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Information technology in health sciences could be a screening tool of great potential and has been shown to be effective in identifying single-drug users at risk. Although there are many published tests for single-drug screening, there is a gap for concomitant drug use screening in general population. The ASSIST (Alcohol, Smoking and Substance Involvement Screening Test) website was launched on February 2015 in Madrid, Spain, as a tool to identify those at risk.
The aim of this study was to describe the use of a tool and to analyze profiles of drug users, their consumption patterns, and associated factors.
Government- and press-released launching of a Spanish-validated ASSIST test from the World Health Organization (WHO) was used for voluntary Web-based screening of people with drug-related problems. The tests completed in the first 6 months were analyzed
A total of 1657 visitors of the 15,867 visits (1657/15,867, 10.44%) completed the whole Web-based screening over a 6-month period. The users had an average age of 37.4 years, and 78.87% (1307/1657) screened positive for at least one of the 9 drugs tested. The drugs with higher prevalence were tobacco (840/1657, 50.69%), alcohol (437/1657, 26.37%), cannabis (361/1657, 21.79%), and sedatives or hypnotics (192/1657, 11.59%). Polyconsumption or concomitant drug use was stated by 31.80% (527/1657) of the users. Male respondents had a higher risk of having alcohol problems (odds ratio, OR 1.55, 95% CI 1.18-2.04;
A Web-based screening test could be useful to detect people at risk. The drug-related problem rates detected by the study are consistent with the current literature. This tool could be useful for users, who use information technology on a daily basis, not seeking medical attention.
The consumption of drugs and addiction are common problems throughout the world and a foreseeable cause of death [
The magnitude of this problem is quantified in Spain by the EDADES (Encuesta De Alcohol y Drogas en ESpaña)[
The screening of problems related to the consumption of drugs such as alcohol and tobacco from a health perspective is a grade B evidence recommendation (meaning the Agency recommends the service to offer or to provide the service) both in the United States (United States Preventive Services Task Force) [
Tools have therefore been designed for screening subjects with addiction to substances, however, these are limited in detecting at-risk people who do not meet substance-related disorder criteria [
The need for other different evaluation means and medical advice to deal with these problems has been highlighted by the users [
Screening based on internet platforms and portals has already proved to be useful in other areas, such as mental health [
ASSIST is designed to detect people at risk of the most common substances used by the general population or in primary care settings. The administration of the ASSIST, by virtue of being a paper-and-pencil assessment requires a great amount of time to be administered in-person in case of subjects complaining about several drugs. Self-administration of the Web-based test could reduce the time required to get the screening done [
As part of the Ministry of Health's National Anti-Drug Abuse Plan, the ASSIST website [
First, the main objective of this study was to describe the user type for the tool, as well as the drug consumption patterns 6 months after it has been launched. Second, factors associated with the severity of substance use as well as polyconsumption were analyzed.
To enable access, coverage, and anonymity, it was decided that the screening would take place on a website. The subjects screened from February 24, 2015 to August 24, 2015 (first 6 months) were included in the analysis. It was an open survey, since access was possible as many times as considered necessary without registering. To control duplicate reporting, a multicomponent checkup was developed ahead with cookies used for identification purposes, and a second checkup was applied during analysis. From a promotion and population scope perspective, no initial contact was made with the potential participants on the internet. The tool was publicly launched in the Government Delegation's National Anti-Drug Abuse Agency on February 24, 2015 and promoted by press releases and coverage during the presentation [
Subjects who claimed to be older than 18 years when accessing the website, who answered all the ASSIST test questions at one time, and whom we identified as coming from a sole device with a sole identifier (internet protocol, IP) were included, although no IP check was done before the analysis. A screen-printed research informed consent was claimed, which had to be read and validated to ensure acknowledgment before entering the test. No incentives (monetary, prizes, or others) were offered.
The entire website [
After completing the test, the users received a report with a summary of the test results, a comparison with the EDADES [
The main result variable was the score obtained from the answers to the self-administered ASSIST test for each of the screened drugs. ASSIST is a brief questionnaire used to identify risky drug use developed by the WHO and adapted and validated in Spanish. The questionnaire consists of 8 questions on recent and lifelong consumption of 9 substances (tobacco, alcohol, cannabis, cocaine, amphetamines or other stimulants, sleeping pills, hallucinogenic drugs, inhalants, and others). Several domains of the questions are considered (time of use, recent use, desire to consume, health issues, social issues, legal issues, difficulty to stop consuming, etc). According to the WHO, from 0 to 3 points means no intervention is recommended as the risk of a condition related to the substance is low; from 4 to 26 points (11-26 for alcohol), brief intervention is recommended; and for scores of 27 points and above, intensive treatment is recommended. The analysis then classifies the risks into 3 levels (low, moderate, and serious). Other social and demographic variables are recorded such as gender, age, and weight.
Users reported weight, gender, and age anonymously. Self-reported anthropometric data have already proven to be a valid method of collecting these data [
Google Analytics was used to compare sociodemographic characteristics reported by the users to their Google accounts’ available information. This tool is normally used by Web designers and search-engine-optimization experts to improved usability [
The descriptive analysis of the variables was conducted using the central trend and spread measures, if they followed normal distribution and, for asymmetric distributions, medians and interquartile ranges (IQR) were used. In the bivariate analysis, the group averages were compared with the Student
Stata v14.0 software was used for the log file statistical analysis with
From February 24, 2015 to August 24, 2015, the website received a total of 15,867 visits.
A total of 76.9% of the access sessions during the period studied took place in the first month after the website was presented, with 3 activity peaks (February 25, launching day, March 4, and March 10, 2015) first one likely related to press release.
A total of 83.1% of the users were from Spain, 2.3% from Mexico, and 2.2% from Argentina. In Spain, the majority of 35.7% of users were from Madrid, followed by Barcelona (8.2%), Bilbao (3.7%), and Valencia (3.5%, see
Users were mainly from 3 internet sources: 40.2% direct traffic (manual access to the website), 33.2% were redirected from another website related to press release, and 20.3% came from a search engine. A total of 6.4% came from social networks. A multilevel regression model was carried out to analyze the possible influence of location clustering in developing any drug risk showing no significant differences (intraclass correlation coefficient, ICC=.01, 95% CI 0.001-0.11).
Flow diagram of participants. PNSD: Plan Nacional Sobre Drogas.
Map of website number of accesses in Spain. The size of the bubble shows the total number of users screened at those locations.
Demographic characteristics referred to by visitors completing the Alcohol, Smoking and Substance Involvement Screening Test (ASSIST).
Characteristics | Total population (n=1657) | Patients screened positive (n=1307) | Patients screened negative (n=350) | ||
Gender, men, n (%) | 990 (59.74) | 812 (62.12) | 178 (50.9) | <.001 | |
Age in years (mean, SD) | 37.5 (12.9) | 37.7 (13.1) | 36.9 (12.2) | .30 | |
Weight (median, IQRa) | 70.9 (60.0-81.0) | 71 (22) | 71 (20) | .45b | |
Number of drugs reported (median, IQR) | 3 (2-4) | 2 (1-3) | 3 (2-5) | <.001b | |
Duration of screening, minutes (median, IQR) | 3 (2-4) | 3 (2-4) | 2 (1.3-3.0) | <.001b | |
February | 526 (32.74) | 417 (25.17) | 109 (6.8) | ||
March | 897 (54.13) | 700 (43.51) | 17 (12.2) | ||
April | 118 (7.12) | 94 (5.79) | 24 (1.5) | ||
May | 40 (2.41) | 28 (1.68) | 12 (0.8) | ||
June | 11 (0.66) | 8 (0.45) | 3 (0.2) | ||
July | 10 (0.61) | 8 (0.45) | 2 (0.1) | ||
August | 8 (0.51) | 6 (0.41) | 2 (0.1) | ||
Personal computer | 1055 (63.66) | 810 (48.89) | 245 (14.8) | .006 | |
Phone/tablet | 602 (36.31) | 497 (30.00) | 105 (6.3) | .006 | |
Direct access | 743 (44.84) | 586 (35.44) | 157 (9.4) | .03 | |
Google search | 288 (17.38) | 228 (13.78) | 60 (3.6) | .03 | |
133 (8.03) | 110 (6.53) | 23 (1.5) | .03 | ||
Government websites | 103 (6.21) | 82 (5.01) | 21 (1.2) | .03 | |
Social media | 51 (3.07) | 41 (2.47) | 10 (0.6) | .03 |
aIQR: interquartile range.
bMann-Whitney
Distribution of risk patterns according to the drug and gender. Statistically significant values are in italics.
Drug risks | Total (n=1657), n (%) | Men (n=990), n (%) | Women (n=667), n (%) | ||
.41 | |||||
Not stated | 0 (0.00) | 0 (0.0) | 0 (0.0) | ||
Low | 817 (49.30) | 485 (29.3) | 332 (20.0) | ||
Moderate | 764 (46.10) | 454 (27.4) | 310 (18.7) | ||
Serious | 76 (4.58) | 51 (3.1) | 25 (1.5) | ||
. |
|||||
Not stated | 200 (12.07) | 118 (7.1) | 82 (5.0) | ||
Low | 1020 (61.55) | 580 (35) | 440 (26.6) | ||
Moderate | 326 (19.67) | 209 (12.6) | 117 (7.1) | ||
Serious | 111 (6.69) | 83 (5.0) | 28 (1.7) | ||
Not stated | 761 (45.92) | 417 (25.1) | 344 (20.8) | ||
Low | 535 (32.28) | 310 (18.7) | 225 (13.6) | ||
Moderate | 274 (16.53) | 198 (12) | 76 (4.5) | ||
Serious | 87 (5.25) | 65 (3.9) | 22 (1.2) | ||
Not stated | 1166 (70.36) | 632 (38.1) | 534 (32.3) | ||
Low | 408 (24.62) | 300 (18.1) | 108 (6.5) | ||
Moderate | 65 (3.92) | 47 (2.8) | 18 (1.1) | ||
Serious | 18 (1.08) | 11 (0.7) | 7 (0.4) | ||
a | |||||
Not stated | 1316 (79.42) | 747 (45.1) | 569 (34.3) | ||
Low | 316 (19.07) | 225 (13.6) | 91 (5.5) | ||
Moderate | 18 (1.08) | 14 (0.8) | 4 (0.2) | ||
Serious | 7 (0.42) | 4 (0.2) | 3 (0.2) | ||
a | |||||
Not stated | 1574 (94.99) | 932 (56.3) | 642 (38.7) | ||
Low | 78 (4.71) | 53 (3.2) | 25 (1.5) | ||
Moderate | 5 (0.30) | 5 (0.3) | 0 (0.0) | ||
Serious | — | — | — | ||
Not stated | 1190 (71.82) | 741 (44.7) | 449 (27.1) | ||
Low | 275 (16.60) | 145 (8.7) | 130 (7.9) | ||
Moderate | 162 (9.78) | 88 (5.3) | 74 (4.5) | ||
Serious | 30 (1.81) | 16 (1) | 14 (0.8) | ||
a | |||||
Not stated | 1423 (85.88) | 820 (49.5) | 603 (36.4) | ||
Low | 233 (14.06) | 169 (10.2) | 64 (3.9) | ||
Moderate | 1 (0.06) | 1 (0.1) | 0 (0.0) | ||
Serious | — | — | — | ||
Not stated | 1552 (93.66) | 918 (55.4) | 634 (38.3) | ||
Low | 91 (5.49) | 65 (3.9) | 26 (1.6) | ||
Moderate | 11 (0.66) | 7 (0.5) | 4 (0.2) | ||
Serious | 3 (0.18) | 0 (0) | 3 (0.2) |
an<5=less than 5 cases.
The average age of the subjects was 37.4 years (standard deviation, SD 12.9) and 59.9% of cases stated to be male. Users could test themselves for drug-related risks and no risks could be showed. Of all the users screened, 21.1% did not have any moderate to high substance-related risks (as opposed to 78.9% with a moderate or high risk in relation to at least 1 substance). The average time spent taking the test was 3 min (IQR: 2-4), with a statistically significant higher time spent for those who screened positive on the test for at least 1 drug. The number of drugs reported was higher for the group who screened positive compared with the group who showed no moderate to high drug-related risks (
The most highly consumed drug by men as well as women was tobacco, which all the self-surveyed subjects stated to smoke at some point, followed by alcohol (87.9%) and cannabis (54.1%). We found moderate or serious substance-related risk for tobacco (50.7%), alcohol (26.4%), cannabis (21.8%), and sedatives or hypnotics (11.6%) as the 4 most frequent.
In the subgroup analysis, significant differences were observed based on gender: men had moderate to high risks for alcohol disorders in 17.6% of cases compared with women in 8.8% (
In the subgroup analysis of age categories, users who screened positive for moderate to high substance-related risks were distributed into groups up to 44 years of age. The highest number of users with drug-related risks was in the 35- to 44-year-old category: tobacco (12.3% moderate, 1.6% serious), alcohol (5.3% moderate; 1.9% serious), cocaine (1.6% moderate), and sedatives/hypnotics (2.6% moderate). The cannabis-related risks were concentrated in the 2 youngest age groups (18-24 and 25-34 years) with 5.7% of all subjects consuming the drug. Growing age increased 3 times risk of developing alcohol problems for people between 45 and 65 years (OR=3.01, 95% CI 1.89-4.79;
With regard to polyconsumption or the consumption of different drugs throughout the same period of time, a moderate or serious risk was observed in 31.6% of the subjects. Number of substances and sex distribution are shown in
Factors associated with moderate to high risky drug use estimates in general people screened at the website. Statistically significant values are in italics.
Sociodemographic factors | Tobacco (n=840) | Alcohol (n=437) | Cannabis (n=361) | ||||
OR (95% CI) | OR (95% CI) | OR (95% CI) | |||||
Female | 1.00 (Ref) | 1.00 (Ref) | 1.00 (Ref) | ||||
Male | 1.21 (0.96-1.51) | .10 | 1.55 (1.18-2.04) | 2.07 (1.46-2.92) | |||
18-24 | 1.00 (Ref) | 1.00 (Ref) | 1.00 (Ref) | ||||
25-34 | 0.74 (0.53-1.03) | .08 | 1.39 (0.89-2.15) | .15 | 0.56 (0.38-0.84) | ||
35-44 | 0.81 (0.58-1.15) | .24 | 1.83 (1.18-2.84) | 0.34 (0.20-0.53) | |||
45-54 | 0.83 (0.57-1.21) | .34 | 3.01 (1.89-4.79) | 0.30 (0.17-0.56) | |||
55-64 | 0.92 (0.59-1.43) | .70 | 3.08 (1.79-5.31) | 0.14 (0.05-0.45) | |||
>65 | 0.84 (0.43-1.64) | .60 | 3.73 (1.69-8.21) | — | |||
Personal computer/mac | 1.00 (Ref) | 1.00 (Ref) | 1.00 (Ref) | ||||
Mobile phone/tablet | 1.47 (1.16-1.86) | 0.80 (0.61-1.07) | .14 | 1.48 (1.06-2.06) | |||
Website referral source | |||||||
None | 1.00 (Ref) | 1.00 (Ref) | 1.00 (Ref) | ||||
Official press releases | 0.98 (0.64-1.50) | .91 | 0.96 (0.57-1.63) | .89 | 1.48 (0.60-1.34) | .20 | |
Google searches | 1.08 (0.82-1.41) | .58 | 1.06 (0.76-1.47) | .74 | 0.90 (0.60-1.34) | .6 | |
Drug information websites | 1.81 (1.20-2.72) | 1.62 (1.02-2.56) | 0.47 (0.27-0.84) | ||||
Social media referrals | 1.27 (0.71-2.26) | .42 | 1.38 (0.71-2.71) | .35 | 0.84 (0.60-1.34) | .68 | |
Time spent in the test | 1.47 (1.16-1.86) | 1.08 (1.04-1.12) | 1.08 (1.02-1.14) |
Gender differences and number of substance-related risks at screening.
Number of substances at risk | Total (n=1657, n (%) | Men (n=990), n (%) | Women (n=657), n (%) | |
No substance | 350 (21.12) | 178 (17.9) | 172 (25.8) | |
1 substance | 780 (47.07) | 452 (45.7) | 328 (49.2) | |
2 substances | 431 (26.01) | 295 (29.8) | 136 (20.4) | |
3 substances | 77 (4.64) | 55 (5.6) | 22 (3.3) | |
4 substances | 13 (0.78) | 7 (0.7) | 6 (0.9) | |
5 or more substances | 6 (0.36) | 3 (0.3) | 3 (0.5) | a |
an<5=less than 5 cases.
Qualitative description of polyconsumers of 2 substances (n=431).
Drugs patterns | Tobacco |
Alcohol |
Cannabis |
Cocaine |
Amphetamine |
Inhalants |
Sedatives |
Opium |
|||||||||
Moda | Serb | Mod | Ser | Mod | Ser | Mod | Ser | Mod | Ser | Mod | Ser | Mod | Ser | Mod | Ser | ||
Mod | |||||||||||||||||
Ser | |||||||||||||||||
Mod | 70 (16.2) | — | |||||||||||||||
Ser | 31 (7.2) | 5 (1.2) | ` | ||||||||||||||
Mod | 135 (31.3) | 8 (1.9) | 8 (1.9) | 1 (0.2) | |||||||||||||
Ser | 45 (10.4) | 8 (1.9) | — | — | |||||||||||||
Mod | 13 (3.0) | 2 (0.5) | 7 (1.6) | 1 (0.2) | 3 (0.7) | 1 (0.2) | |||||||||||
Ser | 1 (0.2) | 1 (0.2) | — | — | 1 (0.2) | 1 (0.2) | |||||||||||
Mod | 2 (0.5) | — | 2 (0.5) | — | 2 (0.5) | — | 1 (0.2) | — | |||||||||
Ser | 2 (0.5) | — | — | — | — | — | — | 1 (0.2) | |||||||||
Mod | 2 (0.5) | — | — | — | 1 (0.2) | — | — | — | — | — | |||||||
Ser | — | — | — | — | — | — | — | — | — | — | |||||||
Mod | 36 (8.4) | 3 (0.7) | 15 (3.5) | 5 (1.2) | 1 (0.2) | 1 (0.2) | 1 (0.2) | — | — | — | — | — | |||||
Ser | 3 (0.7) | 2 (0.5) | 2 (0.5) | 3 (0.7) | — | — | — | — | — | — | — | — | |||||
Mod | 2 (0.5) | — | — | — | — | — | — | — | — | — | — | — | 1 (0.2) | — | |||
Ser | — | — | — | — | — | — | — | — | — | — | — | — | — | — |
aMod: moderate.
bSer: serious.
cHallucinogenic drugs were not included, as there were no positive screening results of polyconsumption (2 or more).
In the first 6 months after the Web-based self-screening for drug consumption website was launched, a total of 15,867 users were recorded, of which 1657 (10.4%) completed the screening. The average age obtained was 37.4 years and 78.9% showed moderate or serious drug-related risks. The most highly consumed drugs stated were tobacco (50.7%), alcohol (26.4%), cannabis (21.8%), and sedatives (11.6%) with men taking more alcohol, cannabis, or cocaine; and young people (aged 18-35 years) using more cannabis. Polyconsumption was observed in 31.6% of the cases.
In terms of the number of users, our website showed data similar to others in the existing literature [
Our website user map (see
Users surfing the Web using Google services are giving their demographic characteristics and interest information for analysis according to the Google privacy terms. A total of 52.1% of users could be analyzed, and differences were observed in relation to age and gender. In the data provided by the users, there were 59.9% men, as opposed to 41.2% in the Google sample, and 7.1% of users stated in the test that they were younger (18-24) than when compared with the data obtained with Google. This could be due to the 50% of incomplete information from the Google remaining sample, or if representation is assumed, due to the fact that some people did the test for others.
Drug-use patterns partially differ with respect to consumption prevalence in Spain according to the EDADES [
In terms of polyconsumption, there are also differences: the definition used in the EDADES survey refers to the combined consumption of substances in the same period [
This is the first drug-related risks screening website related to all common drugs, using a scale that has been internationally validated by the WHO (ASSIST), adapted to Spanish for Web-based screening.
The results also show a target group for potential intervention relating to the tobacco-cannabis polyconsumption pattern, due to the high percentage detected in certain age groups (18-34 years).
One of the major advantages of this type of tool would be the possibility of reaching populations that would otherwise delay resorting to the health system and also offering primary care doctors and other health care professionals a valid tool for handling the risks related to the consumption of substances before it becomes an established medical condition.
The potential impact of applying overall prevention measurements (screening as secondary prevention), instead of those addressed to specific populations is a public health issue that is currently being debated. The screening in itself would be pointless if counteraction was not offered. There is already evidence of decreasing mortality rates in favor of global intervention after screening general population samples by models designed for cardiovascular disease, as opposed to screening specific population alone [
One of the main limitations would be the ability to identify sole users and therefore to eliminate duplicating or overestimating the results. Several users could have access from the same device and even from the same network to complete the test, without being able to make a distinction between them. Therefore, the server data were exploited, limited by the anonymized IP address, compared with the data from the device used for access and the time (day and time) that the test was taken and, together with the aforementioned demographic data, an algorithm was established to identify single and valid users. When the users could not be differentiated, an attempt was made to eliminate “testing users” from the analysis to identify erroneous situations such as the screening of many drugs in a short period of time, several attempts by the same user, etc. For users making several attempts, the last answer was accepted as valid.
Another limitation that can be assumed is related to the scope of publication. Users accessed the website mainly because they had heard about it or were redirected by a different website; 1 of every 5 did so after browsing with Google. Given that the environment was created under a research framework publicized by a public body (National Anti-Drug Abuse Plan), access and users came from locations where there was publicity resulting from the presentation. Future studies would benefit by including a broader population to improve external validity.
Another difficulty encountered was the restriction of the screening age to 18 years and older. At the time we developed the website, validation for the adolescent population had not been published. In 2015, Gryczynksi et al [
Home page.
Drug screening page.
Drug risks evaluation webpage.
Alcohol, Smoking and Substance Involvement Screening Test
internet protocol
odds ratio
Plan Nacional Sobre Drogas
World Health Organization
This project received funding for the 2017 publication funds from the Primary Care Research and Innovation Foundation of Madrid.
None declared.