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Impact of child abuse differs by gender
PARIS – , new research shows.
Investigators found childhood emotional and sexual abuse had a greater effect on women than men, whereas men were more adversely affected by emotional and physical neglect.
“Our findings indicate that exposure to childhood maltreatment increases the risk of having psychiatric symptoms in both men and women,” lead researcher Thanavadee Prachason, PhD, department of psychiatry and neuropsychology, Maastricht (the Netherlands) University Medical Center, said in a press release.
“Exposure to emotionally or sexually abusive experiences during childhood increases the risk of a variety of psychiatric symptoms, particularly in women. In contrast, a history of emotional or physical neglect in childhood increases the risk of having psychiatric symptoms more in men,” Dr. Prachason added.
The findings were presented at the European Psychiatric Association 2023 Congress.
A leading mental illness risk factor
Study presenter Laura Fusar-Poli, MD, PhD, from the department of brain and behavioral sciences, University of Pavia (Italy), said that the differential impact of trauma subtypes in men and women indicate that both gender and the type of childhood adversity experienced need to be taken into account in future studies.
Dr. Fusar-Poli began by highlighting that 13%-36% of individuals have experienced some kind of childhood trauma, with 30% exposed to at least two types of trauma.
Trauma has been identified as a risk factor for a range of mental health problems.
“It is estimated that, worldwide, around one third of all psychiatric disorders are related to childhood trauma,” senior researcher Sinan Gül
Consequently, “childhood trauma is a leading preventable risk factor for mental illness,” he added.
Previous research suggests the subtype of trauma has an impact on subsequent biological changes and clinical outcomes, and that there are gender differences in the effects of childhood trauma.
To investigate, the researchers examined data from TwinssCan, a Belgian cohort of twins and siblings aged 15-35 years without a diagnosis of pervasive mental disorders.
The study included 477 females and 314 males who had completed the Childhood Trauma Questionnaire–Short Form (CTQ) and the Symptom Checklist-90 SR (SCL-90) to determine exposure to childhood adversity and levels of psychopathology, respectively.
Results showed that total CTQ scores were significantly associated with total SCL-90 scores in both men and women, as well as with each of the nine symptom domains of the SCL-90 (P < .001 for all assessments). These included psychoticism, paranoid ideation, anxiety, depression, somatization, obsessive-compulsive, interpersonal sensitivity, hostility, and phobic anxiety.
There were no significant differences in the associations with total CTQ scores between men and women.
However, when the researchers examined trauma subtypes and psychopathology, clear gender differences emerged.
Investigators found a significant association between emotional abuse on the CTQ and total SCL-90 scores in both men (P < .023) and women (P < .001), but that the association was significantly stronger in women (P = .043).
Sexual abuse was significantly associated with total SCL-90 scores in women (P < .001), while emotional neglect and physical neglect were significantly associated with psychopathology scores in men (P = .026 and P < .001, respectively).
“Physical neglect may include experiences of not having enough to eat, wearing dirty clothes, not being taken care of, and not getting taken to the doctor when the person was growing up,” said Dr. Prachason.
“Emotional neglect may include childhood experiences like not feeling loved or important, and not feeling close to the family.”
In women, emotional abuse was significantly associated with all nine symptom domains of the SCL-90, while sexual abuse was associated with seven: psychoticism, paranoid ideation, anxiety, depression, somatization, obsessive-compulsive, and hostility.
Physical neglect, in men, was significantly associated with eight of the symptom domains (all but somatization), but emotional neglect was linked only to depression, Dr. Fusar-Poli reported.
“This study showed a very important consequence of childhood trauma, and not only in people with mental disorders. I would like to underline that this is a general population, composed of adolescents and young adults, which is the age in which the majority of mental disorders starts, Dr. Fusar-Poli said in an interview.
She emphasized that psychotic disorders are only a part of the “broad range” of conditions that may be related to childhood trauma, which “can have an impact on sub-threshold symptoms that can affect functioning and quality of life in the general population.”
Addressing the differential findings in men and women, Dr. Gül
However, he said, this is “something that we really need understand,” as there is likely an underlying mechanism, “and not only a biological mechanism but probably a societal one.”
Dr. Gül
Compromised cognitive, emotional function
Commenting on the findings for this news organization, Elaine F. Walker, PhD, professor of psychology and neuroscience at Emory University in Atlanta, said stress exposure in general, including childhood trauma, “has transdiagnostic effects on vulnerability to mental disorders.”
“The effects are primarily mediated by the hypothalamic-pituitary-adrenal axis, which triggers the release of cortisol. When persistently elevated, this can result in neurobiological processes that have adverse effects on brain structure and circuitry which, in turn, compromises cognitive and emotional functioning,” said Dr. Walker, who was not associated with the study.
She noted that, “while it is possible that there are sex differences in biological sensitivity to certain subtypes of childhood trauma, it may also be the case that sex differences in the likelihood of exposure to trauma subtypes is actually the key factor.”
“At the present time, there are not specific treatment protocols aimed at addressing childhood trauma subtypes, but most experienced therapists will incorporate information about the individual’s trauma history in their treatment,” Dr. Walker added.
Also commenting on the research, Philip Gorwood, MD, PhD, head of the Clinique des Maladies Mentales et de l’Encéphale at Centre Hospitalier Sainte Anne in Paris, said the results are “important … as childhood trauma has been clearly recognized as a major risk factor for the vast majority of psychiatric disorders, but with poor knowledge of gender specificities.”
“Understanding which aspects of trauma are more damaging according to gender will facilitate research on the resilience process. Many intervention strategies will indeed benefit from a more personalized approach,” he said in a statement. Dr. Gorwood was not involved with this study.
The study authors, Dr. Gorwood, and Dr. Walker report no relevant financial relationships.
A version of this article first appeared on Medscape.com.
PARIS – , new research shows.
Investigators found childhood emotional and sexual abuse had a greater effect on women than men, whereas men were more adversely affected by emotional and physical neglect.
“Our findings indicate that exposure to childhood maltreatment increases the risk of having psychiatric symptoms in both men and women,” lead researcher Thanavadee Prachason, PhD, department of psychiatry and neuropsychology, Maastricht (the Netherlands) University Medical Center, said in a press release.
“Exposure to emotionally or sexually abusive experiences during childhood increases the risk of a variety of psychiatric symptoms, particularly in women. In contrast, a history of emotional or physical neglect in childhood increases the risk of having psychiatric symptoms more in men,” Dr. Prachason added.
The findings were presented at the European Psychiatric Association 2023 Congress.
A leading mental illness risk factor
Study presenter Laura Fusar-Poli, MD, PhD, from the department of brain and behavioral sciences, University of Pavia (Italy), said that the differential impact of trauma subtypes in men and women indicate that both gender and the type of childhood adversity experienced need to be taken into account in future studies.
Dr. Fusar-Poli began by highlighting that 13%-36% of individuals have experienced some kind of childhood trauma, with 30% exposed to at least two types of trauma.
Trauma has been identified as a risk factor for a range of mental health problems.
“It is estimated that, worldwide, around one third of all psychiatric disorders are related to childhood trauma,” senior researcher Sinan Gül
Consequently, “childhood trauma is a leading preventable risk factor for mental illness,” he added.
Previous research suggests the subtype of trauma has an impact on subsequent biological changes and clinical outcomes, and that there are gender differences in the effects of childhood trauma.
To investigate, the researchers examined data from TwinssCan, a Belgian cohort of twins and siblings aged 15-35 years without a diagnosis of pervasive mental disorders.
The study included 477 females and 314 males who had completed the Childhood Trauma Questionnaire–Short Form (CTQ) and the Symptom Checklist-90 SR (SCL-90) to determine exposure to childhood adversity and levels of psychopathology, respectively.
Results showed that total CTQ scores were significantly associated with total SCL-90 scores in both men and women, as well as with each of the nine symptom domains of the SCL-90 (P < .001 for all assessments). These included psychoticism, paranoid ideation, anxiety, depression, somatization, obsessive-compulsive, interpersonal sensitivity, hostility, and phobic anxiety.
There were no significant differences in the associations with total CTQ scores between men and women.
However, when the researchers examined trauma subtypes and psychopathology, clear gender differences emerged.
Investigators found a significant association between emotional abuse on the CTQ and total SCL-90 scores in both men (P < .023) and women (P < .001), but that the association was significantly stronger in women (P = .043).
Sexual abuse was significantly associated with total SCL-90 scores in women (P < .001), while emotional neglect and physical neglect were significantly associated with psychopathology scores in men (P = .026 and P < .001, respectively).
“Physical neglect may include experiences of not having enough to eat, wearing dirty clothes, not being taken care of, and not getting taken to the doctor when the person was growing up,” said Dr. Prachason.
“Emotional neglect may include childhood experiences like not feeling loved or important, and not feeling close to the family.”
In women, emotional abuse was significantly associated with all nine symptom domains of the SCL-90, while sexual abuse was associated with seven: psychoticism, paranoid ideation, anxiety, depression, somatization, obsessive-compulsive, and hostility.
Physical neglect, in men, was significantly associated with eight of the symptom domains (all but somatization), but emotional neglect was linked only to depression, Dr. Fusar-Poli reported.
“This study showed a very important consequence of childhood trauma, and not only in people with mental disorders. I would like to underline that this is a general population, composed of adolescents and young adults, which is the age in which the majority of mental disorders starts, Dr. Fusar-Poli said in an interview.
She emphasized that psychotic disorders are only a part of the “broad range” of conditions that may be related to childhood trauma, which “can have an impact on sub-threshold symptoms that can affect functioning and quality of life in the general population.”
Addressing the differential findings in men and women, Dr. Gül
However, he said, this is “something that we really need understand,” as there is likely an underlying mechanism, “and not only a biological mechanism but probably a societal one.”
Dr. Gül
Compromised cognitive, emotional function
Commenting on the findings for this news organization, Elaine F. Walker, PhD, professor of psychology and neuroscience at Emory University in Atlanta, said stress exposure in general, including childhood trauma, “has transdiagnostic effects on vulnerability to mental disorders.”
“The effects are primarily mediated by the hypothalamic-pituitary-adrenal axis, which triggers the release of cortisol. When persistently elevated, this can result in neurobiological processes that have adverse effects on brain structure and circuitry which, in turn, compromises cognitive and emotional functioning,” said Dr. Walker, who was not associated with the study.
She noted that, “while it is possible that there are sex differences in biological sensitivity to certain subtypes of childhood trauma, it may also be the case that sex differences in the likelihood of exposure to trauma subtypes is actually the key factor.”
“At the present time, there are not specific treatment protocols aimed at addressing childhood trauma subtypes, but most experienced therapists will incorporate information about the individual’s trauma history in their treatment,” Dr. Walker added.
Also commenting on the research, Philip Gorwood, MD, PhD, head of the Clinique des Maladies Mentales et de l’Encéphale at Centre Hospitalier Sainte Anne in Paris, said the results are “important … as childhood trauma has been clearly recognized as a major risk factor for the vast majority of psychiatric disorders, but with poor knowledge of gender specificities.”
“Understanding which aspects of trauma are more damaging according to gender will facilitate research on the resilience process. Many intervention strategies will indeed benefit from a more personalized approach,” he said in a statement. Dr. Gorwood was not involved with this study.
The study authors, Dr. Gorwood, and Dr. Walker report no relevant financial relationships.
A version of this article first appeared on Medscape.com.
PARIS – , new research shows.
Investigators found childhood emotional and sexual abuse had a greater effect on women than men, whereas men were more adversely affected by emotional and physical neglect.
“Our findings indicate that exposure to childhood maltreatment increases the risk of having psychiatric symptoms in both men and women,” lead researcher Thanavadee Prachason, PhD, department of psychiatry and neuropsychology, Maastricht (the Netherlands) University Medical Center, said in a press release.
“Exposure to emotionally or sexually abusive experiences during childhood increases the risk of a variety of psychiatric symptoms, particularly in women. In contrast, a history of emotional or physical neglect in childhood increases the risk of having psychiatric symptoms more in men,” Dr. Prachason added.
The findings were presented at the European Psychiatric Association 2023 Congress.
A leading mental illness risk factor
Study presenter Laura Fusar-Poli, MD, PhD, from the department of brain and behavioral sciences, University of Pavia (Italy), said that the differential impact of trauma subtypes in men and women indicate that both gender and the type of childhood adversity experienced need to be taken into account in future studies.
Dr. Fusar-Poli began by highlighting that 13%-36% of individuals have experienced some kind of childhood trauma, with 30% exposed to at least two types of trauma.
Trauma has been identified as a risk factor for a range of mental health problems.
“It is estimated that, worldwide, around one third of all psychiatric disorders are related to childhood trauma,” senior researcher Sinan Gül
Consequently, “childhood trauma is a leading preventable risk factor for mental illness,” he added.
Previous research suggests the subtype of trauma has an impact on subsequent biological changes and clinical outcomes, and that there are gender differences in the effects of childhood trauma.
To investigate, the researchers examined data from TwinssCan, a Belgian cohort of twins and siblings aged 15-35 years without a diagnosis of pervasive mental disorders.
The study included 477 females and 314 males who had completed the Childhood Trauma Questionnaire–Short Form (CTQ) and the Symptom Checklist-90 SR (SCL-90) to determine exposure to childhood adversity and levels of psychopathology, respectively.
Results showed that total CTQ scores were significantly associated with total SCL-90 scores in both men and women, as well as with each of the nine symptom domains of the SCL-90 (P < .001 for all assessments). These included psychoticism, paranoid ideation, anxiety, depression, somatization, obsessive-compulsive, interpersonal sensitivity, hostility, and phobic anxiety.
There were no significant differences in the associations with total CTQ scores between men and women.
However, when the researchers examined trauma subtypes and psychopathology, clear gender differences emerged.
Investigators found a significant association between emotional abuse on the CTQ and total SCL-90 scores in both men (P < .023) and women (P < .001), but that the association was significantly stronger in women (P = .043).
Sexual abuse was significantly associated with total SCL-90 scores in women (P < .001), while emotional neglect and physical neglect were significantly associated with psychopathology scores in men (P = .026 and P < .001, respectively).
“Physical neglect may include experiences of not having enough to eat, wearing dirty clothes, not being taken care of, and not getting taken to the doctor when the person was growing up,” said Dr. Prachason.
“Emotional neglect may include childhood experiences like not feeling loved or important, and not feeling close to the family.”
In women, emotional abuse was significantly associated with all nine symptom domains of the SCL-90, while sexual abuse was associated with seven: psychoticism, paranoid ideation, anxiety, depression, somatization, obsessive-compulsive, and hostility.
Physical neglect, in men, was significantly associated with eight of the symptom domains (all but somatization), but emotional neglect was linked only to depression, Dr. Fusar-Poli reported.
“This study showed a very important consequence of childhood trauma, and not only in people with mental disorders. I would like to underline that this is a general population, composed of adolescents and young adults, which is the age in which the majority of mental disorders starts, Dr. Fusar-Poli said in an interview.
She emphasized that psychotic disorders are only a part of the “broad range” of conditions that may be related to childhood trauma, which “can have an impact on sub-threshold symptoms that can affect functioning and quality of life in the general population.”
Addressing the differential findings in men and women, Dr. Gül
However, he said, this is “something that we really need understand,” as there is likely an underlying mechanism, “and not only a biological mechanism but probably a societal one.”
Dr. Gül
Compromised cognitive, emotional function
Commenting on the findings for this news organization, Elaine F. Walker, PhD, professor of psychology and neuroscience at Emory University in Atlanta, said stress exposure in general, including childhood trauma, “has transdiagnostic effects on vulnerability to mental disorders.”
“The effects are primarily mediated by the hypothalamic-pituitary-adrenal axis, which triggers the release of cortisol. When persistently elevated, this can result in neurobiological processes that have adverse effects on brain structure and circuitry which, in turn, compromises cognitive and emotional functioning,” said Dr. Walker, who was not associated with the study.
She noted that, “while it is possible that there are sex differences in biological sensitivity to certain subtypes of childhood trauma, it may also be the case that sex differences in the likelihood of exposure to trauma subtypes is actually the key factor.”
“At the present time, there are not specific treatment protocols aimed at addressing childhood trauma subtypes, but most experienced therapists will incorporate information about the individual’s trauma history in their treatment,” Dr. Walker added.
Also commenting on the research, Philip Gorwood, MD, PhD, head of the Clinique des Maladies Mentales et de l’Encéphale at Centre Hospitalier Sainte Anne in Paris, said the results are “important … as childhood trauma has been clearly recognized as a major risk factor for the vast majority of psychiatric disorders, but with poor knowledge of gender specificities.”
“Understanding which aspects of trauma are more damaging according to gender will facilitate research on the resilience process. Many intervention strategies will indeed benefit from a more personalized approach,” he said in a statement. Dr. Gorwood was not involved with this study.
The study authors, Dr. Gorwood, and Dr. Walker report no relevant financial relationships.
A version of this article first appeared on Medscape.com.
AT EPA 2023
A new way to gauge suicide risk?
Researchers found SDOH are risk factors for suicide among U.S. 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 this news organization.
Although the study was conducted among U.S. veterans, the results likely hold for 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,” Dr. Yu said.
The study was published online JAMA Network Open.
Improved risk assessment
SDOH, which include factors such as socioeconomic status, 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 EHR. However, the use of unstructured EHR data from clinician notes has received little attention in investigating potential associations between suicide and SDOH.
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).
Participants included 8,821 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.
All of these unstructured clinical notes on SDOH were significantly associated with increased risk for death by suicide.
Legal problems had the largest estimated effect size, more than twice the risk of those with no exposure (adjusted odds ratio 2.62; 95% confidence interval, 2.38-2.89), followed by violence (aOR, 2.34; 95% CI, 2.17-2.52) and social isolation (aOR, 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 (aOR, 2.63; 95% CI, 2.37-2.91).
When combining the structured and NLP-extracted unstructured data, the top three risk factors for death by suicide were legal problems (aOR, 2.66; 95% CI 2.46-2.89), violence (aOR, 2.12; 95% CI, 1.98-2.27), and nonspecific psychosocial needs (aOR, 2.07; 95% CI, 1.92-2.23).
“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.
“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 health care systems,” they conclude.
Dr. Yu said it’s also important to note that their NLP system is built upon “the most advanced deep-learning technologies and therefore is more generalizable than most existing work that mainly used rule-based approaches or traditional machine learning for identifying social determinants of health.”
In an accompanying editorial, Ishanu Chattopadhyay, PhD, of the University of Chicago, said this suggests that unstructured clinical notes “may efficiently identify at-risk individuals even when structured data on the relevant variables are missing or incomplete.”
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,” Dr. Chattopadhyay added.
This research was funded by a grant from the National Institute of Mental Health. The study authors and editorialist report no relevant financial relationships.
A version of this article originally appeared on Medscape.com.
Researchers found SDOH are risk factors for suicide among U.S. 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 this news organization.
Although the study was conducted among U.S. veterans, the results likely hold for 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,” Dr. Yu said.
The study was published online JAMA Network Open.
Improved risk assessment
SDOH, which include factors such as socioeconomic status, 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 EHR. However, the use of unstructured EHR data from clinician notes has received little attention in investigating potential associations between suicide and SDOH.
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).
Participants included 8,821 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.
All of these unstructured clinical notes on SDOH were significantly associated with increased risk for death by suicide.
Legal problems had the largest estimated effect size, more than twice the risk of those with no exposure (adjusted odds ratio 2.62; 95% confidence interval, 2.38-2.89), followed by violence (aOR, 2.34; 95% CI, 2.17-2.52) and social isolation (aOR, 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 (aOR, 2.63; 95% CI, 2.37-2.91).
When combining the structured and NLP-extracted unstructured data, the top three risk factors for death by suicide were legal problems (aOR, 2.66; 95% CI 2.46-2.89), violence (aOR, 2.12; 95% CI, 1.98-2.27), and nonspecific psychosocial needs (aOR, 2.07; 95% CI, 1.92-2.23).
“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.
“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 health care systems,” they conclude.
Dr. Yu said it’s also important to note that their NLP system is built upon “the most advanced deep-learning technologies and therefore is more generalizable than most existing work that mainly used rule-based approaches or traditional machine learning for identifying social determinants of health.”
In an accompanying editorial, Ishanu Chattopadhyay, PhD, of the University of Chicago, said this suggests that unstructured clinical notes “may efficiently identify at-risk individuals even when structured data on the relevant variables are missing or incomplete.”
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,” Dr. Chattopadhyay added.
This research was funded by a grant from the National Institute of Mental Health. The study authors and editorialist report no relevant financial relationships.
A version of this article originally appeared on Medscape.com.
Researchers found SDOH are risk factors for suicide among U.S. 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 this news organization.
Although the study was conducted among U.S. veterans, the results likely hold for 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,” Dr. Yu said.
The study was published online JAMA Network Open.
Improved risk assessment
SDOH, which include factors such as socioeconomic status, 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 EHR. However, the use of unstructured EHR data from clinician notes has received little attention in investigating potential associations between suicide and SDOH.
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).
Participants included 8,821 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.
All of these unstructured clinical notes on SDOH were significantly associated with increased risk for death by suicide.
Legal problems had the largest estimated effect size, more than twice the risk of those with no exposure (adjusted odds ratio 2.62; 95% confidence interval, 2.38-2.89), followed by violence (aOR, 2.34; 95% CI, 2.17-2.52) and social isolation (aOR, 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 (aOR, 2.63; 95% CI, 2.37-2.91).
When combining the structured and NLP-extracted unstructured data, the top three risk factors for death by suicide were legal problems (aOR, 2.66; 95% CI 2.46-2.89), violence (aOR, 2.12; 95% CI, 1.98-2.27), and nonspecific psychosocial needs (aOR, 2.07; 95% CI, 1.92-2.23).
“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.
“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 health care systems,” they conclude.
Dr. Yu said it’s also important to note that their NLP system is built upon “the most advanced deep-learning technologies and therefore is more generalizable than most existing work that mainly used rule-based approaches or traditional machine learning for identifying social determinants of health.”
In an accompanying editorial, Ishanu Chattopadhyay, PhD, of the University of Chicago, said this suggests that unstructured clinical notes “may efficiently identify at-risk individuals even when structured data on the relevant variables are missing or incomplete.”
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,” Dr. Chattopadhyay added.
This research was funded by a grant from the National Institute of Mental Health. The study authors and editorialist report no relevant financial relationships.
A version of this article originally appeared on Medscape.com.
FROM JAMA NETWORK OPEN
Telehealth services tied to a major reduction in opioid overdose deaths
, a new study of Medicare beneficiaries shows.
Telehealth services for opioid use disorder (OUD) were used far more often during the pandemic than before COVID-19, and those who used them were 33% less likely to die of a drug overdose.
Investigators also found a significant increase in MOUD use during the pandemic. Fatal drug overdoses were 59% less likely among individuals who received MOUD from an opioid treatment program and 38% less likely among those treated with buprenorphine in an office-based setting.
The results come as policymakers are preparing for the end of the public health emergency that prompted the expansion of OUD-related telehealth and MOUD prescribing and are deciding whether to make those expansions permanent.
“The expansion of telehealth during the COVID-19 pandemic appears to have had positive effects on patients receiving MOUD, improved retention among patients who received MOUD, and lowered risks for both nonfatal and fatal overdose,” lead investigator Christopher M. Jones, PharmD, DrPH, director of the National Center for Injury Prevention and Control at the Centers for Disease Control and Prevention, Atlanta, Georgia, told this news organization. “Our results suggest that telehealth is a valuable tool in the toolbox for expanding access to and improving retention on MOUD.”
The findings were published online in JAMA Psychiatry.
Increase in treatment
The study included 105,162 Medicare beneficiaries who began OUD treatment between March and August in 2019 (prepandemic cohort; 67.6%
aged 45-74 years), and 70,479 who began treatment between March and August of 2020 (pandemic cohort; 66.3% aged 45-74 years).
Participants had not received OUD treatment in the 6 months leading up to study enrollment and were followed for 6 months after treatment began.
Significantly more study participants received OUD-related telehealth services during the pandemic than prior to 2019 (19.6% vs. 0.6%; P < .001). Receipt of MOUD was also significantly higher in the pandemic cohort (12.6% vs. 10.8%; P < .001).
The rate of drug overdose deaths was higher in the pandemic cohort (5.1 deaths vs. 3.7 deaths per 1,000 beneficiaries; P < .001). But the percentage of deaths from drug overdoses did not differ between groups (4.8% in the prepandemic cohort vs. 5.1% in the pandemic cohort; P = .49).
In the pandemic cohort, fatal drug overdoses were 33% less likely among those who received OUD-related telehealth services (adjusted odds ratio, 0.67; 95% confidence interval, 0.48-0.92); 59% less likely among those who received MOUD from opioid treatment programs (aOR, 0.41; 95% CI, 0.25-0.68), and 38% less likely among those who received buprenorphine in office-based settings (aOR, 0.62; 95% CI, 0.43-0.91).
Risk of fatal overdose was significantly lower among women and those aged 65 years and older. There were no significant differences in risk based on urban or rural residency or on ethnicity.
“Against the backdrop of a highly potent illicit drug supply driven by illicit fentanyl and fentanyl analogues and historically large increases in overdose deaths during the COVID-19 pandemic, MOUD was still highly effective at reducing risk for fatal overdose,” Dr. Jones said.
While the use of buprenorphine in office-based settings was associated with a decreased risk of overdose death, use of extended-release naltrexone was not.
“Prior research has demonstrated the effectiveness of extended-release naltrexone in the treatment of opioid use disorder,” Dr. Jones said. “However, research has also shown that patients have challenges getting started, or inducted, on extended-release naltrexone.”
An earlier study by Dr. Jones and colleagues showed that rates of retention were lower with extended-release naltrexone, compared with buprenorphine in office-based settings or MOUD from opioid treatment programs.
The new study included only a small number of individuals who were receiving extended-release naltrexone, which may have influenced the findings. In addition, challenges with induction and retention may be driving the results, Dr. Jones noted.
“Efforts to improve induction and retention with extended-release naltrexone are important areas for future research and clinical practice,” he added.
An important engagement tool
A number of questions about telehealth care for OUD remain, including whether increased access to care accounts for the reduction in drug overdose risk that the investigators found or whether other factors are at play.
“There is still more we need to understand about telehealth, such as the quality of care provided and the particular aspects of care provided by telehealth and how this influences health outcomes,” Dr. Jones said.
The results also suggest treatments for OUD are still not finding their way to patients who might benefit, he added.
“Despite the positive findings and the prior research showing that MOUD is highly effective, we found that only one in five patients received telehealth services and only one in eight received any MOUD. This really underscores the need to expand these services across clinical settings,” he added.
These and earlier findings demonstrate the potential benefits of continuing pandemic-era expansion of OUD-related telehealth services and MOUD access, Dr. Jones said.
In preparation for the end of the public health emergency on May 1, the Drug Enforcement Agency recently released a proposal that would allow providers to prescribe a 30-day supply of buprenorphine, but for patients to receive additional prescriptions, a face-to-face meeting would be required. The proposal has drawn criticism from addiction medicine specialists.
The current study didn’t explore if or how the proposal might affect patients with OUD or whether it could blunt the positive effects of the findings.
“Prior research shows that keeping individuals engaged in treatment, including on medications, is a critical part of reducing the negative health and social impacts of opioid use disorder. Our results suggest that telehealth can be an important tool in helping patients engage in and stay connected in care,” said Dr. Jones.
The study was funded by the Centers for Disease Control and Prevention, the Centers for Medicare & Medicaid Services, and the National Institutes of Health. Dr. Johnson reports no relevant financial relationships.
A version of this article originally appeared on Medscape.com.
, a new study of Medicare beneficiaries shows.
Telehealth services for opioid use disorder (OUD) were used far more often during the pandemic than before COVID-19, and those who used them were 33% less likely to die of a drug overdose.
Investigators also found a significant increase in MOUD use during the pandemic. Fatal drug overdoses were 59% less likely among individuals who received MOUD from an opioid treatment program and 38% less likely among those treated with buprenorphine in an office-based setting.
The results come as policymakers are preparing for the end of the public health emergency that prompted the expansion of OUD-related telehealth and MOUD prescribing and are deciding whether to make those expansions permanent.
“The expansion of telehealth during the COVID-19 pandemic appears to have had positive effects on patients receiving MOUD, improved retention among patients who received MOUD, and lowered risks for both nonfatal and fatal overdose,” lead investigator Christopher M. Jones, PharmD, DrPH, director of the National Center for Injury Prevention and Control at the Centers for Disease Control and Prevention, Atlanta, Georgia, told this news organization. “Our results suggest that telehealth is a valuable tool in the toolbox for expanding access to and improving retention on MOUD.”
The findings were published online in JAMA Psychiatry.
Increase in treatment
The study included 105,162 Medicare beneficiaries who began OUD treatment between March and August in 2019 (prepandemic cohort; 67.6%
aged 45-74 years), and 70,479 who began treatment between March and August of 2020 (pandemic cohort; 66.3% aged 45-74 years).
Participants had not received OUD treatment in the 6 months leading up to study enrollment and were followed for 6 months after treatment began.
Significantly more study participants received OUD-related telehealth services during the pandemic than prior to 2019 (19.6% vs. 0.6%; P < .001). Receipt of MOUD was also significantly higher in the pandemic cohort (12.6% vs. 10.8%; P < .001).
The rate of drug overdose deaths was higher in the pandemic cohort (5.1 deaths vs. 3.7 deaths per 1,000 beneficiaries; P < .001). But the percentage of deaths from drug overdoses did not differ between groups (4.8% in the prepandemic cohort vs. 5.1% in the pandemic cohort; P = .49).
In the pandemic cohort, fatal drug overdoses were 33% less likely among those who received OUD-related telehealth services (adjusted odds ratio, 0.67; 95% confidence interval, 0.48-0.92); 59% less likely among those who received MOUD from opioid treatment programs (aOR, 0.41; 95% CI, 0.25-0.68), and 38% less likely among those who received buprenorphine in office-based settings (aOR, 0.62; 95% CI, 0.43-0.91).
Risk of fatal overdose was significantly lower among women and those aged 65 years and older. There were no significant differences in risk based on urban or rural residency or on ethnicity.
“Against the backdrop of a highly potent illicit drug supply driven by illicit fentanyl and fentanyl analogues and historically large increases in overdose deaths during the COVID-19 pandemic, MOUD was still highly effective at reducing risk for fatal overdose,” Dr. Jones said.
While the use of buprenorphine in office-based settings was associated with a decreased risk of overdose death, use of extended-release naltrexone was not.
“Prior research has demonstrated the effectiveness of extended-release naltrexone in the treatment of opioid use disorder,” Dr. Jones said. “However, research has also shown that patients have challenges getting started, or inducted, on extended-release naltrexone.”
An earlier study by Dr. Jones and colleagues showed that rates of retention were lower with extended-release naltrexone, compared with buprenorphine in office-based settings or MOUD from opioid treatment programs.
The new study included only a small number of individuals who were receiving extended-release naltrexone, which may have influenced the findings. In addition, challenges with induction and retention may be driving the results, Dr. Jones noted.
“Efforts to improve induction and retention with extended-release naltrexone are important areas for future research and clinical practice,” he added.
An important engagement tool
A number of questions about telehealth care for OUD remain, including whether increased access to care accounts for the reduction in drug overdose risk that the investigators found or whether other factors are at play.
“There is still more we need to understand about telehealth, such as the quality of care provided and the particular aspects of care provided by telehealth and how this influences health outcomes,” Dr. Jones said.
The results also suggest treatments for OUD are still not finding their way to patients who might benefit, he added.
“Despite the positive findings and the prior research showing that MOUD is highly effective, we found that only one in five patients received telehealth services and only one in eight received any MOUD. This really underscores the need to expand these services across clinical settings,” he added.
These and earlier findings demonstrate the potential benefits of continuing pandemic-era expansion of OUD-related telehealth services and MOUD access, Dr. Jones said.
In preparation for the end of the public health emergency on May 1, the Drug Enforcement Agency recently released a proposal that would allow providers to prescribe a 30-day supply of buprenorphine, but for patients to receive additional prescriptions, a face-to-face meeting would be required. The proposal has drawn criticism from addiction medicine specialists.
The current study didn’t explore if or how the proposal might affect patients with OUD or whether it could blunt the positive effects of the findings.
“Prior research shows that keeping individuals engaged in treatment, including on medications, is a critical part of reducing the negative health and social impacts of opioid use disorder. Our results suggest that telehealth can be an important tool in helping patients engage in and stay connected in care,” said Dr. Jones.
The study was funded by the Centers for Disease Control and Prevention, the Centers for Medicare & Medicaid Services, and the National Institutes of Health. Dr. Johnson reports no relevant financial relationships.
A version of this article originally appeared on Medscape.com.
, a new study of Medicare beneficiaries shows.
Telehealth services for opioid use disorder (OUD) were used far more often during the pandemic than before COVID-19, and those who used them were 33% less likely to die of a drug overdose.
Investigators also found a significant increase in MOUD use during the pandemic. Fatal drug overdoses were 59% less likely among individuals who received MOUD from an opioid treatment program and 38% less likely among those treated with buprenorphine in an office-based setting.
The results come as policymakers are preparing for the end of the public health emergency that prompted the expansion of OUD-related telehealth and MOUD prescribing and are deciding whether to make those expansions permanent.
“The expansion of telehealth during the COVID-19 pandemic appears to have had positive effects on patients receiving MOUD, improved retention among patients who received MOUD, and lowered risks for both nonfatal and fatal overdose,” lead investigator Christopher M. Jones, PharmD, DrPH, director of the National Center for Injury Prevention and Control at the Centers for Disease Control and Prevention, Atlanta, Georgia, told this news organization. “Our results suggest that telehealth is a valuable tool in the toolbox for expanding access to and improving retention on MOUD.”
The findings were published online in JAMA Psychiatry.
Increase in treatment
The study included 105,162 Medicare beneficiaries who began OUD treatment between March and August in 2019 (prepandemic cohort; 67.6%
aged 45-74 years), and 70,479 who began treatment between March and August of 2020 (pandemic cohort; 66.3% aged 45-74 years).
Participants had not received OUD treatment in the 6 months leading up to study enrollment and were followed for 6 months after treatment began.
Significantly more study participants received OUD-related telehealth services during the pandemic than prior to 2019 (19.6% vs. 0.6%; P < .001). Receipt of MOUD was also significantly higher in the pandemic cohort (12.6% vs. 10.8%; P < .001).
The rate of drug overdose deaths was higher in the pandemic cohort (5.1 deaths vs. 3.7 deaths per 1,000 beneficiaries; P < .001). But the percentage of deaths from drug overdoses did not differ between groups (4.8% in the prepandemic cohort vs. 5.1% in the pandemic cohort; P = .49).
In the pandemic cohort, fatal drug overdoses were 33% less likely among those who received OUD-related telehealth services (adjusted odds ratio, 0.67; 95% confidence interval, 0.48-0.92); 59% less likely among those who received MOUD from opioid treatment programs (aOR, 0.41; 95% CI, 0.25-0.68), and 38% less likely among those who received buprenorphine in office-based settings (aOR, 0.62; 95% CI, 0.43-0.91).
Risk of fatal overdose was significantly lower among women and those aged 65 years and older. There were no significant differences in risk based on urban or rural residency or on ethnicity.
“Against the backdrop of a highly potent illicit drug supply driven by illicit fentanyl and fentanyl analogues and historically large increases in overdose deaths during the COVID-19 pandemic, MOUD was still highly effective at reducing risk for fatal overdose,” Dr. Jones said.
While the use of buprenorphine in office-based settings was associated with a decreased risk of overdose death, use of extended-release naltrexone was not.
“Prior research has demonstrated the effectiveness of extended-release naltrexone in the treatment of opioid use disorder,” Dr. Jones said. “However, research has also shown that patients have challenges getting started, or inducted, on extended-release naltrexone.”
An earlier study by Dr. Jones and colleagues showed that rates of retention were lower with extended-release naltrexone, compared with buprenorphine in office-based settings or MOUD from opioid treatment programs.
The new study included only a small number of individuals who were receiving extended-release naltrexone, which may have influenced the findings. In addition, challenges with induction and retention may be driving the results, Dr. Jones noted.
“Efforts to improve induction and retention with extended-release naltrexone are important areas for future research and clinical practice,” he added.
An important engagement tool
A number of questions about telehealth care for OUD remain, including whether increased access to care accounts for the reduction in drug overdose risk that the investigators found or whether other factors are at play.
“There is still more we need to understand about telehealth, such as the quality of care provided and the particular aspects of care provided by telehealth and how this influences health outcomes,” Dr. Jones said.
The results also suggest treatments for OUD are still not finding their way to patients who might benefit, he added.
“Despite the positive findings and the prior research showing that MOUD is highly effective, we found that only one in five patients received telehealth services and only one in eight received any MOUD. This really underscores the need to expand these services across clinical settings,” he added.
These and earlier findings demonstrate the potential benefits of continuing pandemic-era expansion of OUD-related telehealth services and MOUD access, Dr. Jones said.
In preparation for the end of the public health emergency on May 1, the Drug Enforcement Agency recently released a proposal that would allow providers to prescribe a 30-day supply of buprenorphine, but for patients to receive additional prescriptions, a face-to-face meeting would be required. The proposal has drawn criticism from addiction medicine specialists.
The current study didn’t explore if or how the proposal might affect patients with OUD or whether it could blunt the positive effects of the findings.
“Prior research shows that keeping individuals engaged in treatment, including on medications, is a critical part of reducing the negative health and social impacts of opioid use disorder. Our results suggest that telehealth can be an important tool in helping patients engage in and stay connected in care,” said Dr. Jones.
The study was funded by the Centers for Disease Control and Prevention, the Centers for Medicare & Medicaid Services, and the National Institutes of Health. Dr. Johnson reports no relevant financial relationships.
A version of this article originally appeared on Medscape.com.
FROM JAMA PSYCHIATRY
Tranq-contaminated fentanyl now in 48 states, DEA warns
The DEA warning comes on the heels of a Food and Drug Administration announcement that it would begin more closely monitoring imports of the raw materials and bulk shipments of xylazine, also known as “tranq” and “zombie drug.”
Xylazine was first approved by the FDA in 1972 as a sedative and analgesic for use only in animals, but is increasingly being detected in illicit street drugs, and is often mixed with fentanyl, cocaine, and methamphetamine.
The FDA warned in November that naloxone (Narcan) would not reverse xylazine-related overdoses because the tranquilizer is not an opioid. It does suppress respiration and repeated exposures may lead to dependence and withdrawal, said the agency. Users are also experiencing severe necrosis at injection sites.
“Xylazine is making the deadliest drug threat our country has ever faced, fentanyl, even deadlier,” said DEA Administrator Anne Milgram in a statement. “The DEA Laboratory System is reporting that in 2022 approximately 23% of fentanyl powder and 7% of fentanyl pills seized by the DEA contained xylazine.”
Xylazine use has spread quickly, from its start in the Philadelphia area to the Northeast, the South, and most recently the West.
Citing data from the Centers for Disease Control and Prevention, the DEA said that 66% of the 107,735 overdose deaths for the year ending August 2022 involved synthetic opioids such as fentanyl. The DEA said that the Sinaloa Cartel and Jalisco Cartel in Mexico, using chemicals sourced from China, are primarily responsible for trafficking fentanyl in the United States.
A version of this article originally appeared on Medscape.com.
The DEA warning comes on the heels of a Food and Drug Administration announcement that it would begin more closely monitoring imports of the raw materials and bulk shipments of xylazine, also known as “tranq” and “zombie drug.”
Xylazine was first approved by the FDA in 1972 as a sedative and analgesic for use only in animals, but is increasingly being detected in illicit street drugs, and is often mixed with fentanyl, cocaine, and methamphetamine.
The FDA warned in November that naloxone (Narcan) would not reverse xylazine-related overdoses because the tranquilizer is not an opioid. It does suppress respiration and repeated exposures may lead to dependence and withdrawal, said the agency. Users are also experiencing severe necrosis at injection sites.
“Xylazine is making the deadliest drug threat our country has ever faced, fentanyl, even deadlier,” said DEA Administrator Anne Milgram in a statement. “The DEA Laboratory System is reporting that in 2022 approximately 23% of fentanyl powder and 7% of fentanyl pills seized by the DEA contained xylazine.”
Xylazine use has spread quickly, from its start in the Philadelphia area to the Northeast, the South, and most recently the West.
Citing data from the Centers for Disease Control and Prevention, the DEA said that 66% of the 107,735 overdose deaths for the year ending August 2022 involved synthetic opioids such as fentanyl. The DEA said that the Sinaloa Cartel and Jalisco Cartel in Mexico, using chemicals sourced from China, are primarily responsible for trafficking fentanyl in the United States.
A version of this article originally appeared on Medscape.com.
The DEA warning comes on the heels of a Food and Drug Administration announcement that it would begin more closely monitoring imports of the raw materials and bulk shipments of xylazine, also known as “tranq” and “zombie drug.”
Xylazine was first approved by the FDA in 1972 as a sedative and analgesic for use only in animals, but is increasingly being detected in illicit street drugs, and is often mixed with fentanyl, cocaine, and methamphetamine.
The FDA warned in November that naloxone (Narcan) would not reverse xylazine-related overdoses because the tranquilizer is not an opioid. It does suppress respiration and repeated exposures may lead to dependence and withdrawal, said the agency. Users are also experiencing severe necrosis at injection sites.
“Xylazine is making the deadliest drug threat our country has ever faced, fentanyl, even deadlier,” said DEA Administrator Anne Milgram in a statement. “The DEA Laboratory System is reporting that in 2022 approximately 23% of fentanyl powder and 7% of fentanyl pills seized by the DEA contained xylazine.”
Xylazine use has spread quickly, from its start in the Philadelphia area to the Northeast, the South, and most recently the West.
Citing data from the Centers for Disease Control and Prevention, the DEA said that 66% of the 107,735 overdose deaths for the year ending August 2022 involved synthetic opioids such as fentanyl. The DEA said that the Sinaloa Cartel and Jalisco Cartel in Mexico, using chemicals sourced from China, are primarily responsible for trafficking fentanyl in the United States.
A version of this article originally appeared on Medscape.com.
FDA approves OTC naloxone, but will cost be a barrier?
Greater access to the drug should mean more lives saved. However, it’s unclear how much the nasal spray will cost and whether pharmacies will stock the product openly on shelves.
Currently, major pharmacy chains such as CVS and Walgreens make naloxone available without prescription, but consumers have to ask a pharmacist to dispense the drug.
“The major question is what is it going to cost,” Brian Hurley, MD, MBA, president-elect of the American Society of Addiction Medicine, said in an interview. “In order for people to access it they have to be able to afford it.”
“We won’t accomplish much if people can’t afford to buy Narcan,” said Chuck Ingoglia, president and CEO of the National Council for Mental Wellbeing, in a statement. Still, he applauded the FDA.
“No single approach will end overdose deaths but making Narcan easy to obtain and widely available likely will save countless lives annually,” he said.
“The timeline for availability and price of this OTC product is determined by the manufacturer,” the FDA said in a statement.
Commissioner Robert M. Califf, MD, called for the drug’s manufacturer to “make accessibility to the product a priority by making it available as soon as possible and at an affordable price.”
Emergent BioSolutions did not comment on cost. It said in a statement that the spray “will be available on U.S. shelves and at online retailers by the late summer,” after it has adapted Narcan for direct-to-consumer use, including more consumer-oriented packaging.
Naloxone’s cost varies, depending on geographic location and whether it is generic. According to GoodRX, a box containing two doses of generic naloxone costs $31-$100, depending on location and coupon availability.
A two-dose box of Narcan costs $135-$140. Emergent reported a 14% decline in naloxone sales in 2022 – to $373.7 million – blaming it in part on the introduction of generic formulations.
Dr. Hurley said he expects those who purchase Narcan at a drug store will primarily already be shopping there. It may or may not be those who most often experience overdose, such as people leaving incarceration or experiencing homelessness.
Having Narcan available over-the-counter “is an important supplement but it doesn’t replace the existing array of naloxone distribution programs,” Dr. Hurley said.
The FDA has encouraged naloxone manufacturers to seek OTC approval for the medication since at least 2019, when it designed a model label for a theoretical OTC product.
In November, the agency said it had determined that some naloxone products had the potential to be safe and effective for OTC use and again urged drugmakers to seek such an approval.
Emergent BioSolutions was the first to pursue OTC approval, but another manufacturer – the nonprofit Harm Reduction Therapeutics – is awaiting approval of its application to sell its spray directly to consumers.
Scott Gottlieb, MD, who was the FDA commissioner from 2017 to 2019, said in a tweet that more work needed to be done.
“This regulatory move should be followed by a strong push by elected officials to support wider deployment of Narcan, getting more doses into the hands of at risk households and frontline workers,” he tweeted.
Mr. Ingoglia said that “Narcan represents a second chance. By giving people a second chance, we also give them an opportunity to enter treatment if they so choose. You can’t recover if you’re dead, and we shouldn’t turn our backs on those who may choose a pathway to recovery that includes treatment.”
A version of this article first appeared on Medscape.com.
Greater access to the drug should mean more lives saved. However, it’s unclear how much the nasal spray will cost and whether pharmacies will stock the product openly on shelves.
Currently, major pharmacy chains such as CVS and Walgreens make naloxone available without prescription, but consumers have to ask a pharmacist to dispense the drug.
“The major question is what is it going to cost,” Brian Hurley, MD, MBA, president-elect of the American Society of Addiction Medicine, said in an interview. “In order for people to access it they have to be able to afford it.”
“We won’t accomplish much if people can’t afford to buy Narcan,” said Chuck Ingoglia, president and CEO of the National Council for Mental Wellbeing, in a statement. Still, he applauded the FDA.
“No single approach will end overdose deaths but making Narcan easy to obtain and widely available likely will save countless lives annually,” he said.
“The timeline for availability and price of this OTC product is determined by the manufacturer,” the FDA said in a statement.
Commissioner Robert M. Califf, MD, called for the drug’s manufacturer to “make accessibility to the product a priority by making it available as soon as possible and at an affordable price.”
Emergent BioSolutions did not comment on cost. It said in a statement that the spray “will be available on U.S. shelves and at online retailers by the late summer,” after it has adapted Narcan for direct-to-consumer use, including more consumer-oriented packaging.
Naloxone’s cost varies, depending on geographic location and whether it is generic. According to GoodRX, a box containing two doses of generic naloxone costs $31-$100, depending on location and coupon availability.
A two-dose box of Narcan costs $135-$140. Emergent reported a 14% decline in naloxone sales in 2022 – to $373.7 million – blaming it in part on the introduction of generic formulations.
Dr. Hurley said he expects those who purchase Narcan at a drug store will primarily already be shopping there. It may or may not be those who most often experience overdose, such as people leaving incarceration or experiencing homelessness.
Having Narcan available over-the-counter “is an important supplement but it doesn’t replace the existing array of naloxone distribution programs,” Dr. Hurley said.
The FDA has encouraged naloxone manufacturers to seek OTC approval for the medication since at least 2019, when it designed a model label for a theoretical OTC product.
In November, the agency said it had determined that some naloxone products had the potential to be safe and effective for OTC use and again urged drugmakers to seek such an approval.
Emergent BioSolutions was the first to pursue OTC approval, but another manufacturer – the nonprofit Harm Reduction Therapeutics – is awaiting approval of its application to sell its spray directly to consumers.
Scott Gottlieb, MD, who was the FDA commissioner from 2017 to 2019, said in a tweet that more work needed to be done.
“This regulatory move should be followed by a strong push by elected officials to support wider deployment of Narcan, getting more doses into the hands of at risk households and frontline workers,” he tweeted.
Mr. Ingoglia said that “Narcan represents a second chance. By giving people a second chance, we also give them an opportunity to enter treatment if they so choose. You can’t recover if you’re dead, and we shouldn’t turn our backs on those who may choose a pathway to recovery that includes treatment.”
A version of this article first appeared on Medscape.com.
Greater access to the drug should mean more lives saved. However, it’s unclear how much the nasal spray will cost and whether pharmacies will stock the product openly on shelves.
Currently, major pharmacy chains such as CVS and Walgreens make naloxone available without prescription, but consumers have to ask a pharmacist to dispense the drug.
“The major question is what is it going to cost,” Brian Hurley, MD, MBA, president-elect of the American Society of Addiction Medicine, said in an interview. “In order for people to access it they have to be able to afford it.”
“We won’t accomplish much if people can’t afford to buy Narcan,” said Chuck Ingoglia, president and CEO of the National Council for Mental Wellbeing, in a statement. Still, he applauded the FDA.
“No single approach will end overdose deaths but making Narcan easy to obtain and widely available likely will save countless lives annually,” he said.
“The timeline for availability and price of this OTC product is determined by the manufacturer,” the FDA said in a statement.
Commissioner Robert M. Califf, MD, called for the drug’s manufacturer to “make accessibility to the product a priority by making it available as soon as possible and at an affordable price.”
Emergent BioSolutions did not comment on cost. It said in a statement that the spray “will be available on U.S. shelves and at online retailers by the late summer,” after it has adapted Narcan for direct-to-consumer use, including more consumer-oriented packaging.
Naloxone’s cost varies, depending on geographic location and whether it is generic. According to GoodRX, a box containing two doses of generic naloxone costs $31-$100, depending on location and coupon availability.
A two-dose box of Narcan costs $135-$140. Emergent reported a 14% decline in naloxone sales in 2022 – to $373.7 million – blaming it in part on the introduction of generic formulations.
Dr. Hurley said he expects those who purchase Narcan at a drug store will primarily already be shopping there. It may or may not be those who most often experience overdose, such as people leaving incarceration or experiencing homelessness.
Having Narcan available over-the-counter “is an important supplement but it doesn’t replace the existing array of naloxone distribution programs,” Dr. Hurley said.
The FDA has encouraged naloxone manufacturers to seek OTC approval for the medication since at least 2019, when it designed a model label for a theoretical OTC product.
In November, the agency said it had determined that some naloxone products had the potential to be safe and effective for OTC use and again urged drugmakers to seek such an approval.
Emergent BioSolutions was the first to pursue OTC approval, but another manufacturer – the nonprofit Harm Reduction Therapeutics – is awaiting approval of its application to sell its spray directly to consumers.
Scott Gottlieb, MD, who was the FDA commissioner from 2017 to 2019, said in a tweet that more work needed to be done.
“This regulatory move should be followed by a strong push by elected officials to support wider deployment of Narcan, getting more doses into the hands of at risk households and frontline workers,” he tweeted.
Mr. Ingoglia said that “Narcan represents a second chance. By giving people a second chance, we also give them an opportunity to enter treatment if they so choose. You can’t recover if you’re dead, and we shouldn’t turn our backs on those who may choose a pathway to recovery that includes treatment.”
A version of this article first appeared on Medscape.com.
Melatonin: A new way to reduce self-harm?
. However, at least one expert has some concerns about the strength of the evidence.
The results suggest improving sleep hygiene in this population may reduce self-injury, study investigator Sarah E. Bergen, PhD, associate professor, department of medical epidemiology and biostatistics, Karolinska Institute, Stockholm, said in an interview.
In addition, she noted, for “pediatric patients who are experiencing sleep problems, melatonin is a safe and effective way to help them.”
Dr. Bergen believes clinicians should recommend melatonin to all teens because “there’s little harm that could come from it and possibly a lot of benefit.”
The findings were published online in the Journal of Child Psychology and Psychiatry.
Few treatments available
Research shows sleep disorders like insomnia are common in youth, particularly among those with psychiatric disorders. Sleep disorders can significantly affect daytime functioning, cognition, emotional regulation, and behavior, and can be a risk factor for unintentional injuries such as falls and vehicular accidents, as well as for intentional self-harm.
The lifetime prevalence of self-harm in youth is estimated to be 17%, but this varies across study designs. There are few treatments for self-harm in youth, although psychosocial treatments appear promising.
Melatonin is a naturally occurring hormone secreted primarily by the pineal gland in response to darkness. It helps promote and maintain the normal sleep-wake cycle and is involved in other biological functions.
In Sweden, melatonin is the most commonly prescribed drug for sleep disturbances in children and adolescents. Prior to 2020, during the course of the study, it was only available by prescription.
The study, which used linked national databases, included 25,575 children and adolescents, 58.2% of them male, who initiated a melatonin treatment between the ages of 6 and 18 years.
Researchers estimated the risks of self-harm, including poisoning (57%) and cutting (34%). The fact that poisoning was more common than cutting was somewhat surprising, said Dr. Bergen. “I would have thought the opposite would be true; that cutting was more prevalent.”
The study examined the risk of self-harm in individual participants by comparing the last unmedicated month with the 12 months after initiating melatonin treatment. In this way, they accounted for potential confounders such as genetics, sleep disorder severity, and psychiatric disorders.
The median age at first melatonin prescription was 13 years for males and 15 years for females.
While there were no statistically significant changes in relative risk for body injuries, falls, and transport accidents, the relative risk for self-injury was statistically significantly lower during the months following melatonin treatment initiation.
The incidence rate ratio in the month following treatment was 0.58 (95% confidence interval, 0.46-0.73) for self-harm and 0.59 (95% CI, 0.45-0.78) for poisoning.
Higher risks in females
The relative risk of self-harm was higher in females than males. This, said Dr. Bergen, is possibly because self-harm is more common in adolescence than in childhood. Female study participants were older than their male counterparts.
Melatonin may help male teens, too, she said. “It’s just that the problem is not that great in males to begin with, so a decrease is not very dramatic after melatonin initiation.”
About 87.2% of participants treated with melatonin were diagnosed with at least one psychiatric disorder. Attention-deficit hyperactivity disorder, the most common comorbidity, was diagnosed in more than 50% of new melatonin users. This isn’t surprising, because sleep disturbances are associated with this psychiatric condition and are frequent side effects of ADHD medications.
After ADHD, anxiety and depression were the next most common psychiatric disorders among study subjects. The analysis found risks for self-harm and poisoning were largely driven by patients suffering from one or both of these disorders, particularly among females.
The IRR in the month following melatonin treatment initiation was 0.46 (95% CI, 0.27-0.76] among adolescent females with psychiatric disorders, after excluding antidepressant users.
Melatonin may reduce the risk of self-harm by treating sleep problems related to psychiatric comorbidities, especially anxiety and depression. It could also decrease pain sensitivity experienced by adolescents who self-harm.
Other factors could play a role in treating sleep problems and/or preventing self-harm in these patients. For example, increased clinician awareness and monitoring, behavioral interventions, a placebo effect, and concurrent use of other medications.
When researchers ran an analysis that excluded individuals taking an antidepressant, “surprisingly, there wasn’t much difference,” said Dr. Bergen. “We thought antidepressants might be causing some of the effect we observed, but when we removed antidepressant users, we saw a very similar pattern of intentional self-harm rates following melatonin use, which suggests melatonin is causal, but we can’t prove that.”
Other sleep medications such as sedatives could also affect self-harm rates by improving sleep. However, these are not typically prescribed to children because of their side effects and overdose potential, said Dr. Bergen.
“Melatonin is extremely safe and side effects are rare; it’s impossible to overdose, and people really can’t hurt themselves with it.”
More research needed
Adrian Jacques Ambrose, MD, medical director, Columbia University Irving Medical Center, and assistant professor of psychiatry, Columbia University, New York, pointed out some evidence in the study is relatively weak.
“When the authors separated out the on- and off-melatonin groups, it looks like there wasn’t a statistically significant difference [in IRRs] between the two groups – for example, in any injury, self-harm, or poisoning – and this weakens their argument that melatonin is associated with self-harm and poisoning.”
Given the current youth mental health crisis, more research “would absolutely be indicated” to better explore possible additional variables, said Dr. Ambrose.
“For example, some additional follow-up studies may add on covariates in conjunction with melatonin usage, such as the number of medical appointments, the presence of psychotherapeutic interventions, dosage of melatonin, or even the sleepiness scale, to evaluate whether the symptoms of sleep disturbances are more directly correlated with the self-harm behaviors.”
The study was supported by the European Union’s Horizon 2020 Research and Innovation Programme. Dr. Bergen and Dr. Ambrose report no relevant financial relationships.
A version of this article first appeared on Medscape.com.
. However, at least one expert has some concerns about the strength of the evidence.
The results suggest improving sleep hygiene in this population may reduce self-injury, study investigator Sarah E. Bergen, PhD, associate professor, department of medical epidemiology and biostatistics, Karolinska Institute, Stockholm, said in an interview.
In addition, she noted, for “pediatric patients who are experiencing sleep problems, melatonin is a safe and effective way to help them.”
Dr. Bergen believes clinicians should recommend melatonin to all teens because “there’s little harm that could come from it and possibly a lot of benefit.”
The findings were published online in the Journal of Child Psychology and Psychiatry.
Few treatments available
Research shows sleep disorders like insomnia are common in youth, particularly among those with psychiatric disorders. Sleep disorders can significantly affect daytime functioning, cognition, emotional regulation, and behavior, and can be a risk factor for unintentional injuries such as falls and vehicular accidents, as well as for intentional self-harm.
The lifetime prevalence of self-harm in youth is estimated to be 17%, but this varies across study designs. There are few treatments for self-harm in youth, although psychosocial treatments appear promising.
Melatonin is a naturally occurring hormone secreted primarily by the pineal gland in response to darkness. It helps promote and maintain the normal sleep-wake cycle and is involved in other biological functions.
In Sweden, melatonin is the most commonly prescribed drug for sleep disturbances in children and adolescents. Prior to 2020, during the course of the study, it was only available by prescription.
The study, which used linked national databases, included 25,575 children and adolescents, 58.2% of them male, who initiated a melatonin treatment between the ages of 6 and 18 years.
Researchers estimated the risks of self-harm, including poisoning (57%) and cutting (34%). The fact that poisoning was more common than cutting was somewhat surprising, said Dr. Bergen. “I would have thought the opposite would be true; that cutting was more prevalent.”
The study examined the risk of self-harm in individual participants by comparing the last unmedicated month with the 12 months after initiating melatonin treatment. In this way, they accounted for potential confounders such as genetics, sleep disorder severity, and psychiatric disorders.
The median age at first melatonin prescription was 13 years for males and 15 years for females.
While there were no statistically significant changes in relative risk for body injuries, falls, and transport accidents, the relative risk for self-injury was statistically significantly lower during the months following melatonin treatment initiation.
The incidence rate ratio in the month following treatment was 0.58 (95% confidence interval, 0.46-0.73) for self-harm and 0.59 (95% CI, 0.45-0.78) for poisoning.
Higher risks in females
The relative risk of self-harm was higher in females than males. This, said Dr. Bergen, is possibly because self-harm is more common in adolescence than in childhood. Female study participants were older than their male counterparts.
Melatonin may help male teens, too, she said. “It’s just that the problem is not that great in males to begin with, so a decrease is not very dramatic after melatonin initiation.”
About 87.2% of participants treated with melatonin were diagnosed with at least one psychiatric disorder. Attention-deficit hyperactivity disorder, the most common comorbidity, was diagnosed in more than 50% of new melatonin users. This isn’t surprising, because sleep disturbances are associated with this psychiatric condition and are frequent side effects of ADHD medications.
After ADHD, anxiety and depression were the next most common psychiatric disorders among study subjects. The analysis found risks for self-harm and poisoning were largely driven by patients suffering from one or both of these disorders, particularly among females.
The IRR in the month following melatonin treatment initiation was 0.46 (95% CI, 0.27-0.76] among adolescent females with psychiatric disorders, after excluding antidepressant users.
Melatonin may reduce the risk of self-harm by treating sleep problems related to psychiatric comorbidities, especially anxiety and depression. It could also decrease pain sensitivity experienced by adolescents who self-harm.
Other factors could play a role in treating sleep problems and/or preventing self-harm in these patients. For example, increased clinician awareness and monitoring, behavioral interventions, a placebo effect, and concurrent use of other medications.
When researchers ran an analysis that excluded individuals taking an antidepressant, “surprisingly, there wasn’t much difference,” said Dr. Bergen. “We thought antidepressants might be causing some of the effect we observed, but when we removed antidepressant users, we saw a very similar pattern of intentional self-harm rates following melatonin use, which suggests melatonin is causal, but we can’t prove that.”
Other sleep medications such as sedatives could also affect self-harm rates by improving sleep. However, these are not typically prescribed to children because of their side effects and overdose potential, said Dr. Bergen.
“Melatonin is extremely safe and side effects are rare; it’s impossible to overdose, and people really can’t hurt themselves with it.”
More research needed
Adrian Jacques Ambrose, MD, medical director, Columbia University Irving Medical Center, and assistant professor of psychiatry, Columbia University, New York, pointed out some evidence in the study is relatively weak.
“When the authors separated out the on- and off-melatonin groups, it looks like there wasn’t a statistically significant difference [in IRRs] between the two groups – for example, in any injury, self-harm, or poisoning – and this weakens their argument that melatonin is associated with self-harm and poisoning.”
Given the current youth mental health crisis, more research “would absolutely be indicated” to better explore possible additional variables, said Dr. Ambrose.
“For example, some additional follow-up studies may add on covariates in conjunction with melatonin usage, such as the number of medical appointments, the presence of psychotherapeutic interventions, dosage of melatonin, or even the sleepiness scale, to evaluate whether the symptoms of sleep disturbances are more directly correlated with the self-harm behaviors.”
The study was supported by the European Union’s Horizon 2020 Research and Innovation Programme. Dr. Bergen and Dr. Ambrose report no relevant financial relationships.
A version of this article first appeared on Medscape.com.
. However, at least one expert has some concerns about the strength of the evidence.
The results suggest improving sleep hygiene in this population may reduce self-injury, study investigator Sarah E. Bergen, PhD, associate professor, department of medical epidemiology and biostatistics, Karolinska Institute, Stockholm, said in an interview.
In addition, she noted, for “pediatric patients who are experiencing sleep problems, melatonin is a safe and effective way to help them.”
Dr. Bergen believes clinicians should recommend melatonin to all teens because “there’s little harm that could come from it and possibly a lot of benefit.”
The findings were published online in the Journal of Child Psychology and Psychiatry.
Few treatments available
Research shows sleep disorders like insomnia are common in youth, particularly among those with psychiatric disorders. Sleep disorders can significantly affect daytime functioning, cognition, emotional regulation, and behavior, and can be a risk factor for unintentional injuries such as falls and vehicular accidents, as well as for intentional self-harm.
The lifetime prevalence of self-harm in youth is estimated to be 17%, but this varies across study designs. There are few treatments for self-harm in youth, although psychosocial treatments appear promising.
Melatonin is a naturally occurring hormone secreted primarily by the pineal gland in response to darkness. It helps promote and maintain the normal sleep-wake cycle and is involved in other biological functions.
In Sweden, melatonin is the most commonly prescribed drug for sleep disturbances in children and adolescents. Prior to 2020, during the course of the study, it was only available by prescription.
The study, which used linked national databases, included 25,575 children and adolescents, 58.2% of them male, who initiated a melatonin treatment between the ages of 6 and 18 years.
Researchers estimated the risks of self-harm, including poisoning (57%) and cutting (34%). The fact that poisoning was more common than cutting was somewhat surprising, said Dr. Bergen. “I would have thought the opposite would be true; that cutting was more prevalent.”
The study examined the risk of self-harm in individual participants by comparing the last unmedicated month with the 12 months after initiating melatonin treatment. In this way, they accounted for potential confounders such as genetics, sleep disorder severity, and psychiatric disorders.
The median age at first melatonin prescription was 13 years for males and 15 years for females.
While there were no statistically significant changes in relative risk for body injuries, falls, and transport accidents, the relative risk for self-injury was statistically significantly lower during the months following melatonin treatment initiation.
The incidence rate ratio in the month following treatment was 0.58 (95% confidence interval, 0.46-0.73) for self-harm and 0.59 (95% CI, 0.45-0.78) for poisoning.
Higher risks in females
The relative risk of self-harm was higher in females than males. This, said Dr. Bergen, is possibly because self-harm is more common in adolescence than in childhood. Female study participants were older than their male counterparts.
Melatonin may help male teens, too, she said. “It’s just that the problem is not that great in males to begin with, so a decrease is not very dramatic after melatonin initiation.”
About 87.2% of participants treated with melatonin were diagnosed with at least one psychiatric disorder. Attention-deficit hyperactivity disorder, the most common comorbidity, was diagnosed in more than 50% of new melatonin users. This isn’t surprising, because sleep disturbances are associated with this psychiatric condition and are frequent side effects of ADHD medications.
After ADHD, anxiety and depression were the next most common psychiatric disorders among study subjects. The analysis found risks for self-harm and poisoning were largely driven by patients suffering from one or both of these disorders, particularly among females.
The IRR in the month following melatonin treatment initiation was 0.46 (95% CI, 0.27-0.76] among adolescent females with psychiatric disorders, after excluding antidepressant users.
Melatonin may reduce the risk of self-harm by treating sleep problems related to psychiatric comorbidities, especially anxiety and depression. It could also decrease pain sensitivity experienced by adolescents who self-harm.
Other factors could play a role in treating sleep problems and/or preventing self-harm in these patients. For example, increased clinician awareness and monitoring, behavioral interventions, a placebo effect, and concurrent use of other medications.
When researchers ran an analysis that excluded individuals taking an antidepressant, “surprisingly, there wasn’t much difference,” said Dr. Bergen. “We thought antidepressants might be causing some of the effect we observed, but when we removed antidepressant users, we saw a very similar pattern of intentional self-harm rates following melatonin use, which suggests melatonin is causal, but we can’t prove that.”
Other sleep medications such as sedatives could also affect self-harm rates by improving sleep. However, these are not typically prescribed to children because of their side effects and overdose potential, said Dr. Bergen.
“Melatonin is extremely safe and side effects are rare; it’s impossible to overdose, and people really can’t hurt themselves with it.”
More research needed
Adrian Jacques Ambrose, MD, medical director, Columbia University Irving Medical Center, and assistant professor of psychiatry, Columbia University, New York, pointed out some evidence in the study is relatively weak.
“When the authors separated out the on- and off-melatonin groups, it looks like there wasn’t a statistically significant difference [in IRRs] between the two groups – for example, in any injury, self-harm, or poisoning – and this weakens their argument that melatonin is associated with self-harm and poisoning.”
Given the current youth mental health crisis, more research “would absolutely be indicated” to better explore possible additional variables, said Dr. Ambrose.
“For example, some additional follow-up studies may add on covariates in conjunction with melatonin usage, such as the number of medical appointments, the presence of psychotherapeutic interventions, dosage of melatonin, or even the sleepiness scale, to evaluate whether the symptoms of sleep disturbances are more directly correlated with the self-harm behaviors.”
The study was supported by the European Union’s Horizon 2020 Research and Innovation Programme. Dr. Bergen and Dr. Ambrose report no relevant financial relationships.
A version of this article first appeared on Medscape.com.
FROM THE JOURNAL OF CHILD PSYCHOLOGY AND PSYCHIATRY
Meet the JCOM Author with Dr. Barkoudah: Residence Characteristics and Nursing Home Compare Quality Measures
Relationships Between Residence Characteristics and Nursing Home Compare Database Quality Measures
From the University of Nebraska, Lincoln (Mr. Puckett and Dr. Ryherd), University of Nebraska Medical Center, Omaha (Dr. Manley), and the University of Nebraska, Omaha (Dr. Ryan).
ABSTRACT
Objective: This study evaluated relationships between physical characteristics of nursing home residences and quality-of-care measures.
Design: This was a cross-sectional ecologic study. The dependent variables were 5 Centers for Medicare & Medicaid Services (CMS) Nursing Home Compare database long-stay quality measures (QMs) during 2019: percentage of residents who displayed depressive symptoms, percentage of residents who were physically restrained, percentage of residents who experienced 1 or more falls resulting in injury, percentage of residents who received antipsychotic medication, and percentage of residents who received anti-anxiety medication. The independent variables were 4 residence characteristics: ownership type, size, occupancy, and region within the United States. We explored how different types of each residence characteristic compare for each QM.
Setting, participants, and measurements: Quality measure values from 15,420 CMS-supported nursing homes across the United States averaged over the 4 quarters of 2019 reporting were used. Welch’s analysis of variance was performed to examine whether the mean QM values for groups within each residential characteristic were statistically different.
Results: Publicly owned and low-occupancy residences had the highest mean QM values, indicating the poorest performance. Nonprofit and high-occupancy residences generally had the lowest (ie, best) mean QM values. There were significant differences in mean QM values among nursing home sizes and regions.
Conclusion: This study suggests that residence characteristics are related to 5 nursing home QMs. Results suggest that physical characteristics may be related to overall quality of life in nursing homes.
Keywords: quality of care, quality measures, residence characteristics, Alzheimer’s disease and related dementias.
More than 55 million people worldwide are living with Alzheimer’s disease and related dementias (ADRD).1 With the aging of the Baby Boomer population, this number is expected to rise to more than 78 million worldwide by 2030.1 Given the growing number of cognitively impaired older adults, there is an increased need for residences designed for the specialized care of this population. Although there are dozens of living options for the elderly, and although most specialized establishments have the resources to meet the immediate needs of their residents, many facilities lack universal design features that support a high quality of life for someone with ADRD or mild cognitive impairment. Previous research has shown relationships between behavioral and psychological symptoms of dementia (BPSD) and environmental characteristics such as acoustics, lighting, and indoor air temperature.2,3 Physical behaviors of BPSD, including aggression and wandering, and psychological symptoms, such as depression, anxiety, and delusions, put residents at risk of injury.4 Additionally, BPSD is correlated with caregiver burden and stress.5-8 Patients with dementia may also experience a lower stress threshold, changes in perception of space, and decreased short-term memory, creating environmental difficulties for those with ADRD9 that lead them to exhibit BPSD due to poor environmental design. Thus, there is a need to learn more about design features that minimize BPSD and promote a high quality of life for those with ADRD.10
Although research has shown relationships between physical environmental characteristics and BPSD, in this work we study relationships between possible BPSD indicators and 4 residence-level characteristics: ownership type, size, occupancy, and region in the United States (determined by location of the Centers for Medicare & Medicaid Services [CMS] regional offices). We analyzed data from the CMS Nursing Home Compare database for the year 2019.11 This database publishes quarterly data and star ratings for quality-of-care measures (QMs), staffing levels, and health inspections for every nursing home supported by CMS. Previous research has investigated the accuracy of QM reporting for resident falls, the impact of residential characteristics on administration of antipsychotic medication, the influence of profit status on resident outcomes and quality of care, and the effect of nursing home size on quality of life.12-16 Additionally, research suggests that residential characteristics such as size and location could be associated with infection control in nursing homes.17
Certain QMs, such as psychotropic drug administration, resident falls, and physical restraint, provide indicators of agitation, disorientation, or aggression, which are often signals of BPSD episodes. We hypothesized that residence types are associated with different QM scores, which could indicate different occurrences of BPSD. We selected 5 QMs for long-stay residents that could potentially be used as indicators of BPSD. Short-stay resident data were not included in this work to control for BPSD that could be a result of sheer unfamiliarity with the environment and confusion from being in a new home.
Methods
Design and Data Collection
This was a cross-sectional ecologic study aimed at exploring relationships between aggregate residential characteristics and QMs. Data were retrieved from the 2019 annual archives found in the CMS provider data catalog on nursing homes, including rehabilitation services.11 The dataset provides general residence information, such as ownership, number of beds, number of residents, and location, as well as residence quality metrics, such as QMs, staffing data, and inspection data. Residence characteristics and 4-quarter averages of QMs were retrieved and used as cross-sectional data. The data used are from 15,420 residences across the United States. Nursing homes located in Guam, the US Pacific Territories, Puerto Rico, and the US Virgin Islands, while supported by CMS and included in the dataset, were excluded from the study due to a severe absence of QM data.
Dependent Variables
We investigated 5 QMs that were averaged across the 4 quarters of 2019. The QMs used as dependent variables were percentage of residents who displayed depressive symptoms (depression), percentage of residents who were physically restrained (restraint), percentage of residents who experienced 1 or more falls resulting in a major injury (falls), percentage of residents who received antipsychotic medication (antipsychotic medication), and percentage of residents who received anti-anxiety or hypnotic medication (anti-anxiety medication).
A total of 2471 QM values were unreported across the 5 QM analyzed: 501 residences did not report depression data; 479 did not report restraint data; 477 did not report falls data; 508 did not report antipsychotic medication data; and 506 did not report anti-anxiety medication data. A residence with a missing QM value was excluded from that respective analysis.
To assess the relationships among the different QMs, a Pearson correlation coefficient r was computed for each unique pair of QMs (Figure). All QMs studied were found to be very weakly or weakly correlated with one another using the Evans classification for very weak and weak correlations (r < 0.20 and 0.20 < r < 0.39, respectively).18
Independent Variables
A total of 15,420 residences were included in the study. Seventy-nine residences did not report occupancy data, however, so those residences were excluded from the occupancy analyses. We categorized the ownership of each nursing home as for-profit, nonprofit, or public. We categorized nursing home size, based on quartiles of the size distribution, as large (> 127 beds), medium (64 to 126 beds), and small (< 64 beds). This method for categorizing the residential characteristics was similar to that used in previous work.19 Similarly, we categorized nursing home occupancy as high (> 92% occupancy), medium (73% to 91% occupancy), and low (< 73% occupancy) based on quartiles of the occupancy distribution. For the regional analysis, we grouped states together based on the CMS regional offices: Atlanta, Georgia; Boston, Massachusetts; Chicago, Illinois; Dallas, Texas; Denver, Colorado; Kansas City, Missouri; New York, New York; Philadelphia, Pennsylvania; San Francisco, California; and Seattle, Washington.20
Analyses
We used Levene’s test to determine whether variances among the residential groups were equal for each QM, using an a priori α = 0.05. For all 20 tests conducted (4 residential characteristics for all 5 QMs), the resulting F-statistics were significant, indicating that the assumption of homogeneity of variance was not met.
We therefore used Welch’s analysis of variance (ANOVA) to evaluate whether the groups within each residential characteristic were the same on their QM means. For example, we tested whether for-profit, nonprofit, and public residences had significantly different mean depression rates. For statistically significant differences, a Games-Howell post-hoc test was conducted to test the difference between all unique pairwise comparisons. An a priori α = 0.05 was used for both Welch’s ANOVA and post-hoc testing. All analyses were conducted in RStudio Version 1.2.5033 (Posit Software, PBC).
Results
Mean Differences
Mean QM scores for the 5 QMs investigated, grouped by residential characteristic for the 2019 year of reporting, are shown in Table 1. It should be noted that the number of residences that reported occupancy data (n = 15,341) does not equal the total number of residences included in the study (N = 15,420) because 79 residences did not report occupancy data. For all QMs reported in Table 1, lower scores are better. Table 2 and Table 3 show results from pairwise comparisons of mean differences for the different residential characteristic and QM groupings. Mean differences and 95% CI are presented along with an indication of statistical significance (when applicable).
Ownership
Nonprofit residences had significantly lower (ie, better) mean scores than for-profit and public residences for 3 QMs: resident depression, antipsychotic medication use, and anti-anxiety medication use. For-profit and public residences did not significantly differ in their mean values for these QMs. For-profit residences had a significantly lower mean score for resident falls than both nonprofit and public residences, but no significant difference existed between scores for nonprofit and public residence falls. There were no statistically significant differences between mean restraint scores among the ownership types.
Size
Large (ie, high-capacity) residences had a significantly higher mean depression score than both medium and small residences, but there was not a significant difference between medium and small residences. Large residences had the significantly lowest mean score for resident falls, and medium residences scored significantly lower than small residences. Medium residences had a significantly higher mean score for anti-anxiety medication use than both small and large residences, but there was no significant difference between small and large residences. There were no statistically significant differences between mean scores for restraint and antipsychotic medication use among the nursing home sizes.
Occupancy
The mean scores for 4 out of the 5 QMs exhibited similar relationships with occupancy rates: resident depression, falls, and antipsychotic and anti-anxiety medication use. Low-occupancy residences consistently scored significantly higher than both medium- and high-occupancy residences, and medium-occupancy residences consistently scored significantly higher than high-occupancy residences. On average, high-occupancy (≥ 92%) residences reported better QM scores than low-occupancy (< 73%) and medium-occupancy (73% to 91%) residences for all the QMs studied except physical restraint, which yielded no significant results. These findings indicate a possible inverse relationship between building occupancy rate and these 4 QMs.
Region
Pairwise comparisons of mean QM scores by region are shown in Table 3. The Chicago region had a significantly higher mean depression score than all other regions, while the San Francisco region’s score was significantly lower than all other regions, except Atlanta and Boston. The Kansas City region had a significantly higher mean score for resident falls than all other regions, with the exception of Denver, and the San Francisco region scored significantly lower than all other regions in falls. The Boston region had a significantly higher mean score for administering antipsychotic medication than all other regions, except for Kansas City and Seattle, and the New York and San Francisco regions both had significantly lower scores than all other regions except for each other. The Atlanta region reported a significantly higher mean score for administering antianxiety medication than all other regions, and the Seattle region’s score for anti-anxiety medication use was significantly lower than all other regions except for San Francisco.
Discussion
This study presented mean percentages for 5 QMs reported in the Nursing Home Compare database for the year 2019: depression, restraint, falls, antipsychotic medication use, and anti-anxiety medication use. We investigated these scores by 4 residential characteristics: ownership type, size, occupancy, and region. In general, publicly owned and low-occupancy residences had the highest scores, and thus the poorest performances, for the 5 chosen QMs during 2019. Nonprofit and high-occupancy residences generally had the lowest (ie, better) scores, and this result agrees with previous findings on long-stay nursing home residents.21 One possible explanation for better performance by high-occupancy buildings could be that increased social interaction is beneficial to nursing home residents as compared with low-occupancy buildings, where less social interaction is probable. It is difficult to draw conclusions regarding nursing home size and region; however, there are significant differences among sizes for 3 out of the 5 QMs and significant differences among regions for all 5 QMs. The analyses suggest that residence-level characteristics are related to QM scores. Although reported QMs are not a direct representation of resident quality of life, this work agrees with previous research that residential characteristics have some impact on the lives of nursing home residents.13-17 Improvements in QM reporting and changes in quality improvement goals since the formation of Nursing Home Compare exist, suggesting that nursing homes’ awareness of their reporting duties may impact quality of care or reporting tendencies.21,22 Future research should consider investigating the impacts of the COVID-19 pandemic on quality-reporting trends and QM scores.
Other physical characteristics of nursing homes, such as noise, lighting levels, and air quality, may also have an impact on QMs and possibly nursing home residents themselves. This type of data exploration could be included in future research. Additionally, future research could include a similar analysis over a longer period, rather than the 1-year period examined here, to investigate which types of residences consistently have high or low scores or how different types of residences have evolved over the years, particularly considering the impact of the COVID-19 pandemic. Information such as staffing levels, building renovations, and inspection data could be accounted for in future studies. Different QMs could also be investigated to better understand the influence of residential characteristics on quality of care.
Conclusion
This study suggests that residence-level characteristics are related to 5 reported nursing home QMs. Overall, nonprofit and high-occupancy residences had the lowest QM scores, indicating the highest performance. Although the results do not necessarily suggest that residence-level characteristics impact individual nursing home residents’ quality of life, they suggest that physical characteristics affect overall quality of life in nursing homes. Future research is needed to determine the specific physical characteristics of these residences that affect QM scores.
Corresponding author: Brian J. Puckett, puckett.brian@huskers.unl.edu.
Disclosures: None reported.
1. Gauthier S, Rosa-Neto P, Morais JA, et al. World Alzheimer report 2021: journey through the diagnosis of dementia. Alzheimer’s Disease International; 2021.
2. Garre-Olmo J, López-Pousa S, Turon-Estrada A, et al. Environmental determinants of quality of life in nursing home residents with severe dementia. J Am Geriatr Soc. 2012;60(7):1230-1236. doi:10.1111/j.1532-5415.2012.04040.x
3. Zeisel J, Silverstein N, Hyde J, et al. Environmental correlates to behavioral health outcomes in Alzheimer’s special care units. Gerontologist. 2003;43(5):697-711. doi:10.1093/geront/43.5.697
4. Brawley E. Environmental design for Alzheimer’s disease: a quality of life issue. Aging Ment Health. 2001;5(1):S79-S83. doi:10.1080/13607860120044846
5. Joosse L. Do sound levels and space contribute to agitation in nursing home residents with dementia? Research Gerontol Nurs. 2012;5(3):174-184. doi:10.3928/19404921-20120605-02
6. Dowling G, Graf C, Hubbard E, et al. Light treatment for neuropsychiatric behaviors in Alzheimer’s disease. Western J Nurs Res. 2007;29(8):961-975. doi:10.1177/0193945907303083
7. Tartarini F, Cooper P, Fleming R, et al. Indoor air temperature and agitation of nursing home residents with dementia. Am J Alzheimers Dis Other Demen. 2017;32(5):272-281. doi:10.1177/1533317517704898
8. Miyamoto Y, Tachimori H, Ito H. Formal caregiver burden in dementia: impact of behavioral and psychological symptoms of dementia and activities of daily living. Geriatr Nurs. 2010;31(4):246-253. doi:10.1016/j.gerinurse.2010.01.002
9. Dementia care and the built environment: position paper 3. Alzheimer’s Australia; 2004.
10. Cloak N, Al Khalili Y. Behavioral and psychological symptoms in dementia. Updated July 21, 2022. In: StatPearls [Internet]. StatPearls Publishing; 2022.
11. Centers for Medicare & Medicaid Services. Nursing homes including rehab services data archive. 2019 annual files. Accessed January 30, 2023. https://data.cms.gov/provider-data/archived-data/nursing-homes
12. Sanghavi P, Pan S, Caudry D. Assessment of nursing home reporting of major injury falls for quality measurement on Nursing Home Compare. Health Serv Res. 2020;55(2):201-210. doi:10.1111/1475-6773.13247
13. Hughes C, Lapane K, Mor V. Influence of facility characteristics on use of antipsychotic medications in nursing homes. Med Care. 2000;38(12):1164-1173. doi:10.1097/00005650-200012000-00003
14. Aaronson W, Zinn J, Rosko M. Do for-profit and not-for-profit nursing homes behave differently? Gerontologist. 1994;34(6):775-786. doi:10.1093/geront/34.6.775
15. O’Neill C, Harrington C, Kitchener M, et al. Quality of care in nursing homes: an analysis of relationships among profit, quality, and ownership. Med Care. 2003;41(12):1318-1330. doi:10.1097/01.MLR.0000100586.33970.58
16. Allen PD, Klein WC, Gruman C. Correlates of complaints made to the Connecticut Long-Term Care Ombudsman program: the role of organizational and structural factors. Res Aging. 2003;25(6):631-654. doi:10.1177/0164027503256691
17. Abrams H, Loomer L, Gandhi A, et al. Characteristics of U.S. nursing homes with COVID-19 cases. J Am Geriatr Soc. 2020;68(8):1653-1656. doi:10.1111/jgs.16661
18. Evans JD. Straightforward Statistics for the Behavioral Sciences. Thomson Brooks/Cole Publishing Co; 1996.
19. Zinn J, Spector W, Hsieh L, et al. Do trends in the reporting of quality measures on the Nursing Home Compare web site differ by nursing home characteristics? Gerontologist. 2005;45(6):720-730.
20. Centers for Medicare & Medicaid Services. CMS Regional Offices. Accessed January 30, 2023. https://www.cms.gov/Medicare/Coding/ICD10/CMS-Regional-Offices
21. Mukamel DB, Weimer DL, Spector WD, et al. Publication of quality report cards and trends in reported quality measures in nursing homes. Health Serv Res. 2008;43(4):1244-1262. doi:10.1093/geront/45.6.720
22. Harris Y, Clauser SB. Achieving improvement through nursing home quality measurement. Health Care Financ Rev. 2002;23(4):5-18.
From the University of Nebraska, Lincoln (Mr. Puckett and Dr. Ryherd), University of Nebraska Medical Center, Omaha (Dr. Manley), and the University of Nebraska, Omaha (Dr. Ryan).
ABSTRACT
Objective: This study evaluated relationships between physical characteristics of nursing home residences and quality-of-care measures.
Design: This was a cross-sectional ecologic study. The dependent variables were 5 Centers for Medicare & Medicaid Services (CMS) Nursing Home Compare database long-stay quality measures (QMs) during 2019: percentage of residents who displayed depressive symptoms, percentage of residents who were physically restrained, percentage of residents who experienced 1 or more falls resulting in injury, percentage of residents who received antipsychotic medication, and percentage of residents who received anti-anxiety medication. The independent variables were 4 residence characteristics: ownership type, size, occupancy, and region within the United States. We explored how different types of each residence characteristic compare for each QM.
Setting, participants, and measurements: Quality measure values from 15,420 CMS-supported nursing homes across the United States averaged over the 4 quarters of 2019 reporting were used. Welch’s analysis of variance was performed to examine whether the mean QM values for groups within each residential characteristic were statistically different.
Results: Publicly owned and low-occupancy residences had the highest mean QM values, indicating the poorest performance. Nonprofit and high-occupancy residences generally had the lowest (ie, best) mean QM values. There were significant differences in mean QM values among nursing home sizes and regions.
Conclusion: This study suggests that residence characteristics are related to 5 nursing home QMs. Results suggest that physical characteristics may be related to overall quality of life in nursing homes.
Keywords: quality of care, quality measures, residence characteristics, Alzheimer’s disease and related dementias.
More than 55 million people worldwide are living with Alzheimer’s disease and related dementias (ADRD).1 With the aging of the Baby Boomer population, this number is expected to rise to more than 78 million worldwide by 2030.1 Given the growing number of cognitively impaired older adults, there is an increased need for residences designed for the specialized care of this population. Although there are dozens of living options for the elderly, and although most specialized establishments have the resources to meet the immediate needs of their residents, many facilities lack universal design features that support a high quality of life for someone with ADRD or mild cognitive impairment. Previous research has shown relationships between behavioral and psychological symptoms of dementia (BPSD) and environmental characteristics such as acoustics, lighting, and indoor air temperature.2,3 Physical behaviors of BPSD, including aggression and wandering, and psychological symptoms, such as depression, anxiety, and delusions, put residents at risk of injury.4 Additionally, BPSD is correlated with caregiver burden and stress.5-8 Patients with dementia may also experience a lower stress threshold, changes in perception of space, and decreased short-term memory, creating environmental difficulties for those with ADRD9 that lead them to exhibit BPSD due to poor environmental design. Thus, there is a need to learn more about design features that minimize BPSD and promote a high quality of life for those with ADRD.10
Although research has shown relationships between physical environmental characteristics and BPSD, in this work we study relationships between possible BPSD indicators and 4 residence-level characteristics: ownership type, size, occupancy, and region in the United States (determined by location of the Centers for Medicare & Medicaid Services [CMS] regional offices). We analyzed data from the CMS Nursing Home Compare database for the year 2019.11 This database publishes quarterly data and star ratings for quality-of-care measures (QMs), staffing levels, and health inspections for every nursing home supported by CMS. Previous research has investigated the accuracy of QM reporting for resident falls, the impact of residential characteristics on administration of antipsychotic medication, the influence of profit status on resident outcomes and quality of care, and the effect of nursing home size on quality of life.12-16 Additionally, research suggests that residential characteristics such as size and location could be associated with infection control in nursing homes.17
Certain QMs, such as psychotropic drug administration, resident falls, and physical restraint, provide indicators of agitation, disorientation, or aggression, which are often signals of BPSD episodes. We hypothesized that residence types are associated with different QM scores, which could indicate different occurrences of BPSD. We selected 5 QMs for long-stay residents that could potentially be used as indicators of BPSD. Short-stay resident data were not included in this work to control for BPSD that could be a result of sheer unfamiliarity with the environment and confusion from being in a new home.
Methods
Design and Data Collection
This was a cross-sectional ecologic study aimed at exploring relationships between aggregate residential characteristics and QMs. Data were retrieved from the 2019 annual archives found in the CMS provider data catalog on nursing homes, including rehabilitation services.11 The dataset provides general residence information, such as ownership, number of beds, number of residents, and location, as well as residence quality metrics, such as QMs, staffing data, and inspection data. Residence characteristics and 4-quarter averages of QMs were retrieved and used as cross-sectional data. The data used are from 15,420 residences across the United States. Nursing homes located in Guam, the US Pacific Territories, Puerto Rico, and the US Virgin Islands, while supported by CMS and included in the dataset, were excluded from the study due to a severe absence of QM data.
Dependent Variables
We investigated 5 QMs that were averaged across the 4 quarters of 2019. The QMs used as dependent variables were percentage of residents who displayed depressive symptoms (depression), percentage of residents who were physically restrained (restraint), percentage of residents who experienced 1 or more falls resulting in a major injury (falls), percentage of residents who received antipsychotic medication (antipsychotic medication), and percentage of residents who received anti-anxiety or hypnotic medication (anti-anxiety medication).
A total of 2471 QM values were unreported across the 5 QM analyzed: 501 residences did not report depression data; 479 did not report restraint data; 477 did not report falls data; 508 did not report antipsychotic medication data; and 506 did not report anti-anxiety medication data. A residence with a missing QM value was excluded from that respective analysis.
To assess the relationships among the different QMs, a Pearson correlation coefficient r was computed for each unique pair of QMs (Figure). All QMs studied were found to be very weakly or weakly correlated with one another using the Evans classification for very weak and weak correlations (r < 0.20 and 0.20 < r < 0.39, respectively).18
Independent Variables
A total of 15,420 residences were included in the study. Seventy-nine residences did not report occupancy data, however, so those residences were excluded from the occupancy analyses. We categorized the ownership of each nursing home as for-profit, nonprofit, or public. We categorized nursing home size, based on quartiles of the size distribution, as large (> 127 beds), medium (64 to 126 beds), and small (< 64 beds). This method for categorizing the residential characteristics was similar to that used in previous work.19 Similarly, we categorized nursing home occupancy as high (> 92% occupancy), medium (73% to 91% occupancy), and low (< 73% occupancy) based on quartiles of the occupancy distribution. For the regional analysis, we grouped states together based on the CMS regional offices: Atlanta, Georgia; Boston, Massachusetts; Chicago, Illinois; Dallas, Texas; Denver, Colorado; Kansas City, Missouri; New York, New York; Philadelphia, Pennsylvania; San Francisco, California; and Seattle, Washington.20
Analyses
We used Levene’s test to determine whether variances among the residential groups were equal for each QM, using an a priori α = 0.05. For all 20 tests conducted (4 residential characteristics for all 5 QMs), the resulting F-statistics were significant, indicating that the assumption of homogeneity of variance was not met.
We therefore used Welch’s analysis of variance (ANOVA) to evaluate whether the groups within each residential characteristic were the same on their QM means. For example, we tested whether for-profit, nonprofit, and public residences had significantly different mean depression rates. For statistically significant differences, a Games-Howell post-hoc test was conducted to test the difference between all unique pairwise comparisons. An a priori α = 0.05 was used for both Welch’s ANOVA and post-hoc testing. All analyses were conducted in RStudio Version 1.2.5033 (Posit Software, PBC).
Results
Mean Differences
Mean QM scores for the 5 QMs investigated, grouped by residential characteristic for the 2019 year of reporting, are shown in Table 1. It should be noted that the number of residences that reported occupancy data (n = 15,341) does not equal the total number of residences included in the study (N = 15,420) because 79 residences did not report occupancy data. For all QMs reported in Table 1, lower scores are better. Table 2 and Table 3 show results from pairwise comparisons of mean differences for the different residential characteristic and QM groupings. Mean differences and 95% CI are presented along with an indication of statistical significance (when applicable).
Ownership
Nonprofit residences had significantly lower (ie, better) mean scores than for-profit and public residences for 3 QMs: resident depression, antipsychotic medication use, and anti-anxiety medication use. For-profit and public residences did not significantly differ in their mean values for these QMs. For-profit residences had a significantly lower mean score for resident falls than both nonprofit and public residences, but no significant difference existed between scores for nonprofit and public residence falls. There were no statistically significant differences between mean restraint scores among the ownership types.
Size
Large (ie, high-capacity) residences had a significantly higher mean depression score than both medium and small residences, but there was not a significant difference between medium and small residences. Large residences had the significantly lowest mean score for resident falls, and medium residences scored significantly lower than small residences. Medium residences had a significantly higher mean score for anti-anxiety medication use than both small and large residences, but there was no significant difference between small and large residences. There were no statistically significant differences between mean scores for restraint and antipsychotic medication use among the nursing home sizes.
Occupancy
The mean scores for 4 out of the 5 QMs exhibited similar relationships with occupancy rates: resident depression, falls, and antipsychotic and anti-anxiety medication use. Low-occupancy residences consistently scored significantly higher than both medium- and high-occupancy residences, and medium-occupancy residences consistently scored significantly higher than high-occupancy residences. On average, high-occupancy (≥ 92%) residences reported better QM scores than low-occupancy (< 73%) and medium-occupancy (73% to 91%) residences for all the QMs studied except physical restraint, which yielded no significant results. These findings indicate a possible inverse relationship between building occupancy rate and these 4 QMs.
Region
Pairwise comparisons of mean QM scores by region are shown in Table 3. The Chicago region had a significantly higher mean depression score than all other regions, while the San Francisco region’s score was significantly lower than all other regions, except Atlanta and Boston. The Kansas City region had a significantly higher mean score for resident falls than all other regions, with the exception of Denver, and the San Francisco region scored significantly lower than all other regions in falls. The Boston region had a significantly higher mean score for administering antipsychotic medication than all other regions, except for Kansas City and Seattle, and the New York and San Francisco regions both had significantly lower scores than all other regions except for each other. The Atlanta region reported a significantly higher mean score for administering antianxiety medication than all other regions, and the Seattle region’s score for anti-anxiety medication use was significantly lower than all other regions except for San Francisco.
Discussion
This study presented mean percentages for 5 QMs reported in the Nursing Home Compare database for the year 2019: depression, restraint, falls, antipsychotic medication use, and anti-anxiety medication use. We investigated these scores by 4 residential characteristics: ownership type, size, occupancy, and region. In general, publicly owned and low-occupancy residences had the highest scores, and thus the poorest performances, for the 5 chosen QMs during 2019. Nonprofit and high-occupancy residences generally had the lowest (ie, better) scores, and this result agrees with previous findings on long-stay nursing home residents.21 One possible explanation for better performance by high-occupancy buildings could be that increased social interaction is beneficial to nursing home residents as compared with low-occupancy buildings, where less social interaction is probable. It is difficult to draw conclusions regarding nursing home size and region; however, there are significant differences among sizes for 3 out of the 5 QMs and significant differences among regions for all 5 QMs. The analyses suggest that residence-level characteristics are related to QM scores. Although reported QMs are not a direct representation of resident quality of life, this work agrees with previous research that residential characteristics have some impact on the lives of nursing home residents.13-17 Improvements in QM reporting and changes in quality improvement goals since the formation of Nursing Home Compare exist, suggesting that nursing homes’ awareness of their reporting duties may impact quality of care or reporting tendencies.21,22 Future research should consider investigating the impacts of the COVID-19 pandemic on quality-reporting trends and QM scores.
Other physical characteristics of nursing homes, such as noise, lighting levels, and air quality, may also have an impact on QMs and possibly nursing home residents themselves. This type of data exploration could be included in future research. Additionally, future research could include a similar analysis over a longer period, rather than the 1-year period examined here, to investigate which types of residences consistently have high or low scores or how different types of residences have evolved over the years, particularly considering the impact of the COVID-19 pandemic. Information such as staffing levels, building renovations, and inspection data could be accounted for in future studies. Different QMs could also be investigated to better understand the influence of residential characteristics on quality of care.
Conclusion
This study suggests that residence-level characteristics are related to 5 reported nursing home QMs. Overall, nonprofit and high-occupancy residences had the lowest QM scores, indicating the highest performance. Although the results do not necessarily suggest that residence-level characteristics impact individual nursing home residents’ quality of life, they suggest that physical characteristics affect overall quality of life in nursing homes. Future research is needed to determine the specific physical characteristics of these residences that affect QM scores.
Corresponding author: Brian J. Puckett, puckett.brian@huskers.unl.edu.
Disclosures: None reported.
From the University of Nebraska, Lincoln (Mr. Puckett and Dr. Ryherd), University of Nebraska Medical Center, Omaha (Dr. Manley), and the University of Nebraska, Omaha (Dr. Ryan).
ABSTRACT
Objective: This study evaluated relationships between physical characteristics of nursing home residences and quality-of-care measures.
Design: This was a cross-sectional ecologic study. The dependent variables were 5 Centers for Medicare & Medicaid Services (CMS) Nursing Home Compare database long-stay quality measures (QMs) during 2019: percentage of residents who displayed depressive symptoms, percentage of residents who were physically restrained, percentage of residents who experienced 1 or more falls resulting in injury, percentage of residents who received antipsychotic medication, and percentage of residents who received anti-anxiety medication. The independent variables were 4 residence characteristics: ownership type, size, occupancy, and region within the United States. We explored how different types of each residence characteristic compare for each QM.
Setting, participants, and measurements: Quality measure values from 15,420 CMS-supported nursing homes across the United States averaged over the 4 quarters of 2019 reporting were used. Welch’s analysis of variance was performed to examine whether the mean QM values for groups within each residential characteristic were statistically different.
Results: Publicly owned and low-occupancy residences had the highest mean QM values, indicating the poorest performance. Nonprofit and high-occupancy residences generally had the lowest (ie, best) mean QM values. There were significant differences in mean QM values among nursing home sizes and regions.
Conclusion: This study suggests that residence characteristics are related to 5 nursing home QMs. Results suggest that physical characteristics may be related to overall quality of life in nursing homes.
Keywords: quality of care, quality measures, residence characteristics, Alzheimer’s disease and related dementias.
More than 55 million people worldwide are living with Alzheimer’s disease and related dementias (ADRD).1 With the aging of the Baby Boomer population, this number is expected to rise to more than 78 million worldwide by 2030.1 Given the growing number of cognitively impaired older adults, there is an increased need for residences designed for the specialized care of this population. Although there are dozens of living options for the elderly, and although most specialized establishments have the resources to meet the immediate needs of their residents, many facilities lack universal design features that support a high quality of life for someone with ADRD or mild cognitive impairment. Previous research has shown relationships between behavioral and psychological symptoms of dementia (BPSD) and environmental characteristics such as acoustics, lighting, and indoor air temperature.2,3 Physical behaviors of BPSD, including aggression and wandering, and psychological symptoms, such as depression, anxiety, and delusions, put residents at risk of injury.4 Additionally, BPSD is correlated with caregiver burden and stress.5-8 Patients with dementia may also experience a lower stress threshold, changes in perception of space, and decreased short-term memory, creating environmental difficulties for those with ADRD9 that lead them to exhibit BPSD due to poor environmental design. Thus, there is a need to learn more about design features that minimize BPSD and promote a high quality of life for those with ADRD.10
Although research has shown relationships between physical environmental characteristics and BPSD, in this work we study relationships between possible BPSD indicators and 4 residence-level characteristics: ownership type, size, occupancy, and region in the United States (determined by location of the Centers for Medicare & Medicaid Services [CMS] regional offices). We analyzed data from the CMS Nursing Home Compare database for the year 2019.11 This database publishes quarterly data and star ratings for quality-of-care measures (QMs), staffing levels, and health inspections for every nursing home supported by CMS. Previous research has investigated the accuracy of QM reporting for resident falls, the impact of residential characteristics on administration of antipsychotic medication, the influence of profit status on resident outcomes and quality of care, and the effect of nursing home size on quality of life.12-16 Additionally, research suggests that residential characteristics such as size and location could be associated with infection control in nursing homes.17
Certain QMs, such as psychotropic drug administration, resident falls, and physical restraint, provide indicators of agitation, disorientation, or aggression, which are often signals of BPSD episodes. We hypothesized that residence types are associated with different QM scores, which could indicate different occurrences of BPSD. We selected 5 QMs for long-stay residents that could potentially be used as indicators of BPSD. Short-stay resident data were not included in this work to control for BPSD that could be a result of sheer unfamiliarity with the environment and confusion from being in a new home.
Methods
Design and Data Collection
This was a cross-sectional ecologic study aimed at exploring relationships between aggregate residential characteristics and QMs. Data were retrieved from the 2019 annual archives found in the CMS provider data catalog on nursing homes, including rehabilitation services.11 The dataset provides general residence information, such as ownership, number of beds, number of residents, and location, as well as residence quality metrics, such as QMs, staffing data, and inspection data. Residence characteristics and 4-quarter averages of QMs were retrieved and used as cross-sectional data. The data used are from 15,420 residences across the United States. Nursing homes located in Guam, the US Pacific Territories, Puerto Rico, and the US Virgin Islands, while supported by CMS and included in the dataset, were excluded from the study due to a severe absence of QM data.
Dependent Variables
We investigated 5 QMs that were averaged across the 4 quarters of 2019. The QMs used as dependent variables were percentage of residents who displayed depressive symptoms (depression), percentage of residents who were physically restrained (restraint), percentage of residents who experienced 1 or more falls resulting in a major injury (falls), percentage of residents who received antipsychotic medication (antipsychotic medication), and percentage of residents who received anti-anxiety or hypnotic medication (anti-anxiety medication).
A total of 2471 QM values were unreported across the 5 QM analyzed: 501 residences did not report depression data; 479 did not report restraint data; 477 did not report falls data; 508 did not report antipsychotic medication data; and 506 did not report anti-anxiety medication data. A residence with a missing QM value was excluded from that respective analysis.
To assess the relationships among the different QMs, a Pearson correlation coefficient r was computed for each unique pair of QMs (Figure). All QMs studied were found to be very weakly or weakly correlated with one another using the Evans classification for very weak and weak correlations (r < 0.20 and 0.20 < r < 0.39, respectively).18
Independent Variables
A total of 15,420 residences were included in the study. Seventy-nine residences did not report occupancy data, however, so those residences were excluded from the occupancy analyses. We categorized the ownership of each nursing home as for-profit, nonprofit, or public. We categorized nursing home size, based on quartiles of the size distribution, as large (> 127 beds), medium (64 to 126 beds), and small (< 64 beds). This method for categorizing the residential characteristics was similar to that used in previous work.19 Similarly, we categorized nursing home occupancy as high (> 92% occupancy), medium (73% to 91% occupancy), and low (< 73% occupancy) based on quartiles of the occupancy distribution. For the regional analysis, we grouped states together based on the CMS regional offices: Atlanta, Georgia; Boston, Massachusetts; Chicago, Illinois; Dallas, Texas; Denver, Colorado; Kansas City, Missouri; New York, New York; Philadelphia, Pennsylvania; San Francisco, California; and Seattle, Washington.20
Analyses
We used Levene’s test to determine whether variances among the residential groups were equal for each QM, using an a priori α = 0.05. For all 20 tests conducted (4 residential characteristics for all 5 QMs), the resulting F-statistics were significant, indicating that the assumption of homogeneity of variance was not met.
We therefore used Welch’s analysis of variance (ANOVA) to evaluate whether the groups within each residential characteristic were the same on their QM means. For example, we tested whether for-profit, nonprofit, and public residences had significantly different mean depression rates. For statistically significant differences, a Games-Howell post-hoc test was conducted to test the difference between all unique pairwise comparisons. An a priori α = 0.05 was used for both Welch’s ANOVA and post-hoc testing. All analyses were conducted in RStudio Version 1.2.5033 (Posit Software, PBC).
Results
Mean Differences
Mean QM scores for the 5 QMs investigated, grouped by residential characteristic for the 2019 year of reporting, are shown in Table 1. It should be noted that the number of residences that reported occupancy data (n = 15,341) does not equal the total number of residences included in the study (N = 15,420) because 79 residences did not report occupancy data. For all QMs reported in Table 1, lower scores are better. Table 2 and Table 3 show results from pairwise comparisons of mean differences for the different residential characteristic and QM groupings. Mean differences and 95% CI are presented along with an indication of statistical significance (when applicable).
Ownership
Nonprofit residences had significantly lower (ie, better) mean scores than for-profit and public residences for 3 QMs: resident depression, antipsychotic medication use, and anti-anxiety medication use. For-profit and public residences did not significantly differ in their mean values for these QMs. For-profit residences had a significantly lower mean score for resident falls than both nonprofit and public residences, but no significant difference existed between scores for nonprofit and public residence falls. There were no statistically significant differences between mean restraint scores among the ownership types.
Size
Large (ie, high-capacity) residences had a significantly higher mean depression score than both medium and small residences, but there was not a significant difference between medium and small residences. Large residences had the significantly lowest mean score for resident falls, and medium residences scored significantly lower than small residences. Medium residences had a significantly higher mean score for anti-anxiety medication use than both small and large residences, but there was no significant difference between small and large residences. There were no statistically significant differences between mean scores for restraint and antipsychotic medication use among the nursing home sizes.
Occupancy
The mean scores for 4 out of the 5 QMs exhibited similar relationships with occupancy rates: resident depression, falls, and antipsychotic and anti-anxiety medication use. Low-occupancy residences consistently scored significantly higher than both medium- and high-occupancy residences, and medium-occupancy residences consistently scored significantly higher than high-occupancy residences. On average, high-occupancy (≥ 92%) residences reported better QM scores than low-occupancy (< 73%) and medium-occupancy (73% to 91%) residences for all the QMs studied except physical restraint, which yielded no significant results. These findings indicate a possible inverse relationship between building occupancy rate and these 4 QMs.
Region
Pairwise comparisons of mean QM scores by region are shown in Table 3. The Chicago region had a significantly higher mean depression score than all other regions, while the San Francisco region’s score was significantly lower than all other regions, except Atlanta and Boston. The Kansas City region had a significantly higher mean score for resident falls than all other regions, with the exception of Denver, and the San Francisco region scored significantly lower than all other regions in falls. The Boston region had a significantly higher mean score for administering antipsychotic medication than all other regions, except for Kansas City and Seattle, and the New York and San Francisco regions both had significantly lower scores than all other regions except for each other. The Atlanta region reported a significantly higher mean score for administering antianxiety medication than all other regions, and the Seattle region’s score for anti-anxiety medication use was significantly lower than all other regions except for San Francisco.
Discussion
This study presented mean percentages for 5 QMs reported in the Nursing Home Compare database for the year 2019: depression, restraint, falls, antipsychotic medication use, and anti-anxiety medication use. We investigated these scores by 4 residential characteristics: ownership type, size, occupancy, and region. In general, publicly owned and low-occupancy residences had the highest scores, and thus the poorest performances, for the 5 chosen QMs during 2019. Nonprofit and high-occupancy residences generally had the lowest (ie, better) scores, and this result agrees with previous findings on long-stay nursing home residents.21 One possible explanation for better performance by high-occupancy buildings could be that increased social interaction is beneficial to nursing home residents as compared with low-occupancy buildings, where less social interaction is probable. It is difficult to draw conclusions regarding nursing home size and region; however, there are significant differences among sizes for 3 out of the 5 QMs and significant differences among regions for all 5 QMs. The analyses suggest that residence-level characteristics are related to QM scores. Although reported QMs are not a direct representation of resident quality of life, this work agrees with previous research that residential characteristics have some impact on the lives of nursing home residents.13-17 Improvements in QM reporting and changes in quality improvement goals since the formation of Nursing Home Compare exist, suggesting that nursing homes’ awareness of their reporting duties may impact quality of care or reporting tendencies.21,22 Future research should consider investigating the impacts of the COVID-19 pandemic on quality-reporting trends and QM scores.
Other physical characteristics of nursing homes, such as noise, lighting levels, and air quality, may also have an impact on QMs and possibly nursing home residents themselves. This type of data exploration could be included in future research. Additionally, future research could include a similar analysis over a longer period, rather than the 1-year period examined here, to investigate which types of residences consistently have high or low scores or how different types of residences have evolved over the years, particularly considering the impact of the COVID-19 pandemic. Information such as staffing levels, building renovations, and inspection data could be accounted for in future studies. Different QMs could also be investigated to better understand the influence of residential characteristics on quality of care.
Conclusion
This study suggests that residence-level characteristics are related to 5 reported nursing home QMs. Overall, nonprofit and high-occupancy residences had the lowest QM scores, indicating the highest performance. Although the results do not necessarily suggest that residence-level characteristics impact individual nursing home residents’ quality of life, they suggest that physical characteristics affect overall quality of life in nursing homes. Future research is needed to determine the specific physical characteristics of these residences that affect QM scores.
Corresponding author: Brian J. Puckett, puckett.brian@huskers.unl.edu.
Disclosures: None reported.
1. Gauthier S, Rosa-Neto P, Morais JA, et al. World Alzheimer report 2021: journey through the diagnosis of dementia. Alzheimer’s Disease International; 2021.
2. Garre-Olmo J, López-Pousa S, Turon-Estrada A, et al. Environmental determinants of quality of life in nursing home residents with severe dementia. J Am Geriatr Soc. 2012;60(7):1230-1236. doi:10.1111/j.1532-5415.2012.04040.x
3. Zeisel J, Silverstein N, Hyde J, et al. Environmental correlates to behavioral health outcomes in Alzheimer’s special care units. Gerontologist. 2003;43(5):697-711. doi:10.1093/geront/43.5.697
4. Brawley E. Environmental design for Alzheimer’s disease: a quality of life issue. Aging Ment Health. 2001;5(1):S79-S83. doi:10.1080/13607860120044846
5. Joosse L. Do sound levels and space contribute to agitation in nursing home residents with dementia? Research Gerontol Nurs. 2012;5(3):174-184. doi:10.3928/19404921-20120605-02
6. Dowling G, Graf C, Hubbard E, et al. Light treatment for neuropsychiatric behaviors in Alzheimer’s disease. Western J Nurs Res. 2007;29(8):961-975. doi:10.1177/0193945907303083
7. Tartarini F, Cooper P, Fleming R, et al. Indoor air temperature and agitation of nursing home residents with dementia. Am J Alzheimers Dis Other Demen. 2017;32(5):272-281. doi:10.1177/1533317517704898
8. Miyamoto Y, Tachimori H, Ito H. Formal caregiver burden in dementia: impact of behavioral and psychological symptoms of dementia and activities of daily living. Geriatr Nurs. 2010;31(4):246-253. doi:10.1016/j.gerinurse.2010.01.002
9. Dementia care and the built environment: position paper 3. Alzheimer’s Australia; 2004.
10. Cloak N, Al Khalili Y. Behavioral and psychological symptoms in dementia. Updated July 21, 2022. In: StatPearls [Internet]. StatPearls Publishing; 2022.
11. Centers for Medicare & Medicaid Services. Nursing homes including rehab services data archive. 2019 annual files. Accessed January 30, 2023. https://data.cms.gov/provider-data/archived-data/nursing-homes
12. Sanghavi P, Pan S, Caudry D. Assessment of nursing home reporting of major injury falls for quality measurement on Nursing Home Compare. Health Serv Res. 2020;55(2):201-210. doi:10.1111/1475-6773.13247
13. Hughes C, Lapane K, Mor V. Influence of facility characteristics on use of antipsychotic medications in nursing homes. Med Care. 2000;38(12):1164-1173. doi:10.1097/00005650-200012000-00003
14. Aaronson W, Zinn J, Rosko M. Do for-profit and not-for-profit nursing homes behave differently? Gerontologist. 1994;34(6):775-786. doi:10.1093/geront/34.6.775
15. O’Neill C, Harrington C, Kitchener M, et al. Quality of care in nursing homes: an analysis of relationships among profit, quality, and ownership. Med Care. 2003;41(12):1318-1330. doi:10.1097/01.MLR.0000100586.33970.58
16. Allen PD, Klein WC, Gruman C. Correlates of complaints made to the Connecticut Long-Term Care Ombudsman program: the role of organizational and structural factors. Res Aging. 2003;25(6):631-654. doi:10.1177/0164027503256691
17. Abrams H, Loomer L, Gandhi A, et al. Characteristics of U.S. nursing homes with COVID-19 cases. J Am Geriatr Soc. 2020;68(8):1653-1656. doi:10.1111/jgs.16661
18. Evans JD. Straightforward Statistics for the Behavioral Sciences. Thomson Brooks/Cole Publishing Co; 1996.
19. Zinn J, Spector W, Hsieh L, et al. Do trends in the reporting of quality measures on the Nursing Home Compare web site differ by nursing home characteristics? Gerontologist. 2005;45(6):720-730.
20. Centers for Medicare & Medicaid Services. CMS Regional Offices. Accessed January 30, 2023. https://www.cms.gov/Medicare/Coding/ICD10/CMS-Regional-Offices
21. Mukamel DB, Weimer DL, Spector WD, et al. Publication of quality report cards and trends in reported quality measures in nursing homes. Health Serv Res. 2008;43(4):1244-1262. doi:10.1093/geront/45.6.720
22. Harris Y, Clauser SB. Achieving improvement through nursing home quality measurement. Health Care Financ Rev. 2002;23(4):5-18.
1. Gauthier S, Rosa-Neto P, Morais JA, et al. World Alzheimer report 2021: journey through the diagnosis of dementia. Alzheimer’s Disease International; 2021.
2. Garre-Olmo J, López-Pousa S, Turon-Estrada A, et al. Environmental determinants of quality of life in nursing home residents with severe dementia. J Am Geriatr Soc. 2012;60(7):1230-1236. doi:10.1111/j.1532-5415.2012.04040.x
3. Zeisel J, Silverstein N, Hyde J, et al. Environmental correlates to behavioral health outcomes in Alzheimer’s special care units. Gerontologist. 2003;43(5):697-711. doi:10.1093/geront/43.5.697
4. Brawley E. Environmental design for Alzheimer’s disease: a quality of life issue. Aging Ment Health. 2001;5(1):S79-S83. doi:10.1080/13607860120044846
5. Joosse L. Do sound levels and space contribute to agitation in nursing home residents with dementia? Research Gerontol Nurs. 2012;5(3):174-184. doi:10.3928/19404921-20120605-02
6. Dowling G, Graf C, Hubbard E, et al. Light treatment for neuropsychiatric behaviors in Alzheimer’s disease. Western J Nurs Res. 2007;29(8):961-975. doi:10.1177/0193945907303083
7. Tartarini F, Cooper P, Fleming R, et al. Indoor air temperature and agitation of nursing home residents with dementia. Am J Alzheimers Dis Other Demen. 2017;32(5):272-281. doi:10.1177/1533317517704898
8. Miyamoto Y, Tachimori H, Ito H. Formal caregiver burden in dementia: impact of behavioral and psychological symptoms of dementia and activities of daily living. Geriatr Nurs. 2010;31(4):246-253. doi:10.1016/j.gerinurse.2010.01.002
9. Dementia care and the built environment: position paper 3. Alzheimer’s Australia; 2004.
10. Cloak N, Al Khalili Y. Behavioral and psychological symptoms in dementia. Updated July 21, 2022. In: StatPearls [Internet]. StatPearls Publishing; 2022.
11. Centers for Medicare & Medicaid Services. Nursing homes including rehab services data archive. 2019 annual files. Accessed January 30, 2023. https://data.cms.gov/provider-data/archived-data/nursing-homes
12. Sanghavi P, Pan S, Caudry D. Assessment of nursing home reporting of major injury falls for quality measurement on Nursing Home Compare. Health Serv Res. 2020;55(2):201-210. doi:10.1111/1475-6773.13247
13. Hughes C, Lapane K, Mor V. Influence of facility characteristics on use of antipsychotic medications in nursing homes. Med Care. 2000;38(12):1164-1173. doi:10.1097/00005650-200012000-00003
14. Aaronson W, Zinn J, Rosko M. Do for-profit and not-for-profit nursing homes behave differently? Gerontologist. 1994;34(6):775-786. doi:10.1093/geront/34.6.775
15. O’Neill C, Harrington C, Kitchener M, et al. Quality of care in nursing homes: an analysis of relationships among profit, quality, and ownership. Med Care. 2003;41(12):1318-1330. doi:10.1097/01.MLR.0000100586.33970.58
16. Allen PD, Klein WC, Gruman C. Correlates of complaints made to the Connecticut Long-Term Care Ombudsman program: the role of organizational and structural factors. Res Aging. 2003;25(6):631-654. doi:10.1177/0164027503256691
17. Abrams H, Loomer L, Gandhi A, et al. Characteristics of U.S. nursing homes with COVID-19 cases. J Am Geriatr Soc. 2020;68(8):1653-1656. doi:10.1111/jgs.16661
18. Evans JD. Straightforward Statistics for the Behavioral Sciences. Thomson Brooks/Cole Publishing Co; 1996.
19. Zinn J, Spector W, Hsieh L, et al. Do trends in the reporting of quality measures on the Nursing Home Compare web site differ by nursing home characteristics? Gerontologist. 2005;45(6):720-730.
20. Centers for Medicare & Medicaid Services. CMS Regional Offices. Accessed January 30, 2023. https://www.cms.gov/Medicare/Coding/ICD10/CMS-Regional-Offices
21. Mukamel DB, Weimer DL, Spector WD, et al. Publication of quality report cards and trends in reported quality measures in nursing homes. Health Serv Res. 2008;43(4):1244-1262. doi:10.1093/geront/45.6.720
22. Harris Y, Clauser SB. Achieving improvement through nursing home quality measurement. Health Care Financ Rev. 2002;23(4):5-18.
Tooth loss and diabetes together hasten mental decline
most specifically in those 65-74 years of age, new findings suggest.
The data come from a 12-year follow-up of older adults in a nationally representative U.S. survey.
“From a clinical perspective, our study demonstrates the importance of improving access to dental health care and integrating primary dental and medical care. Health care professionals and family caregivers should pay close attention to the cognitive status of diabetic older adults with poor oral health status,” lead author Bei Wu, PhD, of New York University, said in an interview. Dr. Wu is the Dean’s Professor in Global Health and codirector of the NYU Aging Incubator.
Moreover, said Dr. Wu: “For individuals with both poor oral health and diabetes, regular dental visits should be encouraged in addition to adherence to the diabetes self-care protocol.”
Diabetes has long been recognized as a risk factor for cognitive decline, but the findings have been inconsistent for different age groups. Tooth loss has also been linked to cognitive decline and dementia, as well as diabetes.
The mechanisms aren’t entirely clear, but “co-occurring diabetes and poor oral health may increase the risk for dementia, possibly via the potentially interrelated pathways of chronic inflammation and cardiovascular risk factors,” Dr. Wu said.
The new study, published in the Journal of Dental Research, is the first to examine the relationships between all three conditions by age group.
Diabetes, edentulism, and cognitive decline
The data came from a total of 9,948 participants in the Health and Retirement Study (HRS) from 2006 to 2018. At baseline, 5,440 participants were aged 65-74 years, 3,300 were aged 75-84, and 1,208 were aged 85 years or older.
They were assessed every 2 years using the 35-point Telephone Survey for Cognitive Status, which included tests of immediate and delayed word recall, repeated subtracting by 7, counting backward from 20, naming objects, and naming the president and vice president of the U.S. As might be expected, the youngest group scored the highest, averaging 23 points, while the oldest group scored lowest, at 18.5 points.
Participants were also asked if they had ever been told by a doctor that they have diabetes. Another question was: “Have you lost all of your upper and lower natural permanent teeth?”
The condition of having no teeth is known as edentulism.
The percentages of participants who reported having both diabetes and edentulism were 6.0%, 6.7%, and 5.0% for those aged 65-74 years, 75-84 years, and 85 years or older, respectively. The proportions with neither of those conditions were 63.5%, 60.4%, and 58.3% in those three age groups, respectively (P < .001).
Compared with their counterparts with neither diabetes nor edentulism at baseline, older adults with both conditions aged 65-74 years (P < .001) and aged 75-84 years had worse cognitive function (P < .001).
In terms of the rate of cognitive decline, compared with those with neither condition from the same age cohort, older adults aged 65-74 years with both conditions declined at a higher rate (P < .001).
Having diabetes alone led to accelerated cognitive decline in older adults aged 65-74 years (P < .001). Having edentulism alone led to accelerated decline in older adults aged 65-74 years (P < .001) and older adults aged 75-84 years (P < 0.01).
“Our study finds the co-occurrence of diabetes and edentulism led to a worse cognitive function and a faster cognitive decline in older adults aged 65-74 years,” say Wu and colleagues.
Study limitations: Better data needed
The study has several limitations, most of them due to the data source. For example, while the HRS collects detailed information on cognitive status, edentulism is its only measure of oral health. There were no data on whether individuals had replacements such as dentures or implants that would affect their ability to eat, which could influence other health factors.
“I have made repeated appeals for federal funding to collect more oral health-related information in large national surveys,” Dr. Wu told this news organization.
Similarly, assessments of diabetes status such as hemoglobin A1c were only available for small subsets and not sufficient to demonstrate statistical significance, she explained.
Dr. Wu suggested that both oral health and cognitive screening might be included in the “Welcome to Medicare” preventive visit. In addition, “Oral hygiene practice should also be highlighted to improve cognitive health. Developing dental care interventions and programs are needed for reducing the societal cost of dementia.”
The study was partially supported by the National Institutes of Health. The authors have reported no relevant financial relationships.
A version of this article first appeared on Medscape.com.
most specifically in those 65-74 years of age, new findings suggest.
The data come from a 12-year follow-up of older adults in a nationally representative U.S. survey.
“From a clinical perspective, our study demonstrates the importance of improving access to dental health care and integrating primary dental and medical care. Health care professionals and family caregivers should pay close attention to the cognitive status of diabetic older adults with poor oral health status,” lead author Bei Wu, PhD, of New York University, said in an interview. Dr. Wu is the Dean’s Professor in Global Health and codirector of the NYU Aging Incubator.
Moreover, said Dr. Wu: “For individuals with both poor oral health and diabetes, regular dental visits should be encouraged in addition to adherence to the diabetes self-care protocol.”
Diabetes has long been recognized as a risk factor for cognitive decline, but the findings have been inconsistent for different age groups. Tooth loss has also been linked to cognitive decline and dementia, as well as diabetes.
The mechanisms aren’t entirely clear, but “co-occurring diabetes and poor oral health may increase the risk for dementia, possibly via the potentially interrelated pathways of chronic inflammation and cardiovascular risk factors,” Dr. Wu said.
The new study, published in the Journal of Dental Research, is the first to examine the relationships between all three conditions by age group.
Diabetes, edentulism, and cognitive decline
The data came from a total of 9,948 participants in the Health and Retirement Study (HRS) from 2006 to 2018. At baseline, 5,440 participants were aged 65-74 years, 3,300 were aged 75-84, and 1,208 were aged 85 years or older.
They were assessed every 2 years using the 35-point Telephone Survey for Cognitive Status, which included tests of immediate and delayed word recall, repeated subtracting by 7, counting backward from 20, naming objects, and naming the president and vice president of the U.S. As might be expected, the youngest group scored the highest, averaging 23 points, while the oldest group scored lowest, at 18.5 points.
Participants were also asked if they had ever been told by a doctor that they have diabetes. Another question was: “Have you lost all of your upper and lower natural permanent teeth?”
The condition of having no teeth is known as edentulism.
The percentages of participants who reported having both diabetes and edentulism were 6.0%, 6.7%, and 5.0% for those aged 65-74 years, 75-84 years, and 85 years or older, respectively. The proportions with neither of those conditions were 63.5%, 60.4%, and 58.3% in those three age groups, respectively (P < .001).
Compared with their counterparts with neither diabetes nor edentulism at baseline, older adults with both conditions aged 65-74 years (P < .001) and aged 75-84 years had worse cognitive function (P < .001).
In terms of the rate of cognitive decline, compared with those with neither condition from the same age cohort, older adults aged 65-74 years with both conditions declined at a higher rate (P < .001).
Having diabetes alone led to accelerated cognitive decline in older adults aged 65-74 years (P < .001). Having edentulism alone led to accelerated decline in older adults aged 65-74 years (P < .001) and older adults aged 75-84 years (P < 0.01).
“Our study finds the co-occurrence of diabetes and edentulism led to a worse cognitive function and a faster cognitive decline in older adults aged 65-74 years,” say Wu and colleagues.
Study limitations: Better data needed
The study has several limitations, most of them due to the data source. For example, while the HRS collects detailed information on cognitive status, edentulism is its only measure of oral health. There were no data on whether individuals had replacements such as dentures or implants that would affect their ability to eat, which could influence other health factors.
“I have made repeated appeals for federal funding to collect more oral health-related information in large national surveys,” Dr. Wu told this news organization.
Similarly, assessments of diabetes status such as hemoglobin A1c were only available for small subsets and not sufficient to demonstrate statistical significance, she explained.
Dr. Wu suggested that both oral health and cognitive screening might be included in the “Welcome to Medicare” preventive visit. In addition, “Oral hygiene practice should also be highlighted to improve cognitive health. Developing dental care interventions and programs are needed for reducing the societal cost of dementia.”
The study was partially supported by the National Institutes of Health. The authors have reported no relevant financial relationships.
A version of this article first appeared on Medscape.com.
most specifically in those 65-74 years of age, new findings suggest.
The data come from a 12-year follow-up of older adults in a nationally representative U.S. survey.
“From a clinical perspective, our study demonstrates the importance of improving access to dental health care and integrating primary dental and medical care. Health care professionals and family caregivers should pay close attention to the cognitive status of diabetic older adults with poor oral health status,” lead author Bei Wu, PhD, of New York University, said in an interview. Dr. Wu is the Dean’s Professor in Global Health and codirector of the NYU Aging Incubator.
Moreover, said Dr. Wu: “For individuals with both poor oral health and diabetes, regular dental visits should be encouraged in addition to adherence to the diabetes self-care protocol.”
Diabetes has long been recognized as a risk factor for cognitive decline, but the findings have been inconsistent for different age groups. Tooth loss has also been linked to cognitive decline and dementia, as well as diabetes.
The mechanisms aren’t entirely clear, but “co-occurring diabetes and poor oral health may increase the risk for dementia, possibly via the potentially interrelated pathways of chronic inflammation and cardiovascular risk factors,” Dr. Wu said.
The new study, published in the Journal of Dental Research, is the first to examine the relationships between all three conditions by age group.
Diabetes, edentulism, and cognitive decline
The data came from a total of 9,948 participants in the Health and Retirement Study (HRS) from 2006 to 2018. At baseline, 5,440 participants were aged 65-74 years, 3,300 were aged 75-84, and 1,208 were aged 85 years or older.
They were assessed every 2 years using the 35-point Telephone Survey for Cognitive Status, which included tests of immediate and delayed word recall, repeated subtracting by 7, counting backward from 20, naming objects, and naming the president and vice president of the U.S. As might be expected, the youngest group scored the highest, averaging 23 points, while the oldest group scored lowest, at 18.5 points.
Participants were also asked if they had ever been told by a doctor that they have diabetes. Another question was: “Have you lost all of your upper and lower natural permanent teeth?”
The condition of having no teeth is known as edentulism.
The percentages of participants who reported having both diabetes and edentulism were 6.0%, 6.7%, and 5.0% for those aged 65-74 years, 75-84 years, and 85 years or older, respectively. The proportions with neither of those conditions were 63.5%, 60.4%, and 58.3% in those three age groups, respectively (P < .001).
Compared with their counterparts with neither diabetes nor edentulism at baseline, older adults with both conditions aged 65-74 years (P < .001) and aged 75-84 years had worse cognitive function (P < .001).
In terms of the rate of cognitive decline, compared with those with neither condition from the same age cohort, older adults aged 65-74 years with both conditions declined at a higher rate (P < .001).
Having diabetes alone led to accelerated cognitive decline in older adults aged 65-74 years (P < .001). Having edentulism alone led to accelerated decline in older adults aged 65-74 years (P < .001) and older adults aged 75-84 years (P < 0.01).
“Our study finds the co-occurrence of diabetes and edentulism led to a worse cognitive function and a faster cognitive decline in older adults aged 65-74 years,” say Wu and colleagues.
Study limitations: Better data needed
The study has several limitations, most of them due to the data source. For example, while the HRS collects detailed information on cognitive status, edentulism is its only measure of oral health. There were no data on whether individuals had replacements such as dentures or implants that would affect their ability to eat, which could influence other health factors.
“I have made repeated appeals for federal funding to collect more oral health-related information in large national surveys,” Dr. Wu told this news organization.
Similarly, assessments of diabetes status such as hemoglobin A1c were only available for small subsets and not sufficient to demonstrate statistical significance, she explained.
Dr. Wu suggested that both oral health and cognitive screening might be included in the “Welcome to Medicare” preventive visit. In addition, “Oral hygiene practice should also be highlighted to improve cognitive health. Developing dental care interventions and programs are needed for reducing the societal cost of dementia.”
The study was partially supported by the National Institutes of Health. The authors have reported no relevant financial relationships.
A version of this article first appeared on Medscape.com.
FROM THE JOURNAL OF DENTAL RESEARCH
Watch for buprenorphine ‘spiking’ in urine drug tests
Urine drug testing can be valuable for monitoring patients undergoing treatment with buprenorphine for opioid use disorder (OUD). However, some patients alter their test results by adding buprenorphine directly to their urine sample to imply adherence, a new study shows.
“I anticipate a much-needed increase” in the number of people gaining access to buprenorphine therapy, given elimination of the X waiver, first author Jarratt D. Pytell, MD, with University of Colorado at Denver, Aurora, said in a statement.
“New prescribers of buprenorphine will need to learn how to conduct the increasingly complex initiation of treatment and then gauge whether it is successful or not,” added Dr. Pytell, a general internist and addiction medicine specialist.
“Spiking suggests that treatment is not working – especially in patients continuing illicit drug use. Detecting spiking allows clinicians to adjust or intensify the treatment plan,” Dr. Pytell said in an interview.
The study was published online in JAMA Psychiatry.
A sign of elevated patient risk
In a cross-sectional study using Millennium Health’s proprietary urine drug test (UDT) database, researchers analyzed 507,735 urine specimens from 58,476 OUD patients collected between January 2017 and April 2022.
A total of 9546 (1.9%) specimens from 4,550 patients (7.6%) were suggestive of spiking.
UDT specimens suggestive of spiking had two times the odds of being positive for other opioids (fentanyl or heroin), compared with opioid negative samples.
UDT specimens obtained from primary care clinics, from patients aged 35-44 years, and from patients living in the South Atlantic region of the United States were also more likely to be suggestive of buprenorphine spiking.
“Our study demonstrated that a buprenorphine to norbuprenorphine ratio of less than 0.02 indicates the possibility of spiking,” Dr. Pytell said in an interview.
“Nevertheless, it is important to note that this cutoff is not a definitive standard and further controlled studies are necessary to determine its predictive value for spiking. But recognizing possible spiking is very important since it demonstrates a point of elevated risk for the patient and the treatment approach should be reconsidered,” Dr. Pytell said.
“At Millennium Health, we have been tracking the enormity of the drug use crisis. This study suggests that spiking is an important patient safety issue, and it is not uncommon,” study coauthor Eric Dawson, PharmD, vice president of clinical affairs, Millennium Health, said in a statement.
“Detection of spiking requires definitive drug testing. Immunoassay-based, point-of-care tests cannot detect spiking because they are generally incapable of quantitative analysis and differentiating buprenorphine from norbuprenorphine,” Dr. Dawson said.
Best practices?
“We need to develop best practices specific for this situation where a patient has added buprenorphine to the urine drug test specimen,” said Dr. Pytell.
“As with all unexpected findings, it is crucial for clinicians to approach this finding in a nonjudgmental manner and work with the patient to understand what might have motivated them to alter their urine specimen,” he added.
Dr. Pytell said a common reaction for clinicians might be to discontinue treatment. However, this is actually a time to try and engage with the patient.
“Clinicians should work collaboratively with patients to identify potential reasons for spiking and determine what changes may need to be made to better support the patient’s recovery,” Dr. Pytell said.
“This could include more frequent monitoring or referral to a higher level of care. In addition, clinicians should be aware that patients who engage in spiking may be experiencing other challenges that impact their ability to adhere to treatment, such as inadequate housing, mental health issues, or financial strain. Addressing these underlying issues may help patients overcome barriers to treatment adherence and reduce the likelihood of future spiking,” Dr. Pytell said.
This study was supported by Millennium Health. The authors have no relevant disclosures.
A version of this article first appeared on Medscape.com.
Urine drug testing can be valuable for monitoring patients undergoing treatment with buprenorphine for opioid use disorder (OUD). However, some patients alter their test results by adding buprenorphine directly to their urine sample to imply adherence, a new study shows.
“I anticipate a much-needed increase” in the number of people gaining access to buprenorphine therapy, given elimination of the X waiver, first author Jarratt D. Pytell, MD, with University of Colorado at Denver, Aurora, said in a statement.
“New prescribers of buprenorphine will need to learn how to conduct the increasingly complex initiation of treatment and then gauge whether it is successful or not,” added Dr. Pytell, a general internist and addiction medicine specialist.
“Spiking suggests that treatment is not working – especially in patients continuing illicit drug use. Detecting spiking allows clinicians to adjust or intensify the treatment plan,” Dr. Pytell said in an interview.
The study was published online in JAMA Psychiatry.
A sign of elevated patient risk
In a cross-sectional study using Millennium Health’s proprietary urine drug test (UDT) database, researchers analyzed 507,735 urine specimens from 58,476 OUD patients collected between January 2017 and April 2022.
A total of 9546 (1.9%) specimens from 4,550 patients (7.6%) were suggestive of spiking.
UDT specimens suggestive of spiking had two times the odds of being positive for other opioids (fentanyl or heroin), compared with opioid negative samples.
UDT specimens obtained from primary care clinics, from patients aged 35-44 years, and from patients living in the South Atlantic region of the United States were also more likely to be suggestive of buprenorphine spiking.
“Our study demonstrated that a buprenorphine to norbuprenorphine ratio of less than 0.02 indicates the possibility of spiking,” Dr. Pytell said in an interview.
“Nevertheless, it is important to note that this cutoff is not a definitive standard and further controlled studies are necessary to determine its predictive value for spiking. But recognizing possible spiking is very important since it demonstrates a point of elevated risk for the patient and the treatment approach should be reconsidered,” Dr. Pytell said.
“At Millennium Health, we have been tracking the enormity of the drug use crisis. This study suggests that spiking is an important patient safety issue, and it is not uncommon,” study coauthor Eric Dawson, PharmD, vice president of clinical affairs, Millennium Health, said in a statement.
“Detection of spiking requires definitive drug testing. Immunoassay-based, point-of-care tests cannot detect spiking because they are generally incapable of quantitative analysis and differentiating buprenorphine from norbuprenorphine,” Dr. Dawson said.
Best practices?
“We need to develop best practices specific for this situation where a patient has added buprenorphine to the urine drug test specimen,” said Dr. Pytell.
“As with all unexpected findings, it is crucial for clinicians to approach this finding in a nonjudgmental manner and work with the patient to understand what might have motivated them to alter their urine specimen,” he added.
Dr. Pytell said a common reaction for clinicians might be to discontinue treatment. However, this is actually a time to try and engage with the patient.
“Clinicians should work collaboratively with patients to identify potential reasons for spiking and determine what changes may need to be made to better support the patient’s recovery,” Dr. Pytell said.
“This could include more frequent monitoring or referral to a higher level of care. In addition, clinicians should be aware that patients who engage in spiking may be experiencing other challenges that impact their ability to adhere to treatment, such as inadequate housing, mental health issues, or financial strain. Addressing these underlying issues may help patients overcome barriers to treatment adherence and reduce the likelihood of future spiking,” Dr. Pytell said.
This study was supported by Millennium Health. The authors have no relevant disclosures.
A version of this article first appeared on Medscape.com.
Urine drug testing can be valuable for monitoring patients undergoing treatment with buprenorphine for opioid use disorder (OUD). However, some patients alter their test results by adding buprenorphine directly to their urine sample to imply adherence, a new study shows.
“I anticipate a much-needed increase” in the number of people gaining access to buprenorphine therapy, given elimination of the X waiver, first author Jarratt D. Pytell, MD, with University of Colorado at Denver, Aurora, said in a statement.
“New prescribers of buprenorphine will need to learn how to conduct the increasingly complex initiation of treatment and then gauge whether it is successful or not,” added Dr. Pytell, a general internist and addiction medicine specialist.
“Spiking suggests that treatment is not working – especially in patients continuing illicit drug use. Detecting spiking allows clinicians to adjust or intensify the treatment plan,” Dr. Pytell said in an interview.
The study was published online in JAMA Psychiatry.
A sign of elevated patient risk
In a cross-sectional study using Millennium Health’s proprietary urine drug test (UDT) database, researchers analyzed 507,735 urine specimens from 58,476 OUD patients collected between January 2017 and April 2022.
A total of 9546 (1.9%) specimens from 4,550 patients (7.6%) were suggestive of spiking.
UDT specimens suggestive of spiking had two times the odds of being positive for other opioids (fentanyl or heroin), compared with opioid negative samples.
UDT specimens obtained from primary care clinics, from patients aged 35-44 years, and from patients living in the South Atlantic region of the United States were also more likely to be suggestive of buprenorphine spiking.
“Our study demonstrated that a buprenorphine to norbuprenorphine ratio of less than 0.02 indicates the possibility of spiking,” Dr. Pytell said in an interview.
“Nevertheless, it is important to note that this cutoff is not a definitive standard and further controlled studies are necessary to determine its predictive value for spiking. But recognizing possible spiking is very important since it demonstrates a point of elevated risk for the patient and the treatment approach should be reconsidered,” Dr. Pytell said.
“At Millennium Health, we have been tracking the enormity of the drug use crisis. This study suggests that spiking is an important patient safety issue, and it is not uncommon,” study coauthor Eric Dawson, PharmD, vice president of clinical affairs, Millennium Health, said in a statement.
“Detection of spiking requires definitive drug testing. Immunoassay-based, point-of-care tests cannot detect spiking because they are generally incapable of quantitative analysis and differentiating buprenorphine from norbuprenorphine,” Dr. Dawson said.
Best practices?
“We need to develop best practices specific for this situation where a patient has added buprenorphine to the urine drug test specimen,” said Dr. Pytell.
“As with all unexpected findings, it is crucial for clinicians to approach this finding in a nonjudgmental manner and work with the patient to understand what might have motivated them to alter their urine specimen,” he added.
Dr. Pytell said a common reaction for clinicians might be to discontinue treatment. However, this is actually a time to try and engage with the patient.
“Clinicians should work collaboratively with patients to identify potential reasons for spiking and determine what changes may need to be made to better support the patient’s recovery,” Dr. Pytell said.
“This could include more frequent monitoring or referral to a higher level of care. In addition, clinicians should be aware that patients who engage in spiking may be experiencing other challenges that impact their ability to adhere to treatment, such as inadequate housing, mental health issues, or financial strain. Addressing these underlying issues may help patients overcome barriers to treatment adherence and reduce the likelihood of future spiking,” Dr. Pytell said.
This study was supported by Millennium Health. The authors have no relevant disclosures.
A version of this article first appeared on Medscape.com.
FROM JAMA PSYCHIATRY