Dear Editor-in-Chief,

We are writing to you in response to Mahar et al.’s (2019) recent publication in your journal (“Suicide in Canadian veterans living in Ontario: a retrospective cohort study linking routinely collected data”).

The authors found a lower risk of suicide in Canadian Armed Forces (CAF) Veterans (former members) using Ontario Health Insurance Plan coverage, in contrast to the findings of higher CAF Veteran suicide risk in the Canadian Forces Cancer and Mortality Study (CF CAMS) and the Veteran Suicide Mortality Study (VSMS), which use nation-wide Vital Statistics and cancer registry data collected by Statistics Canada. We strongly feel that there is synergistic value in having different studies by different investigator groups using different data sources, but it is essential to understand the limitations in these different approaches and their findings.

Mahar et al. state that their study was the “...first study of suicide risk in Canadian veterans...” However, CF CAMS, which looked at suicide risk in Canadian Veterans, was published eight years prior to the Mahar et al. study (Statistics Canada, 2011). This was followed by the Veteran Suicide Mortality Study (VSMS) Technical report was published in 2017 (Simkus et al., 2017), its accompanying peer-reviewed publication (VanTil et al., 2018), and the 2018 VSMS Technical report (Simkus et al., 2018).

We have questions about the authors’ choice of covariates for the adjusted analysis. Specifically, the authors did not adjust for mental illnesses because they asserted that mental illnesses are on the causal pathway to suicide. However, most with mental illness do not die by suicide (Nordentoft et al., 2013). A diagnosis of depression is common in those who die by suicide but remains a poor predictor of a suicidal act (Franklin et al., 2017). Furthermore, the authors’ use of socioeconomic factors and physical health problems as covariates implies that they felt that those factors are not on the suicide causal pathway. This is counter to the epidemiological, clinical and theoretical evidence indicating that such factors are on causal pathways to suicide (Gunnell and Lewis, 2005). The authors wrote that they chose as covariates “established risk factors”, but Franklin et al. (2017) demonstrated that epidemiologists have tended to look at a narrow list of risk indicators over the past 50 years. Just because researchers keep using the same risk indicators does not mean that those indicators are “established” as significant.

We think that Mahar et al.’s statements of discordance between their findings and the CFCAMS findings are overstated, particularly given the lack of statistical significance to support their assertions. The breadth of the confidence intervals in their adjusted models suggest that their findings may actually be aligned with the findings published in studies with census-level coverage of the Veteran population (namely the CF CAMS study [Statistics Canada, 2011] and the VSMS publications [Simkus et al, 2017; Simkus et al, 2018; Van Til et al, 2018]) that found the risk of suicide to be significantly elevated in those with a history of military service. However, a notable exception was the youngest male age group, where the OHIP study reports much lower rates than VSMS. Unfortunately, the authors did not discuss the effect of the OHIP “under-representation of those who released before age 25” on their subsequent comparisons of Veteran and civilian suicides. Future examination of Veterans in the OHIP data should be more cautious in extrapolating conclusions to all Veterans, particularly for conditions that are more common in younger Veterans.

Disagreements notwithstanding, we agree wholeheartedly with the authors’ statement that “...this work raises a need to conduct further high-quality research on the topic of suicide in the military, including veteran populations”, as recommended in the Canadian Armed Forces – Veterans Affairs Canada Joint Suicide Prevention Strategy (CAF and VAC, 2017).

References
Canadian Armed Forces, Veterans Affairs Canada. Joint Suicide Prevention Strategy. Ottawa, ON: Canadian Armed Forces and Veterans Affairs Canada. 2017.

Franklin JC, Ribeiro JD, Fox KR, Bentley KH, Kleiman EM, Huang X, Musacchio KM, Jaroszewski AC, Chang BP, Nock MK. Risk factors for suicidal thoughts and behaviors: a meta-analysis of 50 years of research. Psychological Bulletin. 2017;143(2):187.

Gunnell D, Lewis G. Studying suicide from the life course perspective: implications for prevention. British Journal of Psychiatry. 2005;187:206-8.

Mahar AL, Aiken AB, Whitehead M, Tien H, Cramm H, Fear NT, Kurdyak P. Suicide in Canadian veterans living in Ontario: A retrospective cohort study linking routinely collected data. BMJ Open. 2019;9:e027343.

Nordentoft M, Wahlbeck K, Hällgren J, Westman J, Ösby U, Alinaghizadeh H, Gissler M, Laursen TM. Excess mortality, causes of death and life expectancy in 270,770 patients with recent onset of mental disorders in Denmark, Finland and Sweden. PloSOne. 2013;8(1):e55176.

Simkus K, VanTil LD, Pedlar D. 2017 Veteran Suicide Mortality Study (1976 to 2012). Charlottetown, PEI (Canada): Veterans Affairs Canada, 2017.

Simkus K, VanTil LD. 2018 Veteran Suicide Mortality Study: Identifying risk groups at release. Charlottetown, PEI (Canada): Veterans Affairs Canada, 2018.

Statistics Canada. Canadian Forces Cancer and Mortality Study: Causes of death. Catalogue no. 82-584-X. Ottawa (Canada): Minister for Industry, 2011.

VanTil L, Simkus K, Rolland-Harris E, Pedlar DJ. Veteran suicide mortality in Canada from 1976 to 2012. Journal of Military, Veteran, and Family Health. 2018;4(2):110-6.

The recently published article by Chan et al. questions the value of AAA screening. [1] We question the validity of the arguments and the data they use to support it, which we believe are either irrelevant or support the case for screening. Furthermore, the Chan et al. paper completely neglects the important equity gains that AAA screening can produce in the New Zealand (NZ) population.

Chan and his co-authors’ critique of AAA screening is based on 3 arguments: (i) patients with AAA are too sick with other comorbidities to benefit from screening and die of these other illnesses; (ii) most patients with AAA would be diagnosed by the health system anyway without a screening programme; and (iii) the size of the population that can benefit from screening is too small for it to be cost effective. Let us look more closely at each of these.

Co-morbidities in patients with AAA

The authors emphasise the point that co-morbidities in AAA patients might limit the benefit they attain from screening. They calculate that 77% of those dying of AAA had some other comorbidity. However, to imply that these were fulminant conditions and that preventing AAA death in this group would have been futile because they are moribund is highly misleading. The collection of co-morbidities includes: atrial fibrillation (a condition very prevalent in any elderly population), cardiovascular disease (CVD), also highly prevalent especially for Māori (which could range in severity from stable angina or transient ischaemic attacks to severe stroke), diabetes (which in fact has a negative association with AAA [2]), bronchiectasis (which is acquired in childhood by many in NZ) and previous hip fracture (also a common event in the elderly and one which would not normally on its own be considered a contraindication to life-saving intervention).

Even for the 25.6% with “evidence of malignant cancer” the majority (24.5% of the 25.6%) were non-metastatic cancers and the group excludes two of the most lethal tumour sites (lung, upper gastrointestinal). The malignant cancer group would have included (among others): successfully treated malignant melanoma; chronic haematological malignancies that can remain in remission for many years; and non-metastatic prostate cancer, which is rarely lethal. Note that the five-year net survival in England at ages 55-74 for Stage 1-3 cancers of the anus, colon and rectum, urinary tract including kidney and bladder, liver, and thyroid, plus Hodgkin lymphoma, myeloma, and malignant melanoma (weighted by incidence) is 78%. [3]

Although the authors do not provide a general population comparison of the prevalence of the conditions listed in Table 2, they use the M3 co-morbidity index to argue that the AAA deaths had higher co-morbidities than the general population. The authors fail to countenance the possibility that this could be attributable to reverse causality: in a country without population screening, those with high co-morbidities are far more likely to have their AAA diagnosed than those with AAA and low co-morbidities. It would clearly be difficult for AAA not to be diagnosed in someone with stage 3 renal disease whose abdomen will almost certainly have been imaged by CT or ultrasound as part of the diagnostic work-up. And why compare morbidities in patients who have died from AAA rather than with patients who have been diagnosed with AAA but not died from it? Clearly patients who are dead are likely to have much higher rates of co-morbidity than age-matched peers who are still alive, no matter what the patient died of. Why not compare the co-morbidity index in patients diagnosed with colorectal cancer as a similar screening programme?

Other arguments in relation to co-morbidities in fact support the case for screening. Stating that “out of a group of people with AAA… there were eight times more deaths from other cardiovascular causes than AAA rupture” only serves to underline the high efficacy of modern treatment of AAA disease. They go on to say, “For people with known AAA, the absolute risk of AAA rupture resulting in death is actually low”. That is exactly why screening is effective in preventing those deaths.

The issue of co-morbidities becomes even less relevant if good systems exist to exclude patients with limited life expectancy or poor quality of life from participating in screening. We have piloted a screening programme based in primary care in Auckland, NZ that has ample scope to do just that. [4]

Capacity of the health system to diagnose AAA without a screening programme

A crucial element to Chan et al.’s case against AAA screening is that 31.3% of patients had been diagnosed prior to the acute event resulting in death. This implies that the vast majority – 68.7% – had not been diagnosed before their death and highlights how poor the existing health system is at identifying this treatable disease. Without doubt, a high proportion of deaths in the large undiagnosed majority (of this group) could have been prevented by diagnosis in a screening programme, not to mention all of those who died of AAA without the death being recorded as AAA (see below). And even so, this figure was calculated only in those patients with AAA as their primary cause of death, which is biased to those who had a known history of AAA.

In a country with no population screening many, perhaps even the majority of, AAA deaths will never be recorded as such because the underlying cause of death documented in sudden fatalities frequently relies on a person’s medical history. Sudden deaths in older people, particularly those with a history of CVD, are most often classified as ischaemic heart disease or stroke, and the autopsy rate in NZ, like many other Western countries, is now very low (approximately 13% of all deaths in 2010, but likely to be much lower in older age groups). [5] So just as those with AAA cited as the cause of death are more likely to have previously been diagnosed with AAA and more likely to have had prior aortic surgery, there is likely to be a significant degree of under-reporting of AAA deaths in people without a prior diagnosis of AAA in NZ, since death is more likely to be attributed to AAA in those who have a pre-existing diagnosis of AAA.

Even this low proportion of 31% with a prior AAA diagnosis exaggerates the extent that this might undermine the case for screening. Chan et al. fail to disaggregate the proportion of these cases that would have been diagnosed prior to screening, in which case they would be excluded as ineligible. Our screening pilots have now produced more accurate data on the prevalence of undiagnosed AAA in the NZ population. In just one large rural practice in the north of Auckland we identified 34 previously undiagnosed AAA screening 480 men and women using a risk-based algorithm (set at a threshold of 2%). Three of these undiagnosed aneurysms were large and at significant risk of imminent rupture.

The authors are remarkably sanguine about the likelihood that AAA will be diagnosed (and followed up) as an incidental finding in abdominal CT and ultrasound imaging. It has been our experience that when AAA is not the reason for abdominal imaging, the size of the abdominal aorta is often not examined and/or reported. Others are reported but not followed up by referral to either the patient’s general practitioner or the vascular service. Khashram et al’s NZ study of aneurysms detected through CT colonography noted that 65 of 223 (29.1%) were not reported and only 84 (37.6%) were in surveillance. [6] Similarly, Claridge et al, performed a retrospective audit of all abdominal CT scans in those over 50 years of age in the Otago Southland region of NZ in 2013-14. [7] Of the 187 AAA were identified, 35% were not documented on the original radiology report, including 48% of small (30-39mm) aneurysms. Having a prior diagnosis is manifestly no guarantee of adequate surveillance and treatment. Conversely, formal screening programmes provide the structure and quality control systems to ensure that diagnosed patients receive appropriate follow-up.

The lack of structured surveillance of patients diagnosed with AAA as an incidental finding may help to explain the fact that, according to Chan et al., 89.1% of those who died from AAA had not undergone surgical repair Given this high proportion it is surprising that the authors chose to focus on the small proportion who died with a previously repaired AAA, rather than the huge proportion who, for whatever reason, did not benefit from this highly effective procedure.

Size of benefit and cost-effectiveness

The third theme of Chan et al.’s paper is that AAA does not have a large population health benefit. They claim that “One of the primary objectives for screening for AAA is to reduce all-cause mortality at a population level”. In fact, the aim of screening for AAA is to produce a net benefit in those who are screened (at an acceptable cost), and in NZ at least, to reduce health inequities. AAA is a deadly but relatively uncommon disease that has high prevalence among groups with extensive exposure to tobacco smoke – as is the case for the indigenous Māori population in NZ, as we have ascertained in our recent AAA prevalence research. While there is legitimate debate about whether screening programmes should be required to demonstrate an impact on all-cause mortality [8], there is no doubt that existing programmes, which the authors have not gone on record to criticise, fail to meet their requirement in this regard. For example, evidence is lacking for an all-cause mortality benefit in screening colorectal cancer, a disease with a far higher incidence and mortality than AAA, and one where population screening programmes now exist in many countries, including NZ. The Cochrane Collaboration report on colorectal cancer screening found that “combining the four trials did not show any significant difference between the screening and control groups”. [9] Similarly breast screening may not even reduce breast cancer mortality. [10]

Contrast this with the finding from the most recently published meta-analysis of AAA screening “Invitation to AAA screening in ≥64-year-old men significantly reduced both all-cause and AAA-related mortalities.” [11] In fact, Chan et al. argue that instead of screening for AAA we should be “designing interventions to more directly improve systematic CVD risk management in primary care”. Their inconsistency is ironic given a recently published overview of system reviews of using cardiovascular risk scoring in routine risk assessment in primary prevention of CVD which noted that “none of the included [systematic reviews] reported changes in all-cause mortality” and it concluded that there is “a possible lack of effectiveness of [total risk assessment] in reducing CVD events and mortality, as well as a clinically insignificant influence on individual risk factor levels.” [12]

Space does not permit us to respond point by point to each of the authors’ comments on the cost-effectiveness of AAA screening. We note that of the numerous published studies of AAA screening cost-effectiveness, most of which have reported favourable ratios, a recent one examined it from a NZ perspective and concluded that “a UK-style AAA screening programme would be cost-effective in New Zealand.” [13] Crucially, their cost-effectiveness analysis did not assume that all participants with screen-detected AAA would receive what Chan et al call “the full benefit”; it accounted for not only co-morbidity and opportunistic detection, but also treatment failure, lower life expectancy in AAA cases, non-attendance at screening, complication costs and even unrelated healthcare costs. Rather than simply speculating about the negative impact that certain factors may have had, whilst ignoring the positive effect that others might have had (e.g. the life expectancy gain of almost 3 years in 65-year old men between 1999 and 2017 when the MASS trial ended recruitment), [14] it would have been more useful if Chan et al. had published their own cost-effectiveness estimates.

Other methodological flaws and inaccuracies

Chan et al. incorrectly suggest that there was evidence of rapidly falling prevalence of AAA prior to commencement of screening in the UK. There was evidence of falling rates of mortality and hospital admission, but not of prevalence. The authors have excluded the proportion of patients with an acute AAA presentation that died within 2 days of the admission which further reduces the number of AAA-related deaths in this analysis.

Equity impact?

There is not a single mention of equity in the paper by Chan et al. and none of the published cost-effectiveness studies of AAA screening assign any value to the reductions in health inequities that AAA screening might produce. AAA, like lung cancer, places a disproportionate burden on specific ethnic groups and is also patterned by deprivation and gender. While AAA prevalence rates in 65 year old men in the UK range from 1.0-1.3%, in NZ Māori men it was 3.6% in our own study. [4] The Chan et al paper completely ignores the impact that differential access to primary care, diagnostics and surgery [15-17] contributes to the well-documented inequity in AAA outcomes for Maori compared to non-Māori. [18]

NZ has yet to take the steps required for elimination of unfair, avoidable and remedial health inequalities between its Indigenous and non-Indigenous populations. Such poorly thought through studies illustrate how deeply entrenched and unexamined bias against Māori creates and continues to maintain health inequities in New Zealand.

References

1. Chan WC, Papaconstantinou D, Winnard D, et al. Retrospective review of abdominal aortic aneurysm deaths in New Zealand: what proportion of deaths is potentially preventable by a screening programme in the contemporary setting? BMJ Open 2019; 9: e027291. DOI: 10.1136/bmjopen-2018-027291.

2. Raffort J, Lareyre F, Clément M, et al. Diabetes and aortic aneurysm: current state of the art. Cardiovasc Res 2018; 114: 1702-1713. DOI: 10.1093/cvr/cvy174.

3. Office for National Statistics (UK). Cancer survival in England: adults diagnosed, https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/... (2019, accessed 26 August 2019).

4. Sandiford P, Grey C, Salvetto M, et al. The population prevalence of abdominal aortic aneurysm in New Zealand Māori. J Vasc Surg 2019; In press.

5. New Zealand Law Commission. Final words: death and cremation certification in New Zealand. (Issues paper 23). 2011. Wellington: New Zealand Law Commission.

6. Khashram M, Jones GT and Roake JA. Prevalence of abdominal aortic aneurysm (AAA) in a population undergoing computed tomography colonography in Canterbury, New Zealand. Eur J Vasc Endovasc Surg 2015; 50: 199-205. 2015/06/15. DOI: 10.1016/j.ejvs.2015.04.023.

7. Claridge R, Arnold S, Morrison N, et al. Measuring abdominal aortic diameters in routine abdominal computed tomography scans and implications for abdominal aortic aneurysm screening. J Vasc Surg 2017; 65: 1637-1642. 2017/02/22. DOI: 10.1016/j.jvs.2016.11.044.

8. Editorial. Screening Matters. 2007, p. 1-2.

9. Hewitson P, Glasziou P, Irwig L, et al. Screening for colorectal cancer using the faecal occult blood test, Hemoccult. The Cochrane database of systematic reviews 2007: Cd001216. 2007/01/27. DOI: 10.1002/14651858.CD001216.pub2.

10. Møller MH, Lousdal ML, Kristiansen IS, et al. Effect of organized mammography screening on breast cancer mortality: A population-based cohort study in Norway. Int J Cancer 2019; 144: 697-706. DOI: 10.1002/ijc.31832.

11. Takagi H, Ando T and Umemoto T. Abdominal Aortic Aneurysm Screening Reduces All-Cause Mortality: Make Screening Great Again. Angiology 2018; 69: 205-211. 2017/02/15. DOI: 10.1177/0003319717693107.

12. Studziński K, Tomasik T, Krzysztoń J, et al. Effect of using cardiovascular risk scoring in routine risk assessment in primary prevention of cardiovascular disease: an overview of systematic reviews. BMC Cardiovascular Disorders 2019; 19: 11. DOI: 10.1186/s12872-018-0990-2.

13. Nair N, Kvizhinadze G, Jones GT, et al. Health gains, costs and cost-effectiveness of a population-based screening programme for abdominal aortic aneurysms. BJS 2019; 106: 1043-1054. DOI: 10.1002/bjs.11169.

14. Office for National Statistics (UK). All publications related to life expectancies, https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarri... (accessed Aug 21 2019).

15. Crengle S, Lay-Yee R, Davis P, et al. A Comparison of Māori and Non-Māori Patient Visits to Doctors: The National Primary Medical Care Survey (NatMedCa): 2001/02. Report 6. . Wellington: Ministry of Health, 2005.

16. Rahiri J-L, Alexander Z, Harwood M, et al. Systematic review of disparities in surgical care for Māori in New Zealand. ANZ journal of surgery 2018; 88: 683-689. DOI: 10.1111/ans.14310.

17. Jansen P, Bacal K and Buetow S. A comparison of Māori and non-Māori experiences of general practice. NZ Med J 2011; 124: 24-30.

18. Sandiford P, Mosquera D and Bramley D. Ethnic inequalities in incidence, survival and mortality from abdominal aortic aneurysm in New Zealand. J Epidemiol Community Health 2012; 66: 1097-1103. 2012/07/07. DOI: 10.1136/jech-2011-200754.

T2DM can be divided into Five Stages (1-2). The altered metabolism of uric acid, often associated with DM, may be diagnosed from birth, starting with its Uricemic Constitution-dependent, Inherited Real Risk (3, 4). As all other inherited real risks, such a predisposition to hyperuricemia and gout is removed by Reconstructing Mitochondrial Quantum Therapy (5).

References.
1) Stagnaro-Neri M., Stagnaro S. Introduzione alla Semeiotica Biofisica. Il Terreno Oncologico. Travel Factory, Roma, 2004. http://www.travelfactory.it/semeiotica_biofisica.htm
2) Stagnaro S., Stagnaro-Neri M., Le Costituzioni Semeiotico-Biofisiche.Strumento clinico fondamentale per la prevenzione primaria e la definizione della Single Patient Based Medicine. Travel Factory, Roma, 2004. http://www.travelfactory.it/libro_costituzionisemeiotiche.htm
3) Stagnaro S., West PJ., Hu FB., Manson JE., Willett WC. Diet and Risk of Type 2 Diabetes. N Engl J Med. 2002 Jan 24;346(4):297-298. [Medline]
4) Stagnaro Sergio. Epidemiological evidence for the non-random clustering of the components of the metabolic syndrome: multicentre study of the Mediterranean Group for the Study of Diabetes. Eur J Clin Nutr. 2007 Feb 7; [Epub ahead of print] [Medline]
5) Caramel S., Marchionni M., Stagnaro S. Morinda citrifolia Plays a Central Role in the Primary Prevention of Mitochondrial-dependent Degenerative Disorders. Asian Pac J Cancer Prev. 2015;16(4):1675. http://www.ncbi.nlm.nih.gov/pubmed/25743850 [Medline]

Dear Editor,

Herrmann-Werner et al (2019) brought to our attention an increasingly relevant topic. The study proposed that the obesity simulation suit (OSS) could be used to address the issue of stigmatisation of obesity(1). We are medical students at Imperial College London. As part of our course, we have regularly attended clinical communication sessions in the form of simulated role play.

We believe that whilst using a simulated patient in an OSS may be useful, it is a superficial way of addressing the deep-rooted societal problem of how obesity is viewed and may even reinforce the stigma. The use of simulated patients should be adapted to reduce prejudice towards obese patients, especially with reference to their physical and psychological comorbidities.

The study argues that using healthy weight actors in the OSS was of benefit because using obese actors ‘entails the potential danger of issues becoming too personal for the patient’(1). However, we believe that using actors with a healthy BMI may reinforce to students that obese patients are embarrassed or ashamed to have a conversation about their weight. At Imperial College, we simulate consultations with real patients to better understand their conditions. Rees et al also prefer the use of real patients, arguing that it provides medical students with a more realistic experience(2). Therefore, we recommend that students talk to obese patients who are willing to partake and understand the sensitive nature of the session.

Furthermore, actors with a healthy BMI may not have experienced the same physical and psychosocial problems endured by many obese people. This limits the usefulness of the session, especially the ways in which obese people feel stigmatised. Speaking to real patients during our time at Imperial College has furthered our understanding of the physical and psychosocial impact of disease. In addition, teaching sessions exploring how body image is portrayed in social media, the fashion industry and other areas of society may help medical students recognise and understand their own attitudes towards obese patients.

Allowing students to talk to obese patients with related health conditions would be more beneficial than using actors with an OSS. Furthermore, the physical and psychosocial aspects of disease should be explored alongside formal teaching about disease pathology, to help reduce the stigma surrounding obesity.

1 Herrmann-Werner A, Loda T, Wiesner LM, Erschens RS, Junne F, Zipfel S. Is an obesity simulation suit in an undergraduate medical communication class a valuable teaching tool? A cross-sectional proof of concept study. BMJ open. 2019 Jul 1;9(8):e029738.

2 Rees C, Sheard C, McPherson A. Medical students’ views and experiences of methods of teaching and learning communication skills. Patient Education and Counseling. 2004 July;54(1): 119-121