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Fed Pract
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gaming
gambling
compulsive behaviors
ammunition
assault rifle
black jack
Boko Haram
bondage
child abuse
cocaine
Daech
drug paraphernalia
explosion
gun
human trafficking
ISIL
ISIS
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Islamic state
mixed martial arts
MMA
molestation
national rifle association
NRA
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pedophilia
poker
porn
pornography
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recreational drug
sex slave rings
slot machine
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Texas hold 'em
UFC
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bunges
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butt
butt fuck
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buttfucked
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cock sucker
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A peer-reviewed clinical journal serving healthcare professionals working with the Department of Veterans Affairs, the Department of Defense, and the Public Health Service.

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US Vet Study Identifies Risk Factors for Acral Melanoma

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US Vet Study Identifies Risk Factors for Acral Melanoma

TOPLINE:

Exposure to Agent Orange, the defoliant used by the US Air Force during the Vietnam War, was one of the factors associated with increased odds of acral melanoma (AM), a rare melanoma subtype affecting palms, soles, and nail units.

METHODOLOGY:

  • Researchers conducted a nested case-control study in the Veterans Affairs healthcare system, and identified 1292 veterans (median age, 70.13 years; 94.0% men; 73.4% White, 14.6% Black) with AM through the Veterans Affairs Cancer Registry and a validated natural language processing pipeline from 2000 to 2024.
  • Researchers matched each case of AM to 4 individuals with nonacral cutaneous melanoma (CM) and 4 control individuals without melanoma diagnoses, based on diagnosis year and outpatient visit frequency.
  • Exposures included age, sex, race, ethnicity, rurality, region, military branch, comorbidities, smoking status, alcohol use, BMI, Agent Orange exposure, prior photosensitizing medications, nevi, and keratinocyte carcinoma.

TAKEAWAY:

  • Veterans exposed to Agent Orange had higher odds of AM than individuals with CM (adjusted odds ratio [AOR], 1.31; 95% CI, 1.06-1.62) and control individuals without melanoma (AOR, 1.27; 95% CI, 1.04-1.56).
  • Individuals with current smoking habit had lower odds of AM than those with CM (AOR, 0.65; 95% CI, 0.52-0.81) and control individuals without melanoma (AOR, 0.50; 95% CI, 0.40-0.62).
  • Patients with prior keratinocyte carcinoma and actinic keratosis had higher odds of AM than control individuals without melanoma but lower odds than those with CM.
  • History of nevus was associated with higher odds of acral melanoma compared with individuals without melanoma (AOR, 2.11; 95% CI, 1.49-2.98).

IN PRACTICE:

“Our results support the need for continued investigation of AM as a distinct entity from CM and may inform future evaluations of the associations between [Agent Orange exposure] in veteran populations, as well as those between other environmental exposures in different populations," the study authors wrote. Referring to the “continued search for a better understanding of a potential link” between Agent Orange and melanoma, as well as AM, and other possible etiologic factors for AM, this study “provides a strong impetus to further these research goals and contribute to the investigation of the legacy of the Vietnam War and honor a commitment to the veterans community,” Andrew F. Olshan, PhD, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, wrote in an accompanying editorial.

SOURCE:

The study was led by Jonathan C. Hwang, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, and was published online on February 4 in JAMA Dermatology.

LIMITATIONS:

The case-control design limits causal inference and the findings might not be generalized outside US veterans. Exposure misclassification could be present.

DISCLOSURES:

The study was supported by the Department of Defense and the Department of Veterans Affairs. Several authors reported receiving grants from CU Anschutz Medical Center, Department of Defense, CDMRP Melanoma Research Program, and Merck, Bayer, and Department of Veteran Affairs. They also reported receiving royalty from UpToDate, and being shareholder in many companies, including Apple. NVIDIA, Amazon, Gilead, AstraZeneca, BioNTech, and Moderna. Olshan declared being a member of the National Academies of Sciences, Engineering, and Medicine Veterans and Agent Orange review committee.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

A version of this article first appeared on Medscape.com.

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TOPLINE:

Exposure to Agent Orange, the defoliant used by the US Air Force during the Vietnam War, was one of the factors associated with increased odds of acral melanoma (AM), a rare melanoma subtype affecting palms, soles, and nail units.

METHODOLOGY:

  • Researchers conducted a nested case-control study in the Veterans Affairs healthcare system, and identified 1292 veterans (median age, 70.13 years; 94.0% men; 73.4% White, 14.6% Black) with AM through the Veterans Affairs Cancer Registry and a validated natural language processing pipeline from 2000 to 2024.
  • Researchers matched each case of AM to 4 individuals with nonacral cutaneous melanoma (CM) and 4 control individuals without melanoma diagnoses, based on diagnosis year and outpatient visit frequency.
  • Exposures included age, sex, race, ethnicity, rurality, region, military branch, comorbidities, smoking status, alcohol use, BMI, Agent Orange exposure, prior photosensitizing medications, nevi, and keratinocyte carcinoma.

TAKEAWAY:

  • Veterans exposed to Agent Orange had higher odds of AM than individuals with CM (adjusted odds ratio [AOR], 1.31; 95% CI, 1.06-1.62) and control individuals without melanoma (AOR, 1.27; 95% CI, 1.04-1.56).
  • Individuals with current smoking habit had lower odds of AM than those with CM (AOR, 0.65; 95% CI, 0.52-0.81) and control individuals without melanoma (AOR, 0.50; 95% CI, 0.40-0.62).
  • Patients with prior keratinocyte carcinoma and actinic keratosis had higher odds of AM than control individuals without melanoma but lower odds than those with CM.
  • History of nevus was associated with higher odds of acral melanoma compared with individuals without melanoma (AOR, 2.11; 95% CI, 1.49-2.98).

IN PRACTICE:

“Our results support the need for continued investigation of AM as a distinct entity from CM and may inform future evaluations of the associations between [Agent Orange exposure] in veteran populations, as well as those between other environmental exposures in different populations," the study authors wrote. Referring to the “continued search for a better understanding of a potential link” between Agent Orange and melanoma, as well as AM, and other possible etiologic factors for AM, this study “provides a strong impetus to further these research goals and contribute to the investigation of the legacy of the Vietnam War and honor a commitment to the veterans community,” Andrew F. Olshan, PhD, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, wrote in an accompanying editorial.

SOURCE:

The study was led by Jonathan C. Hwang, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, and was published online on February 4 in JAMA Dermatology.

LIMITATIONS:

The case-control design limits causal inference and the findings might not be generalized outside US veterans. Exposure misclassification could be present.

DISCLOSURES:

The study was supported by the Department of Defense and the Department of Veterans Affairs. Several authors reported receiving grants from CU Anschutz Medical Center, Department of Defense, CDMRP Melanoma Research Program, and Merck, Bayer, and Department of Veteran Affairs. They also reported receiving royalty from UpToDate, and being shareholder in many companies, including Apple. NVIDIA, Amazon, Gilead, AstraZeneca, BioNTech, and Moderna. Olshan declared being a member of the National Academies of Sciences, Engineering, and Medicine Veterans and Agent Orange review committee.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

A version of this article first appeared on Medscape.com.

TOPLINE:

Exposure to Agent Orange, the defoliant used by the US Air Force during the Vietnam War, was one of the factors associated with increased odds of acral melanoma (AM), a rare melanoma subtype affecting palms, soles, and nail units.

METHODOLOGY:

  • Researchers conducted a nested case-control study in the Veterans Affairs healthcare system, and identified 1292 veterans (median age, 70.13 years; 94.0% men; 73.4% White, 14.6% Black) with AM through the Veterans Affairs Cancer Registry and a validated natural language processing pipeline from 2000 to 2024.
  • Researchers matched each case of AM to 4 individuals with nonacral cutaneous melanoma (CM) and 4 control individuals without melanoma diagnoses, based on diagnosis year and outpatient visit frequency.
  • Exposures included age, sex, race, ethnicity, rurality, region, military branch, comorbidities, smoking status, alcohol use, BMI, Agent Orange exposure, prior photosensitizing medications, nevi, and keratinocyte carcinoma.

TAKEAWAY:

  • Veterans exposed to Agent Orange had higher odds of AM than individuals with CM (adjusted odds ratio [AOR], 1.31; 95% CI, 1.06-1.62) and control individuals without melanoma (AOR, 1.27; 95% CI, 1.04-1.56).
  • Individuals with current smoking habit had lower odds of AM than those with CM (AOR, 0.65; 95% CI, 0.52-0.81) and control individuals without melanoma (AOR, 0.50; 95% CI, 0.40-0.62).
  • Patients with prior keratinocyte carcinoma and actinic keratosis had higher odds of AM than control individuals without melanoma but lower odds than those with CM.
  • History of nevus was associated with higher odds of acral melanoma compared with individuals without melanoma (AOR, 2.11; 95% CI, 1.49-2.98).

IN PRACTICE:

“Our results support the need for continued investigation of AM as a distinct entity from CM and may inform future evaluations of the associations between [Agent Orange exposure] in veteran populations, as well as those between other environmental exposures in different populations," the study authors wrote. Referring to the “continued search for a better understanding of a potential link” between Agent Orange and melanoma, as well as AM, and other possible etiologic factors for AM, this study “provides a strong impetus to further these research goals and contribute to the investigation of the legacy of the Vietnam War and honor a commitment to the veterans community,” Andrew F. Olshan, PhD, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, wrote in an accompanying editorial.

SOURCE:

The study was led by Jonathan C. Hwang, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, and was published online on February 4 in JAMA Dermatology.

LIMITATIONS:

The case-control design limits causal inference and the findings might not be generalized outside US veterans. Exposure misclassification could be present.

DISCLOSURES:

The study was supported by the Department of Defense and the Department of Veterans Affairs. Several authors reported receiving grants from CU Anschutz Medical Center, Department of Defense, CDMRP Melanoma Research Program, and Merck, Bayer, and Department of Veteran Affairs. They also reported receiving royalty from UpToDate, and being shareholder in many companies, including Apple. NVIDIA, Amazon, Gilead, AstraZeneca, BioNTech, and Moderna. Olshan declared being a member of the National Academies of Sciences, Engineering, and Medicine Veterans and Agent Orange review committee.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

A version of this article first appeared on Medscape.com.

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US Vet Study Identifies Risk Factors for Acral Melanoma

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US Vet Study Identifies Risk Factors for Acral Melanoma

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Study Finds Racial Gaps in Military Pediatric Asthma Care

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Study Finds Racial Gaps in Military Pediatric Asthma Care

TOPLINE:

Among pediatric beneficiaries in the Military Health System (MHS), racial and ethnic disparities in asthma care persisted, with Black children having the highest odds of an asthma diagnosis and emergency department (ED) visit among all racial and ethnic groups.

METHODOLOGY:

  • This cross-sectional study examined racial and ethnic differences in asthma prevalence and related outcomes among pediatric beneficiaries in the MHS.
  • They included 950,896 dependents aged 2-17 years (50.9% boys) who had ≥ 1 inpatient or outpatient encounter during fiscal year 2023.
  • Race and ethnicity were self-reported by the beneficiary and derived from the sponsor’s demographic records.
  • An asthma diagnosis required at least one inpatient claim or two outpatient claims with an asthma diagnostic code recorded in the primary or secondary diagnosis field.
  • Asthma-related outcomes assessed were potentially avoidable hospitalizations, ED visits, specialist visits, and asthma-related prescriptions.

TAKEAWAY:

  • Overall, 3.3% of children had an asthma diagnosis; the prevalence was higher among children aged 5-10 or 11-17 years, boys, and those with 1 or 2 siblings.
  • The odds of an asthma diagnosis were significantly higher in all racial and ethnic groups than in White children, and were highest in Black children, who had 85% higher odds across all ages (P < .001).
  • Similarly, Black children were 39% more likely than White children to have an asthma-related ED visit; Hispanic children were 36% more likely and Native Hawaiian or Pacific Islander children were 25% more likely (P < .05 for all comparisons).
  • Black children also had slightly higher odds of an asthma-related specialist visit than White children, and both Black and Hispanic children were more likely to receive any asthma prescription.

IN PRACTICE:

These results highlighted how access to low-cost or no-cost care, consistent insurance coverage, and effective prescription practices within the MHS may have helped to improve asthma outcomes. Still, the persistence of racial and ethnic disparities pointed to the need for further action. Efforts to close these gaps should include expanding access to culturally responsive care, increasing availability of specialists, and continuing to assess and improve how care is delivered across the system,” the authors wrote.

SOURCE:

This study was led by Felicia Yeboah Denteh, DrPH, MHA, Center for Health Services Research, Uniformed Services University of the Health Sciences, Bethesda, Maryland. It was published online on January 26, 2026, in JAMA Network Open.

LIMITATIONS:

This study used the sponsor’s race and ethnicity as proxies for children’s race and ethnicity, which could have misclassified multiracial children, adopted children, and wards. It also relied on coding in secondary data and did not include factors such as BMI, pollution, and family history.

DISCLOSURES:

This study was funded by the Department of War, Defense Health Agency. The authors did not report any conflicts of interest.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

A version of this article first appeared on Medscape.com.

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TOPLINE:

Among pediatric beneficiaries in the Military Health System (MHS), racial and ethnic disparities in asthma care persisted, with Black children having the highest odds of an asthma diagnosis and emergency department (ED) visit among all racial and ethnic groups.

METHODOLOGY:

  • This cross-sectional study examined racial and ethnic differences in asthma prevalence and related outcomes among pediatric beneficiaries in the MHS.
  • They included 950,896 dependents aged 2-17 years (50.9% boys) who had ≥ 1 inpatient or outpatient encounter during fiscal year 2023.
  • Race and ethnicity were self-reported by the beneficiary and derived from the sponsor’s demographic records.
  • An asthma diagnosis required at least one inpatient claim or two outpatient claims with an asthma diagnostic code recorded in the primary or secondary diagnosis field.
  • Asthma-related outcomes assessed were potentially avoidable hospitalizations, ED visits, specialist visits, and asthma-related prescriptions.

TAKEAWAY:

  • Overall, 3.3% of children had an asthma diagnosis; the prevalence was higher among children aged 5-10 or 11-17 years, boys, and those with 1 or 2 siblings.
  • The odds of an asthma diagnosis were significantly higher in all racial and ethnic groups than in White children, and were highest in Black children, who had 85% higher odds across all ages (P < .001).
  • Similarly, Black children were 39% more likely than White children to have an asthma-related ED visit; Hispanic children were 36% more likely and Native Hawaiian or Pacific Islander children were 25% more likely (P < .05 for all comparisons).
  • Black children also had slightly higher odds of an asthma-related specialist visit than White children, and both Black and Hispanic children were more likely to receive any asthma prescription.

IN PRACTICE:

These results highlighted how access to low-cost or no-cost care, consistent insurance coverage, and effective prescription practices within the MHS may have helped to improve asthma outcomes. Still, the persistence of racial and ethnic disparities pointed to the need for further action. Efforts to close these gaps should include expanding access to culturally responsive care, increasing availability of specialists, and continuing to assess and improve how care is delivered across the system,” the authors wrote.

SOURCE:

This study was led by Felicia Yeboah Denteh, DrPH, MHA, Center for Health Services Research, Uniformed Services University of the Health Sciences, Bethesda, Maryland. It was published online on January 26, 2026, in JAMA Network Open.

LIMITATIONS:

This study used the sponsor’s race and ethnicity as proxies for children’s race and ethnicity, which could have misclassified multiracial children, adopted children, and wards. It also relied on coding in secondary data and did not include factors such as BMI, pollution, and family history.

DISCLOSURES:

This study was funded by the Department of War, Defense Health Agency. The authors did not report any conflicts of interest.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

A version of this article first appeared on Medscape.com.

TOPLINE:

Among pediatric beneficiaries in the Military Health System (MHS), racial and ethnic disparities in asthma care persisted, with Black children having the highest odds of an asthma diagnosis and emergency department (ED) visit among all racial and ethnic groups.

METHODOLOGY:

  • This cross-sectional study examined racial and ethnic differences in asthma prevalence and related outcomes among pediatric beneficiaries in the MHS.
  • They included 950,896 dependents aged 2-17 years (50.9% boys) who had ≥ 1 inpatient or outpatient encounter during fiscal year 2023.
  • Race and ethnicity were self-reported by the beneficiary and derived from the sponsor’s demographic records.
  • An asthma diagnosis required at least one inpatient claim or two outpatient claims with an asthma diagnostic code recorded in the primary or secondary diagnosis field.
  • Asthma-related outcomes assessed were potentially avoidable hospitalizations, ED visits, specialist visits, and asthma-related prescriptions.

TAKEAWAY:

  • Overall, 3.3% of children had an asthma diagnosis; the prevalence was higher among children aged 5-10 or 11-17 years, boys, and those with 1 or 2 siblings.
  • The odds of an asthma diagnosis were significantly higher in all racial and ethnic groups than in White children, and were highest in Black children, who had 85% higher odds across all ages (P < .001).
  • Similarly, Black children were 39% more likely than White children to have an asthma-related ED visit; Hispanic children were 36% more likely and Native Hawaiian or Pacific Islander children were 25% more likely (P < .05 for all comparisons).
  • Black children also had slightly higher odds of an asthma-related specialist visit than White children, and both Black and Hispanic children were more likely to receive any asthma prescription.

IN PRACTICE:

These results highlighted how access to low-cost or no-cost care, consistent insurance coverage, and effective prescription practices within the MHS may have helped to improve asthma outcomes. Still, the persistence of racial and ethnic disparities pointed to the need for further action. Efforts to close these gaps should include expanding access to culturally responsive care, increasing availability of specialists, and continuing to assess and improve how care is delivered across the system,” the authors wrote.

SOURCE:

This study was led by Felicia Yeboah Denteh, DrPH, MHA, Center for Health Services Research, Uniformed Services University of the Health Sciences, Bethesda, Maryland. It was published online on January 26, 2026, in JAMA Network Open.

LIMITATIONS:

This study used the sponsor’s race and ethnicity as proxies for children’s race and ethnicity, which could have misclassified multiracial children, adopted children, and wards. It also relied on coding in secondary data and did not include factors such as BMI, pollution, and family history.

DISCLOSURES:

This study was funded by the Department of War, Defense Health Agency. The authors did not report any conflicts of interest.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

A version of this article first appeared on Medscape.com.

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Study Finds Racial Gaps in Military Pediatric Asthma Care

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TB, Chronic Bronchitis Tied to Lung Cancer in Never Smokers

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TB, Chronic Bronchitis Tied to Lung Cancer in Never Smokers

TOPLINE:

A history of tuberculosis (TB) and a history of chronic bronchitis were associated with an increased risk for lung cancer in individuals who had never smoked, whereas asthma had a positive, nonsignificant association overall and a significant association in women.

METHODOLOGY:

  • Researchers conducted a systematic review and meta-analysis of clinical databases from inception to July 2025, to assess the association between asthma, TB, and/or chronic bronchitis and the risk for lung cancer among participants aged 18 years or older who had never smoked.
  • They included data from 20 case-control studies involving 54,135 participants and five cohort studies involving 377,983 participants.
  • The primary outcome was the risk for lung cancer among participants with a history of TB, asthma, or chronic bronchitis.
  • Participants were labeled as “never smokers” if they were explicitly described in the manuscripts as having “never smoked” or reported smoking < 100 cigarettes in their lifetime.

TAKEAWAY:

  • In case-control studies, TB (16 studies) and chronic bronchitis (9 studies) were significantly associated with an increased risk for lung cancer (odds ratio [OR], 1.76; P < .001 and OR, 1.36; P = .012, respectively).
  • In four case-cohort studies, TB was associated with an increased but nonsignificant risk for lung cancer (hazard ratio, 1.64).
  • Eleven case-control studies demonstrated a positive but nonsignificant association between asthma and the risk for lung cancer (OR, 1.34). However, a significant association emerged when analyses were limited to women (five studies; OR, 1.61; P < .01).

IN PRACTICE:

History of TB was especially associated with increased LC [lung cancer] risk, meriting particular attention for prospective CT screening studies,” the authors of the study wrote.

SOURCE:

This study was led by Nishwant Swami, MD, Hospital of the University of Pennsylvania, Philadelphia. It was published online on January 11, 2026, in Chest.

LIMITATIONS:

Most studies lacked uniform adjustment for key confounders, increasing the risk for residual confounding. The inclusion of few cohort studies in the analysis may have limited the assessment of temporality and precision. Additionally, differences in covariate adjustment, variable definitions, and language restrictions may have limited comparability and generalizability.

DISCLOSURES:

No specific funding was reported for this study. One author reported serving as a consultant or advisor for various companies, including AstraZeneca, Merck, and Pfizer. Another author reported receiving funding, in part, through the Prostate Cancer Foundation Young Investigator Award and through the Cancer Center Support Grant from the National Cancer Institute.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

A version of this article first appeared on Medscape.com.

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TOPLINE:

A history of tuberculosis (TB) and a history of chronic bronchitis were associated with an increased risk for lung cancer in individuals who had never smoked, whereas asthma had a positive, nonsignificant association overall and a significant association in women.

METHODOLOGY:

  • Researchers conducted a systematic review and meta-analysis of clinical databases from inception to July 2025, to assess the association between asthma, TB, and/or chronic bronchitis and the risk for lung cancer among participants aged 18 years or older who had never smoked.
  • They included data from 20 case-control studies involving 54,135 participants and five cohort studies involving 377,983 participants.
  • The primary outcome was the risk for lung cancer among participants with a history of TB, asthma, or chronic bronchitis.
  • Participants were labeled as “never smokers” if they were explicitly described in the manuscripts as having “never smoked” or reported smoking < 100 cigarettes in their lifetime.

TAKEAWAY:

  • In case-control studies, TB (16 studies) and chronic bronchitis (9 studies) were significantly associated with an increased risk for lung cancer (odds ratio [OR], 1.76; P < .001 and OR, 1.36; P = .012, respectively).
  • In four case-cohort studies, TB was associated with an increased but nonsignificant risk for lung cancer (hazard ratio, 1.64).
  • Eleven case-control studies demonstrated a positive but nonsignificant association between asthma and the risk for lung cancer (OR, 1.34). However, a significant association emerged when analyses were limited to women (five studies; OR, 1.61; P < .01).

IN PRACTICE:

History of TB was especially associated with increased LC [lung cancer] risk, meriting particular attention for prospective CT screening studies,” the authors of the study wrote.

SOURCE:

This study was led by Nishwant Swami, MD, Hospital of the University of Pennsylvania, Philadelphia. It was published online on January 11, 2026, in Chest.

LIMITATIONS:

Most studies lacked uniform adjustment for key confounders, increasing the risk for residual confounding. The inclusion of few cohort studies in the analysis may have limited the assessment of temporality and precision. Additionally, differences in covariate adjustment, variable definitions, and language restrictions may have limited comparability and generalizability.

DISCLOSURES:

No specific funding was reported for this study. One author reported serving as a consultant or advisor for various companies, including AstraZeneca, Merck, and Pfizer. Another author reported receiving funding, in part, through the Prostate Cancer Foundation Young Investigator Award and through the Cancer Center Support Grant from the National Cancer Institute.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

A version of this article first appeared on Medscape.com.

TOPLINE:

A history of tuberculosis (TB) and a history of chronic bronchitis were associated with an increased risk for lung cancer in individuals who had never smoked, whereas asthma had a positive, nonsignificant association overall and a significant association in women.

METHODOLOGY:

  • Researchers conducted a systematic review and meta-analysis of clinical databases from inception to July 2025, to assess the association between asthma, TB, and/or chronic bronchitis and the risk for lung cancer among participants aged 18 years or older who had never smoked.
  • They included data from 20 case-control studies involving 54,135 participants and five cohort studies involving 377,983 participants.
  • The primary outcome was the risk for lung cancer among participants with a history of TB, asthma, or chronic bronchitis.
  • Participants were labeled as “never smokers” if they were explicitly described in the manuscripts as having “never smoked” or reported smoking < 100 cigarettes in their lifetime.

TAKEAWAY:

  • In case-control studies, TB (16 studies) and chronic bronchitis (9 studies) were significantly associated with an increased risk for lung cancer (odds ratio [OR], 1.76; P < .001 and OR, 1.36; P = .012, respectively).
  • In four case-cohort studies, TB was associated with an increased but nonsignificant risk for lung cancer (hazard ratio, 1.64).
  • Eleven case-control studies demonstrated a positive but nonsignificant association between asthma and the risk for lung cancer (OR, 1.34). However, a significant association emerged when analyses were limited to women (five studies; OR, 1.61; P < .01).

IN PRACTICE:

History of TB was especially associated with increased LC [lung cancer] risk, meriting particular attention for prospective CT screening studies,” the authors of the study wrote.

SOURCE:

This study was led by Nishwant Swami, MD, Hospital of the University of Pennsylvania, Philadelphia. It was published online on January 11, 2026, in Chest.

LIMITATIONS:

Most studies lacked uniform adjustment for key confounders, increasing the risk for residual confounding. The inclusion of few cohort studies in the analysis may have limited the assessment of temporality and precision. Additionally, differences in covariate adjustment, variable definitions, and language restrictions may have limited comparability and generalizability.

DISCLOSURES:

No specific funding was reported for this study. One author reported serving as a consultant or advisor for various companies, including AstraZeneca, Merck, and Pfizer. Another author reported receiving funding, in part, through the Prostate Cancer Foundation Young Investigator Award and through the Cancer Center Support Grant from the National Cancer Institute.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

A version of this article first appeared on Medscape.com.

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TB, Chronic Bronchitis Tied to Lung Cancer in Never Smokers

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Fecal Microbiota Transplant Safety Goal Met in Kidney Cancer

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Fecal Microbiota Transplant Safety Goal Met in Kidney Cancer

TOPLINE:

Healthy donor fecal microbiota transplantation (FMT) combined with immune checkpoint inhibitors (ICI) in metastatic renal cell carcinoma demonstrated safety with a 50% objective response rate and no grade 4-5 toxicities. Successful engraftment of diverse, anti-inflammatory microbiota correlated with improved clinical response and reduced immune-related adverse events.

METHODOLOGY:

  • Many patients with metastatic renal cell carcinoma who take ICI experience immune-related adverse events that may require treatment interruption. Recent studies have provided proof of concept for microbiome modulation as a therapeutic adjunct in metastatic renal cell carcinoma, with FMT showing efficacy in resolving TKI-induced toxicities. However, the safety and clinical activity of healthy donor FMT in metastatic renal cell carcinoma remained unexplored, and its mechanism of action was unclear prior to this study.
  • The new phase 1 trial enrolled 20 treatment-naive patients with metastatic renal cell carcinoma classified as intermediate-risk or poor-risk disease, who received encapsulated healthy donor FMT (LND101) combined with ipilimumab plus nivolumab (n = 16), pembrolizumab plus axitinib (n = 3), or pembrolizumab plus lenvatinib (n = 1).
  • Participants underwent polyethylene glycol bowel preparation before receiving one full dose (36-40 capsules containing 80-100 g of stool) and two half-doses (20-25 capsules each containing 50-60 g of stool) of FMT from rigorously screened healthy donors.
  • The primary endpoint was safety assessed through incidence and severity of immune-related adverse events, while secondary endpoints included objective response rate by response evaluation criteria in solid tumors version 1.1, gut microbiome changes, immune correlates, and quality of life.
  • Analysis included longitudinal monitoring of stool and blood samples at five timepoints: baseline, week 1 post-FMT, week 4, week 7, and week 10, with a median follow-up of 21.9 months.

TAKEAWAY:

  • The safety endpoint was met, with half (10 of 20) of patients experiencing grade 3 immune-related adverse events and no serious FMT-related toxicities or grade 4-5 immune-related adverse events. One patient (5%) reported experiencing an FMT-related grade 1 gastrointestinal event.
  • Among evaluable patients (n = 18), the objective response rate was 50% (9 of 18), including two complete responses (11%; 2 of 18), while 67% (12 of 18) achieved clinical benefit defined as complete response, partial response, or stable disease for at least 6 months.
  • Higher alpha diversity and greater functional engraftment of short-chain fatty acid-producing and anti-inflammatory taxa correlated with protection from severe immune-related adverse events (P = .041) and improved therapeutic response (P = .006).
  • Expansion of Segatella copri above 10 counts per million at 10 weeks post-FMT predicted severe toxicity in patients receiving ipilimumab plus nivolumab, regardless of donor or recipient microbiota origin.

IN PRACTICE:

These findings demonstrate the safety and potential for functional microbiome engraftment to optimize response and minimize toxicity in ICI-treated [metastatic renal cell carcinoma]. Together, our results underscore the importance of functional donor screening and targeted modulation of the microbiome in optimizing the safety and efficacy of next-generation immune-based therapies,” wrote the authors of the study.

SOURCE:

The study was led by Ricardo Fernandes, Behnam Jabbarizadeh, and Adnan Rajeh, London Health Sciences Centre, London, Ontario, Canada. It was published online on January 28 in Nature Medicine.

LIMITATIONS:

According to the authors, the study’s primary limitation was its small sample size, which was not powered to define the ideal donor microbiome composition for enhancing immunotherapy efficacy without additional toxicities. The single-center design and highly selected patient population may limit external generalizability, requiring validation in larger, multicenter trials to refine donor selection criteria and clarify microbiome-immunity mechanisms.

DISCLOSURES:

The clinical trial was primarily funded through philanthropic donations to co-authors Saman Maleki Vareki and Fernandes through the London Health Sciences Foundation clinical trials program. Vareki and Michael Silverman, another co-author, reported having US Patent application no. 63/913,940 related to FMT donor screening. Vareki reported receiving grants from the Lotte and John Hecht Memorial Foundation, the Weston Family Foundation, and the Canadian Institutes of Health Research. Additional disclosures are noted in the original article.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

A version of this article first appeared on Medscape.com.

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TOPLINE:

Healthy donor fecal microbiota transplantation (FMT) combined with immune checkpoint inhibitors (ICI) in metastatic renal cell carcinoma demonstrated safety with a 50% objective response rate and no grade 4-5 toxicities. Successful engraftment of diverse, anti-inflammatory microbiota correlated with improved clinical response and reduced immune-related adverse events.

METHODOLOGY:

  • Many patients with metastatic renal cell carcinoma who take ICI experience immune-related adverse events that may require treatment interruption. Recent studies have provided proof of concept for microbiome modulation as a therapeutic adjunct in metastatic renal cell carcinoma, with FMT showing efficacy in resolving TKI-induced toxicities. However, the safety and clinical activity of healthy donor FMT in metastatic renal cell carcinoma remained unexplored, and its mechanism of action was unclear prior to this study.
  • The new phase 1 trial enrolled 20 treatment-naive patients with metastatic renal cell carcinoma classified as intermediate-risk or poor-risk disease, who received encapsulated healthy donor FMT (LND101) combined with ipilimumab plus nivolumab (n = 16), pembrolizumab plus axitinib (n = 3), or pembrolizumab plus lenvatinib (n = 1).
  • Participants underwent polyethylene glycol bowel preparation before receiving one full dose (36-40 capsules containing 80-100 g of stool) and two half-doses (20-25 capsules each containing 50-60 g of stool) of FMT from rigorously screened healthy donors.
  • The primary endpoint was safety assessed through incidence and severity of immune-related adverse events, while secondary endpoints included objective response rate by response evaluation criteria in solid tumors version 1.1, gut microbiome changes, immune correlates, and quality of life.
  • Analysis included longitudinal monitoring of stool and blood samples at five timepoints: baseline, week 1 post-FMT, week 4, week 7, and week 10, with a median follow-up of 21.9 months.

TAKEAWAY:

  • The safety endpoint was met, with half (10 of 20) of patients experiencing grade 3 immune-related adverse events and no serious FMT-related toxicities or grade 4-5 immune-related adverse events. One patient (5%) reported experiencing an FMT-related grade 1 gastrointestinal event.
  • Among evaluable patients (n = 18), the objective response rate was 50% (9 of 18), including two complete responses (11%; 2 of 18), while 67% (12 of 18) achieved clinical benefit defined as complete response, partial response, or stable disease for at least 6 months.
  • Higher alpha diversity and greater functional engraftment of short-chain fatty acid-producing and anti-inflammatory taxa correlated with protection from severe immune-related adverse events (P = .041) and improved therapeutic response (P = .006).
  • Expansion of Segatella copri above 10 counts per million at 10 weeks post-FMT predicted severe toxicity in patients receiving ipilimumab plus nivolumab, regardless of donor or recipient microbiota origin.

IN PRACTICE:

These findings demonstrate the safety and potential for functional microbiome engraftment to optimize response and minimize toxicity in ICI-treated [metastatic renal cell carcinoma]. Together, our results underscore the importance of functional donor screening and targeted modulation of the microbiome in optimizing the safety and efficacy of next-generation immune-based therapies,” wrote the authors of the study.

SOURCE:

The study was led by Ricardo Fernandes, Behnam Jabbarizadeh, and Adnan Rajeh, London Health Sciences Centre, London, Ontario, Canada. It was published online on January 28 in Nature Medicine.

LIMITATIONS:

According to the authors, the study’s primary limitation was its small sample size, which was not powered to define the ideal donor microbiome composition for enhancing immunotherapy efficacy without additional toxicities. The single-center design and highly selected patient population may limit external generalizability, requiring validation in larger, multicenter trials to refine donor selection criteria and clarify microbiome-immunity mechanisms.

DISCLOSURES:

The clinical trial was primarily funded through philanthropic donations to co-authors Saman Maleki Vareki and Fernandes through the London Health Sciences Foundation clinical trials program. Vareki and Michael Silverman, another co-author, reported having US Patent application no. 63/913,940 related to FMT donor screening. Vareki reported receiving grants from the Lotte and John Hecht Memorial Foundation, the Weston Family Foundation, and the Canadian Institutes of Health Research. Additional disclosures are noted in the original article.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

A version of this article first appeared on Medscape.com.

TOPLINE:

Healthy donor fecal microbiota transplantation (FMT) combined with immune checkpoint inhibitors (ICI) in metastatic renal cell carcinoma demonstrated safety with a 50% objective response rate and no grade 4-5 toxicities. Successful engraftment of diverse, anti-inflammatory microbiota correlated with improved clinical response and reduced immune-related adverse events.

METHODOLOGY:

  • Many patients with metastatic renal cell carcinoma who take ICI experience immune-related adverse events that may require treatment interruption. Recent studies have provided proof of concept for microbiome modulation as a therapeutic adjunct in metastatic renal cell carcinoma, with FMT showing efficacy in resolving TKI-induced toxicities. However, the safety and clinical activity of healthy donor FMT in metastatic renal cell carcinoma remained unexplored, and its mechanism of action was unclear prior to this study.
  • The new phase 1 trial enrolled 20 treatment-naive patients with metastatic renal cell carcinoma classified as intermediate-risk or poor-risk disease, who received encapsulated healthy donor FMT (LND101) combined with ipilimumab plus nivolumab (n = 16), pembrolizumab plus axitinib (n = 3), or pembrolizumab plus lenvatinib (n = 1).
  • Participants underwent polyethylene glycol bowel preparation before receiving one full dose (36-40 capsules containing 80-100 g of stool) and two half-doses (20-25 capsules each containing 50-60 g of stool) of FMT from rigorously screened healthy donors.
  • The primary endpoint was safety assessed through incidence and severity of immune-related adverse events, while secondary endpoints included objective response rate by response evaluation criteria in solid tumors version 1.1, gut microbiome changes, immune correlates, and quality of life.
  • Analysis included longitudinal monitoring of stool and blood samples at five timepoints: baseline, week 1 post-FMT, week 4, week 7, and week 10, with a median follow-up of 21.9 months.

TAKEAWAY:

  • The safety endpoint was met, with half (10 of 20) of patients experiencing grade 3 immune-related adverse events and no serious FMT-related toxicities or grade 4-5 immune-related adverse events. One patient (5%) reported experiencing an FMT-related grade 1 gastrointestinal event.
  • Among evaluable patients (n = 18), the objective response rate was 50% (9 of 18), including two complete responses (11%; 2 of 18), while 67% (12 of 18) achieved clinical benefit defined as complete response, partial response, or stable disease for at least 6 months.
  • Higher alpha diversity and greater functional engraftment of short-chain fatty acid-producing and anti-inflammatory taxa correlated with protection from severe immune-related adverse events (P = .041) and improved therapeutic response (P = .006).
  • Expansion of Segatella copri above 10 counts per million at 10 weeks post-FMT predicted severe toxicity in patients receiving ipilimumab plus nivolumab, regardless of donor or recipient microbiota origin.

IN PRACTICE:

These findings demonstrate the safety and potential for functional microbiome engraftment to optimize response and minimize toxicity in ICI-treated [metastatic renal cell carcinoma]. Together, our results underscore the importance of functional donor screening and targeted modulation of the microbiome in optimizing the safety and efficacy of next-generation immune-based therapies,” wrote the authors of the study.

SOURCE:

The study was led by Ricardo Fernandes, Behnam Jabbarizadeh, and Adnan Rajeh, London Health Sciences Centre, London, Ontario, Canada. It was published online on January 28 in Nature Medicine.

LIMITATIONS:

According to the authors, the study’s primary limitation was its small sample size, which was not powered to define the ideal donor microbiome composition for enhancing immunotherapy efficacy without additional toxicities. The single-center design and highly selected patient population may limit external generalizability, requiring validation in larger, multicenter trials to refine donor selection criteria and clarify microbiome-immunity mechanisms.

DISCLOSURES:

The clinical trial was primarily funded through philanthropic donations to co-authors Saman Maleki Vareki and Fernandes through the London Health Sciences Foundation clinical trials program. Vareki and Michael Silverman, another co-author, reported having US Patent application no. 63/913,940 related to FMT donor screening. Vareki reported receiving grants from the Lotte and John Hecht Memorial Foundation, the Weston Family Foundation, and the Canadian Institutes of Health Research. Additional disclosures are noted in the original article.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

A version of this article first appeared on Medscape.com.

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Fecal Microbiota Transplant Safety Goal Met in Kidney Cancer

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Median Income and Clinical Outcomes of Hospitalized Persons With COVID-19 at an Urban Veterans Affairs Medical Center

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Median Income and Clinical Outcomes of Hospitalized Persons With COVID-19 at an Urban Veterans Affairs Medical Center

Large epidemiologic studies have shown disparities in COVID-19 outcomes by race, ethnicity, and socioeconomic status (SES). Racial and ethnic minorities and individuals of lower SES have experienced disproportionately higher rates of intensive care unit (ICU) admission and death. In Washington, DC, Black individuals (47% of the population) accounted for 51% of COVID-19 cases and 75% of deaths. In comparison, White individuals (41% of the population) accounted for 21% of cases and 11% of deaths.1 Place of residence, such as living in socially vulnerable communities, has also been shown to be associated with higher rates of COVID-19 mortality and lower vaccination rates.2-4 Social and structural inequities, such as limited access to health care services and mistrust of the health care system, may explain some of the observed disparities.5 However, data are limited regarding COVID-19 outcomes for individuals with equal access to care.

The Veterans Health Administration (VHA) is the largest integrated US health care system and operates 123 acute care hospitals. Previous research has demonstrated that disparities in outcomes for other diseases are attenuated or erased among veterans receiving VHA care.6,7 Based on literature from the pandemic, markers of health care inequity relating to SES (eg, place of residence, median income) are expected to impact the outcomes of patients acutely hospitalized with COVID-19.4 We hypothesized that the impact on clinical outcomes of infection would be mitigated for veterans receiving VHA care.

This retrospective cohort study included veterans who presented to Washington Veterans Affairs Medical Center (WVAMC) with the goal of determining whether place of residence as a marker of SES, health care access, and median income were predictive of COVID-19 disease severity.

Methods

The WVAMC serves about 125,000 veterans across the metropolitan area, including parts of Maryland and Virginia. It is a high-complexity hospital with 164 acute care beds, 30 psychosocial residential rehabilitation beds, and an adjacent 120-bed community living center providing long-term, hospice, and palliative care.8

The WVAMC developed a dashboard that tracked patients with COVID-19 through on-site testing by admission date, ward, and other key demographics (PowerBi, Corporate Data Warehouse). All patients admitted to WVAMC with a diagnosis of COVID-19 between March 1, 2020, and June 30, 2021, were included in this retrospective review. Using the Computerized Patient Record System (CPRS) and the dashboard, we collected demographic information, baseline clinical diagnoses, laboratory results, and clinical interventions for all patients with documented COVID-19 infection as established by laboratory testing methods available at the time of diagnosis. Veterans treated exclusively outside the WVAMC were excluded. Hospitalization was defined as any acute inpatient admission or transfer recorded within 5 days before and 30 days after the laboratory collection of a positive COVID-19 test. Home testing kits were not widely available during the study period. An ICU stay was defined as any inpatient admission or transfer recorded within 5 days before or 30 days after the laboratory collection of a positive COVID-19 test for which the ward location had the specialty of medical or surgical ICU. Death due to COVID-19 was defined as occurring within 42 days (6 weeks) of a positive COVID-19 test.9 This definition assumed that during the peak of the pandemic, COVID-19 was the attributable cause of death, despite the possible contribution of underlying health conditions.

Patients’ admission periods were based on US Centers for Disease Control and Prevention (CDC) national data and classified as early 2020 (January 2020–April 2020), mid-2020 (May 2020–August 2020), late 2020 (September 2020–December 2020), and early 2021 (January 2021–April 2021).10 We chose to use these time periods as surrogates for the frequent changes in circulating COVID-19 variants, surges in case numbers, therapies and interventions available during the pandemic. The dominant COVID-19 variant during the study period was Alpha (B.1.17). Beta (B.1.351) variants were circulating infrequently, and Delta and Omicron appeared after the study period.11 Treatment strategies evolved rapidly with emerging evidence, including the use of dexamethasone, beginning in June 2020.12 WVAMC followed the Advisory Committee on Immunization Practices guidance on vaccination rollout beginning in December 2020.13

Patients' income was estimated by the median household income of the zip code residence based on US Census Bureau 2021 estimates and was assessed as both a continuous and categorical variable.14 The Charlson Comorbidity Index (CCI) was included in models as a continuous variable.15 Variables contributing to the CCI include myocardial infarction, congestive heart failure, peripheral vascular disease, cerebrovascular disease, dementia, hemiplegia or paraplegia, ulcer disease, hepatic disease, diabetes (with or without end-organ damage), chronic obstructive pulmonary disease (COPD), connective tissue disease, leukemia, lymphoma, moderate or severe renal disease, solid tumor (with or without metastases), and HIV/AIDS. The WVAMC Institutional Review Board approved this study (IRB #1573071).

Variables

This study assessed 3 primary outcomes as indicators of disease severity during hospitalization: need for high-flow oxygen (HFO), intubation, and presumed mortality at any time during hospitalization. The following variables were collected as potential social determinants or clinical risk-adjustment predictors of disease severity outcomes: age; sex; race and ethnicity; median income for patient’s zip code residence, state, and county; wards within Washington, DC; comorbidities, CCI; tobacco use; and body mass index.15 Although medications at baseline, treatments during hospitalization for COVID-19, and laboratory parameters during hospitalization are shown in eAppendices 1 and 2, they are beyond the scope of this analysis.

Statistical Analysis

Three types of logistic regression models were calculated for predicting the disease severity outcomes: (1) simple unadjusted models; (2) models predicting from single variables plus age (age-adjusted); and (3) multivariable models using all nonredundant potential predictors with adequate sample sizes (multivariable). Variables were considered to have inadequate sample sizes if there was nontrivial missing data or small numbers within categories, (eg, AIDS, connective tissue disease). Potential predictors for the multivariable model included age, sex, race, median income by zip code residence, CCI, CDC admission period, obesity, hypertension, chronic kidney disease, obstructive sleep apnea (OSA), diabetes, COPD or asthma, liver disease, antibiotics, and acute kidney injury.

For the multivariable models, the following modifications were made to avoid unreliable parameter estimation and computation problems (quasi-separation): age and CCI were included as continuous rather than categorical variables. Race was recoded as a 2-category variable (Black vs other [White, Hispanic, American Indian, Alaska Native, Asian, Native Hawaiian, and Pacific Islander]), and ethnicity was excluded because of the small number of patients in this group (n = 16). Admission period was included. Predicted probability plots were generated for each outcome with continuous independent predictors (income and CCI), both unadjusted and adjusted for age as a continuous covariate. All analyses were performed using SAS version 9.4.

Heat Maps

Heat maps were generated to visualize the geospatial distribution of COVID-19 cases and median incomes across zip codes in the greater Washington, DC area. Patient case data and median income, aggregated by zip code, were imported using ArcGIS Online. A zip code boundary layer from Esri (United States Zip Code Boundaries) was used to spatially align the case data. Data were joined by matching zip codes or median incomes in the patient dataset to those in the boundary layer. The resulting polygon layer was styled using the Counts and Amounts (Color) symbology in ArcGIS Online, with case counts or median income determining the intensity of the color gradient.

Results

Between March 1, 2020, and June 30, 2021, 348 patients were hospitalized with COVID-19 (Table 1). The mean (SD) age was 68.4 (13.9) years, 313 patients (90.2%) were male, 281 patients (83.4%) were Black, 47 patients (13.6%) were White, and 16 patients (4.8%) were Hispanic. One hundred forty patients (40.2%) resided in Washington, DC, 151 (43.4%) in Maryland, and 19 (5.5%) in Virginia. HFO was received by 86 patients (24.7%), 33 (9.5%) required intubation and mechanical ventilation, and 57 (16.4%) died. All intubations and deaths occurred among patients aged > 50 years, with death occurring in 17.8% of patients aged > 50 years.

FDP04302076_T1

Demographic characteristics and baseline comorbidities associated with COVID-19 disease severity can be found in eAppendix 2. In unadjusted analyses, age was significantly associated with the risk of HFO, with a mean (SD) age of 72.5 (11.7) years among those requiring HFO and 67.1 (14.4) years among patients without HFO (odds ratio [OR], 1.03; 95% CI, 1.01-1.05; P = .002). Although age was not associated with the risk of intubation, it was significantly associated with mortality. Patients who died had a mean (SD) age of 76.8 (11.8) years compared with 66.8 (13.7) years among survivors (OR, 1.06; 95% CI, 1.04-1.09; P < .001).

Compared with patients with no comorbidities, CCI categories of mild, moderate, and severe were associated with increased risk of requiring HFO (eAppendix 3). The adjusted OR (aOR) was highest among patients with severe CCI (aOR, 7.00; 95% CI, 2.42-20.32; P = .0007). In age-adjusted analyses, CCI was not associated with intubation or mortality.

Geospatial Analyses

State of residence, county of residence, and geographic area (including Washington, DC wards, and geographic divisions within counties of residence in Maryland and Virginia) were not associated with the clinical outcomes studied (eAppendix 4). However, zip code-based median income, analyzed as a continuous variable, was associated with a reduced likelihood of receiving HFO (aOR, 0.91; 95% CI, 0.84-0.99; P = .03). Income was not significantly associated with intubation or mortality.

The majority of patients hospitalized for COVID-19 at WVAMC resided in zip codes in eastern Washington, DC, inclusive of wards 7 and 8, and Prince George’s County, Maryland (Figure 1). These areas also corresponded to the lowest median household income by zip code (Figure 2).

FDP04302076_F1
FIGURE 1. Geospatial Heat Map of COVID-19 Cases by Zip
Code
FDP04302076_F2
FIGURE 2. Geospatial Heat Map of Median Income by Zip
Code

 

Multivariable Analysis

Significant predictors of HFO requirement included comorbid diabetes (OR, 2.42; 95% CI, 1.27-4.61; P = .006) and liver disease or cirrhosis (OR, 2.19; 95% CI, 1.09-4.39; P = .02) (Table 2). CDC admission period was also associated with HFO need. Patients admitted after early 2020 had lower odds of receiving HFO. Race and median income based on zip code residence were not associated with HFO requirement.

FDP04302076_T2

Comorbid liver disease or cirrhosis was a significant predictor of intubation (OR, 2.81; 95% CI, 1.07-7.40; P = .03). CDC admission period was associated with intubation with lower odds of intubation for patients admitted after early 2020. Race and median income by zip code were not associated with intubation.

Significant predictors of mortality included age (OR, 2.20; 95% CI, 1.55-3.14; P = .0001), comorbid liver disease or cirrhosis (OR, 2.97; 95% CI, 1.31-6.74; P = .008), and OSA (OR, 3.45; 95% CI, 1.49-7.97; P = .003). CDC admission period was associated with mortality, with lower odds of intubation for patients admitted in mid- and late 2020. Race and median income by zip code residence were not associated with intubation.

Discussion

In this study of COVID-19 disease severity at a large integrated health care system that provides equal access to care, race, ethnicity, and geographic location were not associated with the need for HFO, intubation, or presumed mortality. Median income by zip code residence was associated with reduced HFO use in univariable analyses but not in multivariable models.

These findings support existing literature suggesting that race and ethnicity alone do not explain disparities in COVID-19 outcomes. Multiple studies have demonstrated that disparities in health outcomes have been reduced for patients receiving VHA care.6,16-19 However, even within a health care system with assumed equal access, the finding of an association between income and need for HFO in the univariable analysis may reflect a greater likelihood of delays in care due to structural barriers. Multiple studies suggest low SES may be an independent risk factor for severe COVID-19 disease. Individuals with low SES have higher rates of chronic diseases of obesity, diabetes, heart disease, and lung disease; thus, they are also at greater risk of serious illness with COVID-19.20-24 Socioeconomic disadvantage may also have limited individuals’ ability to engage in protective behaviors to reduce COVID-19 infection risk, including food stockpiling, social distancing, avoidance of public transportation, and refraining from working in “essential jobs.”21

Beyond SES, place of residence also influences health outcomes. Prior literature supports using zip codes to assess area-based SES status and monitor health disparities.25 The Social Vulnerability Index incorporates SES factors for communities and measures social determinates of health at a zip code level exclusive of race and ethnicity.26 Socially vulnerable communities are known to have higher rates of chronic diseases, COVID-19 mortality, and lower vaccination rates.3 Within a defined geographic area, an individual’s outcome for COVID-19 can be influenced by individual resources such as access to care and median income. Disposable income may mitigate COVID-19 risk by facilitating timely care, reducing occupational exposure, improving housing stability, and supporting health-promoting behaviors.21

Limitations

Due to the evolving nature of the COVID-19 pandemic, variants, treatments, and interventions varied throughout the study period and are not included in this analysis. In late December 2020, COVID-19 vaccination was approved with a tiered allocation for at-risk patients and direct health care professionals. Three of the 4 study periods analyzed in this study were prior to vaccine rollout and therefore vaccination history was not assessed. However, we tried to capture the evolving changes in COVID-19 variants, treatments and interventions, and skill in treating the disease through use of CDC-defined time frames. Another limitation is that some studies have shown that use of median income by zip code residence can underestimate mortality.27 Also, shared resources and access to other sources of disposable income can impact the immediate attainment of social needs. For example, during the COVID-19 pandemic, health care systems in Washington, DC assisted vulnerable individuals by providing food, housing, and other resources.28,29 Finally, the modest sample size limits generalizability and power to detect differences for certain variables, including Hispanic ethnicity.

Conclusions

There have been widely described disparities in disease severity and death during the COVID-19 pandemic. In this urban veteran cohort of hospitalized patients, there was no difference in the need for intubation or mortality associated with race. The findings suggest that a lower median income by zip code residence may be associated with greater disease severity at presentation, but do not predict severe outcomes and mortality overall. VHA care, which provides equal access to care, may mitigate the disparities seen in the private sector.

References
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  4. Romano SD, Blackstock AJ, Taylor EV, et al. Trends in racial and ethnic disparities in COVID-19 hospitalizations, by region - United States, March-December 2020. MMWR Morb Mortal Wkly Rep. 2021;70:560-565. doi:10.15585/mmwr.mm7015e2
  5. Kullar R, Marcelin JR, Swartz TH, et al. Racial disparity of coronavirus disease 2019 in African American communities. J Infect Dis. 2020;222:890-893. doi:10.1093/infdis/jiaa372
  6. Riviere P, Luterstein E, Kumar A, et al. Survival of African American and non-Hispanic White men with prostate cancer in an equal-access health care system. Cancer. 2020;126:1683-1690. doi:10.1002/cncr.32666
  7. Ohl ME, Richardson Miell K, Beck BF, et al. Mortality among US veterans admitted to community vs Veterans Health Administration hospitals for COVID-19. JAMA Netw Open. 2023;6:e2315902. doi:10.1001/jamanetworkopen.2023.15902
  8. US Department of Veterans Affairs. VA Washington DC Health Care. Accessed January 16, 2026. https://www.va.gov/washington-dc-health-care/about-us/
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  14. US Census Bureau. Explore census data. Accessed December 10, 2025. https://data.census.gov/profile?q=Income%20by%20Zip%20code%20tabulation%20area
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  16. Zullig LL, Carpenter WR, Provenzale D, Weinberger M, Reeve BB, Jackson GL. Examining potential colorectal cancer care disparities in the Veterans Affairs health care system. J Clin Oncol. 2013;31:3579-3584. doi:10.1200/JCO.2013.50.4753
  17. Grubaugh AL, Slagle DM, Long M, Frueh BC, Magruder KM. Racial disparities in trauma exposure, psychiatric symptoms, and service use among female patients in Veterans Affairs primary care clinics. Womens Health Issues. 2008;18:433-441. doi:10.1016/j.whi.2008.08.001
  18. Bosworth HB, Parsey KS, Butterfield MI, et al. Racial variation in wanting and obtaining mental health services among women veterans in a primary care clinic. J Natl Med Assoc. 2000;92:231-236.
  19. Luo J, Rosales M, Wei G, et al. Hospitalization, mechanical ventilation, and case-fatality outcomes in US veterans with COVID-19 disease between years 2020-2021. Ann Epidemiol. 2022;70:37-44. doi:10.1016/j.annepidem.2022.04.003
  20. Kondo K, Low A, Everson T, et al. Health disparities in veterans: a map of the evidence. Med Care. 2017;55 Suppl 9 Suppl 2:S9-S15. doi:10.1097/MLR.0000000000000756
  21. Grosicki GJ, Bunsawat K, Jeong S, Robinson AT. Racial and ethnic disparities in cardiometabolic disease and COVID-19 outcomes in White, Black/African American, and Latinx populations: Social determinants of health. Prog Cardiovasc Dis. 2022;71:4-10. doi:10.1016/j.pcad.2022.04.004
  22. National Center for Immunization and Respiratory Diseases (U.S.). Division of Viral Diseases. Coronavirus Disease 2019 (COVID-19): COVID-19 in Racial and Ethnic Minority Groups: June 4, 2020. CDC Stacks. June 4, 2020. Accessed January 14, 2026. https://stacks.cdc.gov/view/cdc/88770
  23. Yancy CW. COVID-19 and African Americans. JAMA. 2020;323:1891-1892. doi:10.1001/jama.2020.6548
  24. Magesh S, John D, Li WT, et al. Disparities in COVID-19 outcomes by race, ethnicity, and socioeconomic status: a systematic-review and meta-analysis. JAMA Netw Open. 2021;4:e2134147. doi:10.1001/jamanetworkopen.2021.34147
  25. Berkowitz SA, Traore CY, Singer DE, Atlas SJ. Evaluating area-based socioeconomic status indicators for monitoring disparities within health care systems: results from a primary care network. Health Serv Res. 2015;50:398-417. doi:10.1111/1475-6773.12229
  26. Social Vulnerability Index. Agency for Toxicity and Disease Registry. July 22, 2024. Accessed January 14, 2026. https://www.atsdr.cdc.gov/placeandhealth/svi/index.html
  27. Moss JL, Johnson NJ, Yu M, Altekruse SF, Cronin KA. Comparisons of individual- and area-level socioeconomic status as proxies for individual-level measures: evidence from the Mortality Disparities in American Communities study. Popul Health Metr. 2021;19:1. doi:10.1186/s12963-020-00244-x
  28. DC Department of Human Services. Response to COVID-19. Accessed January 14, 2026. https://dhs.dc.gov/page/responsetocovid19
  29. Wang PG, Brisbon NM, Hubbell H, et al. Is the Gap Closing? Comparison of sociodemographic cisparities in COVID-19 hospitalizations and outcomes between two temporal waves of admissions. J Racial Ethn Health Disparities. 2023;10:593-602. doi:10.1007/s40615-022-01249-y
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Author and Disclosure Information

Matthew G. Tuck, MDa,b; Heather R. Rivasplata, DNPc; Steven A. Towers, MPHd; Angelike P. Liappis, MDa,b; Cherinne Arundel, MDa,b; Anca Dinescu, MDa,b; Zachariah Hamidi, MDe; Haitham Alaithan, MDf; Surabhi Uppal, MDg; Pratish C. Patel, PharmDh; Shikha Khosla, MDa,b; Samuel J. Simmens, PhDd; Gabriel Durham, MSi; Debra A. Benator, MDa,b

Acknowledgments The authors thank Mark Bova, MPH, George Washington University School of Public Health, for his contributions to the design of the statistical analyses performed.

Author affiliations
aWashington Veterans Affairs Medical Center, Washington, DC 
bGeorge Washington University, Washington, DC
cUniformed Services University of the Health Sciences, Bethesda, Maryland 
dGeorge Washington University Milken Institute School of Public Health, Washington, DC 
eBrooke Army Medical Center, San Antonio, Texas 
fBaylor College of Medicine, Houston, Texas 
gMedStar Shah Medical Group, Hollywood, Maryland 
hVanderbilt University Medical Center, Nashville, Tennessee 
iUniversity of Michigan, Ann Arbor

Author disclosures All authors attest to substantially contributing to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; and drafting the work or reviewing it critically for important intellectual content; and giving final approval of the version to be published; and agreeing to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The authors report no actual or potential conflicts of interest with regard to this article.

Disclaimer The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the US Government, or any of its agencies. This article may discuss unlabeled or investigational use of certain drugs. Please review the complete prescribing information for specific drugs or drug combinations—including indications, contraindications, warnings, and adverse effects—before administering pharmacologic therapy to patients.

Ethics and consent The Washington Veterans Affairs Medical Center Institutional Review Board and Research & Development committee reviewed and approved this study (IRB# 1573071). Patients' consent was not obtained for this retrospective study. The authors attest to no competing interests. The datasets during and/or analyzed during the study are available from the corresponding author on reasonable request.

Correspondence: Matthew Tuck ([email protected])

Fed Pract. 2026;43(2). Published online February 16. doi:10.12788/fp.0678

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Matthew G. Tuck, MDa,b; Heather R. Rivasplata, DNPc; Steven A. Towers, MPHd; Angelike P. Liappis, MDa,b; Cherinne Arundel, MDa,b; Anca Dinescu, MDa,b; Zachariah Hamidi, MDe; Haitham Alaithan, MDf; Surabhi Uppal, MDg; Pratish C. Patel, PharmDh; Shikha Khosla, MDa,b; Samuel J. Simmens, PhDd; Gabriel Durham, MSi; Debra A. Benator, MDa,b

Acknowledgments The authors thank Mark Bova, MPH, George Washington University School of Public Health, for his contributions to the design of the statistical analyses performed.

Author affiliations
aWashington Veterans Affairs Medical Center, Washington, DC 
bGeorge Washington University, Washington, DC
cUniformed Services University of the Health Sciences, Bethesda, Maryland 
dGeorge Washington University Milken Institute School of Public Health, Washington, DC 
eBrooke Army Medical Center, San Antonio, Texas 
fBaylor College of Medicine, Houston, Texas 
gMedStar Shah Medical Group, Hollywood, Maryland 
hVanderbilt University Medical Center, Nashville, Tennessee 
iUniversity of Michigan, Ann Arbor

Author disclosures All authors attest to substantially contributing to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; and drafting the work or reviewing it critically for important intellectual content; and giving final approval of the version to be published; and agreeing to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The authors report no actual or potential conflicts of interest with regard to this article.

Disclaimer The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the US Government, or any of its agencies. This article may discuss unlabeled or investigational use of certain drugs. Please review the complete prescribing information for specific drugs or drug combinations—including indications, contraindications, warnings, and adverse effects—before administering pharmacologic therapy to patients.

Ethics and consent The Washington Veterans Affairs Medical Center Institutional Review Board and Research & Development committee reviewed and approved this study (IRB# 1573071). Patients' consent was not obtained for this retrospective study. The authors attest to no competing interests. The datasets during and/or analyzed during the study are available from the corresponding author on reasonable request.

Correspondence: Matthew Tuck ([email protected])

Fed Pract. 2026;43(2). Published online February 16. doi:10.12788/fp.0678

Author and Disclosure Information

Matthew G. Tuck, MDa,b; Heather R. Rivasplata, DNPc; Steven A. Towers, MPHd; Angelike P. Liappis, MDa,b; Cherinne Arundel, MDa,b; Anca Dinescu, MDa,b; Zachariah Hamidi, MDe; Haitham Alaithan, MDf; Surabhi Uppal, MDg; Pratish C. Patel, PharmDh; Shikha Khosla, MDa,b; Samuel J. Simmens, PhDd; Gabriel Durham, MSi; Debra A. Benator, MDa,b

Acknowledgments The authors thank Mark Bova, MPH, George Washington University School of Public Health, for his contributions to the design of the statistical analyses performed.

Author affiliations
aWashington Veterans Affairs Medical Center, Washington, DC 
bGeorge Washington University, Washington, DC
cUniformed Services University of the Health Sciences, Bethesda, Maryland 
dGeorge Washington University Milken Institute School of Public Health, Washington, DC 
eBrooke Army Medical Center, San Antonio, Texas 
fBaylor College of Medicine, Houston, Texas 
gMedStar Shah Medical Group, Hollywood, Maryland 
hVanderbilt University Medical Center, Nashville, Tennessee 
iUniversity of Michigan, Ann Arbor

Author disclosures All authors attest to substantially contributing to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; and drafting the work or reviewing it critically for important intellectual content; and giving final approval of the version to be published; and agreeing to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The authors report no actual or potential conflicts of interest with regard to this article.

Disclaimer The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the US Government, or any of its agencies. This article may discuss unlabeled or investigational use of certain drugs. Please review the complete prescribing information for specific drugs or drug combinations—including indications, contraindications, warnings, and adverse effects—before administering pharmacologic therapy to patients.

Ethics and consent The Washington Veterans Affairs Medical Center Institutional Review Board and Research & Development committee reviewed and approved this study (IRB# 1573071). Patients' consent was not obtained for this retrospective study. The authors attest to no competing interests. The datasets during and/or analyzed during the study are available from the corresponding author on reasonable request.

Correspondence: Matthew Tuck ([email protected])

Fed Pract. 2026;43(2). Published online February 16. doi:10.12788/fp.0678

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Large epidemiologic studies have shown disparities in COVID-19 outcomes by race, ethnicity, and socioeconomic status (SES). Racial and ethnic minorities and individuals of lower SES have experienced disproportionately higher rates of intensive care unit (ICU) admission and death. In Washington, DC, Black individuals (47% of the population) accounted for 51% of COVID-19 cases and 75% of deaths. In comparison, White individuals (41% of the population) accounted for 21% of cases and 11% of deaths.1 Place of residence, such as living in socially vulnerable communities, has also been shown to be associated with higher rates of COVID-19 mortality and lower vaccination rates.2-4 Social and structural inequities, such as limited access to health care services and mistrust of the health care system, may explain some of the observed disparities.5 However, data are limited regarding COVID-19 outcomes for individuals with equal access to care.

The Veterans Health Administration (VHA) is the largest integrated US health care system and operates 123 acute care hospitals. Previous research has demonstrated that disparities in outcomes for other diseases are attenuated or erased among veterans receiving VHA care.6,7 Based on literature from the pandemic, markers of health care inequity relating to SES (eg, place of residence, median income) are expected to impact the outcomes of patients acutely hospitalized with COVID-19.4 We hypothesized that the impact on clinical outcomes of infection would be mitigated for veterans receiving VHA care.

This retrospective cohort study included veterans who presented to Washington Veterans Affairs Medical Center (WVAMC) with the goal of determining whether place of residence as a marker of SES, health care access, and median income were predictive of COVID-19 disease severity.

Methods

The WVAMC serves about 125,000 veterans across the metropolitan area, including parts of Maryland and Virginia. It is a high-complexity hospital with 164 acute care beds, 30 psychosocial residential rehabilitation beds, and an adjacent 120-bed community living center providing long-term, hospice, and palliative care.8

The WVAMC developed a dashboard that tracked patients with COVID-19 through on-site testing by admission date, ward, and other key demographics (PowerBi, Corporate Data Warehouse). All patients admitted to WVAMC with a diagnosis of COVID-19 between March 1, 2020, and June 30, 2021, were included in this retrospective review. Using the Computerized Patient Record System (CPRS) and the dashboard, we collected demographic information, baseline clinical diagnoses, laboratory results, and clinical interventions for all patients with documented COVID-19 infection as established by laboratory testing methods available at the time of diagnosis. Veterans treated exclusively outside the WVAMC were excluded. Hospitalization was defined as any acute inpatient admission or transfer recorded within 5 days before and 30 days after the laboratory collection of a positive COVID-19 test. Home testing kits were not widely available during the study period. An ICU stay was defined as any inpatient admission or transfer recorded within 5 days before or 30 days after the laboratory collection of a positive COVID-19 test for which the ward location had the specialty of medical or surgical ICU. Death due to COVID-19 was defined as occurring within 42 days (6 weeks) of a positive COVID-19 test.9 This definition assumed that during the peak of the pandemic, COVID-19 was the attributable cause of death, despite the possible contribution of underlying health conditions.

Patients’ admission periods were based on US Centers for Disease Control and Prevention (CDC) national data and classified as early 2020 (January 2020–April 2020), mid-2020 (May 2020–August 2020), late 2020 (September 2020–December 2020), and early 2021 (January 2021–April 2021).10 We chose to use these time periods as surrogates for the frequent changes in circulating COVID-19 variants, surges in case numbers, therapies and interventions available during the pandemic. The dominant COVID-19 variant during the study period was Alpha (B.1.17). Beta (B.1.351) variants were circulating infrequently, and Delta and Omicron appeared after the study period.11 Treatment strategies evolved rapidly with emerging evidence, including the use of dexamethasone, beginning in June 2020.12 WVAMC followed the Advisory Committee on Immunization Practices guidance on vaccination rollout beginning in December 2020.13

Patients' income was estimated by the median household income of the zip code residence based on US Census Bureau 2021 estimates and was assessed as both a continuous and categorical variable.14 The Charlson Comorbidity Index (CCI) was included in models as a continuous variable.15 Variables contributing to the CCI include myocardial infarction, congestive heart failure, peripheral vascular disease, cerebrovascular disease, dementia, hemiplegia or paraplegia, ulcer disease, hepatic disease, diabetes (with or without end-organ damage), chronic obstructive pulmonary disease (COPD), connective tissue disease, leukemia, lymphoma, moderate or severe renal disease, solid tumor (with or without metastases), and HIV/AIDS. The WVAMC Institutional Review Board approved this study (IRB #1573071).

Variables

This study assessed 3 primary outcomes as indicators of disease severity during hospitalization: need for high-flow oxygen (HFO), intubation, and presumed mortality at any time during hospitalization. The following variables were collected as potential social determinants or clinical risk-adjustment predictors of disease severity outcomes: age; sex; race and ethnicity; median income for patient’s zip code residence, state, and county; wards within Washington, DC; comorbidities, CCI; tobacco use; and body mass index.15 Although medications at baseline, treatments during hospitalization for COVID-19, and laboratory parameters during hospitalization are shown in eAppendices 1 and 2, they are beyond the scope of this analysis.

Statistical Analysis

Three types of logistic regression models were calculated for predicting the disease severity outcomes: (1) simple unadjusted models; (2) models predicting from single variables plus age (age-adjusted); and (3) multivariable models using all nonredundant potential predictors with adequate sample sizes (multivariable). Variables were considered to have inadequate sample sizes if there was nontrivial missing data or small numbers within categories, (eg, AIDS, connective tissue disease). Potential predictors for the multivariable model included age, sex, race, median income by zip code residence, CCI, CDC admission period, obesity, hypertension, chronic kidney disease, obstructive sleep apnea (OSA), diabetes, COPD or asthma, liver disease, antibiotics, and acute kidney injury.

For the multivariable models, the following modifications were made to avoid unreliable parameter estimation and computation problems (quasi-separation): age and CCI were included as continuous rather than categorical variables. Race was recoded as a 2-category variable (Black vs other [White, Hispanic, American Indian, Alaska Native, Asian, Native Hawaiian, and Pacific Islander]), and ethnicity was excluded because of the small number of patients in this group (n = 16). Admission period was included. Predicted probability plots were generated for each outcome with continuous independent predictors (income and CCI), both unadjusted and adjusted for age as a continuous covariate. All analyses were performed using SAS version 9.4.

Heat Maps

Heat maps were generated to visualize the geospatial distribution of COVID-19 cases and median incomes across zip codes in the greater Washington, DC area. Patient case data and median income, aggregated by zip code, were imported using ArcGIS Online. A zip code boundary layer from Esri (United States Zip Code Boundaries) was used to spatially align the case data. Data were joined by matching zip codes or median incomes in the patient dataset to those in the boundary layer. The resulting polygon layer was styled using the Counts and Amounts (Color) symbology in ArcGIS Online, with case counts or median income determining the intensity of the color gradient.

Results

Between March 1, 2020, and June 30, 2021, 348 patients were hospitalized with COVID-19 (Table 1). The mean (SD) age was 68.4 (13.9) years, 313 patients (90.2%) were male, 281 patients (83.4%) were Black, 47 patients (13.6%) were White, and 16 patients (4.8%) were Hispanic. One hundred forty patients (40.2%) resided in Washington, DC, 151 (43.4%) in Maryland, and 19 (5.5%) in Virginia. HFO was received by 86 patients (24.7%), 33 (9.5%) required intubation and mechanical ventilation, and 57 (16.4%) died. All intubations and deaths occurred among patients aged > 50 years, with death occurring in 17.8% of patients aged > 50 years.

FDP04302076_T1

Demographic characteristics and baseline comorbidities associated with COVID-19 disease severity can be found in eAppendix 2. In unadjusted analyses, age was significantly associated with the risk of HFO, with a mean (SD) age of 72.5 (11.7) years among those requiring HFO and 67.1 (14.4) years among patients without HFO (odds ratio [OR], 1.03; 95% CI, 1.01-1.05; P = .002). Although age was not associated with the risk of intubation, it was significantly associated with mortality. Patients who died had a mean (SD) age of 76.8 (11.8) years compared with 66.8 (13.7) years among survivors (OR, 1.06; 95% CI, 1.04-1.09; P < .001).

Compared with patients with no comorbidities, CCI categories of mild, moderate, and severe were associated with increased risk of requiring HFO (eAppendix 3). The adjusted OR (aOR) was highest among patients with severe CCI (aOR, 7.00; 95% CI, 2.42-20.32; P = .0007). In age-adjusted analyses, CCI was not associated with intubation or mortality.

Geospatial Analyses

State of residence, county of residence, and geographic area (including Washington, DC wards, and geographic divisions within counties of residence in Maryland and Virginia) were not associated with the clinical outcomes studied (eAppendix 4). However, zip code-based median income, analyzed as a continuous variable, was associated with a reduced likelihood of receiving HFO (aOR, 0.91; 95% CI, 0.84-0.99; P = .03). Income was not significantly associated with intubation or mortality.

The majority of patients hospitalized for COVID-19 at WVAMC resided in zip codes in eastern Washington, DC, inclusive of wards 7 and 8, and Prince George’s County, Maryland (Figure 1). These areas also corresponded to the lowest median household income by zip code (Figure 2).

FDP04302076_F1
FIGURE 1. Geospatial Heat Map of COVID-19 Cases by Zip
Code
FDP04302076_F2
FIGURE 2. Geospatial Heat Map of Median Income by Zip
Code

 

Multivariable Analysis

Significant predictors of HFO requirement included comorbid diabetes (OR, 2.42; 95% CI, 1.27-4.61; P = .006) and liver disease or cirrhosis (OR, 2.19; 95% CI, 1.09-4.39; P = .02) (Table 2). CDC admission period was also associated with HFO need. Patients admitted after early 2020 had lower odds of receiving HFO. Race and median income based on zip code residence were not associated with HFO requirement.

FDP04302076_T2

Comorbid liver disease or cirrhosis was a significant predictor of intubation (OR, 2.81; 95% CI, 1.07-7.40; P = .03). CDC admission period was associated with intubation with lower odds of intubation for patients admitted after early 2020. Race and median income by zip code were not associated with intubation.

Significant predictors of mortality included age (OR, 2.20; 95% CI, 1.55-3.14; P = .0001), comorbid liver disease or cirrhosis (OR, 2.97; 95% CI, 1.31-6.74; P = .008), and OSA (OR, 3.45; 95% CI, 1.49-7.97; P = .003). CDC admission period was associated with mortality, with lower odds of intubation for patients admitted in mid- and late 2020. Race and median income by zip code residence were not associated with intubation.

Discussion

In this study of COVID-19 disease severity at a large integrated health care system that provides equal access to care, race, ethnicity, and geographic location were not associated with the need for HFO, intubation, or presumed mortality. Median income by zip code residence was associated with reduced HFO use in univariable analyses but not in multivariable models.

These findings support existing literature suggesting that race and ethnicity alone do not explain disparities in COVID-19 outcomes. Multiple studies have demonstrated that disparities in health outcomes have been reduced for patients receiving VHA care.6,16-19 However, even within a health care system with assumed equal access, the finding of an association between income and need for HFO in the univariable analysis may reflect a greater likelihood of delays in care due to structural barriers. Multiple studies suggest low SES may be an independent risk factor for severe COVID-19 disease. Individuals with low SES have higher rates of chronic diseases of obesity, diabetes, heart disease, and lung disease; thus, they are also at greater risk of serious illness with COVID-19.20-24 Socioeconomic disadvantage may also have limited individuals’ ability to engage in protective behaviors to reduce COVID-19 infection risk, including food stockpiling, social distancing, avoidance of public transportation, and refraining from working in “essential jobs.”21

Beyond SES, place of residence also influences health outcomes. Prior literature supports using zip codes to assess area-based SES status and monitor health disparities.25 The Social Vulnerability Index incorporates SES factors for communities and measures social determinates of health at a zip code level exclusive of race and ethnicity.26 Socially vulnerable communities are known to have higher rates of chronic diseases, COVID-19 mortality, and lower vaccination rates.3 Within a defined geographic area, an individual’s outcome for COVID-19 can be influenced by individual resources such as access to care and median income. Disposable income may mitigate COVID-19 risk by facilitating timely care, reducing occupational exposure, improving housing stability, and supporting health-promoting behaviors.21

Limitations

Due to the evolving nature of the COVID-19 pandemic, variants, treatments, and interventions varied throughout the study period and are not included in this analysis. In late December 2020, COVID-19 vaccination was approved with a tiered allocation for at-risk patients and direct health care professionals. Three of the 4 study periods analyzed in this study were prior to vaccine rollout and therefore vaccination history was not assessed. However, we tried to capture the evolving changes in COVID-19 variants, treatments and interventions, and skill in treating the disease through use of CDC-defined time frames. Another limitation is that some studies have shown that use of median income by zip code residence can underestimate mortality.27 Also, shared resources and access to other sources of disposable income can impact the immediate attainment of social needs. For example, during the COVID-19 pandemic, health care systems in Washington, DC assisted vulnerable individuals by providing food, housing, and other resources.28,29 Finally, the modest sample size limits generalizability and power to detect differences for certain variables, including Hispanic ethnicity.

Conclusions

There have been widely described disparities in disease severity and death during the COVID-19 pandemic. In this urban veteran cohort of hospitalized patients, there was no difference in the need for intubation or mortality associated with race. The findings suggest that a lower median income by zip code residence may be associated with greater disease severity at presentation, but do not predict severe outcomes and mortality overall. VHA care, which provides equal access to care, may mitigate the disparities seen in the private sector.

Large epidemiologic studies have shown disparities in COVID-19 outcomes by race, ethnicity, and socioeconomic status (SES). Racial and ethnic minorities and individuals of lower SES have experienced disproportionately higher rates of intensive care unit (ICU) admission and death. In Washington, DC, Black individuals (47% of the population) accounted for 51% of COVID-19 cases and 75% of deaths. In comparison, White individuals (41% of the population) accounted for 21% of cases and 11% of deaths.1 Place of residence, such as living in socially vulnerable communities, has also been shown to be associated with higher rates of COVID-19 mortality and lower vaccination rates.2-4 Social and structural inequities, such as limited access to health care services and mistrust of the health care system, may explain some of the observed disparities.5 However, data are limited regarding COVID-19 outcomes for individuals with equal access to care.

The Veterans Health Administration (VHA) is the largest integrated US health care system and operates 123 acute care hospitals. Previous research has demonstrated that disparities in outcomes for other diseases are attenuated or erased among veterans receiving VHA care.6,7 Based on literature from the pandemic, markers of health care inequity relating to SES (eg, place of residence, median income) are expected to impact the outcomes of patients acutely hospitalized with COVID-19.4 We hypothesized that the impact on clinical outcomes of infection would be mitigated for veterans receiving VHA care.

This retrospective cohort study included veterans who presented to Washington Veterans Affairs Medical Center (WVAMC) with the goal of determining whether place of residence as a marker of SES, health care access, and median income were predictive of COVID-19 disease severity.

Methods

The WVAMC serves about 125,000 veterans across the metropolitan area, including parts of Maryland and Virginia. It is a high-complexity hospital with 164 acute care beds, 30 psychosocial residential rehabilitation beds, and an adjacent 120-bed community living center providing long-term, hospice, and palliative care.8

The WVAMC developed a dashboard that tracked patients with COVID-19 through on-site testing by admission date, ward, and other key demographics (PowerBi, Corporate Data Warehouse). All patients admitted to WVAMC with a diagnosis of COVID-19 between March 1, 2020, and June 30, 2021, were included in this retrospective review. Using the Computerized Patient Record System (CPRS) and the dashboard, we collected demographic information, baseline clinical diagnoses, laboratory results, and clinical interventions for all patients with documented COVID-19 infection as established by laboratory testing methods available at the time of diagnosis. Veterans treated exclusively outside the WVAMC were excluded. Hospitalization was defined as any acute inpatient admission or transfer recorded within 5 days before and 30 days after the laboratory collection of a positive COVID-19 test. Home testing kits were not widely available during the study period. An ICU stay was defined as any inpatient admission or transfer recorded within 5 days before or 30 days after the laboratory collection of a positive COVID-19 test for which the ward location had the specialty of medical or surgical ICU. Death due to COVID-19 was defined as occurring within 42 days (6 weeks) of a positive COVID-19 test.9 This definition assumed that during the peak of the pandemic, COVID-19 was the attributable cause of death, despite the possible contribution of underlying health conditions.

Patients’ admission periods were based on US Centers for Disease Control and Prevention (CDC) national data and classified as early 2020 (January 2020–April 2020), mid-2020 (May 2020–August 2020), late 2020 (September 2020–December 2020), and early 2021 (January 2021–April 2021).10 We chose to use these time periods as surrogates for the frequent changes in circulating COVID-19 variants, surges in case numbers, therapies and interventions available during the pandemic. The dominant COVID-19 variant during the study period was Alpha (B.1.17). Beta (B.1.351) variants were circulating infrequently, and Delta and Omicron appeared after the study period.11 Treatment strategies evolved rapidly with emerging evidence, including the use of dexamethasone, beginning in June 2020.12 WVAMC followed the Advisory Committee on Immunization Practices guidance on vaccination rollout beginning in December 2020.13

Patients' income was estimated by the median household income of the zip code residence based on US Census Bureau 2021 estimates and was assessed as both a continuous and categorical variable.14 The Charlson Comorbidity Index (CCI) was included in models as a continuous variable.15 Variables contributing to the CCI include myocardial infarction, congestive heart failure, peripheral vascular disease, cerebrovascular disease, dementia, hemiplegia or paraplegia, ulcer disease, hepatic disease, diabetes (with or without end-organ damage), chronic obstructive pulmonary disease (COPD), connective tissue disease, leukemia, lymphoma, moderate or severe renal disease, solid tumor (with or without metastases), and HIV/AIDS. The WVAMC Institutional Review Board approved this study (IRB #1573071).

Variables

This study assessed 3 primary outcomes as indicators of disease severity during hospitalization: need for high-flow oxygen (HFO), intubation, and presumed mortality at any time during hospitalization. The following variables were collected as potential social determinants or clinical risk-adjustment predictors of disease severity outcomes: age; sex; race and ethnicity; median income for patient’s zip code residence, state, and county; wards within Washington, DC; comorbidities, CCI; tobacco use; and body mass index.15 Although medications at baseline, treatments during hospitalization for COVID-19, and laboratory parameters during hospitalization are shown in eAppendices 1 and 2, they are beyond the scope of this analysis.

Statistical Analysis

Three types of logistic regression models were calculated for predicting the disease severity outcomes: (1) simple unadjusted models; (2) models predicting from single variables plus age (age-adjusted); and (3) multivariable models using all nonredundant potential predictors with adequate sample sizes (multivariable). Variables were considered to have inadequate sample sizes if there was nontrivial missing data or small numbers within categories, (eg, AIDS, connective tissue disease). Potential predictors for the multivariable model included age, sex, race, median income by zip code residence, CCI, CDC admission period, obesity, hypertension, chronic kidney disease, obstructive sleep apnea (OSA), diabetes, COPD or asthma, liver disease, antibiotics, and acute kidney injury.

For the multivariable models, the following modifications were made to avoid unreliable parameter estimation and computation problems (quasi-separation): age and CCI were included as continuous rather than categorical variables. Race was recoded as a 2-category variable (Black vs other [White, Hispanic, American Indian, Alaska Native, Asian, Native Hawaiian, and Pacific Islander]), and ethnicity was excluded because of the small number of patients in this group (n = 16). Admission period was included. Predicted probability plots were generated for each outcome with continuous independent predictors (income and CCI), both unadjusted and adjusted for age as a continuous covariate. All analyses were performed using SAS version 9.4.

Heat Maps

Heat maps were generated to visualize the geospatial distribution of COVID-19 cases and median incomes across zip codes in the greater Washington, DC area. Patient case data and median income, aggregated by zip code, were imported using ArcGIS Online. A zip code boundary layer from Esri (United States Zip Code Boundaries) was used to spatially align the case data. Data were joined by matching zip codes or median incomes in the patient dataset to those in the boundary layer. The resulting polygon layer was styled using the Counts and Amounts (Color) symbology in ArcGIS Online, with case counts or median income determining the intensity of the color gradient.

Results

Between March 1, 2020, and June 30, 2021, 348 patients were hospitalized with COVID-19 (Table 1). The mean (SD) age was 68.4 (13.9) years, 313 patients (90.2%) were male, 281 patients (83.4%) were Black, 47 patients (13.6%) were White, and 16 patients (4.8%) were Hispanic. One hundred forty patients (40.2%) resided in Washington, DC, 151 (43.4%) in Maryland, and 19 (5.5%) in Virginia. HFO was received by 86 patients (24.7%), 33 (9.5%) required intubation and mechanical ventilation, and 57 (16.4%) died. All intubations and deaths occurred among patients aged > 50 years, with death occurring in 17.8% of patients aged > 50 years.

FDP04302076_T1

Demographic characteristics and baseline comorbidities associated with COVID-19 disease severity can be found in eAppendix 2. In unadjusted analyses, age was significantly associated with the risk of HFO, with a mean (SD) age of 72.5 (11.7) years among those requiring HFO and 67.1 (14.4) years among patients without HFO (odds ratio [OR], 1.03; 95% CI, 1.01-1.05; P = .002). Although age was not associated with the risk of intubation, it was significantly associated with mortality. Patients who died had a mean (SD) age of 76.8 (11.8) years compared with 66.8 (13.7) years among survivors (OR, 1.06; 95% CI, 1.04-1.09; P < .001).

Compared with patients with no comorbidities, CCI categories of mild, moderate, and severe were associated with increased risk of requiring HFO (eAppendix 3). The adjusted OR (aOR) was highest among patients with severe CCI (aOR, 7.00; 95% CI, 2.42-20.32; P = .0007). In age-adjusted analyses, CCI was not associated with intubation or mortality.

Geospatial Analyses

State of residence, county of residence, and geographic area (including Washington, DC wards, and geographic divisions within counties of residence in Maryland and Virginia) were not associated with the clinical outcomes studied (eAppendix 4). However, zip code-based median income, analyzed as a continuous variable, was associated with a reduced likelihood of receiving HFO (aOR, 0.91; 95% CI, 0.84-0.99; P = .03). Income was not significantly associated with intubation or mortality.

The majority of patients hospitalized for COVID-19 at WVAMC resided in zip codes in eastern Washington, DC, inclusive of wards 7 and 8, and Prince George’s County, Maryland (Figure 1). These areas also corresponded to the lowest median household income by zip code (Figure 2).

FDP04302076_F1
FIGURE 1. Geospatial Heat Map of COVID-19 Cases by Zip
Code
FDP04302076_F2
FIGURE 2. Geospatial Heat Map of Median Income by Zip
Code

 

Multivariable Analysis

Significant predictors of HFO requirement included comorbid diabetes (OR, 2.42; 95% CI, 1.27-4.61; P = .006) and liver disease or cirrhosis (OR, 2.19; 95% CI, 1.09-4.39; P = .02) (Table 2). CDC admission period was also associated with HFO need. Patients admitted after early 2020 had lower odds of receiving HFO. Race and median income based on zip code residence were not associated with HFO requirement.

FDP04302076_T2

Comorbid liver disease or cirrhosis was a significant predictor of intubation (OR, 2.81; 95% CI, 1.07-7.40; P = .03). CDC admission period was associated with intubation with lower odds of intubation for patients admitted after early 2020. Race and median income by zip code were not associated with intubation.

Significant predictors of mortality included age (OR, 2.20; 95% CI, 1.55-3.14; P = .0001), comorbid liver disease or cirrhosis (OR, 2.97; 95% CI, 1.31-6.74; P = .008), and OSA (OR, 3.45; 95% CI, 1.49-7.97; P = .003). CDC admission period was associated with mortality, with lower odds of intubation for patients admitted in mid- and late 2020. Race and median income by zip code residence were not associated with intubation.

Discussion

In this study of COVID-19 disease severity at a large integrated health care system that provides equal access to care, race, ethnicity, and geographic location were not associated with the need for HFO, intubation, or presumed mortality. Median income by zip code residence was associated with reduced HFO use in univariable analyses but not in multivariable models.

These findings support existing literature suggesting that race and ethnicity alone do not explain disparities in COVID-19 outcomes. Multiple studies have demonstrated that disparities in health outcomes have been reduced for patients receiving VHA care.6,16-19 However, even within a health care system with assumed equal access, the finding of an association between income and need for HFO in the univariable analysis may reflect a greater likelihood of delays in care due to structural barriers. Multiple studies suggest low SES may be an independent risk factor for severe COVID-19 disease. Individuals with low SES have higher rates of chronic diseases of obesity, diabetes, heart disease, and lung disease; thus, they are also at greater risk of serious illness with COVID-19.20-24 Socioeconomic disadvantage may also have limited individuals’ ability to engage in protective behaviors to reduce COVID-19 infection risk, including food stockpiling, social distancing, avoidance of public transportation, and refraining from working in “essential jobs.”21

Beyond SES, place of residence also influences health outcomes. Prior literature supports using zip codes to assess area-based SES status and monitor health disparities.25 The Social Vulnerability Index incorporates SES factors for communities and measures social determinates of health at a zip code level exclusive of race and ethnicity.26 Socially vulnerable communities are known to have higher rates of chronic diseases, COVID-19 mortality, and lower vaccination rates.3 Within a defined geographic area, an individual’s outcome for COVID-19 can be influenced by individual resources such as access to care and median income. Disposable income may mitigate COVID-19 risk by facilitating timely care, reducing occupational exposure, improving housing stability, and supporting health-promoting behaviors.21

Limitations

Due to the evolving nature of the COVID-19 pandemic, variants, treatments, and interventions varied throughout the study period and are not included in this analysis. In late December 2020, COVID-19 vaccination was approved with a tiered allocation for at-risk patients and direct health care professionals. Three of the 4 study periods analyzed in this study were prior to vaccine rollout and therefore vaccination history was not assessed. However, we tried to capture the evolving changes in COVID-19 variants, treatments and interventions, and skill in treating the disease through use of CDC-defined time frames. Another limitation is that some studies have shown that use of median income by zip code residence can underestimate mortality.27 Also, shared resources and access to other sources of disposable income can impact the immediate attainment of social needs. For example, during the COVID-19 pandemic, health care systems in Washington, DC assisted vulnerable individuals by providing food, housing, and other resources.28,29 Finally, the modest sample size limits generalizability and power to detect differences for certain variables, including Hispanic ethnicity.

Conclusions

There have been widely described disparities in disease severity and death during the COVID-19 pandemic. In this urban veteran cohort of hospitalized patients, there was no difference in the need for intubation or mortality associated with race. The findings suggest that a lower median income by zip code residence may be associated with greater disease severity at presentation, but do not predict severe outcomes and mortality overall. VHA care, which provides equal access to care, may mitigate the disparities seen in the private sector.

References
  1. District of Columbia: All Race & Ethnicity Data. The COVID Tracking Project. Accessed December 10, 2025. https://covidtracking.com/data/state/district-of-columbia/race-ethnicity
  2. Freese KE, Vega A, Lawrence JJ, et al. Social vulnerability is associated with risk of COVID-19 related mortality in U.S. counties with confirmed cases. J Health Care Poor Underserved. 2021;32:245-257. doi:10.1353/hpu.2021.0022
  3. Saulsberry L, Bhargava A, Zeng S, et al. The social vulnerability metric (SVM) as a new tool for public health. Health Serv Res. 2023;58:873-881. doi:10.1111/1475-6773.14102
  4. Romano SD, Blackstock AJ, Taylor EV, et al. Trends in racial and ethnic disparities in COVID-19 hospitalizations, by region - United States, March-December 2020. MMWR Morb Mortal Wkly Rep. 2021;70:560-565. doi:10.15585/mmwr.mm7015e2
  5. Kullar R, Marcelin JR, Swartz TH, et al. Racial disparity of coronavirus disease 2019 in African American communities. J Infect Dis. 2020;222:890-893. doi:10.1093/infdis/jiaa372
  6. Riviere P, Luterstein E, Kumar A, et al. Survival of African American and non-Hispanic White men with prostate cancer in an equal-access health care system. Cancer. 2020;126:1683-1690. doi:10.1002/cncr.32666
  7. Ohl ME, Richardson Miell K, Beck BF, et al. Mortality among US veterans admitted to community vs Veterans Health Administration hospitals for COVID-19. JAMA Netw Open. 2023;6:e2315902. doi:10.1001/jamanetworkopen.2023.15902
  8. US Department of Veterans Affairs. VA Washington DC Health Care. Accessed January 16, 2026. https://www.va.gov/washington-dc-health-care/about-us/
  9. Trottier C, La J, Li LL, et al. Maintaining the utility of coronavirus disease 2019 pandemic severity surveillance: evaluation of trends in attributable deaths and development and validation of a measurement tool. Clin Infect Dis. 2023;77:1247-1256. doi:10.1093/cid/ciad381
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  11. Centers for Disease Control and Prevention. Covid-surveillance and data analytics. September 5, 2025. Accessed January 16, 2026. cdc.gov/covid/php/surveillance/index.html12.
  12. RECOVERY Collaborative Group, Horby P, Lim WS, et al. Dexamethasone in hospitalized patients with Covid-19. N Engl J Med. 2021;384:693-704. doi:10.1056/NEJMoa2021436
  13. Dooling K, Marin M, Wallace M, et al. The Advisory Committee on Immunization Practices’ updated interim recommendation for allocation of COVID-19 Vaccine - United States, December 2020. MMWR Morb Mortal Wkly Rep. 2021;69:1657-1660. doi:10.15585/mmwr.mm695152e2
  14. US Census Bureau. Explore census data. Accessed December 10, 2025. https://data.census.gov/profile?q=Income%20by%20Zip%20code%20tabulation%20area
  15. Charlson ME, Pompei P, Ales KL, et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40:373-383. doi:10.1016/0021-9681(87)90171-8
  16. Zullig LL, Carpenter WR, Provenzale D, Weinberger M, Reeve BB, Jackson GL. Examining potential colorectal cancer care disparities in the Veterans Affairs health care system. J Clin Oncol. 2013;31:3579-3584. doi:10.1200/JCO.2013.50.4753
  17. Grubaugh AL, Slagle DM, Long M, Frueh BC, Magruder KM. Racial disparities in trauma exposure, psychiatric symptoms, and service use among female patients in Veterans Affairs primary care clinics. Womens Health Issues. 2008;18:433-441. doi:10.1016/j.whi.2008.08.001
  18. Bosworth HB, Parsey KS, Butterfield MI, et al. Racial variation in wanting and obtaining mental health services among women veterans in a primary care clinic. J Natl Med Assoc. 2000;92:231-236.
  19. Luo J, Rosales M, Wei G, et al. Hospitalization, mechanical ventilation, and case-fatality outcomes in US veterans with COVID-19 disease between years 2020-2021. Ann Epidemiol. 2022;70:37-44. doi:10.1016/j.annepidem.2022.04.003
  20. Kondo K, Low A, Everson T, et al. Health disparities in veterans: a map of the evidence. Med Care. 2017;55 Suppl 9 Suppl 2:S9-S15. doi:10.1097/MLR.0000000000000756
  21. Grosicki GJ, Bunsawat K, Jeong S, Robinson AT. Racial and ethnic disparities in cardiometabolic disease and COVID-19 outcomes in White, Black/African American, and Latinx populations: Social determinants of health. Prog Cardiovasc Dis. 2022;71:4-10. doi:10.1016/j.pcad.2022.04.004
  22. National Center for Immunization and Respiratory Diseases (U.S.). Division of Viral Diseases. Coronavirus Disease 2019 (COVID-19): COVID-19 in Racial and Ethnic Minority Groups: June 4, 2020. CDC Stacks. June 4, 2020. Accessed January 14, 2026. https://stacks.cdc.gov/view/cdc/88770
  23. Yancy CW. COVID-19 and African Americans. JAMA. 2020;323:1891-1892. doi:10.1001/jama.2020.6548
  24. Magesh S, John D, Li WT, et al. Disparities in COVID-19 outcomes by race, ethnicity, and socioeconomic status: a systematic-review and meta-analysis. JAMA Netw Open. 2021;4:e2134147. doi:10.1001/jamanetworkopen.2021.34147
  25. Berkowitz SA, Traore CY, Singer DE, Atlas SJ. Evaluating area-based socioeconomic status indicators for monitoring disparities within health care systems: results from a primary care network. Health Serv Res. 2015;50:398-417. doi:10.1111/1475-6773.12229
  26. Social Vulnerability Index. Agency for Toxicity and Disease Registry. July 22, 2024. Accessed January 14, 2026. https://www.atsdr.cdc.gov/placeandhealth/svi/index.html
  27. Moss JL, Johnson NJ, Yu M, Altekruse SF, Cronin KA. Comparisons of individual- and area-level socioeconomic status as proxies for individual-level measures: evidence from the Mortality Disparities in American Communities study. Popul Health Metr. 2021;19:1. doi:10.1186/s12963-020-00244-x
  28. DC Department of Human Services. Response to COVID-19. Accessed January 14, 2026. https://dhs.dc.gov/page/responsetocovid19
  29. Wang PG, Brisbon NM, Hubbell H, et al. Is the Gap Closing? Comparison of sociodemographic cisparities in COVID-19 hospitalizations and outcomes between two temporal waves of admissions. J Racial Ethn Health Disparities. 2023;10:593-602. doi:10.1007/s40615-022-01249-y
References
  1. District of Columbia: All Race & Ethnicity Data. The COVID Tracking Project. Accessed December 10, 2025. https://covidtracking.com/data/state/district-of-columbia/race-ethnicity
  2. Freese KE, Vega A, Lawrence JJ, et al. Social vulnerability is associated with risk of COVID-19 related mortality in U.S. counties with confirmed cases. J Health Care Poor Underserved. 2021;32:245-257. doi:10.1353/hpu.2021.0022
  3. Saulsberry L, Bhargava A, Zeng S, et al. The social vulnerability metric (SVM) as a new tool for public health. Health Serv Res. 2023;58:873-881. doi:10.1111/1475-6773.14102
  4. Romano SD, Blackstock AJ, Taylor EV, et al. Trends in racial and ethnic disparities in COVID-19 hospitalizations, by region - United States, March-December 2020. MMWR Morb Mortal Wkly Rep. 2021;70:560-565. doi:10.15585/mmwr.mm7015e2
  5. Kullar R, Marcelin JR, Swartz TH, et al. Racial disparity of coronavirus disease 2019 in African American communities. J Infect Dis. 2020;222:890-893. doi:10.1093/infdis/jiaa372
  6. Riviere P, Luterstein E, Kumar A, et al. Survival of African American and non-Hispanic White men with prostate cancer in an equal-access health care system. Cancer. 2020;126:1683-1690. doi:10.1002/cncr.32666
  7. Ohl ME, Richardson Miell K, Beck BF, et al. Mortality among US veterans admitted to community vs Veterans Health Administration hospitals for COVID-19. JAMA Netw Open. 2023;6:e2315902. doi:10.1001/jamanetworkopen.2023.15902
  8. US Department of Veterans Affairs. VA Washington DC Health Care. Accessed January 16, 2026. https://www.va.gov/washington-dc-health-care/about-us/
  9. Trottier C, La J, Li LL, et al. Maintaining the utility of coronavirus disease 2019 pandemic severity surveillance: evaluation of trends in attributable deaths and development and validation of a measurement tool. Clin Infect Dis. 2023;77:1247-1256. doi:10.1093/cid/ciad381
  10. Centers for Disease Control and Prevention. CDC Museum COVID-19 Timeline. Updated July 8, 2024. Accessed January 16, 2026. https://www.cdc.gov/museum/timeline/covid19.html#Early-2020
  11. Centers for Disease Control and Prevention. Covid-surveillance and data analytics. September 5, 2025. Accessed January 16, 2026. cdc.gov/covid/php/surveillance/index.html12.
  12. RECOVERY Collaborative Group, Horby P, Lim WS, et al. Dexamethasone in hospitalized patients with Covid-19. N Engl J Med. 2021;384:693-704. doi:10.1056/NEJMoa2021436
  13. Dooling K, Marin M, Wallace M, et al. The Advisory Committee on Immunization Practices’ updated interim recommendation for allocation of COVID-19 Vaccine - United States, December 2020. MMWR Morb Mortal Wkly Rep. 2021;69:1657-1660. doi:10.15585/mmwr.mm695152e2
  14. US Census Bureau. Explore census data. Accessed December 10, 2025. https://data.census.gov/profile?q=Income%20by%20Zip%20code%20tabulation%20area
  15. Charlson ME, Pompei P, Ales KL, et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40:373-383. doi:10.1016/0021-9681(87)90171-8
  16. Zullig LL, Carpenter WR, Provenzale D, Weinberger M, Reeve BB, Jackson GL. Examining potential colorectal cancer care disparities in the Veterans Affairs health care system. J Clin Oncol. 2013;31:3579-3584. doi:10.1200/JCO.2013.50.4753
  17. Grubaugh AL, Slagle DM, Long M, Frueh BC, Magruder KM. Racial disparities in trauma exposure, psychiatric symptoms, and service use among female patients in Veterans Affairs primary care clinics. Womens Health Issues. 2008;18:433-441. doi:10.1016/j.whi.2008.08.001
  18. Bosworth HB, Parsey KS, Butterfield MI, et al. Racial variation in wanting and obtaining mental health services among women veterans in a primary care clinic. J Natl Med Assoc. 2000;92:231-236.
  19. Luo J, Rosales M, Wei G, et al. Hospitalization, mechanical ventilation, and case-fatality outcomes in US veterans with COVID-19 disease between years 2020-2021. Ann Epidemiol. 2022;70:37-44. doi:10.1016/j.annepidem.2022.04.003
  20. Kondo K, Low A, Everson T, et al. Health disparities in veterans: a map of the evidence. Med Care. 2017;55 Suppl 9 Suppl 2:S9-S15. doi:10.1097/MLR.0000000000000756
  21. Grosicki GJ, Bunsawat K, Jeong S, Robinson AT. Racial and ethnic disparities in cardiometabolic disease and COVID-19 outcomes in White, Black/African American, and Latinx populations: Social determinants of health. Prog Cardiovasc Dis. 2022;71:4-10. doi:10.1016/j.pcad.2022.04.004
  22. National Center for Immunization and Respiratory Diseases (U.S.). Division of Viral Diseases. Coronavirus Disease 2019 (COVID-19): COVID-19 in Racial and Ethnic Minority Groups: June 4, 2020. CDC Stacks. June 4, 2020. Accessed January 14, 2026. https://stacks.cdc.gov/view/cdc/88770
  23. Yancy CW. COVID-19 and African Americans. JAMA. 2020;323:1891-1892. doi:10.1001/jama.2020.6548
  24. Magesh S, John D, Li WT, et al. Disparities in COVID-19 outcomes by race, ethnicity, and socioeconomic status: a systematic-review and meta-analysis. JAMA Netw Open. 2021;4:e2134147. doi:10.1001/jamanetworkopen.2021.34147
  25. Berkowitz SA, Traore CY, Singer DE, Atlas SJ. Evaluating area-based socioeconomic status indicators for monitoring disparities within health care systems: results from a primary care network. Health Serv Res. 2015;50:398-417. doi:10.1111/1475-6773.12229
  26. Social Vulnerability Index. Agency for Toxicity and Disease Registry. July 22, 2024. Accessed January 14, 2026. https://www.atsdr.cdc.gov/placeandhealth/svi/index.html
  27. Moss JL, Johnson NJ, Yu M, Altekruse SF, Cronin KA. Comparisons of individual- and area-level socioeconomic status as proxies for individual-level measures: evidence from the Mortality Disparities in American Communities study. Popul Health Metr. 2021;19:1. doi:10.1186/s12963-020-00244-x
  28. DC Department of Human Services. Response to COVID-19. Accessed January 14, 2026. https://dhs.dc.gov/page/responsetocovid19
  29. Wang PG, Brisbon NM, Hubbell H, et al. Is the Gap Closing? Comparison of sociodemographic cisparities in COVID-19 hospitalizations and outcomes between two temporal waves of admissions. J Racial Ethn Health Disparities. 2023;10:593-602. doi:10.1007/s40615-022-01249-y
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Cross-Sectional Analysis of Biologic Use in the Treatment of Veterans With Hidradenitis Suppurativa

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Cross-Sectional Analysis of Biologic Use in the Treatment of Veterans With Hidradenitis Suppurativa

Hidradenitis suppurativa (HS) is a chronic, inflammatory skin disorder characterized by painful nodules, abscesses, and tunnels predominantly affecting intertriginous areas of the body.1,2 The condition poses significant challenges in terms of diagnosis, treatment, and quality of life for affected individuals. Various systemic therapies have been explored to manage this debilitating condition, with the emergence of biologic agents offering hope for improved outcomes. In 2015, adalimumab (ADA) was the first biologic approved by the US Food and Drug Administration (FDA) for the treatment of HS, followed by secukinumab in 2023 and bimekizumab in 2024. However, the off-label use of other biologics and/or tumor necrosis factor inhibitors such as infliximab (IFX) has become common practice.3

Although these therapies have demonstrated promising results in the treatment of HS, their widespread use may be hindered by accessibility and cost barriers. Orenstein et al analyzed data from the IBM Explorys platform from 2015 to 2020 and found that only 1.8% of patients diagnosed with HS had been prescribed ADA or IFX.4 More recently, Garg et al examined IBM MarketScan and IBM US Medicaid data from 2015 to 2018 to evaluate trends in clinical care and treatment. The prevalence of ADA and IFX prescriptions among patients with HS ranged from 2.3% to 8.0% (ADA) and 0.7% to 0.9% (IFX) for patients with commercial insurance, and 1.4% to 4.8% (ADA) and 0.5% to 0.7% (IFX) for patients with Medicaid.5 Biologics are often expensive, and the high cost associated with these therapies has been identified as a significant barrier to access for patients with HS, particularly those who lack adequate insurance coverage or face financial constraints.6

Furthermore, these barriers, particularly the financial barriers, are potentially compounded by the demographics of patients most notably affected by HS. In the US, a disproportionate incidence of HS has been noted in specific groups and age ranges, including women, individuals aged 18 to 29 years, and Black individuals.4 Orenstein et al found a statistically significant difference in use of ADA and IFX biologics based on age, sex, and race.4

The aim of this study was to examine the use of 2 biologics (ADA and IFX) in the Veterans Health Administration (VHA), a unique population in which financial barriers are reduced due to the single-payer government health care system structure. This design allowed for improved isolation and evaluation of variation in ADA and/or IFX prescription rates by demographics and health-related factors among patients with HS. To our knowledge, no studies have analyzed these metrics within the VHA.

Methods

This retrospective, cross-sectional analysis of VHA patients used data from the US Department of Veterans Affairs (VA) Corporate Data Warehouse, a data repository that provides access to longitudinal national electronic health record data for all veterans receiving care through VHA facilities. This study received ethical approval from institutional review boards at the Minneapolis Veterans Affairs Health Care System and VA Salt Lake City Healthcare System. Patient information was deidentified, and patient consent was not required.

Patients with HS were identified using ≥ 1 International Classification of Diseases (ICD) diagnostic code: (ICD-9 [705.83] or ICD-10 [L73.2]) between January 1, 2011, and December 31, 2021. The study included patients aged ≥ 18 years as of January 1, 2011, with ≥ 2 patient encounters during the postdiagnosis follow-up period, and with ≥ 1 encounter 6 months postindex. Patients with a biologic prescription prior to HS diagnosis were excluded. For this study, the term biologics refers to ADA and/or IFX prescriptions, unless otherwise specified. Only ADA and IFX were included in this analysis because ADA, a tumor necrosis factor (TNF)-á inhibitor, was the only FDA-approved medication at the time of the search, and IFX is another common TNF-α inhibitor used for the treatment of HS.

Statistical Analysis

We calculated logistic regression using SAS 9.4 (SAS Institute, Cary, NC). For each variable, the univariate relationship with biologic prescriptions was examined first, followed by the multivariate relationship controlling for all other variables. The following variables were controlled for in the multivariate models and were chosen a priori: sex, age, race, ethnicity, US region, hospital setting, current or previous tobacco use, obesity (defined as body mass index [BMI] ≥ 30), and Charlson Comorbidity Index (CCI).7

Results

Using ICD codes, we identified 29,483 individuals with ≥ 1 HS diagnosis (Figure 1). Of those identified, 1537 patients (5.21%) had been prescribed ≥ 1 biologic. The cohort was predominantly White (60.56%), male (75.27%), obese (59.34%), and had a history of current or previous tobacco use (73.47%) (Table 1). There were significant adjusted differences in prescription rates among veterans with HS based on age, race, and BMI. Notably, there was an age-dependent reduction in the odds of being prescribed a biologic in patients with HS. Compared with patients aged 18 to 44 years, patients aged 45 to 64 years (adjusted odds ratio [aOR], 0.63; 95% CI, 0.54–0.74; P < .001) and patients aged ≥ 65 years (aOR, 0.36; 95% CI, 0.27–0.48; P < .001) had significantly lower odds of receiving a biologic prescription (Table 2). Compared with White patients with HS, Native Hawaiian (NH) or Pacific Islander (PI) patients were less likely to be prescribed a biologic (aOR, 0.23; 95% CI, 0.06–0.92; P = .04). Patients with obesity had significantly higher odds of receiving a biologic prescription compared with patients without obesity (aOR, 1.47; 95% CI, 1.27– 1.71; P < .001).

FDP04302068_F1
FIGURE. STROBE Flowchart of Cohort
Included in Analysis.

 

After adjusting for the variables listed in Table 1, there were no significant differences in biologic prescription rates for men compared with women (aOR, 0.97; 95% CI, 0.83-1.12; P = .68). We observed slight variations in biologic prescriptions between US regions (Midwest 5.0%, East 4.2%, South 5.8%, West 4.6%), none of which were significantly different in the fully adjusted model. No statistically significant differences were found in biologic prescriptions between urban and rural VA settings (5.4% vs 4.8%; aOR, 1.06; 95% CI, 0.90–1.24; P = .47). Tobacco use was not associated with the rate of biologic prescription receipt (aOR, 1.14; 95% CI, 0.97–1.34; P = .11). After adjusting for other variables (as outlined in Table 2), no significant differences were found between CCI of 0 and 1 (aOR, 0.97; 95% CI, 0.82–1.16; P = .77) or between CCI of 0 and 2 (aOR, 0.89; 95% CI, 0.74–1.07; P = .22).7

FDP04302068_T1FDP04302068_T2

Discussion

The aim of the study was to ascertain potential discrepancies in biologic prescription patterns among patients with HS in the VHA by demographic and lifestyle behavior modifiers. Veteran cohorts are unique in composition, consisting predominantly of older White men within a single-payer health care system. The prevalence of biologic prescriptions in this population was low (5.2%), consistent with prior studies (1.8%–8.9%).4,5

We found a significant difference in ADA/IFX prescription patterns between White patients and NH/PI patients (aOR, 0.23; 95% CI, 0.06-0.92; P = .04). Further replication of this result is needed due to the small number of NH/PI patients included in the study (n = 241). Notably, we did not find a significant difference in the odds of Black patients being prescribed a biologic compared with White patients (aOR, 1.07; 95% CI, 0.92–1.25; P = .38), consistent with prior studies.4

In line with prior studies, age was associated with the likelihood of receiving a biologic prescription.4 Using the multivariate model adjusting for variables listed in Table 1, including CCI, patients aged 45 to 64 years and > 64 years were less likely to be prescribed a biologic than patients aged 18 to 44 years. HS disease activity could be a potential confounding variable, as HS severity may subside in some people with increasing age or menopause.8

Because different regions in the US have different sociopolitical ideologies and governing legislation, we hypothesized that there may be dissimilarities in the prevalence rates of biologic prescribing across various US regions. However, no significant differences were found in prescription patterns among US regions or between rural and urban settings. Previous research has demonstrated discernible disparities in both dermatologic care and clinical outcomes based on hospital setting (ie, urban vs rural).9-11

Tobacco use has been demonstrated to be associated with the development of HS.12 In a large retrospective analysis, Garg et al reported increased odds of receiving a new HS diagnosis in known tobacco users (aOR, 1.9; 95% CI, 1.8–2.0).13 The extent to which tobacco use affects HS severity is less understood. While some studies have found an association between smoking and HS severity, other analyses have failed to find this association.14,15 The effects of smoking cessation on the disease course of HS are unknown.16 This analysis, found no significant difference in prescriptions for biologics among patients with HS comparing current or previous tobacco users with nonusers.

There is a known positive correlation between increasing BMI and HS prevalence and severity that may be explained by the downstream effects of adipose tissue secretion of proinflammatory mediators and insulin resistance in the setting of chronic inflammation.12 This analysis found that patients with HS and obesity were 1.47 times more likely to be prescribed a biologic than patients with HS without obesity, which may be confounded by increased HS severity among patients with obesity. The initial concern when analyzing tobacco use and obesity was that clinician bias may result in a decrease in the prevalence of biologic use in these demographics, which was not supported in this study.

Although we identified few disparities, the results demonstrated a substantial underutilization of biologic therapies (5.2%), similar to the other US civilian studies (1.8-8.9%).4,5 While there is no current universal, standardized severity scoring system to evaluate HS (it is difficult to objectively define moderate to severe HS), estimates have shown that 40.3% to 65.8% of patients with HS have Hurley stage II or III.17-19 Therefore, only a small percentage of patients with moderate to severe disease were prescribed the only FDA-approved medication during this time period. The persistence of this underutilization within a medical system that reduces financial barriers suggests that nonfinancial barriers have a notable role in the underutilization of biologics.

For instance, risk of adverse events, particularly lymphoma and infection, has been cited by patients as a reason to avoid biologics. Additionally, treatment fatigue reduced some patients’ willingness to try new treatments, as did lack of knowledge about treatment options.6,20 Other reported barriers included the frequency of injections and fear of needles.6 Additionally, within the VA, ADA may require prior authorization at the local facility level.21 An established relationship with a dermatologist has been shown to significantly increase the odds of being prescribed a biologic medication in the face of these barriers.4 Future system-wide quality improvement initiatives could be implemented to identify patients with HS not followed by dermatology, with the goal of establishing care with a dermatologist.

Limitations

Limitations to this study include an inability to categorize HS disease severity and assess the degree to which disease severity confounded study findings, particularly in relation to tobacco use and obesity. The generalizability of this study is also limited because of the demographic characteristics of the veteran patient population, which is predominantly older, White, and male, whereas HS disproportionately affects younger, Black, and female individuals in the US.22 Despite these limitations, this study contributes valuable insights into the use of biologic therapies for veteran populations with HS using a national dataset.

Conclusions

This study was performed within a single-payer government medical system, likely reducing or removing the financial barriers that some patient populations may face when pursuing biologics for HS treatment. However, the prevalence of biologic use in this population was low overall (5.2%), suggesting that other factors play a role in the underutilization of biologics in HS. Consistent with previous studies, younger individuals were more likely to be prescribed a biologic, and no difference in prescription rates between Black and White patients was observed. Unlike previous studies, no significant difference in prescription rates between men and women was observed.

References
  1. Goldburg SR, Strober BE, Payette MJ. Hidradenitis suppurativa: epidemiology, clinical presentation, and pathogenesis. J Am Acad Dermatol. 2020;82:1045-1058. doi:10.1016/j.jaad.2019.08.090
  2. Tchero H, Herlin C, Bekara F, et al. Hidradenitis suppurativa: a systematic review and meta-analysis of therapeutic interventions. Indian J Dermatol Venereol Leprol. 2019;85:248-257. doi:10.4103/ijdvl.IJDVL_69_18
  3. Shih T, Lee K, Grogan T, et al. Infliximab in hidradenitis suppurativa: a systematic review and meta-analysis. Dermatol Ther. 2022;35:e15691. doi:10.1111/dth.15691
  4. Orenstein LAV, Wright S, Strunk A, et al. Low prescription of tumor necrosis alpha inhibitors in hidradenitis suppurativa: a cross-sectional analysis. J Am Acad Dermatol. 2021;84:1399-1401. doi:10.1016/j.jaad.2020.07.108
  5. Garg A, Naik HB, Alavi A, et al. Real-world findings on the characteristics and treatment exposures of patients with hidradenitis suppurativa from US claims data. Dermatol Ther (Heidelb). 2023;13:581-594. doi:10.1007/s13555-022-00872-1
  6. De DR, Shih T, Fixsen D, et al. Biologic use in hidradenitis suppurativa: patient perspectives and barriers. J Dermatolog Treat. 2022;33:3060-3062. doi:10.1080/09546634.2022.2089336
  7. Charlson ME, Pompei P, Ales KL, et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40:373- 383. doi:10.1016/0021-9681(87)90171-8
  8. von der Werth JM, Williams HC. The natural history of hidradenitis suppurativa. J Eur Acad Dermatol Venereol. 2000;14:389-392. doi:10.1046/j.1468-3083.2000.00087.x
  9. Silverberg JI, Barbarot S, Gadkari A, et al. Atopic dermatitis in the pediatric population: a cross-sectional, international epidemiologic study. Ann Allergy Asthma Immunol. 2021;126:417-428.e2. doi:10.1016/j.anai.2020.12.020
  10. Wu YP, Parsons B, Jo Y, et al. Outdoor activities and sunburn among urban and rural families in a Western region of the US: implications for skin cancer prevention. Prev Med Rep. 2022;29:101914. doi:10.1016/j.pmedr.2022.101914
  11. Mannschreck DB, Li X, Okoye G. Rural melanoma patients in Maryland do not present with more advanced disease than urban patients. Dermatol Online J. 2021;27. doi:10.5070/D327553607
  12. Garg A, Malviya N, Strunk A, et al. Comorbidity screening in hidradenitis suppurativa: evidence-based recommendations from the US and Canadian Hidradenitis Suppurativa Foundations. J Am Acad Dermatol. 2022;86:1092-1101. doi:10.1016/j.jaad.2021.01.059
  13. Garg A, Papagermanos V, Midura M, et al. Incidence of hidradenitis suppurativa among tobacco smokers: a population- based retrospective analysis in the U.S.A. Br J Dermatol. 2018;178:709-714. doi:10.1111/bjd.15939
  14. Sartorius K, Emtestam L, Jemec GBE, et al. Objective scoring of hidradenitis suppurativa reflecting the role of tobacco smoking and obesity. Br J Dermatol. 2009;161:831- 839. doi:10.1111/j.1365-2133.2009.09198.x
  15. Canoui-Poitrine F, Revuz JE, Wolkenstein P, et al. Clinical characteristics of a series of 302 French patients with hidradenitis suppurativa, with an analysis of factors associated with disease severity. J Am Acad Dermatol. 2009;61:51-57. doi:10.1016/j.jaad.2009.02.013
  16. Dufour DN, Emtestam L, Jemec GB. Hidradenitis suppurativa: a common and burdensome, yet under-recognised, inflammatory skin disease. Postgrad Med J. 2014;90:216- 221. doi:10.1136/postgradmedj-2013-131994
  17. Vazquez BG, Alikhan A, Weaver AL, et al. Incidence of hidradenitis suppurativa and associated factors: a population- based study of Olmsted County, Minnesota. J Invest Dermatol. 2013;133:97-103. doi:10.1038/jid.2012.255
  18. Vanlaerhoven AMJD, Ardon CB, van Straalen KR, et al. Hurley III hidradenitis suppurativa has an aggressive disease course. Dermatology. 2018;234:232-233. doi:10.1159/000491547
  19. Shahi V, Alikhan A, Vazquez BG, et al. Prevalence of hidradenitis suppurativa: a population-based study in Olmsted County, Minnesota. Dermatology. 2014;229:154-158. doi:10.1159/000363381
  20. Salame N, Sow YN, Siira MR, et al. Factors affecting treatment selection among patients with hidradenitis suppurativa. JAMA Dermatol. 2024;160:179. doi:10.1001/jamadermatol.2023.5425
  21. VA Formulary Advisor: ADALIMUMAB-BWWD INJ,SOLN. US Department of Veterans Affairs. Updated December 17, 2025. Accessed January 15, 2026. https://www.va.gov/formularyadvisor/drugs/4042383-ADALIMUMAB-BWWD-INJ-SOLN
  22. Garg A, Lavian J, Lin G, et al. Incidence of hidradenitis suppurativa in the United States: a sex- and age-adjusted population analysis. J Am Acad Dermatol. 2017;77:118- 122. doi:10.1016/j.jaad.2017.02.005
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Author and Disclosure Information

Zachary Wendland, MD, MPHa,b; Katelyn Rypka, BSa,b; Lindsey Greenlund, BSb; Claire Herzog, BSb; Fatai Y. Agiri, BSc; Amy A. Gravely, MAa; Lauren Orenstein, MD, MScd; Kathryn M. Pridgen, MAc; Amit Garg, MDe; Julie A. Lynch, PhD, MBA, RNc,f; Noah Goldfarb, MDa,b

Author affiliations
aMinneapolis Veterans Affairs Health Care System, Minnesota
bUniversity of Minnesota, Minneapolis
cVeterans Affairs Salt Lake City Healthcare System, Utah
dEmory University, Atlanta, Georgia
eDonald and Barbara Zucker School of Medicine at Hofstra/ Northwell, Hempstead, New York
fUniversity of Utah School of Medicine, Salt Lake City

Author disclosures NG has participated in clinical trials with AbbVie, Pfizer, Chemocentrix, and DeepX Health, and has served on advisory boards and consulted for Novartis and Boehringer Ingelheim. LO has been an advisor for Chemocentryx, Novartis, and UCB, and has received grants from Pfizer. FYA, KMP, and JAL report receiving grants from Alnylam Pharmaceuticals, Inc., Astellas Pharma, Inc., AstraZeneca Pharmaceuticals LP, Biodesix, Inc., Celgene Corporation, Cerner Enviza, GSK PLC, IQVIA Inc., Janssen Pharmaceuticals, Inc., Kantar Health, Myriad Genetic Laboratories, Inc., Novartis International AG, and Parexel International Corporation through the University of Utah or Western Institute for Veteran Research outside the submitted work. AG is an advisor for AbbVie, Aclaris Therapeutics, Anaptys Bio, Aristea Therapeutics, Boehringer Ingelheim, Bristol Myers Squibb, Incyte, Insmed, Janssen, Novartis, Pfizer, Sonoma Biotherapeutics, UCB, Union Therapeutics, Ventyx Biosciences, and Viela Biosciences, and receives honoraria and research grants from AbbVie, UCB, National Psoriasis Foundation, and CHORD COUSIN Collaboration (C3). He is co-copyright holder of the HS-IGA and HiSQOL instruments. ZW, KR, LG, CH, and AAG report no conflict of interests to disclose.

Disclaimer The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the US Government, or any of its agencies. This article may discuss unlabeled or investigational use of certain drugs. Please review the complete prescribing information for specific drugs or drug combinations— including indications, contraindications, warnings, and adverse effects—before administering pharmacologic therapy to patients.

Ethics and consent Institutional review boards at the Minneapolis Veterans Affairs Health Care System and Veterans Affairs Salt Lake City Healthcare System reviewed and approved this study (IRBNet ID #1698678-5). Patient information was deidentified, and patient consent was not required. Patient data will not be shared with third parties.

Acknowledgments This work was supported using resources and facilities of the US Department of Veterans Affairs Informatics and Computing Infrastructure, including data analytics conducted by its Precision Medicine research team.

Correspondence: Noah Goldfarb ([email protected])

Fed Pract. 2026;43(2). Published online February 16. doi:10.12788/fp.0667

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Zachary Wendland, MD, MPHa,b; Katelyn Rypka, BSa,b; Lindsey Greenlund, BSb; Claire Herzog, BSb; Fatai Y. Agiri, BSc; Amy A. Gravely, MAa; Lauren Orenstein, MD, MScd; Kathryn M. Pridgen, MAc; Amit Garg, MDe; Julie A. Lynch, PhD, MBA, RNc,f; Noah Goldfarb, MDa,b

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bUniversity of Minnesota, Minneapolis
cVeterans Affairs Salt Lake City Healthcare System, Utah
dEmory University, Atlanta, Georgia
eDonald and Barbara Zucker School of Medicine at Hofstra/ Northwell, Hempstead, New York
fUniversity of Utah School of Medicine, Salt Lake City

Author disclosures NG has participated in clinical trials with AbbVie, Pfizer, Chemocentrix, and DeepX Health, and has served on advisory boards and consulted for Novartis and Boehringer Ingelheim. LO has been an advisor for Chemocentryx, Novartis, and UCB, and has received grants from Pfizer. FYA, KMP, and JAL report receiving grants from Alnylam Pharmaceuticals, Inc., Astellas Pharma, Inc., AstraZeneca Pharmaceuticals LP, Biodesix, Inc., Celgene Corporation, Cerner Enviza, GSK PLC, IQVIA Inc., Janssen Pharmaceuticals, Inc., Kantar Health, Myriad Genetic Laboratories, Inc., Novartis International AG, and Parexel International Corporation through the University of Utah or Western Institute for Veteran Research outside the submitted work. AG is an advisor for AbbVie, Aclaris Therapeutics, Anaptys Bio, Aristea Therapeutics, Boehringer Ingelheim, Bristol Myers Squibb, Incyte, Insmed, Janssen, Novartis, Pfizer, Sonoma Biotherapeutics, UCB, Union Therapeutics, Ventyx Biosciences, and Viela Biosciences, and receives honoraria and research grants from AbbVie, UCB, National Psoriasis Foundation, and CHORD COUSIN Collaboration (C3). He is co-copyright holder of the HS-IGA and HiSQOL instruments. ZW, KR, LG, CH, and AAG report no conflict of interests to disclose.

Disclaimer The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the US Government, or any of its agencies. This article may discuss unlabeled or investigational use of certain drugs. Please review the complete prescribing information for specific drugs or drug combinations— including indications, contraindications, warnings, and adverse effects—before administering pharmacologic therapy to patients.

Ethics and consent Institutional review boards at the Minneapolis Veterans Affairs Health Care System and Veterans Affairs Salt Lake City Healthcare System reviewed and approved this study (IRBNet ID #1698678-5). Patient information was deidentified, and patient consent was not required. Patient data will not be shared with third parties.

Acknowledgments This work was supported using resources and facilities of the US Department of Veterans Affairs Informatics and Computing Infrastructure, including data analytics conducted by its Precision Medicine research team.

Correspondence: Noah Goldfarb ([email protected])

Fed Pract. 2026;43(2). Published online February 16. doi:10.12788/fp.0667

Author and Disclosure Information

Zachary Wendland, MD, MPHa,b; Katelyn Rypka, BSa,b; Lindsey Greenlund, BSb; Claire Herzog, BSb; Fatai Y. Agiri, BSc; Amy A. Gravely, MAa; Lauren Orenstein, MD, MScd; Kathryn M. Pridgen, MAc; Amit Garg, MDe; Julie A. Lynch, PhD, MBA, RNc,f; Noah Goldfarb, MDa,b

Author affiliations
aMinneapolis Veterans Affairs Health Care System, Minnesota
bUniversity of Minnesota, Minneapolis
cVeterans Affairs Salt Lake City Healthcare System, Utah
dEmory University, Atlanta, Georgia
eDonald and Barbara Zucker School of Medicine at Hofstra/ Northwell, Hempstead, New York
fUniversity of Utah School of Medicine, Salt Lake City

Author disclosures NG has participated in clinical trials with AbbVie, Pfizer, Chemocentrix, and DeepX Health, and has served on advisory boards and consulted for Novartis and Boehringer Ingelheim. LO has been an advisor for Chemocentryx, Novartis, and UCB, and has received grants from Pfizer. FYA, KMP, and JAL report receiving grants from Alnylam Pharmaceuticals, Inc., Astellas Pharma, Inc., AstraZeneca Pharmaceuticals LP, Biodesix, Inc., Celgene Corporation, Cerner Enviza, GSK PLC, IQVIA Inc., Janssen Pharmaceuticals, Inc., Kantar Health, Myriad Genetic Laboratories, Inc., Novartis International AG, and Parexel International Corporation through the University of Utah or Western Institute for Veteran Research outside the submitted work. AG is an advisor for AbbVie, Aclaris Therapeutics, Anaptys Bio, Aristea Therapeutics, Boehringer Ingelheim, Bristol Myers Squibb, Incyte, Insmed, Janssen, Novartis, Pfizer, Sonoma Biotherapeutics, UCB, Union Therapeutics, Ventyx Biosciences, and Viela Biosciences, and receives honoraria and research grants from AbbVie, UCB, National Psoriasis Foundation, and CHORD COUSIN Collaboration (C3). He is co-copyright holder of the HS-IGA and HiSQOL instruments. ZW, KR, LG, CH, and AAG report no conflict of interests to disclose.

Disclaimer The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the US Government, or any of its agencies. This article may discuss unlabeled or investigational use of certain drugs. Please review the complete prescribing information for specific drugs or drug combinations— including indications, contraindications, warnings, and adverse effects—before administering pharmacologic therapy to patients.

Ethics and consent Institutional review boards at the Minneapolis Veterans Affairs Health Care System and Veterans Affairs Salt Lake City Healthcare System reviewed and approved this study (IRBNet ID #1698678-5). Patient information was deidentified, and patient consent was not required. Patient data will not be shared with third parties.

Acknowledgments This work was supported using resources and facilities of the US Department of Veterans Affairs Informatics and Computing Infrastructure, including data analytics conducted by its Precision Medicine research team.

Correspondence: Noah Goldfarb ([email protected])

Fed Pract. 2026;43(2). Published online February 16. doi:10.12788/fp.0667

Article PDF
Article PDF

Hidradenitis suppurativa (HS) is a chronic, inflammatory skin disorder characterized by painful nodules, abscesses, and tunnels predominantly affecting intertriginous areas of the body.1,2 The condition poses significant challenges in terms of diagnosis, treatment, and quality of life for affected individuals. Various systemic therapies have been explored to manage this debilitating condition, with the emergence of biologic agents offering hope for improved outcomes. In 2015, adalimumab (ADA) was the first biologic approved by the US Food and Drug Administration (FDA) for the treatment of HS, followed by secukinumab in 2023 and bimekizumab in 2024. However, the off-label use of other biologics and/or tumor necrosis factor inhibitors such as infliximab (IFX) has become common practice.3

Although these therapies have demonstrated promising results in the treatment of HS, their widespread use may be hindered by accessibility and cost barriers. Orenstein et al analyzed data from the IBM Explorys platform from 2015 to 2020 and found that only 1.8% of patients diagnosed with HS had been prescribed ADA or IFX.4 More recently, Garg et al examined IBM MarketScan and IBM US Medicaid data from 2015 to 2018 to evaluate trends in clinical care and treatment. The prevalence of ADA and IFX prescriptions among patients with HS ranged from 2.3% to 8.0% (ADA) and 0.7% to 0.9% (IFX) for patients with commercial insurance, and 1.4% to 4.8% (ADA) and 0.5% to 0.7% (IFX) for patients with Medicaid.5 Biologics are often expensive, and the high cost associated with these therapies has been identified as a significant barrier to access for patients with HS, particularly those who lack adequate insurance coverage or face financial constraints.6

Furthermore, these barriers, particularly the financial barriers, are potentially compounded by the demographics of patients most notably affected by HS. In the US, a disproportionate incidence of HS has been noted in specific groups and age ranges, including women, individuals aged 18 to 29 years, and Black individuals.4 Orenstein et al found a statistically significant difference in use of ADA and IFX biologics based on age, sex, and race.4

The aim of this study was to examine the use of 2 biologics (ADA and IFX) in the Veterans Health Administration (VHA), a unique population in which financial barriers are reduced due to the single-payer government health care system structure. This design allowed for improved isolation and evaluation of variation in ADA and/or IFX prescription rates by demographics and health-related factors among patients with HS. To our knowledge, no studies have analyzed these metrics within the VHA.

Methods

This retrospective, cross-sectional analysis of VHA patients used data from the US Department of Veterans Affairs (VA) Corporate Data Warehouse, a data repository that provides access to longitudinal national electronic health record data for all veterans receiving care through VHA facilities. This study received ethical approval from institutional review boards at the Minneapolis Veterans Affairs Health Care System and VA Salt Lake City Healthcare System. Patient information was deidentified, and patient consent was not required.

Patients with HS were identified using ≥ 1 International Classification of Diseases (ICD) diagnostic code: (ICD-9 [705.83] or ICD-10 [L73.2]) between January 1, 2011, and December 31, 2021. The study included patients aged ≥ 18 years as of January 1, 2011, with ≥ 2 patient encounters during the postdiagnosis follow-up period, and with ≥ 1 encounter 6 months postindex. Patients with a biologic prescription prior to HS diagnosis were excluded. For this study, the term biologics refers to ADA and/or IFX prescriptions, unless otherwise specified. Only ADA and IFX were included in this analysis because ADA, a tumor necrosis factor (TNF)-á inhibitor, was the only FDA-approved medication at the time of the search, and IFX is another common TNF-α inhibitor used for the treatment of HS.

Statistical Analysis

We calculated logistic regression using SAS 9.4 (SAS Institute, Cary, NC). For each variable, the univariate relationship with biologic prescriptions was examined first, followed by the multivariate relationship controlling for all other variables. The following variables were controlled for in the multivariate models and were chosen a priori: sex, age, race, ethnicity, US region, hospital setting, current or previous tobacco use, obesity (defined as body mass index [BMI] ≥ 30), and Charlson Comorbidity Index (CCI).7

Results

Using ICD codes, we identified 29,483 individuals with ≥ 1 HS diagnosis (Figure 1). Of those identified, 1537 patients (5.21%) had been prescribed ≥ 1 biologic. The cohort was predominantly White (60.56%), male (75.27%), obese (59.34%), and had a history of current or previous tobacco use (73.47%) (Table 1). There were significant adjusted differences in prescription rates among veterans with HS based on age, race, and BMI. Notably, there was an age-dependent reduction in the odds of being prescribed a biologic in patients with HS. Compared with patients aged 18 to 44 years, patients aged 45 to 64 years (adjusted odds ratio [aOR], 0.63; 95% CI, 0.54–0.74; P < .001) and patients aged ≥ 65 years (aOR, 0.36; 95% CI, 0.27–0.48; P < .001) had significantly lower odds of receiving a biologic prescription (Table 2). Compared with White patients with HS, Native Hawaiian (NH) or Pacific Islander (PI) patients were less likely to be prescribed a biologic (aOR, 0.23; 95% CI, 0.06–0.92; P = .04). Patients with obesity had significantly higher odds of receiving a biologic prescription compared with patients without obesity (aOR, 1.47; 95% CI, 1.27– 1.71; P < .001).

FDP04302068_F1
FIGURE. STROBE Flowchart of Cohort
Included in Analysis.

 

After adjusting for the variables listed in Table 1, there were no significant differences in biologic prescription rates for men compared with women (aOR, 0.97; 95% CI, 0.83-1.12; P = .68). We observed slight variations in biologic prescriptions between US regions (Midwest 5.0%, East 4.2%, South 5.8%, West 4.6%), none of which were significantly different in the fully adjusted model. No statistically significant differences were found in biologic prescriptions between urban and rural VA settings (5.4% vs 4.8%; aOR, 1.06; 95% CI, 0.90–1.24; P = .47). Tobacco use was not associated with the rate of biologic prescription receipt (aOR, 1.14; 95% CI, 0.97–1.34; P = .11). After adjusting for other variables (as outlined in Table 2), no significant differences were found between CCI of 0 and 1 (aOR, 0.97; 95% CI, 0.82–1.16; P = .77) or between CCI of 0 and 2 (aOR, 0.89; 95% CI, 0.74–1.07; P = .22).7

FDP04302068_T1FDP04302068_T2

Discussion

The aim of the study was to ascertain potential discrepancies in biologic prescription patterns among patients with HS in the VHA by demographic and lifestyle behavior modifiers. Veteran cohorts are unique in composition, consisting predominantly of older White men within a single-payer health care system. The prevalence of biologic prescriptions in this population was low (5.2%), consistent with prior studies (1.8%–8.9%).4,5

We found a significant difference in ADA/IFX prescription patterns between White patients and NH/PI patients (aOR, 0.23; 95% CI, 0.06-0.92; P = .04). Further replication of this result is needed due to the small number of NH/PI patients included in the study (n = 241). Notably, we did not find a significant difference in the odds of Black patients being prescribed a biologic compared with White patients (aOR, 1.07; 95% CI, 0.92–1.25; P = .38), consistent with prior studies.4

In line with prior studies, age was associated with the likelihood of receiving a biologic prescription.4 Using the multivariate model adjusting for variables listed in Table 1, including CCI, patients aged 45 to 64 years and > 64 years were less likely to be prescribed a biologic than patients aged 18 to 44 years. HS disease activity could be a potential confounding variable, as HS severity may subside in some people with increasing age or menopause.8

Because different regions in the US have different sociopolitical ideologies and governing legislation, we hypothesized that there may be dissimilarities in the prevalence rates of biologic prescribing across various US regions. However, no significant differences were found in prescription patterns among US regions or between rural and urban settings. Previous research has demonstrated discernible disparities in both dermatologic care and clinical outcomes based on hospital setting (ie, urban vs rural).9-11

Tobacco use has been demonstrated to be associated with the development of HS.12 In a large retrospective analysis, Garg et al reported increased odds of receiving a new HS diagnosis in known tobacco users (aOR, 1.9; 95% CI, 1.8–2.0).13 The extent to which tobacco use affects HS severity is less understood. While some studies have found an association between smoking and HS severity, other analyses have failed to find this association.14,15 The effects of smoking cessation on the disease course of HS are unknown.16 This analysis, found no significant difference in prescriptions for biologics among patients with HS comparing current or previous tobacco users with nonusers.

There is a known positive correlation between increasing BMI and HS prevalence and severity that may be explained by the downstream effects of adipose tissue secretion of proinflammatory mediators and insulin resistance in the setting of chronic inflammation.12 This analysis found that patients with HS and obesity were 1.47 times more likely to be prescribed a biologic than patients with HS without obesity, which may be confounded by increased HS severity among patients with obesity. The initial concern when analyzing tobacco use and obesity was that clinician bias may result in a decrease in the prevalence of biologic use in these demographics, which was not supported in this study.

Although we identified few disparities, the results demonstrated a substantial underutilization of biologic therapies (5.2%), similar to the other US civilian studies (1.8-8.9%).4,5 While there is no current universal, standardized severity scoring system to evaluate HS (it is difficult to objectively define moderate to severe HS), estimates have shown that 40.3% to 65.8% of patients with HS have Hurley stage II or III.17-19 Therefore, only a small percentage of patients with moderate to severe disease were prescribed the only FDA-approved medication during this time period. The persistence of this underutilization within a medical system that reduces financial barriers suggests that nonfinancial barriers have a notable role in the underutilization of biologics.

For instance, risk of adverse events, particularly lymphoma and infection, has been cited by patients as a reason to avoid biologics. Additionally, treatment fatigue reduced some patients’ willingness to try new treatments, as did lack of knowledge about treatment options.6,20 Other reported barriers included the frequency of injections and fear of needles.6 Additionally, within the VA, ADA may require prior authorization at the local facility level.21 An established relationship with a dermatologist has been shown to significantly increase the odds of being prescribed a biologic medication in the face of these barriers.4 Future system-wide quality improvement initiatives could be implemented to identify patients with HS not followed by dermatology, with the goal of establishing care with a dermatologist.

Limitations

Limitations to this study include an inability to categorize HS disease severity and assess the degree to which disease severity confounded study findings, particularly in relation to tobacco use and obesity. The generalizability of this study is also limited because of the demographic characteristics of the veteran patient population, which is predominantly older, White, and male, whereas HS disproportionately affects younger, Black, and female individuals in the US.22 Despite these limitations, this study contributes valuable insights into the use of biologic therapies for veteran populations with HS using a national dataset.

Conclusions

This study was performed within a single-payer government medical system, likely reducing or removing the financial barriers that some patient populations may face when pursuing biologics for HS treatment. However, the prevalence of biologic use in this population was low overall (5.2%), suggesting that other factors play a role in the underutilization of biologics in HS. Consistent with previous studies, younger individuals were more likely to be prescribed a biologic, and no difference in prescription rates between Black and White patients was observed. Unlike previous studies, no significant difference in prescription rates between men and women was observed.

Hidradenitis suppurativa (HS) is a chronic, inflammatory skin disorder characterized by painful nodules, abscesses, and tunnels predominantly affecting intertriginous areas of the body.1,2 The condition poses significant challenges in terms of diagnosis, treatment, and quality of life for affected individuals. Various systemic therapies have been explored to manage this debilitating condition, with the emergence of biologic agents offering hope for improved outcomes. In 2015, adalimumab (ADA) was the first biologic approved by the US Food and Drug Administration (FDA) for the treatment of HS, followed by secukinumab in 2023 and bimekizumab in 2024. However, the off-label use of other biologics and/or tumor necrosis factor inhibitors such as infliximab (IFX) has become common practice.3

Although these therapies have demonstrated promising results in the treatment of HS, their widespread use may be hindered by accessibility and cost barriers. Orenstein et al analyzed data from the IBM Explorys platform from 2015 to 2020 and found that only 1.8% of patients diagnosed with HS had been prescribed ADA or IFX.4 More recently, Garg et al examined IBM MarketScan and IBM US Medicaid data from 2015 to 2018 to evaluate trends in clinical care and treatment. The prevalence of ADA and IFX prescriptions among patients with HS ranged from 2.3% to 8.0% (ADA) and 0.7% to 0.9% (IFX) for patients with commercial insurance, and 1.4% to 4.8% (ADA) and 0.5% to 0.7% (IFX) for patients with Medicaid.5 Biologics are often expensive, and the high cost associated with these therapies has been identified as a significant barrier to access for patients with HS, particularly those who lack adequate insurance coverage or face financial constraints.6

Furthermore, these barriers, particularly the financial barriers, are potentially compounded by the demographics of patients most notably affected by HS. In the US, a disproportionate incidence of HS has been noted in specific groups and age ranges, including women, individuals aged 18 to 29 years, and Black individuals.4 Orenstein et al found a statistically significant difference in use of ADA and IFX biologics based on age, sex, and race.4

The aim of this study was to examine the use of 2 biologics (ADA and IFX) in the Veterans Health Administration (VHA), a unique population in which financial barriers are reduced due to the single-payer government health care system structure. This design allowed for improved isolation and evaluation of variation in ADA and/or IFX prescription rates by demographics and health-related factors among patients with HS. To our knowledge, no studies have analyzed these metrics within the VHA.

Methods

This retrospective, cross-sectional analysis of VHA patients used data from the US Department of Veterans Affairs (VA) Corporate Data Warehouse, a data repository that provides access to longitudinal national electronic health record data for all veterans receiving care through VHA facilities. This study received ethical approval from institutional review boards at the Minneapolis Veterans Affairs Health Care System and VA Salt Lake City Healthcare System. Patient information was deidentified, and patient consent was not required.

Patients with HS were identified using ≥ 1 International Classification of Diseases (ICD) diagnostic code: (ICD-9 [705.83] or ICD-10 [L73.2]) between January 1, 2011, and December 31, 2021. The study included patients aged ≥ 18 years as of January 1, 2011, with ≥ 2 patient encounters during the postdiagnosis follow-up period, and with ≥ 1 encounter 6 months postindex. Patients with a biologic prescription prior to HS diagnosis were excluded. For this study, the term biologics refers to ADA and/or IFX prescriptions, unless otherwise specified. Only ADA and IFX were included in this analysis because ADA, a tumor necrosis factor (TNF)-á inhibitor, was the only FDA-approved medication at the time of the search, and IFX is another common TNF-α inhibitor used for the treatment of HS.

Statistical Analysis

We calculated logistic regression using SAS 9.4 (SAS Institute, Cary, NC). For each variable, the univariate relationship with biologic prescriptions was examined first, followed by the multivariate relationship controlling for all other variables. The following variables were controlled for in the multivariate models and were chosen a priori: sex, age, race, ethnicity, US region, hospital setting, current or previous tobacco use, obesity (defined as body mass index [BMI] ≥ 30), and Charlson Comorbidity Index (CCI).7

Results

Using ICD codes, we identified 29,483 individuals with ≥ 1 HS diagnosis (Figure 1). Of those identified, 1537 patients (5.21%) had been prescribed ≥ 1 biologic. The cohort was predominantly White (60.56%), male (75.27%), obese (59.34%), and had a history of current or previous tobacco use (73.47%) (Table 1). There were significant adjusted differences in prescription rates among veterans with HS based on age, race, and BMI. Notably, there was an age-dependent reduction in the odds of being prescribed a biologic in patients with HS. Compared with patients aged 18 to 44 years, patients aged 45 to 64 years (adjusted odds ratio [aOR], 0.63; 95% CI, 0.54–0.74; P < .001) and patients aged ≥ 65 years (aOR, 0.36; 95% CI, 0.27–0.48; P < .001) had significantly lower odds of receiving a biologic prescription (Table 2). Compared with White patients with HS, Native Hawaiian (NH) or Pacific Islander (PI) patients were less likely to be prescribed a biologic (aOR, 0.23; 95% CI, 0.06–0.92; P = .04). Patients with obesity had significantly higher odds of receiving a biologic prescription compared with patients without obesity (aOR, 1.47; 95% CI, 1.27– 1.71; P < .001).

FDP04302068_F1
FIGURE. STROBE Flowchart of Cohort
Included in Analysis.

 

After adjusting for the variables listed in Table 1, there were no significant differences in biologic prescription rates for men compared with women (aOR, 0.97; 95% CI, 0.83-1.12; P = .68). We observed slight variations in biologic prescriptions between US regions (Midwest 5.0%, East 4.2%, South 5.8%, West 4.6%), none of which were significantly different in the fully adjusted model. No statistically significant differences were found in biologic prescriptions between urban and rural VA settings (5.4% vs 4.8%; aOR, 1.06; 95% CI, 0.90–1.24; P = .47). Tobacco use was not associated with the rate of biologic prescription receipt (aOR, 1.14; 95% CI, 0.97–1.34; P = .11). After adjusting for other variables (as outlined in Table 2), no significant differences were found between CCI of 0 and 1 (aOR, 0.97; 95% CI, 0.82–1.16; P = .77) or between CCI of 0 and 2 (aOR, 0.89; 95% CI, 0.74–1.07; P = .22).7

FDP04302068_T1FDP04302068_T2

Discussion

The aim of the study was to ascertain potential discrepancies in biologic prescription patterns among patients with HS in the VHA by demographic and lifestyle behavior modifiers. Veteran cohorts are unique in composition, consisting predominantly of older White men within a single-payer health care system. The prevalence of biologic prescriptions in this population was low (5.2%), consistent with prior studies (1.8%–8.9%).4,5

We found a significant difference in ADA/IFX prescription patterns between White patients and NH/PI patients (aOR, 0.23; 95% CI, 0.06-0.92; P = .04). Further replication of this result is needed due to the small number of NH/PI patients included in the study (n = 241). Notably, we did not find a significant difference in the odds of Black patients being prescribed a biologic compared with White patients (aOR, 1.07; 95% CI, 0.92–1.25; P = .38), consistent with prior studies.4

In line with prior studies, age was associated with the likelihood of receiving a biologic prescription.4 Using the multivariate model adjusting for variables listed in Table 1, including CCI, patients aged 45 to 64 years and > 64 years were less likely to be prescribed a biologic than patients aged 18 to 44 years. HS disease activity could be a potential confounding variable, as HS severity may subside in some people with increasing age or menopause.8

Because different regions in the US have different sociopolitical ideologies and governing legislation, we hypothesized that there may be dissimilarities in the prevalence rates of biologic prescribing across various US regions. However, no significant differences were found in prescription patterns among US regions or between rural and urban settings. Previous research has demonstrated discernible disparities in both dermatologic care and clinical outcomes based on hospital setting (ie, urban vs rural).9-11

Tobacco use has been demonstrated to be associated with the development of HS.12 In a large retrospective analysis, Garg et al reported increased odds of receiving a new HS diagnosis in known tobacco users (aOR, 1.9; 95% CI, 1.8–2.0).13 The extent to which tobacco use affects HS severity is less understood. While some studies have found an association between smoking and HS severity, other analyses have failed to find this association.14,15 The effects of smoking cessation on the disease course of HS are unknown.16 This analysis, found no significant difference in prescriptions for biologics among patients with HS comparing current or previous tobacco users with nonusers.

There is a known positive correlation between increasing BMI and HS prevalence and severity that may be explained by the downstream effects of adipose tissue secretion of proinflammatory mediators and insulin resistance in the setting of chronic inflammation.12 This analysis found that patients with HS and obesity were 1.47 times more likely to be prescribed a biologic than patients with HS without obesity, which may be confounded by increased HS severity among patients with obesity. The initial concern when analyzing tobacco use and obesity was that clinician bias may result in a decrease in the prevalence of biologic use in these demographics, which was not supported in this study.

Although we identified few disparities, the results demonstrated a substantial underutilization of biologic therapies (5.2%), similar to the other US civilian studies (1.8-8.9%).4,5 While there is no current universal, standardized severity scoring system to evaluate HS (it is difficult to objectively define moderate to severe HS), estimates have shown that 40.3% to 65.8% of patients with HS have Hurley stage II or III.17-19 Therefore, only a small percentage of patients with moderate to severe disease were prescribed the only FDA-approved medication during this time period. The persistence of this underutilization within a medical system that reduces financial barriers suggests that nonfinancial barriers have a notable role in the underutilization of biologics.

For instance, risk of adverse events, particularly lymphoma and infection, has been cited by patients as a reason to avoid biologics. Additionally, treatment fatigue reduced some patients’ willingness to try new treatments, as did lack of knowledge about treatment options.6,20 Other reported barriers included the frequency of injections and fear of needles.6 Additionally, within the VA, ADA may require prior authorization at the local facility level.21 An established relationship with a dermatologist has been shown to significantly increase the odds of being prescribed a biologic medication in the face of these barriers.4 Future system-wide quality improvement initiatives could be implemented to identify patients with HS not followed by dermatology, with the goal of establishing care with a dermatologist.

Limitations

Limitations to this study include an inability to categorize HS disease severity and assess the degree to which disease severity confounded study findings, particularly in relation to tobacco use and obesity. The generalizability of this study is also limited because of the demographic characteristics of the veteran patient population, which is predominantly older, White, and male, whereas HS disproportionately affects younger, Black, and female individuals in the US.22 Despite these limitations, this study contributes valuable insights into the use of biologic therapies for veteran populations with HS using a national dataset.

Conclusions

This study was performed within a single-payer government medical system, likely reducing or removing the financial barriers that some patient populations may face when pursuing biologics for HS treatment. However, the prevalence of biologic use in this population was low overall (5.2%), suggesting that other factors play a role in the underutilization of biologics in HS. Consistent with previous studies, younger individuals were more likely to be prescribed a biologic, and no difference in prescription rates between Black and White patients was observed. Unlike previous studies, no significant difference in prescription rates between men and women was observed.

References
  1. Goldburg SR, Strober BE, Payette MJ. Hidradenitis suppurativa: epidemiology, clinical presentation, and pathogenesis. J Am Acad Dermatol. 2020;82:1045-1058. doi:10.1016/j.jaad.2019.08.090
  2. Tchero H, Herlin C, Bekara F, et al. Hidradenitis suppurativa: a systematic review and meta-analysis of therapeutic interventions. Indian J Dermatol Venereol Leprol. 2019;85:248-257. doi:10.4103/ijdvl.IJDVL_69_18
  3. Shih T, Lee K, Grogan T, et al. Infliximab in hidradenitis suppurativa: a systematic review and meta-analysis. Dermatol Ther. 2022;35:e15691. doi:10.1111/dth.15691
  4. Orenstein LAV, Wright S, Strunk A, et al. Low prescription of tumor necrosis alpha inhibitors in hidradenitis suppurativa: a cross-sectional analysis. J Am Acad Dermatol. 2021;84:1399-1401. doi:10.1016/j.jaad.2020.07.108
  5. Garg A, Naik HB, Alavi A, et al. Real-world findings on the characteristics and treatment exposures of patients with hidradenitis suppurativa from US claims data. Dermatol Ther (Heidelb). 2023;13:581-594. doi:10.1007/s13555-022-00872-1
  6. De DR, Shih T, Fixsen D, et al. Biologic use in hidradenitis suppurativa: patient perspectives and barriers. J Dermatolog Treat. 2022;33:3060-3062. doi:10.1080/09546634.2022.2089336
  7. Charlson ME, Pompei P, Ales KL, et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40:373- 383. doi:10.1016/0021-9681(87)90171-8
  8. von der Werth JM, Williams HC. The natural history of hidradenitis suppurativa. J Eur Acad Dermatol Venereol. 2000;14:389-392. doi:10.1046/j.1468-3083.2000.00087.x
  9. Silverberg JI, Barbarot S, Gadkari A, et al. Atopic dermatitis in the pediatric population: a cross-sectional, international epidemiologic study. Ann Allergy Asthma Immunol. 2021;126:417-428.e2. doi:10.1016/j.anai.2020.12.020
  10. Wu YP, Parsons B, Jo Y, et al. Outdoor activities and sunburn among urban and rural families in a Western region of the US: implications for skin cancer prevention. Prev Med Rep. 2022;29:101914. doi:10.1016/j.pmedr.2022.101914
  11. Mannschreck DB, Li X, Okoye G. Rural melanoma patients in Maryland do not present with more advanced disease than urban patients. Dermatol Online J. 2021;27. doi:10.5070/D327553607
  12. Garg A, Malviya N, Strunk A, et al. Comorbidity screening in hidradenitis suppurativa: evidence-based recommendations from the US and Canadian Hidradenitis Suppurativa Foundations. J Am Acad Dermatol. 2022;86:1092-1101. doi:10.1016/j.jaad.2021.01.059
  13. Garg A, Papagermanos V, Midura M, et al. Incidence of hidradenitis suppurativa among tobacco smokers: a population- based retrospective analysis in the U.S.A. Br J Dermatol. 2018;178:709-714. doi:10.1111/bjd.15939
  14. Sartorius K, Emtestam L, Jemec GBE, et al. Objective scoring of hidradenitis suppurativa reflecting the role of tobacco smoking and obesity. Br J Dermatol. 2009;161:831- 839. doi:10.1111/j.1365-2133.2009.09198.x
  15. Canoui-Poitrine F, Revuz JE, Wolkenstein P, et al. Clinical characteristics of a series of 302 French patients with hidradenitis suppurativa, with an analysis of factors associated with disease severity. J Am Acad Dermatol. 2009;61:51-57. doi:10.1016/j.jaad.2009.02.013
  16. Dufour DN, Emtestam L, Jemec GB. Hidradenitis suppurativa: a common and burdensome, yet under-recognised, inflammatory skin disease. Postgrad Med J. 2014;90:216- 221. doi:10.1136/postgradmedj-2013-131994
  17. Vazquez BG, Alikhan A, Weaver AL, et al. Incidence of hidradenitis suppurativa and associated factors: a population- based study of Olmsted County, Minnesota. J Invest Dermatol. 2013;133:97-103. doi:10.1038/jid.2012.255
  18. Vanlaerhoven AMJD, Ardon CB, van Straalen KR, et al. Hurley III hidradenitis suppurativa has an aggressive disease course. Dermatology. 2018;234:232-233. doi:10.1159/000491547
  19. Shahi V, Alikhan A, Vazquez BG, et al. Prevalence of hidradenitis suppurativa: a population-based study in Olmsted County, Minnesota. Dermatology. 2014;229:154-158. doi:10.1159/000363381
  20. Salame N, Sow YN, Siira MR, et al. Factors affecting treatment selection among patients with hidradenitis suppurativa. JAMA Dermatol. 2024;160:179. doi:10.1001/jamadermatol.2023.5425
  21. VA Formulary Advisor: ADALIMUMAB-BWWD INJ,SOLN. US Department of Veterans Affairs. Updated December 17, 2025. Accessed January 15, 2026. https://www.va.gov/formularyadvisor/drugs/4042383-ADALIMUMAB-BWWD-INJ-SOLN
  22. Garg A, Lavian J, Lin G, et al. Incidence of hidradenitis suppurativa in the United States: a sex- and age-adjusted population analysis. J Am Acad Dermatol. 2017;77:118- 122. doi:10.1016/j.jaad.2017.02.005
References
  1. Goldburg SR, Strober BE, Payette MJ. Hidradenitis suppurativa: epidemiology, clinical presentation, and pathogenesis. J Am Acad Dermatol. 2020;82:1045-1058. doi:10.1016/j.jaad.2019.08.090
  2. Tchero H, Herlin C, Bekara F, et al. Hidradenitis suppurativa: a systematic review and meta-analysis of therapeutic interventions. Indian J Dermatol Venereol Leprol. 2019;85:248-257. doi:10.4103/ijdvl.IJDVL_69_18
  3. Shih T, Lee K, Grogan T, et al. Infliximab in hidradenitis suppurativa: a systematic review and meta-analysis. Dermatol Ther. 2022;35:e15691. doi:10.1111/dth.15691
  4. Orenstein LAV, Wright S, Strunk A, et al. Low prescription of tumor necrosis alpha inhibitors in hidradenitis suppurativa: a cross-sectional analysis. J Am Acad Dermatol. 2021;84:1399-1401. doi:10.1016/j.jaad.2020.07.108
  5. Garg A, Naik HB, Alavi A, et al. Real-world findings on the characteristics and treatment exposures of patients with hidradenitis suppurativa from US claims data. Dermatol Ther (Heidelb). 2023;13:581-594. doi:10.1007/s13555-022-00872-1
  6. De DR, Shih T, Fixsen D, et al. Biologic use in hidradenitis suppurativa: patient perspectives and barriers. J Dermatolog Treat. 2022;33:3060-3062. doi:10.1080/09546634.2022.2089336
  7. Charlson ME, Pompei P, Ales KL, et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40:373- 383. doi:10.1016/0021-9681(87)90171-8
  8. von der Werth JM, Williams HC. The natural history of hidradenitis suppurativa. J Eur Acad Dermatol Venereol. 2000;14:389-392. doi:10.1046/j.1468-3083.2000.00087.x
  9. Silverberg JI, Barbarot S, Gadkari A, et al. Atopic dermatitis in the pediatric population: a cross-sectional, international epidemiologic study. Ann Allergy Asthma Immunol. 2021;126:417-428.e2. doi:10.1016/j.anai.2020.12.020
  10. Wu YP, Parsons B, Jo Y, et al. Outdoor activities and sunburn among urban and rural families in a Western region of the US: implications for skin cancer prevention. Prev Med Rep. 2022;29:101914. doi:10.1016/j.pmedr.2022.101914
  11. Mannschreck DB, Li X, Okoye G. Rural melanoma patients in Maryland do not present with more advanced disease than urban patients. Dermatol Online J. 2021;27. doi:10.5070/D327553607
  12. Garg A, Malviya N, Strunk A, et al. Comorbidity screening in hidradenitis suppurativa: evidence-based recommendations from the US and Canadian Hidradenitis Suppurativa Foundations. J Am Acad Dermatol. 2022;86:1092-1101. doi:10.1016/j.jaad.2021.01.059
  13. Garg A, Papagermanos V, Midura M, et al. Incidence of hidradenitis suppurativa among tobacco smokers: a population- based retrospective analysis in the U.S.A. Br J Dermatol. 2018;178:709-714. doi:10.1111/bjd.15939
  14. Sartorius K, Emtestam L, Jemec GBE, et al. Objective scoring of hidradenitis suppurativa reflecting the role of tobacco smoking and obesity. Br J Dermatol. 2009;161:831- 839. doi:10.1111/j.1365-2133.2009.09198.x
  15. Canoui-Poitrine F, Revuz JE, Wolkenstein P, et al. Clinical characteristics of a series of 302 French patients with hidradenitis suppurativa, with an analysis of factors associated with disease severity. J Am Acad Dermatol. 2009;61:51-57. doi:10.1016/j.jaad.2009.02.013
  16. Dufour DN, Emtestam L, Jemec GB. Hidradenitis suppurativa: a common and burdensome, yet under-recognised, inflammatory skin disease. Postgrad Med J. 2014;90:216- 221. doi:10.1136/postgradmedj-2013-131994
  17. Vazquez BG, Alikhan A, Weaver AL, et al. Incidence of hidradenitis suppurativa and associated factors: a population- based study of Olmsted County, Minnesota. J Invest Dermatol. 2013;133:97-103. doi:10.1038/jid.2012.255
  18. Vanlaerhoven AMJD, Ardon CB, van Straalen KR, et al. Hurley III hidradenitis suppurativa has an aggressive disease course. Dermatology. 2018;234:232-233. doi:10.1159/000491547
  19. Shahi V, Alikhan A, Vazquez BG, et al. Prevalence of hidradenitis suppurativa: a population-based study in Olmsted County, Minnesota. Dermatology. 2014;229:154-158. doi:10.1159/000363381
  20. Salame N, Sow YN, Siira MR, et al. Factors affecting treatment selection among patients with hidradenitis suppurativa. JAMA Dermatol. 2024;160:179. doi:10.1001/jamadermatol.2023.5425
  21. VA Formulary Advisor: ADALIMUMAB-BWWD INJ,SOLN. US Department of Veterans Affairs. Updated December 17, 2025. Accessed January 15, 2026. https://www.va.gov/formularyadvisor/drugs/4042383-ADALIMUMAB-BWWD-INJ-SOLN
  22. Garg A, Lavian J, Lin G, et al. Incidence of hidradenitis suppurativa in the United States: a sex- and age-adjusted population analysis. J Am Acad Dermatol. 2017;77:118- 122. doi:10.1016/j.jaad.2017.02.005
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Cross-Sectional Analysis of Biologic Use in the Treatment of Veterans With Hidradenitis Suppurativa

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AI Tool Helps Patients Assess Bowel Preparation for Colonoscopy

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AI Tool Helps Patients Assess Bowel Preparation for Colonoscopy

TOPLINE:

An artificial intelligence (AI) model, developed using stool images, accurately assessed whether a patient’s bowel preparation was sufficient for colonoscopy. The best version of the model achieved an area under the receiver operating characteristic curve (AUC) of 0.95, accuracy of 0.90, sensitivity of 0.93, and specificity of 0.86.

METHODOLOGY:

  • Patients often need help during bowel preparation for colonoscopy, which increases staff workload; up to 20%-25% of colonoscopies are reported to be inadequately prepared. Researchers developed and tested an AI tool (AI-PREPOO) using stool images to assess whether patients were ready for colonoscopy.
  • They conducted a multicenter observational study in Japan between 2022 and 2023 that included 37 patients scheduled for colonoscopy (median age, 57 years; 45.9% women).
  • After starting consumption of a 2-liter polyethylene glycol solution, patients used smartphones to take photos of their stool in the toilet after each bowel movement and uploaded the images to a secure web server.
  • The images were divided into training and test sets. Images were classified as “ready” for colonoscopy when the stool was clear or light yellow and watery with no solid content.
  • Four image-recognition models based on different deep learning architectures were developed using transfer learning to classify readiness for colonoscopy.

TAKEAWAY:

  • Researchers collected 282 stool images, with 141 classified as ready and 141 as not ready. Of these, 224 images were used for training (the number augmented to 2240 images) and 58 for testing.
  • All four AI-PREPOO models showed high performance, with AUCs ranging from 0.92 to 0.95; pairwise differences in AUCs were not significant.
  • The AI-PREPOO 1 model, based on the MobileNetV3-Small architecture, showed the most balanced performance, with an AUC of 0.95, accuracy of 0.90, sensitivity of 0.93, and specificity of 0.86 on the test set.
  • During colonoscopy, all patients had a Boston Bowel Preparation Scale score of 6 or higher, indicating that “ready” images corresponded to an adequately prepared bowel.

IN PRACTICE:

“If implemented as a mobile application, our model would allow patients to quickly and independently assess bowel preparation adequacy, reducing reliance on nurses and alleviating embarrassment associated with sharing stool images. This approach could also lessen nurses’ workload by minimizing unnecessary inquiries and preventing excessive or insufficient bowel preparation due to uncertainty,” the authors wrote.

SOURCE:

This study was led by Kosuke Kojima, Graduate School of Medical and Dental Sciences, Niigata University in Niigata, Japan. It was published online in the Journal of Gastroenterology and Hepatology.

LIMITATIONS:

The small dataset limited generalizability and increased the risk for overfitting. In real-world practice, stool images might vary in lighting, angle, focus, zoom, and background; thus, a larger and more diverse dataset was needed. The model lacked external validation in an independent or prospective cohort.

DISCLOSURES:

This study received support from the Japanese Foundation for Research and Promotion of Endoscopy. The authors reported having no conflicts of interest.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

A version of this article first appeared on Medscape.com.

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TOPLINE:

An artificial intelligence (AI) model, developed using stool images, accurately assessed whether a patient’s bowel preparation was sufficient for colonoscopy. The best version of the model achieved an area under the receiver operating characteristic curve (AUC) of 0.95, accuracy of 0.90, sensitivity of 0.93, and specificity of 0.86.

METHODOLOGY:

  • Patients often need help during bowel preparation for colonoscopy, which increases staff workload; up to 20%-25% of colonoscopies are reported to be inadequately prepared. Researchers developed and tested an AI tool (AI-PREPOO) using stool images to assess whether patients were ready for colonoscopy.
  • They conducted a multicenter observational study in Japan between 2022 and 2023 that included 37 patients scheduled for colonoscopy (median age, 57 years; 45.9% women).
  • After starting consumption of a 2-liter polyethylene glycol solution, patients used smartphones to take photos of their stool in the toilet after each bowel movement and uploaded the images to a secure web server.
  • The images were divided into training and test sets. Images were classified as “ready” for colonoscopy when the stool was clear or light yellow and watery with no solid content.
  • Four image-recognition models based on different deep learning architectures were developed using transfer learning to classify readiness for colonoscopy.

TAKEAWAY:

  • Researchers collected 282 stool images, with 141 classified as ready and 141 as not ready. Of these, 224 images were used for training (the number augmented to 2240 images) and 58 for testing.
  • All four AI-PREPOO models showed high performance, with AUCs ranging from 0.92 to 0.95; pairwise differences in AUCs were not significant.
  • The AI-PREPOO 1 model, based on the MobileNetV3-Small architecture, showed the most balanced performance, with an AUC of 0.95, accuracy of 0.90, sensitivity of 0.93, and specificity of 0.86 on the test set.
  • During colonoscopy, all patients had a Boston Bowel Preparation Scale score of 6 or higher, indicating that “ready” images corresponded to an adequately prepared bowel.

IN PRACTICE:

“If implemented as a mobile application, our model would allow patients to quickly and independently assess bowel preparation adequacy, reducing reliance on nurses and alleviating embarrassment associated with sharing stool images. This approach could also lessen nurses’ workload by minimizing unnecessary inquiries and preventing excessive or insufficient bowel preparation due to uncertainty,” the authors wrote.

SOURCE:

This study was led by Kosuke Kojima, Graduate School of Medical and Dental Sciences, Niigata University in Niigata, Japan. It was published online in the Journal of Gastroenterology and Hepatology.

LIMITATIONS:

The small dataset limited generalizability and increased the risk for overfitting. In real-world practice, stool images might vary in lighting, angle, focus, zoom, and background; thus, a larger and more diverse dataset was needed. The model lacked external validation in an independent or prospective cohort.

DISCLOSURES:

This study received support from the Japanese Foundation for Research and Promotion of Endoscopy. The authors reported having no conflicts of interest.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

A version of this article first appeared on Medscape.com.

TOPLINE:

An artificial intelligence (AI) model, developed using stool images, accurately assessed whether a patient’s bowel preparation was sufficient for colonoscopy. The best version of the model achieved an area under the receiver operating characteristic curve (AUC) of 0.95, accuracy of 0.90, sensitivity of 0.93, and specificity of 0.86.

METHODOLOGY:

  • Patients often need help during bowel preparation for colonoscopy, which increases staff workload; up to 20%-25% of colonoscopies are reported to be inadequately prepared. Researchers developed and tested an AI tool (AI-PREPOO) using stool images to assess whether patients were ready for colonoscopy.
  • They conducted a multicenter observational study in Japan between 2022 and 2023 that included 37 patients scheduled for colonoscopy (median age, 57 years; 45.9% women).
  • After starting consumption of a 2-liter polyethylene glycol solution, patients used smartphones to take photos of their stool in the toilet after each bowel movement and uploaded the images to a secure web server.
  • The images were divided into training and test sets. Images were classified as “ready” for colonoscopy when the stool was clear or light yellow and watery with no solid content.
  • Four image-recognition models based on different deep learning architectures were developed using transfer learning to classify readiness for colonoscopy.

TAKEAWAY:

  • Researchers collected 282 stool images, with 141 classified as ready and 141 as not ready. Of these, 224 images were used for training (the number augmented to 2240 images) and 58 for testing.
  • All four AI-PREPOO models showed high performance, with AUCs ranging from 0.92 to 0.95; pairwise differences in AUCs were not significant.
  • The AI-PREPOO 1 model, based on the MobileNetV3-Small architecture, showed the most balanced performance, with an AUC of 0.95, accuracy of 0.90, sensitivity of 0.93, and specificity of 0.86 on the test set.
  • During colonoscopy, all patients had a Boston Bowel Preparation Scale score of 6 or higher, indicating that “ready” images corresponded to an adequately prepared bowel.

IN PRACTICE:

“If implemented as a mobile application, our model would allow patients to quickly and independently assess bowel preparation adequacy, reducing reliance on nurses and alleviating embarrassment associated with sharing stool images. This approach could also lessen nurses’ workload by minimizing unnecessary inquiries and preventing excessive or insufficient bowel preparation due to uncertainty,” the authors wrote.

SOURCE:

This study was led by Kosuke Kojima, Graduate School of Medical and Dental Sciences, Niigata University in Niigata, Japan. It was published online in the Journal of Gastroenterology and Hepatology.

LIMITATIONS:

The small dataset limited generalizability and increased the risk for overfitting. In real-world practice, stool images might vary in lighting, angle, focus, zoom, and background; thus, a larger and more diverse dataset was needed. The model lacked external validation in an independent or prospective cohort.

DISCLOSURES:

This study received support from the Japanese Foundation for Research and Promotion of Endoscopy. The authors reported having no conflicts of interest.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

A version of this article first appeared on Medscape.com.

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AI Tool Helps Patients Assess Bowel Preparation for Colonoscopy

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Military-Backed French Biotech Brings Ricin Antidote

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Military-Backed French Biotech Brings Ricin Antidote

France has authorized Ricimed, the first antibody-based treatment specifically indicated for acute ricin intoxication, providing clinicians with a targeted option beyond supportive care for exposure to one of the most lethal naturally occurring toxins.

Fabentech is a French biopharmaceutical company specializing in medical countermeasures against biological threats and infectious diseases.

The polyclonal antibody technology used in the development of Ricimed has received marketing authorization in France as a treatment for ricin poisoning. Ricin is a highly toxic natural substance that can cause death within hours to a few days of exposure.

Supported by the Ministry of Armed Forces and Veterans Affairs (Directorate General of Armaments [DGA] and Armed Forces Health Service) in France, Ricimed is the first approved antidote for ricin poisoning, a condition for which treatment was previously limited to supportive measures alone.

Historical Incident

One incident, in particular, remains etched in espionage history. On September 7, 1978 in London during the Cold War, Bulgarian dissident writer Georgi Markov, living in exile, was struck by the umbrella of a passer-by while waiting at a bus stop. He felt a slight sting. Four days later, he died in the hospital due to a sudden and unexplained illness. An autopsy revealed that he had been poisoned by a tiny metal pellet implanted at the tip of an umbrella containing ricin, a lethal toxin. The legend of the “Bulgarian umbrella,” later invoked in other assassination attempts, was born.

Since then, although Markov remains the only known individual to have been killed by ricin poisoning, this theoretically extremely toxic substance, which can be manufactured relatively easily from castor beans, a widely available plant, has continued to fascinate authors of thrillers and spy novels.

Numerous works of fiction depict characters who succumb to ricin poisoning. The toxin is notably portrayed as a favored weapon of the main character in the hit television series Breaking Bad.

However, ricin is not confined to the realm of science fiction. For several years, authorities in various countries have feared that extremist groups could carry out attacks using ricin. The threat has been taken particularly seriously since 2018, when a clandestine ricin laboratory operated by members of the Islamic State was dismantled in Germany. Since then, several similar attack plots have been thwarted.

This context triggered a race among major powers to develop an effective antidote as quickly as possible. In this effort, Fabentech has risen to a challenge.

“Having demonstrated its ability to target and then neutralize ricin before it causes irreparable damage, Ricimed is a treatment that works based on polyclonal antibodies and compensates for the absence of a vaccine or specific treatment,” Fabentech said in a press release.

The polyclonal antibody technology used by Fabentech offers potential for the development of antidotes against bioterrorist attacks and for the treatment of many infectious diseases.

Ricimed contributed to the deployment of a European health shield against intentional biological threats in France.

Military Backing

Speaking to Le Figaro, France’s oldest national newspaper, Fabentech CEO Sébastien Iva explained that ricin disrupts the body by halting cell function, while noting several other drug candidates in development at the firm.

Typically, the lungs sustain fatal damage. Our treatment interrupts this toxic process. In animals administered the antidote, we observed pulmonary function recovery, allowing survival.

Given that the possibility of terrorist attacks using ricin is considered a national security issue, Fabentech benefited from the support by the Ministry of the Armed Forces and the DGA and lasted nearly a decade of research and development work.

The granting of marketing authorisation was also supported by the French Armed Forces and welcomed by the French Minister of the Armed Forces, Catherine Vautrin, who previously served as France’s Minister of Labour, Health, and Solidarity.

“Supporting the development of companies in France capable of manufacturing antidotes against certain biological agents helps guarantee the operational superiority of our armed forces. Developing and producing such drugs when they do not yet exist on the market is also serving the nation and the public interest,” she said.

Although the threat posed by ricin remains hypothetical, Fabentech reports a strong interest from potential clients, with many countries seeking protection against possible bioterrorist attacks.

The DGA had already placed an order for several doses of Ricimed for deployment in France. For optimal effectiveness, the antidote must be administered within 6 hours of poisoning. Iva confirmed that multiple countries had already expressed interest in acquiring the antidote.

This story was translated from JIM, part of the Medscape Professional Network.

A version of this article first appeared on Medscape.com.

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France has authorized Ricimed, the first antibody-based treatment specifically indicated for acute ricin intoxication, providing clinicians with a targeted option beyond supportive care for exposure to one of the most lethal naturally occurring toxins.

Fabentech is a French biopharmaceutical company specializing in medical countermeasures against biological threats and infectious diseases.

The polyclonal antibody technology used in the development of Ricimed has received marketing authorization in France as a treatment for ricin poisoning. Ricin is a highly toxic natural substance that can cause death within hours to a few days of exposure.

Supported by the Ministry of Armed Forces and Veterans Affairs (Directorate General of Armaments [DGA] and Armed Forces Health Service) in France, Ricimed is the first approved antidote for ricin poisoning, a condition for which treatment was previously limited to supportive measures alone.

Historical Incident

One incident, in particular, remains etched in espionage history. On September 7, 1978 in London during the Cold War, Bulgarian dissident writer Georgi Markov, living in exile, was struck by the umbrella of a passer-by while waiting at a bus stop. He felt a slight sting. Four days later, he died in the hospital due to a sudden and unexplained illness. An autopsy revealed that he had been poisoned by a tiny metal pellet implanted at the tip of an umbrella containing ricin, a lethal toxin. The legend of the “Bulgarian umbrella,” later invoked in other assassination attempts, was born.

Since then, although Markov remains the only known individual to have been killed by ricin poisoning, this theoretically extremely toxic substance, which can be manufactured relatively easily from castor beans, a widely available plant, has continued to fascinate authors of thrillers and spy novels.

Numerous works of fiction depict characters who succumb to ricin poisoning. The toxin is notably portrayed as a favored weapon of the main character in the hit television series Breaking Bad.

However, ricin is not confined to the realm of science fiction. For several years, authorities in various countries have feared that extremist groups could carry out attacks using ricin. The threat has been taken particularly seriously since 2018, when a clandestine ricin laboratory operated by members of the Islamic State was dismantled in Germany. Since then, several similar attack plots have been thwarted.

This context triggered a race among major powers to develop an effective antidote as quickly as possible. In this effort, Fabentech has risen to a challenge.

“Having demonstrated its ability to target and then neutralize ricin before it causes irreparable damage, Ricimed is a treatment that works based on polyclonal antibodies and compensates for the absence of a vaccine or specific treatment,” Fabentech said in a press release.

The polyclonal antibody technology used by Fabentech offers potential for the development of antidotes against bioterrorist attacks and for the treatment of many infectious diseases.

Ricimed contributed to the deployment of a European health shield against intentional biological threats in France.

Military Backing

Speaking to Le Figaro, France’s oldest national newspaper, Fabentech CEO Sébastien Iva explained that ricin disrupts the body by halting cell function, while noting several other drug candidates in development at the firm.

Typically, the lungs sustain fatal damage. Our treatment interrupts this toxic process. In animals administered the antidote, we observed pulmonary function recovery, allowing survival.

Given that the possibility of terrorist attacks using ricin is considered a national security issue, Fabentech benefited from the support by the Ministry of the Armed Forces and the DGA and lasted nearly a decade of research and development work.

The granting of marketing authorisation was also supported by the French Armed Forces and welcomed by the French Minister of the Armed Forces, Catherine Vautrin, who previously served as France’s Minister of Labour, Health, and Solidarity.

“Supporting the development of companies in France capable of manufacturing antidotes against certain biological agents helps guarantee the operational superiority of our armed forces. Developing and producing such drugs when they do not yet exist on the market is also serving the nation and the public interest,” she said.

Although the threat posed by ricin remains hypothetical, Fabentech reports a strong interest from potential clients, with many countries seeking protection against possible bioterrorist attacks.

The DGA had already placed an order for several doses of Ricimed for deployment in France. For optimal effectiveness, the antidote must be administered within 6 hours of poisoning. Iva confirmed that multiple countries had already expressed interest in acquiring the antidote.

This story was translated from JIM, part of the Medscape Professional Network.

A version of this article first appeared on Medscape.com.

France has authorized Ricimed, the first antibody-based treatment specifically indicated for acute ricin intoxication, providing clinicians with a targeted option beyond supportive care for exposure to one of the most lethal naturally occurring toxins.

Fabentech is a French biopharmaceutical company specializing in medical countermeasures against biological threats and infectious diseases.

The polyclonal antibody technology used in the development of Ricimed has received marketing authorization in France as a treatment for ricin poisoning. Ricin is a highly toxic natural substance that can cause death within hours to a few days of exposure.

Supported by the Ministry of Armed Forces and Veterans Affairs (Directorate General of Armaments [DGA] and Armed Forces Health Service) in France, Ricimed is the first approved antidote for ricin poisoning, a condition for which treatment was previously limited to supportive measures alone.

Historical Incident

One incident, in particular, remains etched in espionage history. On September 7, 1978 in London during the Cold War, Bulgarian dissident writer Georgi Markov, living in exile, was struck by the umbrella of a passer-by while waiting at a bus stop. He felt a slight sting. Four days later, he died in the hospital due to a sudden and unexplained illness. An autopsy revealed that he had been poisoned by a tiny metal pellet implanted at the tip of an umbrella containing ricin, a lethal toxin. The legend of the “Bulgarian umbrella,” later invoked in other assassination attempts, was born.

Since then, although Markov remains the only known individual to have been killed by ricin poisoning, this theoretically extremely toxic substance, which can be manufactured relatively easily from castor beans, a widely available plant, has continued to fascinate authors of thrillers and spy novels.

Numerous works of fiction depict characters who succumb to ricin poisoning. The toxin is notably portrayed as a favored weapon of the main character in the hit television series Breaking Bad.

However, ricin is not confined to the realm of science fiction. For several years, authorities in various countries have feared that extremist groups could carry out attacks using ricin. The threat has been taken particularly seriously since 2018, when a clandestine ricin laboratory operated by members of the Islamic State was dismantled in Germany. Since then, several similar attack plots have been thwarted.

This context triggered a race among major powers to develop an effective antidote as quickly as possible. In this effort, Fabentech has risen to a challenge.

“Having demonstrated its ability to target and then neutralize ricin before it causes irreparable damage, Ricimed is a treatment that works based on polyclonal antibodies and compensates for the absence of a vaccine or specific treatment,” Fabentech said in a press release.

The polyclonal antibody technology used by Fabentech offers potential for the development of antidotes against bioterrorist attacks and for the treatment of many infectious diseases.

Ricimed contributed to the deployment of a European health shield against intentional biological threats in France.

Military Backing

Speaking to Le Figaro, France’s oldest national newspaper, Fabentech CEO Sébastien Iva explained that ricin disrupts the body by halting cell function, while noting several other drug candidates in development at the firm.

Typically, the lungs sustain fatal damage. Our treatment interrupts this toxic process. In animals administered the antidote, we observed pulmonary function recovery, allowing survival.

Given that the possibility of terrorist attacks using ricin is considered a national security issue, Fabentech benefited from the support by the Ministry of the Armed Forces and the DGA and lasted nearly a decade of research and development work.

The granting of marketing authorisation was also supported by the French Armed Forces and welcomed by the French Minister of the Armed Forces, Catherine Vautrin, who previously served as France’s Minister of Labour, Health, and Solidarity.

“Supporting the development of companies in France capable of manufacturing antidotes against certain biological agents helps guarantee the operational superiority of our armed forces. Developing and producing such drugs when they do not yet exist on the market is also serving the nation and the public interest,” she said.

Although the threat posed by ricin remains hypothetical, Fabentech reports a strong interest from potential clients, with many countries seeking protection against possible bioterrorist attacks.

The DGA had already placed an order for several doses of Ricimed for deployment in France. For optimal effectiveness, the antidote must be administered within 6 hours of poisoning. Iva confirmed that multiple countries had already expressed interest in acquiring the antidote.

This story was translated from JIM, part of the Medscape Professional Network.

A version of this article first appeared on Medscape.com.

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Publications
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Veterans With Dementia Face Extended Time Away From Home After Emergency Department Care

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Veterans With Dementia Face Extended Time Away From Home After Emergency Department Care

TOPLINE:

Veterans with dementia experienced significant reductions in time spent at home following emergency department (ED) visits, with a mean of 21.7 days away from home within 180 days of the index visit. ED admission was the strongest predictor of extended time away from home, followed by high frailty, an unmarried status, and lack of housing.

METHODOLOGY:

  • Researchers conducted a retrospective cohort study using Department of Veterans Affairs (VA) and Centers for Medicare & Medicaid Services administrative data of 51,707 veterans with dementia (mean age, 79.9 years; 97.6% men; 52.2% married individuals; 73% White individuals) who had an eligible Veterans Health Administration ED visit between October 2016 and September 2018.
  • The primary outcome was home time, defined as days alive and not spent in institutional care settings during the 180 days following the index ED visit; secondary outcomes included ED revisits within 30 days of the index visit and 30-day mortality.

TAKEAWAY:

  • Veterans experienced a mean of 21.7 days away from home within 180 days after the ED visit; 4.5% never returned home, and 18.2% spent the entire 180-day follow-up period at home. Patients admitted from the ED spent a mean of 34.2 days away from home within 180 days, whereas those discharged directly spent a mean of 13.6 days.
  • ED admission had the strongest association with increased days away from home (rate ratio [RR], 3.18), followed by patient factors such as unhoused status (RR, 1.50), very high frailty (RR, 1.27), unmarried status — never married (RR, 1.24) or divorced, separated, or widowed (RR, 1.24) — and depression (RR, 1.13).
  • Compared with the overall cohort, veterans with psychiatric concerns had the highest risk for extended time away from home (RR, 1.31), followed by those with nonspecific concerns and geriatric syndromes.
  • Among all participants, 27.6% had a 30-day ED revisit, and 4% died within 30 days of the index visit. An admission was associated with a lower likelihood of a 30-day ED revisit (hazard ratio [HR], 0.75) but an increased likelihood of 30-day mortality (HR, 4.87).

IN PRACTICE:

"Home time offers a promising, patient-centered measure to align emergency care with patients' and care partners' goals and preferences to remain at home," the authors wrote. However, they emphasized that "refining its application — particularly in accounting for index hospitalizations and long-term care transitions — is critical to accurately capturing quality of care and long-term well-being."

SOURCE:

The study was led by Justine Seidenfeld, MD, MHs, Durham Veterans Affairs Health Care System, Durham, North Carolina. It was published online on December 29, 2025, in JAMA Network Open.

LIMITATIONS:

The study population of veterans aged 65-66 years may have had incomplete dementia confirmation as Medicare data were limited, and the predominantly male cohort limited generalizability. Marriage status served as an imperfect proxy for social and care partner support. The varying severity of dementia among participants could not be fully assessed using VA administrative data. Additionally, some highly emergent ED visits may have been inadvertently included if patients were not properly triaged, and very low-acuity visits could not be reliably identified due to the lack of validated approaches.

DISCLOSURES:

The study was supported by the National Institute on Aging-Veterans Affairs Mentored Physician and Clinical Psychologist Scientist Award in Alzheimer's Disease (AD) and AD-Related Dementias, a project grant from the National Institute on Aging, and a grant from the Veterans Affairs Office of Health Systems Research, Center of Innovation to Accelerate Discovery and Practice Transformation at the Durham VA Health Care System. Several authors reported receiving grants, personal fees, and payments for literature reviews from or serving as consultants for various organizations. Detailed disclosures are noted in the original article.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

A version of this article first appeared on Medscape.com.

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TOPLINE:

Veterans with dementia experienced significant reductions in time spent at home following emergency department (ED) visits, with a mean of 21.7 days away from home within 180 days of the index visit. ED admission was the strongest predictor of extended time away from home, followed by high frailty, an unmarried status, and lack of housing.

METHODOLOGY:

  • Researchers conducted a retrospective cohort study using Department of Veterans Affairs (VA) and Centers for Medicare & Medicaid Services administrative data of 51,707 veterans with dementia (mean age, 79.9 years; 97.6% men; 52.2% married individuals; 73% White individuals) who had an eligible Veterans Health Administration ED visit between October 2016 and September 2018.
  • The primary outcome was home time, defined as days alive and not spent in institutional care settings during the 180 days following the index ED visit; secondary outcomes included ED revisits within 30 days of the index visit and 30-day mortality.

TAKEAWAY:

  • Veterans experienced a mean of 21.7 days away from home within 180 days after the ED visit; 4.5% never returned home, and 18.2% spent the entire 180-day follow-up period at home. Patients admitted from the ED spent a mean of 34.2 days away from home within 180 days, whereas those discharged directly spent a mean of 13.6 days.
  • ED admission had the strongest association with increased days away from home (rate ratio [RR], 3.18), followed by patient factors such as unhoused status (RR, 1.50), very high frailty (RR, 1.27), unmarried status — never married (RR, 1.24) or divorced, separated, or widowed (RR, 1.24) — and depression (RR, 1.13).
  • Compared with the overall cohort, veterans with psychiatric concerns had the highest risk for extended time away from home (RR, 1.31), followed by those with nonspecific concerns and geriatric syndromes.
  • Among all participants, 27.6% had a 30-day ED revisit, and 4% died within 30 days of the index visit. An admission was associated with a lower likelihood of a 30-day ED revisit (hazard ratio [HR], 0.75) but an increased likelihood of 30-day mortality (HR, 4.87).

IN PRACTICE:

"Home time offers a promising, patient-centered measure to align emergency care with patients' and care partners' goals and preferences to remain at home," the authors wrote. However, they emphasized that "refining its application — particularly in accounting for index hospitalizations and long-term care transitions — is critical to accurately capturing quality of care and long-term well-being."

SOURCE:

The study was led by Justine Seidenfeld, MD, MHs, Durham Veterans Affairs Health Care System, Durham, North Carolina. It was published online on December 29, 2025, in JAMA Network Open.

LIMITATIONS:

The study population of veterans aged 65-66 years may have had incomplete dementia confirmation as Medicare data were limited, and the predominantly male cohort limited generalizability. Marriage status served as an imperfect proxy for social and care partner support. The varying severity of dementia among participants could not be fully assessed using VA administrative data. Additionally, some highly emergent ED visits may have been inadvertently included if patients were not properly triaged, and very low-acuity visits could not be reliably identified due to the lack of validated approaches.

DISCLOSURES:

The study was supported by the National Institute on Aging-Veterans Affairs Mentored Physician and Clinical Psychologist Scientist Award in Alzheimer's Disease (AD) and AD-Related Dementias, a project grant from the National Institute on Aging, and a grant from the Veterans Affairs Office of Health Systems Research, Center of Innovation to Accelerate Discovery and Practice Transformation at the Durham VA Health Care System. Several authors reported receiving grants, personal fees, and payments for literature reviews from or serving as consultants for various organizations. Detailed disclosures are noted in the original article.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

A version of this article first appeared on Medscape.com.

TOPLINE:

Veterans with dementia experienced significant reductions in time spent at home following emergency department (ED) visits, with a mean of 21.7 days away from home within 180 days of the index visit. ED admission was the strongest predictor of extended time away from home, followed by high frailty, an unmarried status, and lack of housing.

METHODOLOGY:

  • Researchers conducted a retrospective cohort study using Department of Veterans Affairs (VA) and Centers for Medicare & Medicaid Services administrative data of 51,707 veterans with dementia (mean age, 79.9 years; 97.6% men; 52.2% married individuals; 73% White individuals) who had an eligible Veterans Health Administration ED visit between October 2016 and September 2018.
  • The primary outcome was home time, defined as days alive and not spent in institutional care settings during the 180 days following the index ED visit; secondary outcomes included ED revisits within 30 days of the index visit and 30-day mortality.

TAKEAWAY:

  • Veterans experienced a mean of 21.7 days away from home within 180 days after the ED visit; 4.5% never returned home, and 18.2% spent the entire 180-day follow-up period at home. Patients admitted from the ED spent a mean of 34.2 days away from home within 180 days, whereas those discharged directly spent a mean of 13.6 days.
  • ED admission had the strongest association with increased days away from home (rate ratio [RR], 3.18), followed by patient factors such as unhoused status (RR, 1.50), very high frailty (RR, 1.27), unmarried status — never married (RR, 1.24) or divorced, separated, or widowed (RR, 1.24) — and depression (RR, 1.13).
  • Compared with the overall cohort, veterans with psychiatric concerns had the highest risk for extended time away from home (RR, 1.31), followed by those with nonspecific concerns and geriatric syndromes.
  • Among all participants, 27.6% had a 30-day ED revisit, and 4% died within 30 days of the index visit. An admission was associated with a lower likelihood of a 30-day ED revisit (hazard ratio [HR], 0.75) but an increased likelihood of 30-day mortality (HR, 4.87).

IN PRACTICE:

"Home time offers a promising, patient-centered measure to align emergency care with patients' and care partners' goals and preferences to remain at home," the authors wrote. However, they emphasized that "refining its application — particularly in accounting for index hospitalizations and long-term care transitions — is critical to accurately capturing quality of care and long-term well-being."

SOURCE:

The study was led by Justine Seidenfeld, MD, MHs, Durham Veterans Affairs Health Care System, Durham, North Carolina. It was published online on December 29, 2025, in JAMA Network Open.

LIMITATIONS:

The study population of veterans aged 65-66 years may have had incomplete dementia confirmation as Medicare data were limited, and the predominantly male cohort limited generalizability. Marriage status served as an imperfect proxy for social and care partner support. The varying severity of dementia among participants could not be fully assessed using VA administrative data. Additionally, some highly emergent ED visits may have been inadvertently included if patients were not properly triaged, and very low-acuity visits could not be reliably identified due to the lack of validated approaches.

DISCLOSURES:

The study was supported by the National Institute on Aging-Veterans Affairs Mentored Physician and Clinical Psychologist Scientist Award in Alzheimer's Disease (AD) and AD-Related Dementias, a project grant from the National Institute on Aging, and a grant from the Veterans Affairs Office of Health Systems Research, Center of Innovation to Accelerate Discovery and Practice Transformation at the Durham VA Health Care System. Several authors reported receiving grants, personal fees, and payments for literature reviews from or serving as consultants for various organizations. Detailed disclosures are noted in the original article.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

A version of this article first appeared on Medscape.com.

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Veterans With Dementia Face Extended Time Away From Home After Emergency Department Care

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High-Deductible Plans May Be Linked to Worse Cancer Survival

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High-Deductible Plans May Be Linked to Worse Cancer Survival

TOPLINE:

A new analysis found that high-deductible health plans were associated with worse overall survival and cancer-specific survival among cancer survivors. High-deductible plans, however, were not associated with worse overall survival among adults without a history of cancer.

METHODOLOGY:

  • Previous studies have linked high-deductible health plans with decreased or delayed health utilization among cancer survivors and higher out-of-pocket costs. However, it’s not clear whether these plans influence cancer outcomes.
  • In a cross-sectional study, researchers analyzed data from 147,254 respondents (aged 18 to 84 years) in the National Health Interview Survey from 2011 to 2018 and identified individuals with high-deductible plans — 2331 cancer survivors and 37,473 people without a history of cancer.
  • The researchers acquired linked mortality files from the National Death Index, which included data on mortality events through the end of 2019.
  • High-deductible health plans were identified through survey responses and defined as plans with yearly deductibles of at least $1200-$1350 for individuals or at least $2400-$2700 for families.
  • The primary endpoints included overall survival and cancer-specific survival. Researchers adjusted for insurance status, marital status, sex, comorbidities, education, household income, geographic region, cancer site, and time since diagnosis.

TAKEAWAY:

  • Among cancer survivors, having a high-deductible health plan was associated with worse overall survival (hazard ratio [HR], 1.46) and cancer-specific survival (HR, 1.34). However, sensitivity analyses incorporating time since diagnosis slightly attenuated the cancer-specific survival association (HR, 1.20; 95% CI, 0.92-1.55).
  • Among adults without a history of cancer, having a high-deductible health plan was not associated with significantly worse overall survival (HR, 1.08; 95% CI, 0.96-1.21).
  • General concerns over finances, worry about medical bills, cost-related delays, or forgone care, as well as cost-related underuse of medications were significant mediators of the associations between high-deductible health plan status and mortality outcomes among cancer survivors.
  • High-deductible health plan status was also associated with worse cancer-specific survival among cancer survivors with incomes at least 400% of the federal poverty level (HR, 1.65; P for interaction = .03).

IN PRACTICE:

“These data suggest that insurance coverage that financially discourages medical care may financially discourage necessary care and ultimately worsen cancer outcomes,” the study authors wrote. “This danger appears to be unique to cancer survivors, as [high-deductible health plans] were not associated with survival among adults without a cancer history.”

SOURCE:

The study, led by Justin M. Barnes, MD, MS, Department of Radiation Oncology, Mayo Clinic in Rochester, Minnesota, was published online on January 29 in JAMA Network Open.

LIMITATIONS:

High-deductible health plan status was self-reported and may have been inaccurate for some individuals, with more than half of consumers being unsure about their annual deductible amount. The study lacked specific plan details and exact deductible amounts, and high-deductible health plan status was based on a single time point during survey participation. Additionally, researchers lacked information about cancer stage, cancer-directed therapies, recurrences, or complications, and cancer mortality could be from cancers diagnosed after survey participation.

DISCLOSURES:

Meera Ragavan, MD, MPH, disclosed receiving personal fees from Trial Library and AstraZeneca and grants from Merck, outside the submitted work. Other authors reported receiving personal fees from Costs of Care during the study. Additional disclosures are noted in the original article.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

A version of this article first appeared on Medscape.com.

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TOPLINE:

A new analysis found that high-deductible health plans were associated with worse overall survival and cancer-specific survival among cancer survivors. High-deductible plans, however, were not associated with worse overall survival among adults without a history of cancer.

METHODOLOGY:

  • Previous studies have linked high-deductible health plans with decreased or delayed health utilization among cancer survivors and higher out-of-pocket costs. However, it’s not clear whether these plans influence cancer outcomes.
  • In a cross-sectional study, researchers analyzed data from 147,254 respondents (aged 18 to 84 years) in the National Health Interview Survey from 2011 to 2018 and identified individuals with high-deductible plans — 2331 cancer survivors and 37,473 people without a history of cancer.
  • The researchers acquired linked mortality files from the National Death Index, which included data on mortality events through the end of 2019.
  • High-deductible health plans were identified through survey responses and defined as plans with yearly deductibles of at least $1200-$1350 for individuals or at least $2400-$2700 for families.
  • The primary endpoints included overall survival and cancer-specific survival. Researchers adjusted for insurance status, marital status, sex, comorbidities, education, household income, geographic region, cancer site, and time since diagnosis.

TAKEAWAY:

  • Among cancer survivors, having a high-deductible health plan was associated with worse overall survival (hazard ratio [HR], 1.46) and cancer-specific survival (HR, 1.34). However, sensitivity analyses incorporating time since diagnosis slightly attenuated the cancer-specific survival association (HR, 1.20; 95% CI, 0.92-1.55).
  • Among adults without a history of cancer, having a high-deductible health plan was not associated with significantly worse overall survival (HR, 1.08; 95% CI, 0.96-1.21).
  • General concerns over finances, worry about medical bills, cost-related delays, or forgone care, as well as cost-related underuse of medications were significant mediators of the associations between high-deductible health plan status and mortality outcomes among cancer survivors.
  • High-deductible health plan status was also associated with worse cancer-specific survival among cancer survivors with incomes at least 400% of the federal poverty level (HR, 1.65; P for interaction = .03).

IN PRACTICE:

“These data suggest that insurance coverage that financially discourages medical care may financially discourage necessary care and ultimately worsen cancer outcomes,” the study authors wrote. “This danger appears to be unique to cancer survivors, as [high-deductible health plans] were not associated with survival among adults without a cancer history.”

SOURCE:

The study, led by Justin M. Barnes, MD, MS, Department of Radiation Oncology, Mayo Clinic in Rochester, Minnesota, was published online on January 29 in JAMA Network Open.

LIMITATIONS:

High-deductible health plan status was self-reported and may have been inaccurate for some individuals, with more than half of consumers being unsure about their annual deductible amount. The study lacked specific plan details and exact deductible amounts, and high-deductible health plan status was based on a single time point during survey participation. Additionally, researchers lacked information about cancer stage, cancer-directed therapies, recurrences, or complications, and cancer mortality could be from cancers diagnosed after survey participation.

DISCLOSURES:

Meera Ragavan, MD, MPH, disclosed receiving personal fees from Trial Library and AstraZeneca and grants from Merck, outside the submitted work. Other authors reported receiving personal fees from Costs of Care during the study. Additional disclosures are noted in the original article.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

A version of this article first appeared on Medscape.com.

TOPLINE:

A new analysis found that high-deductible health plans were associated with worse overall survival and cancer-specific survival among cancer survivors. High-deductible plans, however, were not associated with worse overall survival among adults without a history of cancer.

METHODOLOGY:

  • Previous studies have linked high-deductible health plans with decreased or delayed health utilization among cancer survivors and higher out-of-pocket costs. However, it’s not clear whether these plans influence cancer outcomes.
  • In a cross-sectional study, researchers analyzed data from 147,254 respondents (aged 18 to 84 years) in the National Health Interview Survey from 2011 to 2018 and identified individuals with high-deductible plans — 2331 cancer survivors and 37,473 people without a history of cancer.
  • The researchers acquired linked mortality files from the National Death Index, which included data on mortality events through the end of 2019.
  • High-deductible health plans were identified through survey responses and defined as plans with yearly deductibles of at least $1200-$1350 for individuals or at least $2400-$2700 for families.
  • The primary endpoints included overall survival and cancer-specific survival. Researchers adjusted for insurance status, marital status, sex, comorbidities, education, household income, geographic region, cancer site, and time since diagnosis.

TAKEAWAY:

  • Among cancer survivors, having a high-deductible health plan was associated with worse overall survival (hazard ratio [HR], 1.46) and cancer-specific survival (HR, 1.34). However, sensitivity analyses incorporating time since diagnosis slightly attenuated the cancer-specific survival association (HR, 1.20; 95% CI, 0.92-1.55).
  • Among adults without a history of cancer, having a high-deductible health plan was not associated with significantly worse overall survival (HR, 1.08; 95% CI, 0.96-1.21).
  • General concerns over finances, worry about medical bills, cost-related delays, or forgone care, as well as cost-related underuse of medications were significant mediators of the associations between high-deductible health plan status and mortality outcomes among cancer survivors.
  • High-deductible health plan status was also associated with worse cancer-specific survival among cancer survivors with incomes at least 400% of the federal poverty level (HR, 1.65; P for interaction = .03).

IN PRACTICE:

“These data suggest that insurance coverage that financially discourages medical care may financially discourage necessary care and ultimately worsen cancer outcomes,” the study authors wrote. “This danger appears to be unique to cancer survivors, as [high-deductible health plans] were not associated with survival among adults without a cancer history.”

SOURCE:

The study, led by Justin M. Barnes, MD, MS, Department of Radiation Oncology, Mayo Clinic in Rochester, Minnesota, was published online on January 29 in JAMA Network Open.

LIMITATIONS:

High-deductible health plan status was self-reported and may have been inaccurate for some individuals, with more than half of consumers being unsure about their annual deductible amount. The study lacked specific plan details and exact deductible amounts, and high-deductible health plan status was based on a single time point during survey participation. Additionally, researchers lacked information about cancer stage, cancer-directed therapies, recurrences, or complications, and cancer mortality could be from cancers diagnosed after survey participation.

DISCLOSURES:

Meera Ragavan, MD, MPH, disclosed receiving personal fees from Trial Library and AstraZeneca and grants from Merck, outside the submitted work. Other authors reported receiving personal fees from Costs of Care during the study. Additional disclosures are noted in the original article.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

A version of this article first appeared on Medscape.com.

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High-Deductible Plans May Be Linked to Worse Cancer Survival

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High-Deductible Plans May Be Linked to Worse Cancer Survival

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