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Advances in mental health care are continuously emerging, challenging all members of the interprofessional team to remain aware of important updates and how they may improve clinical practice. However, increasing demands on the time and resources of healthcare practitioners make it difficult to stay up-to-date on the latest clinical research and guidelines, as well as the implications for patient care. This article highlights recent advances in our understanding of mental health conditions and strategies to prevent and treat these disorders.
Social determinants of health (SDOH), which include factors such as socioeconomic status, as well as access to healthy food, education, housing, and physical environment, are strong predictors of suicidal behaviors. Several studies have identified a range of common risk factors for suicide using International Classification of Diseases (ICD) codes and other "structured" data from the electronic health records (EHRs). However, the use of unstructured EHR data from clinician notes has received little attention in investigating potential associations between suicide and SDOH.
A recently published study shows that it is possible to flag suicide risk by automatically extracting clinical notes on SDOH from a patient's EHR using natural language processing (NLP), a form of artificial intelligence.[1] Researchers found SDOH are risk factors for suicide among United States veterans and NLP can be leveraged to extract SDOH information from unstructured data in the EHR.
"Since SDOH is overwhelmingly described in EHR notes, the importance of NLP-extracted SDOH can be very significant, meaning that NLP can be used as an effective method for epidemiological and public health study," senior investigator Hong Yu, PhD, from Miner School of Information and Computer Sciences, University of Massachusetts Lowell, told Medscape Medical News.
Although the study was conducted among United States veterans, the results will likely be consistent in the general population as well. "The NLP methods are generalizable. The SDOH categories are generalizable. There may be some variations in terms of the strength of associations in NLP extracted SDOH and suicide death, but the overall findings are generalizable," Yu told Medscape Medical News.
Using the large Veterans Health Administration EHR system, the researchers determined associations between veterans' death by suicide and recent SDOH, identified using both structured data (ICD-10 codes and Veterans Health Administration stop codes) and unstructured data (NLP-processed clinical notes).[1]
Participants included 8821 veterans who committed suicide and 35,284 matched controls. The cohort was mostly male (96%) and White (79%). The mean age was 58 years.
The NLP-extracted SDOH were social isolation, job or financial insecurity, housing instability, legal problems, violence, barriers to care, transition of care, and food insecurity.[1] All of these unstructured clinical notes on SDOH were significantly associated with increased risk for death by suicide (Table).
Legal problems had the largest estimated effect size, more than twice the risk of those with no exposure (adjusted odds ratio [OR] 2.62; 95% CI: 2.38, 2.89), followed by violence (adjusted OR 2.34; 95% CI: 2.17, 2.52) and social isolation (adjusted OR 1.94; 95% CI; 1.83, 2.06).
Similarly, all of the structured SDOH -- social or family problems, employment or financial problems, housing instability, legal problems, violence, and nonspecific psychosocial needs -- also showed significant associations with increased risk for suicide death, once again, with legal problems linked to the highest risk (adjusted OR 2.63; 95% CI: 2.37, 2.91).[1] When combining the structured and NLP-extracted unstructured data, the top 3 risk factors for death by suicide were legal problems (adjusted OR 2.66; 95% CI: 2.46, 2.89), violence (adjusted OR 2.12; 95% CI: 1.98, 2.27), and nonspecific psychosocial needs (adjusted OR 2.07; 95% CI: 1.92, 2.23).
Table. SDOH Associated With Veterans’ Suicide Rate
Adjusted Odds Ratio (95% CI) | |||
---|---|---|---|
SDOH Factors | NLP-extracted | Structures | Combined |
Social problems | 1.94 (1.83, 2.06) | 2.11 (1.94, 2.29) | 1.95 (1.84, 2.07) |
Financial problems | 1.91 (1.79, 2.04) | 2.18 (1.97, 2.42) | 1.92 (1.80, 2.05) |
Housing instability | 1.90 (1.78, 2.03) | 2.28 (2.06, 2.53) | 1.93 (1.80, 2.06) |
Legal problems | 2.62 (2.38, 2.89) | 2.63 (2.37, 2.91) | 2.66 (2.46, 2.89) |
Violence | 2.34 (2.17, 2.52) | 1.96 (1.77, 2.16) | 2.12 (1.98, 2.27) |
Barriers to care | 1.86 (1.74, 1.99) | NA | 1.86 (1.74, 1.98) |
Transition to care | 1.53 (1.44, 1.62) | NA | 1.51 (1.43, 1.60) |
Food insecurity | 1.85 (1.62, 2.11) | NA | 1.85 (1.62, 2.11) |
NLP, natural language processing; NA, not applicable; SDOH, social determinants of health.
"To our knowledge, this the first large-scale study to implement and use an NLP system to extract SDOH information from unstructured EHR data," the researchers write.[1] "We strongly believe that analyzing all available SDOH information, including those contained in clinical notes, can help develop a better system for risk assessment and suicide prevention. However, more studies are required to investigate ways of seamlessly incorporating SDOHs into existing healthcare systems," they conclude.
In an accompanying editorial, Ishanu Chattopadhyay, PhD, from the University of Chicago, Chicago, Illinois, says this suggests that unstructured clinical notes "may efficiently identify at-risk individuals even when structured data on the relevant variables are missing or incomplete."[2] This work may provide "the foundation for addressing the key hurdles in enacting efficient universal assessment for suicide risk among the veterans and perhaps in the general population," Chattopadhyay adds.
Implications for the Interprofessional Healthcare Team
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This research was funded by a grant from the National Institute of Mental Health (NIMH). The study authors and editorialist report no relevant financial relationships.
More than 57 million people currently live with dementia worldwide and alcohol consumption has been identified as a potential modifiable risk factor for dementia, although study results have been inconsistent.[3] New research suggests drinking 1 or 2 cocktails a day may protect against dementia, while having three or more could increase risk. Investigators assessed dementia risk using changes in alcohol consumption over a 2-year period in nearly 4 million people in Korea. After about 7 years, dementia was 21% less likely in mild drinkers and 17% less likely in moderate drinkers. Heavy drinking was linked to an 8% increased risk.
Other studies of the relationship between alcohol and dementia have yielded mixed results, and this study does little to clear those murky waters.[3] Nor do the results mean that drinking is recommended, the investigators note. But the study does offer new information on how risk changes over time as people change their drinking habits, lead investigator Keun Hye Jeon, MD, assistant professor of family medicine at Cha Gumi Medical Center at Cha University, Gumi, Korea, told Medscape Medical News.
"Although numerous studies have shown a relationship between alcohol consumption and dementia, there is a paucity of understanding as to how the incidence of dementia changes with changes in drinking habits," Jeon said. "By measuring alcohol consumption at 2 time points, we were able to study the relationship between reducing, ceasing, maintaining, and increasing alcohol consumption and incident dementia," he added.
Researchers analyzed data from nearly 4 million individuals aged 40 years and older in the Korean National Health Insurance Service who completed questionnaires and underwent physical exams in 2009 and 2011.[3] Study participants completed questionnaires on their drinking habits and were assigned to 1 of 5 groups according to change in alcohol consumption during the study period.
These groups consisted of sustained nondrinkers; those who stopped drinking (quitters); those who reduced their consumption of alcohol but did not stop drinking (reducers); those who maintained the same level of consumption (sustainers); and those who increased their level of consumption (increasers). Mild drinking was defined as < 15 g per day, or one drink; moderate consumption as 15 g to 29.9 g per day, or 1 to 2 drinks; and heavy drinking as ≥ 30 g per day, or 3 or more drinks.
Compared to consistently not drinking, mild and moderate alcohol consumption was associated with a 21% (adjusted HR 0.79; 95% CI: 0.77, 0.81) and 17% (adjusted HR 0.83; 95% CI: 0.79, 0.88) decreased risk for dementia, respectively. Heavy drinking was linked to an 8% increased risk (adjusted HR 1.08; 95% CI: 1.03, 1.12). Similar associations were found between alcohol consumption and risk for Alzheimer's disease and vascular dementia.
Reducing drinking habits from heavy to moderate led to a reduction in risk for dementia and Alzheimer's disease and increasing drinking levels led to an increase in risk for both conditions. But when the researchers analyzed dementia risk for nondrinkers who began drinking at mild levels during the study period, they found something unexpected -- the risk in this group decreased by 7% for dementia (adjusted HR 0.93; 95% CI: 0.90, 0.96) and by 8% for Alzheimer's disease (adjusted HR 0.92; 95% CI: 0.89, 0.95), compared to the risk seen with sustained mild drinkers.
"Our study showed that initiation of mild alcohol consumption leads to a reduced risk of all-cause dementia and Alzheimer's disease, which has never been reported in previous studies," Jeon, the lead investigator, said. However, Jeon was quick to point out that this does not mean that people who do not currently drink should start. Previous studies have shown that heavy alcohol use can triple an individual's dementia risk, while other studies have shown that no amount of alcohol consumption is good for the brain.
"None of the existing health guidelines recommend starting alcohol drinking," Jeon said. "Our findings regarding an initiation of mild alcohol consumption cannot be directly translated into clinical recommendations," but the findings do warrant additional study, he added.
Commenting on the findings for Medscape Medical News, Percy Griffin, PhD, director of scientific engagement for the Alzheimer's Association in Chicago, agrees.
"While this study is interesting, and this topic deserves further study, no one should drink alcohol as a method of reducing risk of Alzheimer's disease or other dementia based on this study."
The exact tipping point in alcohol consumption that can lead to problems with cognition or increased dementia risk is unknown, Griffin said. Nor do researchers understand why mild drinking may have a protective effect. "We do know, however, that excessive alcohol consumption has negative effects on heart health and general health, which can lead to problems with brain function," he said. "Clinicians should have discussions with their patients around their alcohol consumption patterns and the risks associated with drinking in excess, including potential damage to their cognition."
Implications for the Interprofessional Healthcare Team
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Funding for the study was not disclosed. Jeon and Griffin report no relevant financial relationships.
Drug use and addiction represent a growing public health crisis, including increasing emotional and financial costs to families, communities, and society as a whole. In 2021, substance use disorders (SUDs) were reported in more than 46 million people in the United States over the age of 12 years and only 6.3% had received treatment.[4] In addition, approximately 107,000 individuals died of drug overdose in 2021, 37% of which involved simultaneous exposure to both opioids and stimulant drugs. Individual SUDs are heritable and influenced by multiple genes and environmental factors. Genome-wide analysis has emerged as a strategy to identify the genetic factors contributing to addiction.
Findings from a recent study published in Nature Mental Health suggest that SUDs, including alcohol, tobacco, cannabis, or opioids, appear to share a common genetic signature.[5] Researchers say this improved understanding of the biology of addiction could eventually lead to universal therapies to treat multiple and comorbid addictions.
Nora Volkow, MD, director of the National Institute on Drug Abuse (NIDA), stated that "Genetics play a key role in determining health throughout our lives, but they are not destiny. Our hope with genomic studies is to further illuminate factors that may protect or predispose a person to substance use disorders -- knowledge that can be used to expand preventative services and empower individuals to make informed decisions about drug use."[4]
"A better understanding of genetics also brings us one step closer to developing personalized interventions that are tailored to an individual’s unique biology, environment, and lived experience in order to provide the most benefits," Volkow added.[4]
Led by a team at the Washington University of Medicine in St Louis, the study included more than 150 collaborating investigators from around the world.[5]
The risk of developing SUDs is influenced by a complex interplay between genetics and environmental factors. In a genome-wide association study (GWAS), the investigators looked for variations in the genome that were closely associated with SUDs in more than 1 million people of European ancestry and 92,630 people of African ancestry.
Among the European ancestry sample, they discovered 19 single nucleotide polymorphisms (SNPs) that were significantly associated with general addiction risk and 47 genetic variants linked to specific SUDs -- 9 for alcohol, 32 for tobacco, 5 for cannabis, and 1 for opioids.[5] The strongest gene signals consistent across the various SUDs mapped to areas in the genome involved in dopamine signaling regulation, including reward sensitivity and executive functioning. This reinforces the role of dopamine signaling in addiction.
The genomic pattern also predicted higher risk of mental and physical illness, including psychiatric disorders, suicidal behavior, respiratory disease, heart disease, and chronic pain conditions.[5] In children aged 9 or 10 years, presumably without any SUD, these genes correlated with parental substance use and externalizing behavior.
"Substance use disorders and mental disorders often co-occur, and we know that the most effective treatments help people address both issues at the same time. The shared genetic mechanisms between substance use and mental disorders revealed in this study underscore the importance of thinking about these disorders in tandem," Joshua A. Gordon, MD, PhD, director of the National Institute of Mental Health (NIMH), said in an NIMH news release.[4]
Separately, the genomic analysis of individuals of African ancestry showed only one genetic variation associated with general addiction risk and one substance-specific variation for risk of alcohol use disorder.[5] The smaller sample size may be one reason for the more limited findings in this population.
As part of the study, the researchers compiled a list of approved and investigational pharmaceutical drugs that could potentially be repurposed to treat SUDs.[5]
The list includes more than 100 drugs to investigate in future clinical trials, including those that can influence regulation of dopamine signaling.
"There is a tremendous need for treatments that target addiction generally, given patterns of the use of multiple substances, lifetime substance use, and severity seen in the clinic," stated lead researcher Alexander Hatoum, PhD, at Washington University, St Louis.[6] "Our study opens the door to identifying medications that may be leveraged to treat addiction broadly, which may be especially useful for treating more severe forms, including addiction to multiple substances," Hatoum added.
Implications for the Interprofessional Healthcare Team
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This research was supported by the National Institute on Drug Abuse (NIDA), the National Institute on Alcohol Abuse and Alcoholism (NIAAA), the National Institute of Mental Health (NIMH), the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), and the National Institute on Aging (NIA). A complete list of author disclosures is available with the original article.