Ambient Air Pollution and Respiratory Health in Sub-Saharan African Children: A Cross-Sectional Analysis

Factors of acute respiratory infection among under-five children across sub-Saharan African countries using machine learning approaches

Symptoms of Acute Respiratory infections (ARIs) among under-five children are a global health challenge. We aimed to train and evaluate ten machine learning (ML) classification approaches in predicting symptoms of ARIs reported by mothers among children younger than 5 years in sub-Saharan African (sSA) countries. We used the most recent (2012-2022) nationally representative Demographic and Health Surveys data of 33 sSA countries. The air pollution covariates such as global annual surface particulate matter (PM 2.5) and the nitrogen dioxide available in the form of raster images were obtained from the National Aeronautics and Space Administration (NASA). The MLA was used for predicting the symptoms of ARIs among under-five children. We randomly split the dataset into two, 80% was used to train the model, and the remaining 20% was used to test the trained model. Model performance was evaluated using sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve. A total of 327,507 under-five children were included in the study. About 7.10, 4.19, 20.61, and 21.02% of children reported symptoms of ARI, Severe ARI, cough, and fever in the 2 weeks preceding the survey years respectively. The prevalence of ARI was highest in Mozambique (15.3%), Uganda (15.05%), Togo (14.27%), and Namibia (13.65%,), whereas Uganda (40.10%), Burundi (38.18%), Zimbabwe (36.95%), and Namibia (31.2%) had the highest prevalence of cough. The results of the random forest plot revealed that spatial locations (longitude, latitude), particulate matter, land surface temperature, nitrogen dioxide, and the number of cattle in the houses are the most important features in predicting the diagnosis of symptoms of ARIs among under-five children in sSA. The RF algorithm was selected as the best ML model (AUC = 0.77, Accuracy = 0.72) to predict the symptoms of ARIs among children under five. The MLA performed well in predicting the symptoms of ARIs and associated predictors among under-five children across the sSA countries. Random forest MLA was identified as the best classifier to be employed for the prediction of the symptoms of ARI among under-five children.

The impact of environmental conditions on non-communicable diseases in sub-Saharan Africa: A scoping review of epidemiologic evidence

Background

The burden of non-communicable diseases (NCDs) in sub-Saharan Africa (SSA) is increasing. Environmental conditions such as heavy metals and air pollution have been linked with the incidence and mortality of chronic diseases such as cancer, as well as cardiovascular and respiratory diseases. We aimed to scope the current state of evidence on the impact of environmental conditions on NCDs in SSA.

Methods

We conducted a scoping review to identify environmental conditions linked with NCDs in SSA by identifying studies published from January 1986 through February 2023. We searched African Index Medicus, Ovid Medline, Scopus, Web of Science, and Greenfile. Using the PICOS study selection criteria, we identified studies conducted in SSA focussed on physical environmental exposures and incidence, prevalence, and mortality of NCDs. We included only epidemiologic or quantitative studies.

Results

We identified 6754 articles from electronic database searches; only 36 met our inclusion criteria and were qualitatively synthesised. Two studies were conducted in multiple SSA countries, while 34 were conducted across ten countries in SSA. Air pollution (58.3%) was the most common type of environmental exposure reported, followed by exposure to dust (19.4%), meteorological variables (13.8%), heavy metals (2.7%), soil radioactivity (2.7%), and neighbourhood greenness (2.7%). The examined NCDs included respiratory diseases (69.4%), cancer (2.7%), stroke (5.5%), diabetes (2.7%), and two or more chronic diseases (19.4%). The study results suggest an association between environmental exposures and NCDs, particularly for respiratory diseases. Only seven studies found a null association between environmental conditions and chronic diseases.

Conclusions

There is a growing body of research on environmental conditions and chronic diseases in the SSA region. Although some cities in SSA have started implementing environmental monitoring and control measures, there remain high levels of environmental pollution. Investment can focus on improving environmental control measures and disease surveillance.

Exposure to outdoor particulate matter and risk of respiratory diseases: a systematic review and meta-analysis

According to epidemiological studies, particulate matter (PM) is an important air pollutant that poses a significant threat to human health. The relationship between particulate matter and respiratory diseases has been the subject of numerous studies, but these studies have produced inconsistent findings. The purpose of this systematic review was to examine the connection between outdoor particulate matter (PM2.5 and PM10) exposure and respiratory disorders (COPD, lung cancer, LRIs, and COVID-19). For this purpose, we conducted a literature search between 2012 and 2022 in PubMed, Web of Science, and Scopus. Out of the 58 studies that were part of the systematic review, meta-analyses were conducted on 53 of them. A random effect model was applied separately for each category of study design to assess the pooled association between exposure to PM2.5 and PM10 and respiratory diseases. Based on time-series and cohort studies, which are the priorities of the strength of evidence, a significant relationship between the risk of respiratory diseases (COPD, lung cancer, and COVID-19) was observed (COPD: pooled HR = 1.032, 95% CI: 1.004-1.061; lung cancer: pooled HR = 1.017, 95% CI: 1.015-1.020; and COVID-19: pooled RR = 1.004, 95% CI: 1.002-1.006 per 1 μg/m3 increase in PM2.5). Also, a significant relationship was observed between PM10 and respiratory diseases (COPD, LRIs, and COVID-19) based on time-series and cohort studies. Although the number of studies in this field is limited, which requires more investigations, it can be concluded that outdoor particulate matter can increase the risk of respiratory diseases.

Short-term effects of air pollution on hospitalization for acute lower respiratory infections in children: a time-series analysis study from Lanzhou, China

BACKGROUND

Short-term exposure to air pollution is associated with acute lower respiratory infections (ALRI) in children. We investigated the relationship between hospitalization for ALRI in children and air pollutant concentrations from January 1, 2014 to December 31, 2020 in Lanzhou City.

METHODS

We collected data on air pollutant concentrations and children's hospitalization data during the study period. A time series regression analysis was used to assess the short-term effects of air pollutants on ALRI in children, and subgroup analyses and sensitivity analyses were performed.

RESULTS

A total of 51,206 children with ALRI were studied, including 40,126 cases of pneumonia and 11,080 cases of bronchiolitis. The results of the study revealed that PM2.5, PM10, SO2 and NO2 were significantly associated with hospitalization for ALRI in children aged 0-14 years. For each 10 µg/m3 increase in air pollutant concentration in lag0-7, the relative risk of ALRI hospitalization in children due to PM2.5, PM10, SO2 and NO2 increased by 1.089 (95%CI:1.075, 1.103), 1.018 (95%CI:1.014, 1.021), 1.186 (95%CI:1.154. 1.219) and 1.149 (95%CI:1.130, 1.168), respectively.

CONCLUSIONS

PM2.5, PM10, SO2 and NO2 short-term exposures were positively associated with ALRI, pneumonia and bronchiolitis hospitalizations in Lanzhou, China. Local governments should make efforts to improve urban ambient air quality conditions to reduce hospitalization rates for childhood respiratory diseases.

Early-life exposure to Saharan dust storms and adolescence functional disability: Evidence from Cameroon

The direct link between early-life dust storm exposure and later-in-life outcomes is not fully understood. This study examines the association of functional disability in a large sample of adolescent Cameroonians (N = 112,855) with in-utero and early childhood exposure to Saharan dust storms. Adjusting all estimations for temperature, precipitation, time and location fixed-effects, and person and family sociodemographic characteristics, we documented adverse effects on functional disability in female adolescents due to exposure to dense dust storms during the third gestation trimester and the second postnatal trimester. We also found suggestive evidence that an effect exists for the first as well as the third through fifth postnatal trimesters. In the third trimester of gestation and the second postnatal trimester, exposure to an average length dust storm with PM₁₀ levels beyond 190 μg/m³ increased the likelihood of disability among female adolescents by approximately 229 (95 % CI: 10-464) in 100,000. The size of the adverse effects for the other periods followed similar patterns. These results show the value of creating infrastructures to mitigate or adapt to the effects of dust storms. These endeavors should focus on communities and populations in and around the Sahara where international organizations can play a role. In addition, establishing health data infrastructures not only improves public health but also advances our understanding of the long-term effects of dust storms. This study demonstrates the importance of research on the long-term effects of early-life exposure to dust storms and the need for additional work on this topic.

Long-Term PM2.5 Exposure Is Associated with Symptoms of Acute Respiratory Infections among Children under Five Years of Age in Kenya, 2014

Introduction: Short-term exposures to air pollutants such as particulate matter (PM) have been associated with increased risk for symptoms of acute respiratory infections (ARIs). Less well understood is how long-term exposures to fine PM (PM2.5) might increase risk of ARIs and their symptoms. This research uses georeferenced Demographic Health Survey (DHS) data from Kenya (2014) along with a remote sensing based raster of PM2.5 concentrations to test associations between PM2.5 exposure and ARI symptoms in children for up to 12 monthly lags. Methods: Predicted PM2.5 concentrations were extracted from raster of monthly averages for latitude/longitude locations of survey clusters. These data and other environmental and demographic data were used in a logistic regression model of ARI symptoms within a distributed lag nonlinear modeling framework (DLNM) to test lag associations of PM2.5 exposure with binary presence/absence of ARI symptoms in the previous two weeks. Results: Out of 7036 children under five for whom data were available, 46.8% reported ARI symptoms in the previous two weeks. Exposure to PM2.5 within the same month and as an average for the previous 12 months was 18.31 and 22.1 µg/m3, respectively, far in excess of guidelines set by the World Health Organization. One-year average PM2.5 exposure was higher for children who experienced ARI symptoms compared with children who did not (22.4 vs. 21.8 µg/m3, p < 0.0001.) Logistic regression models using the DLNM framework indicated that while PM exposure was not significantly associated with ARI symptoms for early lags, exposure to high concentrations of PM2.5 (90th percentile) was associated with elevated odds for ARI symptoms along a gradient of lag exposure time even when controlling for age, sex, types of cooking fuels, and precipitation. Conclusions: Long-term exposure to high concentrations of PM2.5 may increase risk for acute respiratory problems in small children. However, more work should be carried out to increase capacity to accurately measure air pollutants in emerging economies such as Kenya.