Residential proximity to livestock animals and mortality from respiratory diseases in The Netherlands: A prospective census-based cohort study

Associations between acquired antimicrobial resistance genes in the upper respiratory tract and livestock farm exposures: a case-control study in COPD and non-COPD individuals

BACKGROUND

Livestock-related emissions have been associated with aggravations of respiratory symptoms in patients with chronic obstructive pulmonary disease (COPD), potentially by altering the respiratory resistome.

OBJECTIVES

This study investigates the structure of the acquired oropharyngeal (OP) resistome of patients with COPD and controls, its interplay with the respiratory microbiome and associations with residential livestock exposure.

METHODS

In a matched case-control study in the rural Netherlands, we analysed OP swabs from 35 patients with COPD and 34 controls, none of whom had used antibiotics in the preceding 4 weeks. Resistome profiling was performed using ResCap, complemented by prior characterization of the microbiome via 16S rRNA-based sequencing. Residential livestock farm exposure was defined using distance-based variables alongside modelled concentrations of livestock-emitted microbial pollutants. We compared resistome profiles between patients with COPD and controls, examining alpha and beta diversity as well as differential abundance. Additionally, we assessed the interplay between the resistome and microbiome using co-occurrence networks and Procrustes analysis. Variations in resistome profiles were also analysed based on residential livestock exposures.

RESULTS

Patients with COPD exhibited higher resistome diversity than controls (Shannon diversity, P = 0.047), though resistome composition remained similar between groups (PERMANOVA, P = 0.19). Significant correlations were observed between the OP resistome and microbiome compositions, with distinct patterns in co-occurrence networks. Residential exposure to livestock farms was not associated with resistome alterations.

CONCLUSIONS

Our findings reveal the COPD airway as a hospitable environment for antimicrobial resistance genes, irrespective of recent antimicrobial usage. Demonstrating the interplay between the resistome and microbiome, our study underscores the importance of a deeper understanding of the resistome in respiratory health.

Diagnostic Challenges and Pathogenetic Differences in Biomass-Smoke-Induced versus Tobacco-Smoke-Induced COPD: A Comparative Review

Background: Chronic Obstructive Pulmonary Disease (COPD) is a major global health challenge, primarily driven by exposures to tobacco smoke and biomass smoke. While Tobacco-Smoke-Induced COPD (TSCOPD) has been extensively studied, the diagnostic challenges and distinct pathogenesis of Biomass-Smoke-Induced COPD (BSCOPD), particularly in low- and middle-income countries, remain underexplored. Objective: To explore the differences in clinical manifestations, pulmonary function, and inflammatory profiles between BSCOPD and TSCOPD and highlight the diagnostic complexities of BSCOPD. Methods: This review analyzes the current literature comparing BSCOPD with TSCOPD, focusing on distinctive pathophysiological mechanisms, inflammatory markers, and oxidative stress processes. Results: BSCOPD presents differences in clinical presentation, with less emphysema, smaller airway damage, and higher rates of pulmonary hypertension compared to TSCOPD. BSCOPD is also characterized by bronchial hyperresponsiveness and significant hypoxemia, unlike TSCOPD, which exhibits severe airflow obstruction and emphysema. Additionally, the inflammatory profile of BSCOPD includes distinct mucous hypersecretion and airway remodeling. Conclusions: The unique genetic, epigenetic, and oxidative stress mechanisms involved in BSCOPD complicate its diagnosis and management. Biomass smoke's underrecognized impact on accelerated lung aging and exacerbation mechanisms emphasizes the need for targeted research to refine diagnostic criteria and management strategies for BSCOPD. Future directions: Further research should focus on identifying specific biomarkers and molecular pathways to enhance early diagnosis and improve clinical outcomes in populations exposed to biomass smoke.

Effects of long-term exposure to outdoor air pollution on COVID-19 incidence: A population-based cohort study accounting for SARS-CoV-2 exposure levels in the Netherlands

Several studies have linked air pollution to COVID-19 morbidity and severity. However, these studies do not account for exposure levels to SARS-CoV-2, nor for different sources of air pollution. We analyzed individual-level data for 8.3 million adults in the Netherlands to assess associations between long-term exposure to ambient air pollution and SARS-CoV-2 infection (i.e., positive test) and COVID-19 hospitalisation risks, accounting for spatiotemporal variation in SARS-CoV-2 exposure levels during the first two major epidemic waves (February 2020-February 2021). We estimated average annual concentrations of PM10, PM2.5 and NO2 at residential addresses, overall and by PM source (road traffic, industry, livestock, other agricultural sources, foreign sources, other Dutch sources), at 1 × 1 km resolution, and weekly SARS-CoV-2 exposure at municipal level. Using generalized additive models, we performed interval-censored survival analyses to assess associations between individuals' average exposure to PM10, PM2.5 and NO2 in the three years before the pandemic (2017-2019) and COVID-19-outcomes, adjusting for SARS-CoV-2 exposure, individual and area-specific confounders. In single-pollutant models, per interquartile (IQR) increase in exposure, PM10 was associated with 7% increased infection risk and 16% increased hospitalisation risk, PM2.5 with 8% increased infection risk and 18% increased hospitalisation risk, and NO2 with 3% increased infection risk and 11% increased hospitalisation risk. Bi-pollutant models suggested that effects were mainly driven by PM. Associations for PM were confirmed when stratifying by urbanization degree, epidemic wave and testing policy. All emission sources of PM, except industry, showed adverse effects on both outcomes. Livestock showed the most detrimental effects per unit exposure, whereas road traffic affected severity (hospitalisation) more than infection risk. This study shows that long-term exposure to air pollution increases both SARS-CoV-2 infection and COVID-19 hospitalisation risks, even after controlling for SARS-CoV-2 exposure levels, and that PM may have differential effects on these COVID-19 outcomes depending on the emission source.

Urban environmental monitoring and health risk assessment introducing a fuzzy intelligent computing model

Introduction

To enhance the precision of evaluating the impact of urban environments on resident health, this study introduces a novel fuzzy intelligent computing model designed to address health risk concerns using multi-media environmental monitoring data.

Methods

Three cities were selected for the study: Beijing (B City), Kunming (K City), and Wuxi (W City), representing high, low, and moderate pollution levels, respectively. The study employs a Fuzzy Inference System (FIS) as the chosen fuzzy intelligent computing model, synthesizing multi-media environmental monitoring data for the purpose of urban health risk assessment.

Results

(1) The model reliably estimates health risks across diverse cities and environmental conditions. (2) There is a positive correlation between PM2.5 concentrations and health risks, though the impact of noise levels varies by city. In cities B, K, and W, the respective correlation coefficients are 0.65, 0.55, and 0.7. (3) The Root Mean Square Error (RMSE) values for cities B, K, and W, are 0.0132, 0.0125, and 0.0118, respectively, indicating that the model has high accuracy. The R2 values for the three cities are 0.8963, 0.9127, and 0.9254, respectively, demonstrating the model's high explanatory power. The residual values for the three cities are 0.0087, 0.0075, and 0.0069, respectively, indicating small residuals and demonstrating robustness and adaptability. (4) The model's p-values for the Indoor Air Quality Index (IAQI), Thermal Comfort Index (TCI), and Noise Pollution Index (NPI) all satisfy p < 0.05 for the three cities, affirming the model's credibility in estimating health risks under varied urban environments.

Discussion

These results showcase the model's ability to adapt to diverse geographical conditions and aid in the accurate assessment of existing risks in urban settings. This study significantly advances environmental health risk assessment by integrating multidimensional data, enhancing the formulation of comprehensive environmental protection and health management strategies, and providing scientific support for sustainable urban planning.

Outdoor air pollution as a risk factor for testing positive for SARS-CoV-2: A nationwide test-negative case-control study in the Netherlands

Air pollution is a known risk factor for several diseases, but the extent to which it influences COVID-19 compared to other respiratory diseases remains unclear. We performed a test-negative case-control study among people with COVID-19-compatible symptoms who were tested for SARS-CoV-2 infection, to assess whether their long- and short-term exposure to ambient air pollution (AAP) was associated with testing positive (vs. negative) for SARS-CoV-2. We used individual-level data for all adult residents in the Netherlands who were tested for SARS-CoV-2 between June and November 2020, when only symptomatic people were tested, and modeled ambient concentrations of PM10, PM2.5, NO2 and O3 at geocoded residential addresses. In long-term exposure analysis, we selected individuals who did not change residential address in 2017-2019 (1.7 million tests) and considered the average concentrations of PM10, PM2.5 and NO2 in that period, and different sources of PM (industry, livestock, other agricultural activities, road traffic, other Dutch sources, foreign sources). In short-term exposure analysis, individuals not changing residential address in the two weeks before testing day (2.7 million tests) were included in the analyses, thus considering 1- and 2-week average concentrations of PM10, PM2.5, NO2 and O3 before testing day as exposure. Mixed-effects logistic regression analysis with adjustment for several confounders, including municipality and testing week to account for spatiotemporal variation in viral circulation, was used. Overall, there was no statistically significant effect of long-term exposure to the studied pollutants on the odds of testing positive vs. negative for SARS-CoV-2. However, significant positive associations of long-term exposure to PM10 and PM2.5 from specifically foreign and livestock sources, and to PM10 from other agricultural sources, were observed. Short-term exposure to PM10 (adjusting for NO2) and PM2.5 were also positively associated with increased odds of testing positive for SARS-CoV-2. While these exposures seemed to increase COVID-19 risk relative to other respiratory diseases, the underlying biological mechanisms remain unclear. This study reinforces the need to continue to strive for better air quality to support public health.

Contrasting perspectives on the risks of intensive livestock farming in The Netherlands: a survey study

In the Netherlands, intensive livestock farming is a recurrent topic of societal debate with stakeholders having quite different perspectives on the benefits and harms. In particular, stakeholders appear to have different perceptions on the risks to human and animal health. This paper reports a quantitative analysis of a survey on the perceptions of risks and benefits of intensive livestock farming conducted among the general public, including people living in livestock dense municipalities (n = 808), farmers (n = 237) and other stakeholders (n = 367). Results show that farmers and citizens have contrasting views about the benefits and concerns and in particular about the risks of intensive livestock farming for human health as well as animal well-being. People living in livestock dense communities held a somewhat more positive view than the general public, yet odour hinder and air quality was perceived as a serious health problem, but not by farmers. These differences in risk perceptions may well be explained from differences in interest, experience and options for control of potential hazards. Our study reflects more than just the perceived risks related to intensive livestock farming, but also reveal the global and multidimensional legitimate concerns and views on what matter to different groups of people. We argue that these differences in risk perspectives should be taken into account when communicating about human health risks, and should also be more explicitly addressed in discussions about the risks of intensive livestock farming in order to develop more inclusive policies that are supported by stakeholders.

Residential exposure to livestock farms and lung function in adolescence - The PIAMA birth cohort study

BACKGROUND

There is a growing interest in the impact of air pollution from livestock farming on respiratory health. Studies in adults suggest adverse effects of livestock farm emissions on lung function, but so far, studies involving children and adolescents are lacking.

OBJECTIVES

To study the association of residential proximity to livestock farms and modelled particulate matter ≤10 μm (PM₁₀) from livestock farms with lung function in adolescence.

METHODS

We performed a cross-sectional study among 715 participants of the Dutch prospective PIAMA (Prevention and Incidence of Asthma and Mite Allergy) birth cohort study. Relationships of different indicators of residential livestock farming exposure (distance to farms, distance-weighted number of farms, cattle, pigs, poultry, horses and goats within 3 km; modelled atmospheric PM₁₀ concentrations from livestock farms) with forced expiratory volume in 1 s (FEV₁) and forced vital capacity (FVC) at age 16 were assessed by linear regression taking into account potential confounders. Associations were expressed per interquartile range increase in exposure.

RESULTS

Higher exposure to livestock farming was consistently associated with a lower FEV₁, but not with FVC among participants living in less urbanized municipalities (<1500 addresses/km², N = 402). Shorter distances of homes to livestock farms were associated with a 1.4% (0.2%; 2.7%) lower FEV₁. Larger numbers of farms within 3 km and higher concentrations of PM₁₀ from livestock farming were associated with a 1.8% (0.8%, 2.9%) and 0.9% (0.4%,1.5%) lower FEV₁, respectively.

CONCLUSIONS

Our findings suggest that higher exposure to livestock farming is associated with a lower FEV₁ in adolescents. Replication and more research on the etiologic agents involved in these associations and the underlying mechanisms is needed.

Air pollution from livestock farms and the oropharyngeal microbiome of COPD patients and controls

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.

Proximity to livestock farms and COVID-19 in the Netherlands, 2020-2021

Objectives

In the Netherlands, during the first phase of the COVID-19 epidemic, the hotspot of COVID-19 overlapped with the country's main livestock area, while in subsequent phases this distinct spatial pattern disappeared. Previous studies show that living near livestock farms influence human respiratory health and immunological responses. This study aimed to explore whether proximity to livestock was associated with SARS-CoV-2 infection.

Methods

The study population was the population of the Netherlands excluding the very strongly urbanised areas and border areas, on January 1, 2019 (12, 628, 244 individuals). The cases are the individuals reported with a laboratory-confirmed positive SARS-CoV-2 test with onset before January 1, 2022 (2, 223, 692 individuals). For each individual, we calculated distance to nearest livestock farm (cattle, goat, sheep, pig, poultry, horse, rabbit, mink). The associations between residential (6-digit postal-code) distance to the nearest livestock farm and individuals' SARS-CoV-2 status was studied with multilevel logistic regression models. Models were adjusted for individuals' age categories, the social status of the postal code area, particulate matter (PM₁₀)- and nitrogen dioxide (NO₂)-concentrations. We analysed data for the entire period and population as well as separately for eight time periods (Jan–Mar, Apr–Jun, Jul–Sep and Oct–Dec in 2020 and 2021), four geographic areas of the Netherlands (north, east, west and south), and for five age categories (0–14, 15–24, 25–44, 45–64 and > 65 years).

Results

Over the period 2020–2021, individuals' SARS-CoV-2 status was associated with living closer to livestock farms. This association increased from an Odds Ratio (OR) of 1.01 (95% Confidence Interval [CI] 1.01–1.02) for patients living at a distance of 751–1000 m to a farm to an OR of 1.04 (95% CI 1.04–1.04), 1.07 (95% CI 1.06–1.07) and 1.11 (95% CI 1.10–1.12) for patients living in the more proximate 501–750 m, 251–500m and 0–250 m zones around farms, all relative to patients living further than 1000 m around farms. This association was observed in three out of four quarters of the year in both 2020 and 2021, and in all studied geographic areas and age groups.

Conclusions

In this exploratory study with individual SARS-CoV-2 notification data and high-resolution spatial data associations were found between living near livestock farms and individuals' SARS-CoV-2 status in the Netherlands. Verification of the results in other countries is warranted, as well as investigations into possible underlying exposures and mechanisms.