Lung health in Africa: challenges and opportunities in the context of COVID-19

Impact of Pulmonary Comorbidities on COVID-19: Acute and Long-Term Evaluations

Background/Objectives: Pulmonary comorbidities, such as chronic obstructive pulmonary disease (COPD), asthma, and interstitial lung diseases (ILDs), have emerged as critical factors influencing the severity and outcomes of COVID-19. This review aims to evaluate the interplay between these comorbidities and COVID-19, both during the acute phase and in long-term recovery, focusing on their impact on clinical management and outcomes. Methods: This systematic review examined studies sourced from major medical databases, including PubMed and Scopus, using keywords such as "COVID-19", "pulmonary comorbidities", "long COVID", and "respiratory sequelae". Peer-reviewed articles published from January 2020 to the present were included, with data extracted to evaluate both the acute and long-term effects of these comorbidities on COVID-19 patients. Results: Patients with COPD demonstrated significantly higher risks of severe COVID-19, including increased hospitalization and mortality. Asthma, while less consistently associated with severe outcomes, showed a variable risk based on disease control. ILDs were strongly correlated with poor outcomes, including higher rates of respiratory failure and mortality. Long-term complications, such as persistent dyspnea, impaired lung function, and structural changes like fibrosis, were prevalent in patients recovering from moderate to severe COVID-19. These complications adversely affected quality of life and increased healthcare dependency. Conclusions: Pulmonary comorbidities amplify both the acute severity and long-term respiratory consequences of COVID-19. Effective management necessitates tailored strategies addressing both phases, integrating rehabilitation and continuous monitoring to mitigate chronic impairments. Future research should prioritize understanding the mechanisms behind these interactions to inform public health interventions and improve patient outcomes.

Pediatric Respiratory Pathogens Circulate in Children and Adults in Communities Near Susceptible Wild Great Ape Populations in Uganda

Respiratory infections are a leading cause of death in developing countries. Infants and young children are especially susceptible to disease because they lack immunity, whereas adults who have acquired immunity can be infected asymptomatically. Great ape species, all of which are endangered, are similarly susceptible to respiratory illnesses caused by human respiratory pathogens. We obtained 432 nasopharyngeal swab samples (127 from adults and 305 from children) in a cross-sectional study that took place between February and October 2022 at four sites in Western Uganda (Budongo Central Forest Reserve, Bulindi Town Council, Bwindi Impenetrable National Park, and Kibale National Park) where the participants live in communities where interaction with apes is frequent. Prior research at Kibale has shown that locally circulating human respiratory pathogens have led to multiple lethal outbreaks in wild eastern chimpanzees (Pan troglodytes schweinfurthii). We used a multiplex PCR panel to characterize respiratory pathogens, with the goal of assessing whether respiratory illnesses in the chimpanzees of Budongo and Bulindi and the mountain gorillas (Gorilla beringei beringei) of Bwindi might have originated in local children and been introduced to the apes via asymptomatic adult carriers. The prevalence of respiratory pathogens was twice as high in Bwindi (44.0%) as it was in Budongo (24.0%) and Bulindi (20.8%), while the prevalence was intermediate at Kibale (34.4%). Rates of respiratory pathogen detection were higher but statistically indistinguishable in children compared to adults at Budongo and Bulindi, and children were 15.0 times more likely than adults to have positive detections at Kibale. At Bwindi, however, the pattern was reversed, with adults 2.6 times more likely than children to have positive detections. Rhinovirus, metapneumovirus, human parainfluenza virus 3, respiratory syncytial virus, and coronavirus OC43, all of which have been identified as causative agents of respiratory disease outbreaks in great ape populations across sub-Saharan Africa, accounted for three quarters (73.6%) of detected pathogens. Our data support the idea that human respiratory pathogens that can infect great apes occur at high frequencies in human populations in Western Uganda that live close to and interact with wild apes that have suffered from lethal outbreaks caused by these same pathogens. Reducing respiratory infections in local children, thereby reducing both carriage of those infections into the forest by people and ape exposure to these pathogens when they enter human spaces, should decrease the risk of respiratory disease outbreaks in apes.

Leveraging AI and Machine Learning to Develop and Evaluate a Contextualized User-Friendly Cough Audio Classifier for Detecting Respiratory Diseases: Protocol for a Diagnostic Study in Rural Tanzania

BACKGROUND

Respiratory diseases, including active tuberculosis (TB), asthma, and chronic obstructive pulmonary disease (COPD), constitute substantial global health challenges, necessitating timely and accurate diagnosis for effective treatment and management.

OBJECTIVE

This research seeks to develop and evaluate a noninvasive user-friendly artificial intelligence (AI)-powered cough audio classifier for detecting these respiratory conditions in rural Tanzania.

METHODS

This is a nonexperimental cross-sectional research with the primary objective of collection and analysis of cough sounds from patients with active TB, asthma, and COPD in outpatient clinics to generate and evaluate a noninvasive cough audio classifier. Specialized cough sound recording devices, designed to be nonintrusive and user-friendly, will facilitate the collection of diverse cough sound samples from patients attending outpatient clinics in 20 health care facilities in the Shinyanga region. The collected cough sound data will undergo rigorous analysis, using advanced AI signal processing and machine learning techniques. By comparing acoustic features and patterns associated with TB, asthma, and COPD, a robust algorithm capable of automated disease discrimination will be generated facilitating the development of a smartphone-based cough sound classifier. The classifier will be evaluated against the calculated reference standards including clinical assessments, sputum smear, GeneXpert, chest x-ray, culture and sensitivity, spirometry and peak expiratory flow, and sensitivity and predictive values.

RESULTS

This research represents a vital step toward enhancing the diagnostic capabilities available in outpatient clinics, with the potential to revolutionize the field of respiratory disease diagnosis. Findings from the 4 phases of the study will be presented as descriptions supported by relevant images, tables, and figures. The anticipated outcome of this research is the creation of a reliable, noninvasive diagnostic cough classifier that empowers health care professionals and patients themselves to identify and differentiate these respiratory diseases based on cough sound patterns.

CONCLUSIONS

Cough sound classifiers use advanced technology for early detection and management of respiratory conditions, offering a less invasive and more efficient alternative to traditional diagnostics. This technology promises to ease public health burdens, improve patient outcomes, and enhance health care access in under-resourced areas, potentially transforming respiratory disease management globally.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/54388.

Respiratory disease patterns in rural Western Uganda, 2019-2022

Introduction

Respiratory disease is a major cause of morbidity and mortality in the developing world, but prospective studies of temporal patterns and risk factors are rare.

Methods

We studied people in rural Western Uganda, where respiratory disease is pervasive. We followed 30 adults (ages 22-51 years; 534 observations) and 234 children (ages 3-11 years; 1,513 observations) between May 2019 and July 2022 and collected monthly data on their respiratory symptoms, for a total of 2,047 case records. We examined associations between demographic and temporal factors and respiratory symptoms severity.

Results

The timing of our study (before, during, and after the emergence of COVID-19) allowed us to document the effects of public health measures instituted in the region. Incidence rates of respiratory symptoms before COVID-19 lockdown were 568.4 cases per 1,000 person-months in children and 254.2 cases per 1,000 person-months in adults. These rates were 2.6 times higher than the 2019 global average for children but comparable for adults. Younger children (ages 3-6 years) had the highest frequencies and severities of respiratory symptoms. Study participants were most likely to experience symptoms in February, which is a seasonal pattern not previously documented. Incidence and severity of symptoms in children decreased markedly during COVID-19 lockdown, illustrating the broad effects of public health measures on the incidence of respiratory disease.

Discussion

Our results demonstrate that patterns of respiratory disease in settings such as Western Uganda resemble patterns in developed economies in some ways (age-related factors) but not in others (increased incidence in children and seasonal pattern). Factors such as indoor air quality, health care access, timing of school trimesters, and seasonal effects (rainy/dry seasons) likely contribute to the differences observed.