Original Research

Yoga is an effective clinical intervention for cancer survivors. Studies indicate a wide range of benefits, including improvements in physical functioning, emotional well-being and overall quality of life.^1-7 Two-thirds of National Cancer Institute designated comprehensive cancer centers offer yoga on-site.⁸ Yoga is endorsed by the National Comprehensive Cancer Network and American Society of Clinical Oncology for managing symptoms, such as cancer-related anxiety and depression and for improving overall quality of life.^9,10

Although the positive effects of yoga on cancer patients are well studied, most published research in this area reports on predominantly middle-aged women with breast cancer.^11,12 Less is known about the use of yoga in other groups of cancer patients, such as older adults, veterans, and those from diverse racial or ethnic backgrounds. This gap in the literature is concerning considering that the majority of cancer survivors are aged 60 years or older, and veterans face unique risk factors for cancer associated with herbicide exposure (eg, Agent Orange) and other military-related noxious exposures.^13,14 Older cancer survivors may have more difficulty recovering from treatment-related adverse effects, making it especially important to target recovery efforts to older adults.¹⁵ Yoga can be adapted for older cancer survivors with age-related comorbidities, similar to adaptations made for older adults who are not cancer survivors but require accommodations for physical limitations.^16-20 Similarly, yoga programs targeted to racially diverse cancer survivors are associated with improved mood and well-being in racially diverse cancer survivors, but studies suggest community engagement and cultural adaptation may be important to address the needs of culturally diverse cancer survivors.^21-23

Yoga has been increasingly studied within the Veterans Health Administration (VHA) for treatment of posttraumatic stress disorder (PTSD) and has been found effective in reducing symptoms through the use of trauma-informed and military-relevant instruction as well as a military veteran yoga teacher.^24-26 This work has not targeted older veterans or cancer survivors who may be more difficult to recruit into such programs, but who would nevertheless benefit.

Clinically, the VHA whole health model is providing increased opportunities for veterans to engage in holistic care including yoga.²⁷ Resources include in-person yoga classes (varies by facility), videos, and handouts with practices uniquely designed for veterans or wounded warriors. As clinicians increasingly refer veterans to these programs, it will be important to develop strategies to engage older veterans in these services.

One important strategy to enhancing access to yoga for older veterans is to consider beliefs about yoga. Beliefs about yoga or general expectations about the outcomes of yoga may be critical to consider in expanding access to yoga in underrepresented groups. Beliefs about yoga may include beliefs about yoga improving health, yoga being difficult or producing discomfort, and yoga involving specific social norms.²⁸ For example, confidence in one’s ability to perform yoga despite discomfort predicted class attendance and practice in a sample of 32 breast cancer survivors.²⁹ Relatedly, positive beliefs about the impact of yoga on health were associated with improvements in mood and quality of life in a sample of 66 cancer survivors.³⁰

The aim of this study was to examine avenues to enhance access to yoga for older veterans, including those from diverse backgrounds, with a focus on the role of beliefs. In the first study we investigate the association between beliefs about and barriers to yoga in a group of older cancer survivors, and we consider the role of demographic and clinical variables in such beliefs and how education may alter beliefs. In alignment with the whole health model of holistic health, we posit that yoga educational materials and resources may contribute to yoga beliefs and work to decrease these barriers. We apply these findings in a second study that enrolled older veterans in yoga and examining the impact of program participation on beliefs and the role of beliefs in program outcomes. In the discussion we return to consider how to increase access to yoga to older veterans based on these findings.

Methods

Study 1 participants were identified from VHA tumor registries. Eligible patients had head and neck, esophageal, gastric, or colorectal cancers and were excluded if they were in hospice care, had dementia, or had a psychotic spectrum disorder. Participants completed a face-to-face semistructured interview at 6, 12, and 18 months after their cancer diagnosis with a trained interviewer. Complete protocol methods, including nonresponder information, are described elsewhere.³¹

Questions about yoga were asked at the 12 month postdiagnosis interview. Participants were read the following: “Here is a list of services some patients use to recover from cancer. Please tell me if you have used any of these.” The list included yoga, physical therapy, occupational therapy, exercise, meditation, or massage therapy. Next participants were provided education about yoga via the following description: “Yoga is a practice of stress reduction and exercise with stretching, holding positions and deep breathing. For some, it may improve your sleep, energy, flexibility, anxiety, and pain. The postures are done standing, sitting, or lying down. If needed, it can be done all from a chair.” We then asked whether they would attend if yoga was offered at the VHA hospital (yes, no, maybe). Participants provided brief responses to 2 open-ended questions: (“If I came to a yoga class, I …”; and “Is there anything that might make you more likely to come to a yoga class?”) Responses were transcribed verbatim and entered into a database for qualitative analysis. Subsequently, participants completed standardized measures of health-related quality of life and beliefs about yoga as described below.

Study 2 participants were identified from VHA tumor registries and a cancer support group. Eligible patients had a diagnosis of cancer (any type except basil cell carcinoma) within the previous 3 years and were excluded if they were in hospice care, had dementia, or had a psychotic spectrum disorder. Participants completed face-to-face semistructured interviews with a trained interviewer before and after participation in an 8-week yoga group that met twice per week. Complete protocol methods are described elsewhere.¹⁶ This paper focuses on 28 of the 37 enrolled patients for whom we have complete pre- and postclass interview data. We previously reported on adaptations made to yoga in our pilot group of 14 individuals, who in this small sample did not show statistically significant changes in their quality of life from before to after the class.¹⁶ This analysis includes those 14 individuals and 14 who participated in additional classes, focusing on beliefs, which were not previously reported.

Measures

Participants reported their age, gender, ethnicity (Hispanic/Latino or not), race, and level of education. Information about the cancer diagnosis, American Joint Committee on Cancer (AJCC) cancer stage, and treatments was obtained from the medical record. The Physical Function and Anxiety Subscales from the Patient-Reported Outcomes Measurement Information System were used to measure health-related quality of life (HRQoL).^32-34 Items are rated on a Likert scale from 1 (not at all) to 5 (very much).

The Beliefs About Yoga Scale (BAYS) was used to measure beliefs about the outcomes of engaging in yoga.²⁸ The 11-item scale has 3 factors: expected health benefits (5 items), expected discomfort (3 items), and expected social norms (3 items). Items from the expected discomfort and expected social norms are reverse scored so that a higher score indicates more positive beliefs. To reduce participant burden, in study 1 we selected 1 item from each factor with high factor loadings in the original cross-validation sample.²⁸ It would improve my overall health (Benefit, factor loading = .89); I would have to be more flexible to take a class (Discomfort, factor loading = .67); I would be embarrassed in a class (Social norms, factor loading = .75). Participants in study 2 completed the entire 11-item scale. Items were summed to create subscales and total scales.

Analysis

Descriptive statistics were used in study 1 to characterize participants’ yoga experience and interest. Changes in interest pre- and posteducation were evaluated with χ² comparison of distribution. The association of beliefs about yoga with 3 levels of interest (yes, no, maybe) was evaluated through analysis of variance (ANOVA) comparing the mean score on the summed BAYS items among the 3 groups. The association of demographic (age, education, race) and clinical factors (AJCC stage, physical function) with BAYS was determined through multivariate linear regression.

For analytic purposes, due to small subgroup sample sizes we compared those who identified as non-Hispanic White adults to those who identified as African American/Hispanic/other persons. To further evaluate the relationship of age to yoga beliefs, we examined beliefs about yoga in 3 age groups (40-59 years [n = 24]; 60-69 years [n = 58]; 70-89 years [n = 28]) using ANOVA comparing the mean score on the summed BAYS items among the 3 groups. In study 2, changes in interest before and after the yoga program were evaluated with paired t tests and repeated ANOVA, with beliefs about yoga prior to class as a covariate. The association of demographic and clinical factors with BAYS was determined as in the first sample through multivariate linear regression, except the variable of race was not included due to small sample size (ie, only 3 individuals identified as persons of color).

Thematic analysis in which content-related codes were developed and subsequently grouped together was applied to the data of 110 participants who responded to the open-ended survey questions in study 1 to further illuminate responses to closed-ended questions.³⁵ Transcribed responses to the open-ended questions were transferred to a spreadsheet. An initial code book with code names, definitions, and examples was developed based on an inductive method by one team member (EA).³⁵ Initially, coding and tabulation were conducted separately for each question but it was noted that content extended across response prompts (eg, responses to question 2 “What might make you more likely to come?” were spontaneously provided when answering question 1), thus coding was collapsed across questions. Next, 2 team members (EA, KD) coded the same responses, meeting weekly to discuss discrepancies. The code book was revised following each meeting to reflect refinements in code names and definitions, adding newly generated codes as needed. The process continued until consensus and data saturation was obtained, with 90% intercoder agreement. Next, these codes were subjected to thematic analysis by 2 team members (EA, KD) combining codes into 6 overarching themes. The entire team reviewed the codes and identified 2 supra themes: positive beliefs or facilitators and negative beliefs or barriers.

Consistent with the concept of reflexivity in qualitative research, we acknowledge the influence of the research team members on the qualitative process.³⁶ The primary coding team (EA, KD) are both researchers and employees of Veterans Affairs Boston Healthcare System who have participated in other research projects involving veterans and qualitative analyses but are not yoga instructors or yoga researchers.

Results

Study 1

The sample of 110 military veterans was mostly male (99.1%) with a mean (SD) age of 64.9 (9.4) years (range, 41-88)(Table 1). The majority (70.9%) described their race/ethnicity as White, non-Hispanic followed by Black/African American (18.2%) and Hispanic (8.2%) persons; 50.0% had no more than a high school education. The most common cancer diagnoses were colorectal (50.9%), head and neck (39.1%), and esophageal and gastric (10.0%) and ranged from AJCC stages I to IV.

When first asked, the majority of participants (78.2%) reported that they were not interested in yoga, 16.4% reported they might be interested, and 5.5% reported they had tried a yoga class since their cancer diagnosis. In contrast, 40.9% used exercise, 32.7% used meditation, 14.5% used physical or occupational therapy, and 11.8% used massage therapy since their cancer diagnosis.

Study 2 Intervention Sample

This sample of 28 veterans was mostly male (96.4%) with a mean (SD) age of 69.2 (10.9) years (range, 57-87). The majority (89.3%) described their race as White, followed by Black/African American (10.7%); no participants self-identified in other categories for race/ethnicity. Twelve veterans (42.9%) had no more than a high school education. The most common cancer diagnosis was genitourinary (35.7%) and the AJCC stage ranged from I to IV.

We employed information learned in study 1 to enhance access in study 2. We mailed letters to 278 veterans diagnosed with cancer in the previous 3 years that provided education about yoga based on study 1 findings. Of 207 veterans reached by phone, 133 (64%) stated they were not interested in coming to a yoga class; 74 (36%) were interested, but 30 felt they were unable to attend due to obstacles such as illness or travel. Ultimately 37 (18%) veterans agreed and consented to the class, and 28 (14%) completed postclass surveys.

In multivariate regression, higher expected health benefits of yoga were associated with higher physical function, lower concern about expected discomfort was also associated with higher physical function as well as higher education; similarly, lower concern about expected social norms was associated with higher physical function. Age was not associated with any of the BAYS factors.

Beliefs about yoga improved from before to after class for all 3 domains with greater expected benefit and lower concerns about discomfort or social norms: expected benefit (mean difference, 5.3; t = 4.44, P < .001), expected discomfort (mean difference, 3.0; t = 4.92, P < .001), expected social norms (mean difference, 3.5; t = 4.38, P < .001). Physical function improved from before the class to afterwards (mean difference, 7.5; t = 3.97, P < .001) but there were no changes in anxiety (mean difference, 0.6; t = 0.76, P = .46). Beliefs about yoga moderated changes in physical function, such that those with higher beliefs experienced a greater improvement in physical function (F = 15.24, P < .001).

Discussion

Yoga is an effective clinical intervention for addressing some long-term adverse effects in cancer survivors, although the body of research focuses predominantly on middle aged, female, White, college-educated breast cancer survivors. There is no evidence to suggest yoga would be less effective in other groups, but it has not been extensively studied in survivors from diverse subgroups. Beliefs about yoga are a factor that may enhance interest in yoga interventions and research, and measures aimed at addressing potential beliefs and fears may capture information that can be used to support older cancer survivors in holistic health. The aims of this study were to examine beliefs about yoga in 2 samples of older cancer survivors who received VHA care. The main findings are (1) interest in yoga was initially low and lower than that of other complementary or exercise-based interventions, but increased when participants were provided brief education about yoga; (2) interest in yoga was associated with beliefs about yoga with qualitative comments illuminating these beliefs; (3) demographic characteristics (education, race) and physical function were associated with beliefs about yoga; and (4) positive beliefs about yoga increased following a brief yoga intervention and was associated with improvements in physical function.

Willingness to consider a class appeared to shift for some older veterans when they were presented brief information about yoga that explained what is involved, how it might help, and that it could be done from a chair if needed. These findings clearly indicated that when trying to enhance participation in yoga in clinical or research programs, it will be important that recruitment materials provide such information. This finding is consistent with the qualitative findings that reflected a lack of knowledge or skepticism about benefits of yoga among some participants. Given the finding that physical function was associated with beliefs about yoga and was also a prominent theme in qualitative analyses, when referring older veterans to yoga it may be useful to explicitly state that yoga may enhance some aspects of physical function and can be adapted for level of physical function or pain limitations. The finding that beliefs about yoga were associated with education also suggests that materials should be presented at all levels of education.

Age was not associated with beliefs about yoga in either study. Importantly, in a more detailed study 1 follow-up analysis, beliefs about yoga were equivalent for aged > 70 years compared with those aged 40 to 69 years. It is not entirely clear why older adults have been underrepresented in studies of yoga in cancer survivors. However, older adults are vastly underrepresented in clinical trials for many health conditions, even though they are more likely to experience many diseases, including cancer.³⁷ A new National Institutes of Health policy requires that individuals of all ages, including older adults, must be included in all human subjects research unless there are scientific reasons not to include them.³⁸ It is therefore imperative to consider strategies to address underrepresentation of older adults.

Limitations

These findings have several limitations. First, participants were homogeneous in age, gender, race/ethnicity and veteran status, which provides a window into this understudied population but limits generalizability and our ability to control across populations. Second, the sample size limited the ability to conduct subgroup and interaction analyses, such as examining potential differential effects of cancer type, treatment, and PTSD on yoga beliefs or to consider the relationship of yoga beliefs with changes in quality of life before and after the yoga intervention in study 2. Additionally, age was not associated with beliefs about yoga in these samples that of mostly older adults. We were able to compare middle-aged and older adults but could not compare beliefs about yoga to adults aged in their 20s and 30s. Last, our study excluded people with dementia and psychotic disorders. Further research is needed to examine yoga for older cancer survivors who have these conditions.

Conclusions

Education that specifically informs potential participants about yoga practice, potential modifications, and potential benefits, as well as adaptations to programs that address physical and logistical barriers may be useful in increasing access to and participation in yoga for older Veterans who are cancer survivors.

Acknowledgments/Funding

The authors have no financial or personal relationships to disclose. This work was supported by the US Department of Veterans Affairs (VA) Rehabilitation Research and Development Service. This material is the result of work supported with resources and the use of facilities at the VA Boston Healthcare System, Bedford VA Medical Center, and Michael E. DeBakey VA Medical Center in Houston, Texas. We thank the members of the Veterans Cancer Rehabilitation Study (Vetcares) Research teams in Boston and in Houston and the veterans who have participated in our research studies and allow us to contribute to their health care.
 

Yoga is an effective clinical intervention for cancer survivors. Studies indicate a wide range of benefits, including improvements in physical functioning, emotional well-being and overall quality of life.^1-7 Two-thirds of National Cancer Institute designated comprehensive cancer centers offer yoga on-site.⁸ Yoga is endorsed by the National Comprehensive Cancer Network and American Society of Clinical Oncology for managing symptoms, such as cancer-related anxiety and depression and for improving overall quality of life.^9,10

Although the positive effects of yoga on cancer patients are well studied, most published research in this area reports on predominantly middle-aged women with breast cancer.^11,12 Less is known about the use of yoga in other groups of cancer patients, such as older adults, veterans, and those from diverse racial or ethnic backgrounds. This gap in the literature is concerning considering that the majority of cancer survivors are aged 60 years or older, and veterans face unique risk factors for cancer associated with herbicide exposure (eg, Agent Orange) and other military-related noxious exposures.^13,14 Older cancer survivors may have more difficulty recovering from treatment-related adverse effects, making it especially important to target recovery efforts to older adults.¹⁵ Yoga can be adapted for older cancer survivors with age-related comorbidities, similar to adaptations made for older adults who are not cancer survivors but require accommodations for physical limitations.^16-20 Similarly, yoga programs targeted to racially diverse cancer survivors are associated with improved mood and well-being in racially diverse cancer survivors, but studies suggest community engagement and cultural adaptation may be important to address the needs of culturally diverse cancer survivors.^21-23

Yoga has been increasingly studied within the Veterans Health Administration (VHA) for treatment of posttraumatic stress disorder (PTSD) and has been found effective in reducing symptoms through the use of trauma-informed and military-relevant instruction as well as a military veteran yoga teacher.^24-26 This work has not targeted older veterans or cancer survivors who may be more difficult to recruit into such programs, but who would nevertheless benefit.

Clinically, the VHA whole health model is providing increased opportunities for veterans to engage in holistic care including yoga.²⁷ Resources include in-person yoga classes (varies by facility), videos, and handouts with practices uniquely designed for veterans or wounded warriors. As clinicians increasingly refer veterans to these programs, it will be important to develop strategies to engage older veterans in these services.

One important strategy to enhancing access to yoga for older veterans is to consider beliefs about yoga. Beliefs about yoga or general expectations about the outcomes of yoga may be critical to consider in expanding access to yoga in underrepresented groups. Beliefs about yoga may include beliefs about yoga improving health, yoga being difficult or producing discomfort, and yoga involving specific social norms.²⁸ For example, confidence in one’s ability to perform yoga despite discomfort predicted class attendance and practice in a sample of 32 breast cancer survivors.²⁹ Relatedly, positive beliefs about the impact of yoga on health were associated with improvements in mood and quality of life in a sample of 66 cancer survivors.³⁰

The aim of this study was to examine avenues to enhance access to yoga for older veterans, including those from diverse backgrounds, with a focus on the role of beliefs. In the first study we investigate the association between beliefs about and barriers to yoga in a group of older cancer survivors, and we consider the role of demographic and clinical variables in such beliefs and how education may alter beliefs. In alignment with the whole health model of holistic health, we posit that yoga educational materials and resources may contribute to yoga beliefs and work to decrease these barriers. We apply these findings in a second study that enrolled older veterans in yoga and examining the impact of program participation on beliefs and the role of beliefs in program outcomes. In the discussion we return to consider how to increase access to yoga to older veterans based on these findings.

Methods

Study 1 participants were identified from VHA tumor registries. Eligible patients had head and neck, esophageal, gastric, or colorectal cancers and were excluded if they were in hospice care, had dementia, or had a psychotic spectrum disorder. Participants completed a face-to-face semistructured interview at 6, 12, and 18 months after their cancer diagnosis with a trained interviewer. Complete protocol methods, including nonresponder information, are described elsewhere.³¹

Questions about yoga were asked at the 12 month postdiagnosis interview. Participants were read the following: “Here is a list of services some patients use to recover from cancer. Please tell me if you have used any of these.” The list included yoga, physical therapy, occupational therapy, exercise, meditation, or massage therapy. Next participants were provided education about yoga via the following description: “Yoga is a practice of stress reduction and exercise with stretching, holding positions and deep breathing. For some, it may improve your sleep, energy, flexibility, anxiety, and pain. The postures are done standing, sitting, or lying down. If needed, it can be done all from a chair.” We then asked whether they would attend if yoga was offered at the VHA hospital (yes, no, maybe). Participants provided brief responses to 2 open-ended questions: (“If I came to a yoga class, I …”; and “Is there anything that might make you more likely to come to a yoga class?”) Responses were transcribed verbatim and entered into a database for qualitative analysis. Subsequently, participants completed standardized measures of health-related quality of life and beliefs about yoga as described below.

Study 2 participants were identified from VHA tumor registries and a cancer support group. Eligible patients had a diagnosis of cancer (any type except basil cell carcinoma) within the previous 3 years and were excluded if they were in hospice care, had dementia, or had a psychotic spectrum disorder. Participants completed face-to-face semistructured interviews with a trained interviewer before and after participation in an 8-week yoga group that met twice per week. Complete protocol methods are described elsewhere.¹⁶ This paper focuses on 28 of the 37 enrolled patients for whom we have complete pre- and postclass interview data. We previously reported on adaptations made to yoga in our pilot group of 14 individuals, who in this small sample did not show statistically significant changes in their quality of life from before to after the class.¹⁶ This analysis includes those 14 individuals and 14 who participated in additional classes, focusing on beliefs, which were not previously reported.

Measures

Participants reported their age, gender, ethnicity (Hispanic/Latino or not), race, and level of education. Information about the cancer diagnosis, American Joint Committee on Cancer (AJCC) cancer stage, and treatments was obtained from the medical record. The Physical Function and Anxiety Subscales from the Patient-Reported Outcomes Measurement Information System were used to measure health-related quality of life (HRQoL).^32-34 Items are rated on a Likert scale from 1 (not at all) to 5 (very much).

The Beliefs About Yoga Scale (BAYS) was used to measure beliefs about the outcomes of engaging in yoga.²⁸ The 11-item scale has 3 factors: expected health benefits (5 items), expected discomfort (3 items), and expected social norms (3 items). Items from the expected discomfort and expected social norms are reverse scored so that a higher score indicates more positive beliefs. To reduce participant burden, in study 1 we selected 1 item from each factor with high factor loadings in the original cross-validation sample.²⁸ It would improve my overall health (Benefit, factor loading = .89); I would have to be more flexible to take a class (Discomfort, factor loading = .67); I would be embarrassed in a class (Social norms, factor loading = .75). Participants in study 2 completed the entire 11-item scale. Items were summed to create subscales and total scales.

Analysis

Descriptive statistics were used in study 1 to characterize participants’ yoga experience and interest. Changes in interest pre- and posteducation were evaluated with χ² comparison of distribution. The association of beliefs about yoga with 3 levels of interest (yes, no, maybe) was evaluated through analysis of variance (ANOVA) comparing the mean score on the summed BAYS items among the 3 groups. The association of demographic (age, education, race) and clinical factors (AJCC stage, physical function) with BAYS was determined through multivariate linear regression.

For analytic purposes, due to small subgroup sample sizes we compared those who identified as non-Hispanic White adults to those who identified as African American/Hispanic/other persons. To further evaluate the relationship of age to yoga beliefs, we examined beliefs about yoga in 3 age groups (40-59 years [n = 24]; 60-69 years [n = 58]; 70-89 years [n = 28]) using ANOVA comparing the mean score on the summed BAYS items among the 3 groups. In study 2, changes in interest before and after the yoga program were evaluated with paired t tests and repeated ANOVA, with beliefs about yoga prior to class as a covariate. The association of demographic and clinical factors with BAYS was determined as in the first sample through multivariate linear regression, except the variable of race was not included due to small sample size (ie, only 3 individuals identified as persons of color).

Thematic analysis in which content-related codes were developed and subsequently grouped together was applied to the data of 110 participants who responded to the open-ended survey questions in study 1 to further illuminate responses to closed-ended questions.³⁵ Transcribed responses to the open-ended questions were transferred to a spreadsheet. An initial code book with code names, definitions, and examples was developed based on an inductive method by one team member (EA).³⁵ Initially, coding and tabulation were conducted separately for each question but it was noted that content extended across response prompts (eg, responses to question 2 “What might make you more likely to come?” were spontaneously provided when answering question 1), thus coding was collapsed across questions. Next, 2 team members (EA, KD) coded the same responses, meeting weekly to discuss discrepancies. The code book was revised following each meeting to reflect refinements in code names and definitions, adding newly generated codes as needed. The process continued until consensus and data saturation was obtained, with 90% intercoder agreement. Next, these codes were subjected to thematic analysis by 2 team members (EA, KD) combining codes into 6 overarching themes. The entire team reviewed the codes and identified 2 supra themes: positive beliefs or facilitators and negative beliefs or barriers.

Consistent with the concept of reflexivity in qualitative research, we acknowledge the influence of the research team members on the qualitative process.³⁶ The primary coding team (EA, KD) are both researchers and employees of Veterans Affairs Boston Healthcare System who have participated in other research projects involving veterans and qualitative analyses but are not yoga instructors or yoga researchers.

Results

Study 1

The sample of 110 military veterans was mostly male (99.1%) with a mean (SD) age of 64.9 (9.4) years (range, 41-88)(Table 1). The majority (70.9%) described their race/ethnicity as White, non-Hispanic followed by Black/African American (18.2%) and Hispanic (8.2%) persons; 50.0% had no more than a high school education. The most common cancer diagnoses were colorectal (50.9%), head and neck (39.1%), and esophageal and gastric (10.0%) and ranged from AJCC stages I to IV.

When first asked, the majority of participants (78.2%) reported that they were not interested in yoga, 16.4% reported they might be interested, and 5.5% reported they had tried a yoga class since their cancer diagnosis. In contrast, 40.9% used exercise, 32.7% used meditation, 14.5% used physical or occupational therapy, and 11.8% used massage therapy since their cancer diagnosis.

Study 2 Intervention Sample

This sample of 28 veterans was mostly male (96.4%) with a mean (SD) age of 69.2 (10.9) years (range, 57-87). The majority (89.3%) described their race as White, followed by Black/African American (10.7%); no participants self-identified in other categories for race/ethnicity. Twelve veterans (42.9%) had no more than a high school education. The most common cancer diagnosis was genitourinary (35.7%) and the AJCC stage ranged from I to IV.

We employed information learned in study 1 to enhance access in study 2. We mailed letters to 278 veterans diagnosed with cancer in the previous 3 years that provided education about yoga based on study 1 findings. Of 207 veterans reached by phone, 133 (64%) stated they were not interested in coming to a yoga class; 74 (36%) were interested, but 30 felt they were unable to attend due to obstacles such as illness or travel. Ultimately 37 (18%) veterans agreed and consented to the class, and 28 (14%) completed postclass surveys.

In multivariate regression, higher expected health benefits of yoga were associated with higher physical function, lower concern about expected discomfort was also associated with higher physical function as well as higher education; similarly, lower concern about expected social norms was associated with higher physical function. Age was not associated with any of the BAYS factors.

Beliefs about yoga improved from before to after class for all 3 domains with greater expected benefit and lower concerns about discomfort or social norms: expected benefit (mean difference, 5.3; t = 4.44, P < .001), expected discomfort (mean difference, 3.0; t = 4.92, P < .001), expected social norms (mean difference, 3.5; t = 4.38, P < .001). Physical function improved from before the class to afterwards (mean difference, 7.5; t = 3.97, P < .001) but there were no changes in anxiety (mean difference, 0.6; t = 0.76, P = .46). Beliefs about yoga moderated changes in physical function, such that those with higher beliefs experienced a greater improvement in physical function (F = 15.24, P < .001).

Discussion

Yoga is an effective clinical intervention for addressing some long-term adverse effects in cancer survivors, although the body of research focuses predominantly on middle aged, female, White, college-educated breast cancer survivors. There is no evidence to suggest yoga would be less effective in other groups, but it has not been extensively studied in survivors from diverse subgroups. Beliefs about yoga are a factor that may enhance interest in yoga interventions and research, and measures aimed at addressing potential beliefs and fears may capture information that can be used to support older cancer survivors in holistic health. The aims of this study were to examine beliefs about yoga in 2 samples of older cancer survivors who received VHA care. The main findings are (1) interest in yoga was initially low and lower than that of other complementary or exercise-based interventions, but increased when participants were provided brief education about yoga; (2) interest in yoga was associated with beliefs about yoga with qualitative comments illuminating these beliefs; (3) demographic characteristics (education, race) and physical function were associated with beliefs about yoga; and (4) positive beliefs about yoga increased following a brief yoga intervention and was associated with improvements in physical function.

Willingness to consider a class appeared to shift for some older veterans when they were presented brief information about yoga that explained what is involved, how it might help, and that it could be done from a chair if needed. These findings clearly indicated that when trying to enhance participation in yoga in clinical or research programs, it will be important that recruitment materials provide such information. This finding is consistent with the qualitative findings that reflected a lack of knowledge or skepticism about benefits of yoga among some participants. Given the finding that physical function was associated with beliefs about yoga and was also a prominent theme in qualitative analyses, when referring older veterans to yoga it may be useful to explicitly state that yoga may enhance some aspects of physical function and can be adapted for level of physical function or pain limitations. The finding that beliefs about yoga were associated with education also suggests that materials should be presented at all levels of education.

Age was not associated with beliefs about yoga in either study. Importantly, in a more detailed study 1 follow-up analysis, beliefs about yoga were equivalent for aged > 70 years compared with those aged 40 to 69 years. It is not entirely clear why older adults have been underrepresented in studies of yoga in cancer survivors. However, older adults are vastly underrepresented in clinical trials for many health conditions, even though they are more likely to experience many diseases, including cancer.³⁷ A new National Institutes of Health policy requires that individuals of all ages, including older adults, must be included in all human subjects research unless there are scientific reasons not to include them.³⁸ It is therefore imperative to consider strategies to address underrepresentation of older adults.

Limitations

These findings have several limitations. First, participants were homogeneous in age, gender, race/ethnicity and veteran status, which provides a window into this understudied population but limits generalizability and our ability to control across populations. Second, the sample size limited the ability to conduct subgroup and interaction analyses, such as examining potential differential effects of cancer type, treatment, and PTSD on yoga beliefs or to consider the relationship of yoga beliefs with changes in quality of life before and after the yoga intervention in study 2. Additionally, age was not associated with beliefs about yoga in these samples that of mostly older adults. We were able to compare middle-aged and older adults but could not compare beliefs about yoga to adults aged in their 20s and 30s. Last, our study excluded people with dementia and psychotic disorders. Further research is needed to examine yoga for older cancer survivors who have these conditions.

Conclusions

Education that specifically informs potential participants about yoga practice, potential modifications, and potential benefits, as well as adaptations to programs that address physical and logistical barriers may be useful in increasing access to and participation in yoga for older Veterans who are cancer survivors.

Acknowledgments/Funding

The authors have no financial or personal relationships to disclose. This work was supported by the US Department of Veterans Affairs (VA) Rehabilitation Research and Development Service. This material is the result of work supported with resources and the use of facilities at the VA Boston Healthcare System, Bedford VA Medical Center, and Michael E. DeBakey VA Medical Center in Houston, Texas. We thank the members of the Veterans Cancer Rehabilitation Study (Vetcares) Research teams in Boston and in Houston and the veterans who have participated in our research studies and allow us to contribute to their health care.
 

Yoga is an effective clinical intervention for cancer survivors. Studies indicate a wide range of benefits, including improvements in physical functioning, emotional well-being and overall quality of life.^1-7 Two-thirds of National Cancer Institute designated comprehensive cancer centers offer yoga on-site.⁸ Yoga is endorsed by the National Comprehensive Cancer Network and American Society of Clinical Oncology for managing symptoms, such as cancer-related anxiety and depression and for improving overall quality of life.^9,10

Although the positive effects of yoga on cancer patients are well studied, most published research in this area reports on predominantly middle-aged women with breast cancer.^11,12 Less is known about the use of yoga in other groups of cancer patients, such as older adults, veterans, and those from diverse racial or ethnic backgrounds. This gap in the literature is concerning considering that the majority of cancer survivors are aged 60 years or older, and veterans face unique risk factors for cancer associated with herbicide exposure (eg, Agent Orange) and other military-related noxious exposures.^13,14 Older cancer survivors may have more difficulty recovering from treatment-related adverse effects, making it especially important to target recovery efforts to older adults.¹⁵ Yoga can be adapted for older cancer survivors with age-related comorbidities, similar to adaptations made for older adults who are not cancer survivors but require accommodations for physical limitations.^16-20 Similarly, yoga programs targeted to racially diverse cancer survivors are associated with improved mood and well-being in racially diverse cancer survivors, but studies suggest community engagement and cultural adaptation may be important to address the needs of culturally diverse cancer survivors.^21-23

Yoga has been increasingly studied within the Veterans Health Administration (VHA) for treatment of posttraumatic stress disorder (PTSD) and has been found effective in reducing symptoms through the use of trauma-informed and military-relevant instruction as well as a military veteran yoga teacher.^24-26 This work has not targeted older veterans or cancer survivors who may be more difficult to recruit into such programs, but who would nevertheless benefit.

Clinically, the VHA whole health model is providing increased opportunities for veterans to engage in holistic care including yoga.²⁷ Resources include in-person yoga classes (varies by facility), videos, and handouts with practices uniquely designed for veterans or wounded warriors. As clinicians increasingly refer veterans to these programs, it will be important to develop strategies to engage older veterans in these services.

One important strategy to enhancing access to yoga for older veterans is to consider beliefs about yoga. Beliefs about yoga or general expectations about the outcomes of yoga may be critical to consider in expanding access to yoga in underrepresented groups. Beliefs about yoga may include beliefs about yoga improving health, yoga being difficult or producing discomfort, and yoga involving specific social norms.²⁸ For example, confidence in one’s ability to perform yoga despite discomfort predicted class attendance and practice in a sample of 32 breast cancer survivors.²⁹ Relatedly, positive beliefs about the impact of yoga on health were associated with improvements in mood and quality of life in a sample of 66 cancer survivors.³⁰

The aim of this study was to examine avenues to enhance access to yoga for older veterans, including those from diverse backgrounds, with a focus on the role of beliefs. In the first study we investigate the association between beliefs about and barriers to yoga in a group of older cancer survivors, and we consider the role of demographic and clinical variables in such beliefs and how education may alter beliefs. In alignment with the whole health model of holistic health, we posit that yoga educational materials and resources may contribute to yoga beliefs and work to decrease these barriers. We apply these findings in a second study that enrolled older veterans in yoga and examining the impact of program participation on beliefs and the role of beliefs in program outcomes. In the discussion we return to consider how to increase access to yoga to older veterans based on these findings.

Methods

Study 1 participants were identified from VHA tumor registries. Eligible patients had head and neck, esophageal, gastric, or colorectal cancers and were excluded if they were in hospice care, had dementia, or had a psychotic spectrum disorder. Participants completed a face-to-face semistructured interview at 6, 12, and 18 months after their cancer diagnosis with a trained interviewer. Complete protocol methods, including nonresponder information, are described elsewhere.³¹

Questions about yoga were asked at the 12 month postdiagnosis interview. Participants were read the following: “Here is a list of services some patients use to recover from cancer. Please tell me if you have used any of these.” The list included yoga, physical therapy, occupational therapy, exercise, meditation, or massage therapy. Next participants were provided education about yoga via the following description: “Yoga is a practice of stress reduction and exercise with stretching, holding positions and deep breathing. For some, it may improve your sleep, energy, flexibility, anxiety, and pain. The postures are done standing, sitting, or lying down. If needed, it can be done all from a chair.” We then asked whether they would attend if yoga was offered at the VHA hospital (yes, no, maybe). Participants provided brief responses to 2 open-ended questions: (“If I came to a yoga class, I …”; and “Is there anything that might make you more likely to come to a yoga class?”) Responses were transcribed verbatim and entered into a database for qualitative analysis. Subsequently, participants completed standardized measures of health-related quality of life and beliefs about yoga as described below.

Study 2 participants were identified from VHA tumor registries and a cancer support group. Eligible patients had a diagnosis of cancer (any type except basil cell carcinoma) within the previous 3 years and were excluded if they were in hospice care, had dementia, or had a psychotic spectrum disorder. Participants completed face-to-face semistructured interviews with a trained interviewer before and after participation in an 8-week yoga group that met twice per week. Complete protocol methods are described elsewhere.¹⁶ This paper focuses on 28 of the 37 enrolled patients for whom we have complete pre- and postclass interview data. We previously reported on adaptations made to yoga in our pilot group of 14 individuals, who in this small sample did not show statistically significant changes in their quality of life from before to after the class.¹⁶ This analysis includes those 14 individuals and 14 who participated in additional classes, focusing on beliefs, which were not previously reported.

Measures

Participants reported their age, gender, ethnicity (Hispanic/Latino or not), race, and level of education. Information about the cancer diagnosis, American Joint Committee on Cancer (AJCC) cancer stage, and treatments was obtained from the medical record. The Physical Function and Anxiety Subscales from the Patient-Reported Outcomes Measurement Information System were used to measure health-related quality of life (HRQoL).^32-34 Items are rated on a Likert scale from 1 (not at all) to 5 (very much).

The Beliefs About Yoga Scale (BAYS) was used to measure beliefs about the outcomes of engaging in yoga.²⁸ The 11-item scale has 3 factors: expected health benefits (5 items), expected discomfort (3 items), and expected social norms (3 items). Items from the expected discomfort and expected social norms are reverse scored so that a higher score indicates more positive beliefs. To reduce participant burden, in study 1 we selected 1 item from each factor with high factor loadings in the original cross-validation sample.²⁸ It would improve my overall health (Benefit, factor loading = .89); I would have to be more flexible to take a class (Discomfort, factor loading = .67); I would be embarrassed in a class (Social norms, factor loading = .75). Participants in study 2 completed the entire 11-item scale. Items were summed to create subscales and total scales.

Analysis

Descriptive statistics were used in study 1 to characterize participants’ yoga experience and interest. Changes in interest pre- and posteducation were evaluated with χ² comparison of distribution. The association of beliefs about yoga with 3 levels of interest (yes, no, maybe) was evaluated through analysis of variance (ANOVA) comparing the mean score on the summed BAYS items among the 3 groups. The association of demographic (age, education, race) and clinical factors (AJCC stage, physical function) with BAYS was determined through multivariate linear regression.

For analytic purposes, due to small subgroup sample sizes we compared those who identified as non-Hispanic White adults to those who identified as African American/Hispanic/other persons. To further evaluate the relationship of age to yoga beliefs, we examined beliefs about yoga in 3 age groups (40-59 years [n = 24]; 60-69 years [n = 58]; 70-89 years [n = 28]) using ANOVA comparing the mean score on the summed BAYS items among the 3 groups. In study 2, changes in interest before and after the yoga program were evaluated with paired t tests and repeated ANOVA, with beliefs about yoga prior to class as a covariate. The association of demographic and clinical factors with BAYS was determined as in the first sample through multivariate linear regression, except the variable of race was not included due to small sample size (ie, only 3 individuals identified as persons of color).

Thematic analysis in which content-related codes were developed and subsequently grouped together was applied to the data of 110 participants who responded to the open-ended survey questions in study 1 to further illuminate responses to closed-ended questions.³⁵ Transcribed responses to the open-ended questions were transferred to a spreadsheet. An initial code book with code names, definitions, and examples was developed based on an inductive method by one team member (EA).³⁵ Initially, coding and tabulation were conducted separately for each question but it was noted that content extended across response prompts (eg, responses to question 2 “What might make you more likely to come?” were spontaneously provided when answering question 1), thus coding was collapsed across questions. Next, 2 team members (EA, KD) coded the same responses, meeting weekly to discuss discrepancies. The code book was revised following each meeting to reflect refinements in code names and definitions, adding newly generated codes as needed. The process continued until consensus and data saturation was obtained, with 90% intercoder agreement. Next, these codes were subjected to thematic analysis by 2 team members (EA, KD) combining codes into 6 overarching themes. The entire team reviewed the codes and identified 2 supra themes: positive beliefs or facilitators and negative beliefs or barriers.

Consistent with the concept of reflexivity in qualitative research, we acknowledge the influence of the research team members on the qualitative process.³⁶ The primary coding team (EA, KD) are both researchers and employees of Veterans Affairs Boston Healthcare System who have participated in other research projects involving veterans and qualitative analyses but are not yoga instructors or yoga researchers.

Results

Study 1

The sample of 110 military veterans was mostly male (99.1%) with a mean (SD) age of 64.9 (9.4) years (range, 41-88)(Table 1). The majority (70.9%) described their race/ethnicity as White, non-Hispanic followed by Black/African American (18.2%) and Hispanic (8.2%) persons; 50.0% had no more than a high school education. The most common cancer diagnoses were colorectal (50.9%), head and neck (39.1%), and esophageal and gastric (10.0%) and ranged from AJCC stages I to IV.

When first asked, the majority of participants (78.2%) reported that they were not interested in yoga, 16.4% reported they might be interested, and 5.5% reported they had tried a yoga class since their cancer diagnosis. In contrast, 40.9% used exercise, 32.7% used meditation, 14.5% used physical or occupational therapy, and 11.8% used massage therapy since their cancer diagnosis.

Study 2 Intervention Sample

This sample of 28 veterans was mostly male (96.4%) with a mean (SD) age of 69.2 (10.9) years (range, 57-87). The majority (89.3%) described their race as White, followed by Black/African American (10.7%); no participants self-identified in other categories for race/ethnicity. Twelve veterans (42.9%) had no more than a high school education. The most common cancer diagnosis was genitourinary (35.7%) and the AJCC stage ranged from I to IV.

We employed information learned in study 1 to enhance access in study 2. We mailed letters to 278 veterans diagnosed with cancer in the previous 3 years that provided education about yoga based on study 1 findings. Of 207 veterans reached by phone, 133 (64%) stated they were not interested in coming to a yoga class; 74 (36%) were interested, but 30 felt they were unable to attend due to obstacles such as illness or travel. Ultimately 37 (18%) veterans agreed and consented to the class, and 28 (14%) completed postclass surveys.

In multivariate regression, higher expected health benefits of yoga were associated with higher physical function, lower concern about expected discomfort was also associated with higher physical function as well as higher education; similarly, lower concern about expected social norms was associated with higher physical function. Age was not associated with any of the BAYS factors.

Beliefs about yoga improved from before to after class for all 3 domains with greater expected benefit and lower concerns about discomfort or social norms: expected benefit (mean difference, 5.3; t = 4.44, P < .001), expected discomfort (mean difference, 3.0; t = 4.92, P < .001), expected social norms (mean difference, 3.5; t = 4.38, P < .001). Physical function improved from before the class to afterwards (mean difference, 7.5; t = 3.97, P < .001) but there were no changes in anxiety (mean difference, 0.6; t = 0.76, P = .46). Beliefs about yoga moderated changes in physical function, such that those with higher beliefs experienced a greater improvement in physical function (F = 15.24, P < .001).

Discussion

Yoga is an effective clinical intervention for addressing some long-term adverse effects in cancer survivors, although the body of research focuses predominantly on middle aged, female, White, college-educated breast cancer survivors. There is no evidence to suggest yoga would be less effective in other groups, but it has not been extensively studied in survivors from diverse subgroups. Beliefs about yoga are a factor that may enhance interest in yoga interventions and research, and measures aimed at addressing potential beliefs and fears may capture information that can be used to support older cancer survivors in holistic health. The aims of this study were to examine beliefs about yoga in 2 samples of older cancer survivors who received VHA care. The main findings are (1) interest in yoga was initially low and lower than that of other complementary or exercise-based interventions, but increased when participants were provided brief education about yoga; (2) interest in yoga was associated with beliefs about yoga with qualitative comments illuminating these beliefs; (3) demographic characteristics (education, race) and physical function were associated with beliefs about yoga; and (4) positive beliefs about yoga increased following a brief yoga intervention and was associated with improvements in physical function.

Willingness to consider a class appeared to shift for some older veterans when they were presented brief information about yoga that explained what is involved, how it might help, and that it could be done from a chair if needed. These findings clearly indicated that when trying to enhance participation in yoga in clinical or research programs, it will be important that recruitment materials provide such information. This finding is consistent with the qualitative findings that reflected a lack of knowledge or skepticism about benefits of yoga among some participants. Given the finding that physical function was associated with beliefs about yoga and was also a prominent theme in qualitative analyses, when referring older veterans to yoga it may be useful to explicitly state that yoga may enhance some aspects of physical function and can be adapted for level of physical function or pain limitations. The finding that beliefs about yoga were associated with education also suggests that materials should be presented at all levels of education.

Age was not associated with beliefs about yoga in either study. Importantly, in a more detailed study 1 follow-up analysis, beliefs about yoga were equivalent for aged > 70 years compared with those aged 40 to 69 years. It is not entirely clear why older adults have been underrepresented in studies of yoga in cancer survivors. However, older adults are vastly underrepresented in clinical trials for many health conditions, even though they are more likely to experience many diseases, including cancer.³⁷ A new National Institutes of Health policy requires that individuals of all ages, including older adults, must be included in all human subjects research unless there are scientific reasons not to include them.³⁸ It is therefore imperative to consider strategies to address underrepresentation of older adults.

Limitations

These findings have several limitations. First, participants were homogeneous in age, gender, race/ethnicity and veteran status, which provides a window into this understudied population but limits generalizability and our ability to control across populations. Second, the sample size limited the ability to conduct subgroup and interaction analyses, such as examining potential differential effects of cancer type, treatment, and PTSD on yoga beliefs or to consider the relationship of yoga beliefs with changes in quality of life before and after the yoga intervention in study 2. Additionally, age was not associated with beliefs about yoga in these samples that of mostly older adults. We were able to compare middle-aged and older adults but could not compare beliefs about yoga to adults aged in their 20s and 30s. Last, our study excluded people with dementia and psychotic disorders. Further research is needed to examine yoga for older cancer survivors who have these conditions.

Conclusions

Education that specifically informs potential participants about yoga practice, potential modifications, and potential benefits, as well as adaptations to programs that address physical and logistical barriers may be useful in increasing access to and participation in yoga for older Veterans who are cancer survivors.

Acknowledgments/Funding

The authors have no financial or personal relationships to disclose. This work was supported by the US Department of Veterans Affairs (VA) Rehabilitation Research and Development Service. This material is the result of work supported with resources and the use of facilities at the VA Boston Healthcare System, Bedford VA Medical Center, and Michael E. DeBakey VA Medical Center in Houston, Texas. We thank the members of the Veterans Cancer Rehabilitation Study (Vetcares) Research teams in Boston and in Houston and the veterans who have participated in our research studies and allow us to contribute to their health care.
 

Hidradenitis suppurativa (HS) is a debilitating chronic condition that often is recalcitrant to first-line treatments, and mechanisms underlying its pathology remain unclear. Existing data suggest a multifactorial etiology with different pathophysiologic contributors, including genetic, hormonal, and immune dysregulation factors. At this time, only one medication (adalimumab) is US Food and Drug Administration approved for HS, but multiple medical and procedural therapies are available.¹ Herein, we discuss the concept of treatment stacking, or the combination of unique therapeutic modalities—an approach we believe is key to optimizing management of HS patients.

Stacking Treatments for HS

Unlike psoriasis, in which a single biologic agent may provide 100% clearance (psoriasis area and severity index 100 [PASI 100]) without adjuvant treatment,^2,3 the field of HS currently lacks medications that are efficacious to that degree of success as monotherapy. In HS, the benchmark for a positive treatment outcome is Hidradenitis Suppurativa Clinical Response 50 (HiSCR50),⁴ a 50% reduction in inflammatory lesion count—a far less stringent marker for disease improvement. Thus, providers should design HS treatment regimens with a model of combining therapies and shift away from monotherapy. Targeting different pathophysiologic pathways by stacking multiple treatments may provide synergistic benefits for HS patients. Treatment stacking is a familiar concept in acne; for instance, patients who benefit tremendously from isotretinoin may still require a hormone-modulating treatment (eg, spironolactone) to attain optimal results.

Adherence to a rigid treatment algorithm based on disease severity limits the potential to create comprehensive regimens that account for unique patient characteristics and clinical manifestations. When evaluating an HS patient, providers should systematically consider each pathophysiologic factor and target the ones that appear to be most involved in that particular patient. The North American HS guidelines illustrate this point by supporting use of several treatments across different Hurley stages, such as recommending hormonal treatment in patients with Hurley stages 1, 2, or 3.¹ Of note, treatment stacking also includes procedural therapies. Surgeons typically prefer a patient’s disease management to be optimized prior to surgery, including reduced drainage and inflammation. In addition, even after surgery, patients often still require medical management to prevent continued disease worsening.

Treatment Pathways for HS

A multimodal approach with treatment stacking (Figure) can be useful to all HS patients, from those with the mildest to the most severe disease. Modifiable pathophysiologic factors and examples of their targeted treatments include (1) follicular occlusion (eg, oral retinoids), (2) metabolic dysfunction (eg, metformin), (3) hormones (eg, oral contraceptive pills, spironolactone, finasteride), (4) dysbiosis (eg, antibiotics such as clindamycin and rifampin combination therapy), (5) immune dysregulation (eg, biologic agents), and (6) friction/irritation (eg, weight loss, clothing recommendations).

Combining treatments from different pathways enables potentiation of individual treatment efficacies. A female patient with only a few HS nodules that flare with menses may be well controlled with spironolactone as her only systemic agent; however, she still may benefit from use of an antiseptic wash, topical clindamycin, and lifestyle changes such as weight loss and reduction of mechanical irritation. A patient with severe recalcitrant HS could notably benefit from concomitant biologic, systemic antibiotic, and hormonal/metabolic treatments. If disease control is still inadequate, agents within the same class can be switched (eg, choosing a different biologic) or other disease-modifying agents such as colchicine also can be added. The goal is to create an effective treatment toolbox with therapies targeting different pathophysiologic arms of HS and working together in synergy. Each tool can be refined by modifying dosing frequency and duration of use to strive for optimal response. At this time, the literature on HS combination therapy is sparse. A retrospective study of 31 patients reported promising combinations, including isotretinoin with spironolactone for mild disease, isotretinoin or doxycycline with adalimumab for moderate disease, and cyclosporine with adalimumab for severe disease.⁵ Larger prospective studies on clinical response to different combination regimens are warranted.

Optimizing Therapy for HS and Its Comorbidities

Additional considerations may further optimize treatment plans. Some therapies benefit all patients; for example, providers should counsel all HS patients on healthy weight management, optimized clothing choices,⁶ and friction reduction in the intertriginous folds. Providers also may consider adding therapies with faster onset of efficacy as a bridge to long-term, slower-onset therapies. For instance, female HS patients with menstrual flares who are prescribed spironolactone also may benefit from a course of systemic antibiotics, which typically provides more prompt relief. Treatment regimens also can concomitantly treat HS and its comorbidities.⁷ For example, metformin serves a dual purpose in HS patients with diabetes mellitus, and adalimumab in patients with both HS and inflammatory bowel disease.

Final Thoughts

The last decade has seen tremendous growth in HS research⁸ coupled with a remarkable expansion in the therapeutic pipeline.⁹ However, currently no single therapy for HS can guarantee satisfactory disease remission or durability of remission. The contrast between clinical trials and real-world practice should be acknowledged; the former often is restrictive in design with monotherapy and allowance of very limited concomitant treatments, such as topical or oral antibiotics. This limits our ability to draw conclusions regarding the additive synergistic potential of different therapeutics in combination. In clinical practice, we are not restricted by monotherapy trial protocols. As we await new tools, treatment stacking allows for creating a framework to best utilize the tools that are available to us.

Although HS has continued to affect the lives of many patients, improved understanding of underlying pathophysiology and a well-placed sense of urgency from all stakeholders (ie, patients, clinicians, researchers, industry partners) has pushed this field forward. Until our therapeutic armamentarium has expanded to include highly efficacious monotherapy options, providers should consider treatment stacking for every HS patient.

Hidradenitis suppurativa (HS) is a debilitating chronic condition that often is recalcitrant to first-line treatments, and mechanisms underlying its pathology remain unclear. Existing data suggest a multifactorial etiology with different pathophysiologic contributors, including genetic, hormonal, and immune dysregulation factors. At this time, only one medication (adalimumab) is US Food and Drug Administration approved for HS, but multiple medical and procedural therapies are available.¹ Herein, we discuss the concept of treatment stacking, or the combination of unique therapeutic modalities—an approach we believe is key to optimizing management of HS patients.

Stacking Treatments for HS

Unlike psoriasis, in which a single biologic agent may provide 100% clearance (psoriasis area and severity index 100 [PASI 100]) without adjuvant treatment,^2,3 the field of HS currently lacks medications that are efficacious to that degree of success as monotherapy. In HS, the benchmark for a positive treatment outcome is Hidradenitis Suppurativa Clinical Response 50 (HiSCR50),⁴ a 50% reduction in inflammatory lesion count—a far less stringent marker for disease improvement. Thus, providers should design HS treatment regimens with a model of combining therapies and shift away from monotherapy. Targeting different pathophysiologic pathways by stacking multiple treatments may provide synergistic benefits for HS patients. Treatment stacking is a familiar concept in acne; for instance, patients who benefit tremendously from isotretinoin may still require a hormone-modulating treatment (eg, spironolactone) to attain optimal results.

Adherence to a rigid treatment algorithm based on disease severity limits the potential to create comprehensive regimens that account for unique patient characteristics and clinical manifestations. When evaluating an HS patient, providers should systematically consider each pathophysiologic factor and target the ones that appear to be most involved in that particular patient. The North American HS guidelines illustrate this point by supporting use of several treatments across different Hurley stages, such as recommending hormonal treatment in patients with Hurley stages 1, 2, or 3.¹ Of note, treatment stacking also includes procedural therapies. Surgeons typically prefer a patient’s disease management to be optimized prior to surgery, including reduced drainage and inflammation. In addition, even after surgery, patients often still require medical management to prevent continued disease worsening.

Treatment Pathways for HS

A multimodal approach with treatment stacking (Figure) can be useful to all HS patients, from those with the mildest to the most severe disease. Modifiable pathophysiologic factors and examples of their targeted treatments include (1) follicular occlusion (eg, oral retinoids), (2) metabolic dysfunction (eg, metformin), (3) hormones (eg, oral contraceptive pills, spironolactone, finasteride), (4) dysbiosis (eg, antibiotics such as clindamycin and rifampin combination therapy), (5) immune dysregulation (eg, biologic agents), and (6) friction/irritation (eg, weight loss, clothing recommendations).

Combining treatments from different pathways enables potentiation of individual treatment efficacies. A female patient with only a few HS nodules that flare with menses may be well controlled with spironolactone as her only systemic agent; however, she still may benefit from use of an antiseptic wash, topical clindamycin, and lifestyle changes such as weight loss and reduction of mechanical irritation. A patient with severe recalcitrant HS could notably benefit from concomitant biologic, systemic antibiotic, and hormonal/metabolic treatments. If disease control is still inadequate, agents within the same class can be switched (eg, choosing a different biologic) or other disease-modifying agents such as colchicine also can be added. The goal is to create an effective treatment toolbox with therapies targeting different pathophysiologic arms of HS and working together in synergy. Each tool can be refined by modifying dosing frequency and duration of use to strive for optimal response. At this time, the literature on HS combination therapy is sparse. A retrospective study of 31 patients reported promising combinations, including isotretinoin with spironolactone for mild disease, isotretinoin or doxycycline with adalimumab for moderate disease, and cyclosporine with adalimumab for severe disease.⁵ Larger prospective studies on clinical response to different combination regimens are warranted.

Optimizing Therapy for HS and Its Comorbidities

Additional considerations may further optimize treatment plans. Some therapies benefit all patients; for example, providers should counsel all HS patients on healthy weight management, optimized clothing choices,⁶ and friction reduction in the intertriginous folds. Providers also may consider adding therapies with faster onset of efficacy as a bridge to long-term, slower-onset therapies. For instance, female HS patients with menstrual flares who are prescribed spironolactone also may benefit from a course of systemic antibiotics, which typically provides more prompt relief. Treatment regimens also can concomitantly treat HS and its comorbidities.⁷ For example, metformin serves a dual purpose in HS patients with diabetes mellitus, and adalimumab in patients with both HS and inflammatory bowel disease.

Final Thoughts

The last decade has seen tremendous growth in HS research⁸ coupled with a remarkable expansion in the therapeutic pipeline.⁹ However, currently no single therapy for HS can guarantee satisfactory disease remission or durability of remission. The contrast between clinical trials and real-world practice should be acknowledged; the former often is restrictive in design with monotherapy and allowance of very limited concomitant treatments, such as topical or oral antibiotics. This limits our ability to draw conclusions regarding the additive synergistic potential of different therapeutics in combination. In clinical practice, we are not restricted by monotherapy trial protocols. As we await new tools, treatment stacking allows for creating a framework to best utilize the tools that are available to us.

Although HS has continued to affect the lives of many patients, improved understanding of underlying pathophysiology and a well-placed sense of urgency from all stakeholders (ie, patients, clinicians, researchers, industry partners) has pushed this field forward. Until our therapeutic armamentarium has expanded to include highly efficacious monotherapy options, providers should consider treatment stacking for every HS patient.

Hidradenitis suppurativa (HS) is a debilitating chronic condition that often is recalcitrant to first-line treatments, and mechanisms underlying its pathology remain unclear. Existing data suggest a multifactorial etiology with different pathophysiologic contributors, including genetic, hormonal, and immune dysregulation factors. At this time, only one medication (adalimumab) is US Food and Drug Administration approved for HS, but multiple medical and procedural therapies are available.¹ Herein, we discuss the concept of treatment stacking, or the combination of unique therapeutic modalities—an approach we believe is key to optimizing management of HS patients.

Stacking Treatments for HS

Unlike psoriasis, in which a single biologic agent may provide 100% clearance (psoriasis area and severity index 100 [PASI 100]) without adjuvant treatment,^2,3 the field of HS currently lacks medications that are efficacious to that degree of success as monotherapy. In HS, the benchmark for a positive treatment outcome is Hidradenitis Suppurativa Clinical Response 50 (HiSCR50),⁴ a 50% reduction in inflammatory lesion count—a far less stringent marker for disease improvement. Thus, providers should design HS treatment regimens with a model of combining therapies and shift away from monotherapy. Targeting different pathophysiologic pathways by stacking multiple treatments may provide synergistic benefits for HS patients. Treatment stacking is a familiar concept in acne; for instance, patients who benefit tremendously from isotretinoin may still require a hormone-modulating treatment (eg, spironolactone) to attain optimal results.

Adherence to a rigid treatment algorithm based on disease severity limits the potential to create comprehensive regimens that account for unique patient characteristics and clinical manifestations. When evaluating an HS patient, providers should systematically consider each pathophysiologic factor and target the ones that appear to be most involved in that particular patient. The North American HS guidelines illustrate this point by supporting use of several treatments across different Hurley stages, such as recommending hormonal treatment in patients with Hurley stages 1, 2, or 3.¹ Of note, treatment stacking also includes procedural therapies. Surgeons typically prefer a patient’s disease management to be optimized prior to surgery, including reduced drainage and inflammation. In addition, even after surgery, patients often still require medical management to prevent continued disease worsening.

Treatment Pathways for HS

A multimodal approach with treatment stacking (Figure) can be useful to all HS patients, from those with the mildest to the most severe disease. Modifiable pathophysiologic factors and examples of their targeted treatments include (1) follicular occlusion (eg, oral retinoids), (2) metabolic dysfunction (eg, metformin), (3) hormones (eg, oral contraceptive pills, spironolactone, finasteride), (4) dysbiosis (eg, antibiotics such as clindamycin and rifampin combination therapy), (5) immune dysregulation (eg, biologic agents), and (6) friction/irritation (eg, weight loss, clothing recommendations).

Combining treatments from different pathways enables potentiation of individual treatment efficacies. A female patient with only a few HS nodules that flare with menses may be well controlled with spironolactone as her only systemic agent; however, she still may benefit from use of an antiseptic wash, topical clindamycin, and lifestyle changes such as weight loss and reduction of mechanical irritation. A patient with severe recalcitrant HS could notably benefit from concomitant biologic, systemic antibiotic, and hormonal/metabolic treatments. If disease control is still inadequate, agents within the same class can be switched (eg, choosing a different biologic) or other disease-modifying agents such as colchicine also can be added. The goal is to create an effective treatment toolbox with therapies targeting different pathophysiologic arms of HS and working together in synergy. Each tool can be refined by modifying dosing frequency and duration of use to strive for optimal response. At this time, the literature on HS combination therapy is sparse. A retrospective study of 31 patients reported promising combinations, including isotretinoin with spironolactone for mild disease, isotretinoin or doxycycline with adalimumab for moderate disease, and cyclosporine with adalimumab for severe disease.⁵ Larger prospective studies on clinical response to different combination regimens are warranted.

Optimizing Therapy for HS and Its Comorbidities

Additional considerations may further optimize treatment plans. Some therapies benefit all patients; for example, providers should counsel all HS patients on healthy weight management, optimized clothing choices,⁶ and friction reduction in the intertriginous folds. Providers also may consider adding therapies with faster onset of efficacy as a bridge to long-term, slower-onset therapies. For instance, female HS patients with menstrual flares who are prescribed spironolactone also may benefit from a course of systemic antibiotics, which typically provides more prompt relief. Treatment regimens also can concomitantly treat HS and its comorbidities.⁷ For example, metformin serves a dual purpose in HS patients with diabetes mellitus, and adalimumab in patients with both HS and inflammatory bowel disease.

Final Thoughts

The last decade has seen tremendous growth in HS research⁸ coupled with a remarkable expansion in the therapeutic pipeline.⁹ However, currently no single therapy for HS can guarantee satisfactory disease remission or durability of remission. The contrast between clinical trials and real-world practice should be acknowledged; the former often is restrictive in design with monotherapy and allowance of very limited concomitant treatments, such as topical or oral antibiotics. This limits our ability to draw conclusions regarding the additive synergistic potential of different therapeutics in combination. In clinical practice, we are not restricted by monotherapy trial protocols. As we await new tools, treatment stacking allows for creating a framework to best utilize the tools that are available to us.

Although HS has continued to affect the lives of many patients, improved understanding of underlying pathophysiology and a well-placed sense of urgency from all stakeholders (ie, patients, clinicians, researchers, industry partners) has pushed this field forward. Until our therapeutic armamentarium has expanded to include highly efficacious monotherapy options, providers should consider treatment stacking for every HS patient.

Introduction

There have been incident reports of cardiac-related adverse events (CrAE) from immune checkpoint inhibitors (ICPI); however, the true incidence and subsequent management of these potential side effects have not been defined. It is therefore important to study ICPI related cardiac dysfunction to assist in monitoring and surveillance of these patients.

Methods

63 patients who received nivolumab and pembrolizumab at Stratton VAMC Albany between January 2015 to December 2018 were studied. Retrospective chart review was done to identify the CrAE up to two-year post-therapy completion or discontinuation. Naranjo score was used to assess drug-related side effect. IRB approval was obtained.

Results 

CrAE were defined as new onset arrythmia identified on electrocardiogram, evidence of cardiomyopathy on echocardiogram, an acute coronary event, and hospitalizations from primary cardiac disorder following ICPI administration. Of the 63 patients, 6 patients developed CrAE. Our review showed 3 patients developed new arrythmias including 1 with atrial fibrillation, and 2 with atrial flutter. There was 1 case each of new heart failure with reduced ejection fraction and pericarditis with pericardial tamponade. 1 patient developed acute coronary syndrome in addition to complete heart block. Of the 6 patients, 2 had elevated brain natriuretic peptide (BNP) prior to onset of CrAE. Elevated markers including BNP and troponin-I were also seen in 13 patients with preexisting heart conditions without CrAE. Duration of therapy was variable for all patients with CrAE. Therapy was continued for 3 patients without recurrence of CrAE. Therapy was permanently discontinued in the patient who developed pericardial effusion (grade IV toxicity). The remaining 2 patients had additional concurrent immune-related toxicities that required discontinuation of therapy. Our analysis showed 25/63 patients with pre-existing cardiac conditions (including arrhythmia, heart failure or coronary artery disease) who did not develop new CrAE; however 6 of these patients required hospitalization for exacerbation related to these pre-existing conditions.

Conclusions

CrAE can occur with ICPIs, and vigilance is required in high-risk patient including those with pre-existing cardiac comorbidity. Further studies are required to establish if baseline screening EKG and echocardiogram should be obtained for all patients starting ICPI.

Introduction

There have been incident reports of cardiac-related adverse events (CrAE) from immune checkpoint inhibitors (ICPI); however, the true incidence and subsequent management of these potential side effects have not been defined. It is therefore important to study ICPI related cardiac dysfunction to assist in monitoring and surveillance of these patients.

Methods

63 patients who received nivolumab and pembrolizumab at Stratton VAMC Albany between January 2015 to December 2018 were studied. Retrospective chart review was done to identify the CrAE up to two-year post-therapy completion or discontinuation. Naranjo score was used to assess drug-related side effect. IRB approval was obtained.

Results 

CrAE were defined as new onset arrythmia identified on electrocardiogram, evidence of cardiomyopathy on echocardiogram, an acute coronary event, and hospitalizations from primary cardiac disorder following ICPI administration. Of the 63 patients, 6 patients developed CrAE. Our review showed 3 patients developed new arrythmias including 1 with atrial fibrillation, and 2 with atrial flutter. There was 1 case each of new heart failure with reduced ejection fraction and pericarditis with pericardial tamponade. 1 patient developed acute coronary syndrome in addition to complete heart block. Of the 6 patients, 2 had elevated brain natriuretic peptide (BNP) prior to onset of CrAE. Elevated markers including BNP and troponin-I were also seen in 13 patients with preexisting heart conditions without CrAE. Duration of therapy was variable for all patients with CrAE. Therapy was continued for 3 patients without recurrence of CrAE. Therapy was permanently discontinued in the patient who developed pericardial effusion (grade IV toxicity). The remaining 2 patients had additional concurrent immune-related toxicities that required discontinuation of therapy. Our analysis showed 25/63 patients with pre-existing cardiac conditions (including arrhythmia, heart failure or coronary artery disease) who did not develop new CrAE; however 6 of these patients required hospitalization for exacerbation related to these pre-existing conditions.

Conclusions

CrAE can occur with ICPIs, and vigilance is required in high-risk patient including those with pre-existing cardiac comorbidity. Further studies are required to establish if baseline screening EKG and echocardiogram should be obtained for all patients starting ICPI.

Introduction

There have been incident reports of cardiac-related adverse events (CrAE) from immune checkpoint inhibitors (ICPI); however, the true incidence and subsequent management of these potential side effects have not been defined. It is therefore important to study ICPI related cardiac dysfunction to assist in monitoring and surveillance of these patients.

Methods

63 patients who received nivolumab and pembrolizumab at Stratton VAMC Albany between January 2015 to December 2018 were studied. Retrospective chart review was done to identify the CrAE up to two-year post-therapy completion or discontinuation. Naranjo score was used to assess drug-related side effect. IRB approval was obtained.

Results 

CrAE were defined as new onset arrythmia identified on electrocardiogram, evidence of cardiomyopathy on echocardiogram, an acute coronary event, and hospitalizations from primary cardiac disorder following ICPI administration. Of the 63 patients, 6 patients developed CrAE. Our review showed 3 patients developed new arrythmias including 1 with atrial fibrillation, and 2 with atrial flutter. There was 1 case each of new heart failure with reduced ejection fraction and pericarditis with pericardial tamponade. 1 patient developed acute coronary syndrome in addition to complete heart block. Of the 6 patients, 2 had elevated brain natriuretic peptide (BNP) prior to onset of CrAE. Elevated markers including BNP and troponin-I were also seen in 13 patients with preexisting heart conditions without CrAE. Duration of therapy was variable for all patients with CrAE. Therapy was continued for 3 patients without recurrence of CrAE. Therapy was permanently discontinued in the patient who developed pericardial effusion (grade IV toxicity). The remaining 2 patients had additional concurrent immune-related toxicities that required discontinuation of therapy. Our analysis showed 25/63 patients with pre-existing cardiac conditions (including arrhythmia, heart failure or coronary artery disease) who did not develop new CrAE; however 6 of these patients required hospitalization for exacerbation related to these pre-existing conditions.

Conclusions

CrAE can occur with ICPIs, and vigilance is required in high-risk patient including those with pre-existing cardiac comorbidity. Further studies are required to establish if baseline screening EKG and echocardiogram should be obtained for all patients starting ICPI.

Background

We sought to evaluate passive recruitment efforts of VA patients into a precision medicine research program. Access to clinical trials and other research opportunities is important to discovering new disease treatments and better ways to detect, diagnose, and reduce disease risk. The WISDOM (Women Informed to Screen Depending on Measures of risk) Study is a multi-site, pragmatic trial with webbased participation based at the University of California at San Francisco (UCSF) that aims to move breast cancer screening away from its current one-size-fitsall approach to one that is personalized based on each woman’s individual risk.

Methods

We created a hub and spoke recruitment model with the San Francisco VA Medical Center (SFVAMC) serving as the central hub of passive recruitment activities and eligible VA facilities that agreed to participate serving as the spoke recruitment sites. Eligible facilities had at least 3,000 women patients, VA clinical genetic services available, a site lead from the VA Women’s Health-Practice-Based Research Network, and mammography on site. Site participation involved permission for the research team to email eligible patients (women aged 40-74 without prior breast cancer diagnosis) about the WISDOM Study. We evaluated the effectiveness of the recruitment by assessing trends in enrollment and monitoring participation of VA patients in the WISDOM Study. Analysis: Pre/post frequencies of women consenting to participate in the WISDOM Study.

Results

From 5/24/2021 through 6/21/2021, we emailed 27,061 eligible VA patients from six participating VA facilities. Prior to the VA emailing, an average of 22 women per week consented to participating in the WISDOM Study and none were Veterans. After the first month of the VA emailing, an average of 186 women per week consented – a 7.5-fold increase. Additionally, during the first month of VA emailing, 81% of women registering with the WISDOM Study said they heard about the study from the VA.

Implications

The VA has recently approved of emailing as a method for recruiting research subjects. Our results demonstrate this is a feasible approach for precision medicine research, a growing area of research in VA and at academic affiliates.

Background

We sought to evaluate passive recruitment efforts of VA patients into a precision medicine research program. Access to clinical trials and other research opportunities is important to discovering new disease treatments and better ways to detect, diagnose, and reduce disease risk. The WISDOM (Women Informed to Screen Depending on Measures of risk) Study is a multi-site, pragmatic trial with webbased participation based at the University of California at San Francisco (UCSF) that aims to move breast cancer screening away from its current one-size-fitsall approach to one that is personalized based on each woman’s individual risk.

Methods

We created a hub and spoke recruitment model with the San Francisco VA Medical Center (SFVAMC) serving as the central hub of passive recruitment activities and eligible VA facilities that agreed to participate serving as the spoke recruitment sites. Eligible facilities had at least 3,000 women patients, VA clinical genetic services available, a site lead from the VA Women’s Health-Practice-Based Research Network, and mammography on site. Site participation involved permission for the research team to email eligible patients (women aged 40-74 without prior breast cancer diagnosis) about the WISDOM Study. We evaluated the effectiveness of the recruitment by assessing trends in enrollment and monitoring participation of VA patients in the WISDOM Study. Analysis: Pre/post frequencies of women consenting to participate in the WISDOM Study.

Results

From 5/24/2021 through 6/21/2021, we emailed 27,061 eligible VA patients from six participating VA facilities. Prior to the VA emailing, an average of 22 women per week consented to participating in the WISDOM Study and none were Veterans. After the first month of the VA emailing, an average of 186 women per week consented – a 7.5-fold increase. Additionally, during the first month of VA emailing, 81% of women registering with the WISDOM Study said they heard about the study from the VA.

Implications

The VA has recently approved of emailing as a method for recruiting research subjects. Our results demonstrate this is a feasible approach for precision medicine research, a growing area of research in VA and at academic affiliates.

Background

We sought to evaluate passive recruitment efforts of VA patients into a precision medicine research program. Access to clinical trials and other research opportunities is important to discovering new disease treatments and better ways to detect, diagnose, and reduce disease risk. The WISDOM (Women Informed to Screen Depending on Measures of risk) Study is a multi-site, pragmatic trial with webbased participation based at the University of California at San Francisco (UCSF) that aims to move breast cancer screening away from its current one-size-fitsall approach to one that is personalized based on each woman’s individual risk.

Methods

We created a hub and spoke recruitment model with the San Francisco VA Medical Center (SFVAMC) serving as the central hub of passive recruitment activities and eligible VA facilities that agreed to participate serving as the spoke recruitment sites. Eligible facilities had at least 3,000 women patients, VA clinical genetic services available, a site lead from the VA Women’s Health-Practice-Based Research Network, and mammography on site. Site participation involved permission for the research team to email eligible patients (women aged 40-74 without prior breast cancer diagnosis) about the WISDOM Study. We evaluated the effectiveness of the recruitment by assessing trends in enrollment and monitoring participation of VA patients in the WISDOM Study. Analysis: Pre/post frequencies of women consenting to participate in the WISDOM Study.

Results

From 5/24/2021 through 6/21/2021, we emailed 27,061 eligible VA patients from six participating VA facilities. Prior to the VA emailing, an average of 22 women per week consented to participating in the WISDOM Study and none were Veterans. After the first month of the VA emailing, an average of 186 women per week consented – a 7.5-fold increase. Additionally, during the first month of VA emailing, 81% of women registering with the WISDOM Study said they heard about the study from the VA.

Implications

The VA has recently approved of emailing as a method for recruiting research subjects. Our results demonstrate this is a feasible approach for precision medicine research, a growing area of research in VA and at academic affiliates.

Within the nationwide effort to combat the opioid epidemic and reduce opioid prescribing, researchers have described different prescribing patterns for non-White racial and ethnic groups, including Black and LatinX populations. This remains a largely unexplored area within hospital medicine. Earlier studies of racial disparities demonstrate how some patients are assessed less often for pain and prescribed fewer opioids from the emergency department, surgical settings, and outpatient primary care practices. Researchers also have documented racial and ethnic disparities in analgesia for cancer pain and chronic noncancer pain.^1-11 Studies have demonstrated that White patients are more likely to receive opioid prescriptions compared with Black patients. Even with similar documented pain scores, there is evidence that Black patients receive fewer analgesics compared with White patients. For example, a recent study found that Black and Hispanic patients presenting to the emergency room for renal colic received less opioid medication compared with White patients.³ A study across 22 sites in Northern California found that racial minorities with long-bone fractures received fewer opioids at discharge than White patients.¹

It is unknown whether differential prescribing patterns by race exist among patients hospitalized on general medicine services. The objective of our study was to assess whether race and ethnicity were associated with the likelihood of opioids being prescribed and the duration of opioids prescribed when these patients are discharged from the hospital. Quantifying and seeking to understand these differences are the first steps toward ensuring racial and ethnic health equity in patient care.

Study Population and Data Sources

We identified all adults (age ≥18 years) discharged from the acute care inpatient general medicine services between June 1, 2012, and November 30, 2018, at the University of California, San Francisco (UCSF) Helen Diller Medical Center at Parnassus Heights, a 785-bed urban academic teaching hospital. All data were obtained from the hospital’s Epic-based electronic medical record (Epic Systems Corporation). Data elements were extracted from Clarity, the relationship database that stores Epic inpatient data. Patients discharged from the inpatient cardiology or bone marrow transplant services were not included. We excluded patients who did not receive opioids in the last 24 hours of their hospitalization. Patients with cancer-related pain diagnoses or sickle cell disease pain crises and patients who were discharged to hospice or followed by palliative care were excluded from the study based on International Classification of Diseases, Tenth Revision (ICD-10) codes (available on request) or service codes, when available, or admitting provider electronic health record documentation (Appendix Figure 1). Palliative care and hospice patients have significantly different pain needs, with management often directed by specialists. Patients with sickle cell disease are disproportionately Black and have distinct opioid prescribing patterns.^12,13 We also excluded discharge opioid prescriptions that were a resumption of the patient’s opioid prescription before admission based on medication documentation. Only new prescriptions signed by the discharging hospitalist, including different doses and formulations, were included in this study.

We performed a subgroup analysis of patients who were not prescribed opioids before their admission based on medication reconciliation but were started on opioids while hospitalized.

Primary Outcomes

We examined two primary outcomes: whether a patient received an opioid prescription at discharge, and, for patients prescribed opioids, the number of days prescribed. Days of opioids at discharge were calculated as total morphine milligram equivalents (MMEs) prescribed divided by MMEs administered during the final 24 hours of hospitalization. This metric was used as a patient-specific approach to calculating how long an opioid prescription will last after discharge, standardized according to the actual opioid requirements from hospitalization.¹⁴ If a patient was discharged with prescriptions for several opioids, the longest single prescription duration was used.

Primary Predictors

The primary predictor was the patient’s primary self-reported race/ethnicity, categorized as White, Black, LatinX, Asian, Native Hawaiian or other Pacific Islander, American Indian or Alaska Native, and other/unknown. Other/unknown included patients who were listed as other, declined, or who were otherwise unspecified. Self-reported race/ethnicity is obtained through reporting to the registrar. These race/ethnicity groupings were done in concordance with US Census Bureau definitions. Researchers classified patients as LatinX if they had Hispanic documented as their ethnicity, no matter their racial identification. These categorizations were chosen to be consistent with the existing literature, recognizing the role of a combined race/ethnicity definition for Hispanic or LatinX populations.¹⁵ These definitions of race/ethnicity are self-reported and reflect socially—not genetically defined—groupings.¹⁶ This variable serves as a surrogate for the structural factors that contribute to racism, the determining factor for racially disparate outcomes.¹⁷

Covariate Data Collection

Additional data were obtained regarding patient demographics, hospitalization factors, and medical diagnoses. Demographic factors included age, sex, and limited English proficiency (LEP) status. LEP was defined as having a primary language other than English and requiring an interpreter. Hospitalization factors included length of stay, whether they required intensive care unit (ICU) management, average daily MMEs administered during their entire hospitalization, MMEs administered during the final 24 hours of their hospitalization, whether the patient was on a teaching service or direct-care hospitalist service, their disposition on discharge, and year. Medical diagnosis variables included the adjusted Elixhauser Comorbidity Index based on ICD-10 codes; whether the patient was taking opioids at admission; and specific diagnoses of cancer, posttraumatic stress disorder (PTSD), and mood, anxiety, or psychotic disorder based on ICD-10 documentation.¹⁸

Statistical Analysis

All statistical analyses were performed using Stata software version 16 (StataCorp LP). Baseline demographic variables, hospitalization factors, and medical diagnosis variables were stratified by race/ethnicity. Within group comparisons were performed using chi-square or analysis of varianace (ANOVA) testing. For regression analyses, we fit two models. First, we fit a multivariable logistic regression model on all patients who received opioids during the last 24 hours of their hospitalization to examine the association between patient race/ethnicity and whether a patient received opioids at discharge, adjusting for additional patient, hospitalization, and medical covariates. Then we fit a negative binomial regression model on patients who were prescribed opioids at discharge to examine the association between patient race/ethnicity and the amount of opioids prescribed at discharge, adjusting for covariates. We used a negative binomial model because of the overdispersed distribution of discharge opioid prescriptions and only examined patients with an opioid prescription at discharge. We included the listed variables in our model because they were all found a priori to be associated with discharge opioid prescriptions.¹⁹ Instead of using days of opioids based on the last 24 hours, we performed a secondary analysis using the actual days of opioids supplied as the outcome. For example, a prescription of 12 tablets with every 6 hours dosing would be 3 days’ duration.

For both models, subgroup analyses were performed using the adjusted models restricted to patients newly prescribed opioids during their hospitalization and who were not previously taking opioids based on admission medication reconciliation. After testing for effect modification, this subgroup analysis was performed to reduce selection bias associated with earlier opioid use.

For all models, we reported predicted population opioid prescribing rates from the average marginal effects (AME).²⁰ Marginal effects were used because ours was a population level study and the outcome of interest was relatively common, limiting the effective interpretation of odds ratios.²¹ Marginal effects allow us to observe the instantaneous effect a given independent variable has on a dependent variable, while holding all other variables constant. It is implemented using the margins command in Stata. Marginal effects enable us to present our results as differences in probabilities, which is a more accurate way to describe the differences found among patient groups. Further, marginal effects are less sensitive to changes in model specifications.²²The UCSF Institutional Review Board for Human Subjects Research approved this study with a waiver of informed consent.

Unadjusted Results

We identified 10,953 patients who received opioids during the last 24 hours of hospitalization (see Appendix Figure 1 for study consort diagram). The patient population was 52.2% White, 18.4% Black, 11.5% Latinx, 10.1% Asian, 6.2% other/unknown, 0.9% Native Hawaiian/Other Pacific Islander, and 0.8% American Indian/Alaska Native (Table 1, Appendix Table 1). Black patients had fewer cancer diagnoses and the highest rate of prescribed opioids on admission. Asian patients were older and more likely to be female, and had higher rates of cancer, the highest median comorbidity index, and the smallest median daily MME during both the last 24 hours and total duration of hospitalization. Representative of general medicine patients, the most common principal discharge diagnoses in our dataset were pneumonia, cellulitis, altered mental status, sepsis, and abdominal pain.

Overall, 5541 (50.6%) patients who received opioids in the last 24 hours of their hospitalization received an opioid prescription at discharge. There were significant differences among racial/ethnic groups receiving an opioid prescription at discharge. Black patients were less likely to be discharged with an opioid compared with White patients (47.7% vs 50.3%; P < .001) (Table 2). The median discharge prescription duration for all patients was 9.3 days (interquartile range [IQR], 3.8-20.0). Black patients received the fewest median days of opioids at 7.5 days (IQR, 3.2-16.7) compared with White patients at 8.8 days (IQR, 3.7-20.0; P < .001) (Table 2).

Adjusted Regression Results

Demographic, clinical, and diagnosis specific factors were significantly associated with opioid prescriptions, including previous opioid use, sex, and a concurrent cancer diagnosis. There were fewer opioid prescriptions over time (Figure).

Following multivariable logistic regression for the association between race/ethnicity and opioid on discharge and controlling for covariates, we found that Black patients were less likely to receive an opioid prescription on discharge compared with White patients (predicted population rate, 47.6% vs 50.7%; AME −3.1%; 95% CI, −5.5% to −0.8%). Asian patients were more likely to receive a prescription on discharge compared with White patients (predicted population rate, 55.6% vs 50.7%; AME +4.9; 95% CI, 1.5%-8.3%).

Following multivariable negative binomial regression for the association between race/ethnicity and the number of opioid days on discharge, we found that Black patients received a shorter duration of opioid days compared with White patients (predicted days of opioids on discharge, 15.7 days vs 17.8 days; AME −2.1 days; 95% CI, −3.3 to −0.87) (Table 3). There were no significant differences among patients and the other racial/ethnic groups.

Our secondary analysis from the negative binomial regression with the days of opioids supplied metric yielded similar results to our primary analysis showing that Black patients received statically significantly fewer days of opioid therapy compared with White patients (Appendix Table 2).

Subgroup Regression Results

After testing for effect modification, which was negative, we examined the relationships for patients started on opioids during their hospitalization (Appendix Table 3 and Appendix Table 4). There were 5101 patients with newly prescribed opioids during their hospitalization. Adjusting for covariates, we found that Black patients were less likely to receive opioids at discharge compared with White patients (predicted population rate, 34.9% vs 40.4%; AME −5.5%; 95% CI, −9.2% to −1.9%). American Indian or Alaska Native patients were more likely to receive opioids on discharge (predicted population rate, 58.3% vs 40.4%; AME +17.9%; 95% CI, 1.0%-34.8%). We also found that Asian patients received more days of opioids on discharge (predicted days of opioid on discharge, 16.7 vs 13.7 days; AME +3.0 days; 95% CI, 0.6-5.3 days) (Appendix Table 4, Appendix Figure 2).

We found that Black patients discharged from the general medicine service were less likely to receive opioids and received shorter courses on discharge compared with White patients, adjusting for demographic, hospitalization, and medical diagnosis variables. Asian patients were more likely to receive an opioid prescription at discharge—a finding not reported in the literature on opioid prescribing disparities in most other practice settings.¹

Previous studies have shown racial disparities in pain management in emergency and surgical settings, but these relationships have not been characterized in an inpatient medicine population. Medicine patients comprise the majority of admitted patients in the United States and reflect a wide diversity of medical conditions, many requiring opioids for pain management. Determining the etiology of these differential prescribing patterns was not within the scope of our study, but earlier studies demonstrate a number of reasons why these patterns exist across racial and ethnic groups in other practice settings.^23,24 These reports give us insight into potential mechanisms for our study population.

Differences in pain management likely represent the multiple structural mechanisms by which racism operates.¹⁷ Drawing from the existing literature and the socioecological model, we hypothesize the ways that individual, interpersonal relationships, organizations, communities, and public policy impact opioid prescribing.^25,26 Using this model and considering the framework of Critical Race Theory (CRT), we can work towards understanding how race and ethnicity stand in as surrogates for racism and how this manifests in different outcomes and identify areas for intervention. CRT draws attention to race consciousness, contemporary orientation, centering in the margins, and praxis. In the context of this analysis, we recognize race consciousness and the interactions among factors such as race/ethnicity, language, and diagnoses such as PTSD.²⁷ This approach is necessary because racism is a multilevel construct influenced by macrolevel factors.²⁸

Individually and interpersonally, there is clinician-driven bias in pain assessment, which is activated under times of stress and diagnostic uncertainty and is amplified by a lack of clear guidelines for pain management prescriptions.^23,29-32 Institutional and organizational culture contribute to disparities through ingrained culture, practice patterns, and resource allocation.^29,33 Last, public policy and the larger sociopolitical environment worsen disparities through nondiverse workforces, state and federal guidelines, criminal justice policy, supply chain regulation, and access to care.

As individual clinicians, departments, and health systems leaders, we must identify areas for intervention. At the individual and interpersonal levels, there is evidence that taking implicit association tests could help clinicians become more aware of their negative associations, and empathy-inducing, perspective-taking interventions can reduce pain treatment bias.^31,34 At the institutional level, we must report data on disparities, create guidelines for pain management, and reevaluate the educational curriculum and culture to assess how certain biases could be propagated. The lack of straightforward guidelines leads to unclear indications for opioid prescriptions, exacerbating provider-level differences in prescribing. At the policy level, legislation that promotes workplace diversity, increases training for and access to pain specialists, and incentivizes data collection and reporting could help reduce disparities.³⁵ Equitable access to prescriptions and care is essential. Pharmacies often understock opioids in minority neighborhoods, meaning that even if a patient is prescribed an opioid on discharge, he or she might have difficulty filling the prescription.³⁶

One could question whether fewer opioid prescriptions for Black patients protects against the harms of opioid overprescribing, and therefore is not reflective of harmful inequity.³⁷ Ongoing national programs aim to reduce the harmful effects of opioids, which is reflected in the reduction in opioid prescribing over time in our institution. Our point is that differences in prescribing could reflect practices that do result in patient harm, such as less adequately controlled pain among Black patients.^1,3 Undertreated pain has negative health and social consequences and further contributes to substance-use stigma within minority communities.³⁸ Moreover, Black people who describe more discrimination in medical settings were more likely to report subsequent opioid misuse.³⁹

Although the above mechanisms might partially explain our findings among Black patients, the higher rate of prescribing for Asian patients is more challenging to explain. Our models adjusted for clinical factors. Notably, our Asian patients had the highest baseline comorbidity index, oldest mean age, and highest cancer rates, and it is possible that we were unable to fully account for illness severity or related pain needs (Table 1). It also is possible—although speculative—that factors such as language, provider concordance, and the type of disease process all contribute.⁴⁰ Some researchers have proposed a “stereotype content model” that seeks to establish a pathway among social structure (status of a patient) to clinician stereotypes (is this patient warm and/or competent) to emotional prejudices (envy, pride) and ultimately to discrimination (active/passive, help/harm).²³Our study has limitations. Our model was limited by the available data collected on our patients. Covariates including primary care follow-up, pain scores, and overdose history were not available. Furthermore, our categorization of race/ethnicity was based on self-reported data. We had 676 patients with race/ethnicity specified as other/unknown. We recognize the heterogeneity within these racial/ethnic categorizations. For example, within the LatinX or Asian communities, there are large differences based on region, country, ethnic, or cultural groups. Our study only included patients presenting to a hospital in San Francisco, which is different from the racial/ethnic makeup of other cities across the nation. Our electronic health record capture of history of opioid use disorder and mood disorders is contingent on individual clinician documentation. We did not account for provider-level differences, which is an important part of variation in prescribing differences. We also did not examine differences at the diagnosis-specific level. Finally, we could not determine the indication or appropriateness of opioid prescriptions.

Future studies will be necessary to characterize this relationship at a diagnosis-specific level and to describe causal pathways. Within our own institution, these findings present an opportunity for positive change. We hope to continue to explore the etiology of these disparities and identify areas where differences could impact patient outcomes, such as pain control. It is essential to develop appropriate recommendations for inpatient and discharge opioid prescribing to help minimize disparities and to mitigate potential harms of overprescribing. All health systems should continue to collect data on their own disparities in opioid prescribing and educate clinicians on promoting more equitable practices.

The authors thank Sneha Daya, MD, Sachin Shah, MD, MPH, and the UCSF Division of Hospital Medicine Data Core.

Within the nationwide effort to combat the opioid epidemic and reduce opioid prescribing, researchers have described different prescribing patterns for non-White racial and ethnic groups, including Black and LatinX populations. This remains a largely unexplored area within hospital medicine. Earlier studies of racial disparities demonstrate how some patients are assessed less often for pain and prescribed fewer opioids from the emergency department, surgical settings, and outpatient primary care practices. Researchers also have documented racial and ethnic disparities in analgesia for cancer pain and chronic noncancer pain.^1-11 Studies have demonstrated that White patients are more likely to receive opioid prescriptions compared with Black patients. Even with similar documented pain scores, there is evidence that Black patients receive fewer analgesics compared with White patients. For example, a recent study found that Black and Hispanic patients presenting to the emergency room for renal colic received less opioid medication compared with White patients.³ A study across 22 sites in Northern California found that racial minorities with long-bone fractures received fewer opioids at discharge than White patients.¹

It is unknown whether differential prescribing patterns by race exist among patients hospitalized on general medicine services. The objective of our study was to assess whether race and ethnicity were associated with the likelihood of opioids being prescribed and the duration of opioids prescribed when these patients are discharged from the hospital. Quantifying and seeking to understand these differences are the first steps toward ensuring racial and ethnic health equity in patient care.

Study Population and Data Sources

We identified all adults (age ≥18 years) discharged from the acute care inpatient general medicine services between June 1, 2012, and November 30, 2018, at the University of California, San Francisco (UCSF) Helen Diller Medical Center at Parnassus Heights, a 785-bed urban academic teaching hospital. All data were obtained from the hospital’s Epic-based electronic medical record (Epic Systems Corporation). Data elements were extracted from Clarity, the relationship database that stores Epic inpatient data. Patients discharged from the inpatient cardiology or bone marrow transplant services were not included. We excluded patients who did not receive opioids in the last 24 hours of their hospitalization. Patients with cancer-related pain diagnoses or sickle cell disease pain crises and patients who were discharged to hospice or followed by palliative care were excluded from the study based on International Classification of Diseases, Tenth Revision (ICD-10) codes (available on request) or service codes, when available, or admitting provider electronic health record documentation (Appendix Figure 1). Palliative care and hospice patients have significantly different pain needs, with management often directed by specialists. Patients with sickle cell disease are disproportionately Black and have distinct opioid prescribing patterns.^12,13 We also excluded discharge opioid prescriptions that were a resumption of the patient’s opioid prescription before admission based on medication documentation. Only new prescriptions signed by the discharging hospitalist, including different doses and formulations, were included in this study.

We performed a subgroup analysis of patients who were not prescribed opioids before their admission based on medication reconciliation but were started on opioids while hospitalized.

Primary Outcomes

We examined two primary outcomes: whether a patient received an opioid prescription at discharge, and, for patients prescribed opioids, the number of days prescribed. Days of opioids at discharge were calculated as total morphine milligram equivalents (MMEs) prescribed divided by MMEs administered during the final 24 hours of hospitalization. This metric was used as a patient-specific approach to calculating how long an opioid prescription will last after discharge, standardized according to the actual opioid requirements from hospitalization.¹⁴ If a patient was discharged with prescriptions for several opioids, the longest single prescription duration was used.

Primary Predictors

The primary predictor was the patient’s primary self-reported race/ethnicity, categorized as White, Black, LatinX, Asian, Native Hawaiian or other Pacific Islander, American Indian or Alaska Native, and other/unknown. Other/unknown included patients who were listed as other, declined, or who were otherwise unspecified. Self-reported race/ethnicity is obtained through reporting to the registrar. These race/ethnicity groupings were done in concordance with US Census Bureau definitions. Researchers classified patients as LatinX if they had Hispanic documented as their ethnicity, no matter their racial identification. These categorizations were chosen to be consistent with the existing literature, recognizing the role of a combined race/ethnicity definition for Hispanic or LatinX populations.¹⁵ These definitions of race/ethnicity are self-reported and reflect socially—not genetically defined—groupings.¹⁶ This variable serves as a surrogate for the structural factors that contribute to racism, the determining factor for racially disparate outcomes.¹⁷

Covariate Data Collection

Additional data were obtained regarding patient demographics, hospitalization factors, and medical diagnoses. Demographic factors included age, sex, and limited English proficiency (LEP) status. LEP was defined as having a primary language other than English and requiring an interpreter. Hospitalization factors included length of stay, whether they required intensive care unit (ICU) management, average daily MMEs administered during their entire hospitalization, MMEs administered during the final 24 hours of their hospitalization, whether the patient was on a teaching service or direct-care hospitalist service, their disposition on discharge, and year. Medical diagnosis variables included the adjusted Elixhauser Comorbidity Index based on ICD-10 codes; whether the patient was taking opioids at admission; and specific diagnoses of cancer, posttraumatic stress disorder (PTSD), and mood, anxiety, or psychotic disorder based on ICD-10 documentation.¹⁸

Statistical Analysis

All statistical analyses were performed using Stata software version 16 (StataCorp LP). Baseline demographic variables, hospitalization factors, and medical diagnosis variables were stratified by race/ethnicity. Within group comparisons were performed using chi-square or analysis of varianace (ANOVA) testing. For regression analyses, we fit two models. First, we fit a multivariable logistic regression model on all patients who received opioids during the last 24 hours of their hospitalization to examine the association between patient race/ethnicity and whether a patient received opioids at discharge, adjusting for additional patient, hospitalization, and medical covariates. Then we fit a negative binomial regression model on patients who were prescribed opioids at discharge to examine the association between patient race/ethnicity and the amount of opioids prescribed at discharge, adjusting for covariates. We used a negative binomial model because of the overdispersed distribution of discharge opioid prescriptions and only examined patients with an opioid prescription at discharge. We included the listed variables in our model because they were all found a priori to be associated with discharge opioid prescriptions.¹⁹ Instead of using days of opioids based on the last 24 hours, we performed a secondary analysis using the actual days of opioids supplied as the outcome. For example, a prescription of 12 tablets with every 6 hours dosing would be 3 days’ duration.

For both models, subgroup analyses were performed using the adjusted models restricted to patients newly prescribed opioids during their hospitalization and who were not previously taking opioids based on admission medication reconciliation. After testing for effect modification, this subgroup analysis was performed to reduce selection bias associated with earlier opioid use.

For all models, we reported predicted population opioid prescribing rates from the average marginal effects (AME).²⁰ Marginal effects were used because ours was a population level study and the outcome of interest was relatively common, limiting the effective interpretation of odds ratios.²¹ Marginal effects allow us to observe the instantaneous effect a given independent variable has on a dependent variable, while holding all other variables constant. It is implemented using the margins command in Stata. Marginal effects enable us to present our results as differences in probabilities, which is a more accurate way to describe the differences found among patient groups. Further, marginal effects are less sensitive to changes in model specifications.²²The UCSF Institutional Review Board for Human Subjects Research approved this study with a waiver of informed consent.

Unadjusted Results

We identified 10,953 patients who received opioids during the last 24 hours of hospitalization (see Appendix Figure 1 for study consort diagram). The patient population was 52.2% White, 18.4% Black, 11.5% Latinx, 10.1% Asian, 6.2% other/unknown, 0.9% Native Hawaiian/Other Pacific Islander, and 0.8% American Indian/Alaska Native (Table 1, Appendix Table 1). Black patients had fewer cancer diagnoses and the highest rate of prescribed opioids on admission. Asian patients were older and more likely to be female, and had higher rates of cancer, the highest median comorbidity index, and the smallest median daily MME during both the last 24 hours and total duration of hospitalization. Representative of general medicine patients, the most common principal discharge diagnoses in our dataset were pneumonia, cellulitis, altered mental status, sepsis, and abdominal pain.

Overall, 5541 (50.6%) patients who received opioids in the last 24 hours of their hospitalization received an opioid prescription at discharge. There were significant differences among racial/ethnic groups receiving an opioid prescription at discharge. Black patients were less likely to be discharged with an opioid compared with White patients (47.7% vs 50.3%; P < .001) (Table 2). The median discharge prescription duration for all patients was 9.3 days (interquartile range [IQR], 3.8-20.0). Black patients received the fewest median days of opioids at 7.5 days (IQR, 3.2-16.7) compared with White patients at 8.8 days (IQR, 3.7-20.0; P < .001) (Table 2).

Adjusted Regression Results

Demographic, clinical, and diagnosis specific factors were significantly associated with opioid prescriptions, including previous opioid use, sex, and a concurrent cancer diagnosis. There were fewer opioid prescriptions over time (Figure).

Following multivariable logistic regression for the association between race/ethnicity and opioid on discharge and controlling for covariates, we found that Black patients were less likely to receive an opioid prescription on discharge compared with White patients (predicted population rate, 47.6% vs 50.7%; AME −3.1%; 95% CI, −5.5% to −0.8%). Asian patients were more likely to receive a prescription on discharge compared with White patients (predicted population rate, 55.6% vs 50.7%; AME +4.9; 95% CI, 1.5%-8.3%).

Following multivariable negative binomial regression for the association between race/ethnicity and the number of opioid days on discharge, we found that Black patients received a shorter duration of opioid days compared with White patients (predicted days of opioids on discharge, 15.7 days vs 17.8 days; AME −2.1 days; 95% CI, −3.3 to −0.87) (Table 3). There were no significant differences among patients and the other racial/ethnic groups.

Our secondary analysis from the negative binomial regression with the days of opioids supplied metric yielded similar results to our primary analysis showing that Black patients received statically significantly fewer days of opioid therapy compared with White patients (Appendix Table 2).

Subgroup Regression Results

After testing for effect modification, which was negative, we examined the relationships for patients started on opioids during their hospitalization (Appendix Table 3 and Appendix Table 4). There were 5101 patients with newly prescribed opioids during their hospitalization. Adjusting for covariates, we found that Black patients were less likely to receive opioids at discharge compared with White patients (predicted population rate, 34.9% vs 40.4%; AME −5.5%; 95% CI, −9.2% to −1.9%). American Indian or Alaska Native patients were more likely to receive opioids on discharge (predicted population rate, 58.3% vs 40.4%; AME +17.9%; 95% CI, 1.0%-34.8%). We also found that Asian patients received more days of opioids on discharge (predicted days of opioid on discharge, 16.7 vs 13.7 days; AME +3.0 days; 95% CI, 0.6-5.3 days) (Appendix Table 4, Appendix Figure 2).

We found that Black patients discharged from the general medicine service were less likely to receive opioids and received shorter courses on discharge compared with White patients, adjusting for demographic, hospitalization, and medical diagnosis variables. Asian patients were more likely to receive an opioid prescription at discharge—a finding not reported in the literature on opioid prescribing disparities in most other practice settings.¹

Previous studies have shown racial disparities in pain management in emergency and surgical settings, but these relationships have not been characterized in an inpatient medicine population. Medicine patients comprise the majority of admitted patients in the United States and reflect a wide diversity of medical conditions, many requiring opioids for pain management. Determining the etiology of these differential prescribing patterns was not within the scope of our study, but earlier studies demonstrate a number of reasons why these patterns exist across racial and ethnic groups in other practice settings.^23,24 These reports give us insight into potential mechanisms for our study population.

Differences in pain management likely represent the multiple structural mechanisms by which racism operates.¹⁷ Drawing from the existing literature and the socioecological model, we hypothesize the ways that individual, interpersonal relationships, organizations, communities, and public policy impact opioid prescribing.^25,26 Using this model and considering the framework of Critical Race Theory (CRT), we can work towards understanding how race and ethnicity stand in as surrogates for racism and how this manifests in different outcomes and identify areas for intervention. CRT draws attention to race consciousness, contemporary orientation, centering in the margins, and praxis. In the context of this analysis, we recognize race consciousness and the interactions among factors such as race/ethnicity, language, and diagnoses such as PTSD.²⁷ This approach is necessary because racism is a multilevel construct influenced by macrolevel factors.²⁸

Individually and interpersonally, there is clinician-driven bias in pain assessment, which is activated under times of stress and diagnostic uncertainty and is amplified by a lack of clear guidelines for pain management prescriptions.^23,29-32 Institutional and organizational culture contribute to disparities through ingrained culture, practice patterns, and resource allocation.^29,33 Last, public policy and the larger sociopolitical environment worsen disparities through nondiverse workforces, state and federal guidelines, criminal justice policy, supply chain regulation, and access to care.

As individual clinicians, departments, and health systems leaders, we must identify areas for intervention. At the individual and interpersonal levels, there is evidence that taking implicit association tests could help clinicians become more aware of their negative associations, and empathy-inducing, perspective-taking interventions can reduce pain treatment bias.^31,34 At the institutional level, we must report data on disparities, create guidelines for pain management, and reevaluate the educational curriculum and culture to assess how certain biases could be propagated. The lack of straightforward guidelines leads to unclear indications for opioid prescriptions, exacerbating provider-level differences in prescribing. At the policy level, legislation that promotes workplace diversity, increases training for and access to pain specialists, and incentivizes data collection and reporting could help reduce disparities.³⁵ Equitable access to prescriptions and care is essential. Pharmacies often understock opioids in minority neighborhoods, meaning that even if a patient is prescribed an opioid on discharge, he or she might have difficulty filling the prescription.³⁶

One could question whether fewer opioid prescriptions for Black patients protects against the harms of opioid overprescribing, and therefore is not reflective of harmful inequity.³⁷ Ongoing national programs aim to reduce the harmful effects of opioids, which is reflected in the reduction in opioid prescribing over time in our institution. Our point is that differences in prescribing could reflect practices that do result in patient harm, such as less adequately controlled pain among Black patients.^1,3 Undertreated pain has negative health and social consequences and further contributes to substance-use stigma within minority communities.³⁸ Moreover, Black people who describe more discrimination in medical settings were more likely to report subsequent opioid misuse.³⁹

Although the above mechanisms might partially explain our findings among Black patients, the higher rate of prescribing for Asian patients is more challenging to explain. Our models adjusted for clinical factors. Notably, our Asian patients had the highest baseline comorbidity index, oldest mean age, and highest cancer rates, and it is possible that we were unable to fully account for illness severity or related pain needs (Table 1). It also is possible—although speculative—that factors such as language, provider concordance, and the type of disease process all contribute.⁴⁰ Some researchers have proposed a “stereotype content model” that seeks to establish a pathway among social structure (status of a patient) to clinician stereotypes (is this patient warm and/or competent) to emotional prejudices (envy, pride) and ultimately to discrimination (active/passive, help/harm).²³Our study has limitations. Our model was limited by the available data collected on our patients. Covariates including primary care follow-up, pain scores, and overdose history were not available. Furthermore, our categorization of race/ethnicity was based on self-reported data. We had 676 patients with race/ethnicity specified as other/unknown. We recognize the heterogeneity within these racial/ethnic categorizations. For example, within the LatinX or Asian communities, there are large differences based on region, country, ethnic, or cultural groups. Our study only included patients presenting to a hospital in San Francisco, which is different from the racial/ethnic makeup of other cities across the nation. Our electronic health record capture of history of opioid use disorder and mood disorders is contingent on individual clinician documentation. We did not account for provider-level differences, which is an important part of variation in prescribing differences. We also did not examine differences at the diagnosis-specific level. Finally, we could not determine the indication or appropriateness of opioid prescriptions.

Future studies will be necessary to characterize this relationship at a diagnosis-specific level and to describe causal pathways. Within our own institution, these findings present an opportunity for positive change. We hope to continue to explore the etiology of these disparities and identify areas where differences could impact patient outcomes, such as pain control. It is essential to develop appropriate recommendations for inpatient and discharge opioid prescribing to help minimize disparities and to mitigate potential harms of overprescribing. All health systems should continue to collect data on their own disparities in opioid prescribing and educate clinicians on promoting more equitable practices.

The authors thank Sneha Daya, MD, Sachin Shah, MD, MPH, and the UCSF Division of Hospital Medicine Data Core.

Within the nationwide effort to combat the opioid epidemic and reduce opioid prescribing, researchers have described different prescribing patterns for non-White racial and ethnic groups, including Black and LatinX populations. This remains a largely unexplored area within hospital medicine. Earlier studies of racial disparities demonstrate how some patients are assessed less often for pain and prescribed fewer opioids from the emergency department, surgical settings, and outpatient primary care practices. Researchers also have documented racial and ethnic disparities in analgesia for cancer pain and chronic noncancer pain.^1-11 Studies have demonstrated that White patients are more likely to receive opioid prescriptions compared with Black patients. Even with similar documented pain scores, there is evidence that Black patients receive fewer analgesics compared with White patients. For example, a recent study found that Black and Hispanic patients presenting to the emergency room for renal colic received less opioid medication compared with White patients.³ A study across 22 sites in Northern California found that racial minorities with long-bone fractures received fewer opioids at discharge than White patients.¹

It is unknown whether differential prescribing patterns by race exist among patients hospitalized on general medicine services. The objective of our study was to assess whether race and ethnicity were associated with the likelihood of opioids being prescribed and the duration of opioids prescribed when these patients are discharged from the hospital. Quantifying and seeking to understand these differences are the first steps toward ensuring racial and ethnic health equity in patient care.

Study Population and Data Sources

We identified all adults (age ≥18 years) discharged from the acute care inpatient general medicine services between June 1, 2012, and November 30, 2018, at the University of California, San Francisco (UCSF) Helen Diller Medical Center at Parnassus Heights, a 785-bed urban academic teaching hospital. All data were obtained from the hospital’s Epic-based electronic medical record (Epic Systems Corporation). Data elements were extracted from Clarity, the relationship database that stores Epic inpatient data. Patients discharged from the inpatient cardiology or bone marrow transplant services were not included. We excluded patients who did not receive opioids in the last 24 hours of their hospitalization. Patients with cancer-related pain diagnoses or sickle cell disease pain crises and patients who were discharged to hospice or followed by palliative care were excluded from the study based on International Classification of Diseases, Tenth Revision (ICD-10) codes (available on request) or service codes, when available, or admitting provider electronic health record documentation (Appendix Figure 1). Palliative care and hospice patients have significantly different pain needs, with management often directed by specialists. Patients with sickle cell disease are disproportionately Black and have distinct opioid prescribing patterns.^12,13 We also excluded discharge opioid prescriptions that were a resumption of the patient’s opioid prescription before admission based on medication documentation. Only new prescriptions signed by the discharging hospitalist, including different doses and formulations, were included in this study.

We performed a subgroup analysis of patients who were not prescribed opioids before their admission based on medication reconciliation but were started on opioids while hospitalized.

Primary Outcomes

We examined two primary outcomes: whether a patient received an opioid prescription at discharge, and, for patients prescribed opioids, the number of days prescribed. Days of opioids at discharge were calculated as total morphine milligram equivalents (MMEs) prescribed divided by MMEs administered during the final 24 hours of hospitalization. This metric was used as a patient-specific approach to calculating how long an opioid prescription will last after discharge, standardized according to the actual opioid requirements from hospitalization.¹⁴ If a patient was discharged with prescriptions for several opioids, the longest single prescription duration was used.

Primary Predictors

The primary predictor was the patient’s primary self-reported race/ethnicity, categorized as White, Black, LatinX, Asian, Native Hawaiian or other Pacific Islander, American Indian or Alaska Native, and other/unknown. Other/unknown included patients who were listed as other, declined, or who were otherwise unspecified. Self-reported race/ethnicity is obtained through reporting to the registrar. These race/ethnicity groupings were done in concordance with US Census Bureau definitions. Researchers classified patients as LatinX if they had Hispanic documented as their ethnicity, no matter their racial identification. These categorizations were chosen to be consistent with the existing literature, recognizing the role of a combined race/ethnicity definition for Hispanic or LatinX populations.¹⁵ These definitions of race/ethnicity are self-reported and reflect socially—not genetically defined—groupings.¹⁶ This variable serves as a surrogate for the structural factors that contribute to racism, the determining factor for racially disparate outcomes.¹⁷

Covariate Data Collection

Additional data were obtained regarding patient demographics, hospitalization factors, and medical diagnoses. Demographic factors included age, sex, and limited English proficiency (LEP) status. LEP was defined as having a primary language other than English and requiring an interpreter. Hospitalization factors included length of stay, whether they required intensive care unit (ICU) management, average daily MMEs administered during their entire hospitalization, MMEs administered during the final 24 hours of their hospitalization, whether the patient was on a teaching service or direct-care hospitalist service, their disposition on discharge, and year. Medical diagnosis variables included the adjusted Elixhauser Comorbidity Index based on ICD-10 codes; whether the patient was taking opioids at admission; and specific diagnoses of cancer, posttraumatic stress disorder (PTSD), and mood, anxiety, or psychotic disorder based on ICD-10 documentation.¹⁸

Statistical Analysis

All statistical analyses were performed using Stata software version 16 (StataCorp LP). Baseline demographic variables, hospitalization factors, and medical diagnosis variables were stratified by race/ethnicity. Within group comparisons were performed using chi-square or analysis of varianace (ANOVA) testing. For regression analyses, we fit two models. First, we fit a multivariable logistic regression model on all patients who received opioids during the last 24 hours of their hospitalization to examine the association between patient race/ethnicity and whether a patient received opioids at discharge, adjusting for additional patient, hospitalization, and medical covariates. Then we fit a negative binomial regression model on patients who were prescribed opioids at discharge to examine the association between patient race/ethnicity and the amount of opioids prescribed at discharge, adjusting for covariates. We used a negative binomial model because of the overdispersed distribution of discharge opioid prescriptions and only examined patients with an opioid prescription at discharge. We included the listed variables in our model because they were all found a priori to be associated with discharge opioid prescriptions.¹⁹ Instead of using days of opioids based on the last 24 hours, we performed a secondary analysis using the actual days of opioids supplied as the outcome. For example, a prescription of 12 tablets with every 6 hours dosing would be 3 days’ duration.

For both models, subgroup analyses were performed using the adjusted models restricted to patients newly prescribed opioids during their hospitalization and who were not previously taking opioids based on admission medication reconciliation. After testing for effect modification, this subgroup analysis was performed to reduce selection bias associated with earlier opioid use.

For all models, we reported predicted population opioid prescribing rates from the average marginal effects (AME).²⁰ Marginal effects were used because ours was a population level study and the outcome of interest was relatively common, limiting the effective interpretation of odds ratios.²¹ Marginal effects allow us to observe the instantaneous effect a given independent variable has on a dependent variable, while holding all other variables constant. It is implemented using the margins command in Stata. Marginal effects enable us to present our results as differences in probabilities, which is a more accurate way to describe the differences found among patient groups. Further, marginal effects are less sensitive to changes in model specifications.²²The UCSF Institutional Review Board for Human Subjects Research approved this study with a waiver of informed consent.

Unadjusted Results

We identified 10,953 patients who received opioids during the last 24 hours of hospitalization (see Appendix Figure 1 for study consort diagram). The patient population was 52.2% White, 18.4% Black, 11.5% Latinx, 10.1% Asian, 6.2% other/unknown, 0.9% Native Hawaiian/Other Pacific Islander, and 0.8% American Indian/Alaska Native (Table 1, Appendix Table 1). Black patients had fewer cancer diagnoses and the highest rate of prescribed opioids on admission. Asian patients were older and more likely to be female, and had higher rates of cancer, the highest median comorbidity index, and the smallest median daily MME during both the last 24 hours and total duration of hospitalization. Representative of general medicine patients, the most common principal discharge diagnoses in our dataset were pneumonia, cellulitis, altered mental status, sepsis, and abdominal pain.

Overall, 5541 (50.6%) patients who received opioids in the last 24 hours of their hospitalization received an opioid prescription at discharge. There were significant differences among racial/ethnic groups receiving an opioid prescription at discharge. Black patients were less likely to be discharged with an opioid compared with White patients (47.7% vs 50.3%; P < .001) (Table 2). The median discharge prescription duration for all patients was 9.3 days (interquartile range [IQR], 3.8-20.0). Black patients received the fewest median days of opioids at 7.5 days (IQR, 3.2-16.7) compared with White patients at 8.8 days (IQR, 3.7-20.0; P < .001) (Table 2).

Adjusted Regression Results

Demographic, clinical, and diagnosis specific factors were significantly associated with opioid prescriptions, including previous opioid use, sex, and a concurrent cancer diagnosis. There were fewer opioid prescriptions over time (Figure).

Following multivariable logistic regression for the association between race/ethnicity and opioid on discharge and controlling for covariates, we found that Black patients were less likely to receive an opioid prescription on discharge compared with White patients (predicted population rate, 47.6% vs 50.7%; AME −3.1%; 95% CI, −5.5% to −0.8%). Asian patients were more likely to receive a prescription on discharge compared with White patients (predicted population rate, 55.6% vs 50.7%; AME +4.9; 95% CI, 1.5%-8.3%).

Following multivariable negative binomial regression for the association between race/ethnicity and the number of opioid days on discharge, we found that Black patients received a shorter duration of opioid days compared with White patients (predicted days of opioids on discharge, 15.7 days vs 17.8 days; AME −2.1 days; 95% CI, −3.3 to −0.87) (Table 3). There were no significant differences among patients and the other racial/ethnic groups.

Our secondary analysis from the negative binomial regression with the days of opioids supplied metric yielded similar results to our primary analysis showing that Black patients received statically significantly fewer days of opioid therapy compared with White patients (Appendix Table 2).

Subgroup Regression Results

After testing for effect modification, which was negative, we examined the relationships for patients started on opioids during their hospitalization (Appendix Table 3 and Appendix Table 4). There were 5101 patients with newly prescribed opioids during their hospitalization. Adjusting for covariates, we found that Black patients were less likely to receive opioids at discharge compared with White patients (predicted population rate, 34.9% vs 40.4%; AME −5.5%; 95% CI, −9.2% to −1.9%). American Indian or Alaska Native patients were more likely to receive opioids on discharge (predicted population rate, 58.3% vs 40.4%; AME +17.9%; 95% CI, 1.0%-34.8%). We also found that Asian patients received more days of opioids on discharge (predicted days of opioid on discharge, 16.7 vs 13.7 days; AME +3.0 days; 95% CI, 0.6-5.3 days) (Appendix Table 4, Appendix Figure 2).

We found that Black patients discharged from the general medicine service were less likely to receive opioids and received shorter courses on discharge compared with White patients, adjusting for demographic, hospitalization, and medical diagnosis variables. Asian patients were more likely to receive an opioid prescription at discharge—a finding not reported in the literature on opioid prescribing disparities in most other practice settings.¹

Previous studies have shown racial disparities in pain management in emergency and surgical settings, but these relationships have not been characterized in an inpatient medicine population. Medicine patients comprise the majority of admitted patients in the United States and reflect a wide diversity of medical conditions, many requiring opioids for pain management. Determining the etiology of these differential prescribing patterns was not within the scope of our study, but earlier studies demonstrate a number of reasons why these patterns exist across racial and ethnic groups in other practice settings.^23,24 These reports give us insight into potential mechanisms for our study population.

Differences in pain management likely represent the multiple structural mechanisms by which racism operates.¹⁷ Drawing from the existing literature and the socioecological model, we hypothesize the ways that individual, interpersonal relationships, organizations, communities, and public policy impact opioid prescribing.^25,26 Using this model and considering the framework of Critical Race Theory (CRT), we can work towards understanding how race and ethnicity stand in as surrogates for racism and how this manifests in different outcomes and identify areas for intervention. CRT draws attention to race consciousness, contemporary orientation, centering in the margins, and praxis. In the context of this analysis, we recognize race consciousness and the interactions among factors such as race/ethnicity, language, and diagnoses such as PTSD.²⁷ This approach is necessary because racism is a multilevel construct influenced by macrolevel factors.²⁸

Individually and interpersonally, there is clinician-driven bias in pain assessment, which is activated under times of stress and diagnostic uncertainty and is amplified by a lack of clear guidelines for pain management prescriptions.^23,29-32 Institutional and organizational culture contribute to disparities through ingrained culture, practice patterns, and resource allocation.^29,33 Last, public policy and the larger sociopolitical environment worsen disparities through nondiverse workforces, state and federal guidelines, criminal justice policy, supply chain regulation, and access to care.

As individual clinicians, departments, and health systems leaders, we must identify areas for intervention. At the individual and interpersonal levels, there is evidence that taking implicit association tests could help clinicians become more aware of their negative associations, and empathy-inducing, perspective-taking interventions can reduce pain treatment bias.^31,34 At the institutional level, we must report data on disparities, create guidelines for pain management, and reevaluate the educational curriculum and culture to assess how certain biases could be propagated. The lack of straightforward guidelines leads to unclear indications for opioid prescriptions, exacerbating provider-level differences in prescribing. At the policy level, legislation that promotes workplace diversity, increases training for and access to pain specialists, and incentivizes data collection and reporting could help reduce disparities.³⁵ Equitable access to prescriptions and care is essential. Pharmacies often understock opioids in minority neighborhoods, meaning that even if a patient is prescribed an opioid on discharge, he or she might have difficulty filling the prescription.³⁶

One could question whether fewer opioid prescriptions for Black patients protects against the harms of opioid overprescribing, and therefore is not reflective of harmful inequity.³⁷ Ongoing national programs aim to reduce the harmful effects of opioids, which is reflected in the reduction in opioid prescribing over time in our institution. Our point is that differences in prescribing could reflect practices that do result in patient harm, such as less adequately controlled pain among Black patients.^1,3 Undertreated pain has negative health and social consequences and further contributes to substance-use stigma within minority communities.³⁸ Moreover, Black people who describe more discrimination in medical settings were more likely to report subsequent opioid misuse.³⁹

Although the above mechanisms might partially explain our findings among Black patients, the higher rate of prescribing for Asian patients is more challenging to explain. Our models adjusted for clinical factors. Notably, our Asian patients had the highest baseline comorbidity index, oldest mean age, and highest cancer rates, and it is possible that we were unable to fully account for illness severity or related pain needs (Table 1). It also is possible—although speculative—that factors such as language, provider concordance, and the type of disease process all contribute.⁴⁰ Some researchers have proposed a “stereotype content model” that seeks to establish a pathway among social structure (status of a patient) to clinician stereotypes (is this patient warm and/or competent) to emotional prejudices (envy, pride) and ultimately to discrimination (active/passive, help/harm).²³Our study has limitations. Our model was limited by the available data collected on our patients. Covariates including primary care follow-up, pain scores, and overdose history were not available. Furthermore, our categorization of race/ethnicity was based on self-reported data. We had 676 patients with race/ethnicity specified as other/unknown. We recognize the heterogeneity within these racial/ethnic categorizations. For example, within the LatinX or Asian communities, there are large differences based on region, country, ethnic, or cultural groups. Our study only included patients presenting to a hospital in San Francisco, which is different from the racial/ethnic makeup of other cities across the nation. Our electronic health record capture of history of opioid use disorder and mood disorders is contingent on individual clinician documentation. We did not account for provider-level differences, which is an important part of variation in prescribing differences. We also did not examine differences at the diagnosis-specific level. Finally, we could not determine the indication or appropriateness of opioid prescriptions.

Future studies will be necessary to characterize this relationship at a diagnosis-specific level and to describe causal pathways. Within our own institution, these findings present an opportunity for positive change. We hope to continue to explore the etiology of these disparities and identify areas where differences could impact patient outcomes, such as pain control. It is essential to develop appropriate recommendations for inpatient and discharge opioid prescribing to help minimize disparities and to mitigate potential harms of overprescribing. All health systems should continue to collect data on their own disparities in opioid prescribing and educate clinicians on promoting more equitable practices.

The authors thank Sneha Daya, MD, Sachin Shah, MD, MPH, and the UCSF Division of Hospital Medicine Data Core.

The COVID-19 pandemic has led to more than 40 million infections and more than 650,000 deaths in the United States alone.¹ Morbidity and mortality have disproportionately affected older adults.^2-4 However, acute infection and delayed effects, such as multisystem inflammatory syndrome in children (MIS-C), occur and can lead to severe complications, hospitalization, and death in pediatric patients.^5,6 Due to higher clinical disease prevalence and morbidity in the adult population, we have learned much about the clinical factors associated with severe adult COVID-19 disease.^5,7-9 Such clinical factors include older age, concurrent comorbidities, smoke exposure, and Black race or Hispanic ethnicity, among others.^5,7-10 However, there is a paucity of data on severe COVID-19 disease in pediatric patients.^5,11,12 In addition, most immunization strategies and pharmacologic treatments for COVID-19 have not been evaluated or approved for use in children.¹³ To guide targeted prevention and treatment strategies, there is a critical need to identify children and adolescents—who are among the most vulnerable patient populations—at high risk for severe disease.

Identifying the clinical factors associated with severe COVID-19 disease will help with prioritizing and allocating vaccines when they are approved for use in patients younger than 12 years. It also can provide insight for clinicians and families faced with decisions wherein individual risk assessment is crucial (eg, in-person schooling, other group activities). The objective of this study was to determine the clinical factors associated with severe COVID-19 among children and adolescents in the United States.

Study Design

We conducted a multicenter retrospective cohort study of patients presenting for care at pediatric hospitals that report data to the Pediatric Health Information System (PHIS) database. The PHIS administrative database includes billing and utilization data from 45 US tertiary care hospitals affiliated with the Children’s Hospital Association (Lenexa, Kansas). Data quality and reliability are ensured through a joint validation effort between the Children’s Hospital Association and participating hospitals. Hospitals submit discharge data, including demographics, diagnoses, and procedures using International Classification of Diseases, 10th Revision (ICD-10) codes, along with daily detailed information on pharmacy, location of care, and other services.

Study Population

Patients 30 days to 18 years of age discharged from the emergency department (ED) or inpatient setting with a primary diagnosis of COVID-19 (ICD-10 codes U.071 and U.072) between April 1, 2020, and September 30, 2020, were eligible for inclusion.¹⁴ In a prior study, the positive predictive value of an ICD-10–coded diagnosis of COVID-19 among hospitalized pediatric patients was 95.5%, compared with reverse transcription polymerase reaction results or presence of MIS-C.¹⁵ The diagnostic code for COVID-19 (ICD-10-CM) also had a high sensitivity (98.0%) in the hospitalized population.¹⁶ Acknowledging the increasing practice of screening patients upon admission, and in an attempt to minimize potential misclassification, we did not include encounters with secondary diagnoses of COVID-19 in our primary analyses. Pediatric patients with surgical diagnoses and neonates who never left the hospital were also excluded.

Factors Associated With Severe COVID-19 Disease

Exposures of interest were determined a priori based on current evidence in the literature and included patient age (0-4 years, 5-11 years, and 12-18 years), sex, race and ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, Asian, other non-White race [defined as Pacific Islander, Native American, or other]), payor type, cardiovascular complex chronic conditions (CCC), neuromuscular CCC, obesity/type 2 diabetes mellitus (DM), pulmonary CCC, asthma (defined using ICD-10 codes¹⁷), and immunocompromised CCC. Race and ethnicity were included as covariates based on previous studies reporting differences in COVID-19 outcomes among racial and ethnic groups.⁹ The CCC covariates were defined using the pediatric CCC ICD-10 classification system version 2.¹⁸

Pediatric Complications and Conditions Associated With COVID-19

Based on current evidence and expert opinion of study members, associated diagnoses and complications co-occurring with a COVID-19 diagnosis were defined a priori and identified through ICD-10 codes (Appendix Table 1). These included acute kidney injury, acute liver injury, aseptic meningitis, asthma exacerbation, bronchiolitis, cerebral infarction, croup, encephalitis, encephalopathy, infant fever, febrile seizure, gastroenteritis/dehydration, Kawasaki disease/MIS-C, myocarditis/pericarditis, pneumonia, lung effusion or empyema, respiratory failure, sepsis, nonfebrile seizure, pancreatitis, sickle cell complications, and thrombotic complications.

Outcomes

COVID-19 severity outcomes were assessed as follows: (1) mild = ED discharge; (2) moderate = inpatient admission; (3) severe = intensive care unit (ICU) admission without mechanical ventilation, shock, or death; and (4) very severe = ICU admission with mechanical ventilation, shock, or death.¹⁹ This ordinal ranking system did not violate the proportional odds assumption. Potential reasons for admission to the ICU without mechanical ventilation, shock, or death include, but are not limited to, need for noninvasive ventilation, vital sign instability, dysrhythmias, respiratory insufficiency, or complications arising from concurrent conditions (eg, thrombotic events, need for continuous albuterol therapy). We examined several secondary, hospital-based outcomes, including associated diagnoses and complications, all-cause 30-day healthcare reutilization (ED visit or rehospitalization), length of stay (LOS), and ICU LOS.

Statistical Analysis

Demographic characteristics were summarized using frequencies and percentages for categorical variables and geometric means with SD and medians with interquartile ranges (IQR) for continuous variables, as appropriate. Factors associated with hospitalization (encompassing severity levels 2-4) vs ED discharge (severity level 1) were assessed using logistic regression. Factors associated with increasing severity among hospitalized pediatric patients (severity levels 2, 3, and 4) were assessed using ordinal logistic regression. Covariates in these analyses included race and ethnicity, age, sex, payor, cardiovascular CCC, neurologic/neuromuscular CCC, obesity/type 2 DM, pulmonary CCC, asthma, and immunocompromised CCC. Adjusted odds ratios (aOR) and corresponding 95% CI for each risk factor were generated using generalized linear mixed effects models and random intercepts for each hospital. Given the potential for diagnostic misclassification of pediatric patients with COVID-19 based on primary vs secondary diagnoses, we performed sensitivity analyses defining the study population as those with a primary diagnosis of COVID-19 and those with a secondary diagnosis of COVID-19 plus a concurrent primary diagnosis of a condition associated with COVID-19 (Appendix Table 1).

All analyses were performed using SAS version 9.4 (SAS Institute, Inc), and P < .05 was considered statistically significant. The Institutional Review Board at Vanderbilt University Medical Center determined that this study of de-identified data did not meet the criteria for human subjects research.

Study Population

A total of 19,976 encounters were included in the study. Of those, 15,913 (79.7%) were discharged from the ED and 4063 (20.3%) were hospitalized (Table 1). The most common race/ethnicity was Hispanic (9741, 48.8%), followed by non-Hispanic White (4217, 21.1%). Reference race/ethnicity data for the overall 2019 PHIS population can be found in Appendix Table 2.

The severity distribution among the hospitalized population was moderate (3222, 79.3%), severe (431, 11.3%), and very severe (380, 9.4%). The frequency of COVID-19 diagnoses increased late in the study period (Figure). Among those hospitalized, the median LOS for the index admission was 2 days (IQR, 1-4), while among those admitted to the ICU, the median LOS was 3 days (IQR, 2-5).

Overall, 10.1% (n = 2020) of the study population had an all-cause repeat encounter (ie, subsequent ED encounter or hospitalization) within 30 days following the index discharge. Repeat encounters were more frequent among patients hospitalized than among those discharged from the ED (Appendix Table 3).

Prevalence of Conditions and Complications Associated With COVID-19

Overall, 3257 (16.3%) patients had one or more co-occurring diagnoses categorized as a COVID-19–associated condition or complication. The most frequent diagnoses included lower respiratory tract disease (pneumonia, lung effusion, or empyema; n = 1415, 7.1%), gastroenteritis/dehydration (n = 1068, 5.3%), respiratory failure (n = 731, 3.7%), febrile infant (n = 413, 2.1%), and nonfebrile seizure (n = 425, 2.1%). Aside from nonfebrile seizure, neurological complications were less frequent and included febrile seizure (n = 155, 0.8%), encephalopathy (n = 63, 0.3%), aseptic meningitis (n = 16, 0.1%), encephalitis (n = 11, 0.1%), and cerebral infarction (n = 6, <0.1%). Kawasaki disease and MIS-C comprised 1.7% (n = 346) of diagnoses. Thrombotic complications occurred in 0.1% (n = 13) of patients. Overall, these conditions and complications associated with COVID-19 were more frequent in hospitalized patients than in those discharged from the ED (P < .001) (Table 2).

Factors Associated With COVID-19 Disease Severity

Compared to pediatric patients with COVID-19 discharged from the ED, factors associated with increased odds of hospitalization included private payor insurance; obesity/type 2 DM; asthma; and cardiovascular, immunocompromised, neurologic/neuromuscular, and pulmonary CCCs (Table 3). Factors associated with decreased risk of hospitalization included Black race or Hispanic ethnicity compared with White race; female sex; and age 5 to 11 years and age 12 to 17 years (vs age 0-4 years). Among children and adolescents hospitalized with COVID-19, factors associated with greater disease severity included Black or other non-White race; age 5 to 11 years; age 12 to 17 years; obesity/type 2 DM; immunocompromised conditions; and cardiovascular, neurologic/neuromuscular, and pulmonary CCCs (Table 3).

Sensitivity Analysis

We performed a sensitivity analysis that expanded the study population to include those with a secondary diagnosis of COVID-19 plus a diagnosis of a COVID-19–associated condition or complication. Analyses using the expanded population (N = 21,247) were similar to the primary analyses (Appendix Table 4 and Appendix Table 5).

In this large multicenter study evaluating COVID-19 disease severity in more than 19,000 patients presenting for emergency care at US pediatric hospitals, approximately 20% were hospitalized, and among those hospitalized almost a quarter required ICU care. Clinical risk factors associated with increased risk of hospitalization include private payor status and selected comorbidities (obesity/type 2 DM; asthma; and cardiovascular, pulmonary, immunocompromised, neurologic/neuromuscular CCCs), while those associated with decreased risk of hospitalization include older age, female sex, and Black race or Hispanic ethnicity. Factors associated with severe disease among hospitalized pediatric patients include Black or other non-White race, school age (≥5 years), and certain chronic conditions (cardiovascular disease, obesity/type 2 DM, neurologic or neuromuscular disease). Sixteen percent of patients had a concurrent diagnosis for a condition or complication associated with COVID-19.

While the study population (ie, children and adolescents presenting to the ED) represents a small fraction of children and adolescents in the community with SARS-CoV-2 infection, the results provide important insight into factors of severe COVID-19 in the pediatric population. A report from France suggested ventilatory or hemodynamic support or death were independently associated with older age (≥10 years), elevated C-reactive protein, and hypoxemia.¹² An Italian study found that younger age (0-4 years) was associated with less severe disease, while preexisting conditions were more likely in patients with severe disease.¹¹ A single-center case series of 50 patients (aged ≤21 years) hospitalized at a children’s hospital in New York City found respiratory failure (n = 9) was more common in children older than 1 year, patients with elevated inflammatory markers, and patients with obesity.²⁰

Our study confirms several factors for severe COVID-19 found in these studies, including older age,^11,12,20 obesity,²⁰ and preexisting conditions.¹¹ Our findings also expand on these reports, including identification of factors associated with hospitalization. Given the rate of 30-day re-encounters among pediatric patients with COVID-19 (10.1%), identifying risk factors for hospitalization may aid ED providers in determining optimal disposition (eg, home, hospital admission, ICU). We also identified specific comorbidities associated with more severe disease in those hospitalized with COVID-19, such as cardiovascular disease, obesity/type 2 DM, and pulmonary, neurologic, or neuromuscular conditions. We also found that asthma increased the risk for hospitalization but not more severe disease among those hospitalized. This latter finding also aligns with recent single-center studies,^21,22 whereas a Turkish study of pediatric patients aged 0 to 18 years found no association between asthma and COVID-19 hospitalizations.²³We also examined payor type and racial/ethnic factors in our analysis. In 2019, patients who identified as Black or Hispanic comprised 52.3% of all encounters and 40.7% of hospitalizations recorded in the PHIS database. During the same year, encounters for influenza among Black or Hispanic pediatric patients comprised 58.7% of all influenza diagnoses and 47.0% of pediatric influenza hospitalizations (Appendix Table 2). In this study, patients who identified as Black or Hispanic race represented a disproportionately large share of patients presenting to children’s hospitals (68.5%) and of those hospitalized (60.8%). Hispanic ethnicity, in particular, represented a disproportionate share of patients seeking care for COVID-19 compared to the overall PHIS population (47.7% and 27.1%, respectively). After accounting for other factors, we found Black and other non-White race—but not of Hispanic ethnicity—were independently associated with more disease severity among those hospitalized. This contrasts with findings from a recent adult study by Yehia et al,²⁴ who found (after adjusting for other clinical factors) no significant difference in mortality between Black patients and White patients among adults hospitalized due to COVID-19. It also contrasts with a recent large population-based UK study wherein pediatric patients identifying as Asian, but not Black or mixed race or ethnicity, had an increased risk of hospital admission and admission to the ICU compared to children identifying as White. Children identifying as Black or mixed race had longer hospital admissions.²⁵ However, as the authors of the study note, residual confounders and ascertainment bias due to differences in COVID testing may have influenced these findings.

Our findings of differences in hospitalization and disease severity among those hospitalized by race and ethnicity should be interpreted carefully. These may reflect a constellation of factors that are difficult to measure, including differences in healthcare access, inequalities in care (including hospital admission inequalities), and implicit bias—all of which may reflect structural racism. For example, it is possible that children who identify as Black or Hispanic have different access to care compared to children who identify as White, and this may affect disease severity on presentation.² Alternatively, it is possible that White pediatric patients are more likely to be hospitalized as compared to non-White pediatric patients with similar illness severity. Our finding that pediatric patients who identify as Hispanic or Black had a lower risk of hospitalization should be also interpreted carefully, as this may reflect higher utilization of the ED for SARS-CoV-2 testing, increased use of nonemergency services among those without access to primary care, or systematic differences in provider decision-making among this segment of the population.² Further study is needed to determine specific drivers for racial and ethnic differences in healthcare utilization in children and adolescents with COVID-19.²⁶

Complications and co-occurring diagnoses in adults with COVID-19 are well documented.^27-30 However, there is little information to date on the co-occurring diagnoses and complications associated with COVID-19 in children and adolescents. We found that complications and co-occurring conditions occurred in 16.3% of the study population, with the most frequent conditions including known complications of viral infections such as pneumonia, respiratory failure, and seizures. Acute kidney and liver injury, as well as thrombotic complications, occurred less commonly than in adults.^26-29 Interestingly, neurologic complications were also uncommon compared to adult reports^8,31 and less frequent than in other viral illnesses in children and adolescents. For example, neurologic complications occur in approximately 7.5% of children and adolescents hospitalized with influenza.³²

Limitations of the present study include the retrospective design, as well as incomplete patient-level clinical data in the PHIS database. The PHIS database only includes children’s hospitals, which may limit the generalizability of findings to community hospitals. We also excluded newborns, and our findings may not be generalizable to this population. We only included children and adolescents with a primary diagnosis of COVID-19, which has the potential for misclassification in cases where COVID-19 was a secondary diagnosis. However, results of our sensitivity analysis, which incorporated secondary diagnoses of COVID-19, were consistent with findings from our main analyses. Our study was designed to examine associations between certain prespecified factors and COVID-19 severity among pediatric patients who visited the ED or were admitted to the hospital during the COVID-19 pandemic. Thus, our findings must be interpreted in light of these considerations and may not be generalizable outside the ED or hospital setting. For example, it could be that some segments of the population utilized ED resources for testing, whereas others avoided the ED and other healthcare settings for fear of exposure to SARS-CoV-2. We also relied on diagnosis codes to identify concurrent diagnoses, as well as mechanical ventilation in our very severe outcome cohort, which resulted in this classification for some of these diagnoses. Despite these limitations, our findings represent an important step in understanding the risk factors associated with severe clinical COVID-19 disease in pediatric patients.

Our findings may inform future research and clinical interventions. Future studies on antiviral therapies and immune modulators targeting SARS-CoV-2 infection in children and adolescents should focus on high-risk populations, such as those identified in the study, as these patients are most likely to benefit from therapeutic interventions. Similarly, vaccine-development efforts may benefit from additional evaluation in high-risk populations, some of which may have altered immune responses. Furthermore, with increasing vaccination among adults and changes in recommendations, societal mitigation efforts (eg, masking, physical distancing) will diminish. Continued vigilance and COVID-19–mitigation efforts among high-risk children, for whom vaccines are not yet available, are critical during this transition.

Among children with COVID-19 who received care at children’s hospitals and EDs, 20% were hospitalized, and, of those, 21% were admitted to the ICU. Older children and adolescent patients had a lower risk of hospitalization; however, when hospitalized, they had greater illness severity. Those with selected comorbidities (eg, cardiovascular, obesity/type 2 DM, pulmonary and neurologic or neuromuscular disease) had both increased odds of hospitalization and in-hospital illness severity. While there were observed differences in COVID-19 severity by race and ethnicity, additional research is needed to clarify the drivers of such disparities. These factors should be considered when prioritizing mitigation strategies to prevent infection (eg, remote learning, avoidance of group activities, prioritization of COVID-19 vaccine when approved for children aged <12 years).

The COVID-19 pandemic has led to more than 40 million infections and more than 650,000 deaths in the United States alone.¹ Morbidity and mortality have disproportionately affected older adults.^2-4 However, acute infection and delayed effects, such as multisystem inflammatory syndrome in children (MIS-C), occur and can lead to severe complications, hospitalization, and death in pediatric patients.^5,6 Due to higher clinical disease prevalence and morbidity in the adult population, we have learned much about the clinical factors associated with severe adult COVID-19 disease.^5,7-9 Such clinical factors include older age, concurrent comorbidities, smoke exposure, and Black race or Hispanic ethnicity, among others.^5,7-10 However, there is a paucity of data on severe COVID-19 disease in pediatric patients.^5,11,12 In addition, most immunization strategies and pharmacologic treatments for COVID-19 have not been evaluated or approved for use in children.¹³ To guide targeted prevention and treatment strategies, there is a critical need to identify children and adolescents—who are among the most vulnerable patient populations—at high risk for severe disease.

Identifying the clinical factors associated with severe COVID-19 disease will help with prioritizing and allocating vaccines when they are approved for use in patients younger than 12 years. It also can provide insight for clinicians and families faced with decisions wherein individual risk assessment is crucial (eg, in-person schooling, other group activities). The objective of this study was to determine the clinical factors associated with severe COVID-19 among children and adolescents in the United States.

Study Design

We conducted a multicenter retrospective cohort study of patients presenting for care at pediatric hospitals that report data to the Pediatric Health Information System (PHIS) database. The PHIS administrative database includes billing and utilization data from 45 US tertiary care hospitals affiliated with the Children’s Hospital Association (Lenexa, Kansas). Data quality and reliability are ensured through a joint validation effort between the Children’s Hospital Association and participating hospitals. Hospitals submit discharge data, including demographics, diagnoses, and procedures using International Classification of Diseases, 10th Revision (ICD-10) codes, along with daily detailed information on pharmacy, location of care, and other services.

Study Population

Patients 30 days to 18 years of age discharged from the emergency department (ED) or inpatient setting with a primary diagnosis of COVID-19 (ICD-10 codes U.071 and U.072) between April 1, 2020, and September 30, 2020, were eligible for inclusion.¹⁴ In a prior study, the positive predictive value of an ICD-10–coded diagnosis of COVID-19 among hospitalized pediatric patients was 95.5%, compared with reverse transcription polymerase reaction results or presence of MIS-C.¹⁵ The diagnostic code for COVID-19 (ICD-10-CM) also had a high sensitivity (98.0%) in the hospitalized population.¹⁶ Acknowledging the increasing practice of screening patients upon admission, and in an attempt to minimize potential misclassification, we did not include encounters with secondary diagnoses of COVID-19 in our primary analyses. Pediatric patients with surgical diagnoses and neonates who never left the hospital were also excluded.

Factors Associated With Severe COVID-19 Disease

Exposures of interest were determined a priori based on current evidence in the literature and included patient age (0-4 years, 5-11 years, and 12-18 years), sex, race and ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, Asian, other non-White race [defined as Pacific Islander, Native American, or other]), payor type, cardiovascular complex chronic conditions (CCC), neuromuscular CCC, obesity/type 2 diabetes mellitus (DM), pulmonary CCC, asthma (defined using ICD-10 codes¹⁷), and immunocompromised CCC. Race and ethnicity were included as covariates based on previous studies reporting differences in COVID-19 outcomes among racial and ethnic groups.⁹ The CCC covariates were defined using the pediatric CCC ICD-10 classification system version 2.¹⁸

Pediatric Complications and Conditions Associated With COVID-19

Based on current evidence and expert opinion of study members, associated diagnoses and complications co-occurring with a COVID-19 diagnosis were defined a priori and identified through ICD-10 codes (Appendix Table 1). These included acute kidney injury, acute liver injury, aseptic meningitis, asthma exacerbation, bronchiolitis, cerebral infarction, croup, encephalitis, encephalopathy, infant fever, febrile seizure, gastroenteritis/dehydration, Kawasaki disease/MIS-C, myocarditis/pericarditis, pneumonia, lung effusion or empyema, respiratory failure, sepsis, nonfebrile seizure, pancreatitis, sickle cell complications, and thrombotic complications.

Outcomes

COVID-19 severity outcomes were assessed as follows: (1) mild = ED discharge; (2) moderate = inpatient admission; (3) severe = intensive care unit (ICU) admission without mechanical ventilation, shock, or death; and (4) very severe = ICU admission with mechanical ventilation, shock, or death.¹⁹ This ordinal ranking system did not violate the proportional odds assumption. Potential reasons for admission to the ICU without mechanical ventilation, shock, or death include, but are not limited to, need for noninvasive ventilation, vital sign instability, dysrhythmias, respiratory insufficiency, or complications arising from concurrent conditions (eg, thrombotic events, need for continuous albuterol therapy). We examined several secondary, hospital-based outcomes, including associated diagnoses and complications, all-cause 30-day healthcare reutilization (ED visit or rehospitalization), length of stay (LOS), and ICU LOS.

Statistical Analysis

Demographic characteristics were summarized using frequencies and percentages for categorical variables and geometric means with SD and medians with interquartile ranges (IQR) for continuous variables, as appropriate. Factors associated with hospitalization (encompassing severity levels 2-4) vs ED discharge (severity level 1) were assessed using logistic regression. Factors associated with increasing severity among hospitalized pediatric patients (severity levels 2, 3, and 4) were assessed using ordinal logistic regression. Covariates in these analyses included race and ethnicity, age, sex, payor, cardiovascular CCC, neurologic/neuromuscular CCC, obesity/type 2 DM, pulmonary CCC, asthma, and immunocompromised CCC. Adjusted odds ratios (aOR) and corresponding 95% CI for each risk factor were generated using generalized linear mixed effects models and random intercepts for each hospital. Given the potential for diagnostic misclassification of pediatric patients with COVID-19 based on primary vs secondary diagnoses, we performed sensitivity analyses defining the study population as those with a primary diagnosis of COVID-19 and those with a secondary diagnosis of COVID-19 plus a concurrent primary diagnosis of a condition associated with COVID-19 (Appendix Table 1).

All analyses were performed using SAS version 9.4 (SAS Institute, Inc), and P < .05 was considered statistically significant. The Institutional Review Board at Vanderbilt University Medical Center determined that this study of de-identified data did not meet the criteria for human subjects research.

Study Population

A total of 19,976 encounters were included in the study. Of those, 15,913 (79.7%) were discharged from the ED and 4063 (20.3%) were hospitalized (Table 1). The most common race/ethnicity was Hispanic (9741, 48.8%), followed by non-Hispanic White (4217, 21.1%). Reference race/ethnicity data for the overall 2019 PHIS population can be found in Appendix Table 2.

The severity distribution among the hospitalized population was moderate (3222, 79.3%), severe (431, 11.3%), and very severe (380, 9.4%). The frequency of COVID-19 diagnoses increased late in the study period (Figure). Among those hospitalized, the median LOS for the index admission was 2 days (IQR, 1-4), while among those admitted to the ICU, the median LOS was 3 days (IQR, 2-5).

Overall, 10.1% (n = 2020) of the study population had an all-cause repeat encounter (ie, subsequent ED encounter or hospitalization) within 30 days following the index discharge. Repeat encounters were more frequent among patients hospitalized than among those discharged from the ED (Appendix Table 3).

Prevalence of Conditions and Complications Associated With COVID-19

Overall, 3257 (16.3%) patients had one or more co-occurring diagnoses categorized as a COVID-19–associated condition or complication. The most frequent diagnoses included lower respiratory tract disease (pneumonia, lung effusion, or empyema; n = 1415, 7.1%), gastroenteritis/dehydration (n = 1068, 5.3%), respiratory failure (n = 731, 3.7%), febrile infant (n = 413, 2.1%), and nonfebrile seizure (n = 425, 2.1%). Aside from nonfebrile seizure, neurological complications were less frequent and included febrile seizure (n = 155, 0.8%), encephalopathy (n = 63, 0.3%), aseptic meningitis (n = 16, 0.1%), encephalitis (n = 11, 0.1%), and cerebral infarction (n = 6, <0.1%). Kawasaki disease and MIS-C comprised 1.7% (n = 346) of diagnoses. Thrombotic complications occurred in 0.1% (n = 13) of patients. Overall, these conditions and complications associated with COVID-19 were more frequent in hospitalized patients than in those discharged from the ED (P < .001) (Table 2).

Factors Associated With COVID-19 Disease Severity

Compared to pediatric patients with COVID-19 discharged from the ED, factors associated with increased odds of hospitalization included private payor insurance; obesity/type 2 DM; asthma; and cardiovascular, immunocompromised, neurologic/neuromuscular, and pulmonary CCCs (Table 3). Factors associated with decreased risk of hospitalization included Black race or Hispanic ethnicity compared with White race; female sex; and age 5 to 11 years and age 12 to 17 years (vs age 0-4 years). Among children and adolescents hospitalized with COVID-19, factors associated with greater disease severity included Black or other non-White race; age 5 to 11 years; age 12 to 17 years; obesity/type 2 DM; immunocompromised conditions; and cardiovascular, neurologic/neuromuscular, and pulmonary CCCs (Table 3).

Sensitivity Analysis

We performed a sensitivity analysis that expanded the study population to include those with a secondary diagnosis of COVID-19 plus a diagnosis of a COVID-19–associated condition or complication. Analyses using the expanded population (N = 21,247) were similar to the primary analyses (Appendix Table 4 and Appendix Table 5).

In this large multicenter study evaluating COVID-19 disease severity in more than 19,000 patients presenting for emergency care at US pediatric hospitals, approximately 20% were hospitalized, and among those hospitalized almost a quarter required ICU care. Clinical risk factors associated with increased risk of hospitalization include private payor status and selected comorbidities (obesity/type 2 DM; asthma; and cardiovascular, pulmonary, immunocompromised, neurologic/neuromuscular CCCs), while those associated with decreased risk of hospitalization include older age, female sex, and Black race or Hispanic ethnicity. Factors associated with severe disease among hospitalized pediatric patients include Black or other non-White race, school age (≥5 years), and certain chronic conditions (cardiovascular disease, obesity/type 2 DM, neurologic or neuromuscular disease). Sixteen percent of patients had a concurrent diagnosis for a condition or complication associated with COVID-19.

While the study population (ie, children and adolescents presenting to the ED) represents a small fraction of children and adolescents in the community with SARS-CoV-2 infection, the results provide important insight into factors of severe COVID-19 in the pediatric population. A report from France suggested ventilatory or hemodynamic support or death were independently associated with older age (≥10 years), elevated C-reactive protein, and hypoxemia.¹² An Italian study found that younger age (0-4 years) was associated with less severe disease, while preexisting conditions were more likely in patients with severe disease.¹¹ A single-center case series of 50 patients (aged ≤21 years) hospitalized at a children’s hospital in New York City found respiratory failure (n = 9) was more common in children older than 1 year, patients with elevated inflammatory markers, and patients with obesity.²⁰

Our study confirms several factors for severe COVID-19 found in these studies, including older age,^11,12,20 obesity,²⁰ and preexisting conditions.¹¹ Our findings also expand on these reports, including identification of factors associated with hospitalization. Given the rate of 30-day re-encounters among pediatric patients with COVID-19 (10.1%), identifying risk factors for hospitalization may aid ED providers in determining optimal disposition (eg, home, hospital admission, ICU). We also identified specific comorbidities associated with more severe disease in those hospitalized with COVID-19, such as cardiovascular disease, obesity/type 2 DM, and pulmonary, neurologic, or neuromuscular conditions. We also found that asthma increased the risk for hospitalization but not more severe disease among those hospitalized. This latter finding also aligns with recent single-center studies,^21,22 whereas a Turkish study of pediatric patients aged 0 to 18 years found no association between asthma and COVID-19 hospitalizations.²³We also examined payor type and racial/ethnic factors in our analysis. In 2019, patients who identified as Black or Hispanic comprised 52.3% of all encounters and 40.7% of hospitalizations recorded in the PHIS database. During the same year, encounters for influenza among Black or Hispanic pediatric patients comprised 58.7% of all influenza diagnoses and 47.0% of pediatric influenza hospitalizations (Appendix Table 2). In this study, patients who identified as Black or Hispanic race represented a disproportionately large share of patients presenting to children’s hospitals (68.5%) and of those hospitalized (60.8%). Hispanic ethnicity, in particular, represented a disproportionate share of patients seeking care for COVID-19 compared to the overall PHIS population (47.7% and 27.1%, respectively). After accounting for other factors, we found Black and other non-White race—but not of Hispanic ethnicity—were independently associated with more disease severity among those hospitalized. This contrasts with findings from a recent adult study by Yehia et al,²⁴ who found (after adjusting for other clinical factors) no significant difference in mortality between Black patients and White patients among adults hospitalized due to COVID-19. It also contrasts with a recent large population-based UK study wherein pediatric patients identifying as Asian, but not Black or mixed race or ethnicity, had an increased risk of hospital admission and admission to the ICU compared to children identifying as White. Children identifying as Black or mixed race had longer hospital admissions.²⁵ However, as the authors of the study note, residual confounders and ascertainment bias due to differences in COVID testing may have influenced these findings.

Our findings of differences in hospitalization and disease severity among those hospitalized by race and ethnicity should be interpreted carefully. These may reflect a constellation of factors that are difficult to measure, including differences in healthcare access, inequalities in care (including hospital admission inequalities), and implicit bias—all of which may reflect structural racism. For example, it is possible that children who identify as Black or Hispanic have different access to care compared to children who identify as White, and this may affect disease severity on presentation.² Alternatively, it is possible that White pediatric patients are more likely to be hospitalized as compared to non-White pediatric patients with similar illness severity. Our finding that pediatric patients who identify as Hispanic or Black had a lower risk of hospitalization should be also interpreted carefully, as this may reflect higher utilization of the ED for SARS-CoV-2 testing, increased use of nonemergency services among those without access to primary care, or systematic differences in provider decision-making among this segment of the population.² Further study is needed to determine specific drivers for racial and ethnic differences in healthcare utilization in children and adolescents with COVID-19.²⁶

Complications and co-occurring diagnoses in adults with COVID-19 are well documented.^27-30 However, there is little information to date on the co-occurring diagnoses and complications associated with COVID-19 in children and adolescents. We found that complications and co-occurring conditions occurred in 16.3% of the study population, with the most frequent conditions including known complications of viral infections such as pneumonia, respiratory failure, and seizures. Acute kidney and liver injury, as well as thrombotic complications, occurred less commonly than in adults.^26-29 Interestingly, neurologic complications were also uncommon compared to adult reports^8,31 and less frequent than in other viral illnesses in children and adolescents. For example, neurologic complications occur in approximately 7.5% of children and adolescents hospitalized with influenza.³²

Limitations of the present study include the retrospective design, as well as incomplete patient-level clinical data in the PHIS database. The PHIS database only includes children’s hospitals, which may limit the generalizability of findings to community hospitals. We also excluded newborns, and our findings may not be generalizable to this population. We only included children and adolescents with a primary diagnosis of COVID-19, which has the potential for misclassification in cases where COVID-19 was a secondary diagnosis. However, results of our sensitivity analysis, which incorporated secondary diagnoses of COVID-19, were consistent with findings from our main analyses. Our study was designed to examine associations between certain prespecified factors and COVID-19 severity among pediatric patients who visited the ED or were admitted to the hospital during the COVID-19 pandemic. Thus, our findings must be interpreted in light of these considerations and may not be generalizable outside the ED or hospital setting. For example, it could be that some segments of the population utilized ED resources for testing, whereas others avoided the ED and other healthcare settings for fear of exposure to SARS-CoV-2. We also relied on diagnosis codes to identify concurrent diagnoses, as well as mechanical ventilation in our very severe outcome cohort, which resulted in this classification for some of these diagnoses. Despite these limitations, our findings represent an important step in understanding the risk factors associated with severe clinical COVID-19 disease in pediatric patients.

Our findings may inform future research and clinical interventions. Future studies on antiviral therapies and immune modulators targeting SARS-CoV-2 infection in children and adolescents should focus on high-risk populations, such as those identified in the study, as these patients are most likely to benefit from therapeutic interventions. Similarly, vaccine-development efforts may benefit from additional evaluation in high-risk populations, some of which may have altered immune responses. Furthermore, with increasing vaccination among adults and changes in recommendations, societal mitigation efforts (eg, masking, physical distancing) will diminish. Continued vigilance and COVID-19–mitigation efforts among high-risk children, for whom vaccines are not yet available, are critical during this transition.

Among children with COVID-19 who received care at children’s hospitals and EDs, 20% were hospitalized, and, of those, 21% were admitted to the ICU. Older children and adolescent patients had a lower risk of hospitalization; however, when hospitalized, they had greater illness severity. Those with selected comorbidities (eg, cardiovascular, obesity/type 2 DM, pulmonary and neurologic or neuromuscular disease) had both increased odds of hospitalization and in-hospital illness severity. While there were observed differences in COVID-19 severity by race and ethnicity, additional research is needed to clarify the drivers of such disparities. These factors should be considered when prioritizing mitigation strategies to prevent infection (eg, remote learning, avoidance of group activities, prioritization of COVID-19 vaccine when approved for children aged <12 years).

The COVID-19 pandemic has led to more than 40 million infections and more than 650,000 deaths in the United States alone.¹ Morbidity and mortality have disproportionately affected older adults.^2-4 However, acute infection and delayed effects, such as multisystem inflammatory syndrome in children (MIS-C), occur and can lead to severe complications, hospitalization, and death in pediatric patients.^5,6 Due to higher clinical disease prevalence and morbidity in the adult population, we have learned much about the clinical factors associated with severe adult COVID-19 disease.^5,7-9 Such clinical factors include older age, concurrent comorbidities, smoke exposure, and Black race or Hispanic ethnicity, among others.^5,7-10 However, there is a paucity of data on severe COVID-19 disease in pediatric patients.^5,11,12 In addition, most immunization strategies and pharmacologic treatments for COVID-19 have not been evaluated or approved for use in children.¹³ To guide targeted prevention and treatment strategies, there is a critical need to identify children and adolescents—who are among the most vulnerable patient populations—at high risk for severe disease.

Identifying the clinical factors associated with severe COVID-19 disease will help with prioritizing and allocating vaccines when they are approved for use in patients younger than 12 years. It also can provide insight for clinicians and families faced with decisions wherein individual risk assessment is crucial (eg, in-person schooling, other group activities). The objective of this study was to determine the clinical factors associated with severe COVID-19 among children and adolescents in the United States.

Study Design

We conducted a multicenter retrospective cohort study of patients presenting for care at pediatric hospitals that report data to the Pediatric Health Information System (PHIS) database. The PHIS administrative database includes billing and utilization data from 45 US tertiary care hospitals affiliated with the Children’s Hospital Association (Lenexa, Kansas). Data quality and reliability are ensured through a joint validation effort between the Children’s Hospital Association and participating hospitals. Hospitals submit discharge data, including demographics, diagnoses, and procedures using International Classification of Diseases, 10th Revision (ICD-10) codes, along with daily detailed information on pharmacy, location of care, and other services.

Study Population

Patients 30 days to 18 years of age discharged from the emergency department (ED) or inpatient setting with a primary diagnosis of COVID-19 (ICD-10 codes U.071 and U.072) between April 1, 2020, and September 30, 2020, were eligible for inclusion.¹⁴ In a prior study, the positive predictive value of an ICD-10–coded diagnosis of COVID-19 among hospitalized pediatric patients was 95.5%, compared with reverse transcription polymerase reaction results or presence of MIS-C.¹⁵ The diagnostic code for COVID-19 (ICD-10-CM) also had a high sensitivity (98.0%) in the hospitalized population.¹⁶ Acknowledging the increasing practice of screening patients upon admission, and in an attempt to minimize potential misclassification, we did not include encounters with secondary diagnoses of COVID-19 in our primary analyses. Pediatric patients with surgical diagnoses and neonates who never left the hospital were also excluded.

Factors Associated With Severe COVID-19 Disease

Exposures of interest were determined a priori based on current evidence in the literature and included patient age (0-4 years, 5-11 years, and 12-18 years), sex, race and ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, Asian, other non-White race [defined as Pacific Islander, Native American, or other]), payor type, cardiovascular complex chronic conditions (CCC), neuromuscular CCC, obesity/type 2 diabetes mellitus (DM), pulmonary CCC, asthma (defined using ICD-10 codes¹⁷), and immunocompromised CCC. Race and ethnicity were included as covariates based on previous studies reporting differences in COVID-19 outcomes among racial and ethnic groups.⁹ The CCC covariates were defined using the pediatric CCC ICD-10 classification system version 2.¹⁸

Pediatric Complications and Conditions Associated With COVID-19

Based on current evidence and expert opinion of study members, associated diagnoses and complications co-occurring with a COVID-19 diagnosis were defined a priori and identified through ICD-10 codes (Appendix Table 1). These included acute kidney injury, acute liver injury, aseptic meningitis, asthma exacerbation, bronchiolitis, cerebral infarction, croup, encephalitis, encephalopathy, infant fever, febrile seizure, gastroenteritis/dehydration, Kawasaki disease/MIS-C, myocarditis/pericarditis, pneumonia, lung effusion or empyema, respiratory failure, sepsis, nonfebrile seizure, pancreatitis, sickle cell complications, and thrombotic complications.

Outcomes

COVID-19 severity outcomes were assessed as follows: (1) mild = ED discharge; (2) moderate = inpatient admission; (3) severe = intensive care unit (ICU) admission without mechanical ventilation, shock, or death; and (4) very severe = ICU admission with mechanical ventilation, shock, or death.¹⁹ This ordinal ranking system did not violate the proportional odds assumption. Potential reasons for admission to the ICU without mechanical ventilation, shock, or death include, but are not limited to, need for noninvasive ventilation, vital sign instability, dysrhythmias, respiratory insufficiency, or complications arising from concurrent conditions (eg, thrombotic events, need for continuous albuterol therapy). We examined several secondary, hospital-based outcomes, including associated diagnoses and complications, all-cause 30-day healthcare reutilization (ED visit or rehospitalization), length of stay (LOS), and ICU LOS.

Statistical Analysis

Demographic characteristics were summarized using frequencies and percentages for categorical variables and geometric means with SD and medians with interquartile ranges (IQR) for continuous variables, as appropriate. Factors associated with hospitalization (encompassing severity levels 2-4) vs ED discharge (severity level 1) were assessed using logistic regression. Factors associated with increasing severity among hospitalized pediatric patients (severity levels 2, 3, and 4) were assessed using ordinal logistic regression. Covariates in these analyses included race and ethnicity, age, sex, payor, cardiovascular CCC, neurologic/neuromuscular CCC, obesity/type 2 DM, pulmonary CCC, asthma, and immunocompromised CCC. Adjusted odds ratios (aOR) and corresponding 95% CI for each risk factor were generated using generalized linear mixed effects models and random intercepts for each hospital. Given the potential for diagnostic misclassification of pediatric patients with COVID-19 based on primary vs secondary diagnoses, we performed sensitivity analyses defining the study population as those with a primary diagnosis of COVID-19 and those with a secondary diagnosis of COVID-19 plus a concurrent primary diagnosis of a condition associated with COVID-19 (Appendix Table 1).

All analyses were performed using SAS version 9.4 (SAS Institute, Inc), and P < .05 was considered statistically significant. The Institutional Review Board at Vanderbilt University Medical Center determined that this study of de-identified data did not meet the criteria for human subjects research.

Study Population

A total of 19,976 encounters were included in the study. Of those, 15,913 (79.7%) were discharged from the ED and 4063 (20.3%) were hospitalized (Table 1). The most common race/ethnicity was Hispanic (9741, 48.8%), followed by non-Hispanic White (4217, 21.1%). Reference race/ethnicity data for the overall 2019 PHIS population can be found in Appendix Table 2.

The severity distribution among the hospitalized population was moderate (3222, 79.3%), severe (431, 11.3%), and very severe (380, 9.4%). The frequency of COVID-19 diagnoses increased late in the study period (Figure). Among those hospitalized, the median LOS for the index admission was 2 days (IQR, 1-4), while among those admitted to the ICU, the median LOS was 3 days (IQR, 2-5).

Overall, 10.1% (n = 2020) of the study population had an all-cause repeat encounter (ie, subsequent ED encounter or hospitalization) within 30 days following the index discharge. Repeat encounters were more frequent among patients hospitalized than among those discharged from the ED (Appendix Table 3).

Prevalence of Conditions and Complications Associated With COVID-19

Overall, 3257 (16.3%) patients had one or more co-occurring diagnoses categorized as a COVID-19–associated condition or complication. The most frequent diagnoses included lower respiratory tract disease (pneumonia, lung effusion, or empyema; n = 1415, 7.1%), gastroenteritis/dehydration (n = 1068, 5.3%), respiratory failure (n = 731, 3.7%), febrile infant (n = 413, 2.1%), and nonfebrile seizure (n = 425, 2.1%). Aside from nonfebrile seizure, neurological complications were less frequent and included febrile seizure (n = 155, 0.8%), encephalopathy (n = 63, 0.3%), aseptic meningitis (n = 16, 0.1%), encephalitis (n = 11, 0.1%), and cerebral infarction (n = 6, <0.1%). Kawasaki disease and MIS-C comprised 1.7% (n = 346) of diagnoses. Thrombotic complications occurred in 0.1% (n = 13) of patients. Overall, these conditions and complications associated with COVID-19 were more frequent in hospitalized patients than in those discharged from the ED (P < .001) (Table 2).

Factors Associated With COVID-19 Disease Severity

Compared to pediatric patients with COVID-19 discharged from the ED, factors associated with increased odds of hospitalization included private payor insurance; obesity/type 2 DM; asthma; and cardiovascular, immunocompromised, neurologic/neuromuscular, and pulmonary CCCs (Table 3). Factors associated with decreased risk of hospitalization included Black race or Hispanic ethnicity compared with White race; female sex; and age 5 to 11 years and age 12 to 17 years (vs age 0-4 years). Among children and adolescents hospitalized with COVID-19, factors associated with greater disease severity included Black or other non-White race; age 5 to 11 years; age 12 to 17 years; obesity/type 2 DM; immunocompromised conditions; and cardiovascular, neurologic/neuromuscular, and pulmonary CCCs (Table 3).

Sensitivity Analysis

We performed a sensitivity analysis that expanded the study population to include those with a secondary diagnosis of COVID-19 plus a diagnosis of a COVID-19–associated condition or complication. Analyses using the expanded population (N = 21,247) were similar to the primary analyses (Appendix Table 4 and Appendix Table 5).

In this large multicenter study evaluating COVID-19 disease severity in more than 19,000 patients presenting for emergency care at US pediatric hospitals, approximately 20% were hospitalized, and among those hospitalized almost a quarter required ICU care. Clinical risk factors associated with increased risk of hospitalization include private payor status and selected comorbidities (obesity/type 2 DM; asthma; and cardiovascular, pulmonary, immunocompromised, neurologic/neuromuscular CCCs), while those associated with decreased risk of hospitalization include older age, female sex, and Black race or Hispanic ethnicity. Factors associated with severe disease among hospitalized pediatric patients include Black or other non-White race, school age (≥5 years), and certain chronic conditions (cardiovascular disease, obesity/type 2 DM, neurologic or neuromuscular disease). Sixteen percent of patients had a concurrent diagnosis for a condition or complication associated with COVID-19.

While the study population (ie, children and adolescents presenting to the ED) represents a small fraction of children and adolescents in the community with SARS-CoV-2 infection, the results provide important insight into factors of severe COVID-19 in the pediatric population. A report from France suggested ventilatory or hemodynamic support or death were independently associated with older age (≥10 years), elevated C-reactive protein, and hypoxemia.¹² An Italian study found that younger age (0-4 years) was associated with less severe disease, while preexisting conditions were more likely in patients with severe disease.¹¹ A single-center case series of 50 patients (aged ≤21 years) hospitalized at a children’s hospital in New York City found respiratory failure (n = 9) was more common in children older than 1 year, patients with elevated inflammatory markers, and patients with obesity.²⁰

Our study confirms several factors for severe COVID-19 found in these studies, including older age,^11,12,20 obesity,²⁰ and preexisting conditions.¹¹ Our findings also expand on these reports, including identification of factors associated with hospitalization. Given the rate of 30-day re-encounters among pediatric patients with COVID-19 (10.1%), identifying risk factors for hospitalization may aid ED providers in determining optimal disposition (eg, home, hospital admission, ICU). We also identified specific comorbidities associated with more severe disease in those hospitalized with COVID-19, such as cardiovascular disease, obesity/type 2 DM, and pulmonary, neurologic, or neuromuscular conditions. We also found that asthma increased the risk for hospitalization but not more severe disease among those hospitalized. This latter finding also aligns with recent single-center studies,^21,22 whereas a Turkish study of pediatric patients aged 0 to 18 years found no association between asthma and COVID-19 hospitalizations.²³We also examined payor type and racial/ethnic factors in our analysis. In 2019, patients who identified as Black or Hispanic comprised 52.3% of all encounters and 40.7% of hospitalizations recorded in the PHIS database. During the same year, encounters for influenza among Black or Hispanic pediatric patients comprised 58.7% of all influenza diagnoses and 47.0% of pediatric influenza hospitalizations (Appendix Table 2). In this study, patients who identified as Black or Hispanic race represented a disproportionately large share of patients presenting to children’s hospitals (68.5%) and of those hospitalized (60.8%). Hispanic ethnicity, in particular, represented a disproportionate share of patients seeking care for COVID-19 compared to the overall PHIS population (47.7% and 27.1%, respectively). After accounting for other factors, we found Black and other non-White race—but not of Hispanic ethnicity—were independently associated with more disease severity among those hospitalized. This contrasts with findings from a recent adult study by Yehia et al,²⁴ who found (after adjusting for other clinical factors) no significant difference in mortality between Black patients and White patients among adults hospitalized due to COVID-19. It also contrasts with a recent large population-based UK study wherein pediatric patients identifying as Asian, but not Black or mixed race or ethnicity, had an increased risk of hospital admission and admission to the ICU compared to children identifying as White. Children identifying as Black or mixed race had longer hospital admissions.²⁵ However, as the authors of the study note, residual confounders and ascertainment bias due to differences in COVID testing may have influenced these findings.

Our findings of differences in hospitalization and disease severity among those hospitalized by race and ethnicity should be interpreted carefully. These may reflect a constellation of factors that are difficult to measure, including differences in healthcare access, inequalities in care (including hospital admission inequalities), and implicit bias—all of which may reflect structural racism. For example, it is possible that children who identify as Black or Hispanic have different access to care compared to children who identify as White, and this may affect disease severity on presentation.² Alternatively, it is possible that White pediatric patients are more likely to be hospitalized as compared to non-White pediatric patients with similar illness severity. Our finding that pediatric patients who identify as Hispanic or Black had a lower risk of hospitalization should be also interpreted carefully, as this may reflect higher utilization of the ED for SARS-CoV-2 testing, increased use of nonemergency services among those without access to primary care, or systematic differences in provider decision-making among this segment of the population.² Further study is needed to determine specific drivers for racial and ethnic differences in healthcare utilization in children and adolescents with COVID-19.²⁶

Complications and co-occurring diagnoses in adults with COVID-19 are well documented.^27-30 However, there is little information to date on the co-occurring diagnoses and complications associated with COVID-19 in children and adolescents. We found that complications and co-occurring conditions occurred in 16.3% of the study population, with the most frequent conditions including known complications of viral infections such as pneumonia, respiratory failure, and seizures. Acute kidney and liver injury, as well as thrombotic complications, occurred less commonly than in adults.^26-29 Interestingly, neurologic complications were also uncommon compared to adult reports^8,31 and less frequent than in other viral illnesses in children and adolescents. For example, neurologic complications occur in approximately 7.5% of children and adolescents hospitalized with influenza.³²

Limitations of the present study include the retrospective design, as well as incomplete patient-level clinical data in the PHIS database. The PHIS database only includes children’s hospitals, which may limit the generalizability of findings to community hospitals. We also excluded newborns, and our findings may not be generalizable to this population. We only included children and adolescents with a primary diagnosis of COVID-19, which has the potential for misclassification in cases where COVID-19 was a secondary diagnosis. However, results of our sensitivity analysis, which incorporated secondary diagnoses of COVID-19, were consistent with findings from our main analyses. Our study was designed to examine associations between certain prespecified factors and COVID-19 severity among pediatric patients who visited the ED or were admitted to the hospital during the COVID-19 pandemic. Thus, our findings must be interpreted in light of these considerations and may not be generalizable outside the ED or hospital setting. For example, it could be that some segments of the population utilized ED resources for testing, whereas others avoided the ED and other healthcare settings for fear of exposure to SARS-CoV-2. We also relied on diagnosis codes to identify concurrent diagnoses, as well as mechanical ventilation in our very severe outcome cohort, which resulted in this classification for some of these diagnoses. Despite these limitations, our findings represent an important step in understanding the risk factors associated with severe clinical COVID-19 disease in pediatric patients.

Our findings may inform future research and clinical interventions. Future studies on antiviral therapies and immune modulators targeting SARS-CoV-2 infection in children and adolescents should focus on high-risk populations, such as those identified in the study, as these patients are most likely to benefit from therapeutic interventions. Similarly, vaccine-development efforts may benefit from additional evaluation in high-risk populations, some of which may have altered immune responses. Furthermore, with increasing vaccination among adults and changes in recommendations, societal mitigation efforts (eg, masking, physical distancing) will diminish. Continued vigilance and COVID-19–mitigation efforts among high-risk children, for whom vaccines are not yet available, are critical during this transition.

Among children with COVID-19 who received care at children’s hospitals and EDs, 20% were hospitalized, and, of those, 21% were admitted to the ICU. Older children and adolescent patients had a lower risk of hospitalization; however, when hospitalized, they had greater illness severity. Those with selected comorbidities (eg, cardiovascular, obesity/type 2 DM, pulmonary and neurologic or neuromuscular disease) had both increased odds of hospitalization and in-hospital illness severity. While there were observed differences in COVID-19 severity by race and ethnicity, additional research is needed to clarify the drivers of such disparities. These factors should be considered when prioritizing mitigation strategies to prevent infection (eg, remote learning, avoidance of group activities, prioritization of COVID-19 vaccine when approved for children aged <12 years).