County-level interventions to reduce drug overdose rates and alcohol-impaired deaths
Introduction
In 2022, 46.8 million Americans who were twelve years of age or older struggled with a drug use disorder. Eight million Americans aged twelve and older battled alcohol and drug use issues at the same time that year. Use of drugs and alcohol has substantial financial consequences. Every year, excessive alcohol use costs 249 billion dollars in lost productivity, medical expenditures, and criminal justice costs. In contrast, illicit drug usage costs 193 billion dollars in lost productivity, criminality, and medical costs. Communities can benefit from adopting and putting into practice policies that lessen prescription drug abuse and excessive alcohol consumption.
Research Question: Is there demographic and social factors that are predictors of drug overdose and alcohol-related incidents (e.g., driving accidents)?
Hypothesis: The chances of drug overdoses and alcohol-related driving incidents can be predicted by demographic factors like age, gender, and race as well as social factors like substance use habits, education, socioeconomic status.
Certain demographic variables—like age, gender, and race—have a major impact on the risk of drug overdoses and incidents involving alcohol and driving. For example, middle-aged people may be at risk from long-term substance use, while younger people may be at risk from peer pressure and experimentation. Another important factor is gender: it’s possible that men experience higher rates of substance abuse and related incidents than women do, possibly as a result of differing social behaviors and stress reactions. Differences in socioeconomic status, community support, and access to healthcare can all contribute to racial disparities. Social factors hold equal significance. Substance use patterns, such as the kind and quantity of drugs or alcohol consumed, are strong indicators of incidents linked to substance use. Furthermore, access to treatment or preventive resources, as well as patterns of substance use, can be influenced by socioeconomic status, which includes factors like education, work status, and income level. Tailored interventions can be devised through the identification and comprehension of these demographic and social variables. By customizing these interventions to target particular high-risk populations, their effectiveness can be increased and the population’s overall rate of drug overdoses and alcohol-related driving accidents can be decreased.
Data
The data is collected by the University of Wisconsin Population Health Institute and includes health, clinical, social/economic, and demographic variables measured at the county level. The two response variables we are modeling are drug overdose rates and alcohol-impaired driving deaths. Drug overdose rates measure number of drug poisoning deaths per 100,000 from 2019-2021. Alcohol-impaired driving deaths measure the percentage of driving deaths with alcohol involvement from 2017-2021.
We considered the following social-economic and demographic variables:
Unemployment: Compared to the employed population, the unemployed population excessively drinks and do drugs more to cope with the stress from their financial situation.
Median Income/Income Inequality: Higher income levels often correlate with better access to resources such as healthcare, education, and social services, which might reduce risky behaviors like excessive alcohol consumption and substance abuse. In areas of high income inequality, illegal drugs may be more readily available especially in economically distressed neighborhoods where the drug trade may flourish due to lack of economic opportunities.
Disconnected Youth: Disconnected youth is defined as the percentage of teens and young adults (ages 16-19) who are neighter working nor in school. Disconnected youth are more likely to smoke, drink, and use marijuana. They are also more prone to having mental health disorders
High School Graduation: A higher level of education is linked to a lower likelihood of smoking. Adults with higher levels of education typically have more employment opportunities and make more money overall.
Social Associations: Reduced participation in communal life and little social interaction with others are linked to higher rates of illness and early death. Compared to those with strong networks, those without strong social networks are less likely to choose healthy lifestyles.
% Female: Historically, men have been more likely to drive under the influence of alcohol. Men are also more likely to die from drug overdoses; however, there are increasing rates of drug overdose deaths among women.
Intial EDA
Looking at the states with respect to drug overdose deaths and alcohol-impaired driving deaths, the areas with the most issues can be identified, and solutions can eventually be developed.
In Figure 1, West Virginia has the highest amount of drug overdose deaths. By conducting further research on West Virginia, it was found that the state leads the nation in opioid-related drug overdose deaths. According to the Drug Enforcement Administration, West Virginia has one of the highest prescription rates for opioids in the country and also has a high prevalence of controlled prescription drug addiction and trafficking. Furthermore, the usage of illegal prescription drugs was a factor in almost 61% of state overdose deaths in 2015.
In Figure 2, alcohol-impaired driving deaths, Montana had the highest amount of deaths resulting from alcohol-impaired driving. According to Forbes, Montana has the highest rate of drunk driving, with 8.57 fatal crashes involving drunk drivers for every 100,000 licensed drivers and 7.14 fatal crashes involving drunk drivers for every 100,000 state population. These rates are the highest in the country. Montana has the highest percentage of drunk drivers in the country—more than two-fifths (43.51%) of road fatalities.
By analyzing the predictors of both drug overdose deaths and alcohol-impaired driving deaths, solutions and regulations can hopefully be developed to help these states with their ongoing problems.
Figure 3, which is broken down by income level, shows the correlation between adult smoking rates and the number of drug overdose deaths per 100,000 people. The trend line shows a positive association, implying that there is a connection between an increase in drug overdose deaths and rising smoking rates. Dark red areas are low-income areas, which tend to cluster in the higher ranges of both smoking rates and overdose deaths. High-income areas, indicated in dark blue, on the other hand, typically have lower smoking rates and fewer overdose deaths.
Methods
Drug Overdose Rates
To model drug overdose rates, we used mixed effects model with random intercept to account for variability across geographical regions. We hypothesized that counties within the same region (e.g. state) are correlated–overdose death rates could vary by state due to differences in locally-regulated healthcare systems, law enforcement involving drugs, drug accessibility (variables not examined in the analysis). For example, West Virginia has by far the highest drug overdose deaths with opioid accounting for 83% of all drug overdose deaths in 2021. This is higher than the state average opioid overdose deaths accounting for 75% of drug overdose deaths. Uncoincidentally, this is attributed to West Virginia having the highest opioid prescription rate. This baseline variability could also be attributed to regional differences (across the US and within state) in specific drug availability and use.
Since counties could be correlated within different geographic region levels, we tested various mixed effects models with a random intercept term for:
1. Urbanization level (three levels)– CDC found that urban counties generally had higher drug overdoses due to higher population density and easier access to drugs from the greater number of distributors and markets in the cities.
2. Public Health regions as defined by the U.S. Department of Health and Human Services– Counties in these regions may vary in drug overdoses as different public health initiatives are implemented based on the specific needs of the territories/states within each region.
3. The 50 states–for reasons mentioned above.
We assumed that the effect of social and demographic predictors on drug overdose is constant, making the predictors fixed effects.
We included models with only fixed effects as controls to determine if random intercepts are even necessary. We used AIC–an approximation of leave-one-out cross validation–for model selection. The model is fit to n-1 observations to predict the response of one observation. This is done n times in which a different observation is left out from the training set each time.
Ultimately, the model with random intercepts for states has the lowest AIC and therefore the best fit.
df AIC
fixed_no_urban 11 15717.90
fixed_urban 12 15719.89
random_urban 12 15685.25
random_region 13 15412.36
random_state 13 15073.35However, there is a cone shape pattern which indicates heteroscedastic.
To resolve this, we applied a log transformation on Drug Overdose Deaths.
Alcohol-impaired Deaths
To model alcohol-impaired deaths rate, we decided on a linear regression model and variations of linear regression–Ridge, Lasso, and Elastic Net to assist with feature selection. Ridge and Lasso regression are methods that reduce multicollinearity by imposing regularization penalties. Ridge is more conservative as it reduces the magnitude of coefficients by shrinking coefficients to near zero, while retaining all variables. Lasso on the other hand penalizes the model more by shrinking coefficients to exactly zero which removes unimportant variables from the model, making it extremely effective in reducing overfitting. Lasso Elastic Net is a balanced approach to variable selection and regularization that incorporates the strengths of both Ridge and Lasso. Finally, Random Forest, an ensemble method, is a potent instrument for managing intricate datasets due to its ability to capture non-linear relationships and interactions between variables, which provides both flexibility and robustness.
We used RMSE to determine the best model for alcohol-impaired driving deaths. To ensure that the model performance is not dependent on the train-test division of the dataset, we used a 5-fold cross validation that averages RMSE across the five folds.
We chose the Linear model for predicting alcohol-impaired driving deaths because it demonstrated the lowest and most consistent RMSE values, as shown in Figure 5. However, given the overlapping error margins with other models, one could argue for the use of alternative methods, as no single model shows a clear advantage.
Results
Predictors of Drug Overdose Rates
Looking at the fixed effects plot below, it is evident that high county-level income inequality ratios is associated with high drug overdose deaths. Fatal drug overdoses are more common among areas with more economic distress which explains why a greater income inequality ratio is attributed to higher drug overdoses: if the income inequality ratio increases by 1, drug overdoses increase by 1.12 deaths per 100,000 (assuming the average drug overdose is 25 deaths/100,000) which is a 4.5% increase.
% female, surprisingly, is also associated with drug overdose. While males historically have had higher rates of drug overdose deaths than females, the model shows since 2019, counties with higher female populations have higher drug overdose rates. If a county’s female population increases by 1%, drug overdoses (per 100,000) is expected to increase by 2.4% or about 1 death /166,000. This jump might be explained by a 3-fold increase in overdose mortality from 2018-2021 for pregnant and postpartum women in 2018-2021. Dr. Nora Volkow, director of the National Institute on Drug Abuse, attributed this to the fact that drug use is even more stigmatized for pregnant women, making them less likely to seek or receive help for dependence on opioids and other drugs.
Suicide rate has a weaker association compared to the other two–increasing suicide rate by 1 per 100,000 results in 1.81% increase in drug overdoses or 1 death per 200,000.
Each $10,000 increase in median house income is associated with a drug overdose decrease by 3.045% or decrease in 1 death per 115,000. This is in line with previous studies that found a link between low socioeconomic status and long-term opioid use for management of chronic pain and conversely, people with higher education level and income are more likely to accept costlier, non-pharmacological treatments like physical therapy.
Membership organization have less of an effect–increasing membership organizations (i.e. bowling centers, golf clubs, fitness centers, religious organizations) per 100,000 decreases drug overdoses by 1.64% or 1 death per 200,000. Number of membership associations can be a metric for social support networks, indicating that counties with more opportunities for community building could decrease drug overdoses.
Rural counties have slightly fewer drug overdoses than urban counties with 5.28% fewer drug overdoses or 1.3 fewer drug overdoses per 100,000 (using the mean 25 drug overdose deaths/100,000). This is a very marginal difference.
Percentage of high school completion, adult smoking rate, median house income are highly correlated with each other.
Variability in Drug Overdose Rates Not Accounted for by Predictors
Ignoring the effect of predictors on drug overdose, we consider the baseline drug overdoses for each state indicated by the random intercepts to see if there are external factors contributing to drug overdose rates. West Virginia has by far the highest drug overdose rate which aligns with the state statistic in having the highest opioid prescription rate in the United States. This is largely attributed to the high number of heavy manual labor professions like mining and timbering which often cause injuries to workers. Delaware’s high drug overdose rate is also primarily related to the opioid crisis and high opioid prescription rate—opioid overdose accounted for 88% of the state’s drug overdose deaths in 2021. In Delaware and its surrounding states, new drugs and combinations are frequently used in conjunction with opioids, making traditional treatment options not as effective. For example, 90% of opioid street samples were found to contain xylazine which does not respond to the opioid overdose reversal drug naloxone. More immediate solutions are needed to combat the constantly evolving usage of opioid adulterants.
Predictors of Alcohol-Impaired Deaths
Unemployment is the top predictor for alcohol impaired driving deaths. Holding all other variables constant, if unemployment rate increases by 1%, alcohol-impaired driving deaths is projected to increase by 1.3%. Another significant predictor is % of high school completion or percentage of adults ages 25 and over with high school diploma. If high school completion increases by 1%, alcohol-impaired driving deaths is projected to decrease by 0.2%. In other words if high school completion increases by 5%, alcohol-impaired driving deaths will decrease by 1%.
Discussion
Recommendations for Drug Overdose Rates
1. Limit prescription of drugs for low income communities and introduce alternatives
Prescription drug monitoring programs (PDMP) programs have already been implemented in many states for pharmacies and providers to communicate about patients’ previous and current prescriptions to prevent patients from getting multiple prescriptions at the same time. Socioeconomic data could be additionally integrated into PDMPs, allowing providers to identify patients from low-income counties who may be at higher risk for long-term opioid use and dependence. By understanding these contextual factors, physicians can make more informed decisions and adopt a more conservative approach to prescribing opioids for these populations. Counties might consider increasing funding to provide non-pharmacological treatments such as therapy for low-income communities.
2. Implement tailored interventions for pregnant and postpartum women dealing with drug abuse
Efforts should be made to destigmatize drug use among pregnant women and reform punitive policies that discourage them from seeking help. Counties might consider referring pregnant and postpartum women with substance use disorders to specialized treatment and support services rather than the criminal justice system. These programs would provide access to addiction treatment, mental health counseling, and prenatal care. Additionally, policies could require pregnant women to join peer support/counseling groups that bring other pregnant women dealing with drug abuse together to normalize and destigmatize being pregnant while battling drug addiction.
Counties should enact policies that protect the parental rights of women undergoing treatment for substance use disorders, ensuring that they are not automatically at risk of losing custody of their children solely due to their substance use history. Child welfare services should focus on family preservation and support, rather than separation, when safe and appropriate.
Counties can provide pregnant women access to supportive housing programs that allow them to maintain custody of their children while receiving treatment. These programs can offer on-site childcare, parenting classes, and family therapy to strengthen family units.
3. Fund and support community-based programs
County legislation can allocate funds or provide grants to community organizations that run support groups, mentorship programs, or outreach efforts targeting at-risk populations. This funding could be used to create new programs or expand existing ones.
Counties can also Invest in the development or expansion of community centers that offer safe spaces for social interaction, peer support groups, and educational workshops on substance abuse prevention and recovery.
4. Closely monitor opioid prescription rate and opioid adulterants in West Virginia and neighboring states.
From a legislative standpoint, legislators should focus on creating flexible regulatory frameworks that allow for the rapid scheduling and control of new substances as they enter the market. This might involve granting agencies like the DEA the authority to temporarily classify emerging drugs under controlled substance categories, pending further evaluation. Counties can implement real-time surveillance systems to track the presence and spread of opioid adulterants within the local drug supply. This can involve collaboration with local hospitals, law enforcement, and public health agencies to collect and analyze data on overdose cases and drug testing results.
Counties can launch public education campaigns to inform residents about the dangers of opioid adulterants, including the risks associated with substances like xylazine.
To keep up with the rapidly evolving adulterants landscape, counties can pass ordinances that require regular testing of street drugs by local law enforcement or public health departments to identify the presence of dangerous adulterants. This information can be used to issue public warnings and guide local response efforts.
Recommendations for Alcohol-impaired deaths rates
1. Provide job-specific training to support those with limited skills and education
Counties can foster collaborations between local eduational institutions and vocational schools to offer low-cost or free job-specific training in high-demand industries especially in low-income areas. These programs can quckly equip workers with the skills necessary to re-enter the work force.
People in low-income areas, especially those who never finished high school, tend to have fewer job opportunies as they don’t have ties to educational institutions that offer a plethora of career support/services. Counties should establish independent job placement programs to help match job seekers with available positions. These programs can offer resume workshops, inerview coaching, and job fairs to help individuals find jobs.
2. Create family engagement programs to encourage children to stay in schoool
Schools can partner with community organizations to offer programs that engage parents and families in the education process. By providing parent support–family counseling, resources on college planning, financial aid, and academic support–schools can engage parents more on their children’s educational journey. Engaging families on their kids’ academic journey can influence them to encourage their children to complete high school.
Counties can also foster collaboration between nonprofits and schools to create support networks that provide resources for familes facing economic and social hardships. These networks can provide food assistance, housing support, or mental health services which can help alleviate barriers that may affect a student’s ability to focus on school. When families feel supported by the community, students are more likely to attend school regularly and achieve academic success.
Limitations
Since we are working with county level data, all individuals’ data in a county are simply averaged so we might be losing important granular differences within counties–this might be an issue especially for larger counties that have multiple urban and rural areas and therefore are more socioeconomically and demographically diverse. This makes it difficult to generalize findings. For example, Los Angeles county is huge (population-wise) with 88 cities and nearly as many unincorporated areas. All these areas have vastly different socioeconomic conditions, access to healthcare, education levels, and employment opportunities. Aggregating by county masks these differences. Thus, we might not capture localized trends in finer geographic regions like cities.
Additionally, while education (measured by high school completion rate) and adult smoking rate are not significant predictors of drug overdose, it is highly correlated with median household income. A next step could be to explore potential interaction effects between education and income to determine if the combined impact of these factors influences drug overdose rates. This could reveal whether the effect of income on overdose risk varies depending on education levels and adult smoking rate.
It is possible that the effect of predictors on drug overdose might vary by state (or another group), so a potential next step is to include random slopes. Additionally, the correlation of these predictors might vary by demographic. Since we have data on drug overdoses for different ethnic groups, we could make regression models for each and identify the most correlated predictors for each group to design demographic-tailored interventions.
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