Alcohol, cannabis, and nicotine use have distinct associations with COVID-19 pandemic-related experiences: An exploratory Bayesian network analysis across two timepoints
2023

Santiago Papini
Division of Research
Kaiser Permanente Northern California
2000, CA94612Broadway, OaklandUSA

Teresa López-Castro
Department of Psychology
Colin Powell School of Civic and Global Leadership
The City College of New York
The City University of New York
160 Convent Avenue, NAC 7/120NY10031New YorkUSA

Margaret Swarbrick
Center of Alcohol and Substance Use Studies
Rutgers University-New Brunswick
607 Allison RoadNJ08854PiscatawayUSA

Lynn K Paul
Division of the Humanities and Social Sciences
California Institute of Technology
1200 East California BoulevardCA91125PasadenaUSA

Damian Stanley
Division of the Humanities and Social Sciences
California Institute of Technology
1200 East California BoulevardCA91125PasadenaUSA

Gordon F. Derner School of Psychology
Adelphi University
One South AvenueNY11530Garden CityUSA

Alexandria Bauer
Center of Alcohol and Substance Use Studies
Rutgers University-New Brunswick
607 Allison RoadNJ08854PiscatawayUSA

Covid-Dynamic Team
Denise A Hien
Center of Alcohol and Substance Use Studies
Rutgers University-New Brunswick
607 Allison RoadNJ08854PiscatawayUSA

Alcohol, cannabis, and nicotine use have distinct associations with COVID-19 pandemic-related experiences: An exploratory Bayesian network analysis across two timepoints

Drug and Alcohol Dependence
248109929202310.1016/j.drugalcdep.2023.109929Method: In July 2020 and January 2021Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre -including this research content -immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. 0376-8716/ A R T I C L E I N F O Keywords: Substance use COVID-19 pandemic Bayesian network analysis Alcohol Cannabis Nicotine A B S T R A C T Background: Substance use trends during the COVID-19 pandemic have been extensively documented. However, relatively less is known about the associations between pandemic-related experiences and substance use.
a broad U.S. community sample (N = 1123) completed online assessments of past month alcohol, cannabis, and nicotine use and the 92-item Epidemic-Pandemic Impacts Inventory, a multidimensional measure of pandemic-related experiences. We examined links between substance use frequency, and pandemic impact on emotional, physical, economic, and other key domains, using Bayesian Gaussian graphical networks in which edges represent significant associations between variables (referred to as nodes). Bayesian network comparison approaches were used to assess the evidence of stability (or change) in associations between the two timepoints. Results: After controlling for all other nodes in the network, multiple significant edges connecting substance use nodes and pandemic-experience nodes were observed across both time points, including positive-(r range 0.07-0.23) and negative-associations (r range − 0.25 to − 0.11). Alcohol was positively associated with social and emotional pandemic impacts and negatively associated with economic impacts. Nicotine was positively associated with economic impact and negatively associated with social impact. Cannabis was positively associated with emotional impact. Network comparison suggested these associations were stable across the two timepoints. Conclusion: Alcohol, nicotine, and cannabis use had unique associations to a few specific domains among a broad range of pandemic-related experiences. Given the cross-sectional nature of these analyses with observational

Introduction

The COVID-19 pandemic had sudden, stark, and long-term impact across multiple life domains at the individual, family, and community levels, including social isolation and loneliness induced by stay-at-home orders, economic instability, physical illness, and pandemic-related anxiety, stress, and worry. Our aim was to examine cross-sectionally whether (and how) these known pandemic impacts were associated with three of the most widely used and co-used substances: alcohol, nicotine, and cannabis. A large body of evidence documenting changes in substance use in the early stages of the pandemic has been summarized in multiple meta-analytic and systematic reviews. Findings from these reviews suggest changes in alcohol use varied by country (Acuff et al., 2022;Bakaloudi et al., 2021;Kilian et al., 2022;Sohi et al., 2022) and the most common correlates of increased alcohol were mental health factors including stress, depression, anxiety, and pre-pandemic level of use (Acuff et al., 2022;Kilian et al., 2022;Kumar et al., 2022;Roberts et al., 2021;Schmidt et al., 2021). Many of these mental health factors were also associated with increases in cannabis use (Chong et al., 2021). Sociodemographic characteristics such as age, sex, and race, as well as changes in work and childcare during COVID-19 were predictive of increased drinking (Acuff et al., 2022). Global trends in tobacco use suggested similar proportions of individuals either increased or reduced their use, and approximately half showed no changes (Sarich et al., 2022). Among youth samples, alcohol, cannabis, and nicotine had an overall trend of decline during the pandemic (Layman et al., 2022). Together, these existing reviews indicate that there is considerable heterogeneity in pandemic substance use trends, which warrants further investigation.

Network analysis provides an avenue for understanding factors that may contribute to variation in substance use and/or that are impacted by substance use. In this approach, variables are represented as nodes in a network connected by edges that typically measure conditional associations (i.e., the associations between two nodes after controlling for all other nodes in the network) (Borsboom et al., 2021). This framework has been used to investigate relations among substance use disorder (SUD) symptoms (Rhemtulla et al., 2016;Rutten et al., 2021); to improve the understanding of links between symptoms of SUD and other mental health syndromes or personality traits (Afzali et al., 2020;Blondino and Prom-Wormley, 2022;López-Toro et al., 2022;Moriarity et al., 2021;van Buitenen et al., 2020;Wasil et al., 2020); to identify SUD symptoms associated with overdose (Ingram et al., 2022); to examine the potential impact of stress on SUD symptom connectivity (Lin et al., 2020); and to compare substance use patterns between clinical and population samples (Huth et al., 2022). Understanding the links between use of specific substance types and pandemic related stressors may generate testable hypotheses about potential points of intervention, which can facilitate development of targeted prevention and early intervention strategies for future pandemics.

In the context of the COVID-19 pandemic, pandemic-related thoughts (e.g., belief that threat is exaggerated), emotions (e.g., worry), and behaviors (e.g., compulsive checking), have been examined in network analyses which revealed links between alcohol use (including at-risk drinking behavior) and traumatic stress symptoms related to the pandemic as well as disregard for social distancing (Taylor et al., 2021). We aimed to expand on this work in several key ways. First, we examined cannabis and nicotine in addition to alcohol, given that the use of these substances often overlaps (Schlienz and Lee, 2018), and that changes in the use of one have been shown to impact changes in the use of the others (Allsop et al., 2014;Metrik et al., 2011). Second, our analysis included a wide range of pandemic impacts in addition to stress reactions. Lastly, we compared networks across two timepoints which allowed us to test whether relations among substance use and pandemic-related experiences were temporally stable. We expected there would be co-use of the three substances, and the network models were designed to test whether there were unique associations between specific substances and pandemic-related experiences. Given the exploratory nature of this analysis, we did not make specific hypotheses about the cross-sectional associations between substances and pandemic-related experience, or hypotheses about the temporal stability of relationships.

Material and methods

Study design and cohort

We used data from the COVID-Dynamic project (http://www.covidd ynamic.caltech.edu; Rusch et al., 2023; study preregistration http://osf. io/sb6qx), a longitudinal U.S. dataset of self-report measures and behavioral data collected in 16 waves from April 2020 to January 2021. Participant (N=1797) recruitment began on April 4, 2020 via the online platform Prolific (prolific.co) according to the following criteria: aged 18-100, fluent in English, current resident of one of the 50 U.S. states, previous participation in a minimum of 5 Prolific studies, and Prolific approval rating ≥98%. To ensure geographic diversity, recruitment was spread equally across the U.S. East Coast, Middle U.S., and U.S. West Coast (see Rusch et al., 2023 for details). To ensure diversity in participant age, a portion of the recruitment targeted individuals between the ages of 40 and 100. Each wave, participants were given 140 minutes to complete an hour-long survey (survey measure composition varied from wave to wave; see Rusch et al., 2023 for details). Participants were compensated at a rate of $10 per estimated hour of survey time as well as with regular pseudo-randomly distributed bonuses. Participants were excluded from the longitudinal study based on performance and commitment metrics (see Rusch et al., 2023 for a detailed characterization of excluded participants).

Our analyses focused on measures collected in July 2020 and January 2021 (waves 9 and 16 of the COVID-Dynamic project, henceforth referred to as T1 and T2, respectively). These were the only two timepoints that included the key assessments of interest to our research question. Inclusion criteria for this study were: 1) participated in at least one of the two timepoints and 2) had no more than one incorrect attention check. From the original sample of 1797 individuals that were recruited for the COVID-Dynamic project, 993 participated in the T1 assessment and 861 participated in the T2 assessment (N = 1123 unique participants after removing 14 that did not meet the attention check criterion). The percentage of missing data for the variables included in our network analyses ranged from 11.6% to 11.8% at T1 and from 23.3% to 25.5% at T2 (Table 1), which primarily reflects the proportion of individuals that did not participate in one of the two time-points. This human-subjects study was deemed exempt by the Institutional Review Boards of California Institute of Technology and Rutgers University.

Measures

The Monthly Substance Use Questionnaire was created for COVID-Dynamic to assess past 30 day substance-use frequency, where 0 = no use, 1= 1-2 days, 2 = 3-5 days, 3 = 6-9 days, 4 = 10-19 days, and 5 = 20-29 days (Rusch et al., 2023). In this study, the nicotine category reflects the highest response for cigarettes, e-cigarettes, tobacco from a water pipe, cigars/little cigars/cloves, or smokeless tobacco.

The Epidemic-Pandemic Impacts Inventory (EPII; http://www.phe nxtoolkit.org/toolkit_content/PDF/Grasso_EPII.pdf), is a 92-item inventory of pandemic-related experiences that was developed by an interdisciplinary team of experts (Grasso et al., 2021). Items were grouped across several domains: work impact (e.g., "reduced work hours or furloughed", "hard time making the transition to working from home"), home impact (e.g., "had to spend a lot more time taking care of a family member", "increase in verbal arguments or conflict with other adults in home"), social impact (e.g., "separated from family or close friends", "planned travel or vacations canceled"), economic impact (e.g., "unable to pay important bills like rent or utilities", "unable to get enough food or healthy food"), emotional health impact (e.g., "increase in mental health problems or symptoms", "increase in sleep problems or poor sleep quality"), physical health impact (e.g., "got less medical care than usual", "increase in health problems not related to this disease"), infection and isolation (e.g., "isolated or quarantined due to symptoms of this disease", "tested and currently have this disease"), and positive impact (e.g., "paid more attention to personal health", "increase in exercise or physical activity"). Summary results were generated by creating a sum score for each domain of impact composed of the items answered "yes" (self or person in household) (Table 1). Since our goal was to examine how these pandemic experiences were related to substance use reported in the Monthly Substance Use Questionnaire, we removed two items from the EPII that asked participants about substance use. Specifically, we removed "Increase in use of alcohol or substances" from the group of items related to emotional health, and we removed "Less use of alcohol or substances" from the group of items related to positive change.

Network analyses

Analyses were conducted in the open-source statistical software R version 4.2.1 (R Core Team, 2013); the full analytic code and output are provided in Appendix 1 of the Supplementary Material. Bayesian Gaussian graphical models (BGGM) were estimated with the BGGM package (Williams, 2021). We selected the BGGM package because it handles non-normal and zero-inflated data using a semi-parametric copula approach, uses multiple imputation to estimate the strength of each edge within a network, and estimates differences in edge strength across networks using unimputed data (Williams et al., 2020). This allowed us to 1) examine the links between substance use and pandemic-related experiences, and 2) test whether these associations were stable or different across time. Edges in the networks with 95% credible intervals (CI) that did not cross zero were considered significant. Networks were compared by subtracting posterior distributions of edge strengths across the two time points; edges were considered significantly different when the 95% CI of the estimated difference did not cross zero. Although all potential edges in the network were estimated, our focus was on the links connecting substance use to pandemic experiences. Given the number of potential edges, it is important to consider whether this approach is prone to identifying false edges (i.e., spurious associations). In extensive simulation studies that compared multiple network analytic methods , BGGM with 95% CIs had a precision of 0.94 (i.e., 94% of edges included in the estimated network also appeared in the true network) and a specificity of 0.95 (i.e., 95% of the edges that were not part of the true network were also excluded from the estimated network).

Post hoc analyses

In the review process, reviewers suggested controlling for demographic variables and testing associations in negative binomial regression models that treat variables as a count. To address these suggestions we re-analyzed each substance use outcome (alcohol, cannabis, and nicotine) across each of the two time-points in negative binomial regression models that included the other substances and the pandemicrelated experiences, and controlled for demographic variables that were self-reported in the baseline assessment of the parent study: age (in years), sex (female or male), Hispanic or Latino ethnicity (yes or no), race (binary 1/0 indicators for Asian, Black, multiracial, and White), education level (eight levels ranging from some high school to a doctorate degree), and a binary variable indicating whether the participant was in a committed relationship (including marriage) or not. Individuals coded 0 on all racial categories identified as American Indian or Alaskan Native, Native Hawaiian or Other Pacific Islander, or other; creating separate indicators for these categories was not possible because the sample sizes were too low. These analyses included all participants with complete data (965 at T1 and 834 at T2). The full analytic code and output of these analyses are provided in Appendix 2 of the Supplementary Material.

Results

Approximately half of participants were female (50.4%), with a mean age of 39.5 (SD = 13.9, range 18-77), and 52.2% reported being in a committed relationship (including marriage). The self-identified race of most participants was White (75.7%), followed by Asian (9.9%), Black (7.1%), multiracial (4.5%), other (2.2%), American Indian or Alaskan Native (0.4%), and Native Hawaiian or Other Pacific Islander (0.1%); 10.2% reported Hispanic or Latino ethnicity. Most participants reported that their highest level of education included a college degree: Associate's (10.6%), Bachelor's (34.5%), some graduate school (3.2%), Master's (13.3%), or doctorate (3.4%); the remainder reported some college (23.1%), a high school degree (11.1%), or some high school (0.9%). At T1, 50.0% reported no children living in the household, 23.5% reported living with children, and 26.4% had missing responses. Table 1 provides descriptive statistics of all the variables included in the network analyses. Across the two time points, there were no meaningful differences in the frequency of alcohol, cannabis, or nicotine use, or in the number of pandemic experiences reported across the different EPII domains. Fig. 1 shows the network plots across the two waves; here we report partial r and 95% CIs for the edges containing substance use nodes. While these values are generally small, they reflect the strength of the association across each node pair after controlling for all other nodes in the network. In the T1 network there were five significant edges connecting substance use nodes with pandemic experiences.  Note. Networks included 1123 participants with data in at least one of the two time points. The percentage of missing data ranged from 11.6% to 13.7%at T1 and from 23.3% to 25.5% at T2. Across both timepoints, there were a total of seven edges linking substance use and pandemic experiences; of these, three were significant across both networks. The Bayesian model comparison of model edges linking substance use to pandemic experiences all had 95% CI that included zero. This suggests that the links between substance use and pandemic experiences were not significantly different across time. In fact, when considering all edges in the networks, only the association between work and economic impact of the pandemic was significantly different (weaker) at T2 compared to T1, Δr = − 0.18 [− 0.34, − 0.01].

Supplementary Tables 1-6 contain results from the negative binomial regressions for each substance use outcome at each timepoint that contained the other substance use variables, the EPII variables, and adjusted for an additional nine demographic variables. Consistent with the network analyses at T1, social and work impacts were significantly associated with alcohol use, and home and social impacts were significantly associated with nicotine use (Supplementary Tables 1-3). Consistent with the network analyses at T2, emotional impacts were associated with cannabis use, economic impacts were associated with alcohol use, and social impacts were associated with nicotine use (Supplementary Tables 4-6). The remaining significant associations that were observed in the network analyses did not reach significance in the regression analyses; however, all estimates were in the same direction (i. e., positive or negative coefficients) across the two analytic approaches. Note that because these are negative binomial models, the coefficients reflect the predicted difference in log odds of the expected count in the response variable per unit change in the predictor variable (after controlling for all other variables in the model).

Discussion

Results from our Bayesian network analysis suggest associations among the use of alcohol, cannabis, and nicotine; additionally, each of these substance use variables showed distinct associations to negative impacts of the pandemic on emotional, social, economic, home, and work domains. Alcohol was the most frequently used substance in this sample, and endorsement of more types of social impacts was associated with greater alcohol use across both timepoints (this association was also significant at T1 in the negative binomial regression that controlled for an additional nine variables). This is consistent with a large systematic review that found social isolation was consistently linked with increases in alcohol use during the pandemic (Roberts et al., 2021). Although these cross-sectional network analyses cannot determine whether alcohol use exacerbated or was exacerbated by these pandemic experiences, they highlight the specificity of links between alcohol use and social consequences of the pandemic, which were present after controlling for all other pandemic experiences in the network. Alcohol use also showed a significant but negative association with economic impact across both timepoints (this association was also significant at T2 in the negative binomial regression that controlled for an additional nine variables). Items in this domain included inability to afford food, rent, utilities, and medications, which suggests that economic factors may have limited access to alcohol. Together, these findings are consistent with a systematic review that suggested two opposite patterns of consumption were likely operating during the pandemic: potential increase related to distress and decrease due to economic impact (Rehm et al., 2020).

Nicotine use was the second most frequently used substance in this sample and showed strong associations to both alcohol and cannabis use. However, in contrast to alcohol use, nicotine showed a negative association with social impact across both timepoints (these associations were also significant in the negative binomial regression that controlled for an additional nine variables). It is plausible that individuals that primarily smoked in social situations reduced their smoking if the pandemic impacted their social life and reduced contextual exposure to smoking-related cues (Jackson et al., 2021). At T2 there were higher rates of nicotine use among participants that reported more economic impact, which is consistent with research preceding the pandemic that showed that financial stress was associated with lower likelihood of quitting smoking, and higher likelihood of smoking relapse (Siahpush and Carlin, 2006). At T2, cannabis use showed a positive association with negative emotional experiences resulting from the pandemic (this association was also significant in the negative binomial regression that controlled for an additional nine variables). Although this association should not be interpreted causally, other studies have documented that reduction of psychological distress is frequently cited as the primary reason for cannabis use (Glodosky and Cuttler, 2020). Moreover, having a mental health condition was associated with nearly double the odds of increased use of medicinal cannabis during the pandemic (Vidot et al., 2021).

In contrast to the regression analyses, network analyses also provided estimates of associations between pandemic experiences. Although our goal was to examine links between pandemic experiences and substance use, several things stand out in the associations among pandemic impact domains. First, it is clear from Fig. 1 that there are many positive associations among pandemic variables, which indicates that the impact of the pandemic was highly distributed across the diverse domains measured. Interestingly, reporting more types of negative experiences across work, home, and social domains was associated with reporting more positive types of pandemic experiences, such as increasing physical activity and exercise, spending more time outdoors or engaging in hobbies, having time to cook to improve diet and nutrition, and spending more time with family and friends (even if virtually). This suggests that many individuals may have adapted to the negative pandemic impacts by adjusting behavioral lifestyle habits and engaging with social supporters.

The only edge in the network to show a significant change across the two timepoints was between economic and work impact, which showed a positive relationship at T1 that significantly reduced by T2. It should be noted that within the work domain, the measured impacts varied from layoff to difficulty adjusting to remote work; it is possible that as time progressed through the pandemic, the types of work-related impact that were reported were less likely to be associated with economic challenges.

Limitations

We note several limitations. An online survey platform was used to facilitate collection of data, given pandemic-related restrictions and safety protocols, which has the potential for biased responses (e.g., social desirability considerations around reporting of substance use; community sample, rather than population-based sample). Although the sample included participants with a wide age range and nearly equal representation between males and females, several racial and ethnic groups including Hispanic or Latino and Black were underrepresented. Additionally, the sample included participants from all 50 states in the US but given the international differences in pandemic-related policies, findings may not generalize to other countries. Although we report frequency of substance use over the past month assessment period, we did not document quantity of use; this is a study limitation. The inclusion of two timepoints allowed us to test the stability of the estimated networks, but given the subsequent Delta and Omicron waves, it is possible that these relationships continued to evolve throughout the remainder of the pandemic. Moreover, the associations in these crosssectional network analyses should not be interpreted as causal. Most (but not all) of the associations observed in the network model remained significant when we examined associations between pandemic variables and substance use using negative binomial regressions that controlled for an additional nine demographic variables. This suggests that some of the results were sensitive to distributional assumptions and/or to confounding related to the demographic covariates. It is also possible that the strength of associations between EPII variables and substance use is moderated by demographic variables. We did not explore this given the number of potential comparisons; however, future work can test targeted hypotheses about potential sex, age, race, and/or ethnic differences in the associations of the variables we examined. Finally, some findings (or lack of) may have been confounded by the fact that there are several items in the EPII home and work domains that are only applicable to individuals who have (and live with) children.

Conclusions

These network analyses suggest specific and nuanced relationships between pandemic-related emotional, economic, and social impacts and the use of alcohol, cannabis, and nicotine in a sample with a broad range of use patterns. Notably, more endorsement of emotional impacts was positively associated with cannabis use at the latter timepoint, whereas links with social impacts were positive or negative depending on the substance. Although findings are consistent with other studies showing some associations between the use of substances for coping with emotional impact and social isolation, further research is needed to investigate potential causal links.

Funding

This work was supported by grants from the National Institute of Mental Health (2P50MH094258), the Caltech Chen Neuroscience Institute, and the Caltech Merkin Institute (RA), by the Oscar M. Ruebhausen Fund at Yale Law School, the Rutgers Center of Alcohol & Substance Use Studies, the John Templeton Foundation and the Kay Family COVID-19 Rapid Response Research Awards at Chapman University. No other funding sources were declared. The funding sources had no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the article for publication.

CRediT authorship contribution statement

Declaration of Competing Interest

Alcohol use was positively associated with social impact, r = 0.17 [0.07, 0.23], and work impact, r = 0.07 [0.01, 0.18], and negatively associated with economic impact, r = − 0.15 [− 0.27, − 0.05]. Nicotine use was positively associated with home impact, r = 0.13 [0.03, 0.25] and negatively

associated with social impact, r = − 0.21 [− 0.28, − 0.07]. Nicotine use also had significant associations with alcohol use, r = 0.36 [0.22, 0.43], and with cannabis use, r = 0.32 [0.22, 0.45].In the T2 network, there were five significant edges connecting substance use nodes with pandemic experiences, all of which were also significant in the T1 network.Cannabis use was positively associated with emotional impact, r = 0.12 [0.002, 0.23]. Alcohol use was positively associated with social impact, r = 0.15 [0.05, 0.23], and negatively associated with economic impact, r = − 0.25 [− 0.35, − 0.10]. Nicotine use was positively associated with economic impact, r = 0.23 [0.06, 0.37], and negatively associated with social impact, r = − 0.11 [− 0.26, − 0.03]. Nicotine use also had significant associations with alcohol use, r = 0.28 [0.14, 0.37], and with cannabis use, r = 0.24 [0.14, 0.41]. Cannabis and alcohol use were also associated, r = 0.16 [0.05, 0.29].

Fig. 1 .
1Plots of the Bayesian Gaussian graphical network models. Note. Green nodes represent substance use variables, and blue nodes represent pandemic-related impacts. Solid green edges indicate positive associations, and dashed red edges indicate negative associations. Edge thickness conveys the strength of the pairwise association, after controlling for all other nodes in the network.

Table 1
1Descriptive statistics of variables included in the networks.Network node
T1 (July 2020)
T2 (January 2021)

Alcohol use (last month)
none
504 (44.9%)
447 (39.8%)
1-2 days
124 (11.0%)
118 (10.5%)
3-5 days
115 (10.2%)
91 (8.1%)
6-9 days
80 (7.1%)
55 (4.9%)
10-19 days
71 (6.3%)
68 (6.1%)
20-29 days
59 (5.3%)
40 (3.6%)
daily
39 (3.5%)
39 (3.5%)
missing
131 (11.7%)
265 (23.6%)
Cannabis use (last month)
none
826 (73.6%)
732 (65.2%)
1-2 days
33 (2.9%)
22 (2.0%)
3-5 days
25 (2.2%)
22 (2.0%)
6-9 days
16 (1.4%)
12 (1.1%)
10-19 days
14 (1.2%)
14 (1.2%)
20-29 days
23 (2.0%)
14 (1.2%)
daily
53 (4.7%)
45 (4.0%)
missing
133 (11.8%)
262 (23.3%)
Nicotine use (last month)
none
784 (69.8%)
693 (61.7%)
1-2 days
14 (1.2%)
13 (1.2%)
3-5 days
15 (1.3%)
6 (0.5%)
6-9 days
9 (0.8%)
8 (0.7%)
10-19 days
9 (0.8%)
8 (0.7%)
20-29 days
11 (1.0%)
8 (0.7%)
daily
127 (11.3%)
101 (9.0%)
missing
154 (13.7%)
286 (25.5%)
Work impact, range 0-8
mean (SD)
1.19 (1.34)
1.19 (1.38)
median [Q1, Q3]
1.00 [0.00, 2.00]
1.00 [0.00, 2.00]
Home impact, range 0-12
mean (SD)
0.79 (1.39)
0.79 (1.39)
median [Q1, Q3]
0.00 [0.00, 1.00]
0.00 [0.00, 1.00]
Social impact, range 0-10
mean (SD)
3 (2.04)
3.27 (2.23)
median [Q1, Q3]
3.00 [1.00, 5.00]
3.00 [1.00, 5.00]
Economic impact, range 0-5
mean (SD)
0.25 (0.66)
0.22 (0.6)
median [Q1, Q3]
0.00 [0.00, 0.00]
0.00 [0.00, 0.00]
Emotional impact, range 0-7
mean (SD)
1.69 (1.35)
1.69 (1.4)
median [Q1, Q3]
1.00 [1.00, 3.00]
1.00 [1.00, 3.00]
Physical impact, range 0-8
mean (SD)
2.04 (1.55)
2.17 (1.6)
median [Q1, Q3]
2.00 [1.00, 3.00]
2.00 [1.00, 3.00]
Infection and isolation, range 0-9
mean (SD)
0.9 (1.34)
1.23 (1.62)
median [Q1, Q3]
0.00 [0.00, 1.00]
1.00 [0.00, 2.00]
Positive impact, range 0-18
mean (SD)
5.58 (3.88)
5.5 (3.69)
median [Q1, Q3]
5.00 [2.00, 8.00]
5.00 [3.00, 8.00]

Santiago Papini :
PapiniConceptualization, Methodology, Software, Formal analysis, Validation, Visualization, Writingoriginal draft, Writingreview & editing. Teresa López-Castro: Conceptualization, Funding acquisition, Project administration, Writingreview & editing. Peggy Swarbrick: Conceptualization, Writingoriginal draft, Writingreview & editing. Lynn K. Paul: Data curation, Investigation, Writingoriginal draft, Writingreview & editing. Damian Stanley: Conceptualization, Data curation, Investigation, Methodology, Project administration, Validation, Writingreview & editing. Alexandria Bauer: Conceptualization, Writingoriginal draft, Writingreview & editing. Denise A. Hien: Conceptualization, Funding acquisition, Project administration, Resources, Writingoriginal draft, Writingreview & editing.
AcknowledgmentsWe thank the COVID-Dynamic team (http://coviddynamic.caltech. edu/investigators) for their contributions to the dataset used in this study, including specifically (alphabetical order): Yanting Han, Dehua Liang, Uri Maoz, and Tessa Rusch. We thank the COVID-Dynamic team (http://coviddynamic.caltech.edu/investigators) for their contributions to the dataset used in this study.Appendix A. Supporting informationSupplementary data associated with this article can be found in the online version at doi:10.1016/j.drugalcdep.2023.109929.
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Real-time understanding of lignocellulosic bioethanol fermentation by Raman spectroscopy

Shannon M Ewanick
Wesley J Thompson
Brian J Marquardt
Renata Bura
Real-time understanding of lignocellulosic bioethanol fermentation by Raman spectroscopy
R E S E A R C H Open Access
Background: A substantial barrier to commercialization of lignocellulosic ethanol production is a lack of process specific sensors and associated control strategies that are essential for economic viability. Current sensors and analytical techniques require lengthy offline analysis or are easily fouled in situ. Raman spectroscopy has the potential to continuously monitor fermentation reactants and products, maximizing efficiency and allowing for improved process control.Results: In this paper we show that glucose and ethanol in a lignocellulosic fermentation can be accurately monitored by a 785 nm Raman spectroscopy instrument and novel immersion probe, even in the presence of an elevated background thought to be caused by lignin-derived compounds. Chemometric techniques were used to reduce the background before generating calibration models for glucose and ethanol concentration. The models show very good correlation between the real-time Raman spectra and the offline HPLC validation. Conclusions: Our results show that the changing ethanol and glucose concentrations during lignocellulosic fermentation processes can be monitored in real-time, allowing for optimization and control of large scale bioconversion processes.

Background

The growing bioethanol industry produced 22.3 billion gallons of ethanol worldwide in 2011 [1] and is replacing many non-renewable products with products derived from biomass. The cost to produce many of these products, however, is still not competitive with petroleum-derived counterparts. Processes to produce fuels and chemicals from petroleum have a wealth of online analytical sensors that permit them to operate at or near capacity with optimal process yields. This hyper efficiency is a necessary condition for profitability in producing high volume, narrow-profit margin products such as fuels and some commodity chemicals. The need for process efficiencyand hence the need for online sensorsis especially acute in biomass fed biorefineries due to the complexity and expense of the feedstock. Development of robust sensors for lignocellulosic biorefineries is as critical as the research that has gone into developing the processes themselves, but has received little or no attention. Process improvements (feedstock pretreatment, microorganisms, enzymes, etc.) will likely reduce costs in the future, but in both the short and long term, improving the efficiency of existing operations will have the greatest effect on overall process economics.

In a typical ethanol production process, raw biomass is first pretreated, then saccharified, fermented and purified. The liquid fraction following acidic pretreatment and saccharification is high in soluble lignin, phenolics, sugar degradation products (e.g. furfural) and monomeric and oligomeric hemicellulosic sugars. Monomeric sugars are fermented using microorganisms that primarily produce ethanol, so both high and low concentrations of sugar and ethanol must be monitored over the course of fermentation in order to ensure that the fermentation is proceeding optimally. Such a diverse mix of compounds can pose a challenge to current analytical methods; high performance liquid chromatography (HPLC) with refractive index detection is one of the only methods currently in use that can measure ethanol and carbohydrates simultaneously in the presence of the aforementioned compounds. Although capable of very high sensitivity, good separation and quantification of multiple component mixtures, sample preparation and analysis can be time consuming, costly and not suitable for a process environment. As such, HPLC is usually limited to offline analysis of samples, precluding its use for real-time, continuous analysis. Spectroscopic methods have the potential to rapidly and nondestructively analyze multiple components of a reaction mixture. Raman spectroscopy in particular is an established vibrational spectroscopy technique useful for determining both qualitative and quantitative molecular information from almost any type of sample (e.g. solid, liquid or gas) [2,3]. A Raman spectrum is obtained by exciting a sample with a laser and measuring the inelastic scattering of photons from the vibrations within the molecules. Raman spectroscopy has been used successfully to measure ethanol alone during fermentations [4][5][6], but these techniques have as yet not been fully utilized to provide on-line, real time measurements of lignocellulose-derived materials.

Our objective in this research was to evaluate the possibility of real-time, continuous lignocellulosic fermentation monitoring using Raman spectroscopy. We monitored the progress of fermentation of both a synthetic fermentation broth containing glucose and a steam-pretreated switchgrass hydrolysate in a controlled bioreactor using a novel Raman immersion probe inserted in a fast loop parallel sampling system. Chemometric analysis of the reactants and products was done using Principal Component Analysis (PCA) of the Raman spectra and a Partial Least Squares (PLS) model was developed and validated using HPLC data.

Results

Glucose fermentation

To first evaluate the effectiveness of the Raman probe under ideal fermentation conditions, a glucose solution was fermented using Saccharomyces cerevisiae. Although fermentation of glucose to ethanol is typically carried out as a batch process, a stepwise fed-batch experiment was run to follow the product consumption and formation rates more clearly over short periods of time. The experiment began with a glucose concentration of 5 g/L and additional aliquots of glucose were added at regular intervals when ethanol production had ceased to increase (determined by monitoring the intensity of the ethanol Raman peak at 883 cm -1 in real time) until a total of 25 g/L had been added. HPLC validation samples for determination of ethanol and glucose were withdrawn from the vessel at 10-15 minute intervals and Raman spectra were measured automatically every 30 seconds. Figure 1 (inset) shows the full Raman spectrum of the reaction mixture. The region from 350-1800 cm -1 shows an increased background due to water and some evidence of cosmic ray interference. To mitigate these effects and evaluate the elements of the spectra changing over time it was necessary to process the data with in-house data  Figure 1 Raw spectra (inset) were pretreated with a polynomial fitting routine to reduce the elevated background and a cosmic ray removal algorithm to remove spurious peaks caused by the high energy particles from the sun. In the pretreated spectra, the ethanol peak can be easily seen at 883 cm -1 .

pretreatment algorithms. The background water spectrum removal algorithm is a modified polyfit algorithm [7], applying a moving window to the polynomial subtraction routine that reduces the effects of baseline shifts and fluctuating spectral background. Data were also processed using a cosmic ray removal filter [8] which compares each spectrum to the ones preceding and succeeding it, identifying the transient cosmic spikes and removing them. The remainder of Figure 1 shows the region of interest after all spectral pretreatment algorithms. These data were used for the development of multivariate models. Principal Component Analysis (PCA) of the Raman spectroscopy data determined two components comprised 89% of the variance in the data and the scores of these components correlated well to the HPLC concentration data of ethanol on the first principal component (PC) and glucose on the second PC ( Figure 2).

While simple monitoring of the fermentation rates provides some information, determining the actual concentration of the reactants is essential to compare the process to past and future processes. The spectroscopic data were evaluated by Partial Least Squares (PLS) using the HPLC results as the calibration concentration data set ( Figure 3). The data were conditioned with Orthogonal Signal Correction (OSC) and mean centering to mitigate any non-relevant variation in the models. The PLS models were cross validated by removing random prediction subsets. Cross validation provides a means to evaluate the performance of the models by removing a subset of the data, generating a model from the remaining data and applying the subset as a test set. The Root Mean Square Error of Cross Validation (RMSECV) defines the model's ability to accurately predict the test set samples. The models correlated well with the HPLC data over the full range of concentrations (0.1 -11 g/Lethanol and 0.1-5.5 g/Lglucose), and a standard limit of detection as defined as 2x the RMSECV allows quantification of 1 g/L and above for either ethanol or glucose (Table 1). These results indicate that even with the varied rates of glucose uptake and ethanol production, the reaction components were detected and followed over the course of the reaction.

Lignocellulosic hydrolysate fermentation

The initial experiments demonstrated that Raman spectroscopy could identify glucose and ethanol within the spectrum of a synthetic fermentation broth and monitor the process. The same sampling, spectral preprocessing, modelling and data analysis techniques were then applied to the fermentation of steam-exploded switchgrass hydrolysate; a dark brown solution produced by the reaction of switchgrass for a short time under high heat and pressure in the presence of SO 2 [9]. The hydrolysate is high in lignin and sugar degradation products, as well as monomeric and oligomeric carbohydrates from cellulose and hemicellulose. The hydrolysate was fermented in the same step-wise fashion as the synthetic fermentation. The concentration of monomeric glucose in the hydrolysate was relatively low (1.5 g/L), so additional glucose (3.5 g/L) was added at the beginning and 5 g/L of glucose added at regular intervals. Fermentation of each added amount of glucose proceeded until the glucose was consumed and ethanol production had ceased to increase as determined by monitoring the intensity of the ethanol Raman peak at 883 cm -1 in real time and verified by offline HPLC analysis.  As expected, the switchgrass hydrolysate spectra exhibited a highly elevated spectral background compared to the synthetic fermentation broth, presumably due to the presence of fluorescent lignin-derived compounds (Figure 4, inset). The modified polyfit and cosmic ray removal algorithms greatly reduced the spectral background, illustrating that the spectra were reproducible and the ethanol peak at 883 cm -1 was readily apparent (Figure 4). Compared to the spectra of the synthetic broth, these spectra have visibly increased noise due to the heteroscedastic nature of the noise remaining after spectral pretreatment. Future work will focus on the reduction of background signal to improve our signal to noise ratio and modelling ability.

The hydrolysate fermentation spectra correlated well with the offline HPLC analysis for ethanol ( Figure 5). The ethanol peak at 883 cm -1 is visually distinct from the baseline and models well even in the presence of an elevated spectral background, with a RMSECV only 0.2 g/L lower than the synthetic fermentation broth ( Table 1). The concentration of glucose was more difficult to predict after the background pretreatment. The low concentration of glucose in our fermentation, combined with a high loading of yeast cells, yielded a glucose concentration that rapidly decreased culminating in calibration HPLC data below our current spectroscopic limit of detection (LOD). The synthetic fermentation glucose models had a limit of detection of about 1 g/L. Many of the calibration data for glucose in the hydrolysate fermentation fell below 0.03 g/L; removing these data provided a more robust model for glucose in the hydrolysate fermentation however this left only 19 reference points for modelling. Additional reference points may increase the robustness of the models; however, more important is reducing the spectral background signal in order to improve the signal-to-noise ratio and reduce prediction errors.

We have demonstrated quantitative models for following ethanol production during fermentation and while these models show promising results with a low concentration system, further study is necessary to alleviate the background effects that inhibit monitoring glucose consumption. The glucose concentration in a typical fermentation broth following hydrolysis of polymeric and oligomeric carbohydrates is 20-50 g/L, much higher than the starting glucose concentration in our stepwise fed batch hydrolysate fermentation. In the batch fermentation, the initial glucose was rapidly consumed resulting in low glucose concentrations during much of the fermentation. A realistic, large scale fermentation, however, would have a much larger range of concentrations of glucose which would improve the results from our real time analysis. Ethanol Figure 4 Raw spectra from the hydrolysate fermentation (inset) were treated similarly to the synthetic fermentation data to remove the elevated background. Noise is intensified in the pretreated spectra due to the greater intensity of the background signal and the heteroscedastic nature of the noise.

Discussion

Despite widespread use in other areas, Raman spectroscopy has not yet been utilized extensively for continuous analysis of fermentation of lignocellulosic-derived materials. However, small scale ethanol fermentations have been measured by Raman spectroscopy, both through periodic removal and measurement of samples [10] and continuous monitoring of nano-scale reactions with in situ measurement [5]. In addition, Shih et al [11,12] used a 785 nm Raman microscope to measure offline aliquots of both ethanol and sugar from enzymatic hydrolysis and fermentation of pretreated corn stover. Multiple sugars and ethanol were measured simultaneously, but high background fluorescence in the spectra was problematic. Attempts to decrease the spectral background and increase the detection limits by extraction of the biomass prior to sample pretreatment with solvents (ethanol, hexane or water) were successful in reducing the LOD for glucose from 20 g/L to 4 g/L, but such treatments are impractical on a larger scale [11]. An elevated spectral background signal is a persistent issue when dealing with lignocellulosic biomass fermentation processeslignin is made up of highly conjugated phenolic groups [13], which can lead to an elevated background signal in the same spectral region as the compounds of interest, potentially masking the Raman features of the spectra [14]. Analysis of ethanol and glucose has also been conducted non-spectroscopically in a number of ways in order to eliminate the need for manual sampling and the associated delay in data procurement. Sequential injection analysis (SIA) with enzyme or amperometric detection [15,16] can measure both ethanol and glucose in solution. Indirect monitoring of fermentation progress by measurement of headspace CO 2 [17] and electrochemical detection of ethanol by microelectrode array [18] provide information about the progress of the reaction, but cannot pinpoint a cause if the reaction deviates from normal conditions. These methods are an improvement over offline HPLC methods, but still cannot provide information in real time.  Figure 5 Partial Least Squares models generated from hydrolysate fermentation data. Ethanol (top) and glucose (bottom) data were pretreated similarly to previous models. For the glucose model, data below 0.03 g/L were assumed to be below the limit of detection and removed.

The advantages of online Raman spectroscopy over other methods lie mainly in the speed of analysis and the reduction of user interaction. The Raman spectroscopy method was capable of collecting a full spectrum every 30 seconds, from which both ethanol and glucose concentrations could be determined within seconds using multivariate control models. Following the quantities of ethanol and glucose in near real-time provides insight regarding fermentation performance and allows for control decisions to be made in time to affect the quality of the product being formed in the bioprocess.

Many reactions are run for a set period of time depending on their initial sugar concentration, yeast loading, temperature, etc. These parameters are determined based on theoretical values and previous experiments. However, other factors may influence the fermentation rate and so often fermentation processes are run longer than necessary to ensure completion. Raman spectroscopy allows the user to determine if the cell loading was sufficient, detect possible contamination, determine the rate of fermentation and see exactly when the fermentation has completed. In addition, process upsets or problems can be spotted early, reducing costs and increasing efficiency.

Conclusions

Fermentation of both a synthetic fermentation broth and a lignocellulosic hydrolysate was measured continuously by 785 nm Raman spectroscopy. Despite an elevated background present in the lignocellulosic hydrolysate, effective data pretreatment methods allowed for measurement of ethanol and glucose over the course of the reaction. These results show that Raman spectroscopy has the potential to be an effective tool to improve the efficiency of existing bioconversion processes. With precision sensors continuously monitoring large scale reactions, time and resources can be conserved to help ensure economic sustainability of biomass-based biorefineries in the long term.

Methods

Steam-pretreated switchgrass hydrolysate

The liquid hydrolysate was prepared as described by Ewanick and Bura [9]. Briefly, four 50 g aliquots of SO 2impregnated switchgrass were sequentially steam-exploded using a 1.5 L batch steam gun (HM 3 Energy Inc, Gresham OR) at 195°C for 7.5 minutes. The resulting slurry, at 14% consistency, was separated by vacuum filtration into a liquid fraction (used in this study) and solid fraction and stored at 4°C.

Fermentation

Saccharomyces cerevisiae ATCC 96581 isolated from spent sulphite liquor [19] (obtained from ATCC) was streaked onto YPD agar plates and allowed to grow for 48 hours. Prior to fermentation, pre-culture cells were grown by adding one colony from the plate to liquid media containing 10 g/L each of glucose, yeast extract and peptone. After 24 hours of growth at 30°C and 150 rpm orbital shaking, the cells were centrifuged and the spent supernatant removed and replaced with fresh media. The cells were then grown for another 24 hours under the same conditions; the cells were again spun down, washed twice in deionized water, and then resuspended in a small volume of 0.9% sodium chloride. Cell concentration was determined by measuring the optical density of the suspension at 600 nm and comparing to a calibration curve prepared using oven dried cells at varying optical densities.

Synthetic fermentation broth solutions, as well as the steam-pretreated hydrolysate were adjusted to pH 6 using dilute NaOH. Nutrients in the form of ammonium phosphate (2 g/L), sodium sulfate (0.2 g/L) and sodium nitrate (2 g/L) were added and the solution was heated to 30°C in a 1.3 L New Brunswick Scientific BioFlo 115 bioreactor equipped with a water jacket, exhaust condenser and pH probe. The pH was monitored and maintained at pH 6 for the duration of the fermentation with 1 M HCl and 2 M NaOH. The total solution volume was 800 mL with a cell concentration of 5 g/L and the mixture was stirred continuously with a Rushton impellor at 400 rpm. The initial glucose concentration was 5 g/L at time zero, and further 4 g aliquots of glucose were added when the Raman ethanol peak at 883 cm -1 reached equilibrium, roughly every 90 minutes. One milliliter samples were removed every 10-15 minutes for HPLC analysis.

Raman data collection and analysis

Real-time analysis data were collected using a RamanRxn1 instrument (Kaiser Optical Systems, Ann Arbor, MI). The excitation wavelength was 785nm with a power at the sample of 250mW. Spectra were collected as an average of six, five-second exposures resulting in a collection time of 30 seconds per spectrum. A ballprobe immersion optic (Matrix Solutions, WA) was used for collection of the spectroscopic data. The spherical lens of the ballprobe collects the signal from a small volume very close to the ball surface, providing a constant focal length and greatly enhanced measurement precision. The spherical tip of the probe causes high shear forces as the reaction liquid circulates in the sampling system, preventing accumulation of cells or debris on the probe surface.

A custom sampling loop system that rapidly pumped the fermentation broth out of the fermenter, past the probe and back into the vessel was used with the ballprobe to reduce the possibility of fouling and improve the sampling reproducibility of the fermentation. The slightly increased pressure generated in the sampling loop maintains gases in solution, thus preventing CO 2 bubbles produced during fermentation from interacting with the excitation laser and potentially causing erroneous data points. The sampling loop was designed using NeSSI (New Sampling/Sensor Initiative) sampling blocks that provide a simplified flow path past the ballprobe. NeSSI defines a standard physical format (ANSI/ISA SP76.00.02) to simplify development and installation, and reduce the size of fluid handling systems. The fast loop system was developed using Parker Intraflow (Cleveland, OH) substrates and top mount components and had a volume of approximately 10 ml. The fermentation broth was pumped through the fast loop at 500 mL/min to ensure a rapid sample turnover in the fermenter.

HPLC analysis

Ethanol and glucose were measured using refractive index detection on a Shimadzu Prominence LC. Samples were diluted as appropriate, filtered through 0.22 μm syringe filters and 20 μL of sample were injected onto a Phenomenex Rezex RHM H + column at 63°C with an isocratic mobile phase elution of 0.05 mM H 2 SO 4 at 0.6 ml/min. Standards were prepared and used to quantify the unknown samples.

Data analysis

Data models were created and analyzed using Matlab (TheMathWorks, MA) and the PLS_Toolbox (Eigenvector Research, Inc., WA).

Figure 2
2There is a strong correlation of the scores data to the reference ethanol and glucose concentrations from HPLC. The blue lines show the concentration of the analytes by HPLC while the green shows the principal component score of the Raman data. Ethanol correlates to PC1 (top) while glucose can be seen on PC2 (bottom). The red lines indicate added aliquots of glucose.

Figure 3
3Partial Least Squares models from the glucose fermentation. Ethanol (top) and glucose (bottom) models were pretreated with orthogonal signal correction and cross validated using random subsets.

Table 1
1Prediction model data for both glucose and switchgrass hydrolysate fermentation modelsR 2
RMSEC
RMSECV

Glucose Fermentation
Ethanol 0.984 0.010942 0.40995

Glucose 0.920 0.32228
0.5335

Hydrolysate Fermentation Ethanol 0.935 0.2009
0.60326

Glucose 0.513 0.20828
1.0614

http://www.biotechnologyforbiofuels.com/content/6/1/28
AcknowledgementsWe would like to thank the Center for Process Analysis and Control (CPAC) and the Denman Professorship in Bioresource Science and Engineering both at the University of Washington for funding and HM 3 Energy in Gresham OR for the use of their steam gun. The authors would also like to thank Drs. Sergey Mozharov and Thomas Dearing for their insights and useful comments regarding Raman spectroscopy and data analysis.Competing interestsThe authors declare that they have no competing interests.Authors' contributions SME performed fermentation and data analysis and participated in drafting the manuscript. WJT performed Raman data collection and analysis and participated in drafting the manuscript. RB and BJM conceived experiments and participated in drafting the manuscript. All authors read and approved the final manuscript.Submit your next manuscript to BioMed
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Real-time understanding of lignocellulosic bioethanol fermentation by Raman spectroscopy. Ewanick, Biotechnology for Biofuels. 628Cite this article asCite this article as: Ewanick et al.: Real-time understanding of lignocellulosic bioethanol fermentation by Raman spectroscopy. Biotechnology for Biofuels 2013 6:28.