Residential microbial composition likely contributes to the development of lower respiratory tract infections (LRTI) among children, but the association is poorly understood. We aimed to study the ...relationship between the indoor airborne dust bacterial and fungal microbiota and childhood LRTI in Ibadan, Nigeria.
Ninety-eight children under the age of five years hospitalized with LRTI were recruited and matched by age (±3 months), sex, and geographical location to 99 community-based controls without LRTI. Participants’ homes were visited and sampled over a 14-day period for airborne house dust using electrostatic dustfall collectors (EDC). In airborne dust samples, the composition of bacterial and fungal communities was characterized by a meta-barcoding approach using amplicons targeting simultaneously the bacterial 16S rRNA gene and the internal-transcribed-spacer (ITS) region-1 of fungi in association with the SILVA and UNITE database respectively.
A 100-unit change in house dust bacterial, but not fungal, richness (OR 1.06; 95%CI 1.03–1.10) and a 1-unit change in Shannon diversity (OR 1.92; 95%CI 1.28–3.01) were both independently associated with childhood LRTI after adjusting for other indoor environmental risk factors. Beta-diversity analysis showed that bacterial (PERMANOVA p < 0.001, R2 = 0.036) and fungal (PERMANOVA p < 0.001, R2 = 0.028) community composition differed significantly between homes of cases and controls. Pair-wise differential abundance analysis using both DESEq2 and MaAsLin2 consistently identified the bacterial phyla Deinococcota (Benjamini-Hochberg (BH) adjusted p-value <0.001) and Bacteriodota (BH-adjusted p-value = 0.004) to be negatively associated with LRTI. Within the fungal microbiota, phylum Ascomycota abundance (BH adjusted p-value <0.001) was observed to be directly associated with LRTI, while Basidiomycota abundance (BH adjusted p-value <0.001) was negatively associated with LRTI.
Our study suggests that early-life exposure to certain airborne bacterial and fungal communities is associated with LRTI among children under the age of five years.
•In house dust samples, bacterial abundance was dominated by the phylum Proteobacteria while fungal abundance by the Ascomycota.•House dust bacterial, but not fungal, richness and shannon diversity were independently associated with childhood LRTI.•Bacterial and fungal community composition differed significantly between homes of children with and without LRTI.•Taxa belonging to the fungal phylum Ascomycota were positively associated with LRTI among under-five children.•Taxa belonging to the bacterial phyla Deinococcota and Bacteriodota, and fungal phylum Basidiomycota were negatively associated with childhood LRTI.
Abstract
Although there is scientific evidence for an increased prevalence of sleep disorders during the coronavirus disease 2019 (COVID-19) pandemic, there is still limited information on how ...lifestyle factors might have affected sleep patterns. Therefore, we followed a large cohort of participants in the Netherlands (n = 5,420) for up to 1 year (September 2020–2021) via monthly Web-based questionnaires to identify lifestyle changes (physical activity, cigarette smoking, alcohol consumption, electronic device use, and social media use) driven by anti–COVID-19 measures and their potential associations with self-reported sleep (latency, duration, and quality). We used the Containment and Health Index (CHI) to assess the stringency of anti–COVID-19 measures and analyzed associations through multilevel ordinal response models. We found that more stringent anti–COVID-19 measures were associated with higher use of electronic devices (per interquartile-range increase in CHI, odds ratio (OR) = 1.47, 95% confidence interval (CI): 1.40, 1.53), less physical activity (OR = 0.94, 95% CI: 0.90, 0.98), lower frequency of alcohol consumption (OR = 0.63, 95% CI: 0.60, 0.66), and longer sleep duration (OR = 1.11, 95% CI: 1.05, 1.16). Lower alcohol consumption frequency and higher use of electronic devices and social media were associated with longer sleep latency. Lower physical activity levels and higher social media and electronic device use were related to poorer sleep quality and shorter sleep duration.
Living close to livestock farms has been associated with increased symptoms in patients with chronic obstructive pulmonary disease (COPD). The causes of these effects are still poorly understood. ...This panel study attempts to assess the acute effects of livestock-related air pollution in patients with COPD living in an area with intensive livestock farming in the Netherlands.
Between February 2015 and July 2016, 82 participants took spirometry measurements twice daily (morning and evening) during a 3-month period, resulting in 12,672 FEV1 and PEF records. Participants also kept a diary on respiratory symptoms as well as livestock-related odor annoyance. Daily average ammonia (NH3) (a proxy for livestock-related air pollution) and fine particulate matter (PM10) levels were collected from monitoring stations in the area. Lung function was analyzed as decrements of >10% and >20% from their median as well as absolute values. Self-reported odor annoyance was analyzed as a dichotomous variable. All analyses were done using generalized estimated equations. We adjusted for humidity, temperature, linear trend, and took multiple testing into account.
We found an odds ratio of 1.14 95%CI 1.05; 1.25 for decrements >20% in morning FEV1 per interquartile range (12 µg/m3) increase in NH3 concentration (lag 2). Odor annoyance was negatively associated with evening PEF (−4.46 l/min 95%CI −7.59; −1.33). Sensitivity analyses showed a stronger effect in participants with worse baseline lung function. No associations with symptoms were found.
Our results show acute effects of livestock-related air pollution on lung function in COPD patients living in close proximity to livestock farms.
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Air pollution from livestock farms is known to affect respiratory health of patients with chronic obstructive pulmonary disease (COPD). The mechanisms behind this relationship, ...however, remain poorly understood. We hypothesise that air pollutants could influence respiratory health through modulation of the airway microbiome. Therefore, we studied associations between air pollution exposure and the oropharyngeal microbiota (OPM) composition of COPD patients and controls in a livestock-dense area.
Oropharyngeal swabs were collected from 99 community-based (mostly mild) COPD cases and 184 controls (baseline), and after 6 and 12 weeks. Participants were non-smokers or former smokers. Annual average livestock-related outdoor air pollution at the home address was predicted using dispersion modelling. OPM composition was analysed using 16S rRNA-based sequencing in all baseline samples and 6-week and 12-week repeated samples of 20 randomly selected subjects (n = 323 samples). A random selection of negative control swabs, taken every sampling day, were also included in the downstream analysis.
Both farm-emitted endotoxin and PM10 levels were associated with increased OPM richness in COPD patients (p < 0.05) but not in controls. COPD case-control status was not associated with community structure, while correcting for known confounders (multivariate PERMANOVA p > 0.05). However, members of the genus Streptococcus were more abundant in COPD patients (Benjamini-Hochberg adjusted p < 0.01). Moderate correlation was found between ordinations of 20 subjects analysed at 0, 6, and 12 weeks (Procrustes r = 0.52 to 0.66; p < 0.05; Principal coordinate analysis of Bray-Curtis dissimilarity), indicating that the OPM is relatively stable over a 12 week period and that a single sample sufficiently represents the OPM.
Air pollution from livestock farms is associated with OPM richness of COPD patients, suggesting that the OPM of COPD patients is susceptible to alterations induced by exposure to air pollutants.
This longitudinal study aimed to assess the impact of COVID-19 containment measures on perceived health, health protective behavior and risk perception, and investigate whether chronic disease status ...and urbanicity of the residential area modify these effects. Participants (n = 5420) were followed for up to 14 months (September 2020-October 2021) by monthly questionnaires. Chronic disease status was obtained at baseline. Urbanicity of residential areas was assessed based on postal codes or neighborhoods. Exposure to containment measures was assessed using the Containment and Health Index (CHI). Bayesian multilevel-models were used to assess effect modification of chronic disease status and urbanicity by CHI. CHI was associated with higher odds for worse physical health in people with chronic disease (OR = 1.09, 95% credibility interval (CrI) = 1.01, 1.17), but not in those without (OR = 1.01, Crl = 0.95, 1.06). Similarly, the association of CHI with higher odds for worse mental health in urban dwellers (OR = 1.31, Crl = 1.23, 1.40) was less pronounced in rural residents (OR = 1.20, Crl = 1.13, 1.28). Associations with behavior and risk perception also differed between groups. Our study suggests that individuals with chronic disease and those living in urban areas are differentially affected by government measures put in place to manage the COVID-19 pandemic. This highlights the importance of considering vulnerable subgroups in decision making regarding containment measures.
Adverse health effects have been linked with exposure to livestock farms, likely due to airborne microbial agents. Accurate exposure assessment is crucial in epidemiological studies, however limited ...studies have modelled bioaerosols. This study used measured concentrations in air of livestock commensals (Escherichia coli (E. coli) and Staphylococcus species (spp.)), and antimicrobial resistance genes (tetW and mecA) at 61 residential sites in a livestock-dense region in the Netherlands. For each microbial agent, land use regression (LUR) and random forest (RF) models were developed using Geographic Information System (GIS)-derived livestock-related characteristics as predictors. The mean and standard deviation of annual average concentrations (gene copies/m3) of E. coli, Staphylococcus spp., tetW and mecA were as follows: 38.9 (±1.98), 2574 (±3.29), 20991 (±2.11), and 15.9 (±2.58). Validated through 10-fold cross-validation (CV), the models moderately explained spatial variation of all microbial agents. The best performing model per agent explained respectively 38.4%, 20.9%, 33.3% and 27.4% of the spatial variation of E. coli, Staphylococcus spp., tetW and mecA. RF models had somewhat better performance than LUR models. Livestock predictors related to poultry and pig farms dominated all models. To conclude, the models developed enable enhanced estimates of airborne livestock-related microbial exposure in future epidemiological studies. Consequently, this will provide valuable insights into the public health implications of exposure to specific microbial agents.
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•Improving livestock exposure assessment is crucial for accurately assessing health risks.•Empirical spatial models effectively predict airborne microbial concentrations.•Random Forest models slightly outperform Land Use Regression models.•Variables related to poultry and pig farms are large contributors to the models.•Modelling residential exposure will benefit future epidemiological studies.
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