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In January 2019, we used the advanced search interface on Twitter to retrieve original Tweets within the West Germanic that contain references to the show or its main characters. Our search terms were 13RW, 13 Reasons Why, Thirteen Reasons Why, Hannah Baker, and Clay Jensen. This search allowed us to get an exhaustive data set with all mentions of the show, excluding Tweets produced by accounts that Twitter considered malicious bots, up to the retrieval date. This method was accustomed gather 1 416 175 Tweets, generated by 870 056 users, for the amount April 1, 2017, to June 30, 2017.
To measure the eye received on Instagram, we used data from InfluencerDB, an organization that owns a database that features an exhaustive record of metadata of media posted on Instagram by influencers (ie, users with a minimum of 15 000 followers). We processed the information for April to June 2017, selecting content with mentions of the show almost like those on Twitter. We further filtered non-English content with the textcat R package (R Foundation for Statistical Computing), yielding a knowledge set of 26 322 Instagram posts produced by 7875 influencers.
The weekly number of Twitter users and Instagram influencers who posted about 13 Reasons Why for the primary time between April 1, 2017, and June 30, 2017. Social media attention peaked in April, during which 84% of initial Tweets and 74% of initial Instagram posts about the show occurred. This general trend is supported by Netflix, which reported that the show was the third most binge-watched on Netflix in 2017. Thus, this analysis considered the exposure to the show to be sudden during April 2017. due to the absence of social media attention after June 2017, we defined the exposure window as April to June.
We downloaded monthly suicide data from the Centers for Disease Control and Prevention WONDER (Wide-ranging Online Data for Epidemiologic Research) system21 for the amount January 1, 1999, to New Year's Eve, 2017. Suicide data were extracted for the age groups 10 to 19 years (the main target market for 13 Reasons Why), 20 to 29 years, and 30 years or older for both males and females. Identification with the life circumstances of a highschool student like Hannah Baker and related issues like school bullying were expected to be most prominent among individuals aged 10 to 19 years. Therefore, the prespecified hypothesis of this study was that any potential associated effects of 13 Reasons Why would be most pronounced within the 10- to 19-year cohort. Similarly, we expected the implications to be stronger in females, due to the show’s target Hannah’s suicide. We also extracted data on suicide methods for the 10-to 19-year age bracket, including cutting (the method of suicide employed by Hannah), hanging, and shooting with firearms.
Time series models were fitted to the info, in line with the analysis of the pre–April 2017 period. For the choice of models, we used SPSS Expert Modeler function, version 25 (IBM), to settle on the model with all-time low Bayesian information criterion value, highest stationary R2 value (the variance accounted for by the fitted statistic model), and a not significant Ljung-Box Q statistic (indicating whether residuals might be assumed racket, with stated df).
The models were subsequently fitted to the complete statistic. On the idea of social media data shown in Figure 1, we investigated a short lived association of the discharge of 13 Reasons Why with suicides (1) for April 2017, which was in step with the amount of strong interest within the show, and (2) for April to June 2017, including the whole period with some indication of public interest within the show. We used dummy variables to model these associations as discrete pulses and calculated the amount of excess suicides for every model. Two-sided tests of significance were performed. P < .05 was considered significant.
Observed suicides from April to June 2017 exceeded the 95% CIs of model forecasts fitted to pre–April 2017 data for 10- to 19-year-old males and females (Figure 2B, D). This observation was also true for the suicide method of hanging during this people (Figure 2F).
Models including a discrete pulse for April (Figure 2B, D, and F) indicated 38.2 (95% CI, 10.5-65.9) excess suicides among 10- to 19-year-old individuals of both sexes (14.6% increase; 95% CI, 4.0%-25.3%). Gender-specific models indicated 27.9 (95% CI, 2.3-53.5) excess suicides among males (14.2% increase; 95% CI, 1.2%-27.3%) and 16 (95% CI, 3.5-28.4) excess suicides among females (27.1% increase; 95% CI, 6.0%-48.2%).
Models testing discrete pulses from April to June 2017 indicated 94.4 (95% CI, 39.3-149.6) excess suicides among 10- to 19-year-old individuals within the 3-month period after the show’s release, such as a rise of 13.3% (95% CI, 5.5%-21.1%) in comparison with the expected number of suicides. For 10- to 19-year-old males, the model indicated 66 (95% CI, 16.3-115.7) excess suicides (12.4% increase; 95% CI, 3.1%-21.8%). Among females, 37 (95% CI, 12.4-61.5) excess suicides were estimated (21.7% increase; 95% CI, 7.3%-36.2%). No associated differences in suicide mortality were seen within the 20- to 29-year and therefore the 30-year-or-older age groups (Table 2).
With reference to suicide methods, cutting (the method portrayed within the show) was rare, with typically no quite 2 cases per month among individuals within the 10- to 19-year cohort. due to the low number of suicides by cutting, these data weren't amenable to statistic analysis. Increases in suicide by hanging were found. The model testing a discrete pulse in April 2017 indicated 34.7 (95% CI, 16.8-52.7) excess suicides by hanging (33.6% increase; 95% CI, 16.2%-51.0%) within the month with the best volume of public attention to the show. The model testing 3-month associated suicide mortality estimated 79.8 (95% CI, 45.6-114.1) excess suicides by hanging (26.9% increase; 95% CI, 15.3%-38.4%). No associations were seen for suicide by firearm.
The skewness of the statistic data ranged from 0.33 (females ≥30 years) to 1.11 (all 10- to 19-year-olds; males 10-19 years of age). When a root transformation was applied to scale back the possible consequence of nonnormality, all associations reported in Table 2 retained statistical significance, aside from the 1-month period of April 2017, among the 10- to 19-year-old males and females, which only closely missed nominal significance. the particular parameter estimates (with SEs; all on a root scale) of discrete pulses were as follows: All aged 10 to 19 years 1-month estimate, 1.08 (0.54; P = .045), and 3-month estimate, 3.01 (1.10; P = .007); males aged 10 to 19 years 1-month estimate, 0.91 (0.56; P = .11), and 3-month estimate, 2.48 (1.09; P = .02); females aged 10 to 19 years 1-month estimate, 0.86 (0.53; P = .10), and 3-month estimate, 2.24 (1.04; P = .03); hanging among all youths aged 10 to 19 years 1-month estimate, 1.13 (0.52; P = .03), and 3-month estimate, 4.05 (1.55; P = .01).
To our knowledge, this study is that the first to research the association between 13 Reasons Why and suicides within the us. Although these results must be interpreted with substantial caution, they are doing identify an increase in youth suicides above and beyond the commonly increasing trend within the country.22 This increase was concurrent with the amount of strongest interest within the show, as reflected by Instagram and Twitter data, and occurred only within the cohort targeted by the show. statistic modeling from April to June 2017 suggested the magnitude of increase was 13.3% in those aged 10 to 19 years, which might be meaningful from a clinical and public health standpoint at any value within its 95% CI (5.5%-21.1%).
Ecological studies have inherent limitations; however, we believe this method is that the best available to answer the research question posed here. an in depth examination of the findings may help to clarify the degree of confidence with which to conclude that the association between 13 Reasons Why and increased suicides is causal. The immediate increase in suicides after the discharge of 13 Reasons Why among this people is in step with the prespecified expectation. Studies on how people self-select for online content strengthen the argument that the majority viewings of the show (and therefore potentially harmful exposures) occurred in April 2017, when attention on social media was greatest.23 Previous research on suicide contagion succeeding fictional media portrayals has generally found that the associations were strongest within the first month after public release.5,6 However, 13 Reasons Why was a media phenomenon, which remains available on Netflix, that generated unusually intense press interest for months and was expected to own implications beyond the primary month. As indicated by social media data, the associations might need been present for a minimum of 3 months, until June 2017, when social media interest within the show was reduced. Therefore, the timing of the observed associations is in step with possible contagion by media.
With relation to the specificity of those associations, teenagers were the clear target demographic of 13 Reasons Why, which portrayed issues like bullying at schools and life problems in adolescence. Increases in suicide were seen only during this age bracket with no associations observed for people aged 20 to 29 years and 30 years or older, and this finding is potentially according to contagion by media.
Potentially greater proportional increases in suicides among females were noted. Previous research indicated that contagion by media presumably (but not exclusively) occurs among individuals of the identical sex and age as fictional characters who die by suicide.5 there's no expectation that this association would be exclusive to females, only if a number of the life problems presented as causes of Hannah’s suicide and discussed within the show (eg, bullying) similarly adversely affect both female and male adolescents.24 the rise in male suicide may, in part, reflect that suicide deaths are more prevalent in male adolescents, whereas females have higher rates of suicide attempts, which weren't analyzed during this study.25
Hanging stood out because the method related to increased suicides among 10- to 19-year-old individuals within the months after the discharge of 13 Reasons Why. If the association were causal, the inference could also be that suicide increases should occur by cutting (the suicide method depicted within the show) instead of hanging. However, cutting could be a method with generally low lethality and will be more likely to rise in suicide attempt instead of suicide death data. Research indicates that cutting has all-time low case rate among suicide methods.26 In contrast, hanging is one amongst the foremost lethal methods,26 and therefore the availability of hanging is high. Furthermore, research conducted immediately after the discharge of 13 Reasons Why indicated that web searches for suicide methods and queries on a way to strive increased immediately after the discharge of the show within the u. s..1 Hannah’s controversial suicide scene was discussed on social media, and therefore the discussions highlighted that the tactic was difficult to hold out.27
Taken together, the findings may reflect a variety of selection bias, highlighting only the increases within the most typical method of suicide death in adolescents but offering no information on changes in low-lethality methods that may are present in suicide attempt data. In support of this conjecture, public mass media that speculated on the potential association between youth suicides and also the show repeatedly reported about teens who died by hanging within the aftermath of the discharge of the show.
This study doesn't provide definitive proof that 13 Reasons Why is related to harmful outcomes, but the findings are sufficiently concerning so on warrant greater care and a spotlight by Netflix and other entertainment producers. These findings support the urgent necessity for active engagement between those within the industry and mental state and suicide prevention experts to reduce or avoid potentially harmful suicide portrayals. specifically, media recommendations for responsible reporting of suicide within the news are readily available,31,32 but few resources are provided for those that create content within the industry.33,34 National recommendations for depicting suicide with a particular target the show biz were recently released by the National Action Alliance for Suicide Prevention.35 Strong collaborations between different sectors could lead to on-screen portrayals that not only do no harm but also act as a force permanently in suicide prevention.
A strength of this study was the length of the statistic analysis data set: It used monthly data of 19 years to estimate expected suicide counts. statistic models can produce accurate estimates without measuring exogenous variables, and that they control for issues like autocorrelation and seasonal changes in suicide. The structural characteristics of the statistic, including trends, temporal fluctuations, and seasonality (eg, known spring peaks in adolescent suicides) were adequately adjusted for in autoregressive integrated moving average statistic models, as applied here.
The main limitation of the study was that it was based on ecological data. Thus, it was not possible to ascertain whether the excess suicide decedents had actually watched 13 Reasons Why. Furthermore, viewership data of the show were not available, and therefore the timing of exposure was modeled only through the proxy of interest on social media. The ecological nature of the study also meant that this study could identify only associations and not causation. Many factors are associated with suicide across any population, let alone a country the size of the United States. The wide CIs of the time series analyses underscore this point. The models could not account for other suicide-related media events that occurred during the study period that might have affected suicide counts. For example, on April 28, 2017, the rapper Logic released his song 1-800-273-8255, which shared the telephone number for the National Suicide Prevention Lifeline. The release was followed by the second-highest call volume in the history of the service, and overall calls to the hotline rose approximately 33% over the corresponding time in 2016. This outcome might have helped mitigate any harmful consequences of 13 Reasons Why. Furthermore, mental health and suicide prevention organizations shared material for educating teachers, adolescents, clinicians, and parents about how to discuss the show in schools,3 and Netflix added content warnings to the show in May 2017.