Bronchitis

We collected demographic data, pharmacy records and the primary ICD-9 code for all outpatient and inpatient visits during the exact calendar date one year pre- and one year post the index date utilizing the VA computerized medical record system.
Patients with any of the following primary ICD-9 codes were considered to have a COPD-related visit: 491.xx - chronic bronchitis, 492.xx - emphysema, 493.2 - chronic obstructive asthma, 496.xx - chronic airway obstruction, not elsewhere classified. We did not include 490 - Bronchitis, not specified as acute or chronic in our administrative definition of COPD because the definition itself lacks specificity which increases the concern about misclassification [23 (link),24 (link)]. Outpatient primary and secondary ICD-9 codes were those recorded during a patient encounter in any outpatient clinic while inpatient primary ICD-9 codes were those recorded during an admission to the hospital. ICD-9 codes generated during visits to the pulmonary function laboratory were not considered in this analysis. Although secondary ICD-9 codes were considered for defining a COPD-related visit these were uncommonly (<7% of visits) coded by providers. Comorbid conditions relevant to patients with COPD were determined using ICD-9 codes for all previous outpatient visits in the one year period prior to the index date. These included a diagnosis of lung cancer (162.x, 163.x), acute coronary syndrome (410.xx, 411.xx), congestive heart failure (398.91,415.xx, 416.xx,425.x, 428.x), diabetes (250.x), hypertension (401.xx-405.xx), atrial fibrillation (427.xx), depression (311, 300.4, 296.2x, 296.3x), and schizophrenia (295.xx).
Smoking was assessed at the time of spirometry; patients were classified as never/former or current based upon self report. We determined the total number of metered dose inhaler (MDI) canisters prescribed over the two year period to each patient for both albuterol and ipratropium bromide (categorized as: albuterol - 0, 1-5, 6+ MDI; ipratropium - 0, 1-2, 3+) using the Veterans Integrated Service Network (VISN) data warehouse. The VISN data warehouse contains the complete pharmacy records for patients who filled prescriptions within the VISN region. These data include the drug name, class, prescription identification number, prescription fill dates (primary and refills), number of allowable refills, date of next allowable refill, amount dispensed, day supply, unit price of the medication and directions for use. Nebulized medications were not included in the calculation of these totals. Tiotropium was not included in our analysis as it was adopted slowly in the VA because of formulary restriction.

A cross sectional study of a quasi-random sample of 327 pharmacies was conducted in Riyadh, the capital of Saudi Arabia with about 5 million habitants, in November 2010. The sample was intended to be representative of all Riyadh pharmacies. The sample was stratified by the five regions of Riyadh (Eastern, Western, Northern, Southern, Central) regardless of the pharmacy's size, deprivation level of the area. A convenience sample of streets was chosen from each region and a complete enumeration of all pharmacies in each street was considered. Each pharmacy was visited once by two investigators (total of 6 male physicians and 2 male medical students participated) who simulated having a brother/sister with a predetermined clinical scenario according to simulated-client method pharmacy surveys [19 (link),20 ]. The scenarios included sore throat, acute bronchitis, otitis media, acute sinusitis, diarrhea, and urinary tract infection in a pregnant (childbearing age) women. The investigators concealed their identity and the study objective of their visits from the approached pharmacists who were identified by their licenses and pictures on the front wall of the pharmacy. The clinical scenarios were presented as follow; one investigator talked to the pharmacist while the other observed the discussion and memorized the responses. Immediately after leaving the pharmacy, both investigators completed a standardized data form that included information about the location of the pharmacy, antibiotics dispensing practice, pharmacists' inquiries about associated symptoms (e.g. fever/shortness of breath/abdominal pain/loin pain), allergy history, pregnancy status in case of UTI; type of antibiotic, if dispensed; and information about drug interactions if this was provided by the pharmacist.
Two sessions of standardization took place in the presence of all actors. Each group rehearsed simulating all the clinical scenarios to the senior investigator using the same complaints (terminology and statements). Rehearsal was repeated to ensure reliability of the simulated scenario. The actors used lay language and refrained from using any jargon.
Only the following clinical information was presented to the pharmacist. Any additional information was only provided if the pharmacist inquired about it. The sore throat scenario: a healthy young male relative was described as having difficulties in swallowing with slight fever of 24 hours duration. Acute bronchitis scenario: an elderly man relative was described as having sore throat, cough with sputum production. Additional information provided upon request was the patient had multiple comorbid conditions and was using warfarin.
Acute sinusitis scenario: a young male relative was described as having running nose, facial pain, and headache. Otitis media scenario: a 5-year-old relative child was described as having ear pain and discharge. Urinary tract infection scenario: a childbearing female relative was described as having dysuria and urinary frequency. Diarrhea scenario: a young male relative was described with loose bowel motion for one day.
Three levels of demand were used sequentially until an antibiotic was dispensed or denied [4 (link)]: 1) Can I have something to relieve my symptoms?: 2) Can I have something stronger? 3) I would like to have an antibiotic.
Data are presented as percentage of the pharmacists' responses toward the simulated clinical scenarios.
The study was approved by the Institutional Review Board at King Fahd Medical City. Deception and incomplete disclosure to study subjects (pharmacists) were considered ethically acceptable because this was a minimal risk study and it could not have been performed with complete disclosure of investigator entity. Data were kept anonymous.

Electronic medical records updated on January 1, 2020, were used to obtain date of birth to calculate age at infection, obstructive airway disease and upper respiratory disease diagnoses, lifetime posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) diagnoses, and the most recent available measure of body mass index (BMI). Obstructive airway disease category includes diagnoses of asthma and bronchitis, upper respiratory disease includes chronic rhinitis and chronic sinusitis [18 ]. Respiratory diagnoses were determined based on systemic examination by a physician or nurse practitioner and included repeated pulmonary function tests, physical examination, and medical history. BMI scores were analyzed as continuous variable to maximize statistical power [19 , 20 (link)]. Genetic data were used to obtain sex and ancestry information.

Participants in the analytic sample were categorized into four severity groups according to their symptoms: asymptomatic (N = 92, 9.4%), mild (N = 378, 38.5%), moderate (N = 408, 41.5%), and severe (N = 75, 7.6%). This categorization was based on the NIH COVID-19 clinical spectrum updated October 2021 (National Institutes of Health, 2021). The asymptomatic category consisted of responders who reported a positive SARS-CoV-2 virologic test result without any symptoms associated with COVID-19. The mild category included responders with at least one symptom associated with COVID-19 but no shortness of breath or difficulty breathing. Moderate cases were in responders who reported shortness of breath and/or diagnosis of lower respiratory disease (pneumonia/bronchitis) during clinical assessment or imaging. These responders maintained oxygen saturation (SpO₂)≥94% on room air at sea level. Mild and moderate cases were medically managed primarily at home, even if they initially visited a healthcare facility for medical treatment and/or testing. Severe cases included responders with SpO₂<93% on room air, respiratory rate >30 breaths/min, heart rate greater than 100 beats per minute, acute respiratory distress syndrome, septic shock, cardiac dysfunction, or an exaggerated inflammatory response in addition to pulmonary disease, or severe illness causing cardiac, hepatic, renal, central nervous system, or thrombotic disease during COVID-19. Responders were also categorized as severe if they were admitted to the hospital, or received intensive care or mechanical ventilation, or if they eventually died from COVID-19.
Two complimentary analytic variables were created: an ordinal COVID-19 severity variable on 1–4 scale corresponding to asymptomatic, mild, moderate, and severe symptoms, and a binary COVID-19 severe category variable, with asymptomatic, mild, and moderate patients in one category, and severe patients in the second category.