Livestock-associated risk factors for pneumonia in an area of intensive animal farming in the Netherlands

Unsustainable production patterns and disease emergence: The paradigmatic case of Highly Pathogenic Avian Influenza H5N1

Current food production systems are causing severe environmental damage, including the emergence of dangerous pathogens that put humans and wildlife at risk. Several dangerous pathogens (e.g., the 2009 A(H1N1) Influenza Virus, Nipah virus) have emerged associated with the dominant intensive food production systems. In this article, we use the case of the emergence and spillover of the Highly Pathogenic Avian Influenza virus H5N1 (hereafter, H5N1) to illustrate how intensive food production methods provide a breeding ground for dangerous pathogens. We also discuss how emerging pathogens, such as H5N1, may affect not only ecosystem health but also human well-being and the economy. The current H5N1 panzootic (2020-2024) is producing a catastrophic impact: the millions of domestic birds affected by this virus have led to significant economic losses globally, and wild birds and mammals have suffered alarming mortalities, with the associated loss of their material and non-material ecosystem services. Transformative actions are required to reduce the emergence and impact of pathogens such as H5N1; we particularly need to reconsider the ways we are producing food. Governments should redirect funds to the promotion of alternative production systems that reduce the risk of new emerging pathogens and produce environmentally healthy food. These systems need to have a positive relationship with nature rather than being systems based on business as usual to the detriment of the environment. Sustainable food production systems may save many lives, economies, and biodiversity, together with the ecosystem services species provide.

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.

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.

Dietary strategies can increase cloacal endotoxin levels and modulate the resident microbiota in broiler chickens

Endotoxins released from poultry feces have been associated with impaired human health. Because endotoxins are released from gram-negative intestinal bacteria, it was hypothesized that dietary strategies may influence endotoxin excretion via modulation of gut microbiota. We therefore tested dietary strategies that could potentially reduce cloacal endotoxin levels in broiler chickens. One-day-old male Ross 308 (N = 1,344) broilers were housed in 48 pens (N = 8 pens/treatment, 28 chickens per pen) and fed 1 of 6 diets for 35 days (d) in a 3-phase feeding program: a basic diet (CON) that served as the reference diet, or basic diet supplemented with butyrate (BUT), inulin (INU), medium-chain fatty acids (MCFA) or Original XPC™LS (XPC), or a high-fiber-low-protein (HF-LP) diet. A significant (P < 0.05) increase in cloacal endotoxin concentration at d 35 was observed in BUT as compared to CON. Analysis of cloacal microbiota showed a trend (P < 0.07) for a higher gram-negative/gram-positive ratio and for a higher relative abundance of gram-negative bacteria at d 35 (P ≤ 0.08) in BUT and HF-LP as compared to CON. A significant (P < 0.05) increase in average daily gain (ADG) and improved feed conversion ratio (P < 0.05) were observed in MCFA during the grower phase (d 14-28), and a significant (P < 0.05) increase in average daily feed intake (ADFI) was observed in MCFA during d 0 to 28. Broilers fed HF-LP had a significantly (P < 0.05) higher FCR and lower ADG throughout the rearing period. No treatment effects were found on footpad dermatitis, but BUT had worst hock burn scores at d 35 (P < 0.01) and MCFA had worst cleanliness scores at d 21 but not at d 35 (treatment*age P < 0.05), while INU had better cleanliness as compared to CON at d 35 (P < 0.05). In conclusion, especially BUT and HF-LP were able to modulate resident microbiota and BUT also increased cloacal endotoxin levels, which was opposite to our hypothesis. The present study indicates that cloacal endotoxin release can be affected by the diet but further study is needed to find dietary treatments that can reduce cloacal endotoxin release.

Risk of pneumonia in the vicinity of goat farms: a comparative assessment of temporal variation based on longitudinal health data

BACKGROUND

Although the association between living in the vicinity of a goat farm and the occurrence of pneumonia is well-documented, it is unclear whether the higher risk of pneumonia in livestock dense areas is season-specific or not. This study explored the temporal variation of the association between exposure to goat farms and the occurrence of pneumonia.

METHODS

A large population-based study was conducted in the Netherlands, based on electronic health records from 49 general practices, collected for a period of six consecutive years (2014-2019). Monthly incidence rates of pneumonia in a livestock dense area were compared with those of a control group (areas with low livestock density) both per individual year and cumulatively for the entire six-year period. Using individual estimates of livestock exposure, it was also examined whether incidence of pneumonia differed per month if someone lived within a certain radius from a goat farm, compared to residents who lived further away.

RESULTS

Pneumonia was consistently more common in the livestock dense area throughout the year, compared to the control area. Analyses on the association between the individual livestock exposure estimates and monthly pneumonia incidence for the whole six-year period, yielded a generally higher risk for pneumonia among people living within 500 m from a goat farm, compared to those living further away. Significant associations were observed for March (IRR 1.68, 95% CI 1.02-2.78), August (IRR 2.67, 95% CI 1.45-4.90) and September (IRR 2.52, 95% CI 1.47-4.32).

CONCLUSIONS

The increased occurrence of pneumonia in the vicinity of goat farms is not season-specific. Instead, pneumonia is more common in livestock dense areas throughout the year, including summer months.

Data analysis supports monitoring and surveillance of goat health and welfare in the Netherlands

Monitoring and surveillance systems have an increasingly important role in contemporary society ensuring high levels of animal health and welfare, securing export positions, and protecting public health by ensuring animal health and product safety. In the Netherlands, a voluntary monitoring and surveillance system is in place since 2003 to provide a broad overview of livestock trends in addition to disease-specific surveillance systems, including insight into the occurrence and prevalence of new and emerging non-notifiable diseases and disorders. Being a major surveillance component of this monitoring and surveillance system for small ruminant health in the Netherlands, an annual data analysis on routine census data is performed to retrospectively monitor trends and developments regarding goat health and welfare. This paper aims to describe the process of the data analysis on goat farms in the Netherlands in 2020 and subsequent results are discussed. The data analysis provides key monitoring indicators such as animal and farm density, mortality, animal movements, and numbers and origin of imported small ruminants. Trends were analysed over a five-year, period and associations between herd characteristics and herd health are evaluated. Results showed that in 2020 the Dutch goat population consisted of 670,842 goats, distributed over 14,730 unique herds and increased by 2.3 % compared to 2019. Between 2016 and 2020, although probably underestimated, recorded mortality rates showed a decline on both small-scale and professional farms, with a strongest decrease on farms with herd sizes over more than 200 animals. Seventy-five percent of all professional farms registered animal introductions, in addition to 63 % of small-scale farms, including 2439 imported goats. Performing risks analyses requires demographic knowledge of the goat industry. During and after several disease outbreaks, such as bluetongue and Schmallenberg virus disease, the data analysis proved to function as a valuable tool, however, appeared just as important for recording outbreak-free data. Since its start in 2006, the concept of the data-analysis has continuously been improved, and will in the future be further developed, especially if more complete data sets become available. Subsequently, data analysis will increasingly support monitoring and surveillance of goat health and welfare.

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.

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.

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

BACKGROUND

There is increasing evidence of associations between residential proximity to livestock farms and respiratory morbidity, but less is known about potential effects on respiratory mortality among residents.

OBJECTIVES

We aimed to assess potential associations between respiratory mortality and residential proximity to (intensive) livestock farming.

METHODS

In DUELS, a national census-based cohort, we selected all inhabitants from rural and semi-urban areas of the Netherlands, aged ≥30 years and living at the same address for five years up to baseline (2004). We followed these ∼4 million individuals for respiratory mortality (respiratory system diseases, chronic lower respiratory diseases, pneumonia) from 2005 to 2012. We computed the average number of cattle, pigs, chicken, and mink present in 500 m, 1000 m, 1500 m and 2000 m of each individual's residence in the period 1999-2003. Analyses were conducted using Cox proportional hazards regression, adjusting for potential confounders at individual and neighbourhood level.

RESULTS

We found evidence that living up to 2000 m of pig farms was associated with respiratory mortality, namely from chronic lower respiratory diseases, with Hazard Ratios ranging from 1.06 (1.02, 1.10) in people living close to low numbers (<median number of animals) of pigs in 1000 m and 1.18 (1.13, 1.24) in those living near high numbers (≥median) of pigs in 2000 m. We also found indications of higher pneumonia mortality in people living near mink farms.

CONCLUSION

Our results are in line with previous findings of adverse respiratory effects in people living near livestock farms. Little is known about the physical, chemical, and biological exposures leading to respiratory morbidity and mortality warranting further explorations of air contaminants in the vicinity of livestock farms.

Identified micro-organisms in hospitalized community-acquired pneumonia patients living near goat and poultry farms

BACKGROUND

In the Netherlands, an increased risk of community-acquired pneumonia (CAP) has been reported for adults living near goat and poultry farms. Previous results of respiratory microbiome studies in hospitalized CAP patients near poultry farms suggested a higher relative abundance of Streptococcus pneumoniae. This retrospective study, using routine laboratory data from hospitalized CAP patients, aims to explore possible aetiologic micro-organisms of CAP in relation to livestock exposure.

METHODS

Patient characteristics and PCR and urinary antigen test results were retrieved retrospectively from electronic medical records of CAP patients admitted to the Jeroen Bosch Hospital or Gelre Hospital in the Netherlands during 2016-2017. Distances between the patients' home address and the nearest poultry and goat farm were calculated. Differences in laboratory test results between CAP patients with and without goat or poultry farms within 2 km of their home address were analyzed using Fisher's exact test.

RESULTS

In total, 2230 CAP episodes with diagnostic results were included. In only 25% of the CAP episodes, a micro-organism was detected. A positive urinary antigen test for S. pneumoniae was found more often in patients living within two kilometers of goat (15.2% vs. 11.3%) and poultry farms (14.4% vs. 11.3%), however these differences were not statistically significant (p = 0.1047 and p = 0.1376).

CONCLUSION

Our retrospective analysis did not show statistically significant differences in the identified micro-organisms in hospitalized CAP patients related to livestock farming. The study was hampered by limited statistical power and limited laboratory results. Therefore, the potential increased CAP risk around goat and poultry farms will be further explored in a prospective study among CAP patients in primary care.

Use of Antibiotics among Residents Living Close to Poultry or Goat Farms: A Nationwide Analysis in The Netherlands

Prior regional studies found a high risk of pneumonia for people living close to poultry and goat farms. This epidemiological study in the Netherlands used nationwide antibiotic prescription data as a proxy for pneumonia incidence to investigate whether residents of areas with poultry and goat farms use relatively more antibiotics compared to areas without such farms. We used prescription data on antibiotics most commonly prescribed to treat pneumonia in adults and livestock farming data, both with nationwide coverage. Antibiotic use was expressed as defined daily doses per (4-digit Postal Code (PC4) area)-(age group)-(gender)-(month) combination for the year 2015. We assessed the associations between antibiotic use and farm exposure using negative binomial regression. The amoxicillin, doxycycline, and co-amoxiclav use was significantly higher (5-10% difference in use) in PC4 areas with poultry farms present compared to areas without, even after adjusting for age, gender, smoking, socio-economic status, and goat farm presence. The adjusted models showed no associations between antibiotic use and goat farm presence. The variables included in this study could only partly explain the observed regional differences in antibiotic use. This was an ecological study that precludes inference about causal relations. Further research using individual-level data is recommended.

Prediction of human active mobility in rural areas: development and validity tests of three different approaches

BACKGROUND/AIM

Active mobility may play a relevant role in the assessment of environmental exposures (e.g. traffic-related air pollution, livestock emissions), but data about actual mobility patterns are work intensive to collect, especially in large study populations, therefore estimation methods for active mobility may be relevant for exposure assessment in different types of studies. We previously collected mobility patterns in a group of 941 participants in a rural setting in the Netherlands, using week-long GPS tracking. We had information regarding personal characteristics, self-reported data regarding weekly mobility patterns and spatial characteristics. The goal of this study was to develop versatile estimates of active mobility, test their accuracy using GPS measurements and explore the implications for exposure assessment studies.

METHODS

We estimated hours/week spent on active mobility based on personal characteristics (e.g. age, sex, pre-existing conditions), self-reported data (e.g. hours spent commuting per bike) or spatial predictors such as home and work address. Estimated hours/week spent on active mobility were compared with GPS measured hours/week, using linear regression and kappa statistics.

RESULTS

Estimated and measured hours/week spent on active mobility had low correspondence, even the best predicting estimation method based on self-reported data, resulted in a R² of 0.09 and Cohen's kappa of 0.07. A visual check indicated that, although predicted routes to work appeared to match GPS measured tracks, only a small proportion of active mobility was captured in this way, thus resulting in a low validity of overall predicted active mobility.

CONCLUSIONS

We were unable to develop a method that could accurately estimate active mobility, the best performing method was based on detailed self-reported information but still resulted in low correspondence. For future studies aiming to evaluate the contribution of home-work traffic to exposure, applying spatial predictors may be appropriate. Measurements still represent the best possible tool to evaluate mobility patterns.

Effects of Dutch livestock production on human health and the environment

Observed multiple adverse effects of livestock production have led to increasing calls for more sustainable livestock production. Quantitative analysis of adverse effects, which can guide public debate and policy development in this area, is limited and generally scattered across environmental, human health, and other science domains. The aim of this study was to bring together and, where possible, quantify and aggregate the effects of national-scale livestock production on 17 impact categories, ranging from impacts of particulate matter, emerging infectious diseases and odor annoyance to airborne nitrogen deposition on terrestrial nature areas and greenhouse gas emissions. Effects were estimated and scaled to total Dutch livestock production, with system boundaries including feed production, manure management and transport, but excluding slaughtering, retail and consumption. Effects were expressed using eight indicators that directly express Impact in the sense of the Drivers-Pressures-State-Impact-Response framework, while the remaining 14 express Pressures or States. Results show that livestock production may contribute both positively and negatively to human health with a human disease burden (expressed in disability-adjusted life years) of up to 4% for three different health effects: those related to particulate matter, zoonoses, and occupational accidents. The contribution to environmental impact ranges from 2% for consumptive water use in the Netherlands to 95% for phosphorus transfer to soils, and extends beyond Dutch borders. While some aggregation across impact categories was possible, notably for burden of disease estimates, further aggregation of disparate indicators would require normative value judgement. Despite difficulty of aggregation, the assessment shows that impacts receive a different contribution of different animal sectors. While some of our results are country-specific, the overall approach is generic and can be adapted and tuned according to specific contexts and information needs in other regions, to allow informed decision making across a broad range of impact categories.

Relationship between Coxiella burnetii (Q fever) antibody serology and time spent outdoors

BACKGROUND/AIM

From 2007 through 2010, the Netherlands experienced the largest recorded Q fever outbreak to date. People living closer to Coxiella burnetii infected goat farms were at increased risk for acute Q fever. Time spent outdoors near infected farms may have contributed to exposure to C. burnetii. The aim of this study was to retrospectively evaluate whether hours/week spent outdoors, in the vicinity of previously C. burnetii infected goat farms, was associated with presence of antibodies against C. burnetii in residents of a rural area in the Netherlands.

METHODS

Between 2014-2015, we collected C. burnetii antibody serology and self-reported data about habitual hours/week spent outdoors near the home from 2494 adults. From a subgroup we collected 941 GPS tracks, enabling analyses of active mobility in the outbreak region. Participants were categorised as exposed if they spent time within specified distances (500m, 1000m, 2000m, or 4000m) of C. burnetii infected goat farms. We evaluated whether time spent near these farms was associated with positive C. burnetii serology using spline analyses and logistic regression.

RESULTS

People that spent more hours/week outdoors near infected farms had a significantly increased risk for positive C. burnetii serology (time spent within 2000m of a C. burnetii abortion-wave positive farm, OR 3.6 (1.2-10.6)), compared to people spending less hours/week outdoors.

CONCLUSIONS

Outdoor exposure contributed to the risk of becoming C. burnetii serology positive. These associations were stronger if people spent more time near C. burnetii infected farms. Outdoor exposure should, if feasible, be included in outbreak investigations.

Morbidity Rates in an Area with High Livestock Density: A Registry-Based Study Including Different Groups of Patients with Respiratory Health Problems

There is continuing debate and public health concern regarding the previously confirmed association between high livestock density and human health. The primary aim of the current study is to assess the prevalence of respiratory and other health problems in a livestock dense area in the Netherlands, based on recent longitudinal health data and a large sample. Analyses are expanded with the investigation of different subgroups of patients with respiratory health problems and the inclusion of various chronic and acute health outcomes, as well as prescribed medication. Prevalence of health symptoms and chronic conditions was assessed for the period 2014-2016, based on electronic health records registered in 26 general practices located in areas with intensive livestock farming in the Netherlands ("livestock dense area", n = 117,459 unique residents in total). These were compared with corresponding health data from general practices (n = 22) in different rural regions with a low density of livestock farms or other major environmental exposures ("control area", n = 85,796 unique residents in total). Multilevel regression models showed a significantly higher prevalence of pneumonia in the total sample in the livestock dense area, which was also observed among susceptible subgroups of children, the elderly, and patients with chronic obstructive pulmonary disease (COPD). Lower respiratory tract infections, respiratory symptoms, vertigo, and depression were also more common in the livestock dense area compared to the control area. In general, there were no significant differences in chronic conditions such as asthma, COPD, or lung cancer. Prescription rates for broad-spectrum antibiotics were more common among patients with pneumonia in the livestock dense area. Acute respiratory infections and symptoms, but not chronic conditions, were considerably more common in areas with a high livestock density. Identification of causal pathogens on the basis of serological analyses could further elucidate the underlying mechanisms behind the observed health effects.

Particulate air pollution from different sources and mortality in 7.5 million adults - The Dutch Environmental Longitudinal Study (DUELS)

BACKGROUND

Long-term exposure to particulate air pollution has been associated with mortality in urban cohort studies. Few studies have investigated the association between emission contributions from different particle sources and mortality in large-scale population registries, including non-urban populations.

OBJECTIVES

The aim of the study was to evaluate the associations between long-term exposure to particulate air pollution from different source categories and non-accidental mortality in the Netherlands based on existing national databases.

METHODS

We used existing Dutch national databases on mortality, individual characteristics, residence history, neighbourhood characteristics and modelled air pollution concentrations from different sources and air pollution components: particulate matter PM10, primary particulate matter PM10 (PPM10), particulate matter PM2.5, primary particulate matter PM2.5 (PPM2.5), elemental carbon (EC), nitrogen dioxide (NO₂) and secondary inorganic aerosol (SIA) in PM10 (SIA10) or in PM2.5 (SIA2.5). We established a cohort of 7.5 million individuals 30 years or older. We followed the cohort for eight years (2008-2015). We applied Cox proportional hazard regression models adjusting for potential individual and area-specific confounders.

RESULTS

We found statistically significant associations between total and primary particulate matter (PM10 and PM2.5), elemental carbon and mortality. Adjustment for nitrogen dioxide did not change the associations. Secondary inorganic aerosol showed less consistent associations. All primary PM sources were associated with mortality, except agricultural emissions and, depending on the statistical model, industrial PM emissions.

CONCLUSIONS

We could not identify one or more specific source categories of particulate air pollution as main determinants of the mortality effects found in this and in a previous study. This suggests that present policy measures should be focussed on the wider spectrum of air pollution sources instead of on specific sources.

Risk of pneumonia among residents living near goat and poultry farms during 2014-2016

In the Netherlands, an association was found between the prevalence of pneumonia and living near goat and poultry farms in 2007-2013. This association then led to regulatory decisions to restrict the building of new goat farms and to reduce emissions of poultry farms. Confirmation of these results, however, is required because the period of previous analyses overlapped a Q-fever epidemic in 2007-2010. To confirm the association, we performed a population-based study during 2014-2016 based on general practitioner (GP) data. Electronic medical records of 90,183 persons were used to analyze the association between pneumonia and the population living in the proximity (within 500-2000 m distance) of goat and poultry farms. Data were analyzed with three types of logistic regression (with and without GP practice as a random intercept and with stratified analyses per GP practice) and a kernel model to discern the influence of different statistical methods on the outcomes. In all regression analyses involving adults, a statistically significant association between pneumonia and residence within 500 meters of goat farms was found (odds ratio [OR] range over all analyses types: 1.33-1.60), with a decreasing OR for increasing distances. In kernel analyses (including all ages), a population-attributable risk between 6.0 and 7.8% was found for a distance of 2000 meters in 2014-2016. The associations were consistent across all years and robust for mutual adjustment for proximity to other animals and for several other sensitivity analyses. However, associations with proximity to poultry farms are not supported by the present study. As the causes of the elevated pneumonia incidence in persons living close to goat farms remain unknown, further research into potential mechanisms is required for adequate prevention.

Insights into Livestock-Related Microbial Concentrations in Air at Residential Level in a Livestock Dense Area

Microbial air pollution from livestock farms has raised concerns regarding public health. Little is known about airborne livestock-related microbial levels in residential areas. We aimed to increase insights into this issue. Air measurements were performed in 2014 and 2015 at 61 residential sites in The Netherlands. Quantitative-PCR was used to assess DNA concentrations of selected bacteria (commensals: Escherichia coli and Staphylococcus spp.; a zoonotic pathogen: Campylobacter jejuni) and antimicrobial resistance (AMR) genes ( tetW, mecA) in airborne dust. Mixed models were used to explore spatial associations (temporal adjusted) with livestock-related characteristics of the surroundings. DNA from commensals and AMR genes was detectable even at sites furthest away from farms (1200 m), albeit at lower levels. Concentrations, distinctly different between sites, were strongly associated with the density of farms in the surroundings especially with poultry and pigs. C. jejuni DNA was less prevalent (42% of samples positive). Presence of C. jejuni was solely associated with poultry (OR: 4.7 (95% CI: 1.7-14), high versus low poultry density). Residential exposure to livestock-related bacteria and AMR genes was demonstrated. Identified associations suggest contribution of livestock farms to microbial air pollution in general and attribution differences between farm types. This supports the plausibility of recent studies showing health effects in relation to residential proximity to farms.

Patients with overlapping diagnoses of asthma and COPD: is livestock exposure a risk factor for comorbidity and coexisting symptoms and infections?

BACKGROUND

Epidemiological research on health effects of livestock exposure in population subgroups with compromised respiratory health is still limited. The present study explored the association between livestock exposure and comorbid/concurrent conditions in patients with overlapping diagnoses of asthma and COPD.

METHODS

Electronic health record data from 23 general practices in the Netherlands were collected from 425 patients diagnosed with both asthma and COPD, living in rural areas with high livestock density ("study area"). Data of 341 patients with the same overlapping diagnoses, living in rural areas with lower livestock density ("control areas") were obtained from 19 general practices. First, the prevalence of comorbid disorders and symptoms/infections were compared between the study and control area. Second, the examined health outcomes were analyzed in relation to measures of individual livestock exposure.

RESULTS

Pneumonia was twice as common among patients living in areas with a high livestock density (OR 2.29, 99% CI 0.96-5.47); however, there were generally no statistically significant differences in the investigated outcomes between the study and control area. Significant associations were observed between presence of goats within 1000 m and allergic rhinitis (OR 5.71, 99% CI 1.11-29.3, p < 0.01), number of co-occurring symptoms (IRR 1.69, 99% CI 1.03-2.77, p < 0.01) and anxiety (OR 8.18, 99% 1.5-44.7, p < 0.01). Presence of cattle within 500 m was associated with pneumonia prevalence (OR 2.48, 99% CI 1.05-5.84, p < 0.01).

CONCLUSION

Livestock exposure is not associated with comorbid chronic conditions but appears to be a risk factor for symptomatic effects in patients with overlapping diagnoses of asthma and COPD.

Attitude toward livestock farming does not influence the earlier observed association between proximity to goat farms and self-reported pneumonia

Attitudes toward environmental risks may be a source of bias in environmental health studies because concerns about environmental hazards may influence self-reported outcomes.

OBJECTIVE

The main aim was to assess whether earlier observed associations between proximity to goat farms and self-reported pneumonia were biased by participants' attitude toward farming.

METHODS

We developed an attitude-score for 2,457 participants of the Dutch Livestock Farming and Neighbouring Residents' Health Study (veehouderij en gezondheid omwonenden) by factor analysis of 13 questionnaire items related to attitude toward livestock farming. Linear regression analysis was used to assess associations between attitude and potential determinants. The effect of attitude on the association between goat farm proximity and pneumonia was analyzed by evaluating (1) misclassification of the outcome, (2) effect modification by attitude, and (3) exclusion of participants reporting health problems due to farms in their environment.

RESULTS

In general, the study population had a positive attitude toward farming, especially if participants were more familiar with farming. Older participants, females, ex-smokers, and higher-educated individuals had a more negative attitude. Both self-reported respiratory symptoms and exposure to livestock farms were associated with a more negative attitude. Misclassification of self-reported pneumonia was nondifferential with regard to participants' attitude. Furthermore, no indication was found that the association between proximity to goat farms and pneumonia was modified by attitude. Excluding subjects who attributed their health symptoms to livestock farms did also not change the association.

CONCLUSIONS

The association between goat farm proximity and pneumonia was not substantially biased by study participants' attitude toward livestock farming.

Associations between pneumonia and residential distance to livestock farms over a five-year period in a large population-based study

In a recent study of electronic health records (EHR) of general practitioners in a livestock-dense area in The Netherlands in 2009, associations were found between residential distance to poultry farms and the occurrence of community-acquired pneumonia (CAP). In addition, in a recent cross-sectional study in 2494 adults in 2014/2015 an association between CAP and proximity to goat farms was observed. Here, we extended the 2009 EHR analyses across a wider period of time (2009–2013), a wider set of health effects, and a wider set of farm types as potential risk sources. A spatial (transmission) kernel model was used to investigate associations between proximity to farms and CAP diagnosis for the period from 2009 to 2013, obtained from EHR of in total 140,059 GP patients. Also, associations between proximity to farms and upper respiratory infections, inflammatory bowel disease, and (as a control disease) lower back pain were analysed. Farm types included as potential risk sources in these analyses were cattle, (dairy) goats, mink, poultry, sheep, and swine. The previously found association between CAP occurrence and proximity to poultry farms was confirmed across the full 5-year study period. In addition, we found an association between increased risk for pneumonia and proximity to (dairy) goat farms, again consistently across all years from 2009 to 2013. No consistent associations were found for any of the other farm types (cattle, mink, sheep and swine), nor for the other health effects considered. On average, the proximity to poultry farms corresponds to approximately 119 extra patients with CAP each year per 100,000 people in the research area, which accounts for approximately 7.2% extra cases. The population attributable risk percentage of CAP cases in the research area attributable to proximity to goat farms is approximately 5.4% over the years 2009–2013. The most probable explanation for the association of CAP with proximity to poultry farms is thought to be that particulate matter and its components are making people more susceptible to respiratory infections. The causes of the association with proximity to goat farms is still unclear. Although the 2007–2010 Q-fever epidemic in the area probably contributed Q-fever related pneumonia cases to the observed additional cases in 2009 and 2010, it cannot explain the association found in later years 2011–2013.

Pneumonia risk of people living close to goat and poultry farms - Taking GPS derived mobility patterns into account

We previously observed an increased incidence of pneumonia in persons living near goat and poultry farms, using animal presence around the home to define exposure. However, it is unclear to what extent individual mobility and time spent outdoors close to home contributes to this increased risk. Therefore, the aim of the current study was to investigate the role of mobility patterns and time spent outdoors in the vicinity of goat or poultry farms in relation to pneumonia risk. In a rural Dutch cohort, 941 members logged their mobility using GPS trackers for 7 days. Pneumonia was diagnosed in 83 subjects (participants reported that pneumonia had been diagnosed by a medical doctor, or recorded in EMR from general practitioners, 2011-2014). We used logistic regression to evaluate pneumonia-risk by presence of goat farms within 500 and 1000 m around the home and around GPS-tracks (only non-motorised mobility), also we evaluated whether more time spent outdoors increased pneumonia-risks. We observed a clearly increased risk of pneumonia among people living in close proximity to goat farms, ORs increased with closer distances of homes to farms (500 m: 6.2 (95% CI 2.2-16.5) 1000 m: 2.5 (1.4-4.3)) The risk increased for individuals who spent more time outdoors close to home, but only if homes were close to goat farms (within 500 m and often outdoors: 12.7 (3.6-45.4) less often: 2.0 (0.3-9.2), no goat farms and often outdoors: 1.0 (0.6-1.6)). For poultry we found no increased risks. Pneumonia-risks increased when people lived near goat farms, especially when they spent more time outdoors, mobility does not seem to add to these risks.

Residential proximity to livestock farms is associated with a lower prevalence of atopy

Objectives

Exposure to farm environments during childhood and adult life seems to reduce the risk of atopic sensitisation. Most studies have been conducted among farmers, but people living in rural areas may have similar protective effects for atopy. This study aims to investigate the association between residential proximity to livestock farms and atopy among non-farming adults living in a rural area in the Netherlands.

Methods

We conducted a cross-sectional study among 2443 adults (20–72 years). Atopy was defined as specific IgE to common allergens and/or total IgE ≥100 IU/mL. Residential proximity to livestock farms was assessed as 1) distance to the nearest pig, poultry, cattle or any farm, 2) number of farms within 500 m and 1000 m, and 3) modelled annual average fine dust emissions from farms within 500 m and 1000 m. Data were analysed with multiple logistic regression and generalised additive models.

Results

The prevalence of atopy was 29.8%. Subjects living at short distances from farms (<327 m, first tertile) had a lower odds for atopy compared with subjects living further away (>527 m, third tertile) (OR 0.79, 95% CI 0.63 to 0.98). Significant associations in the same direction were found with distance to the nearest pig or cattle farm. The associations between atopy and livestock farm exposure were somewhat stronger in subjects who grew up on a farm.

Conclusions

Living in close proximity to livestock farms seems to protect against atopy. This study provides evidence that protective effects of early-life and adult farm exposures may extend beyond farming populations.

Bacterial-Host Interactions: Physiology and Pathophysiology of Respiratory Infection

It has long been thought that respiratory infections are the direct result of acquisition of pathogenic viruses or bacteria, followed by their overgrowth, dissemination, and in some instances tissue invasion. In the last decades, it has become apparent that in contrast to this classical view, the majority of microorganisms associated with respiratory infections and inflammation are actually common members of the respiratory ecosystem and only in rare circumstances do they cause disease. This suggests that a complex interplay between host, environment, and properties of colonizing microorganisms together determines disease development and its severity. To understand the pathophysiological processes that underlie respiratory infectious diseases, it is therefore necessary to understand the host-bacterial interactions occurring at mucosal surfaces, along with the microbes inhabiting them, during symbiosis. Current knowledge regarding host-bacterial interactions during asymptomatic colonization will be discussed, including a plausible role for the human microbiome in maintaining a healthy state. With this as a starting point, we will discuss possible disruptive factors contributing to dysbiosis, which is likely to be a key trigger for pathobionts in the development and pathophysiology of respiratory diseases. Finally, from this renewed perspective, we will reflect on current and potential new approaches for treatment in the future.

A cross sectional study on Dutch layer farms to investigate the prevalence and potential risk factors for different Chlamydia species

In poultry several Chlamydia species have been detected, but Chlamydia psittaci and Chlamydia gallinacea appear to be most prevalent and important. Chlamydia psittaci is a well-known zoonosis and is considered to be a pathogen of poultry. Chlamydia gallinacea has been described more recently. Its avian pathogenicity and zoonotic potential have to be further elucidated. Within the Netherlands no data were available on the presence of Chlamydia on poultry farms. As part of a surveillance programme for zoonotic pathogens in farm animals, we investigated pooled faecal samples from 151 randomly selected layer farms. On a voluntary base, 69 farmers, family members or farm workers from these 151 farms submitted a throat swab. All samples were tested with a generic 23S Chlamydiaceae PCR followed by a species specific PCR for C. avium, C. gallinacea and C. psittaci. C. avium and psittaci DNA was not detected at any of the farms. At 71 farms the positive result could be confirmed as C. gallinacea. Variables significantly associated with the presence of C. gallinacea in a final multivariable model were ‘age of hens,’ ‘use of bedding material’ and ‘the presence of horses.’ The presence of C. gallinacea was associated with neither clinical signs, varying from respiratory symptoms, nasal and ocular discharges to diarrhoea, nor with a higher mortality rate the day before the visit. All throat swabs from farmers, family members or farm workers tested negative for Chlamydia DNA, giving no further indication for possible bird-to-human (or human-to-bird) transmission.

Remarkable spatial variation in the seroprevalence of Coxiella burnetii after a large Q fever epidemic

Background

Prior to the 2007–2010 Q fever epidemic in the Netherlands, the seroprevalence of antibodies against Coxiella burnetii in the general population was 1.5%, which is low compared to other countries. We aimed to determine the seroprevalence after the Q fever epidemic among people living in the affected area, compare the seroprevalence with the incidence of Q fever notifications during the 2007–2010 Q fever epidemic, and to identify farm exposures associated with having antibodies against C. burnetii.

Methods

During the period March 2014–February 2015, residents aged 18–70 years from two provinces were invited by general practitioners to complete a questionnaire on their symptoms and personal characteristics and to submit a blood sample. We used the mandatory provincial database of livestock licences to calculate distance to farms/farm animals for each participant. To compare ELISA-positive participants for C. burnetii antibodies with those who were negative, we calculated prevalence ratios (PR) using binominal regression. We compared the C. burnetii seroprevalence in the period March 2014–February 2015 with the incidence of Q fever notifications during the 2007–2010 Q fever epidemic at municipal level by calculating the Spearman correlation coefficient.

Results

Of the 2296 participants (response rate: 34%), 6.1% (n = 139, 95% CI 5.1–7.1%) had C. burnetii antibodies (range in municipalities: 1.7–14.1%). C. burnetii seroprevalence was higher in individuals living within 1000 m of goat farms (PR 3.0; 95% CI 1.4–6.4) or within 1000 m of > 50 goats (PR 1.9; 95% CI 1.2–3.0). Seroprevalence increased with decreasing distance to the closest goat farm that was infected during the epidemic years (< 500 m, PR 9.5, 95% CI 2.8–32; 500–1000 m, PR 4.5, 95% CI 2.6–7.7; 1000–1500 m, PR 2.2, 95% CI 1.1–4.3, 1500–2000 m, PR 1.2, 95% CI 0.6–2.5; > 2000 reference group). There was no significant correlation between C. burnetii seroprevalence and Q fever incidence during the 2007–2010 epidemic (r_s = 0.42, p = 0.156).

Conclusions

Results showed a remarkable spatial variation in C. burnetii seroprevalence in a relatively small livestock dense area. It confirms previous evidence that the Q fever epidemic was primarily the result of airborne C. burnetii transmission from Q fever affected goat farms.

Impacts of Intensive Livestock Production on Human Health in Densely Populated Regions

In several regions worldwide, the presence of livestock in close proximity to residential areas raises questions about public health implications. The rapid expansion of large-scale livestock farms, increasingly interwoven with urbanized areas, and its potential impact on neighboring residents' health has hardly been accompanied by any research. The current situation in densely populated livestock farming areas could be regarded as a "natural experiment." Most scientific and public health initiatives have focused on emerging zoonoses and antimicrobial resistance as potential health threats. In this commentary, we emphasize the importance of respiratory health effects of noninfectious air pollutant emissions from livestock farms.

Space-time analysis of pneumonia hospitalisations in the Netherlands

Community acquired pneumonia is a major global public health problem. In the Netherlands there are 40,000-50,000 hospital admissions for pneumonia per year. In the large majority of these hospital admissions the etiologic agent is not determined and a real-time surveillance system is lacking. Localised and temporal increases in hospital admissions for pneumonia are therefore only detected retrospectively and the etiologic agents remain unknown. Here, we perform spatio-temporal analyses of pneumonia hospital admission data in the Netherlands. To this end, we scanned for spatial clusters on yearly and seasonal basis, and applied wavelet cluster analysis on the time series of five main regions. The pneumonia hospital admissions show strong clustering in space and time superimposed on a regular yearly cycle with high incidence in winter and low incidence in summer. Cluster analysis reveals a heterogeneous pattern, with most significant clusters occurring in the western, highly urbanised, and in the eastern, intensively farmed, part of the Netherlands. Quantitatively, the relative risk (RR) of the significant clusters for the age-standardised incidence varies from a minimum of 1.2 to a maximum of 2.2. We discuss possible underlying causes for the patterns observed, such as variations in air pollution.