brought the question of GM mosquito introduction in the U.S. out of questionnaire and survey data collection to the potential for real implementation. While 57% of residents in Monroe County Florida voted in favor of the trail with GM mosquitos, 65% of the 643 residents who voted in Key Haven (the study site) were opposed [31]. Given the nature of the referenda, it is not clear what decisions will result [31], although this recent example from Florida highlights the potential for locale-specific differences, not just regional differences, in acceptance, in particular in those locales targeted for release.

Conclusions The objective of this study was to explore U.S. resident’s acceptance of GM across five potential categories or uses, with special focus on GM mosquitos as means of controlling ZIKV spread. A total of 964 responses were collected. Less than half (44%) of the sample accepted the use of GM in livestock production, 49% in grain production, 48% in fruit and vegetable production, 62% in human medicine, and 68% for human health. Statistically significant differences in the proportion of the sample accepting GM uses for human health and human medicine versus GM uses for food production were found. Overall, a significantly higher proportion of respondents were willing to accept GM uses for human medicine and health reasons than for food production (grain, fruits and vegetables, and livestock). Respondents reporting being male, being younger, having higher incomes, and being college educated were more likely to agree with GM technology for any of the five uses. Interestingly, the lower income segment was least likely to support GM uses in agriculture, which may run counter to their own self-interest in that loss of GM varieties would lead to higher food prices, which disproportionally affect the poor. Specific to ZIKV, 83.6% of respondents were aware of ZIKV, 80.6% were aware that mosquitoes could spread viruses among humans, 72% were not aware of the development of a GM mosquito (Aedes Aegypti); however, 75.2% would support the use of GM mosquito technology for use in the Caribbean and 78.0% for use in the U.S. Generally, males, younger respondents, college degree holders, and those with higher incomes were more likely to be aware of the development of a GM mosquito. Several limitations to this study exist, including the potential for overstating acceptance of GM uses impacting human health, and in particular for disease vector control, given the incorporation of significant focus on ZIKV and control mechanisms. As with any survey analysis, one must consider the specific wording of questions and context in which they were asked. Certainly questions surrounding ZIKV may have led respondents to have more urgency in addressing human health needs (especially vector control) than they might have otherwise. In this way, the dual-focus of the data collection effort on both GM uses and ZIKV as a case study may have influenced responses. In addition, timing of data collection for this analysis may have influenced responses. Data for this study was collected when ZIKV was heavily featured in media. Admittedly, data collection at any point in time is likely to be influenced by the media or current event happenings of that time. However, additional analyses of GM use acceptance, and perhaps analyses focusing on different aspects of GM awareness (aside from disease vector control) may be advantageous. These findings provide insight into understanding GM acceptance and the potential impact of GM technology on the human condition. GM mosquitos align with respondent GM acceptance for the uses of human medicine and human health which could benefit regions highly impacted by ZIKV as the GM mosquito technology is used and supported to control the spread of the virus. Future studies could consider the overlap of GM food and GM health technology (such as Golden Rice, a genetically engineered rice cultivar that produces vitamin A). Furthermore, future studies should consider acceptance of GM uses concurrently, rather than independently; studying GM uses concurrently may involve study of more than the five uses analyzed here, likely with more specific GM uses focused upon to facilitate study of specific processes or uses of the technology.

Ethics statement The survey utilized in this study was approved by the Purdue University Social Sciences Institutional Review Board (IRB) Human Research Protection Program (IRB Protocol Number 1602017146).

Author Contributions Conceptualization: NJOW WT. Data curation: SRD AR NJOW. Formal analysis: NJOW SRD AR WT. Funding acquisition: NJOW. Investigation: WT NJOW. Methodology: NJOW SRD. Project administration: NJOW. Resources: NJOW. Software: NJOW SRD. Supervision: NJOW WT. Validation: NJOW WT. Visualization: NJOW SRD AR WT. Writing – original draft: NJOW SRD AR WT. Writing – review & editing: NJOW SRD AR WT.