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      Differences in adherence to COVID-19 pandemic containment measures: psychopathy traits, empathy, and sex

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      Trends in Psychiatry and Psychotherapy
      Associação de Psiquiatria do Rio Grande do Sul

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          Abstract

          The coronavirus disease 2019 (COVID-19) pandemic required the implementation of containment measures to slow the spread of the virus. 1 , 2 The main measures are social distance, personal hygiene, and the use of face masks. 3 Adherence to containment measures depends on individual factors, 4 including personality traits. 5 , 6 Previous evidence indicates that people with high levels of empathy tend to adhere more to containment measures. 7 In contrast, people presenting high scores on the dark triad traits tend to adhere less to these measures. 8 , 9 The expression dark triad refers to the set of three constellations of subclinical socially aversive traits: machiavellianism, narcissism, and psychopathy. 10 Each component of the dark triad has specific characteristics, although they overlap in terms of manipulation and insensitivity traits. 11 In this study, our focus was on one of the components of the dark triad, namely, psychopathy. Psychopathy is characterized mainly by callousness and lack of empathy, 12 including traits of irresponsibility, a tendency to behave in a socially deviant manner, tendency to deceive, grandiosity, recklessness, and impulsiveness. 13 , 14 Moreover, psychopathy is associated with antisocial and criminal behaviors. 15 , 16 Studies indicate that men score higher than women in psychopathy traits. 17 , 18 Taking into consideration the association between individual differences and adherence to COVID-19 containment measures, 4 as well as previous evidence indicating that typical psychopathic traits are associated with transgressive behaviors, this study aimed to investigate relationships between psychopathy traits and adherence to containment measures of the COVID-19 pandemic, also observing differences between men and women. A total of 893 adult participants were included in the study. Age ranged from 18 to 79 years (mean [M] = 34.77; standard deviation [SD] = 11.98), they were mostly women (80%) and Caucasian (71.2%), and most reported having a graduate degree (39%). Participants answered a web-based questionnaire released on online social networks containing questions about adherence to COVID-19 pandemic containment measures, facets of the Personality Inventory for DSM-5 (PID-5), 19 which assesses pathological personality traits, and the Affective and Cognitive Measure of Empathy (ACME), 20 which evaluates the empathy trait through the affective resonance indicator. Regarding adherence indicators, the items focused on four dimensions: social distancing (engagement to social distance measure; three items), hygiene (engagement in hygienic recommendations; three items), face mask (using face mask; two items), and staying home (never leaving home; one item). After approval by the Universidade São Francisco research ethics committee, data collection was performed online using Google Forms. We shared the survey link on the social media website Facebook and also via the WhatsApp application, inviting individuals to participate and relying on the snowball strategy to reach a larger number of participants. We used latent profile analysis to empirically discriminate groups according to the scores obtained on the personality measures. Latent profile analysis is recommended to the investigation of different subpopulations, according to distinct answer patterns to a group of variables. 21 , 22 For this analysis, we used the following indicators: scores on affective resonance (ACME), callousness, deceitfulness, grandiosity, impulsivity, irresponsibility, and risk-taking (PID-5). Previously to this analysis, we standardized the scores in z (M = 0; SD = 1). Comparisons between means were conducted using analysis of variance (ANOVA) to assess differences in adherence to the containment measures, including the groups identified by the latent profile analysis and the sex variable. For ANOVA, we used 0.05 as significance level, and the partial eta squared was used as an effect size indicator. The partial eta squared was interpreted as 0.01 (small), 0.09 (medium) and 0.25 (large). 23 Latent profile analyses were performed in the software Mplus version 7, and ANOVA in the Statistical Package for the Social Sciences (SPSS) version 23. For the latent profile analysis, we tested solutions with two, three, and four profiles. Although the two-profile solution did not demonstrate the best-fit indices, it did have acceptable fit indices, 24 - 26 and better interpretability for the observed profiles. The adjustment indices were adjusted Bayesian information criteria (aBIC) = 16883.167; entropy = 0.978; Lo-Mendell-Rubin adjusted likelihood ratio test (LMRT) = 0.0001. Figure 1A shows the two profiles observed. Figure 1 A) Composition of profiles obtained via latent profile analysis; B) means obtained according to profile and sex. The tendency to psychopathy traits group (profile 1) comprised 77 participants who showed higher means in psychopathy traits and lower affective resonance; 7.4% of the all the participating women and 12.8% of all the men were in this group. The tendency to empathy group (profile 2) comprised 816 people, and had a higher level of affective resonance and lower scores on psychopathy traits; 92.6% of women and 87.2% of men were in this group. The groups found in the latent profile analysis were compared in terms of adherence to containment measures against the COVID-19 pandemic. The sex variable was also considered in the analysis. Profiles differed significantly regarding adherence to containment measures, except in the hygiene dimension. There were no significant differences regarding sex. The results are shown in Table 1 . Table 1 Profile and gender comparison regarding adherence to containment measures. Group/variable Sum of squares df F p Partial η2 Sex           Stay home 2.51 1.00 2.16 0.14 0.00 Social distancing 0.11 1.00 0.41 0.52 0.00 Hygiene 0.42 1.00 0.96 0.33 0.00 Face mask 0.09 1.00 0.08 0.78 0.00 Profile           Stay home 4.47 1.00 3.85 0.05 0.01* Social distancing 3.45 1.00 12.65 0.00 0.01* Hygiene 0.45 1.00 1.03 0.31 0.00 Face mask 5.61 1.00 5.20 0.02 0.01* Sex*profile           Stay home 0.49 1.00 0.42 0.52 0.00 Social distancing 0.00 1.00 0.00 0.98 0.00 Hygiene 0.47 1.00 1.08 0.30 0.00 Face mask 0.86 1.00 0.80 0.37 0.00 Error           Stay home 1032.13 889.00       Social distancing 242.11 889.00       Hygiene 390.39 889.00       Face mask 959.82 889.00       * Small partial η2. This study aimed to investigate relationships between indicators of adherence to COVID-19 containment measures and indicators of psychopathy. In addition, we assessed the impact of the sex variable on that relationship. The results indicated that people with increased psychopathy traits and low levels of empathy tend to adhere less to containment measures in comparison to people not showing these characteristics, which is in line with previous findings suggesting personality traits as associated with adherence to containment measures in the COVID-19 pandemic. 5 - 6 , 8 - 9 Furthermore, our findings add to the existing literature 7 , 15 - 16 by indicating traits of psychopathy as associated with transgressive behaviors, and empathy traits as associated with cooperation. Conversely, even though there is evidence suggesting that men are more likely to exhibit behaviors typical of psychopathy than women, 17 , 18 no significant differences were found regarding adherence to containment measures and sex. These findings may be related to the manifestation of psychopathy traits in men and women. For instance, male psychopaths often manifest impulsivity and conduct problems such as violent behavior, whereas female psychopaths usually engage in running away, self-harming behaviors, manipulation, and property crimes such as theft or fraud. 27 Our findings indicate that psychopathy traits should be accounted for as relevant while establishing public policies to increase and maintain adherence to COVID-19 containment measures. These findings should be considered for both men and women, as we did not observe differences regarding sex. The present findings should be considered in light of the methodological limitations of our study. First, the data were collected online, which may imply a bias regarding the demographic characteristics of the sample. Second, the sample consisted of a larger number of women (80%), which may have skewed the findings. Given these limitations, we recommend that this study be replicated using representative samples. We also suggest that other studies investigate the interaction between psychopathic traits and other variables and their influence on adherence to containment measures.

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          How will country-based mitigation measures influence the course of the COVID-19 epidemic?

          Governments will not be able to minimise both deaths from coronavirus disease 2019 (COVID-19) and the economic impact of viral spread. Keeping mortality as low as possible will be the highest priority for individuals; hence governments must put in place measures to ameliorate the inevitable economic downturn. In our view, COVID-19 has developed into a pandemic, with small chains of transmission in many countries and large chains resulting in extensive spread in a few countries, such as Italy, Iran, South Korea, and Japan. 1 Most countries are likely to have spread of COVID-19, at least in the early stages, before any mitigation measures have an impact. What has happened in China shows that quarantine, social distancing, and isolation of infected populations can contain the epidemic. 1 This impact of the COVID-19 response in China is encouraging for the many countries where COVID-19 is beginning to spread. However, it is unclear whether other countries can implement the stringent measures China eventually adopted. Singapore and Hong Kong, both of which had severe acute respiratory syndrome (SARS) epidemics in 2002–03, provide hope and many lessons to other countries. In both places, COVID-19 has been managed well to date, despite early cases, by early government action and through social distancing measures taken by individuals. The course of an epidemic is defined by a series of key factors, some of which are poorly understood at present for COVID-19. The basic reproduction number (R0), which defines the mean number of secondary cases generated by one primary case when the population is largely susceptible to infection, determines the overall number of people who are likely to be infected, or more precisely the area under the epidemic curve. For an epidemic to take hold, the value of R0 must be greater than unity in value. A simple calculation gives the fraction likely to be infected without mitigation. This fraction is roughly 1–1/R0. With R0 values for COVID-19 in China around 2·5 in the early stages of the epidemic, 2 we calculate that approximately 60% of the population would become infected. This is a very worst-case scenario for a number of reasons. We are uncertain about transmission in children, some communities are remote and unlikely to be exposed, voluntary social distancing by individuals and communities will have an impact, and mitigation efforts, such as the measures put in place in China, greatly reduce transmission. As an epidemic progresses, the effective reproduction number (R) declines until it falls below unity in value when the epidemic peaks and then decays, either due to the exhaustion of people susceptible to infection or the impact of control measures. The speed of the initial spread of the epidemic, its doubling time, or the related serial interval (the mean time it takes for an infected person to pass on the infection to others), and the likely duration of the epidemic are determined by factors such as the length of time from infection to when a person is infectious to others and the mean duration of infectiousness. For the 2009 influenza A H1N1 pandemic, in most infected people these epidemiological quantities were short with a day or so to infectiousness and a few days of peak infectiousness to others. 3 By contrast, for COVID-19, the serial interval is estimated at 4·4–7·5 days, which is more similar to SARS. 4 First among the important unknowns about COVID-19 is the case fatality rate (CFR), which requires information on the denominator that defines the number infected. We are unaware of any completed large-scale serology surveys to detect specific antibodies to COVID-19. Best estimates suggest a CFR for COVID-19 of about 0·3–1%, 4 which is higher than the order of 0·1% CFR for a moderate influenza A season. 5 The second unknown is the whether infectiousness starts before onset of symptoms. The incubation period for COVID-19 is about 5–6 days.4, 6 Combining this time with a similar length serial interval suggests there might be considerable presymptomatic infectiousness (appendix 1). For reference, influenza A has a presymptomatic infectiousness of about 1–2 days, whereas SARS had little or no presymptomatic infectiousness. 7 There have been few clinical studies to measure COVID-19 viraemia and how it changes over time in individuals. In one study of 17 patients with COVID-19, peak viraemia seems to be at the end of the incubation period, 8 pointing to the possibility that viraemia might be high enough to trigger transmission for 1–2 days before onset of symptoms. If these patterns are verified by more extensive clinical virological studies, COVID-19 would be expected to be more like influenza A than SARS. For SARS, peak infectiousness took place many days after first symptoms, hence the success of quarantine of patients with SARS soon after symptoms started 7 and the lack of success for this measure for influenza A and possibly for COVID-19. The third uncertainty is whether there are a large number of asymptomatic cases of COVID-19. Estimates suggest that about 80% of people with COVID-19 have mild or asymptomatic disease, 14% have severe disease, and 6% are critically ill, 9 implying that symptom-based control is unlikely to be sufficient unless these cases are only lightly infectious. The fourth uncertainty is the duration of the infectious period for COVID-19. The infectious period is typically short for influenza A, but it seems long for COVID-19 on the basis of the few available clinical virological studies, perhaps lasting for 10 days or more after the incubation period. 8 The reports of a few super-spreading events are a routine feature of all infectious diseases and should not be overinterpreted. 10 What do these comparisons with influenza A and SARS imply for the COVID-19 epidemic and its control? First, we think that the epidemic in any given country will initially spread more slowly than is typical for a new influenza A strain. COVID-19 had a doubling time in China of about 4–5 days in the early phases. 3 Second, the COVID-19 epidemic could be more drawn out than seasonal influenza A, which has relevance for its potential economic impact. Third, the effect of seasons on transmission of COVID-19 is unknown; 11 however, with an R0 of 2–3, the warm months of summer in the northern hemisphere might not necessarily reduce transmission below the value of unity as they do for influenza A, which typically has an R0 of around 1·1–1·5. 12 Closely linked to these factors and their epidemiological determinants is the impact of different mitigation policies on the course of the COVID-19 epidemic. A key issue for epidemiologists is helping policy makers decide the main objectives of mitigation—eg, minimising morbidity and associated mortality, avoiding an epidemic peak that overwhelms health-care services, keeping the effects on the economy within manageable levels, and flattening the epidemic curve to wait for vaccine development and manufacture on scale and antiviral drug therapies. Such mitigation objectives are difficult to achieve by the same interventions, so choices must be made about priorities. 13 For COVID-19, the potential economic impact of self-isolation or mandated quarantine could be substantial, as occurred in China. No vaccine or effective antiviral drug is likely to be available soon. Vaccine development is underway, but the key issues are not if a vaccine can be developed but where phase 3 trials will be done and who will manufacture vaccine at scale. The number of cases of COVID-19 are falling quickly in China, 4 but a site for phase 3 vaccine trials needs to be in a location where there is ongoing transmission of the disease. Manufacturing at scale requires one or more of the big vaccine manufacturers to take up the challenge and work closely with the biotechnology companies who are developing vaccine candidates. This process will take time and we are probably a least 1 year to 18 months away from substantial vaccine production. So what is left at present for mitigation is voluntary plus mandated quarantine, stopping mass gatherings, closure of educational institutes or places of work where infection has been identified, and isolation of households, towns, or cities. Some of the lessons from analyses of influenza A apply for COVID-19, but there are also differences. Social distancing measures reduce the value of the effective reproduction number R. With an early epidemic value of R0 of 2·5, social distancing would have to reduce transmission by about 60% or less, if the intrinsic transmission potential declines in the warm summer months in the northern hemisphere. This reduction is a big ask, but it did happen in China. School closure, a major pillar of the response to pandemic influenza A, 14 is unlikely to be effective given the apparent low rate of infection among children, although data are scarce. Avoiding large gatherings of people will reduce the number of super-spreading events; however, if prolonged contact is required for transmission, this measure might only reduce a small proportion of transmissions. Therefore, broader-scale social distancing is likely to be needed, as was put in place in China. This measure prevents transmission from symptomatic and non-symptomatic cases, hence flattening the epidemic and pushing the peak further into the future. Broader-scale social distancing provides time for the health services to treat cases and increase capacity, and, in the longer term, for vaccines and treatments to be developed. Containment could be targeted to particular areas, schools, or mass gatherings. This approach underway in northern Italy will provide valuable data on the effectiveness of such measures. The greater the reduction in transmission, the longer and flatter the epidemic curve (figure ), with the risk of resurgence when interventions are lifted perhaps to mitigate economic impact. Figure Illustrative simulations of a transmission model of COVID-19 A baseline simulation with case isolation only (red); a simulation with social distancing in place throughout the epidemic, flattening the curve (green), and a simulation with more effective social distancing in place for a limited period only, typically followed by a resurgent epidemic when social distancing is halted (blue). These are not quantitative predictions but robust qualitative illustrations for a range of model choices. The key epidemiological issues that determine the impact of social distancing measures are what proportion of infected individuals have mild symptoms and whether these individuals will self-isolate and to what effectiveness; how quickly symptomatic individuals take to isolate themselves after the onset of symptoms; and the duration of any non-symptomatic infectious period before clear symptoms occur with the linked issue of how transmissible COVID-19 is during this phase. Individual behaviour will be crucial to control the spread of COVID-19. Personal, rather than government action, in western democracies might be the most important issue. Early self-isolation, seeking medical advice remotely unless symptoms are severe, and social distancing are key. Government actions to ban mass gatherings are important, as are good diagnostic facilities and remotely accessed health advice, together with specialised treatment for people with severe disease. Isolating towns or even cities is not yet part of the UK Government action plan. 15 This plan is light on detail, given the early stages of the COVID-19 epidemic and the many uncertainties, but it outlines four phases of action entitled contain, delay, research, and mitigate. 15 The UK has just moved from contain to delay, which aims to flatten the epidemic and lower peak morbidity and mortality. If measures are relaxed after a few months to avoid severe economic impact, a further peak is likely to occur in the autumn (figure). Italy, South Korea, Japan, and Iran are at the mitigate phase and trying to provide the best care possible for a rapidly growing number of people with COVID-19. The known epidemiological characteristics of COVID-19 point to urgent priorities. Shortening the time from symptom onset to isolation is vital as it will reduce transmission and is likely to slow the epidemic (appendices 2, 3) However, strategies are also needed for reducing household transmission, supporting home treatment and diagnosis, and dealing with the economic consequences of absence from work. Peak demand for health services could still be high and the extent and duration of presymptomatic or asymptomatic transmission—if this turns out to be a feature of COVID-19 infection—will determine the success of this strategy. 16 Contact tracing is of high importance in the early stages to contain spread, and model-based estimates suggest, with an R0 value of 2·5, that about 70% of contacts will have to be successfully traced to control early spread. 17 Analysis of individual contact patterns suggests that contact tracing can be a successful strategy in the early stages of an outbreak, but that the logistics of timely tracing on average 36 contacts per case will be challenging. 17 Super-spreading events are inevitable, and could overwhelm the contact tracing system, leading to the need for broader-scale social distancing interventions. Data from China, South Korea, Italy, and Iran suggest that the CFR increases sharply with age and is higher in people with COVID-19 and underlying comorbidities. 18 Targeted social distancing for these groups could be the most effective way to reduce morbidity and concomitant mortality. During the outbreak of Ebola virus disease in west Africa in 2014–16, deaths from other causes increased because of a saturated health-care system and deaths of health-care workers. 19 These events underline the importance of enhanced support for health-care infrastructure and effective procedures for protecting staff from infection. In northern countries, there is speculation that changing contact patterns and warmer weather might slow the spread of the virus in the summer. 11 With an R0 of 2·5 or higher, reductions in transmission by social distancing would have to be large; and much of the changes in transmission of pandemic influenza in the summer of 2009 within Europe were thought to be due to school closures, but children are not thought to be driving transmission of COVID-19. Data from the southern hemisphere will assist in evaluating how much seasonality will influence COVID-19 transmission. Model-based predictions can help policy makers make the right decisions in a timely way, even with the uncertainties about COVID-19. Indicating what level of transmission reduction is required for social distancing interventions to mitigate the epidemic is a key activity (figure). However, it is easy to suggest a 60% reduction in transmission will do it or quarantining within 1 day from symptom onset will control transmission, but it is unclear what communication strategies or social distancing actions individuals and governments must put in place to achieve these desired outcomes. A degree of pragmatism will be needed for the implementation of social distancing and quarantine measures. Ongoing data collection and epidemiological analysis are therefore essential parts of assessing the impacts of mitigation strategies, alongside clinical research on how to best manage seriously ill patients with COVID-19. There are difficult decisions ahead for governments. How individuals respond to advice on how best to prevent transmission will be as important as government actions, if not more important. Government communication strategies to keep the public informed of how best to avoid infection are vital, as is extra support to manage the economic downturn.
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            Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study

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              The Dark Triad of personality: Narcissism, Machiavellianism, and psychopathy

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                Author and article information

                Journal
                Trends Psychiatry Psychother
                Trends Psychiatry Psychother
                trends
                Trends in Psychiatry and Psychotherapy
                Associação de Psiquiatria do Rio Grande do Sul
                2237-6089
                2238-0019
                22 September 2020
                Oct-Dec 2020
                : 42
                : 4
                : 389-392
                Affiliations
                [1 ] orgnameUniversidade São Francisco Campinas SP Brazil original Universidade São Francisco, Campinas, SP, Brazil.
                Author notes
                Correspondence: Lucas de Francisco Carvalho Rua Waldemar César da Silveira, 105, Jardim Cura D’ars 13045-510 - Campinas, SP - Brazil E-mail: lucas@ 123456labape.com.br

                Disclosure

                No conflicts of interest declared concerning the publication of this article.

                Author information
                https://orcid.org/0000-0002-3274-9724
                https://orcid.org/0000-0003-1983-6636
                Article
                2237-6089-2020-0055
                10.1590/2237-6089-2020-0055
                7879082
                32997044
                74df295a-996a-48cb-b767-581086acbae4

                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 work is properly cited.

                History
                : 18 May 2020
                : 07 August 2020
                Page count
                Figures: 1, Tables: 1, Equations: 0, References: 27, Pages: 3
                Categories
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