Validation of a survey questionnaire for screening of obstructive airway disease by primary care physicians

Dear Editor,

Obstructive airway diseases (OADs) have assumed the scale of an epidemic. The term covers a large spectrum of diseases including some named and some still orphan entities.[1] Chronic obstructive pulmonary disease (COPD) and asthma are held to be the two poles of the spectrum of airway diseases and also most relevant in clinical practice. Both are characterised by common presentation of dyspnoea and cough but essentially have different pathology, management, and prognosis.

In spite of great strides in respiratory medicine and availability of trained specialists, OADs are predominantly taken care of by primary care physicians, family physicians, and internists.

However, the spectrum of OAD is a vast one making it difficult to distinguish at times. Still, it needs a holistic approach for early diagnosis to prevent progression and improve patient outcomes.[2]

Though there are separate guidelines for asthma and COPD, there is no single guideline and/or consensus approach for the whole spectrum from a single viewpoint.[3]

There was an unmet need of a questionnaire to be used by the primary care physicians in low-cost settings which would aid the appropriate diagnosis of an OAD. Here, we present a pilot study to develop and validate a questionnaire for screening of OADs in a primary care setting.

The validation study was conducted in a primary and secondary care hospital in eastern India over a 6-month period. The participants were physicians involved in treating patients with airway diseases. All the participant physicians had a good understanding of the English language, so we aimed to construct and validate only the English version of the questionnaire [Table 1].

Table 1.

Questionnaire and scores

Age	<20y	21-30	31-40	41-50	50-60	>60
SCORE	3	5	8	15	20	30
Family H/O, Allergy	Present		Absent
SCORE	10		30
Nature of symptoms	Intermittent symptoms Waxing and waning		Continuous and progressive symptoms
SCORE	10		30
Allergic rhinitis	Present		Absent
SCORE	10		30
Smoking Quit (Years before)	Non-smoker	>11-20 years quit	21-30 years quit	>30 pack Years quit	Recent quit (less than 10 years)	Current smoker <10 pack years	Current smoker 11-20 pack years	Current smoker >20 pack years
SCORE	10	50	40	30	20	30	40	60
Biomass Hours of exposure (13 years or more)	None	2 hours	2-4 hours	4-6 hours	>6
SCORE	10	20	30	40	50
Biomass exposure years	None	13 years	14-26 years	26-39 years	>40 years
SCORE	10	20	30	40	50

This interviewer-led questionnaire was designed for primary care physicians. The purpose was to screen for the presence of various risk factors which would ultimately help in indicating the presence of OADs of the asthma–COPD spectrum. The literature on the PubMed database was reviewed with the keywords “Asthma, COPD, ACO, and practice questionnaires,” and a list of items were constructed. After reviewing the literature on the PubMed database with keywords “Asthma, COPD, ACO, and practice questionnaires,” a list of items were constructed. The final questionnaire has seven domains which are independent risk factors for asthma and COPD or the chronic irreversible disease. Each variable or domain was assigned an empiric weighted score based on its established contribution to airway inflammation, obstruction, and progression, as supported by current clinical and epidemiological evidence. The cumulative score thus represents the overall airway disease risk profile, where higher values correspond to a greater probability of chronic, irreversible airway involvement to be present.

We distributed the questionnaire to 12 experts from the field of respiratory medicine, epidemiology, and biostatistics for the evaluation regarding the relevance of each item to the targeted domain and scored each item on a Likert scale from 1 to 9, with 1 being least relevant and 9 being most relevant, and a median cut-off of ≥5 was considered relevant. We calculated the Content Validity Index (CVI) to evaluate the validity of the content of the items. CVI assesses the degree to which the content of an instrument is relevant to and representative of the construct it is intended to measure.[4] All the items had an item-level CVI (I-CVI) >0.8, which showed a high level of agreement among the experts. The scale-level CVI (S-CVI) score was 0.83, which also showed a high level of overall agreement on the relevance of the items.

A sample size of 30 per item was taken to increase the robustness of the study. We calculated our sample size to be 225 for six questions with a 20% probable non-responsive rate. We checked the reliability of the instrument using Cronbach’s α. Spearman’s correlation was performed to test the associations among the items of the questionnaire among all participants and stratified by disease type. To examine whether the data were suitable for principal component analysis (PCA), we performed the Kaiser–Meyer–Olkin (KMO) test of sampling adequacy. An overall KMO >0.50 indicates adequate sampling and that the items were sufficiently correlated to conduct PCA. PCA of the questionnaire items was performed to seek information of the major component(s) which represent(s) the maximum variance within the questionnaire instrument with varimax rotation and components with Eigenvalues >1 was primarily considered. Test results were considered significant if the P value was smaller than 0.05. Components were visually represented using scree plot and loading plots.

The inter-item correlation matrix showed significant correlation of biomass exposure with age, nature of symptoms with age, and presence of allergic rhinitis with age [Tables 2A and 2B].

Table 2A.

Inter-item correlation matrix of the instrument

	Age	Family H/O allergy	Nature of symptoms	Allergic rhinitis	Smoking status	Biomass exposure years
Age	1
Family H/O allergy	0.08 (0.26)	1
Nature of symptoms	0.24 (0.002)	0.01 (0.89)	1
Allergic rhinitis	0.44 (<0.001)	0.17 (0.01)	0.13 (0.05)	1
Smoking status	0.14 (0.03)	-0.09 (0.19)	0.16 (0.02)	0.13 (0.06)	1
Biomass exposure years	0.24 (<0.001)	-0.05 (0.46)	0.15 (0.03)	0.12 (0.06)	-0.002 (0.98)	1

Data presented as Spearman’s rank-order correlation coefficient (ρ). Values in the parentheses indicate P-values

Table 2B.

Principal component loading (unrotated)

	Component 1	Component 2	Component 3	Component 4	Component 5	Component 6
Age	0.61	0.07	- 0.08	- 0.16	- 0.23	- 0.74
Family H/O allergy	0.11	0.75	0.25	0.28	0.53	- 0.09
Nature of symptoms	0.40	- 0.25	0.17	0.82	- 0.21	0.17
Allergic rhinitis	0.53	0.30	0.10	- 0.39	- 0.28	0.62
Smoking status	0.25	- 0.50	0.61	- 0.27	0.50	- 0.01
Biomass exposure years	0.35	- 0.19	- 0.72	0.03	0.54	0.17

The validation was done on 225 patients above 18 years of age, who were able to perform spirometry as per the ATS criteria.[5] Primary care physicians and interns administered the questionnaire on patients with a diagnosis of asthma, COPD, ACO, and bronchiectasis. COPD was clinically diagnosed by clinical history and spirometry as per GOLD guidelines.[6] Similarly, asthma was diagnosed by clinical history and spirometry as per recent GINA guideline.[7] True positives were those who were diagnosed of a disease by both the questionnaire and anamnesis with spirometry, false positives were those who were positive by questionnaire but negative by the standard method, true negatives were those where the disease was not diagnosed by either the questionnaire or the standard method, and the false negatives were those who were not diagnosed by the questionnaire but had the disease.

Scores range from 63 to 280. Higher scores are associated with COPD, while lower scores are associated with asthma. Scores ranging from 63 to 110 are more likely to be asthma, scores from 200 to 280 are more likely to be COPD, and scores from 110 till 200 are asthma–COPD overlap. The AUC for the diagnosis of asthma in the ROC curve with a cut-off score of 110 is 0.74, and that with a cut-off score of 63 is 0.93, while the AUC for the diagnosis of COPD for a cut-off score of 200 is 0.86 and that for 280 is 0.95.

The questionnaire had a sensitivity of 68.2%, a specificity of 73.5%, a PPV of 43.4%, and an NPV of 89.6%. This questionnaire has a low sensitivity and PPV but higher specificity and NPV. This limits the questionnaire to be used as a tool to rule in the disease, but high NPV shows this tool could be used to rule out the disease under study. This may be attributed to the weighted scoring of the domains and using a limited set of risk factors. The domain was kept few to keep it handy for the use by a primary care physician as a screening tool for day-to-day use and not as a research tool or a tool for specialist use in a tertiary care facility. This questionnaire has similar sensitivity and specificity to another questionnaire validated by Mahesh et al.[8] for COPD patients in Mysore.

Thus, this questionnaire would serve as a screening tool to identify the patients who need to be investigated more for the diagnosis of obstructive airway disease and thus would aid a primary care physician to use it in his/her general OPD.

Conflicts of interest

There are no conflicts of interest.

Funding Statement

Nil.