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      Smoking identified as preferred mode of opioid safe supply use; investigating correlates of smoking preference through a 2021 cross-sectional study in British Columbia

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          Abstract

          Background

          The increasing number of illicit drug toxicity deaths in British Columbia (BC) has led to calls for a regulated (pharmaceutical grade) supply of substances (“safe supply”). In order to inform safe supply recommendations, we aimed to identify why people currently smoke opioids and assess the preferred mode of consumption if people who use opioids were provided with opioid safe supply.

          Methods

          The BC Harm Reduction Client Survey (HRCS) is an annual survey that gathers information about people who use drugs' (PWUD) substance use characteristic with the goal of contributing to evidence-based policy. This study utilized data from the 2021 HRCS. The outcome variable was “prefer smoking opioid safe supply” (‘yes/no’). Explanatory variables included participants’ demographics, drug use, and overdose characteristics. Bivariate and hierarchical multivariable logistic regressions were conducted to identify factors associated with the outcome.

          Results

          Of 282 total participants who indicated a preference for a mode of consumption for opioid safe supply, 62.4% preferred a smokable option and 19.9% preferred to inject if provided with opioid safe supply. Variables significantly associated with the outcome (preferred smoking) included: being 19-29 years old (AOR=5.95, CI =1.93 – 18.31) compared to >50 years old, having witnessed an overdose in the last 6 months (AOR=2.26, CI=1.20 – 4.28), having smoked opioids in the last 3 days (AOR=6.35, CI=2.98 – 13.53) and having a preference to smoke stimulants safe supply (AOR=5.04, CI=2.53 – 10.07).

          Conclusion

          We found that over half of participants prefer smokable options when accessing opioid safe supply. Currently in BC, there are limited smokable opioid safe supply options as alternatives to the toxic street supply. To reduce overdose deaths, safe supply options should be expanded to accommodate PWUD that prefer smoking opioids.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s13011-023-00515-4.

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          Most cited references17

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          Applied Logistic Regression

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            Trends and Geographic Patterns in Drug and Synthetic Opioid Overdose Deaths — United States, 2013–2019

            Deaths involving synthetic opioids other than methadone (synthetic opioids), which largely consist of illicitly manufactured fentanyl; psychostimulants with abuse potential (e.g., methamphetamine); and cocaine have increased in recent years, particularly since 2013 ( 1 , 2 ). In 2019, a total of 70,630 drug overdose deaths occurred, corresponding to an age-adjusted rate of 21.6 per 100,000 population and a 4.3% increase from the 2018 rate (20.7) ( 3 ). CDC analyzed trends in age-adjusted overdose death rates involving synthetic opioids, psychostimulants, cocaine, heroin, and prescription opioids during 2013–2019, as well as geographic patterns in synthetic opioid- and psychostimulant-involved deaths during 2018–2019. From 2013 to 2019, the synthetic opioid-involved death rate increased 1,040%, from 1.0 to 11.4 per 100,000 age-adjusted (3,105 to 36,359). The psychostimulant-involved death rate increased 317%, from 1.2 (3,627) in 2013 to 5.0 (16,167) in 2019. In the presence of synthetic opioid coinvolvement, death rates for prescription opioids, heroin, psychostimulants, and cocaine increased. In the absence of synthetic opioid coinvolvement, death rates increased only for psychostimulants and cocaine. From 2018 to 2019, the largest relative increase in the synthetic opioid-involved death rate occurred in the West (67.9%), and the largest relative increase in the psychostimulant-involved death rate occurred in the Northeast (43.8%); these increases represent important changes in the geographic distribution of drug overdose deaths. Evidence-based prevention and response strategies including substance use disorder treatment and overdose prevention and response efforts focused on polysubstance use must be adapted to address the evolving drug overdose epidemic. Drug overdose deaths were identified in the National Vital Statistics System multiple cause-of-death mortality files* by using International Classification of Diseases, Tenth Revision (ICD-10) underlying cause-of-death codes X40–44 (unintentional), X60–64 (suicide), X85 (homicide), or Y10–14 (undetermined intent). Drug categories were defined using the following ICD-10 multiple cause-of-death codes: synthetic opioids other than methadone (T40.4), psychostimulants with abuse potential (T43.6), cocaine (T40.5), prescription opioids (T40.2 or T40.3), and heroin (T40.1). Deaths involving more than one type of drug were included in the rates for each applicable drug category; categories are not mutually exclusive. † Annual age-adjusted death rates § were examined during 2013–2019 and stratified by drug category and synthetic opioid coinvolvement. The percentage of 2019 drug overdose deaths and change in 2018–2019 age-adjusted death rates involving synthetic opioids and psychostimulants were examined by U.S Census region ¶ and state. States with inadequate drug specificity, too few deaths to calculate stable estimates, or too few deaths to meet confidentiality requirements were excluded from state-level analyses.** ,†† Analyses of rate changes used z-tests when deaths were ≥100 and nonoverlapping confidence intervals based on a gamma distribution when deaths were 80% of drug overdose death certificates named at least one specific drug in 2019 and ≥10 deaths occurred in 2019 in the specific drug category. † Rate per 100,000 population age-adjusted to the 2000 U.S. standard population using the vintage year population of the data year. § Z-tests were used if the number of deaths was ≥100 in both 2018 and 2019, and p 80% of drug overdose death certificates named at least one specific drug in 2018 and 2019 and ≥20 deaths occurred during 2018 and 2019 in the drug category examined. †† Deaths were classified using the International Classification of Diseases, Tenth Revision. Drug overdoses are identified using underlying cause-of-death codes X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), and Y10–Y14 (undetermined). §§ Drug overdose deaths, as defined, that involve synthetic opioids other than methadone (T40.4). ¶¶ Drug overdose deaths, as defined, that involve psychostimulants with abuse potential (T43.6). *** Because deaths might involve more than one drug, some deaths are included in more than one category. In 2019, 6.3% of drug overdose deaths did not include information on the specific type of drug(s) involved. The figure is a series of maps showing percent and relative change in age-adjusted rates of drug overdose deaths involving synthetic opioids other than methadone and psychostimulants with abuse potential in the United States during 2013–2019. From 2018 to 2019, the age-adjusted synthetic opioid-involved death rate increased 15.2%, from 9.9 to 11.4. In 2019, the Northeast had the highest percentage and rate of deaths involving synthetic opioids, but the smallest relative (5.2%) and absolute (1.0) rate increases from the previous year (19.1 in 2018 to 20.1 in 2019). In contrast, the West experienced the largest relative (67.9%) and absolute (1.9) rate increases from 2.8 in 2018 to 4.7 in 2019. From 2018 to 2019, a total of 20 states experienced relative increases in their synthetic opioid-involved death rate, with the highest rate in 2019 in Delaware (38.4). The largest relative rate increase occurred in Colorado (95.5%), and the largest absolute rate increase occurred in the District of Columbia (7.6). No state experienced a significant decrease. The percentage of deaths involving psychostimulants was highest in the West (43.5%) and lowest in the Northeast (7.9%) in 2019. The same geographic pattern was observed with psychostimulant-involved deaths that did not coinvolve synthetic opioids. In all northeastern states, fewer than 20% of drug overdose deaths involved psychostimulants. In 12 states, mostly in the West and Midwest, ≥40% of overdose deaths involved psychostimulants. Among these, the percentage was highest in Hawaii (70.2%) and Oklahoma (50.7%). The percentage was lowest in Maryland (3.3%). From 2018 to 2019, the age-adjusted rate of psychostimulant-involved deaths increased 28.2%, from 3.9 to 5.0. The Northeast experienced the largest relative (43.8%), but smallest absolute (0.7), rate increase. The Midwest (36.1%) and South (32.4%) experienced similar relative but slightly larger absolute (1.3 and 1.2, respectively) rate increases. Although the percentage of 2019 drug overdose deaths involving psychostimulants was highest in the West, the relative rate increase (17.5%) was lowest there. Twenty-four states experienced an increase in the rate of psychostimulant-involved deaths. Kansas experienced the largest relative increase (107.1%) and third largest absolute rate increase (3.0). West Virginia had the highest 2019 rate (24.4) and the largest absolute rate increase (5.1); New York had the lowest 2019 rate (1.3). No state had a significant decrease (Supplementary Table, https://stacks.cdc.gov/view/cdc/101757). Discussion In 2019, a total of 70,630 drug overdose deaths occurred in the United States; approximately one half involved synthetic opioids. From 2013 to 2019, the age-adjusted synthetic opioid death rate increased sharply by 1,040%, from 1.0 to 11.4. Death rates involving prescription opioids and heroin increased in the presence of synthetic opioids (from 0.3 to 1.8 and from 0.1 to 2.7, respectively), but not in their absence. Death rates involving psychostimulants increased 317% overall, regardless of synthetic opioid coinvolvement. Synthetic opioid- and psychostimulant-involved deaths shifted geographically from 2018 to 2019. From 2015 to 2016, states in the East had the largest increases in deaths involving synthetic opioids, and from 2016 to 2017, the Midwest had the largest increases in deaths involving psychostimulants ( 2 , 4 ). In contrast, from 2018 to 2019, the largest relative increase in death rates involving synthetic opioids occurred in the West (67.9%); the largest relative increase in death rate involving psychostimulants occurred in the Northeast (43.8%). Sharp increases in synthetic opioid- and psychostimulant-involved overdose deaths in 2019 are consistent with recent trends indicating a worsening and expanding drug overdose epidemic ( 1 , 2 , 4 – 6 ). Synthetic opioids, particularly illicitly manufactured fentanyl and fentanyl analogs, are highly potent, increasingly available across the United States, and found in the supplies of other drugs ( 7 , 8 ). Co-use of synthetic opioids with other drugs can be deliberate or inadvertent (i.e., products might be adulterated with illicitly manufactured fentanyl or fentanyl analogs unbeknownst to the user). Similarly, psychostimulant-involved deaths are likely rising because of increases in potency, availability, and reduced cost of methamphetamine in recent years ( 9 ). The increase in synthetic-opioid involved deaths in the West and in psychostimulant-involved deaths in the Northeast signal broadened geographic use of these substances, consistent with increases in the number of drug submissions to forensic laboratories in those regions during 2018–2019 ( 8 ). The findings in this report are subject to at least two limitations. First, forensic toxicology testing protocols varied by time and jurisdiction, particularly for synthetic opioids. Therefore, some of the increases in overdose deaths reported by drug categories could be attributed to the increases in testing as well as the use of more comprehensive tests. Second, geographic analyses excluded states with inadequate drug specificity or too few deaths to calculate stable rates. The worsening and expanding drug overdose epidemic in the United States now involves potent synthetic drugs, often in combination with other substances, and requires urgent action. As involved substances and geographic trends in drug overdose deaths change, timely surveillance and evidence-based prevention and response strategies remain essential. CDC’s Overdose Data to Action ¶¶ cooperative agreement funds health departments in 47 states, the District of Columbia, two territories, and 16 cities and counties to obtain high-quality, comprehensive, and timely data on fatal and nonfatal drug overdoses to inform prevention and response efforts. To help curb this epidemic, Overdose Data to Action strategies focus on enhancing linkage to and retention in substance use disorder treatment, improving prescription drug monitoring programs, implementing postoverdose protocols in emergency departments, including naloxone provision to patients who use opioids or other illicit drugs, and strengthening public health and public safety partnerships, enabling data sharing to help inform comprehensive interventions.*** Other approaches ††† should include expanded naloxone distribution and education that potent opioids might require multiple doses of naloxone, improved access to substance use disorder treatment (including medications for opioid use disorder or programs addressing polysubstance use), expanded harm reduction services, and continued partnerships with public safety to monitor trends in the illicit drug supply, including educating the public that drug products might be adulterated with fentanyl or fentanyl analogs unbeknownst to users. A comprehensive and coordinated approach from clinicians, public health, public safety, community organizations, and the public must incorporate innovative and established prevention and response strategies, including those focused on polysubstance use. Summary What is already known about this topic? Deaths involving synthetic opioids other than methadone, cocaine, and psychostimulants have increased in recent years. What is added by this report? From 2013 to 2019, the age-adjusted rate of deaths involving synthetic opioids other than methadone increased 1,040%, and for psychostimulants increased 317%. During 2018–2019, the largest relative increase in synthetic opioid-involved death rates occurred in the West (67.9%), and the largest relative increase in psychostimulant-involved death rates occurred in the Northeast (43.8%). What are the implications for public health practice? Evidence-based prevention and response strategies, including substance use disorder treatment and overdose prevention and response efforts focused on polysubstance use, must be adapted to address the changing drug overdose epidemic.
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              Statistical notes for clinical researchers: Chi-squared test and Fisher's exact test

              When we try to compare proportions of a categorical outcome according to different independent groups, we can consider several statistical tests such as chi-squared test, Fisher's exact test, or z-test. The chi-squared test and Fisher's exact test can assess for independence between two variables when the comparing groups are independent and not correlated. The chi-squared test applies an approximation assuming the sample is large, while the Fisher's exact test runs an exact procedure especially for small-sized samples. Chi-squared test 1. Independency test The chi-squared test is used to compare the distribution of a categorical variable in a sample or a group with the distribution in another one. If the distribution of the categorical variable is not much different over different groups, we can conclude the distribution of the categorical variable is not related to the variable of groups. Or we can say the categorical variable and groups are independent. For example, if men have a specific condition more than women, there is bigger chance to find a person with the condition among men than among women. We don't think gender is independent from the condition. If there is equal chance of having the condition among men and women, we will find the chance of observing the condition is the same regardless of gender and can conclude their relationship as independent. Examples 1 and 2 in Table 1 show perfect independent relationship between condition (A and B) and gender (male and female), while example 3 represents a strong association between them. In example 3, women had a greater chance to have the condition A (p = 0.7) compared to men (p = 0.3). The chi-squared test performs an independency test under following null and alternative hypotheses, H0 and H1, respectively. H0: Independent (no association) H1: Not independent (association) The test statistic of chi-squared test: χ 2 = ∑ ( 0 - E ) 2 E ~ χ 2 with degrees of freedom (r - 1)(c - 1), Where O and E represent observed and expected frequency, and r and c is the number of rows and columns of the contingency table. The first step of the chi-squared test is calculation of expected frequencies (E). E is calculated under the assumption of independent relation or, in other words, no association. Under independent relationship, the cell frequencies are determined only by marginal proportions, i.e., proportion of A (60/200 = 0.3) and B (1400/200 = 0.7) in example 2. In example 2, the expected frequency of the male and A cell is calculated as 30 that is the proportion of 0.3 (proportion of A) in 100 Males. Similarly, the expected frequency of the male and A cell is 50 that is the proportion of 0.5 (proportion of A = 100/200 = 0.5) in 100 Males in example 3 (Table 1). Expected frequency (E) of Male & A = Number of A * Number of Male Total number = p A * p male * total number The second step is obtaining (O - E)2/E for each cell and summing up the values over each cell. The final summed value follows chi-squared distribution. For the ‘male and A’ cell in example 3, (O - E)2/E = (30 - 50)2/50 = 8. Chi-squared statistic calculated = ∑ ( 0 - E ) 2 E = 8 + 8 + 8 + 8 = 32 in example 3. For examples 1 and 2, the chi-squared statistics equal zero. A big difference between observed value and expected value or a large chi-squared statistic implies that the assumption of independency applied in calculation of expected value is irrelevant to the observed data that is being tested. The degrees of freedom is one as the data has two rows and two columns: (r - 1) * (c - 1) = (2 - 1) * (2 - 1) = 1. The final step is making conclusion referring to the chi-squared distribution. We reject the null hypothesis of independence if the calculated chi-squared statistic is larger than the critical value from the chi-squared distribution. In the chi-squared distribution, the critical values are 3.84, 5.99, 7.82, and 9.49, with corresponding degrees of freedom of 1, 2, 3, and 4, respectively, at an alpha level of 0.5. Larger chi-square statistics than these critical values of specific corresponding degrees of freedom lead to the rejection of null hypothesis of independence. In examples 1 and 2, the chi-squared statistic is zero which is smaller than the critical value of 3.84, concluding independent relationship between gender and condition. However, data in example 3 have a large chi-squared statistic of 32 which is larger than 3.84; it is large enough to reject the null hypothesis of independence, concluding a significant association between two variables. The chi-squared test needs an adequate large sample size because it is based on an approximation approach. The result is relevant only when no more than 20% of cells with expected frequencies < 5 and no cell have expected frequency < 1.1 2. Effect size As the significant test does not tell us the degree of effect, displaying effect size is helpful to show the magnitude of effect. There are three different measures of effect size for chi-squared test, Phi (φ), Cramer's V (V), and odds ratio (OR). Among them φ and OR can be used as the effect size only in 2 × 2 contingency tables, but not for bigger tables. φ = χ 2 n V = χ 2 n · d f , where n is total number of observation, and df is degrees of freedom calculated by (r - 1) * (c - 1). Here, r and c are the numbers of rows and columns of the contingency table. In example 3, we can calculate them as φ = χ 2 n = 32 200 = 0.4 , V = χ 2 n · d f = 32 200 · 1 = 0.4 , and O R = 70 · 70 30 · 30 = 5.44 . Referring to Table 2, the effect size V = 0.4 is interpreted medium to large. If number of rows and/or columns are larger than 2, only Cramer's V is available. 3. Post-hoc pairwise comparison of chi-squared test The chi-squared test assesses a global question whether relation between two variables is independent or associated. If there are three or more levels in either variable, a post-hoc pairwise comparison is required to compare the levels of each other. Let's say that there are three comparative groups like control, experiment 1, and experiment 2 and we try to compare the prevalence of a certain disease. If the chi-squared test concludes that there is significant association, we may want to know if there is any significant difference in three compared pairs, between control and experiment 1, between control and experiment 2, and between experiment 1 and experiment 2. We can reduce the table into multiple 2 × 2 contingency tables and perform the chi-squared test with applying the Bonferroni corrected alpha level (corrected α = 0.05/3 compared pairs = 0.017). Fisher's exact test Fisher's exact test is practically applied only in analysis of small samples but actually it is valid for all sample sizes. While the chi-squared test relies on an approximation, Fisher's exact test is one of exact tests. Especially when more than 20% of cells have expected frequencies < 5, we need to use Fisher's exact test because applying approximation method is inadequate. Fisher's exact test assesses the null hypothesis of independence applying hypergeometric distribution of the numbers in the cells of the table. Many packages provide the results of Fisher's exact test for 2 × 2 contingency tables but not for bigger contingency tables with more rows or columns. For example, the SPSS statistical package automatically provides an analytical result of Fisher's exact test as well as chi-squared test only for 2 × 2 contingency tables. For Fisher's exact test of bigger contingency tables, we can use web pages providing such analyses. For example, the web page ‘Social Science Statistics’ (http://www.socscistatistics.com/tests/chisquare2/Default2.aspx) permits performance of Fisher exact test for up to 5 × 5 contingency tables. The procedure of chi-squared test and Fisher's exact test using IBM SPSS Statistics for Windows Version 23.0 (IBM Corp., Armonk, NY, USA) is as follows:
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                Author and article information

                Contributors
                jane.buxton@bccdc.ca
                Journal
                Subst Abuse Treat Prev Policy
                Subst Abuse Treat Prev Policy
                Substance Abuse Treatment, Prevention, and Policy
                BioMed Central (London )
                1747-597X
                16 May 2023
                16 May 2023
                2023
                : 18
                : 27
                Affiliations
                [1 ]GRID grid.415368.d, ISNI 0000 0001 0805 4386, Public Health Agency of Canada, ; Ottawa, Ontario Canada
                [2 ]GRID grid.418246.d, ISNI 0000 0001 0352 641X, British Columbia Centre for Disease Control, ; Vancouver, British Columbia Canada
                [3 ]GRID grid.17091.3e, ISNI 0000 0001 2288 9830, School of Population and Public Health, , University of British Columbia, ; Vancouver, British Columbia Canada
                Article
                515
                10.1186/s13011-023-00515-4
                10186314
                37194018
                b174cb1c-64cc-4dc7-aacd-3420d5b14eed
                © The Author(s) 2023

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 18 September 2022
                : 4 January 2023
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2023

                Health & Social care
                opioids,harm reduction,safe supply,mode of consumption,smoking,people who use drugs (pwud)

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