Risk Factors Associated With Dyspnea
Patient-related risk factors were consideredﬁrst, and re-
sults of spirometry considered afterward. The spirom-
etry risk factors chosen for the second stage analysis
included the spirometry-based diagnosis of the patient
(asthma, COPD, PRISm, or normal) and lung function
results indicative of the severity of physiologic impair-
ment. Severity was gauged by assessing three principal
lung function measures: (1) post-BD FEV1 % predicted,
(2) post-BD FEV 1/FVC ratio, and (3) percentage
reversal of FEV1 with BD.
Dyspnea Impact and Health Care Use, Quality of
Life, and Work Productivity
The impact of dyspnea and its associations with health
care use, quality of life, and work productivity were exam-
ined. Health care utilization was assessed through self-
reported data. Quality of life was assessed using the 36-
Item Short Form Health Survey questionnaire, where
higher scores indicate better health status. Work produc-
tivity was assessed using the Work Productivity and Activ-
ity Impairment questionnaire, where higher scores
indicate greater impairment in work productivity and
daily activities.
Statistical Analysis
Box plots were used to compare distribution patterns of
dyspnea impact assessments among the disease groups.
Pairwise comparison tests were conducted to evaluate
mean dyspnea differences between groups. Multiple
linear regression analysis was used to measure contribu-
tions to variability of dyspnea by selected patient-speciﬁc
risk factors, spirometry disease classiﬁcation, and key
lung function measures. The selected sets of risk factors
were evaluated using successive regression analyses.
Analysis of variance sums of squares from the successive
regression analyses provided the cumulative percentage
contributions to variability of dyspnea. Simple, multiple,
and logistic regression analyses were used to study asso-
ciations between dyspnea and health care utilization,
quality of life, and work productivity outcomes. All sta-
tistical analyses were done using STATA 16 statistical
software (StataCorp).
Results
Figure 1 illustrates the results of the caseﬁnding
approach, including the enrollment of the control group.
Among 5,631 potentially eligible participants, 1,359
participants (24%) did not meet the threshold of$ 6
points on the ASQ or$ 20 points on the COPD-
Diagnostic Questionnaire and were thus excluded,
leaving 4,272 individuals deemed eligible for spirometry.
38,353 individuals indicated that they had respiratory
symptoms and were phoned back by study personnel
    26,905 were symptomatic
    11,448 had no respiratory symptoms
5,631 potentially eligible for the
case finding study
1,359 did not score ≥ 6
points on ASQ or ≥ 20
points on the COPD-DQ
2,090 (73.2%) had normal
spirometry
265 (9.3%) had
undiagnosed asthma
330 (11.5%) had
undiagnosed COPD 172 (6.0%) had PRISM
21,274 excluded
8,273 Previous diagnosis of asthma
5,363 Previous diagnosis of COPD
190 Age < 18 years
1,763 Previous diagnosis of CF, bronchiectasis, pulmonary
fibrosis, or lung cancer
1,331 History of MI, heart problems, stroke, aortic or cerebral
aneurysm, eye surgery, or detached retina in past 3 mos.
19 Pregnant, in the third trimester
3,715 Under care of respirologist or using an inhaled respiratory
1,415 did not complete spirometry
  1,337 refused to travel to study site
  67 participants unable to complete acceptable
  spirometry
  11 participants deemed ineligible after consent
4,272 potentially eligible for the
case finding study
2,857 completed pre and post
bronchodilator spirometry and
could be evaluated for a
diagnosis of asthma or COPD
231 healthy controls with
no respiratory symptoms
who scored 0 points on the
ASQ were selected and
completed pre and post
bronchodilator spirometry
Figure 1– Study ﬂow diagram demonstrating the caseﬁnding and control group recruitment and allocation. ASQ¼ Asthma Screening Questionnaire;