Disparities in α1-Antitrypsin Deficiency Testing and Treatment in US Adults

Arianne K Baldomero; Lisa Smith; Amy Gravely; Vickram Tejwani; Chris H Wendt

doi:10.1001/jamanetworkopen.2026.39328

. 2026 Oct 15;9(10):e2639328. doi: 10.1001/jamanetworkopen.2026.39328

Disparities in α₁-Antitrypsin Deficiency Testing and Treatment in US Adults

Arianne K Baldomero ^1,^2,^3,^✉, Lisa Smith ⁴, Amy Gravely ⁴, Vickram Tejwani ^5,⁶, Chris H Wendt ^1,²

¹Pulmonary, Allergy, Critical Care, and Sleep Medicine, Minneapolis VA Health Care System, Minneapolis, Minnesota

²Pulmonary, Allergy, Critical Care, and Sleep Medicine, University of Minnesota, Minneapolis

³Center for Care Delivery and Outcomes Research, Minneapolis VA Health Care System, Minneapolis, Minnesota

⁴Research Department, Minneapolis VA Health Care System, Minneapolis, Minnesota

⁵Pulmonary and Critical Care Medicine, Integrated Hospital Care Institute, Cleveland Clinic, Cleveland, Ohio

⁶Genomic Sciences and Systems Biology, Cleveland Clinic Research, Cleveland Clinic, Cleveland, Ohio

Accepted for Publication: August 23, 2026.

Published: October 15, 2026. doi:10.1001/jamanetworkopen.2026.39328

Open Access: This is an open access article distributed under the terms of the CC-BY-NC-ND License, which does not permit alteration or commercial use, including those for text and data mining, AI training, and similar technologies. © 2026 Baldomero AK et al. JAMA Network Open.

^✉

Corresponding Author: Arianne K. Baldomero, MD, MS, Minneapolis VA Health Care System, Pulmonary, Allergy, Critical Care, and Sleep Medicine (111N), One Veterans Dr, Minneapolis, MN 55417 (baldo004@umn.edu).

Author Contributions: Dr Baldomero and Ms Gravely had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Baldomero, Gravely, Tejwani, Wendt.

Acquisition, analysis, or interpretation of data: Baldomero, Smith, Gravely, Wendt.

Drafting of the manuscript: All authors.

Critical review of the manuscript for important intellectual content: All authors.

Statistical analysis: Gravely.

Obtained funding: Wendt.

Administrative, technical, or material support: Baldomero.

Conflict of Interest Disclosures: Dr Baldomero reported receiving grants from the National Institutes of Health (NIH) and Patient-Centered Outcomes Research Institute outside the submitted work. Dr Tejwani reported receiving grants from NIH, 4D Medical, Takeda, and AstraZeneca, and personal fees from Sanofi/Regeneron and AstraZeneca outside the submitted work. Dr Wendt reported being a volunteer member of the medical and scientific advisory committee for the Alpha-1 Foundation. No other disclosures were reported.

Funding/Support: This research was supported by the Alpha-1 Foundation (grant to Dr Wendt) and the Veterans Administration Office of Research and Development, Health Services Research and Development (grant CDA2 IK2RD000406 to Dr Baldomero). This material is the result of work supported with resources and the use of facilities at the Minneapolis VA Health Care System.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The views expressed in this article are those of the authors and do not reflect the views of the US government, the Department of Veterans Affairs, the Alpha-1 Foundation, or any of the authors’ affiliated academic institutions.

Data Sharing Statement: See Supplement 2.

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Corresponding author.

PMCID: PMC13673321 PMID: 42879152

Key Points

Question

What factors are associated with α₁-antitrypsin deficiency (AATD) testing and augmentation therapy among individuals with chronic obstructive pulmonary disease (COPD) and/or asthma?

Findings

In this cohort study of more than 2 million individuals with COPD and/or asthma, only 5.8% underwent AATD testing, and 3.7% of those who received a diagnosis were prescribed augmentation therapy. AATD testing and treatment were lower among older individuals, those actively using tobacco, patients from minoritized racial and ethnic groups, and those facing longer drive times to specialty care.

Meaning

In this study, AATD testing and treatment were low, indicating that system-level reforms are needed to bridge gaps driven by sociodemographic, clinical, and health care access.

This cohort study examines factors associated with low α₁-antitrypsin deficiency testing and augmentation therapy in individuals with chronic obstructive pulmonary disease and asthma.

Abstract

Importance

α₁-Antitrypsin deficiency (AATD) is underdiagnosed despite guidelines for broad testing, and the factors associated with low testing and treatment are not well known.

Objective

To identify factors associated with low AATD testing and augmentation therapy in individuals with chronic obstructive pulmonary disease (COPD) and asthma.

Design, Setting, and Participants

This is a retrospective, multicenter cohort study using national electronic health record data from the Veterans Health Administration from January 1, 2020, to December 31, 2024. Data analysis was conducted from July to October 2025. Participants were individuals with a diagnosis of COPD and/or asthma (≥2 International Classification of Diseases, Ninth Revision, or International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes).

Exposure

Individual demographics, comorbidities, tobacco use, rural-urban residence, Social Vulnerability Index (SVI), and drive time to the specialty care facility.

Main Outcomes and Measures

The primary outcomes were prevalence of and factors associated with AATD testing and augmentation therapy prescription. Multivariable regression models were used, adjusting sequentially for demographics, comorbidities, tobacco use, rural-urban residence, SVI, and drive time to the specialty care facility.

Results

Among 2 384 913 individuals with COPD and/or asthma (mean [SD] age, 64.2 [14.3] years; 178 898 female [7.5%]; 16 778 [0.7%] American Indian or Alaska Native; 14 996 [0.7%] Asian; 331 202 Black or African American [15.3%]; 19 029 [0.9%] Native Hawaiian or Pacific Islander; 1 787 951 White [82.4%]), 138 278 (5.8%) underwent testing. In fully adjusted models, cirrhosis (adjusted odds ratio [aOR], 6.40; 95% CI, 6.29-6.51) and bronchiectasis (aOR, 3.73; 95% CI, 3.59-3.87) were associated with higher odds of testing. Older ages and active tobacco use were associated with lower odds of testing. Rural-urban residence and SVI had similar odds across categories. Longer drive times were associated with progressively lower odds of testing (>90 vs ≤30 minutes; aOR, 0.63; 95% CI, 0.62-0.65). Of 8564 with AATD diagnosis, 320 (3.7%) were prescribed augmentation therapy and showed a similar pattern as testing, occurring less often among patients from minoritized racial and ethnic groups, those actively using tobacco, and those with longer drive times to specialty care, and more often among those with cirrhosis and bronchiectasis.

Conclusions and Relevance

In this cohort study of individuals with COPD and/or asthma, AATD testing and augmentation therapy were profoundly low. System-level reform is needed to bridge gaps across age, comorbidities, tobacco use, and health care access.

Introduction

α₁-Antitrypsin deficiency (AATD) is a common inherited disease caused by a deficiency of the α₁-antitrypsin (AAT) protein, a major circulating serine protease inhibitor encoded by the SERPINA1 gene.¹ The normal allele is designated M, with Pi*MM representing the usual genotype. More than 100 SERPINA1 gene variants have been described, although the most common deficiency alleles are S and Z. In the US and Europe, the prevalence of severe AATD (Pi*ZZ) is estimated at approximately 1 in 2500 to 5000 individuals, yet only a small fraction receive a diagnosis.^2,3 AATD is associated with an increased risk for obstructive lung disease, liver disease, c-antineutrophil cytoplasmic antibodies vasculitis, panniculitis, and bronchiectasis.⁴ Despite its frequency and broad disease spectrum, AATD remains relatively underdiagnosed, largely because of low testing rates.^5,6 However, the factors associated with this low testing are not well established.

Testing for AATD is generally prompted by 2 indications: evaluation of individuals with AATD-associated diseases and/or testing those with a family history of AATD. The American Thoracic Society/European Respiratory Society recommends testing all individuals with chronic obstructive pulmonary disease (COPD), unresponsive asthma, cryptogenic liver disease, bronchiectasis, panniculitis, and c-antineutrophil cytoplasmic antibodies vasculitis.⁷ However, even among individuals with COPD and asthma, which are highly prevalent, testing for AATD remains uncommon, and the variables associated with low testing rates are not well known.

COPD is particularly prevalent among US veterans, with spirometry-based assessments identifying COPD in up to 45% of this population.⁸ Veterans are at heightened risk for COPD in part because they are more likely than nonveterans to currently smoke tobacco or to have previously smoked tobacco (74% vs 48%).⁹ Emerging data suggest that AATD may be underdiagnosed in veterans with COPD and related conditions.¹⁰ Factors intrinsic to the veteran population, including older age, a predominantly male demographic with high tobacco use prevalence, and a substantial burden of comorbidities, may contribute to low testing rates for AATD. Furthermore, geographic barriers, especially for veterans living in resource-limited rural settings, may restrict access to specialty care, where AATD testing and treatment are most likely to occur. This is especially relevant given that the Veterans Health Administration population has a higher prevalence of rural residents compared with the general US population (37% vs 24%).¹¹

The primary objective of this study is to determine the prevalence of AATD testing among individuals with COPD and/or asthma and to identify factors, including sociodemographic and geographic characteristics, associated with undergoing AATD testing. The secondary objective is to determine the prevalence of augmentation therapy among those with a diagnosis of AATD and to identify factors associated with prescription of augmentation therapy. Characterizing these variables may help identify barriers both to AATD testing and to augmentation therapy and inform strategies to improve AATD detection and treatment.

Methods

This cohort study was approved with a waiver of informed consent by the Minneapolis Veterans Affairs (VA) Health Care System institutional review board because of its retrospective design and minimal risk to participants. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines.¹²

Data Sources and Study Populations

We extracted electronic health record data from the national VA Corporate Data Warehouse, a network of national databases that integrates patient electronic data throughout the VA.¹³ We identified individuals who received care at the VA with diagnosis of COPD and/or asthma between January 2000 and December 2024. Diagnosis codes are listed in eTable 1 in Supplement 1. We included individuals who had at least 2 encounters with International Classification of Diseases, Ninth Revision codes or International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes for COPD (490-492.x, 496; J40, J41.x, J42, J43.x, and J44) and/or asthma (493.x, J45.x, J82.83, J44.89, and J46).¹⁴ Because COPD and asthma share clinical features, risk misdiagnosis, and both warrant for testing, we included individuals with COPD, asthma, asthma-COPD overlap, or spirometry-confirmed airflow obstruction (ratio of forced expiratory volume in first second of expiration [FEV₁] to forced vital capacity [FVC] of <0.70).^1,15 This approach aligns with World Health Organization, American Thoracic Society/European Respiratory Society, and other guideline recommendations that adults with COPD, adult-onset or treatment-resistant asthma, or persistent airflow obstruction be tested for AATD, and reflects diagnostic uncertainty in clinical practice between asthma, COPD, and overlap syndromes.^1,15,16

AATD Testing

The primary outcome was AATD testing at any time during the study period. AATD testing was defined as either a serum AAT level and/or AAT genotype testing.⁷ We classified AATD testing as present if at least 1 eligible test was recorded.

AATD Diagnosis

Among tested individuals, AATD diagnosis was defined by serum AAT less than 100 mg/dL (to convert to micromoles per liter, multiply by 0.184); genotypes ZZ, SZ, or SS; or augmentation therapy prescription.^7,15 Serum AAT concentrations were measured using standard clinical laboratory assays (predominantly nephelometry or turbidimetry), with normal reference ranges typically 100 to 200 mg/dL. We selected less than 100 mg/dL as the cutoff as it corresponds to the lower limit of normal for these assays, enabling us to capture individuals with subnormal AAT levels who may be at increased risk of disease, rather than restricting the cohort solely to severe deficiency (<80 mg/dL).¹⁷ Although genotyping is required for a definitive diagnosis, for this epidemiologic study, individuals meeting the serum cutoff were classified as having AATD, including those with MM, MS, or MZ genotypes to account for rare SERPINA1 variants undetected by standard genotyping.^18,19 We categorized individuals as having been prescribed AAT augmentation therapy if they had any prescription for an intravenous AAT augmentation product during the study period.

Variables

Demographics (age, sex, and race and ethnicity), comorbidities, and geographic locations were obtained from the VA Corporate Data Warehouse.¹² Self-reported race and ethnicity were extracted from VA electronic records to evaluate demographic disparities in testing and treatment. Comorbidities were defined using International Classification of Diseases, Ninth Revision codes or International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes requiring at least 2 encounters over the study period.^20,21 Tobacco use status (current, former, and never) was obtained from the VA tobacco health factor data, which are updated at routine clinical visits (94.3% specificity for identifying individuals who are currently using tobacco).²²

Geocoded individual home addresses, rural-urban residence, and drive time to the closest VA facility offering specialty care services, including pulmonary care, were obtained from the VA Planning Systems Support Group files.²³ Addresses were linked to the Centers for Disease Control and Prevention Social Vulnerability Index (SVI) via census tracts, which ranks 16 socioeconomic factors using American Community Survey Data.²⁴ The VA classifies each address as urban, rural or highly rural using the 2010 Rural-Urban Community Area codes (urban, 1.0 or 1.1; highly rural, 10.0; rural, all others).^23,25,26 Drive time from home addresses to the nearest VA facility offering specialty care services was estimated using VA geospatial methods incorporating average routes, traffic, and road conditions.²⁷

Statistical Analysis

Data analysis was conducted between July and October 2025. We compared demographic characteristics, comorbidities, tobacco use, pulmonary function, rural-urban status, neighborhood-level SVI, and drive time to specialty care between those who underwent AATD testing vs no AATD testing (a binary outcome at the patient level). The primary outcome was AATD testing, and the secondary outcome was augmentation therapy prescription.

We fit a series of nested multivariable logistic regression models to estimate adjusted odds ratios (aORs) for these outcomes. Model 1 included age, sex, and race and ethnicity. Model 2 additionally adjusted for comorbidities and tobacco use status. Model 3 further added rural-urban status and neighborhood-level SVI percentile quartiles as measures of geographic and socioeconomic characteristics. Model 4 additionally adjusted for drive time to the closest specialty care facility (categorized as ≤30, 31-60, 61-90, and >90 minutes) as a measure of health care access; this final model is referred to as the full model. This modeling sequence was repeated for augmentation therapy. All tests were 2-sided (with significance set at P < .05) and performed using SAS statistical software version 9.4 (SAS Institute).

Results

Among 2 384 913 individuals with COPD and/or asthma, the mean (SD) age was 64.2 (14.3) years, 178 898 (7.5%) were female, 16 778 (0.7%) were American Indian or Alaska Native, 14 996 (0.7%) were Asian, 331 202 (15.3%) were Black or African American, 19 029 (0.9%) were Native Hawaiian or Pacific Islander, and 1 787 951 (82.4%) were White (Table 1). Comorbidities included coronary artery disease (985 908 patients [41.3%]), congestive heart failure (738 204 patients [31.0%]), and diabetes (944 632 patients [39.6%]). Overall, 936 173 (39.3%) lived in rural or highly rural areas, 679 235 (33.2%) had drive times to specialty care greater than 60 minutes, and 1 102 473 (55.5%) lived in neighborhoods with SVI greater than or equal to the 50th percentile (Table 1).

Table 1. Characteristics of Individuals With COPD and/or Asthma Tested vs Not Tested for AATD^a.

Characteristic	Patients, No. (%)
Characteristic	Tested (n = 138 278)	Not tested (n = 2 246 635)	Total (N = 2 384 913)
Age, mean (SD), y	58.3 (11.9)	64.6 (14.3)	64.2 (14.3)
Sex
Female	10 380 (7.5)	168 518 (7.5)	178 898 (7.5)
Male	127 898 (92.5)	2 078 117 (92.5)	2 206 015 (92.5)
Race and ethnicity
American Indian or Alaska Native	980 (0.8)	15 798 (0.8)	16 778 (0.7)
Asian	907 (0.7)	14 089 (0.7)	14 996 (0.7)
Black or African American	23 528 (18.1)	307 674 (15.1)	331 202 (15.3)
Native Hawaiian or Pacific Islander	1209 (0.9)	17 820 (0.9)	19 029 (0.9)
White	103 361 (79.5)	1 684 590 (82.6)	1 787 951 (82.4)
Obstructive lung disease
COPD	95 103 (68.8)	1 657 238 (73.8)	1 752 341 (73.5)
Asthma	16 649 (12.0)	360 269 (16.0)	376 916 (15.8)
COPD and asthma	26 517 (19.2)	229 128 (10.2)	255 656 (10.7)
Pulmonary function tests, mean (SD)
FVC percent predicted	91.0 (21.9)	93.8 (22.2)	93.6 (22.2)
FVC z score	−0.6 (1.4)	−0.4 (1.3)	−0.4 (1.3)
FEV₁ percent predicted	81.4 (28.7)	85.5 (26.6)	85.2 (26.7)
FEV₁ z score	−1.08 (1.7)	−0.80 (1.5)	−0.82 (1.5)
FEV₁/FVC ratio	66.8 (25.9)	68.3 (37.1)	68.2 (36.5)
Comorbidities
Cirrhosis	35 364 (25.6)	94 275 (4.2)	129 639 (5.4)
Bronchiectasis	4749 (3.4)	22 291 (1.0)	27 040 (1.1)
Coronary artery disease	56 950 (41.2)	928 958 (41.4)	985 908 (41.3)
Congestive heart failure	47 577 (34.4)	690 627 (30.7)	738 204 (31.0)
Diabetes	63 274 (45.8)	881 358 (39.2)	944 632 (39.6)
Chronic kidney disease	46 258 (33.5)	658 718 (29.3)	704 976 (29.6)
Vasculitis	3169 (2.3)	36 513 (1.6)	39 682 (1.7)
Tobacco use
Active	45 442 (33.1)	661 790 (30.2)	707 232 (30.4)
Former	57 617 (41.9)	948 151 (43.3)	1 005 768 (43.2)
Never	34 456 (25.1)	580 525 (26.5)	614 981 (26.4)
Education attainment
Completed graduate school	9499 (10.4)	161 441 (11.1)	170 940 (11.0)
Completed college	30 087 (33.1)	506 817 (34.7)	536 904 (34.6)
Attended vocational or technical school	1276 (1.4)	17 395 (1.2)	18 672 (1.2)
Completed high school	50 091 (55.1)	774 418 (53.0)	824 509 (53.2)
Rural-urban residence^b
Urban	88 697 (64.2)	1 355 497 (60.5)	1 444 194 (60.7)
Rural	45 651 (33.1)	813 962 (36.3)	859 613 (36.1)
Highly rural	3774 (2.7)	72 786 (3.3)	76 560 (3.2)
Region
Midwest	34 331 (24.8)	529 283 (23.6)	563 614 (23.6)
Northeast	15 502 (11.2)	283 965 (12.6)	299 467 (12. 6)
South	63 658 (46.0)	991 628 (44.1)	1 055 286 (44.3)
West	23 060 (16.7)	422 194 (18.8)	445 254 (18.7)
Social Vulnerability Index percentile^c
<25	20 531 (16.7)	335 736 (18.0)	356 267 (17.9)
25-50	31 390 (25.5)	499 094 (26.7)	530 484 (26.7)
51-75	36 521 (29.7)	553 219 (29.6)	589 740 (29.7)
76-100	34 496 (28.1)	478 237 (25.6)	512 733 (25.8)
Drive time to specialty care, min^d
≤30	53 371 (44.2)	732 741 (38.0)	786 112 (38.4)
31-60	34 955 (28.9)	548 024 (28.4)	582 979 (28.5)
61-90	18 245 (15.1)	324 854 (16.9)	343 099 (16.8)
>90	14 197 (11.8)	321 939 (16.7)	336 136 (16.4)

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Abbreviations: AATD, α₁-antitrypsin deficiency; COPD, chronic obstructive pulmonary disease; FEV₁, forced expiratory volume in first second of expiration; FVC, forced vital capacity.

^{^a}

AATD testing was defined as any serum level α₁-antitrypsin and/or α₁-antitrypsin genotype test at any time during the study period.

^{^b}

Rural-urban residence categories were assigned by the Veterans Administration using 2010 Rural-Urban Commuting Area codes, with urban defined as codes 1.0 or 1.1, highly rural as code 10.0, and rural as all other codes.

^{^c}

Social Vulnerability Index combines 16 American Community Survey variables (eg, poverty, education, unemployment, insurance status, household characteristics, and housing type) to provide census tract percentile rankings of neighborhood-level social vulnerability.

^{^d}

Drive time to specialty care represents the estimated travel time from each patient’s home address to the nearest Veterans Administration facility offering specialty care services, including pulmonary care, calculated using geospatial methods that account for expected driving routes, traffic, and average road conditions.

Overall, 138 278 patients (5.8%) were tested for AATD; of those patients, 136 069 (98.4%) had serum levels, 10 691 (7.7%) underwent genotyping, and 8482 (6.1%) received both (Table 2). Of those tested, 8564 (6.2%) received a diagnosis of AATD. Among genotyped individuals, 7.69% carried at least 1 Z allele (MZ, 677 patients [6.3%]; SZ, 86 patients [0.8%]; and ZZ, 59 patients [0.6%]). ZZ homozygotes represented 0.0025% of the total cohort (Table 2).

Table 2. AAT Serum Levels, Genotype, and Augmentation Therapy Among Individuals Tested for AATD.

Characteristic	Total tested (n = 138 278)^a		AATD diagnosis (n = 8564)^b		No AATD diagnosis (n = 129 714)
Characteristic	Patients, No. (%)	AAT level, mean (SD), mg/dL	Patients, No. (%)	AAT level, mean (SD), mg/dL	Patients, No. (%)	AAT level, mean (SD), mg/dL
Serum levels
Overall	136 069 (98.4)	150.5 (39.4)	NA	NA	NA	NA
<100 mg/dL	7492 (5.4)	74.6 (24.7)	7492 (87.5)	74.6 (24.7)	NA	NA
≥100 mg/dL	128 577 (93.0)	154.9 (35.3)	NA	NA	128 577 (99.1)	154.9 (35.3)
Genotype^c
Overall	10 691 (7.7)	142.8 (39.2)	NA	NA	NA	NA
MM	9019 (6.5)	150.5 (34.1)	152 (1.8)	81.2 (18.4)	8867 (6.8)	152.0 (32.8)
MS	825 (0.6)	129.0 (31.7)	91 (1.1)	89.3 (14.7)	734 (0.6)	135.2 (29.1)
MZ	677 (0.5)	94.2 (25.5)	431 (5.0)	83.1 (16.4)	248 (0.2)	122.0 (22.9)
SS	25 (0.1)	90.6 (19.4)	25 (0.3)	90.6 (19.4)	NA	NA
SZ	86 (0.1)	64.9 (15.4)	86 (1.0)	64.9 (15.4)	NA	NA
ZZ	59 (0.1)	26.1 (8.2)	59 (0.7)	26.1 (8.2)	NA	NA
Augmentation therapy^d	320 (0.2)	52.0 (38.9)	320 (3.7)	52.0 (38.9)	NA	NA

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Abbreviations: AAT, α₁-antitrypsin; AATD, α₁-antitrypsin deficiency; NA, not applicable.

SI conversion factor: To convert AAT to micromoles per liter, multiply by 0.184.

^{^a}

AATD testing was defined as any serum AAT level and/or AAT genotype test at any time during the study period. A total of 8482 individuals had both serum levels and genotype tests.

^{^b}

AATD diagnosis was defined as serum AAT level less than 100 mg/dL; ZZ, SZ, or SS genotypes; or a prescription augmentation therapy.

^{^c}

Individuals with MM, MS, or MZ genotypes received a diagnosis of AATD on the basis of serum AAT levels less than 100 mg/dL.

^{^d}

Augmentation therapy indicates prescription of intravenous AAT augmentation during the study period.

Tested individuals were younger than nontested individuals (mean [SD] age, 58.3 [11.9] vs 64.6 [14.3] years) and were more likely to have cirrhosis (35 364 patients [25.6%] vs 94 275 patients [4.2%]), diabetes (63 274 patients [45.8%] vs 881 358 patients [39.2%]), and chronic kidney disease (46 258 patients [33.5%] vs 658 718 patients [29.3%]) (Table 1). Pulmonary function, tobacco use, and education were similar. Tested individuals predominantly lived in urban vs rural areas (88 697 patients [64.2%] vs 45 651 patients [33.1%]) and had shorter drive times to specialty care (≤30 minutes: 53 371 patients [44.2%] vs >90 minutes: 14 197 patients [11.8%]).

In fully adjusted models (model 4), older ages (aOR for 61-80 years, 0.50 [95% CI, 0.48-0.51]; aOR for >80 years, 0.12 [95% CI, 0.11-0.13]) and female sex (aOR, 0.84 [95% CI, 0.81-0.85]) were associated with lower odds of AATD testing (Figure 1 and eTable 2 in Supplement 1). Cirrhosis (aOR 6.40 [95% CI, 6.29-6.51]) and bronchiectasis (aOR, 3.73 [95% CI, 3.59-3.87]) were associated with higher odds of AATD testing. Compared with never smokers, former tobacco use was associated with higher odds of testing (aOR, 1.10 [95% CI, 1.05-1.15]), whereas active tobacco use was associated with lower odds of AATD testing (aOR, 0.93 [95% CI, 0.92-0.95]). Individuals living in rural or highly rural areas (aOR, 1.01 [95% CI, 1.00-1.03]) had odds of testing similar to those for individuals living in urban areas. Similarly, the odds of testing did not differ across SVI percentiles. Longer drive times to specialty care facilities were associated with progressively lower odds of testing (>90 minutes vs ≤30 minutes, aOR, 0.63 [95% CI, 0.62-0.65]) (Figure 1 and eTable 2 in Supplement 1).

Forest plot of adjusted odds ratios for testing across patient characteristics. Two-part data figure with a table on the left and a forest plot on the right. Table columns read, from left to right: Characteristics; No. with subcolumns Tested and Total; Adjusted O R with 95 percent C I. Section header Age, y with rows: less than or equal to 40, Tested 11,716, Total 171,259, Adjusted O R 1 Reference; 41 to 60, 63,840, 670,242, 1.03 (1.00 to 1.06); 61 to 80, 59,928, 1,258,514, 0.50 (0.48 to 0.51); greater than or equal to 80, 2,794, 284,898, 0.12 (0.11 to 0.13). Section header Sex with rows: Female, 10,380, 178,898, 0.84 (0.81 to 0.85); Male, 127,898, 2,206,015, 1 Reference. Section header Race and ethnicity with rows: American Indian or Alaska Native, 980, 16,778, 0.87 (0.80 to 0.93); Asian, 907, 14,996, 1.05 (0.97 to 1.13); Black or African American, 23,528, 331,202, 0.99 (0.97 to 1.01); Native Hawaiian or Pacific Islander, 1,209, 19,029, 1.04 (0.97 to 1.11); White, 103,361, 1,787,951, 1 Reference. Section header Comorbidities with rows: Cirrhosis, 35,364, 129,639, 6.40 (6.29 to 6.51); Bronchiectasis, 4,749, 27,040, 3.73 (3.59 to 3.87); Coronary artery disease, 56,950, 985,908, 1.00 (0.99 to 1.02); Congestive heart failure, 47,577, 738,204, 1.07 (1.05 to 1.09); Diabetes, 63,274, 944,632, 1.14 (1.12 to 1.16); Chronic kidney disease, 46,258, 704,976, 1.01 (1.00 to 1.03); Vasculitis, 3,169, 39,682, 1.17 (1.12 to 1.22). Section header Tobacco use with rows: Active, 45,442, 707,232, 0.93 (0.92 to 0.95); Former, 57,617, 1,005,768, 1.10 (1.08 to 1.12); Never, 34,456, 614,981, 1 Reference. Section header Rural-urban status with rows: Urban, 88,697, 1,444,194, 1 Reference; Rural or highly rural, 49,425, 936,173, 1.01 (1.00 to 1.03). Section header Social Vulnerability Index, percentile with rows: less than or equal to 25, 20,531, 356,267, 1 Reference; 25 to 50, 31,390, 530,484, 1.02 (1.00 to 1.04); 51 to 75, 36,521, 589,740, 1.04 (1.02 to 1.06); 76 to 100, 34,496, 512,733, 1.05 (1.03 to 1.07). Section header Drive time to specialty care, min with rows: less than or equal to 30, 53,371, 786,112, 1 Reference; 31 to 60, 34,955, 582,979, 0.92 (0.90 to 0.93); 61 to 90, 18,245, 343,099, 0.81 (0.79 to 0.83); greater than or equal to 90, 14,197, 336,136, 0.63 ( — α₁-Antitrypsin deficiency testing was defined as any serum α₁-antitrypsin level and/or α₁-antitrypsin level genotype test at any time during the study period. Rural-urban residence categories were assigned by the Veterans Administration using 2010 Rural-Urban Commuting Area (RUCA) codes, with urban defined as RUCA codes 1.0 or 1.1, highly rural as RUCA code 10.0, and rural as all other RUCA codes. Social Vulnerability Index combines 16 American Community Survey variables (eg, poverty, education, unemployment, insurance status, household characteristics, and housing type) to provide census tract percentile rankings of neighborhood-level social vulnerability. Drive time to specialty care represents the estimated travel time from each patient’s home address to the nearest Veterans Administration facility offering specialty care services, including pulmonary care, calculated using geospatial methods that account for expected driving routes, traffic, and average road conditions. OR indicates odds ratio.

Among 8244 individuals classified as having an AATD diagnosis, 320 (3.7%) were prescribed augmentation therapy (Table 3). The mean age was similar between treated and untreated groups. Treated individuals had higher rates of bronchiectasis (27 patients [8.4%] vs 271 patients [3.3%]) and former tobacco use (186 patients [58.1%] vs 3663 patients [44.4%]) than untreated individuals, but lower rates of active tobacco use (36 patients [11.3%] vs 2038 patients [24.7%]) and more severe airflow obstruction (mean [SD] FEV₁ percent predicted, 52.8% [28.5%] vs 80.6% [32.3%]; mean [SD] FEV₁/FVC, 47.6 [16.7] vs 66.3 [6.5]). In model 4, age 41 to 60 years vs 40 years or younger (aOR, 2.02 [95% CI, 1.24-3.26]), bronchiectasis (aOR, 2.98 [95% CI, 1.90-4.67]), and cirrhosis (aOR, 1.49 [95% CI, 1.07-2.06]) were associated with higher odds of treatment. Patients from minoritized racial and ethnic groups had significantly lower odds of augmentation therapy compared with White patients (aOR, 0.05 [95% CI, 0.01-0.22]), and active vs never tobacco use was associated with lower odds of treatment (aOR, 0.30 [95% CI, 0.19-0.48]). The odds of augmentation therapy prescription were similar across rural-urban status and did not vary by SVI percentiles. However, drive time of greater than 90 minutes to specialty care were associated with lower odds of therapy compared with drive times of 30 minutes or less (aOR, 0.56 [95% CI, 0.35-0.91]) (Figure 2 and eTable 3 in Supplement 1).

Table 3. Characteristics of Individuals Prescribed Augmentation Therapy for α₁-Antitrypsin Deficiency.

Characteristic	Patients, No. (%)
Characteristic	Augmentation therapy (n = 320)^a	No augmentation therapy (n = 8244)	Total (n = 8564)
Age, mean (SD), y	56.3 (11.5)	57.0 (12.8)	57.0 (12.7)
Sex
Female	28 (8.8)	584 (7.1)	612 (7.2)
Male	291 (91.2)	7653 (92.9)	7944 (92.8)
Race and ethnicity
American Indian or Alaska Native	0	55 (0.7)	55 (0.7)
Asian	0	58 (0.7)	58 (0.7)
Black or African American	4 (1.3)	804 (9.8)	808 (10.1)
Native Hawaiian or Pacific Islander	0	65 (0.8)	65 (0.8)
White	300 (93.8)	6730 (81.6)	7030 (87.7)
Unknown or declined^b	16 (5.0)	532 (6.5)	548 (6.4)
Obstructive lung disease
COPD	230 (71.9)	5286 (64.1)	5516 (64.4)
Asthma	2 (0.6)	1267 (15.4)	1269 (14.8)
COPD and asthma	88 (27.5)	1691 (20.5)	1779 (20.8)
Pulmonary function tests, mean (SD)
FVC percent predicted	85.3 (30.0)	94.5 (27.3)	93.9 (27.5)
FVC z score	−1.0 (1.9)	−0.4 (1.7)	−0.4 (1.8)
FEV₁ percent predicted	52.8 (28.5)	80.6 (32.3)	79.1 (32.7)
FEV₁ z score	−2.8 (1.7)	−1.1 (1.9)	−1.2 (1.9)
FEV₁/FVC ratio	47.6 (16.7)	66.3 (16.5)	64.2 (18.9)
Comorbidities
Cirrhosis	68 (21.3)	1781 (21.6)	1849 (21.6)
Bronchiectasis	27 (8.4)	271 (3.3)	298 (3.5)
Coronary artery disease	96 (30.0)	2912 (35.3)	3008 (35.1)
Congestive heart failure	2333 (28.3)	83 (25.9)	2416 (28.2)
Diabetes	83 (25.9)	3347 (40.6)	3430 (40.1)
Chronic kidney disease	68 (25.3)	2460 (29.8)	2528 (29.5)
Vasculitis	8 (2.5)	170 (2.1)	178 (2.1)
Tobacco use
Active	36 (11.3)	2038 (24.7)	2074 (24.4)
Former	186 (58.1)	3663 (44.4)	3849 (45.3)
Never	95 (30.0)	2485 (30.1)	2580 (30.3)
Education attainment
Completed graduate school	28 (11.9)	621 (11.0)	649 (11.0)
Completed college	74 (31.4)	1926 (34.1)	2000 (34.0)
Attended vocational or technical school	1 (0.4)	61 (1.1)	62 (1.1)
Completed high school	133 (56.4)	3038 (53.8)	3171 (53.9)
Rural-urban home addresses^c
Urban	174 (54.4)	5060 (61.5)	5234 (61.2)
Rural	136 (42.5)	2920 (35.5)	3056 (35.7)
Highly rural	10 (3.1)	255 (3.1)	265 (3.1)
Region
Midwest	93 (29.1)	1910 (23.2)	2003 (23.4)
Northeast	41 (12.8)	8,28 (10.0)	869 (10.2)
South	132 (41.2)	3266 (39.6)	3398 (39.7)
West	53 (16.3)	2137 (25.9)	2190 (25.6)
Social Vulnerability Index percentile^d
<25	46 (14.4)	824 (10.0)	1412 (18.5)
25-50	53 (16.6)	1273 (15.4)	2044 (26.8)
51-75	37 (11.6)	1375 (16.7)	2271 (29.8)
76-100	39 (12.2)	795 (9.6)	1955 (25.6)
Drive time to specialty care, min^e
≤30	109 (37.9)	2923 (40.8)	3032 (40.7)
31-60	90 (31.3)	2136 (29.8)	2226 (29.9)
61-90	54 (18.8)	1068 (14.9)	1122 (15.1)
>90	35 (12.2)	1042 (14.5)	1077 (14.4)

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Abbreviations: COPD, chronic obstructive pulmonary disease; FEV₁, forced expiratory volume in first second of expiration; FVC, forced vital capacity.

^{^a}

Augmentation therapy indicates prescription of intravenous α₁-antitrypsin augmentation during the study period.

^{^b}

Other or declined category includes individuals with missing data or self-reported categories not encompassed by standard federal reporting definitions for race and ethnicity.

^{^c}

^{^d}

^{^e}

Forest plot of adjusted odds ratios for treatment by patient characteristics. Left side table with three columns under the header Characteristics, No., and Adjusted O R with 95 percent C I. Under No. are two subcolumns labeled Treated and Total. Rows grouped by section headers: Age, y; Sex; Race and ethnicity; Comorbidities; Tobacco use; Rural-urban status; Social Vulnerability Index, percentile; Drive time to specialty care, min. Age, y rows: less than or equal to 40, Treated 26, Total 1005, Adjusted O R 1 Reference; 41 to 60, 172, 3890, 2.02 with 95 percent C I 1.24 to 3.26; 61 to 80, 115, 3516, 1.13 with 0.68 to 1.87; greater than or equal to 80, 7, 153, 1.47 with 0.53 to 4.10. Sex rows: Female 28, 612, 0.76 with 0.44 to 1.32; Male 291, 7944, 1 Reference. Race and ethnicity rows: Any race or ethnicity other than White 20, 1534, 0.05 with 0.01 to 0.22; White 300, 7030, 1 Reference. Comorbidities rows: Cirrhosis 68, 1849, 1.49 with 1.07 to 2.06; Bronchiectasis 27, 298, 2.98 with 1.90 to 4.67; Coronary artery disease 96, 3008, 0.78 with 0.57 to 1.07; Congestive heart failure 83, 2416, 1.17 with 0.85 to 1.62; Diabetes 83, 3430, 0.50 with 0.37 to 0.68; Chronic kidney disease 68, 2528, 0.72 with 0.51 to 1.01; Vasculitis 8, 178, 1.10 with 0.47 to 2.57. Tobacco use rows: Active 36, 2074, 0.30 with 0.19 to 0.48; Former 186, 3849, 1.10 with 0.82 to 1.46; Never 95, 2580, 1 Reference. Rural-urban status rows: Urban 174, 5234, 1 Reference; Rural 136, 3056, 1.31 with 0.97 to 1.78; Highly rural 10, 265, 1.17 with 0.54 to 2.54. Social Vulnerability Index, percentile rows: less than or equal to 25, 46, 1412, 1 Reference; 25 to 50, 53, 2044, 0.86 with 0.59 to 1.24; 51 to 75, 37, 2271, 0.79 with 0.54 to 1.15; 76 to 100, 39, 1955, 0.95 with 0.65 to 1.41. Drive time to specialty care, min rows: less than or equal to 30, 109, 3032, 1 Reference; 31 to 60, 90, 2226, 0.91 with 0.66 to 1.26; 61 to 90, 54, 1122, 1.01 with 0.68 to 1.50; greater than or equal to 90, 35, 1077, 0.56 with 0.35 to 0.91. Right side forest plot aligned to rows, with dark teal squares and horizontal confidence interval lines. A vertical dotted reference line at 1.0. One confidence interval for Any race or ethnicity other than White extends far left with a left-pointing arrow indicating truncation. Bottom horizontal axis labeled Adjusted O R with 95 percent C I, tick labels 0.1, 0.5, 1, 2.0, and 6.0. — α₁-Antitrypsin deficiency testing was defined as any serum α₁-antitrypsin level and/or α₁-antitrypsin level genotype test at any time during the study period. Rural-urban residence categories were assigned by the Veterans Administration using 2010 Rural-Urban Commuting Area (RUCA) codes, with urban defined as RUCA codes 1.0 or 1.1, highly rural as RUCA code 10.0, and rural as all other RUCA codes. Social Vulnerability Index combines 16 American Community Survey variables (eg, poverty, education, unemployment, insurance status, household characteristics, and housing type) to provide census tract percentile rankings of neighborhood-level social vulnerability. Drive time to specialty care represents the estimated travel time from each patient’s home address to the nearest Veterans Administration facility offering specialty care services, including pulmonary care, calculated using geospatial methods that account for expected driving routes, traffic, and average road conditions. OR indicates odds ratio.

Discussion

In this national cohort study of individuals with COPD and/or asthma, we found substantial disparities in both testing and augmentation therapy for AATD. Despite guideline recommendations for broad testing in all patients with COPD and persistent asthma, only 5.8% of eligible patients underwent AATD testing. Testing was less frequent among individuals who were older, female, actively using tobacco, and those with longer drive times to specialty care, and was more frequent among individuals with comorbidities, including cirrhosis and bronchiectasis. Augmentation therapy showed a similar pattern, occurring less often among individuals from minoritized racial and ethnic groups, those actively using tobacco, and those with longer drive times to specialty care, and occurring more often among individuals ages 41 to 60 years and those with cirrhosis and bronchiectasis. These findings suggest that demographic characteristics, comorbidities, and health care access shape AATD care. Together, these findings highlight persistent gaps in implementation of evidence-based AATD care and identify populations at particular risk of being missed.

Our findings align with prior work documenting widespread AATD underrecognition and undertreatment, while extending the literature in several important ways.^28,29,30 Consistent with registry-based studies, younger age was associated with testing, likely reflecting the traditional association of AATD with early-onset airflow obstruction and modest smoking exposure.^30,31 However, by leveraging a large, integrated national cohort spanning the full spectrum of disease severity and care settings, we further demonstrate that testing is also associated with comorbidities, with particularly high testing rates among those with cirrhosis and bronchiectasis, which are AATD-associated conditions emphasized in current guidelines.^7,15

In contrast to general population non-VA registries, our study evaluated a targeted at-risk cohort with established obstructive lung disease. Our observed testing rate (5.8%) aligns closely with prior VA data in patients with COPD (4.04%),¹⁰ while extending these findings by demonstrating persistently low rates of augmentation therapy utilization across the VA system.

Genotyped individuals in this at-risk cohort showed marked enrichment for ZZ homozygosity (0.56%; an 18-fold increase over general European population estimates of 0.03%) and Z-allele carriage (7.69% vs 4.0% in general European populations).²⁸ This reflects appropriate clinical selection for high-risk patients. However, with only 5.8% of the cohort screened, substantial population-level underdiagnosis persists.

Tobacco exposure is a known risk factor for the progression of lung and liver disease in individuals with AATD.^1,32 In our AATD-tested veteran cohort, we observed a high prevalence of active and former tobacco use. This contrasts sharply with landmark AATD cohorts, such as the Genetic Modifiers of AATD study, QUANTUM1, GRADS, and A1BC, where active smoking rates at enrollment are consistently under 10%, a difference driven by higher military tobacco exposure and cohort composition centered on veterans with COPD.^33,34,35,36 However, testing in our cohort was less frequent among individuals who were actively using tobacco. In contrast, nearly all tested patients had prior tobacco use, mirroring the emphasis in previous studies on tobacco as a key driver of AATD progression.³² This dichotomy suggests that while clinicians recognize tobacco as a risk factor, there may bias against testing in those actively smoking.^30,37 These findings highlight both a critical clinical bias to overcome and the urgent need for aggressive smoking cessation and diligent monitoring in veterans with AATD.

Unlike earlier work that lacked detailed contextual information on rurality and social vulnerability,³⁷ we found no clear association between testing and neighborhood-level social vulnerability or urban vs rural residence; instead, longer drive times to specialty care were associated with lower odds of testing, suggesting that geographic access barriers, rather than traditional sociodemographic measures, may be a more proximate determinant of AATD evaluation.^{34,35,36,37,38,39,40,41}

Augmentation therapy in our cohort was more often prescribed to individuals with more severe airflow obstruction and to those with bronchiectasis and cirrhosis, consistent with observational registry data showing that treatment is typically concentrated among patients with advanced physiologic impairment.^42,43 Concurrent bronchiectasis increased the odds of treatment, suggesting clinicians are more likely to initiate therapy in patients presenting with a more severe or complex pulmonary phenotype. In the National Heart, Lung, and Blood Institute Registry, for example, two-thirds of severely deficient participants ultimately received augmentation therapy, yet the FEV₁ and diffusing capacity of the lungs for carbon monoxide at diagnosis were already about half of predicted, and nearly one-third reported medical disability by a mean age of 46 years.^44,45 Our finding that augmentation therapy was less common among individuals actively using tobacco and more frequent among those with prior tobacco use parallels registry data, supporting guideline emphasis on tobacco cessation as a prerequisite for treatment, given the attenuated benefit in the setting of ongoing tobacco exposure.^7,15 Our results extend the existing observational and registry literature by demonstrating that, even in an integrated national system, augmentation therapy remains concentrated among a subset of clinically severe patients and is shaped not only by disease phenotype and tobacco status but also by structural factors.⁴⁶ Augmentation therapy did not differ by rurality or neighborhood-level social vulnerability, yet declined among those with the longest drive times to specialty care, suggesting that geographic access to infusion-capable centers is a key, previously underappreciated barrier to equitable augmentation therapy.^38,39,40,41

Our findings have important clinical and health-policy implications. Clinically, failure to test broadly for AATD risks missing a diagnosis that can influence tobacco cessation counseling, family screening, transplant evaluation, and eligibility for augmentation therapy.^1,15 Underuse of augmentation therapy among appropriate candidates may contribute to avoidable morbidity, accelerated lung function decline, and increased mortality. Furthermore, there are several pipeline therapies, including small molecule inhibitors, RNA-based therapies, and others,⁴⁷ for which patients without a diagnosis will not be known to be eligible. From a health system perspective, our findings suggest that guideline recommendations for universal or near-universal testing in eligible individuals are not being systematically operationalized, particularly for groups found to be at higher risk of undertesting. Interventions such as electronic health record alerts for AATD testing in patients with COPD, care pathways embedded in COPD clinics, or reflex testing on certain spirometry or radiographic patterns could help standardize practice.^48,49 At the policy level, efforts to ensure coverage and reduce administrative burden for testing and augmentation therapy, as well as targeted outreach individuals with geographic barriers to receiving specialized care, may be needed to reduce inequities. Given the VA’s integrated structure, there is an opportunity to pilot and rigorously evaluate systemwide quality improvement initiatives around AATD care that could be emulated in other health systems.

Limitations

Several limitations should be considered when interpreting these results. First, our analysis relies on administrative data, which may misclassify clinical diagnoses, AATD testing, and prescription of augmentation therapy, and cannot capture testing or treatment received outside the system. Second, this cohort is drawn from an integrated VA health system and consists of veterans who are predominantly male and White, which may limit generalizability to other populations. Third, because our study focused on testing and treatment utilization, radiological features and longitudinal functional data (eg, serial spirometry) were not evaluated to assess imaging predictors of testing or track disease progression on or off therapy. Fourth, our observational design precludes causal inference about the impact of testing or augmentation therapy on outcomes.

Conclusions

In this cohort study of individuals with COPD and/or asthma, AATD testing and augmentation therapy remain low, with systematic shortfalls by age, comorbidity profile, tobacco use, and drive time to specialty care. Ensuring that all eligible individuals are tested for AATD and facilitating testing and treatment for those at highest risk of being missed are actionable targets for system-level reform.

Supplement 1.

eTable 1. Diagnosis Codes for COPD and Asthma

eTable 2. Logistic Regression Analysis for Alpha-1 Antitrypsin Testing Among Individuals With COPD and/or Asthma

eTable 3. Logistic Regression Analysis for Augmentation Therapy Among Those With Alpha-1 Antitrypsin Deficiency Diagnosis

jamanetwopen-e2639328-s001.pdf^{(349.5KB, pdf)}

Supplement 2.

Data Sharing Statement

jamanetwopen-e2639328-s002.pdf^{(13.8KB, pdf)}

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25.US Department of Agriculture, Economic Research Service . Rural-Urban Community Area Codes, version 2010. November 16, 2021. Accessed September 11, 2026. https://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes/
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38.Baldomero AK, Kunisaki KM, Wendt CH, et al. Drive time and receipt of guideline-recommended screening, diagnosis, and treatment. JAMA Netw Open. 2022;5(11):e2240290. doi: 10.1001/jamanetworkopen.2022.40290 [DOI] [PMC free article] [PubMed] [Google Scholar]
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40.Baldomero AK, Kunisaki KM, Wendt CH, et al. Guideline-discordant inhaler regimens after COPD hospitalization: associations with rurality, drive time to care, and fragmented care—a United States cohort study. Lancet Reg Health Am. 2023;26:100597. doi: 10.1016/j.lana.2023.100597 [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Baldomero AK, Dudley RA, Wendt CH. Geographic disparities in chronic obstructive pulmonary disease inhalers by rurality, drive time, and neighborhood disadvantage. Am J Respir Crit Care Med. 2026;212(9):2087-2090. doi: 10.1093/ajrccm/aamag159 [DOI] [PubMed] [Google Scholar]
42.Vreim CE, Wu M, Crystal RG, et al. ; The Alpha-1-Antitrypsin Deficiency Registry Study Group . Survival and FEV₁ decline in individuals with severe deficiency of α₁-antitrypsin. Am J Respir Crit Care Med. 1998;158(1):49-59. doi: 10.1164/ajrccm.158.1.9712017 [DOI] [PubMed] [Google Scholar]
43.Miravitlles M, Turner AM, Torres-Duran M, et al. Clinical and functional characteristics of individuals with alpha-1 antitrypsin deficiency: EARCO international registry. Respir Res. 2022;23(1):352. doi: 10.1186/s12931-022-02275-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
44.McElvaney NG, Stoller JK, Buist AS, et al. ; Alpha-1-Antitrypsin Deficiency Registry Study Group . Baseline characteristics of enrollees in the National Heart, Lung and Blood Institute Registry of α1-antitrypsin deficiency. Chest. 1997;111(2):394-403. doi: 10.1378/chest.111.2.394 [DOI] [PubMed] [Google Scholar]
45.Stoller JK, Sandhaus RA, Turino G, Dickson R, Rodgers K, Strange C. Delay in diagnosis of alpha1-antitrypsin deficiency: a continuing problem. Chest. 2005;128(4):1989-1994. doi: 10.1378/chest.128.4.1989 [DOI] [PubMed] [Google Scholar]
46.Greulich T, Albert A, Cassel W, et al. Opinions and attitudes of pulmonologists about augmentation therapy in patients with alpha-1 antitrypsin deficiency: a survey of the EARCO Group. Int J Chron Obstruct Pulmon Dis. 2022;17:53-64. doi: 10.2147/COPD.S346051 [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Rahaghi FF. Alpha-1 antitrypsin deficiency research and emerging treatment strategies: what’s down the road? Ther Adv Chronic Dis. Published online July 29, 2021. doi: 10.1177/20406223211014025 [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Campos M, Hagenlocker B, Lascano J, Riley L. Impact of a computerized clinical decision support system to improve chronic obstructive pulmonary disease diagnosis and testing for alpha-1 antitrypsin deficiency. Ann Am Thorac Soc. 2023;20(8):1116-1123. doi: 10.1513/AnnalsATS.202211-954OC [DOI] [PubMed] [Google Scholar]
49.Nathani A, Stoller JK. An electronic health record-based strategy to enhance detection of alpha-1 antitrypsin deficiency. Respir Care. Published online September 10, 2024. doi: 10.4187/respcare.12200 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement 1.

eTable 1. Diagnosis Codes for COPD and Asthma

eTable 2. Logistic Regression Analysis for Alpha-1 Antitrypsin Testing Among Individuals With COPD and/or Asthma

eTable 3. Logistic Regression Analysis for Augmentation Therapy Among Those With Alpha-1 Antitrypsin Deficiency Diagnosis

jamanetwopen-e2639328-s001.pdf^{(349.5KB, pdf)}

Supplement 2.

Data Sharing Statement

jamanetwopen-e2639328-s002.pdf^{(13.8KB, pdf)}

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[zoi261063r37] 37.Riley L, Sriram A, Brantly M, Lascano J. Testing patterns and disparities for alpha-1 antitrypsin deficiency. Am J Med. 2023;136(10):1011-1017. doi: 10.1016/j.amjmed.2023.06.020 [DOI] [PubMed] [Google Scholar]

[zoi261063r38] 38.Baldomero AK, Kunisaki KM, Wendt CH, et al. Drive time and receipt of guideline-recommended screening, diagnosis, and treatment. JAMA Netw Open. 2022;5(11):e2240290. doi: 10.1001/jamanetworkopen.2022.40290 [DOI] [PMC free article] [PubMed] [Google Scholar]

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[zoi261063r40] 40.Baldomero AK, Kunisaki KM, Wendt CH, et al. Guideline-discordant inhaler regimens after COPD hospitalization: associations with rurality, drive time to care, and fragmented care—a United States cohort study. Lancet Reg Health Am. 2023;26:100597. doi: 10.1016/j.lana.2023.100597 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi261063r41] 41.Baldomero AK, Dudley RA, Wendt CH. Geographic disparities in chronic obstructive pulmonary disease inhalers by rurality, drive time, and neighborhood disadvantage. Am J Respir Crit Care Med. 2026;212(9):2087-2090. doi: 10.1093/ajrccm/aamag159 [DOI] [PubMed] [Google Scholar]

[zoi261063r42] 42.Vreim CE, Wu M, Crystal RG, et al. ; The Alpha-1-Antitrypsin Deficiency Registry Study Group . Survival and FEV₁ decline in individuals with severe deficiency of α₁-antitrypsin. Am J Respir Crit Care Med. 1998;158(1):49-59. doi: 10.1164/ajrccm.158.1.9712017 [DOI] [PubMed] [Google Scholar]

[zoi261063r43] 43.Miravitlles M, Turner AM, Torres-Duran M, et al. Clinical and functional characteristics of individuals with alpha-1 antitrypsin deficiency: EARCO international registry. Respir Res. 2022;23(1):352. doi: 10.1186/s12931-022-02275-4 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi261063r44] 44.McElvaney NG, Stoller JK, Buist AS, et al. ; Alpha-1-Antitrypsin Deficiency Registry Study Group . Baseline characteristics of enrollees in the National Heart, Lung and Blood Institute Registry of α1-antitrypsin deficiency. Chest. 1997;111(2):394-403. doi: 10.1378/chest.111.2.394 [DOI] [PubMed] [Google Scholar]

[zoi261063r45] 45.Stoller JK, Sandhaus RA, Turino G, Dickson R, Rodgers K, Strange C. Delay in diagnosis of alpha1-antitrypsin deficiency: a continuing problem. Chest. 2005;128(4):1989-1994. doi: 10.1378/chest.128.4.1989 [DOI] [PubMed] [Google Scholar]

[zoi261063r46] 46.Greulich T, Albert A, Cassel W, et al. Opinions and attitudes of pulmonologists about augmentation therapy in patients with alpha-1 antitrypsin deficiency: a survey of the EARCO Group. Int J Chron Obstruct Pulmon Dis. 2022;17:53-64. doi: 10.2147/COPD.S346051 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi261063r47] 47.Rahaghi FF. Alpha-1 antitrypsin deficiency research and emerging treatment strategies: what’s down the road? Ther Adv Chronic Dis. Published online July 29, 2021. doi: 10.1177/20406223211014025 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi261063r48] 48.Campos M, Hagenlocker B, Lascano J, Riley L. Impact of a computerized clinical decision support system to improve chronic obstructive pulmonary disease diagnosis and testing for alpha-1 antitrypsin deficiency. Ann Am Thorac Soc. 2023;20(8):1116-1123. doi: 10.1513/AnnalsATS.202211-954OC [DOI] [PubMed] [Google Scholar]

[zoi261063r49] 49.Nathani A, Stoller JK. An electronic health record-based strategy to enhance detection of alpha-1 antitrypsin deficiency. Respir Care. Published online September 10, 2024. doi: 10.4187/respcare.12200 [DOI] [PubMed] [Google Scholar]

PERMALINK

Disparities in α1-Antitrypsin Deficiency Testing and Treatment in US Adults

Arianne K Baldomero, MD, MS

Lisa Smith, BSN

Amy Gravely, BS

Vickram Tejwani, MD

Chris H Wendt, MD

Key Points

Question

Findings

Meaning

Abstract

Importance

Objective

Design, Setting, and Participants

Exposure

Main Outcomes and Measures

Results

Conclusions and Relevance

Introduction

Methods

Data Sources and Study Populations

AATD Testing

AATD Diagnosis

Variables

Statistical Analysis

Results

Table 1. Characteristics of Individuals With COPD and/or Asthma Tested vs Not Tested for AATDa.

Table 2. AAT Serum Levels, Genotype, and Augmentation Therapy Among Individuals Tested for AATD.

Figure 1. Dot Plot for α1-Antitrypsin Deficiency Testing Among Individuals With Chronic Obstructive Pulmonary Disease and/or Asthma.

Table 3. Characteristics of Individuals Prescribed Augmentation Therapy for α1-Antitrypsin Deficiency.

Figure 2. Dot Plot for Augmentation Therapy Among Individuals With α1-Antitrypsin Deficiency.

Discussion

Limitations

Conclusions

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases

Disparities in α₁-Antitrypsin Deficiency Testing and Treatment in US Adults

Table 1. Characteristics of Individuals With COPD and/or Asthma Tested vs Not Tested for AATD^a.

Figure 1. Dot Plot for α₁-Antitrypsin Deficiency Testing Among Individuals With Chronic Obstructive Pulmonary Disease and/or Asthma.

Table 3. Characteristics of Individuals Prescribed Augmentation Therapy for α₁-Antitrypsin Deficiency.

Figure 2. Dot Plot for Augmentation Therapy Among Individuals With α₁-Antitrypsin Deficiency.