eLetters

29 e-Letters

  • The authors' response to criticism

    We appreciate the feedback from Streed et al., Sullins, Vesterinen, and Meyerowitz-Katz.

    There has been discussion regarding whether suicide mortality differs between gender-referred adolescents who proceeded to gender reassignment (GR) and those who did not. We did not examine this. Our key finding was that suicides are rare among gender-referred adolescents, primarily explained by severe psychiatric morbidity. We consider the division of the gender-referred group into those who proceeded to GR (GR+) and those who did not (GR-) as additional information. No difference in suicide mortality was found between either GR group and matched controls in our final model. More detailed subgroup analyses could not be presented due to data security regulations by Statistics Finland, given the small number of suicides. For the same reason, we could not present the Kaplan-Meier curve requested by Streed et al., nor conduct analyses one variable at a time. Special permission was required to compare the GR subgroups with the controls in the final model.

    There has also been debate about our use of p<0.01 as the threshold for statistical significance and the wide confidence intervals in suicide mortality between the GR subgroups. We consider this threshold justified given the sample size and use of multivariate models to minimise the role of chance. Even if p<0.05 were used, the finding would still be only borderline significant, and the wide confidence intervals of the...

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  • Letter to the Editor: "Perfect storm: emotionally based school avoidance in the post-COVID-19 pandemic context"

    Dear Editor,

    I eagerly write in response to the perspective piece entitled “Perfect storm: emotionally based school avoidance in the post-COVID-19 pandemic context.”1 The authors provide a timely and crucial analysis of the rising trend in emotionally based school avoidance (EBSA) following the COVID-19 pandemic. However, while their overview of the issue and proposed interventions are commendable, I believe there are several critical points that warrant further inquiry and discussion.

    The authors rightly identify the complex array of factors contributing to EBSA, including school, family, and child-based risk factors. However, their analysis would benefit from a more nuanced investigation of the socioeconomic disparities exacerbated by the pandemic. Research has previously shown that children from lower-income families were disproportionately affected by school closures and faced greater challenges in accessing remote learning resources.2 Such a preexisting inequality may have further exacerbated EBSA patterns among vulnerable populations—this deserves greater emphasis in developing targeted interventions.

    The authors acknowledge the need for multi-component approaches across education, health, and social care sectors, and their call for early intervention that does not impose strict absenteeism criteria is laudable. My only worry is that this approach may present challenges in terms of resource allocation and identifying those most in need of support...

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  • Possible improvements

    Thank you for your article, it is always good to see more visual techniques to display research data. I would like to suggest some improvements for your consideration.

    I am a little concerned that the scale of your suggested plots is linear, which means that larger segments have a disproportionately larger area. The human visual processing system tends to judge by area when comparing objects, something that was well understood by Florence Nightingale when she created her rose diagrams. Common practice in data visualisation is to use area for circles and arcs for this reason. This could be addressed by using a square root based scale rather than a linear one.

    I also wonder whether all of the wedges having the same scale is appropriate? I would think that some outcomes (such as mortality) might have very small differences, but these would be of great consequence, compared with a minor adverse event which would look visually quite a bit more important. Having a different scale for each wedge - perhaps based on clinically important differences - could be more intuitive.

    Also, a minor point, but red-green colour scales can be challenging to interpret for some people with different colour perception.

    I am also concerned that it would be challenging to compare many different plots for large numbers of interventions. I wonder if the use of parallel coordinates, an established technique for multivariate comparisons, might address some of these issues?...

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  • Medical intervention appears to reduce the risk of suicide in the treatment of gender dysphoria.

    The authors present a retrospective cohort study of mostly adults who were referred to clinics in Finland for the treatment of gender dysphoria. However, one of the most important findings in this paper seems to have been missed in the discussion section.

    The authors report that suicide risk was not statistically different between people referred for treatment and a matched cohort, with a hazard ratio of 1.8 (0.6-4.8) for people referred for gender dysphoria when compared to the control in the fully adjusted model (Table 3). However, the authors also conducted this analysis using only people who had accessed gender-affirming medical interventions (categorized as "Hormonal or surgical gender reassignment interventions" in Table 1) and those who hadn't (GR+ and GR-). In many ways, this is the more important analysis, as it addresses the question of medical treatment rather than medical referral.

    The authors do note in their conclusion that there were no statistically significant differences in all-cause mortality when the data is split up into these groups, with GR- having a HR of 1.4 (0.6-3.3) and GR+ 0.7 (0.2-2). However, the results also show that the adjusted suicide mortality HRs for the GR- and GR+ groups compared to the matched control were 3.2 (1-10.2) and 0.8 (0.2-4) respectively. While the authors do not present an adjusted analysis of suicide mortality comparing these two groups directly, this implies a statistically significant associ...

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  • Two questions about potential methodological and interpretative shortcomings

    Hi

    I am writing here to express my concern that this paper might have some serious flaws that have apparently passed the peer review and editorial processes.

    I am a doctoral candidate in Classics and unfortunately lack sophisticated skills at statistics, but due to my background both as a doctoral researcher in another field and as a former patient in a gender identity policlinic in Finland, I do believe I am capable of raising one question about the methodology and another about the analysis of the results.

    The paper compares the all-cause mortality and suicide rates between individuals referred to gender identity clinics in Finland between 1996 and 2019 and a control group. The age limit is <23. The methodological problem that I perceive is that the paper fails to take into consideration that before 2011, minors were generally not granted referrals to gender identity clinics (see e.g. https://yle.fi/a/3-10707095 (in Finnish); https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4396787/). Consequently, any mortalities among the gender-incongruent youth under 18 years old would not be associated in the statistics with referrals to gi clinics but might be included in the control group. For example, had I succeeded in my suicide attempt at the age of 15 in 2008, this would not be classified as a gender-dysphoria-linked death in this paper despite my...

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  • Inaccurate representation of ANU-ADRI and biased comparison of ANU-ADRI with UKBDR concerning age distribution of the UK Biobank sample

    The article [1] reports the development of a new dementia risk score, leveraging off superior area under the curve (AUC) statistics compared with previously published risk scores. However, the representation and use of at least one of those prior risk scores is highly inaccurate and this raises concerns about the overall integrity of the publication.

    1. The authors incorrectly state that the ANU-ADRI risk index[2] was ‘developed in cohorts in Australia’ (abstract and page 2). This is wrong, it was not developed directly from any other cohorts. Rather, as described in the original publication[2] it was developed using an evidence-based medicine approach that collated the effect sizes of risk factors drawn from systematic reviews. The systematic reviews draw from the wider literature, with most cohorts being from North America, the UK, and Europe. The tool was validated three external cohort studies. Data from Australia was rarely included in the meta-analyses from which the risk score was derived [2].

    2. The authors say that the ANU-ADRI ‘was developed for older individuals (60+), ….however our sensitivity analysis also performed poorly when restricting our cohort to an age range matching its development sample’.
    There are two problems with this sentence:
    a. There was no development cohort for the ANU-ADRI so it could not have been possible for the described sensitivity analysis to have been undertaken.
    b. Most cohort studies that contribut...

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  • Significantly higher suicide mortality (p=.05) without gender reassignment should not be dismissed

    Ruuska et al.’s [1] analysis incorrectly concluded that medical gender reassignment (GR) did not reduce suicide mortality because they had assessed that “the suicide mortality of both those [presenting with gender dysphoria (GD)] who proceeded and did not proceed to GR did not statistically significantly differ from that of controls.” On the contrary, by conventional criteria, the suicide mortality of the non-GR group was significantly higher than controls while that of the GR group was not. Ruuska et al. [1] reported: "Adjusted HRs [hazard rates] for suicide mortality were 3.2 [for non-GR] (95% CI 1.0 to 10.2; p=0.05) and 0.8 [for GR] (95% CI 0.2 to 4.0; p=0.8), respectively." By this finding, those dysphorics who had undergone GR were no more likely to have committed suicide than were general population controls, while those who had not undergone GR were more than three times as likely to have committed suicide. The latter difference is reported with a p-value of .05 and a 95% confidence interval that does not extend below 1.0.

    By the prevailing standards of scientific inference, and in virtually any other study, such a finding would be assessed as statistically significant (or perhaps, depending on rounding error, trivially below significance), but Ruuska et al. [1] judged it not to be so. They achieved this anomaly by fiat, announcing: “In order to avoid type 1 error due to multiple testing and the large data size, the cut- off for statistical sign...

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  • Methods and results do not support conclusions of Ruuska et al.

    We appreciate the interest in understanding the health and well-being of transgender persons and their unique care needs, particularly youth and adolescents. There are, however, several methodological missteps in the recent article by Ruuska et al. that has been published in BMJ Mental Health. The authors have fallen into a number of methodological mistakes and fallacies that make quite untenable their conclusions that gender-affirming interventions have no effect on suicide mortality.
    First, the authors have not shared sufficient data to support their conclusions that gender-affirming interventions do not reduce suicide. A properly reported analysis must show the events and characteristics of all transgender persons referred for care, as well as the sub-groups (hormonal and/or surgical interventions vs. no interventions). Similarly, with respect to the shortfalls of their analytic methodology, the authors have not demonstrated that they checked the proportional hazards assumption on which their Cox models rely. Given the rapidly changing political and social environments for transgender people in countries around the world, including Finland, the assumption that the hazards are proportional over time must be examined and explained. The authors also violate standard practice by not showing Kaplan-Meier curves for each of the outcomes of interest, in addition to providing the rates of all-cause mortality and suicide in each risk group discussed.
    Second, with onl...

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  • Suicide prediction following self-harm: implications of the new science of risk modelling and being open to the evidence

    The comments by Quinlivan and colleagues provide an opportunity to respond to some common misunderstandings of suicide risk assessment tools, and more broadly, prediction modelling. First, their comments are based on the mistaken assumption that all suicide prediction tools invariably have to classify individuals into low-risk versus high-risk groups. Unlike the earlier tools referred to in the response (all of which are classifiers, i.e. stratify people into risk categories), OxSATS provides probabilistic estimates of suicide risk. The benefits of probability estimation over classification have been discussed widely in the methodological literature,[1,2] and models which produce continuous risk scores are routinely used in other areas of medicine (such as the Framingham and QRISK models for cardiovascular disease risk prediction).

    Second, Quinlivan and colleagues have compared the area under the curve (AUC) of OxSATS to earlier tools and highlighted the discrepancy in the interpretation of the findings. However, this misses the methodological point that what is considered good discrimination performance for a prediction model depends on the clinical area and available alternatives. While very high AUC values (e.g. above 0.90) can be reported for diagnostic prediction,[3] such values are rare in prognostic modelling, where AUC values in the 0.70s are found for best-performing models for incident cardiovascular disease[4] and adverse health outcomes (including mortal...

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  • Critique on Methodological Aspects in Culturally Adapted Counselling Study: Addressing Self-Reported Measures and Counsellor Training

    The published study provides valuable insights into the effectiveness of culturally adapted counselling (CAC) for ethnic minorities, there are two critical aspects that warrant further discussion: the reliance on self-reported measures and the training and supervision of counsellors.

    Firstly, the primary outcome measures in the study were self-reported by participants. While self-reporting is a common practice in psychological research, it is not without its limitations. Self-reported data are susceptible to biases, such as social desirability bias, where participants may provide responses they believe are more socially acceptable rather than their true feelings or experiences. Additionally, response bias can occur, particularly in longitudinal studies where participants might answer questions based on their memory of previous answers rather than their current state. These biases could significantly influence the study's findings, potentially overestimating the effectiveness of the CAC intervention. To enhance the robustness of future research, incorporating objective measures or third-party assessments could provide a more comprehensive and unbiased evaluation of the intervention's effectiveness.

    Secondly, the study involved training counsellors in the culturally adapted intervention. However, the depth and effectiveness of this training, as well as the consistency of its application across counsellors, are not extensively discussed. The quality a...

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