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Keywords:

  • illicit drugs;
  • dependence;
  • burden of disease;
  • epidemiology

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Acknowledgements
  5. References

Introduction

The Global Burden of Disease (GBD) 2010 study updated the findings of earlier exercises. It provided regional and global estimates of the burden of disease attributable to diseases, injuries and risk factors. Here we provide a brief summary of the work for illicit drug use.

Design

Systematic reviews were undertaken to estimate the major epidemiological parameters (incidence, prevalence, duration/remission and mortality) for each drug. Reviews evaluated the nature and quality of evidence for illicit drug use as a risk factor for many health outcomes, for the comparative risk assessment (CRA) exercise.

Results

Substantial gaps existed in basic epidemiological parameters. Following modelling and imputation of missing data, it was estimated that opioid and amphetamine dependence were the most common forms of illicit drug dependence in 2010; opioid dependence was responsible for the greatest burden. Few putative consequences of illicit drug use had the quality or quantity of data required to be included in the CRA.

Discussion

Estimates of the extent and distribution of disease burden are likely to shape global and regional health policy development. The GBD exercise will be repeated on an annual basis; GBD 2010 clearly demonstrated that although the illicit drug field is generating more and better epidemiological data on the health risks of drug use, there is still much work to be done to generate defensible estimates of the magnitude of risk, particularly for impactful and prevalent outcomes, such as injuries, violence and mental health complications. Until then, burden of disease attributable to illicit drugs will be underestimated. [Degenhardt L, Whiteford H, Hall WD. The Global Burden of Disease projects: What have we learned about illicit drug use and dependence and their contribution to the global burden of disease? Drug Alcohol Rev 2014;33:4–12]


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Acknowledgements
  5. References

There is good evidence that illicit drug use and mental disorders produce substantial loss of life and disability, but their impact upon population health needs to be better understood. Governments, policy-makers and funding bodies need information on the comparative population health impact of different diseases and risk factors when making decisions about where to focus policy, service and research planning, and implementation. It is important that illicit drugs and mental health are understood with that context.

‘Burden of disease’ studies have identified the large proportion of disease burden arising from mental disorders and illicit drug use [1,2]. The high prevalence and chronic nature of some drug use and mental disorders led to them being prominent in the league table of disorders ranked in order of burden in the first Global Burden of Disease (GBD) study [3]. The often quoted finding that depression was a leading cause of disability in the world has been used to promote funding for programs to treat and prevent mental disorders [4]. Burden of disease estimates have been even more important in countries where disease advocacy groups are not well established and where external agencies [such as the World Health Organization (WHO) and World Bank] have been influential in setting priorities for health spending.

Started in 2007, with capstone papers published in December 2012 [5–9] and more specific papers published during 2013 [10,11], the GBD 2010 study comprehensively update the findings of the first GBD exercise. It provided regional and global estimates of the burden of disease attributable to hundreds of diseases, injuries and their risk factors. In this commentary, we provide a brief summary of the work undertaken by the expert group examining illicit drug use and mental disorders, with a specific focus on illicit drugs. We draw on work that has already been published from both GBD 2010 and in the work leading up to the overview publications throughout this paper.

The history of burden of disease studies

Until the early 1990s, guidance on public health funding allocations largely came from studies of population mortality. These studies ignored morbidity arising from disorders and injuries that were not fatal but nonetheless adversely affected a person's functioning [12]. Measuring the impact of disease was revolutionised in 1993, when the World Bank provided estimates of causes of global disease burden using a new summary measure, the disability-adjusted life year (DALY) [13]. The DALY is a summary measure of population health that integrates mortality with morbidity and disability information to produce a single measure of disease burden that enables the relative importance of health problems to be compared. One DALY represents the loss of one healthy year of life.

For each disease or injury, DALYs are calculated as the sum of years lost due to premature mortality and the years of lost health due to disability (see Figure 1). The DALY combines measures of premature mortality (years of life lost) and morbidity (years lived with disability) that are attributable to diseases (e.g. depression, cancer and heart disease). The DALY allows the mortality and morbidity of various diseases to be compared, with the aim of ‘disconnecting advocacy from epidemiology’ [3]. It is used by the World Bank and the WHO to estimate disease burden and the cost-effectiveness of health interventions [14–16], that is, the cost and impact of interventions on the burden of a particular disease or injury. This information is critical for informed priority setting in health care.

figure

Figure 1. Metrics used in global burden of disease.

Download figure to PowerPoint

A revised set of estimates was published in 1996 as part of the first GBD study [3]. Regular updates have been included in the WHO's World Health Reports [17] and the Disease Control Priorities publications [16]. In 2002, the World Health Report estimated disease burden attributable to various risk factors—the so-called ‘comparative risk assessment’ (CRA) exercise. This was finalised in 2006 [18]. The CRA estimated that alcohol, tobacco and injecting drug use were important risk factors for global disease burden [18].

GBD 2010

GBD 2010 was led by a core team of researchers from a consortium that included the Institute for Health Metrics and Evaluation at the University of Washington (Seattle, USA), the University of Queensland (Australia), Johns Hopkins University (USA), Harvard University (USA) and the WHO (Switzerland).

It was the first major effort since the original GBD study to produce systematic and comprehensive estimates of the burden of diseases and injuries. It updated the comparative estimates of the burden of risk factors for 1990, 2005 and 2010. The 1990 estimates were recalculated using the improved methods and data that have become available since the original study was undertaken. The new GBD study produced estimates for 21 regions of the world that were published in 2012 [5–9].

The study included epidemiological reviews of all diseases, injuries and risk factors and estimates of the mortality and cause of death for all countries in the world. This involved multiple systematic reviews to estimate the major epidemiological parameters (incidence, prevalence, duration/remission and mortality) for each disorder and a critical synthesis of the existing evidence. The intent of the GBD was to understand and incorporate all existing data on the epidemiology of diseases and risk factors. It did not conduct new studies to estimate these parameters.

Experts across the range of diseases and injuries included in the study provided input to this core team, with disease/disorder groups organised into ‘expert groups’. The expert groups were asked to synthesise existing data on the incidence, prevalence, duration and excess mortality of diseases, exposures to important risk factors, and then to critically assess the estimates of disease burden produced by the core project team. More information on the process is provided at http://www.gbd.unsw.edu.au.

Advances in our understanding of disease epidemiology allowed the GBD group to expand the number of mental disorders and illicit drugs included beyond those in the original GBD study. For example, a greater number of anxiety disorders and childhood mental disorders were included. The original GBD study focused on heroin dependence, because this was the form of illicit drug use for which there were the best prevalence estimates and mortality data [3]. This time, estimates of disease burden were made for dependence on heroin and other illicit opioid use, cocaine, amphetamines and cannabis.

Other drugs were not separately estimated because of data limitations and lack of research on their risks of dependence and other harms. This does not mean that the use of these drugs is without risk to users. However, the inclusion of an ‘other drugs’ category will have captured some of this burden in a non-drug-specific manner [10].

Detail on results of systematic reviews, by drug type and parameter

There are major gaps in the most basic of epidemiological parameters for illicit drug dependence. Systematic reviews conducted for each of the four major drug types revealed that the greatest amount of data were available for cannabis and opioids, with data on amphetamines and cocaine more sparse (Table 1). Incidence estimates were extremely rare. Estimates of ‘remission’ from dependence were very uncommon [3]; and mortality had been most widely studied among regular or dependent opioid users [21]. When examining prevalence, use estimates were far more common than dependence estimates; and school surveys were more common than adult surveys [19].

Table 1. Data located on epidemiological parameters for illicit drugs
 Evidence that use or dependence occurs (no. of countries)Estimates of prevalence of use (no. of countries)Estimates of dependence prevalence (no. of countries)Prospective studies examining remissionProspective studies examining mortality
  1. Sources for these estimates are as follows: evidence of use/dependence [19]; estimates of use prevalence [19]; estimates of dependence prevalence [19]; prospective studies of remission from dependence [20]; prospective studies of mortality [21–24].

Amphetamines18177938
Cannabis201957319
Cocaine18286547
Heroin/opioids19289251057

Although we found improvements over time in the quality and scope of data on the epidemiology of drug use disorders, nonetheless huge gaps remained. In most countries, there was only a single measure of prevalence and limited knowledge of the natural history of these disorders; in others, such as countries in the Caribbean and Pacific regions and Africa, there were often no data. Expert opinion and advice was sought to produce the most plausible estimates and uncertainty bounds for countries without data. In all stages of data extraction, all available study details were extracted into an access database specifically designed for the project. These databases facilitated the use of modelling and regression techniques to impute data and estimate error around the estimates.

Drug dependence prevalence and disease burden

The results of the epidemiological modelling undertaken in GBD 2010 produced novel results about levels of dependent drug use across the globe; the results presented here have been reported in detail elsewhere previously [10,25, Degenhardt et al., unpublished data]. Opioid and amphetamine dependence were the two most common forms of illicit drug dependence globally in 2010 (15.4 million and 17.2 million estimated cases, respectively; Table 2). There were 13.1 million cannabis-dependent and 6.9 million cocaine-dependent persons. Males formed the majority of cases (64% each for cannabis and amphetamines and 70% each for opioids and cocaine).

Table 2. Age-standardised prevalence of illicit drug dependence, and estimated number drug dependent people, by drug type and GBD region, 2010
 CannabisAmphetaminesCocaineOpioids
n%95% CIn%95% CIn%95% CIn%95% CI
  1. aNote: reproduced with permission from a previous Lancet publication [10]. CI, confidence interval; GBD, Global Burden of Disease.

Asia Pacific, high income390 0000.28(0.18–0.41)372 0000.24(0.17–0.34)257 0000.06(0.05–0.07)456 0000.28(0.17–0.44)
Asia Central197 0000.22(0.17–0.29)203 0000.23(0.18–0.29)52 0000.02(0.01–0.02)209 0000.24(0.18–0.33)
Asia East2 402 0000.17(0.09–0.28)2 634 0000.18(0.12–0.26)234 0000.16(0.11–0.24)2 180 0000.14(0.08–0.24)
Asia South2 649 0000.15(0.13–0.18)3 993 0000.24(0.16–0.37)1 086 0000.07(0.04–0.10)4 331 0000.26(0.22–0.31)
Asia South East977 0000.15(0.11–0.19)2 724 0000.42(0.34–0.54)114 0000.02(0.01–0.02)956 0000.15(0.11–0.20)
Australasia154 0000.68(0.60–0.78)98 0000.41(0.29–0.56)32 0000.14(0.09–0.20)110 0000.46(0.41–0.53)
Caribbean69 0000.16(0.12–0.21)88 0000.20(0.16–0.25)143 0000.33(0.26–0.42)109 0000.26(0.18–0.36)
Europe Central249 0000.23(0.18–0.29)365 0000.31(0.27–0.37)63 0000.05(0.04–0.06)230 0000.19(0.15–0.26)
Europe Eastern432 0000.22(0.15–0.33)298 0000.14(0.11–0.19)117 0000.05(0.04–0.07)607 0000.27(0.17–0.44)
Europe Western1,141 0000.34(0.28–0.41)938 0000.26(0.24–0.28)641 0000.18(0.16–0.19)1 318 0000.35(0.32–0.39)
Latin America, Andean62 0000.11(0.08–0.15)76 0000.14(0.12–0.17)145 0000.26(0.20–0.34)153 0000.28(0.18–0.42)
Latin America, Central220 0000.09(0.07–0.13)710 0000.30(0.23–0.39)274 0000.12(0.09–0.14)572 0000.24(0.17–0.35)
Latin America, Southern169 0000.28(0.19–0.43)153 0000.26(0.20–0.33)184 0000.30(0.21–0.42)208 0000.35(0.22–0.54)
Latin America, Tropical286 0000.14(0.08–0.23)708 0000.33(0.26–0.43)920 0000.43(0.30–0.59)491 0000.23(0.12–0.39)
North Africa/Middle East735 0000.14(0.12–0.18)1 145 0000.24(0.20–0.28)691 0000.14(0.11–0.17)1 374 0000.29(0.22–0.37)
North America, High Income1 755 0000.60(0.53–0.68)717 0000.23(0.18–0.28)1 604 0000.53(0.39–0.72)959 0000.30(0.25–0.36)
Oceania21 0000.20(0.13–0.31)25 0000.26(0.18–0.37)3 0000.03(0.02–0.05)19 0000.20(0.12–0.31)
Sub-Saharan Africa Central151 0000.16(0.11–0.23)207 0000.24(0.17–0.34)40 0000.05(0.03–0.07)118 0000.15(0.09–0.23)
Sub-Saharan Africa East589 0000.16(0.13–0.20)798 0000.24(0.20–0.29)105 0000.03(0.03–0.04)488 0000.15(0.12–0.19)
Sub-Saharan Africa South149 0000.18(0.12–0.28)188 0000.24(0.17–0.34)37 0000.05(0.03–0.07)157 0000.21(0.13–0.35)
Sub-Saharan Africa West276 0000.08(0.06–0.11)742 0000.24(0.19–0.32)149 0000.05(0.04–0.07)435 0000.15(0.11–0.20)
Females4 696 0000.14(0.12–0.16)6 256 0000.18(0.16–0.22)2 090 0000.06(0.05–0.07)4 698 0000.14(0.12–0.16)
Males8 377 0000.23(0.20–0.27)10 928 0000.31(0.27–0.37)4 801 0000.14(0.12–0.16)10 781 0000.31(0.27–0.35)
Overall13 073 0000.19(0.17–0.21)17 184 0000.25(0.22–0.28)6 891 0000.10(0.09–0.11)15 479 0000.22(0.20–0.25)

The geographic distribution of cases reflected variations in prevalence and country populations (Table 2). An estimated 57.8% of amphetamine dependence cases were found across the Asian regions (9.3 million cases), but the highest prevalence estimates were for Southeast Asia [0.42%; 95% uncertainty interval (UI) 0.34–0.54%] and Australasia (0.41%; 95% UI 0.29–0.56%). North America High-Income was estimated to contain 13.4% of cannabis-dependent people, with a high prevalence (0.6%; 0.5–0.7%). The highest levels of cocaine dependence were estimated in North America High-Income (0.53%; 95% UI 0.39–0.72%) and Latin America. Australasia had among the highest levels of opioid dependence (0.46%; 95% UI 0.41–0.53%), although the largest populations were in East and South Asia. Estimated levels of illicit drug dependence were generally lower in African and Asian regions.

Drug use disorders directly accounted for 20.0 million DALYs in 2010 (95% UI 15.3–25.4 million; Table 3). This was 0.8% (0.6%–1.0%) of global all-cause DALYs. This was an increase of 52% from estimates for 1990 (using the same methods), when the estimated direct burden was 13.1 million DALYs or 0.5% (0.4–0.7%) of all-cause DALYs.

Table 3. Estimated YLDs, YLLs and DALYs for drug use disorders, by sex, 2010
 PersonsMalesFemales
Lower CIMeanUpper CILower CIMeanUpper CILower CIMeanUpper CI
  1. aNote: reproduced with permission from a previous Lancet publication [10]. bFor these causes, the mean value is outside of the 95% uncertainty interval. This occurs because the full distribution of 1000 draws is asymmetric with a long tail. A small number of high values in the uncertainty distribution raises the mean above the 97.5 percentile of the distribution. CI, confidence interval; DALY, disability-adjusted life year; YLD, years lived with disability; YLL, years of life lost.

Cannabis dependence         
YLDs1 348 0002 057 0002 929 000849 0001 323 0001 936 000481 000734 0001 063 000
YLLs
DALYs1 348 0002 057 0002 929 000849 0001 323 0001 936 000481 000734 0001 063 000
Amphetamine dependence         
YLDs1 460 0002 596 0003 957 000928 0001 657 0002 562 000522 000939 0001 502 000
YLLs6 00021 000b15 0004 00015 000b13 0001 0005 000b4 000
DALYs1 470 0002 617 0004 109 000933 0001 673 0002 653 000524,000944 0001 520 000
Cocaine dependence         
YLDs633 0001 085 0001 639 000443 000760 0001 168 000187 000325 000503 000
YLLs7 00025 000b22 0005 00018 000b17 0002 0006 000b5 000
DALYs645 0001 110 0001 727 000452 000778 0001 200 000189 500331 800518 700
Opioid dependence         
YLDs5 143 0007 170 0009 258 0003 550 0005 017 0006 536 0001 484 0002 153 0002 877 000
YLLs1 233 0001 981 0003 133 000771 0001 460 0002 419 000287 000522 000792 000
DALYs7 066 0009 152 00011 443 0004 860 0006 477 0008 298 0001 963 0002 675 0003 453 000
Other drug use disorders         
YLDs2 108 0003 503 0005 170 0001 380 0002 306 0003 439 000723 0001 198 0001 821 000
YLLs1 008 0001 555 0002 552 000590 0001 114 0001 941 000249 000441 000739 000
DALYs3 555 0005 059 0007 042 0002 390 0003 420 0004 798 0001 128 0001 639 0002 348 000
All drugs         
YLDs11 837 00016 411 00021 584 0007 934 00011 063 00014 572 0003 763 0005 349 0007 095 000
YLLs2 225 0003 582 0005 683 0001 340 0002 607 0004 409 000538 000975 0001 510 000
DALYs15 255 00019 995 00025 367 00010 214 00013 670 00017 454 0004 715 0006 324 0008 199 000

Much of the change over time could be attributed to population growth. The exception was opioid dependence, where 42% of the increase was attributed to increased prevalence between 1990 and 2010; overall opioid dependence burden increased by 74% across the period.

Two-thirds (69.3%) of all drug disorder DALYs were explained by years lived with disability and 30.7% by years of life lost. Opioid dependence accounted for the highest proportion (46%) of illicit drug burden (9.2 million DALYs, 95% UI 7.1–11.4 million). Cocaine dependence accounted for the smallest burden (5.5% of illicit drug burden; 1.1 million DALYs, 95% UI 0.65–1.7 million). Cannabis dependence was not estimated to cause any years of life lost but contributed 2.1 million DALYs in the form of years lived with disability (95% UI 1.3–2.9 million; 10.3% of illicit drug burden).

It is important to note that we did not estimate harmful use/abuse of illicit drugs (as defined by WHO's International Classification of Diseases (ICD) and American Psychiatric Association's Diagnostic and Statistical Manual (DSM) of mental disorders) in GBD 2010. The same decision was made by the GBD alcohol expert group. The reasons for this decision were the limited data on these disorders, ongoing debate about the validity of these diagnoses and the likely small disability associated with such disorders. Future iterations of GBD might reconsider this decision.

The health consequences of illicit drug use

As mentioned previously, one component of GBD 2010 was the CRA exercise, which examines risk factors for health outcomes, including illicit drug use. The adverse health effects of illicit drug use can be considered conceptually under four headings [26]: acute toxic effects, including overdose; acute effects of intoxication, such as injuries and violence; dependence on the drug; and adverse health effects of sustained regular use, such as chronic physical disease (e.g. cardiovascular disease and cirrhosis), blood-borne infections, and mental disorders.

For GBD 2010, it was necessary to evaluate the nature and quality of evidence for illicit drug use as a risk factor for many health outcomes [27]. In order for risk outcomes to be eligible for inclusion in the CRA component, a number of eligibility criteria needed to be met (see Table 4). In order to make a causal inference, it is necessary to document an association, confirm that drug use preceded the outcome and exclude alternative explanations of the association, such as reverse causation and confounding [29].

Table 4. Criteria for including risk factors for health outcomes in the CRA component of GBD 2010
  1. aNote: Summarised from [5]. CRA, comparative risk assessment; GBD, Global Burden of Disease.

Inclusion criteria for each risk-outcome pair:
  • 1) 
    The importance of a risk factor to disease burden or policy;
  • 2) 
    Sufficient data to enable estimates of exposure by country for at least one of the study periods (1990 and 2010);
  • 3) 
    Sufficient evidence for causal effects based on high-quality epidemiological studies, whose findings were unlikely to be due to bias or chance, as per the World Cancer Research Fund grading system [28], and sufficient data to estimate outcome-specific etiological effect sizes;
  • 4) 
    Evidence to support generalisability of effect sizes to populations other than those in existing studies.

The results of these reviews were sobering, in that very few putative consequences of illicit drug use had anywhere near the quality or quantity of data, or enumeration of effect sizes, required to be eligible for inclusion in the CRA (Table 5). Many studies report associations between illicit drug use and various health-related harms, but it has been more challenging to decide whether these are causal relationships. Our review of the availability of evidence, the quality of evidence and the strength of associations observed for each drug type for a range of putative acute and chronic outcomes revealed several things [27]: (i) the risks of cannabis use are much more modest than those of other illicit drugs, largely because cannabis does not produce fatal overdoses and it cannot easily be injected; (ii) the quality of evidence varies widely across drug and health outcomes: there are more data on cannabis use from prospective population-based cohorts, and for the use of other drug types, more data from selected treatment cohorts; and (iii) the magnitude of the effect is often poorly quantified. In the end, GBD 2010 only included the outcomes of drug use listed in Table 6, despite considering dozens more [27].

Table 5. Summary of evidence on some of the major potential acute and chronic consequences of illicit drug use
 CannabisOpioidsAmphetaminesCocaine
Effect Effect Effect Effect 
  1. aNotes on codes used in this table. bNote: summarised from [27]. cPresence or absence of effect: ✗ This drug not appear to have a significant effect upon the outcome. ✓ This outcome may be increased by the use of this drug. ? There is insufficient data on this drug and this outcome to permit conclusions about the association between the two. CRA, comparative risk assessment.

Acute toxic effects (fatal overdose)    
Acute intoxication effects        
Accidental injury? One of the most common causes of death among opioid users, however not included as it was decided confounding was not adequately addressed in existing cohorts.?Plausible, however too few data to assess?Plausible, however too few data to assess
Motor vehicle accidentsEvidence suggests an association, but existing epidemiological studies not thought to have adequately controlled for confounding?Plausible, however too few data to assess?Plausible, however too few data to assess?Plausible, however too few data to assess
Drug-induced psychotic symptoms  Limited controlled data on riskLimited controlled data on risk
Violence  Plausible, however too few data to assessPlausible, however too few data to assess
Myocardial infarction?Emerging evidence, considered too limited at present. Not included in CRA Plausible, however too few data to assessPlausible, however too few data to assess
DependenceIncluded in CRAIncluded in CRAIncluded in CRAIncluded in CRA
Adverse health effects of chronic use        
Cardiovascular pathology?Emerging evidence, considered too limited at present. Not included in CRA?Evidence largely cross-sectional studies of users or case series. Poor control for confounding. Not included.    
Liver disease Pathology in chronic users, however poor control for confoundingPathology in chronic users, however poor control for confoundingPathology in chronic users, however poor control for confounding
Pulmonary disease?Emerging evidence, considered too limited at present. Not included in CRA?Pathology in chronic users, however poor control for confounding? ? 
Cancers?Emerging evidence, considered too limited at present. Not included in CRA?Some evidence of increased risk but confounding not controlled? ? 
Neurotoxic effects?Emerging evidence, considered too limited at present. Not included in CRA Some evidence of increased risk but confounding not controlledSome evidence of increased risk but confounding not controlled
Psychotic disordersIncluded in CRA Plausible, but Insufficient controlled prospective data. Not included in CRAPlausible, but Insufficient controlled prospective data. Not included in CRA
Common mental disorders?Inconsistent evidence on the association with depression and anxiety, although clearer for depression. Not included.Depression and anxiety elevated among this group, although few prospective data examining risks. Not included.Depression and anxiety elevated among this group, although few prospective data examining risks. Not included.Depression and anxiety elevated among this group, although few prospective data examining risks. Not included.
SuicideFew epidemiological data, poor control for confounding, inconsistent results.Consistent finding of elevations In suicide. Included in CRAConsistent finding of elevations In suicide. Included in CRAConsistent finding of elevations In suicide. Included in CRA
Elevated mortality    
Consequences of unsafe drug injectionEffect       
HIVIncluded in CRA      
HCVIncluded in CRA      
HBVIncluded in CRA      
Infective endocarditisA likely outcome of unsafe injection, however risk rarely quantified. Not included in CRA
TuberculosisHas been noted as prevalent in some countries among injectors, particularly as an HIV co-infection, but few data on prevalence across countries. Not included in CRA
Table 6. Outcomes of illicit drug use that were included in the GBD 2010 CRA component of GBD 2010
  1. CRA, comparative risk assessment; GBD, Global Burden of Disease.

  • Cannabis as a risk factor for schizophrenia, modelled as: triggering an earlier onset of schizophrenia among those who would develop the disorder regardless, and as greater time spent in the ‘acute’ state of schizophrenia. No impact upon incidence of schizophrenia was modelled;
  • Opioid, cocaine and amphetamine dependence as risk factors for suicide;
  • Injecting drug use as a risk factor for HIV, Hepatitis C virus and Hepatitis B virus.

Where to from here?

Estimates of the extent and distribution of disease burden for different disorders are likely to shape global and regional health policy development. Existing estimates were used in debates to evaluate (and justify) WHO's global funding distribution [30,31]. They have been used in multiple discussions of funding allocation and priorities [32–34]. There have been consistent increases in the extent of funding for non-communicable diseases since 1990 (http://go.worldbank.org/851WC143G0).

There are limitations and controversies surrounding the GBD methodology, arising from the way in which ‘disability’ is estimated, the lack of consideration of social, economic and crime aspects of ‘burden’; and the inherent limitations of any enumerative exercise that is constrained by limited and potentially low quality data. Nonetheless, the GBD 2010 results have already been widely discussed and used, with demand from countries for specific country-level data to assist in their health service and policy planning [35,36]. These data will no doubt be used in future to inform funding allocation across multiple sectors, perhaps also including the drug and alcohol field.

The GBD team (now GBD 2.0) will continue to collect new data, and generate improved models and outputs, on an annual basis (see http://www.healthmetricsandevaluation.org/gbd/2013/protocol). At present, data are being collected for the 2013 round of GBD estimates. An important component of this ongoing work is the formation of the GBD Scientific Council. One of the roles of this council will be to evaluate proposals for new risk-outcome pairs, in a transparent and rigourous process that adheres to the principles for inclusion of such pairs in the CRA (Table 4). GBD 2010 clearly demonstrated that although the field is generating more and better epidemiological data on the health risks of drug use in more recent years, there is still much work to be done to generate defensible estimates of the magnitude of risk, particularly for impactful and prevalent outcomes, such as injuries, violence and mental health complications drug use. Until such data are generated, estimates of the burden of disease attributable to illicit drug use will be vast underestimates.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. Acknowledgements
  5. References

Professor Louisa Degenhardt is supported by an Australian National Health and Medical Research Council Principal Research Fellowship. Professor Wayne Hall is supported by an National Health and Medical Research Council Australia Fellowship. The National Drug and Alcohol Research Centre at the University of NSW is supported by funding from the Australian Government under the Substance Misuse Prevention and Service Improvements Grants Fund. Professor Harvey Whiteford is affiliated with the Queensland Centre for Mental Health Research, which receives its core funding from the Queensland Department of Health. More information can be found at: http://www.gbd.unsw.edu.au and http://www.healthmetricsandevaluation.org/gbd.

Conflict of interest

Professor Louisa Degenhardt has received untied educational grants from Reckitt Benckiser for the conduct of post-marketing surveillance studies of the diversion and injection of opioids prescribed for opioid substitution therapy. That funder had no knowledge of this paper.

References

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  2. Abstract
  3. Introduction
  4. Acknowledgements
  5. References
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