Chronic Obstructive Pulmonary Disease in Cameroon: Prevalence and Predictors-A Multisetting Community-Based Study.

Objective: Little is known concerning chronic obstructive pulmonary disease (COPD) in Sub-Saharan Africa (SSA), where the disease remains underdiagnosed. We aimed to estimate its prevalence in Cameroon and look for its predictors. Methods: Adults aged 19 years and older were randomly selected in 4 regions of Cameroon to participate in a cross-sectional community-based study. Data were collected in the participant's home or place of work. Spirometry was performed on selected participants. COPD was defined as the postbronchodilator forced expiratory volume in 1 second/forced vital capacity ratio (FEV1/FVC) < lower limit of normal, using the global lung initiative (GLI) equations for Black people. Binomial logistic regression was used to seek COPD-associated factors. The strength of the association was measured using the adjusted odds ratio (aOR). Results: A total of 5055 participants (median age (25th-75th percentile) = 43 (30-56) years, 54.9% of women) were enrolled. COPD prevalence (95% confidence interval (95% CI)) was 2.9% (2.4, 3.3)%. Independent predictors of COPD (aOR (95% CI)) were a high educational level (4.7 (2.0, 11.1)), living in semiurban or rural locality (1.7 (1.4, 3.0)), tobacco smoking (1.7 (1.1, 2.5)), biomass fuel exposure (1.9 (1.1, 3.3)), experience of dyspnea (2.2 (1.4, 3.5)), history of tuberculosis (3.6 (1.9, 6.7)), and history of asthma (6.3 (3.4, 11.6)). Obesity was protective factor (aOR (95%CI) = 0.3 (0.2, 0.5)). Conclusion: The prevalence of COPD was relatively low. Alternative risk factors such as biomass fuel exposure, history of tuberculosis, and asthma were confirmed as predictors.

On-Field Test of Tuberculosis Diagnosis through Exhaled Breath Analysis with a Gas Sensor Array.

Tuberculosis (TB) is among the more frequent causes of death in many countries. For pulmonary TB, early diagnosis greatly increases the efficiency of therapies. Although highly sensitive tests based on nucleic acid amplification tests (NAATs) and loop-mediated isothermal amplification (TB-LAMP) are available, smear microscopy is still the most widespread diagnostics method in most low-middle-income countries, and the true positive rate of smear microscopy is lower than 65%. Thus, there is a need to increase the performance of low-cost diagnosis. For many years, the use of sensors to analyze the exhaled volatile organic compounds (VOCs) has been proposed as a promising alternative for the diagnosis of several diseases, including tuberculosis. In this paper, the diagnostic properties of an electronic nose (EN) based on sensor technology previously used to identify tuberculosis have been tested on-field in a Cameroon hospital. The EN analyzed the breath of a cohort of subjects including pulmonary TB patients (46), healthy controls (38), and TB suspects (16). Machine learning analysis of the sensor array data allows for the identification of the pulmonary TB group with respect to healthy controls with 88% accuracy, 90.8% sensitivity, 85.7% specificity, and 0.88 AUC. The model trained with TB and healthy controls maintains its performance when it is applied to symptomatic TB suspects with a negative TB-LAMP. These results encourage the investigation of electronic noses as an effective diagnostic method for future inclusion in clinical practice.