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. Author manuscript; available in PMC: 2019 Oct 1.
Published in final edited form as: Addiction. 2018 Aug 9;113(10):1768–1774. doi: 10.1111/add.14383

Competing global statistics on prevalence of injecting drug use – why does it matter and what can be done?

Matthew Hickman 1, Sarah Larney 2, Amy Peacock 2, Hayley Jones 1, Jason Grebely 3, Louisa Degenhardt 2
PMCID: PMC6717512  NIHMSID: NIHMS1037304  PMID: 29989282

Abstract

Variation in global estimates of the prevalence of injecting or opioid use from recent reports are partly explained by use of alternative information sources and value given to unreferenced country estimates; but also highlights a prevailing problem with the reporting and robustness of prevalence estimates. Unfortunately, there is no quick solution: we need investment both in ongoing information or surveillance of drug related harms and clinical and other interventions and in the implementation of more refined statistical methods to estimate prevalence.

Addiction presents the latest global statistics on alcohol, tobacco and illicit drug use and highlighted discrepancies in relation to injecting drug use between the 2017 World Drug Report led by the United Nations Office on Drugs and Crime and the most recent 2017 systematic review led by the global consortium (1). As highlighted by the systematic review, evidence on the prevalence of injecting drug use has increased and improved over the past decade. Evidence of injecting was identified from 179 countries covering 99% of the global population and the latest review synthesising information from multiple sources for 83 countries covering 83% of the global population (1–3).

The estimates of global injecting prevalence overlap substantially between the 2017 World Drug Report (estimate 0.25%; range 0.18, 0.36) and the 2017 systematic review (0.33%; 0.21, 0.49). However, there are more marked differences by country and region (see table below or link), including Latin America (estimate ranges 1,392,000 to 2,380,000 vs. 380,000 to 1,340,000), Central Asia (189,500 to 416,500 vs. 400,000 to 510,000), and South Asia (783,500 to 1,263,000 vs. 290,000 to 300,000).

Table 1.

Comparing global estimates of prevalence of injecting drug use by country and source: Global Consortium and WDR.

Global consortium World Drug Report 2018
Region Prevalence (%) Prevalence (#) Prevalence (%) Prevalence (#)
 Country low mid high low mid high low mid high low mid high
OCEANIA 98,000 138,000 181,500 0.47 0.61 0.75 120,000 160,000 190,000
Australasia/Oceania (UNODC) 0.42 0.6 0.75 83000 115500 148000 0.47 0.61 0.75 120,000 160,000 190,000
Australia 0.43 0.60 0.76 68,000 93,000 118,000 0.44 0.60 0.76 68,000 93,000 118,000
New Zealand 0.49 0.73 0.97 15,031 22,393 29,755 0.68 20,000
Pacific Island States and Territories/Oceania (UNODC) 0.22 0.33 0.49 15,000 22,500 33,500 0.47 0.61 0.75 120,000 160,000 190,000
American Samoa NE
Fed. States of Micronesia NE
Fiji NE
French Polynesia NE
Guam NE
Kiribati NE
Marshall Islands NE
Nauru NE
New Caledonia NE
Northern Mariana Islands NE
Palau NE
Papua New Guinea NE
Samoa NE
Solomon Islands NE
Tonga NE
Tuvalu NE
Vanuatu NE
AMERICA 2,943,500 4,459,500 6,926,000 0.33 0.42 0.57 2,180,000 2,790,000 3,730,000
North America 0.62 1.06 1.83 1,498,500 2,557,000 4,428,000 0.56 0.65 0.75 1,800,000 2,080,000 2,390,000
Canada 1.04 1.22 1.40 262,109 308,198 354,286 1.00 1.30 1.70 220,690 286,987 375,173
United States 0.57 1.04 1.88 1,236,391 2,248,583 4,073,771 0.65 0.74 0.84 1,316,703 1,520,054 1,723,405
Caribbean/Latin America & the Caribbean (UNODC) 0.30 0.44 0.66 53,000 79,500 118,000 0.11 0.21 0.39 380,000 700,000 1,340,000
Antigua & Barbuda NE
Bahamas NE
Barbados NE
Bermuda NE
Commonwealth of Puerto Rico 1.15 18,964 28,234 41,931 1.50 NK
Cuba NE
Dominica NE
Dominican Republic NE
Grenada NE
Haiti NE
Jamaica NE
Saint Kitts & Nevis NE
Saint Lucia NE
Saint Vincent & Grenadines NE
Trinidad & Tobago NE
Latin America/Latin America & the Caribbean (UNODC) 0.35 0.46 0.60 1,392,000 1,823,000 2,380,000 0.11 0.21 0.39 380,000 700,000 1,340,000
Argentina 0.29 0.29 0.30 79,115 80,745 82,375 0.39 NK
Belize NE
Bolivia NE
Brazil 0.67 734,658 962,069 1,256,232 0.10 0.21 0.43 107,639 227,253 462,847
Chile 0.38 35,858 46,958 61,316 0.18 16,257
Colombia 0.10 14,893
Costa Rica 0.12 3,845
Ecuador NE
El Salvador 0.17 6,532
Guatemala 0.03 2,500
Guyana NE
Honduras NE
Mexico 0.12 0.18 0.25 100,487 150,731 209,348 0.21 164,157
Nicaragua NE
Panama NE
Paraguay 0.14 5,714
Peru NE
Suriname NE
Uruguay 0.10 0.30 0.87 2,201 6,604 19,152 0.03 443
Venezuela NE
ASIA 4,014,000 5,294,000 6,634,500 0.12 0.16 0.20 3,480,000 4,700,000 5,970,000
Central Asia 0.43 0.63 0.94 189,500 281,500 416,500 0.71 0.79 0.90 400,000 440,000 510,000
Kazakhstan 0.96 75,468 112,358 166,867 1.09 127,800
Kyrgyzstan 0.74 19,126 28,475 42,290 0.74 25,000
Tajikistan 0.45 15,783 23,499 34,899 0.45 23,100
Turkmenistan NE
Uzbekistan 0.47 63,225 94,130 139,796 0.47 80,000
East and South East Asia 0.19 0.25 0.31 3,041,000 3,989,000 4,955,000 0.14 0.20 0.26 2,220,000 3,180,000 4,130,000
Brunei Darussalam NE
Cambodia 0.10 0.11 0.23 9,713 10,514 22,731 0.01 0.01 0.03 1,200 1,300 2,800
China 0.19 0.25 0.31 1,963,844 2,563,908 3,163,971 0.13 0.19 0.25 1,310,000 1,930,000 2,540,000
Hong Kong (China) 0.04 1,907
Indonesia 0.09 0.11 0.13 155,786 190,405 225,024 0.04 0.05 0.05 61,901 74,326 88,320
Japan 0.47 281,566 368,670 458,832 0.47 400,000
Lao PDR 0.03 1,317
Malaysia 1.11 1.33 1.56 233,625 281,724 329,823 0.94 170,000
Mongolia NE
Myanmar 0.32 0.48 0.65 115,710 173,565 235,036 0.23 83,000
North Korea NE
Philippines 0.03 0.04 0.05 19,175 25,566 31,958 0.04 0.09 10,000 21,700
Singapore NE
South Korea NE
Taiwan NE
Thailand 0.03 0.11 0.18 15,813 51,491 87,169 0.15 71,000
Timor Leste 0.00 0.01 0.02 6 49 123 0.01 53
Viet Nam 0.25 122,972 161,014 200,392 0.16 0.43 0.54 100,000 271,506 335,990
South Asia 0.07 0.09 0.11 783,500 1,023,500 1,263,000 0.03 0.03 0.03 290,000 290,000 300,000
South-West Asia (UNODC) 0.29 0.38 0.48 550,000 720,000 910,000
Afghanistan 0.50 0.80 1.09 87,756 139,028 190,301 0.09 0.27 0.53 13,500 40,900 80,000
Bangladesh 0.06 0.07 0.07 63,348 68,627 73,906 0.02 0.03 0.03 26,186 29,626 33,067
Bhutan NE
India 0.01 0.02 0.03 127,428 197,174 266,920 0.02 180,000
Iran, Islamic Republic 0.19 0.28 0.37 107,214 157,999 208,784 0.33 0.38 0.43 183,000 208,000 238,000
Maldives 0.26 0.60 0.94 648 1,483 2,318 0.32 0.37 0.42 690 793 896
Nepal 0.19 0.20 0.21 33,299 35,237 36,998 0.32 52,174
Pakistan 0.32 0.37 0.42 363,060 422,893 482,607 0.30 0.40 0.50 430,000
Sri Lanka 0.00 0.00 0.00 274 411 411 0.01 994
AFRICA 823,000 1,727,500 3,601,500 0.05 0.10 0.33 350,000 650,000 2,160,000
Middle East and North Africa/Near and Middle East (UNODC) 0.06 0.12 0.18 177,500 349,500 521,500 0.02 0.07 0.12 20,000 70,000 130,000
Algeria NE
Bahrain NE
Cyprus 0.04 0.08 0.12 283 526 769 0.04 0.06 0.08 358 465 641
Egypt 0.35 0.37 0.48 86,142 93,314 119,412
Iraq NE
Israel 0.11 5,000
Jordan 0.02 605
Kuwait NE
Lebanon 0.02 0.08 0.14 812 3,114 5,416
Libyan Arab Jamahiriya 0.05 480 1,956 4,278 0.05 1,685
Morocco 0.07 0.13 0.20 15,388 30,341 45,317 0.01 1,500
Occupied Palestinian Territories NE
Oman NE
Qatar NE
Saudi Arabia NE
South Sudan NE
Sudan NE
Syrian Arab Republic NE
Tunisia 0.13 10,000
Turkey 0.02 0.03 0.05 11,126 12,733 26,537
United Arab Emirates NE
Yemen NE
Sub Saharan Africa/Africa (UNODC) 0.13 0.28 0.62 645,500 1,378,000 3,080,000 0.05 0.10 0.33 350,000 650,000 2,160,000
Angola NE
Benin NE
Botswana NE
Burkina Faso NE
Burundi NE
Cameroon NE
Cape Verde NE
Central African Republic NE
Chad NE
Comoros NE
Cote d’Ivoire 0.00 0.01 0.01 334 631 928 NE
Dem Rep of the Congo 0.00 0.01 0.40 79 3,550 157,758 0.12 44,023
Djibouti NE
Equatorial Guinea NE
Eritrea NE
Ethiopia NE
Gabon NE
Gambia NE
Ghana NE
Guinea NE
Guinea-Bissau NE
Kenya 0.03 0.12 0.20 8,860 30,502 52,118 0.04 0.21 0.52 10,000 49,167 120,000
Lesotho 0.21 2,600
Liberia 0.02 457
Madagascar 0.02 0.12 0.59 3,091 15,589 79,426 0.12 14,445
Malawi NE
Mali NE
Mauritania NE
Mauritius 0.39 0.78 1.54 3,549 7,062 13,944 1.17 1.30 1.42 10,542 11,677 12,812
Mozambique 0.00 0.20 0.41 - 28,804 59,001 0.01 1,910
Namibia NE
Niger NE
Nigeria 0.02 18,801
Republic of the Congo NE
Rwanda 0.00 0.03 0.07 - 1,956 4,564 0.00 0.02 100 1,200
Sao Tome & Principe NE
Senegal 0.02 0.02 0.02 1,281 1,324 1,367
Seychelles 1.54 2.30 3.43 1,033 1,546 2,304 2.30 1,283
Sierra Leone 0.04 0.04 0.04 1,242 1,419 1,561 0.04 1,500
Somalia NE
South Africa 0.06 0.21 0.74 21,730 76,055 268,002 0.21 75,701
Swaziland NE
Togo 0.01 0.06 0.49 241 2,330 19,684 0.06 2,289
Uganda NE
United Republic of Tanzania 0.72 1.24 1.76 199,892 342,838 485,784 0.08 0.12 0.17 20,000 30,000 42,500
Zambia 0.03 2,281
Zimbabwe NE
EUROPE 2,340,000 4,029,500 6,394,500 0.45 0.65 0.99 2,420,000 3,510,000 5,370,000
Western Europe 0.23 0.34 0.47 686,500 1,009,500 1,386,500 0.20 0.22 0.25 640,000 700,000 780,000
Albania 0.20 0.31 4,000 6,000
Andorra NE
Austria 0.22 0.32 0.42 12,723 18,555 24,387 0.19 0.26 11,000 15,000
Belgium 0.24 0.35 0.49 18,251 26,137 36,953 0.24 0.35 0.49 17,638 25,295 35,699
Croatia 0.18 0.23 0.29 5,147 6,344 8,255 0.04 0.04 0.06 998 1,274 1,746
Denmark 0.35 0.45 0.52 12,911 16,600 19,183 0.28 0.36 0.46 10,066 13,000 16,821
England (U.K.) 0.55 0.59 0.63 196,346 210,626 224,906 0.29 0.30 0.32 117,370 122,894 131,869
Finland 0.41 0.46 0.67 15,154 17,002 24,764 0.41 0.46 0.67 13,770 15,611 22,665
France 0.16 0.20 0.23 66,317 81,847 96,957 0.20 81,000
FYR of Macedonia 0.68 10,200
Germany 0.03 0.24 0.45 14,048 131,702 249,410 0.14 0.17 NK
Greece 0.06 0.07 0.09 4,137 5,048 6,240 0.06 0.07 0.09 4,209 5,120 6,303
Greenland NE
Iceland 0.17 350
Ireland 0.20 0.27 0.33 6,371 8,536 10,701 0.04 1,098
Italy 0.83 246,176 341,708 482,438 0.38 NK
Liechtenstein NE
Luxembourg 0.45 0.57 0.69 1,748 2,187 2,637 0.45 0.57 0.69 1,524 1,907 2,301
Malta 0.27 744
Monaco NE
Montenegro 0.30 1,283
Netherlands 0.02 0.03 0.04 2,341 3,298 4,575 0.02 0.02 0.02 2,336 2,390 2,444
Northern Ireland (U.K.) 0.29 0.30 0.32 117,370 122,894 131,869
Norway 0.21 0.24 0.29 7,137 8,385 10,117 0.25 8,400
Portugal 0.19 0.22 0.25 13,965 15,837 17,708 0.19 0.22 0.25 12,732 14,426 16,101
San Marino NE
Scotland (U.K.) 0.38 0.44 0.49 13,656 15,812 17,608 0.29 0.30 0.32 117,370 122,894 131,869
Serbia 0.41 0.49 0.58 23,880 29,128 34,316 0.32 0.40 0.56 16,000 20,000 28,000
Slovenia 0.30 0.42 0.55 4,115 5,881 7,661 0.52 7,310
Spain 0.01 0.03 0.05 3,467 10,501 17,569 0.02 0.03 0.04 7,971 9,879 11,786
Svalbard
Sweden 0.03 0.13 0.62 1,750 8,189 38,506 0.13 8,021
Switzerland 0.19 0.24 0.29 10,807 13,551 16,183 0.51 0.65 0.78 24,907 31,653 38,399
United Kingdom 0.26 0.54 0.91 109,214 228,036 387,287 0.29 0.30 0.32 117,370 122,894 131,869
Wales (U.K.) 0.29 0.30 0.32 117,370 122,894 131,869
Eastern Europe 0.71 1.30 2.15 1,653,500 3,020,000 5,008,000 0.79 1.25 2.04 1,780,000 2,810,000 4,580,000
Armenia 0.41 0.62 1.35 8,759 13,245 28,839 0.45 9,507
Azerbaijan 0.49 0.61 0.74 34,637 43,337 52,036 0.66 1.08 1.50 43,736 71,283 98,830
Belarus 0.22 0.59 0.96 15,164 40,666 66,169 0.23 1.00 15,509 66,504
Bosnia & Herzegovina 0.36 0.47 0.58 9,500 12,500 15,500
Bulgaria 0.30 0.38 0.45 14,877 18,596 22,315 0.50 24,000
Czech Republic 0.61 0.64 0.67 44,633 46,828 49,023 0.61 0.64 0.68 43,200 45,600 48,000
Estonia 0.69 0.94 1.73 6,307 8,592 15,812 0.43 0.59 1.08 3,906 5,362 9,837
Georgia 0.48 4.19 7.90 13,162 115,038 216,914 2.00 2.02 2.04 49,208 49,700 50,192
Hungary 0.03 0.06 0.08 2,084 3,969 5,854 0.08 5,699
Latvia 0.73 0.92 1.17 11,134 14,032 17,845 0.59 0.92 1.38 7,983 12,573 18,778
Lithuania 0.22 2,534 4,781 7,291 0.27 5,225
Moldova 0.25 0.40 0.54 7,504 11,970 16,436 1.23 31,562
Poland 0.02 0.03 0.04 4,270 7,284 10,299
Romania 0.46 0.62 0.84 60,282 81,250 110,081 0.39 0.53 0.72 4,703 6,288 8,492
Russian Federation 1.78 996,956 1,881,062 2,868,387 1.30 2.29 4.00 NK
Slovakia 0.35 0.49 0.89 14,470 19,854 36,190 0.35 0.49 0.89 13,732 18,841 34,343
Ukraine 0.52 0.97 1.79 172,153 319,421 590,400 1.09 341,500
Global 0.21 0.33 0.49 10,219,000 15,648,000 23,737,500 0.18 0.25 0.36 8,550,000 11,810,000 17,420,000
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There are several potential reasons for this variation. First, the criteria for inclusion of studies differs between the two reports. The World Drug Report uses sources without detail on the origin of the study data, such as estimates reported via its Annual Reports Questionnaire received from member states, which had a range of estimates, the source of which could not be located or obtained. The systematic review excluded new estimates that did not have information on the origin of the source study included. Second, the two reports used different imputation methods in the absence of local data. The systematic review pooled estimates from studies of similar quality to maximise geographic coverage within a country (where possible), whereas the World Drug Report tended to use data from single studies.

An issue that is faced when trying to provide global estimates of this nature is that that many country estimates are based on methods that are likely to be biased, given that the original data used to derive the prevalence estimate are not reported transparently. Estimates of injecting prevalence from the United States (US) are a notable example. These estimates were based on several forms of multipliers across different years, including population and other data for specific areas in the US, and then imputed for the US (4–7). Further, the exact method of generating multipliers and the data on which it is based were confusingly spread across multiple publications (4–7). In Europe, the surveillance of “problem drug use”, including injecting drug use, is moribund – few countries report up to date estimates, with no consistency in methods between countries (e.g. see http://www.emcdda.europa.eu/data/stats2017/pdu_en). Estimates are overly reliant on inherently biased methods (8).

This sorry state of affairs has led the consortium at IHME leading the Global Burden of Disease (GBD) study – which has done so much to raise the profile of addictive behaviours and mental health as key drivers of ill health across the globe (9, 10) – to consider excluding most of the evidence from so called “indirect” estimates, and re-calibrate estimates of the prevalence of opioid and injecting drug use from “direct” population surveys (based on extrapolation from a few countries that have both a “reliable” indirect estimate and a general population survey). If carried through, this action would be both deleterious and retrograde. It is likely that the prevalence estimates for opioid disorders and injecting drug use would be lower than current estimates, down-weighting their public health importance as the modelled contribution to disease burden also would fall (11). Indubitably, the estimates would be wrong, based on the heroically unjustifiable assumptions: that only a few countries have reliable indirect estimates and that the relationship between these and population surveys in these countries can apply to the rest of the world.

Unfortunately, the right approach to fixing the problem is going to take more time. Yet there are several pressing reasons to improve prevalence estimates. Addressing the opioid overdose crisis in North America – where opioid related deaths have increased 3-fold in the last 10 years [https://www.drugabuse.gov/related-topics/trends-statistics/overdose-death-rates] - and HCV elimination targets in many countries - where the majority of new infections are in PWID (12) - would have benefited from better information on the dynamics and size of the population of opioid users and PWID. In order to appropriately scale and monitor impact accurately, we must both scale-up interventions and improve the epidemiological evidence base.

Data used for prevalence estimation need to be reported more transparently – so that the methods can be replicated, and the information be made more readily available for quality assessment and synthesis. Such action could be taken now by academic journals, global agencies and those who fund work to deliver interventions to address injecting drug use and illicit drug harms to prevent unreferenced, opaque estimates being circulated.

However, this is only part of the solution. It is widely acknowledged that population surveys will under-estimate prevalence of comparatively rare, hidden and stigmatised behaviours – such as opioid, cocaine, methamphetamine and injecting drug use (13, 14). Alternative indirect estimates that utilise information on drug related harms, and partial data sets of people in treatment or criminal justice, have been developed, but there has been a lack of investment in critical evaluation – in testing, applying, developing and then implementing the best methods. More importantly, there has been a lack of investment in strengthening the epidemiological data in countries and cities affected by problematic drug use, which is the foundation of any prevalence estimate. Further work is needed to improve the design (e.g. clear definitions of “recent” injecting drug use, inclusion of multiple study sites across different regions, and the collection of samples which permit the accurate assessment of viral infections, e.g. HCV and HIV), implementation and reporting (e.g. clear and transparent reporting of results) of studies to estimate epidemiologic data related to injecting drug use (and its sequalae). As an epidemic of injecting or opioid use matures, the connections between data sources (and potential biases) also become more complex. In such contexts, investment in record linkage between administrative data sources will support better estimates of drug related harm, and more refined statistical approaches to prevalence estimation.

It has been demonstrated that it is feasible to measure drugs consumed by a population in their sewage/wastewater. Disappointingly, there have been few widescale implementations of waste-water monitoring or integration of wastewater data with other epidemiological data to size the drug market or estimate prevalence of drug use in the population(15, 16).

So, let us ask IHME, the UN system and other agencies with an interest in monitoring and preventing drug-related harm to support countries to invest in information systems that better document key harms, and to test and implement the most reliable statistical approaches to estimating disease burden and prevalence.

References

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