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      Beliefs and misperceptions about naloxone and overdose among U.S. laypersons: a cross-sectional study

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          Abstract

          Background

          Overdose education and naloxone distribution (OEND) to laypersons are key approaches to reduce the incidence of opioid-involved overdoses. While some research has examined attitudes toward OEND, especially among pharmacists and first responders, our understanding of what laypersons believe about overdose and naloxone is surprisingly limited. Further, some scholars have expressed concerns about the prevalence of non-evidence-based beliefs about overdose and naloxone. We designed this study to analyze the prevalence, nature, and context of beliefs about naloxone and overdose among U.S. laypersons.

          Methods

          We conducted a cross-sectional study ( n = 702) using Prolific.co (representative of the U.S. population by age, gender, and race). Primary outcomes were the believability of six statements about overdose/naloxone on a seven-point Likert-type scale. Five statements were unsupported, and one was supported, by current scientific evidence. We used latent profile analysis to classify participants into belief groups, then used regression to study correlates of profile classification.

          Results

          Believability of the statements (7: extremely believable) ranged from m = 5.57 (SD = 1.38) for a scientifically supported idea (trained bystanders can reverse overdose with naloxone), to m = 3.33 (SD = 1.83) for a statement claiming opioid users can get high on naloxone. Participants were classified into three latent belief profiles: Profile 1 (most aligned with current evidence; n = 246), Profile 2 (moderately aligned; n = 351), and Profile 3 (least aligned, n = 105). Compared to Profile 1, several covariates were associated with categorization into Profiles 2 and 3, including lower trust in science (RRR = 0.36, 95%CI = 0.24–0.54; RRR = 0.21, 95%CI = 0.12–0.36, respectively), conservative political orientation (RRR = 1.41, 95%CI = 1.23–1.63; 3:RRR = 1.62, 95%CI = 1.35–1.95, respectively), and never being trained about naloxone (Profile 3: RRR = 3.37, 95%CI = 1.16–9.77).

          Conclusions

          Preliminary evidence suggests some U.S. laypersons simultaneously believe that bystander overdose prevention with naloxone can prevent overdose and one or more scientifically unsupported claims about naloxone/overdose. Categorization into clusters displaying such belief patterns was associated with low trust in science, conservative political orientation, and not having been trained about naloxone.

          Preregistration

          This cross-sectional study was preregistered prior to any data collection using the Open Science Framework: https://osf.io/c6ufv

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12889-022-13298-3.

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          Most cited references44

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          The ASA's Statement onp-Values: Context, Process, and Purpose

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            Statistical Power to Detect the Correct Number of Classes in Latent Profile Analysis.

            Little research has examined factors influencing statistical power to detect the correct number of latent classes using latent profile analysis (LPA). This simulation study examined power related to inter-class distance between latent classes given true number of classes, sample size, and number of indicators. Seven model selection methods were evaluated. None had adequate power to select the correct number of classes with a small (Cohen's d = .2) or medium (d = .5) degree of separation. With a very large degree of separation (d = 1.5), the Lo-Mendell-Rubin test (LMR), adjusted LMR, bootstrap likelihood-ratio test, BIC, and sample-size adjusted BIC were good at selecting the correct number of classes. However, with a large degree of separation (d = .8), power depended on number of indicators and sample size. The AIC and entropy poorly selected the correct number of classes, regardless of degree of separation, number of indicators, or sample size.
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              Opioid overdose rates and implementation of overdose education and nasal naloxone distribution in Massachusetts: interrupted time series analysis

              Objective To evaluate the impact of state supported overdose education and nasal naloxone distribution (OEND) programs on rates of opioid related death from overdose and acute care utilization in Massachusetts. Design Interrupted time series analysis of opioid related overdose death and acute care utilization rates from 2002 to 2009 comparing community-year strata with high and low rates of OEND implementation to those with no implementation. Setting 19 Massachusetts communities (geographically distinct cities and towns) with at least five fatal opioid overdoses in each of the years 2004 to 2006. Participants OEND was implemented among opioid users at risk for overdose, social service agency staff, family, and friends of opioid users. Intervention OEND programs equipped people at risk for overdose and bystanders with nasal naloxone rescue kits and trained them how to prevent, recognize, and respond to an overdose by engaging emergency medical services, providing rescue breathing, and delivering naloxone. Main outcome measures Adjusted rate ratios for annual deaths related to opioid overdose and utilization of acute care hospitals. Results Among these communities, OEND programs trained 2912 potential bystanders who reported 327 rescues. Both community-year strata with 1-100 enrollments per 100 000 population (adjusted rate ratio 0.73, 95% confidence interval 0.57 to 0.91) and community-year strata with greater than 100 enrollments per 100 000 population (0.54, 0.39 to 0.76) had significantly reduced adjusted rate ratios compared with communities with no implementation. Differences in rates of acute care hospital utilization were not significant. Conclusions Opioid overdose death rates were reduced in communities where OEND was implemented. This study provides observational evidence that by training potential bystanders to prevent, recognize, and respond to opioid overdoses, OEND is an effective intervention.
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                Author and article information

                Contributors
                jagley@indiana.edu
                Journal
                BMC Public Health
                BMC Public Health
                BMC Public Health
                BioMed Central (London )
                1471-2458
                10 May 2022
                10 May 2022
                2022
                : 22
                : 924
                Affiliations
                [1 ]GRID grid.411377.7, ISNI 0000 0001 0790 959X, Prevention Insights, Department of Applied Health Science, School of Public Health Bloomington, , Indiana University Bloomington, ; 809 E. 9th St., Bloomington, IN 47405 USA
                [2 ]GRID grid.5386.8, ISNI 000000041936877X, Department of Population Health Sciences, , Weill Cornell Medicine, ; New York, NY USA
                [3 ]GRID grid.255364.3, ISNI 0000 0001 2191 0423, College of Health and Human Performance, East Carolina University, ; Greenville, NC USA
                [4 ]GRID grid.134563.6, ISNI 0000 0001 2168 186X, Southwest Institute for Research On Women, College of Social & Behavioral Sciences, , University of Arizona, ; Tucson, AZ USA
                [5 ]GRID grid.411377.7, ISNI 0000 0001 0790 959X, Biostatistics Consulting Center, School of Public Health Bloomington, , Indiana University Bloomington, ; Bloomington, IN USA
                Article
                13298
                10.1186/s12889-022-13298-3
                9086153
                35538566
                0e7c1195-4f5a-47db-955a-560d1b94f44f
                © The Author(s) 2022

                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
                : 24 February 2022
                : 25 April 2022
                Categories
                Research
                Custom metadata
                © The Author(s) 2022

                Public health
                naloxone,overdose,misinformation,trust in science,opioids,opioid epidemic
                Public health
                naloxone, overdose, misinformation, trust in science, opioids, opioid epidemic

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