LEARNING OUTCOMES
After completing this module, the paramedic will be able to:
The misuse of opioids is a worldwide crisis that affects public health and welfare (National Institute on Drug Abuse, 2021; World Health Organization (WHO), 2021). Opioid overdose requires rapid response with naloxone (Kim and Nelson, 2015). In 2017, opioid overdose deaths accounted for approximately two-thirds of all drug overdose deaths in the United States (Scholl et al, 2018; Wilson et al, 2020). Worldwide, about half a million deaths are attributable to drug use, of which more than 70% are related to opioids, with over 30% of those deaths caused by overdose (WHO, 2020).
In recent years, opioid overdose deaths have been driven primarily by synthetic opioids such as fentanyl (Scholl et al, 2018; Wilson et al, 2020). In 2017–2018, deaths involving all opioids, prescription opioids and heroin decreased by 2%, 14% and 4% respectively. However, deaths involving synthetic opioids increased by 10%, likely driven by illicitly manufactured fentanyl, including fentanyl analogues (Guy et al, 2017; Scholl et al, 2018; Hedegaard et al, 2020; O'Donnell et al, 2020; Wilson et al, 2020).
Among the Centers for Disease Control's chief recommendations for preventing opioid-involved deaths is naloxone distribution and use (Wilson et al, 2020). Naloxone—an opioid antagonist—can safely prevent fatal overdose when administered in a timely and appropriate manner (Kim and Nelson, 2015).
When provided in conjunction with community-based opioid overdose education and training, naloxone has proven to play a significant role in harm reduction (Green, 2005; Galea et al, 2006; Albert et al, 2011; Walley et al, 2013; Clark et al, 2014; Wagner et al, 2014; Mueller et al, 2015; Wheeler et al, 2015; Oliva et al, 2016).
Western New York, which consists of eight contiguous counties (Allegany, Cattaraugus, Chautauqua, Erie, Genesee, Niagara, Orleans and Wyoming), has been particularly impacted by the ongoing opioid overdose epidemic (Erie County Opiate Epidemic Task Force (ECOETF), 2020). In the most populous county, Erie, opioid overdose deaths more than doubled between 2014 and 2015 (127 to 256 people), eventually peaking at 301 people in 2016. More than half of the 2015 and 2016 overdose victims in Erie County were aged under aged 40 years (ECOETF, 2020). In line with national trends, Erie County opioid deaths between 2017 and 2019 were largely been driven by an increased proportion of deaths caused by synthetic opioids (ECOETF, 2020).
To combat this crisis, the ECOETF was established in 2016. It consists of: community experts from law enforcement and first responder teams; members of the medical community, including emergency medicine, primary care, inpatient services as well as medical schools' mental health and addictions treatment providers; public information and community education sectors; and personally affected individuals, including victims' family members, friends and loved ones.
Since its inception, ECOETF has been addressing the opioid epidemic through the implementation of evidence-based harm reduction initiatives, including the Opioid Overdose Prevention Program/Narcan (Naloxone) Administration Project, which focuses on training and administration of naloxone to reverse opioid overdoses. These efforts have coincided with steady decreases in Erie County's opioid overdose deaths since 2017 (156 people in 2019).
To expand and strengthen the infrastructure of existing programmes that provide first responder training, the Erie County Department of Health (ECDOH) used funding from the Substance Abuse and Mental Health Services Administration's First Responders-Comprehensive Addiction and Recovery Act grant to increase the availability of naloxone and to train first responders and key community sectors to carry and administer it.
In conjunction with this training, ECDOH developed a brief survey that was administered before and after each training session. The survey assessed trainees' knowledge and attitudes concerning opioid use and overdose.
Aims
Using data collected from these surveys, this study aimed to evaluate the effectiveness of the regional naloxone training initiative on participants' knowledge and attitudes and explore differences in training outcomes between groups of trainees, such as first responders, health professional students and members of the public—the general community.
Methods
Training development and implementation
The training on opioid overdose recognition and naloxone use was developed by ECDOH staff. Four ECDOH employees conduct the training sessions: a community health educator nurse with more than 20 years of experience, as well as two Master's-level public health educators and one ECDOH employee, each with several years' experience.
The 1.5-hour sessions were led by an ECDOH trainer and covered: the history and scope of the opioid epidemic locally; signs and symptoms of an overdose; overdose response and naloxone use; reporting and follow-up after administering naloxone; syringe access and exchange; and legal protections around naloxone use. All training sessions included a lecture, time for questions and hands-on practice with all forms of naloxone available to laypersons.
Surveys were administered before and after 126 sessions between 28 July 2019 and 12 March 2020. They were anonymous, and each participant's pre and post surveys were linked by having the same unique number. Each participant received the pre survey and an envelope with the post survey stapled together. Pre-surveys were completed and collected before training started. At the end of the session, participants were instructed to open the sealed envelopes and complete the post survey.
The training curriculum is available at https://tinyurl.com/vm86khf9.
Recruitment of trainees
Trainees were recruited through media messaging (papers, radio, social media), word of mouth, and outreach to first responder departments and community organisations. ECDOH used its longstanding relationships with the local police academy to recruit trainees from police departments.
Survey development and design
After validated measurement tools were reviewed, pre and post surveys were drafted to assess knowledge and attitudes concerning opioid use and overdose (Appendices A–D).
The final surveys consisted of 23–24 questions that assessed: knowledge of substance use disorder and overdose rescue; attitudes toward opioid users and the opioid epidemic; intent to assist during an opioid overdose; and confidence in one's ability to properly intervene in an opioid overdose.
Knowledge questions were adapted from a previously used opioid overdose knowledge scale (Williams et al, 2013). Attitude questions were chosen from the Opioid Overdose Attitudes Scale (Williams et al, 2013) and from mental health assessment questions (Link, 1989; Wahl et al, 2011; Meier, 2015; Taylor et al, 2019) and amended to measure attitudes related to opioid use and overdose. The post survey contained one optional open-ended question asking respondents to identify something new that they learned from the training.
Survey questions for first responder and community member trainees were largely the same with the exception of the following items. Only members of the public were asked ‘If I witnessed and overdose, I would call 911 right away’ and ‘I would be able to deal effectively with an overdose’. Only first responders were asked to answer whether ‘Opioid addiction is a chronic disease’.
Data collection
ECDOH trainers conducted the sessions and administered paper surveys, the results of which were later entered into a spreadsheet. The electronic data were shared with the evaluation team for analysis. Participants completed a 10-minute paper survey immediately before and after the training in the room where the session was conducted. No identifying information was collected on the surveys. Pre and post surveys were linked using an anonymous ID code.
A zip code, based on the location of the training session, was attached to each completed survey and professional and educational backgrounds were inferred based on training venue, rather than being directly collected from participants. We used the Rural-Urban Commuting Area (RUCA) codes (version 2.0) developed by the Office of Rural Health Policy with the University of Washington and the Department of Agriculture's Economic Research Service to classify zip codes into urban or rural categories. RUCA is an established census tract-based classification scheme that uses the Bureau of the Census Urbanized Area and Urban Cluster definitions in combination with work commuting information to characteristics all of the nation's census tracts regarding their rural and urban status.
The University at Buffalo institutional review board approved all study procedures under the designation of ‘not human subject research’.
Data analysis
Quantitative analysis included calculating descriptive statistics (frequencies, medians and interquartile ranges (IQR)) for all variables. For knowledge questions, we calculated the proportion of respondents who correctly answered each item and compared pre- and post-survey data using McNemar's test. To determine whether there had been a change in attitudes, we calculated the median score and the IQR for each item. Attitudes questions had Likert-scale responses that were coded 1–5 for analysis.
Items were organised so all responses for attitude questions moved in the same direction, with a decrease in score between the pre and post survey indicating an improvement.
For attitudes questions, the Wilcoxon signed-rank test was used to compare median pre- and post-survey scores. Knowledge and attitudes sub-scale scores were calculated to assess overall changes between the two surveys and compared using the Wilcoxon signed-rank test. Non-parametric tests were used as the distribution of score differences between the pre and post survey were not normally distributed.
Cronbach's alpha was used to ensure internal consistency within each sub-scale category. Multivariate linear regression analysis was conducted to assess whether location or trainee educational and professional background were independently associated with changes in knowledge and attitudes sub-scale scores.
All analyses were conducted in SAS 9.4 software.
Results
A total of 2321 pre and post surveys were completed by trainees between 28 July 2019 and 12 March 2020. More than 90% of these participants attended the session in an urban location (n=2,097; 90.4%).
Participants were categorised according to their professional and educational background or if they were members of the general community. Members of the public accounted for the largest proportion of participants (n=913; 39.6%). Firefighters were the next largest group (n=401; 17.4%) (Table 1). Professional and educational background categories were assigned to participant surveys according to survey venue.
| n | % | |
|---|---|---|
| Trainee type * | ||
| General community | 913 | 39.6% |
| First responders | ||
| Emergency medical services | 47 | 2.0% |
| Firefighters | 401 | 17.4% |
| Law enforcement | 59 | 2.6% |
| Providers | ||
| Medical | 41 | 1.8% |
| Social service and mental health providers | 82 | 3.6% |
| Health professional students/trainees | ||
| Dental | 302 | 13.1% |
| Nursing | 224 | 9.7% |
| Pharmacy | 234 | 10.2% |
| Location | ||
| Rural/isolated | 223 | 9.6 |
| Urban | 2098 | 90.4 |
Before and after comparison for questions and question categories
Almost all knowledge questions showed statistically significant improvements (Table 2).
| Check all that apply | Pre-test | Post-test | |||
|---|---|---|---|---|---|
| n correct | % correct | n correct | % correct | P | |
| Which of the following factors increase someone's risk of an opioid overdose? | |||||
| Using opioids again after not having used for a while (true) | 1846 | 79.5% | 2003 | 86.3% | <0.0001 |
| A long history of drug use (true) | 1800 | 77.6% | 1852 | 79.8% | 0.0443 |
| Using opioids again after detox or jail time (true) | 1790 | 77.1% | 2020 | 87.0% | <0.0001 |
| Which of the following may be signs of an opioid overdose? | |||||
| Not responsive (true) | 2011 | 86.6% | 2131 | 91.8% | <0.0001 |
| Very shallow breathing, gurgling (true) | 1926 | 83.0% | 2080 | 89.6% | <0.0001 |
| Skin changes; blue lips and nails (true) | 1800 | 77.6% | 1905 | 82.1% | <0.0001 |
| Which of the following should be done to help during an opioid overdose? | |||||
| Call 911 (true) | 2069 | 89.1% | 2144 | 92.4% | <0.0001 |
| Place the person in the recovery position (on their side with mouth clear) (true) | 1834 | 79.0% | 2086 | 89.9% | <0.0001 |
| Give Narcan (naloxone)—opioid antidote (true) | 1996 | 86.0% | 2163 | 93.2% | <0.0001 |
| Check for breathing (true) | 1912 | 82.4% | 2076 | 89.4% | <0.0001 |
| Put the person in bed to sleep it off (false) | 2246 | 96.8% | 2241 | 96.6% | 0.6573 |
| Pour ice into armpits or body cavities (false) | 2168 | 93.4% | 2284 | 98.4% | <0.0001 |
| What is Narcan (naloxone) used for? | |||||
| To reverse the effects of an opioid overdose (e.g. heroin, fentanyl, prescriptions) (true) | 2017 | 86.9% | 2145 | 92.4% | <0.0001 |
| To reverse the effects of an amphetamine overdose (false) | 1999 | 86.1% | 2037 | 87.8% | 0.0452 |
| To reverse the effects of a cocaine overdose (false) | 1997 | 86.0% | 1969 | 84.8% | 0.1626 |
| To reverse the effects of a molly (MDMA) overdose (false) | 2096 | 90.3% | 2089 | 90.0% | 0.6741 |
| To reverse the effects of any overdose (false) | 1984 | 85.5% | 1990 | 85.7% | 0.757 |
| How can Narcan (naloxone) be given to a person? | |||||
| Into a muscle (intramuscular) (true) | 878 | 37.8% | 1820 | 78.4% | <0.0001 |
| Into a vein (intravenous) (true) | 699 | 30.1% | 1092 | 47.1% | <0.0001 |
| Into a nostril (intranasal) (true) | 1672 | 72.0% | 2144 | 92.4% | <0.0001 |
| Which type of person is likely to have an addiction to opioids? | |||||
| Homeless person (true) | 1930 | 83.2% | 2031 | 87.5% | <0.0001 |
| Nurse (true) | 1563 | 67.3% | 1821 | 78.5% | <0.0001 |
| Sex worker (true) | 1812 | 78.1% | 1924 | 82.9% | <0.0001 |
| Bank executive (true) | 1452 | 62.6% | 1764 | 76.0% | <0.0001 |
| College student (true) | 1703 | 73.4% | 1904 | 82.0% | <0.0001 |
| Food service worker (true) | 1464 | 63.1% | 1786 | 77.0% | <0.0001 |
| Doctor (true) | 1536 | 66.2% | 1776 | 76.5% | <0.0001 |
| Construction worker (true) | 1537 | 66.2% | 1913 | 82.4% | <0.0001 |
| Police officer (true) | 1437 | 61.9% | 1735 | 74.8% | <0.0001 |
| The New York State 911 Good Samaritan Law offers protection from arrest and prosecution for certain charges for: | |||||
| All people at the party (false) | 1736 | 74.8% | 2023 | 87.2% | <0.0001 |
| Person experiencing the medical event or overdose (true) | 811 | 34.9% | 1793 | 77.3% | <0.0001 |
| Person who called 911 (true) | 1503 | 64.8% | 2021 | 87.1% | <0.0001 |
| Property owner where medical event or overdose occurred (false) | 1752 | 75.5% | 1978 | 85.5% | <0.0001 |
| Median | IQR | Median | IQR | P | |
| Overall (total possible score=33) | 26 | 21–29 | 30 | 26–32 | <0.0001 |
Four items did not show statistically significant changes between pre and post survey. One item (put the person in bed to sleep it off to help during an opioid overdose) had a high percentage of being correctly identified as false in the pre survey (96.8%) so there was little room for improvement at post survey. The remaining three items all dealt with the use of naloxone for overdose to reverse the effect of: a cocaine overdose; a molly (MDMA/ecstasy) overdose; and any overdose. The percentage of correct answers at pre survey for those four items ranged from ~86% to ~90%, while the post-survey scores ranged from ~85% to ~90%.
The median knowledge sub-scale score showed a statistically significant improvement (26; IQR 21–29 versus 30; 26–32; P<0.0001, where higher scores indicate an improvement) demonstrating that the training was effective overall. The Cronbach's alphas of the knowledge sub-scale were 0.88 and 0.91 for pre and post surveys respectively.
The median scores for each of the attitude questions showed statistically significant improvements (Table 3). The items that showed the largest improvements between the pre and post survey were ‘I am going to need more training before I would feel confident to help someone who had overdosed’ and ‘If I gave Narcan (naloxone) to someone not overdosing on opioids, I might accidentally harm them’. In both instances, respondents were more likely to disagree with those statements in the post-survey response than in the pre-survey response. The median attitudes sub-scale score showed a statistically significant improvement (2.5; IQR 2.1–2.8 versus 1.8; IQR 1.4–2.1; P<0.0001, where lower scores indicate an improvement). The Cronbach's alpha scores of the knowledge sub-scale were 0.77 and 0.86 for pre and post surveys respectively.
| Pre-test | Post-test | ||||
|---|---|---|---|---|---|
| Lower score desirable | Mean | Median (IQR) | Mean | Median (IQR) | P |
| Most opioid-related deaths in Western New York are caused by heroin (n=2039) | 3.3 | 3 (3–4) | 2.3 | 2 (1–3) | <0.0001 |
| Opioid overdose in Western New York is limited to inner-city populations (n=2060) | 2.0 | 2 (1–2) | 1.7 | 2 (1–2) | <0.0001 |
| In general, a person who is addicted to opioids is dangerous (n=2035) | 2.7 | 2 (2–4) | 2.3 | 2 (2–3) | <0.0001 |
| A person who is addicted to opioids is to blame for their own problems (n=2035) | 2.4 | 2 (2–3) | 2.0 | 2 (1–2) | <0.0001 |
| A person who is addicted to opioids is unable to work (n=2056) | 2.3 | 2 (2–3) | 1,9 | 2 (1–2) | <0.0001 |
| Providing Narcan (naloxone) just enables drug users to continue (n=2044) | 2.4 | 2 (2–3) | 2.0 | 2 (1–2) | <0.0001 |
| If someone overdoses, I want to be able to help them (n=2046) | 1.7 | 2 (1–2) | 1.5 | 1 (1–2) | <0.0001 |
| I would be afraid of doing something wrong in an overdose situation (n=2046) | 3.0 | 3 (2–4) | 2.1 | 2 (1–2) | <0.0001 |
| Everyone in the US should be trained to recognize an overdose, use Narcan (naloxone), and have access to Narcan (naloxone) (n=2045) | 2.1 | 2 (1–3) | 1.7 | 2 (1–2) | <0.0001 |
| I am going to need more training before I would feel confident to help someone who had overdosed (n=2039) | 3.4 | 4 (2–4) | 2.1 | 2 (1–2) | <0.0001 |
| Medication addiction treatment (e.g. methadone and suboxone) is the evidenced-based treatment recognised today (n=2018) | 2.5 | 3 (2–3) | 2.0 | 2 (1–2) | <0.0001 |
| If I gave Narcan (naloxone) to someone not overdosing on opioids, I might accidently harm them (n=2034) | 2.6 | 3 (2–3) | 1.5 | 1 (1–2) | <0.0001 |
| If I witnessed an overdose, I would call 911 right away (n=1992)* | 1.6 | 1 (1–2) | 1.4 | 1 (1–2) | <0.0001 |
| Opioid addiction is a chronic disease (n=43)** | 2.8 | 2 (2–4) | 2.5 | 2 (2–4) | 0.0352 |
| If someone overdoses, I would know what to do to help them (n=2029) | 2.6 | 3 (2–3) | 1.6 | 2 (1–2) | <0.0001 |
| I would be able to place someone who had overdosed in the recovery position (n=2042) | 2.2 | 2 (2–3) | 1.5 | 1 (1–2) | <0.0001 |
| I would be able to deal effectively with an overdose (n=1999)* | 2.6 | 3 (2–3) | 1.7 | 2 (1–2) | <0.0001 |
| Overall (n=2068) | – | 2.5 (2.1–2.8) | – | 1.8 (1.4–2.1) | <0.0001 |
Univariate/multivariate results
Linear regression models were developed to assess the independent effects of location and trainee educational and professional background on the mean change in knowledge and attitudes sub-scale scores. The reference categories for each model were urban location and emergency medical service trainee. Models were also adjusted for trainer. The reference category model estimates for the knowledge and attitudes sub-scale score differences were 2.88 and 0.61 respectively.
In the model where the mean knowledge sub-scale score difference was the dependent variable, there was a significant association with location (rural). Compared to the reference, being a trainee from a rural location was associated with a 1.64-point decrease in the mean knowledge sub-scale score difference, independent of trainer (data not shown) or trainee educational and professional background (Table 4).
| n=2321 | Parameter estimate | 95% confidence interval | P | |
|---|---|---|---|---|
| Intercept | 2.88 | – | – | – |
| Trainee type | ||||
| General community | 0.12 | −1.91 | 2.16 | 0.91 |
| First responders | ||||
| Firefighters | 0.85 | −1.27 | 2.96 | 0.43 |
| Law enforcement | −1.99 | −4.58 | 0.60 | 0.13 |
| Providers | ||||
| Medical | −2.06 | −5.08 | 0.97 | 0.18 |
| Social service, and mental health providers | −0.76 | −3.27 | 1.74 | 0.55 |
| Health professional students/trainees | ||||
| Dental | 1.27 | −0.94 | 3.48 | 0.26 |
| Nursing | −0.13 | −2.27 | 2.01 | 0.90 |
| Pharmacy | −0.94 | −3.23 | 1.35 | 0.42 |
| Location | ||||
| Rural | −1.64 | −3.18 | −0.10 | 0.04 |
Multivariate models are controlled for trainer, trainee type and location
In the model where the mean attitudes sub-scale score difference was the dependent variable, there was a significant association with trainee educational and professional background (dental, general community and pharmacy). Compared to the reference, being a dental school trainee, a pharmacy school trainee or being from the general community sub-groups was associated with a 0.17, 0.14, and 0.17 increase respectively in the mean attitudes sub-scale score difference, independent of trainer (data not shown) and location (Table 5).
| n=2068 | Parameter estimate | 95% Confidence Interval | P | |
|---|---|---|---|---|
| Intercept | 0.61 | – | – | – |
| Trainee type | ||||
| General community | 0.14 | 0.01 | 0.28 | 0.04 |
| First responders | ||||
| Firefighters | −0.08 | −0.22 | 0.06 | 0.24 |
| Law enforcement | −0.04 | −0.21 | 0.12 | 0.62 |
| Providers | ||||
| Medical | 0.04 | −0.16 | 0.24 | 0.70 |
| Social service and mental health providers | −0.03 | −0.19 | 0.13 | 0.70 |
| Health professional students/trainees | ||||
| Dental | 0.17 | 0.03 | 0.32 | 0.02 |
| Nursing | 0.07 | −0.08 | 0.21 | 0.36 |
| Pharmacy | 0.17 | 0.02 | 0.32 | 0.03 |
| Location | ||||
| Rural | 0.01 | −0.09 | 0.10 | 0.86 |
Multivariate models are controlled for trainer, trainee type and location
Discussion
This paper reports on the success of a training programme for first responders and key community sectors to carry and administer emergency treatment for opioid overdose. To our knowledge, the current study is unique in that the sample is composed of a large, diverse group of trainees from multiple educational and professional backgrounds and members of the general public.
We found an improvement in opioid overdose knowledge and attitudes among 2321 people attending training sessions conducted throughout Western New York.
One set of items from the knowledge sub-scale did not show statistically significant improvements between pre and post survey. These items dealt with naloxone use for the reversal of overdose from cocaine, molly or any other illicit drugs. One explanation for this finding could be that the training included local data showing the increase in fatal overdoses with both cocaine and fentanyl present, and a media campaign around fentanyl in the local cocaine and methamphetamine supply. Trainers encouraged the use of naloxone during an overdose with other illicit drugs as it will not cause harm to the patient and fentanyl may be present. This media campaign message may have affected the lack of change between pre and post survey results for these items.
The multivariate models indicate there were significant differences between subgroups depending on location and trainee educational and professional background regarding the magnitude of changes between pre and post survey scores in the knowledge and attitudes sub-scales.
Trainees in rural settings had smaller increases in overall knowledge than their urban counterparts. Historically, ECOETF activities have largely focused their activities in urban regions of Western New York such as the Buffalo-Niagara metropolitan area. These endogenous factors may account for the differences observed in the present study's data, although this study did not collect the data necessary to explore that theory.
Changes in scores for the attitudes sub-scale also varied by trainee educational and professional background. Two health professional student trainee groups (dental and pharmacy) as well as the general community showed larger changes than emergency medical technicians. It is possible that these health professional trainees have received similar education or have been involved in opioid rescues, which may help to account for the different scores, though no data were collected within the present study to further explore this.
Finally, there were substantial differences in sub-scale scores between the reference group and medical providers (−2.06) and law enforcement staff (−1.09), although these were not statistically significant.
These results warrant future study on whether sessions should be tailored for trainees with different education and professional backgrounds.
The results from the present study are consistent with those of previous studies using smaller, less diverse samples.
Recent studies involving pharmacy students have shown: significant increases in knowledge of risks and signs of overdose and use of naloxone (Skoy et al, 2019; Kwon et al, 2020); better attitudes toward opioid overdose management (Skoy et al, 2019); improved self-efficacy in regard to readiness to manage an opioid overdose (Kwon et al, 2020); and improved ability to administer and counsel about naloxone (Schartel et al, 2018; Eukel et al, 2019), and understand the role of the pharmacist in fighting the opioid epidemic (Schartel et al, 2018; Bachyrycz et al, 2019; Eukel et al, 2019; Skoy et al, 2019).
Similar successes were observed for trainees from other health-related fields. A study of nursing students showed significant increases in naloxone knowledge, reduced stigma toward naloxone and improved self-efficacy (Carter and Caudill, 2020). Among medical students, researchers observed they became more comfortable about using and teaching patients to administer naloxone (Jawa et al, 2020), while emergency medical technicians demonstrated increased support for the idea of giving naloxone to people at risk of overdose (Zhang et al, 2018). Studies of law enforcement staff and firefighters showed improvements in knowledge (Crocker et al, 2019; Dahlem et al, 2017; Wagner et al, 2016), competency (Purviance et al, 2017; Wagner et al, 2016) and confidence (Purviance et al, 2017; Crocker et al, 2019). Conversely, one study found no change in attitudes towards overdose victims (Wagner et al, 2016).
Two studies of general community members showed significant increases in knowledge about how to help a person overdosing (Lowenstein et al, 2021), confidence in teaching someone else about overdose response (Lowenstein et al, 2021), and more positive attitudes around opioid overdose (Wolfson-Stofko et al, 2018).
Limitations
The current study has several limitations. Surveys were collected from trainees in the Western New York region, limiting the generalisability of the results. Trainees were not randomly sampled to attend sessions and complete surveys. The results may therefore be subject to bias since first responders and community members who seek training may be more likely to show greater improvements than those do not seek training.
Data were collected as part of a larger programme evaluation, so data collection elements were limited to items necessary for that initiative.
Demographic factors such as age, race, sex and socioeconomic status were not available for part of the analysis. Additionally, professional and educational backgrounds were inferred based on training venue, not collected directly from participants. This may result in non-differential misclassification and a biasing of regression estimates toward the null.
No longitudinal data were collected, which limits the ability to determine whether knowledge and attitude changes persisted over time or whether increases in knowledge or attitudes resulted in additional overdose rescues.
Conclusion
Though the analyses in the present study revealed significant differences among select sub-groups, most groups did not exhibit statistically significant differences in knowledge and attitude changes before and after the training session.
Further study is needed to examine whether trainees could benefit further from material that is tailored toward their educational and professional backgrounds.