Preserved Ratio Impaired Spirometry

The investigators aim to evaluate whether it is feasible for healthcare professionals to implement a case-finding tool to identify individuals at high risk for chronic respiratory diseases (CRDs) during routine care for non-communicable diseases (NCDs) in primary healthcare services in Brazil. The investigators will complete a cross-sectional observational study involving 20 healthcare professionals (physicians, physiotherapists, community health agents, and nurses) from five primary healthcare services who will apply the COLA-6 tool during routine NCD care. A total sample of 1,000 patients is expected. Qualitative interviews will be conducted with healthcare professionals to explore barriers and facilitators to implementing COLA-6 in clinical practice.

The investigators aim to make it easier to find people with undiagnosed long-term lung health problems (such as asthma, and chronic obstructive pulmonary disease, COPD, collectively called 'Chronic Respiratory Diseases', CRDs) living in Brasil. Because people with undiagnosed CRDs often have other medical problems too, such as heart problems, high blood pressure and diabetes (collectively called 'Long Term Conditions', LTC), the investigators will look for undiagnosed CRD in people with LTC when they are attending appointments at local health centres in the cities of Sao Carlos and Sao Paulo. To look for undiagnosed CRDs the investigators will use two questionnaire-based methods called 'COLA' and 'SBQ'. These are quick and simple to do. All participants will also have tests of their lung function completed including spirometry and Fractional Exhaled Nitric Oxide so that the investigators can make a definite diagnosis of CRDs. When the study is complete, the investigators will examine how well the COLA and SBQ questionnaires perform in identifying people with a definite diagnosis of CRD. In total, the investigators aim to recruit 859 people.

The goal of this observational study is to develop an advanced expiratory algorithm model utilizing exhaled breath volatile organic compound (VOC) marker molecules. This model aims to accurately diagnose mutiple pulmonary diseases. The primary objectives it strives to accomplish are: 1. To assess the diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in diagnose several common pulmonary diseases. 2. To assess the diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in diagnose more pulmonary diseases.

Establish early TCM prevention and treatment program to reduce the incidence rate of COPD.