Commentary on Milloy <i>et al</i>. (2010): The stark reality of overdose mortality among indigenous peoples—a(nother) plea for action
Bibliographic record
Abstract
The paper by Milloy and colleagues highlights the significant problem of injecting drug use among First Nations people in Vancouver [1] which is perhaps mirrored in other areas of Canada and for indigenous peoples living within dominant non-indigenous surrounds, globally. The authors have clearly shown the troubling rates of injecting drug use and its devastating effects, particularly in terms of overdose-related mortality, among Status Indians. Especially apparent are the high rates of overdose-related mortality amongst women in Downtown Eastside Vancouver, a finding that has been reiterated in other studies [2]. The authors recognise their findings are likely to be an underestimate of overdose mortality burden for the study period (2001–2005) and that the true magnitude of the problem is likely to be much greater. Understanding the demographics of English colonized indigenous populations is complex. The counting and classification of indigenous populations in the four major countries colonized by the English—Australia, New Zealand, Canada and the USA—differ greatly, which makes comparing illicit drug use within these populations problematic. However, the findings of Milloy and colleagues are consistent with what we know about higher rates of drug use and harms among indigenous peoples in other settings. In the case of Australia, data on illicit drug use and harms are limited. Currently there is no national surveillance system that accurately quantifies the number of indigenous people who inject drugs, their demographic details or risk behaviours, let alone morbidity and mortality data that are described in this paper. Despite this, we do know that the burden of disease and death estimated to be caused by illicit drug use is substantially larger among indigenous Australians than their non-indigenous counterparts [3]—similar to this study. Milloy and colleagues report that, during the study period, First Nations people experienced a standardised mortality rate close to three times higher than for non-First Nations British Columbians [1]. This data adds heavily to the already high rates of early mortality experienced by First Nations people, largely attributed to chronic disease and other factors associated with early mortality [4,5]. These startling statistics are significant for four key reasons. First, is the question of why indigenous peoples and other identifiable indigenous populations living within dominant worldview societies continue to be overrepresented in injecting drug use morbidity and mortality-related data. The ongoing effects of colonisation, including cultural breakdown, intergenerational trauma, and systematic failures of policy directed at indigenous peoples rather than developed by indigenous peoples, all contribute. Failure to address gaps in indigenous education, employment and health has predisposed many young indigenous peoples into pathways of vulnerability [6,7,8]. Clearly these issues should be addressed to reduce mortality among vulnerable populations. Second, the paper provides an insight to the task that lies ahead for British Columbia, and for the Canadian health system, in trying to reduce early preventable mortality among marginalised First Nations peoples. In Australia, the notion of “Closing the Gap” in life expectancy between indigenous and non-indigenous Australians endeavours to address this by improving outcomes related to overcrowded housing and education, and by striving for a better start in life for Aboriginal babies beginning in pregnancy. However, it would be fair to say that we are at the tip of the iceberg in trying to resolve the issues that have resulted from over 200 years of colonisation. Third, there are some clear steps that need to be prioritised in order to turn around overdose-related mortality among First Nations peoples in Canada. First Nations peoples of Canada must have access to detoxification and rehabilitation services for injecting drug users that are controlled and run by Canadian First Nations peoples; these services must aim to provide culturally appropriate programs that are encompassing of the cultural and social needs of indigenous peoples [9]. Importantly, these services need to be accessible in areas of greatest need, such as the Downtown Eastside area. A recent study by Gray and colleagues in the Australian context provides a timely warning [10]. The study found that there was no direct relationship between expenditure on indigenous drug and alcohol services and indigenous population patterns; some urban centres, where the majority of indigenous Australians live, were poorly resourced. Studies such as this highlight the necessity for services to be placed where need is greatest. Finally, the article opens up the festering sore of research gaps in indigenous worlds, and the authors must be commended for presenting this data. Given that there is little research on overdose mortality among indigenous peoples in developed nations, we can safely assume an even greater dearth for indigenous peoples in developing nations. Various barriers, practical and statistical, hamper the collection of reliable, indigenous-specific alcohol and other drug data, as well as other health-related data. Furthermore, processes for identifying indigenous status are often insufficient [11,12]. The pressing need for better quality data at a population level and improved coordination of data collection, preferably owned and led by indigenous peoples themselves, will enable identification of vulnerable groups and areas of greatest need. Most importantly, there is a need to use this data to analyse, interpret and promote action through the development of community responses, policy formulation and informed, targeted response to injecting drug use among indigenous peoples. A failure to lead in these areas equates to a failure of a global human rights obligation to indigenous peoples. In conclusion, the data presented in this paper represents an opportunity to turn around early mortality related to injecting drug use by highlighting the disparity that exists among injecting drug users, related to ethnicity. It also emphasizes the vulnerability of First Nations peoples, especially women, to injecting-related overdose [1]. We know indigenous peoples around the world experience harms from drug use disproportionate to non-indigenous people. However, the harmful effects of injecting drug use in indigenous communities are very much an unknown. This paper is a good first step. Information on overdose rates – and improved health data on indigenous populations generally [13]—would greatly assist to develop a knowledge base and allow for informed policy growth and improvement. None
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".