Skip to main content
Download PDF

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

BMC Pulmonary Medicine volume 19, Article number: 105 (2019) Cite this article

Abstract

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.

Peer Review reports

Background

Patients with obstructive lung diseases such as asthma and chronic obstructive pulmonary disease (COPD) are characterized by substantial morbidity. Among the different phenotypes that have been identified for asthma and COPD [1,2,3], the largest one represents a patient group that has features and characteristics of both diseases [4, 5]. Overlapping diagnoses of COPD and asthma is commonly referred to as Asthma and COPD Overlap Syndrome (ACOS) [6]. The health-care burden of ACOS seems to be considerable; recent studies suggest that, compared to COPD or asthma only, ACOS patients have higher comorbidity and exacerbation rates, lower health-related quality of life and make more often use of healthcare [7,8,9].

It is well documented that particulate matter and its components contribute to the development and exacerbation of respiratory diseases [10,11,12]. Individuals with asthma for instance suffer more exacerbations and they appear to be at high risk of developing ACOS when exposed to air pollution [13, 14]. Livestock farming is one of the continuously expanding sources of ambient air pollution worldwide [15, 16]. In the international literature different population-based studies have described the relationship between livestock farming and health effects such as asthma, allergy, respiratory complaints, pneumonia, and lung function deficits [17,18,19,20,21,22,23,24,25].

Fewer studies have focused on specific respiratory patient groups, although these seem to respond to a greater extent to exposure from livestock farms. In an experimental setting, the study of Sigurdarson et al. [26] demonstrated adverse reactions in individuals with asthma after being exposed to inhaled grain dust and Harting et al. [27] found that COPD patients had greater systemic responsiveness to ex vivo stimulation with swine dust extract than healthy volunteers. In a more recent epidemiological study in the Netherlands, it was shown that COPD patients living at shorter distance from livestock farms reported more respiratory symptoms [28] and had a higher exacerbation rate when compared with a control area in COPD [29]; however, no consistent associations were observed between individual exposure estimates and diagnosed comorbidity or exacerbations in COPD patients [29, 30].

Considering the growing evidence, it seems important to lay more emphasis on investigating whether the effects of livestock exposure on morbidity and symptomatic reactions are more pronounced among respiratory patients with other clinical phenotypes. To date, no epidemiological study has been conducted on the association between health outcomes and livestock density in patients with ACOS. The present study uses electronic health record data from general practices to address the following questions: 1) Does the prevalence of the examined health outcomes in those patients differ between areas with high livestock farm density (“study areas”) and rural areas with a substantially lower livestock density (“control areas”); 2) What is the association between indicators of exposure to livestock farms and comorbidities and symptoms and infections in ACOS patients? Following prior findings on other respiratory vulnerable subgroups, it is hypothesized that a) the prevalence of the investigated health outcomes will be higher in the “exposed” area; b) higher individual exposure, as indicated by the employed proxies, will be more consistently associated with higher prevalence of comorbid conditions and symptoms.

Methods

Study design and population

This study is part of a larger study in the Netherlands, entitled “Livestock Farming and Neighbouring Residents’ Health study” (VGO). Patients were sampled from 23 general practices in rural areas with high livestock farm density (eastern part of Noord-Brabant province as well as the northern part of Limburg); more than 95% of the included patients were living within a km from an animal farm. General practices in the “control area” (n = 19) were located in other rural areas in the Netherlands with a substantially lower livestock farm density (control area) [29, 31], particularly in the provinces of Noord-Holland, Zuid- Holland, Utrecht, Gelderland, Zeeland, Overijssel and Groningen (for additional details, see van Dijk et al., 2017 [31]).

Diagnosed conditions were registered by the general practitioners (GPs) on the basis of the International Classification of Primary Care (ICPC) [32]. For the present analysis, 766 (n = 425 in the “study area” and n = 341 in the control group) ACOS patients (registered with ICPC codes R95 or R91: COPD and R96: Asthma) with an age of ≥40 years were included. The study focused on non-occupational exposure, therefore patients possibly working or residing on a farm (defined as a distance of < 50 m between house address and the closest farm) were not included. Additional information regarding study design is presented in previous publications [29, 31].

Health outcome assessment

Data from electronic health records (EHRs) from primary care (general practices) were used [33]. These practices participated in the Primary Care Database (PCD) of NIVEL [33, 34]. Prevalence rates of the year 2012 were estimated [30]. Contacts for the same health problem within a specific time frame were clustered into episodes of care, which were used to estimate the prevalence rates. For the construction of care episodes, all records with an ICPC code were considered [31, 34]. Selection of comorbid conditions (Fig. 1) was based on the literature [9, 35,36,37,38].

Fig. 1
figure1

Prevalence (and corresponding ICPC codes) of comorbid conditions and coexisting symptoms and infections in the sample of ACOS patients living in the study area (n = 425) and control area (n = 341). Abbreviations: ICPC; International Classification of Primary Care; GERD; Gastro-oesophageal reflux disease. * The heart disease (risk) cluster consists of coronary heart disease, heart failure, atherosclerosis, hyperlipidemia

Full size image

Exposure indicators

For the same year (2012), data regarding characteristics of farms was obtained from databases of environmental licenses for livestock (“Bestand Veehouderij Bedrijven”). These include information on animal type and number, geographic coordinates of farms and fine dust and ammonia (NH3) emission estimates from each farm per year. The distance between home addresses and farms was estimated based on a geographic information system (GIS) (ArcGis 9.3.1, Esri, Redlands, CA). Similar to recent studies [28,29,30,31] we examined the following variables: 1) distance (meters) from participant’s home to the nearest animal farm (binary) and 2) presence of farm animals (various types) within 500 m and 1000 m (binary). In additional analyses, inverse-distance weighted fine dust and NH3 emissions from all farms within 500 m and 1000 m were also analyzed, as continuous variables. These were rescaled using interquartile range (IQR) as a scaling factor.

Statistical analysis

The prevalence of morbidity rates and coexisting symptoms in the sample as well as basic sample characteristics were estimated on the basis of descriptive analyses. Given that the study data have a hierarchical structure (observations clustered in general practices), exposure-outcome associations were examined using multilevel logistic and Poisson regression models. Analyses were adjusted for gender and age (including a quadratic variable to allow for a potential non-linear trend between age and morbidity). For each regression model, Odds ratios (OR) for the binary outcomes and incidence rate ratios for the count variables (IRR) and 99% confidence intervals (CI) were calculated. Statistical significance was set at p < 0.01, to correct for multiple testing. Analyses were performed with STATA version 13.0 (StataCorp LP, College Station, TX, USA).

Results

Descriptive analysis

An overview of sample characteristics is shown in Table 1. Patients with ACOS in the study area were slightly more often female (52%) and were on average 66 years of age. At least one comorbid condition was present in 67% of the patients while about 54% had at least one coexisting symptom/infection. About 50% of the patients in the study area lived within 500 m from an animal farm. Patients in the study area were also frequently living within 500 m of farms with cattle (31%) and pigs (19%). These rates were much higher within 1000 m (Table 1).

Table 1 Characteristics of ACOS patients included in the study
Full size table

As shown in Fig. 1, among the examined chronic conditions, heart disease (study group 33.7% vs. control group 34.6%), hypertension (33.2% vs. 38.4%), diabetes (15.1% vs. 21.7%) and depression (9.7% vs. 11.7%) were the most prevalent in both the study and control group. Upper respiratory tract infections (22.1% vs. 26.7%), respiratory symptoms (19.1% vs. 20.8%), pneumonia (11.1% vs. 6.5%) and sleep problems (7.3% vs. 7.3%) were the most prevalent acute conditions.

Comorbid diseases and symptoms: comparison between study and control area

No significant differences were found in the majority of examined chronic conditions; in some cases (e.g. diabetes) prevalence was lower in the study area (Table 2). Higher but nonsignificant OR were observed for several coexisting symptoms/infections such as pneumonia (OR 2.29, 99% CI 0.96–5.47) and also for the total symptom/infection prevalence (OR 1.28, 99% CI 0.71–2.30) (Table 2).

Table 2 Differences (OR, 99% CI) a in prevalence of comorbidity and coexisting symptoms and infections, between ACOS patients living in the study area and those living in the control area (significant associations in bold)
Full size table

Association of livestock exposure with comorbid chronic conditions

As shown in Table 3, analyses yielded no significant associations between distance to the nearest farm and the investigated outcomes. Only presence of goats within 1000 m was significantly associated with a higher prevalence of anxiety (Table 5). There were no significant findings in relation to modeled fine dust and ammonia emissions (see Appendix).

Table 3 Association (OR, 99% CI) a between distance to the nearest farm and primary outcomes among ACOS patients in the study area
Full size table

Association between livestock exposure and coexisting symptoms/infections

Several significant associations were found between the examined exposure proxies and prevalence of co-occurring symptoms/infections. More specifically, presence of cattle within 500 m was associated with a higher prevalence of pneumonia (Table 4) and presence of goats within 1000 m with allergic rhinitis/hay fever and also with a number of co-occurring symptoms (Table 5). Overall, OR were consistently higher for most of the associations between presence of goat farms within 1000 m and acute conditions; a borderline nonsignificant association with pneumonia was also observed (OR 3.65, 99% CI 0.96–13.8). No statistically significant results were found in relation to distance to the nearest farm and modeled emissions but pneumonia risk was again, consistently higher (see Table 2 and Appendix). Analyses also showed a significant inverse association between pigs within 500 m and total prevalence (Table 4).

Table 4 Association (OR, 99% CI) a between presence of farm animals within 500 m and primary outcomes among ACOS patients in the study area (significant associations in bold)
Full size table
Table 5 Association (OR, 99% CI) a between presence of farm animals within 1000 m and primary outcomes among ACOS patients in the study area (significant associations in bold)
Full size table

Discussion

Asthma and COPD overlap syndrome is a recently recognized phenotype gaining attention due to the associated morbidity and rapid decline in lung function, compared to other respiratory conditions. While there is increasing evidence that livestock density can be an environmental risk factor for adverse effects in respiratory patients such as those with COPD [28, 29], its role in ACOS has not been previously investigated.

Determining the health effects of modifiable risk factors could decrease (multi)morbidity and burden of disease associated with ACOS. The present study comprises a first effort to fill this gap. General practice-registered patients with a dual diagnosis of asthma and COPD were included as a subgroup of respiratory patients potentially susceptible to livestock exposure. We used two approaches to assess the associations between exposure and various comorbid conditions and symptoms: First, we compared rural areas of high livestock density with other rural areas of substantially lower livestock density. Second, we tested the association between health outcomes and individual measures of livestock exposure, based on distance between patients’ home addresses and livestock farms.

In line with the broader literature, presence of comorbid conditions was found to be high in patients with ACOS [9, 37]. Overall, prevalence of the investigated outcomes did not differ significantly between the study and control area, but analyses in the study area showed several statistically significant associations with individual exposure. More specifically, presence of goats within 1000 m was associated with anxiety, allergic rhinitis and number of co-occurring symptoms. In addition, presence of cattle within 500 m was associated with pneumonia; the latter was clearly more prevalent among patients in the study area. Risk of pneumonia was consistently higher in relation to exposure estimates such as presence of goats within 1000 m and distance to the nearest farm, though these results did not reach statistical significance.

Considering the lack of studies on the effects of livestock exposure in this patient group, a direct comparison with prior findings was not feasible. A previous study on patients with COPD (without asthma) living in the same study area showed a comparatively lower prevalence rates of symptoms and infections and no convincing evidence for an association between livestock exposure and health outcomes [30]. Another study [28], showed a higher risk for self-reported respiratory symptoms among individuals with COPD who lived within 500 m from cattle, but that was not statistically significant. In the general population, a higher risk of pneumonia when living in the vicinity of livestock farms is well-documented, but only in relation to goat and poultry [22,23,24,25].

A plausible biological mechanism to explain the significant association observed between presence of goats and anxiety is unknown. Nevertheless, people who live in close proximity of goat farms, seem to have a more negative attitude towards farming [39], which might be related to the Q-fever outbreak in the study area several years prior to the study [40]. Concerns regarding the health effects of environmental exposures as well as perceived exposure are well-documented determinants of various clusters of “non-specific” symptoms [41,42,43], but this is highly unlikely for GP-diagnosed disorders or infections such as pneumonia [39].

For some health outcomes we could verify the risk of living nearby livestock farms. Although it is yet unclear which environmental agents could be responsible for the observed associations, it was recently shown that livestock farms are a source of endotoxins in the areas included in the present study [44]. Moreover, the proxies of livestock exposure that we used were significantly associated with measured endotoxin concentrations [45]. Despite our focus on risk, interestingly enough, a lower prevalence of comorbid conditions was often observed in the control area, and in some cases, in relation to exposure proxies in the study area. Currently the mechanisms of how exposure and other factors interact and develop across time are not well-understood. The analysis described in this article contributes to this lacuna, but further research into these mechanisms is needed to determine which risk mitigation interventions are recommended from a public health perspective.

An important strength of the study was the use of objective outcome data from medical records of general practices, based on a reliable registration system. We also employed two approaches to assess associations; a study vs. control area comparison and use of individual exposure estimates of livestock exposure. In addition, exposure was objectively assessed based on different proxies and information on livestock farm licenses and health outcome data were obtained for the same year.

The cross-sectional design constitutes a study limitation. Second, analyses were not adjusted for covariates such as history of tobacco use and socioeconomic status, since this information was not provided/registered in the EHRs. Nevertheless, previous research has shown that controlling for socioeconomic status or smoking habits did not alter the associations between livestock exposure and health outcomes [28, 46]. Third, considering that this study focused on neighbouring residents and the fact that data on occupational livestock exposure were not available, we excluded patients living within 50 m from a farm. It is therefore possible that not all occupationally exposed people have been excluded, but this probably concerns a small fraction of the sample. A previous investigation in the same study area, using questionnaire data on occupational exposure, showed that only about 2.5% of the residents were living or working on a livestock farm, after exclusion of subjects living within 50 m from a farm [28]. Fourth, only health outcomes were included for which people visit a general practitioner; this could have influenced the prevalence of symptoms, to some extent. Fifth, the lack of a universally accepted case-definition and objective biomarkers of ACOS is an obstacle to the investigation and understanding of its pathophysiology and epidemiology [4, 5, 47]. As a result, estimation of prevalence as well as exacerbation risk is highly depended on the case definitions used in different studies. We therefore employed a rather arbitrary definition that requires further validation. Also considering that this is one of the first studies to explore ACOS comorbidity based on EHR data within the context of environmental health, we cannot ensure comparability with previous research. Finally, a larger sample would increase the study power for the identification of possible exposure-outcome associations.

Conclusion

In conclusion, this study supports the hypothesis that livestock farm exposures may increase susceptibility to respiratory infections in patients with overlapping diagnoses of asthma and COPD. However, we found no convincing evidence for an association between estimates of livestock density and prevalence of chronic comorbid conditions. Focusing on patient subgroups with different respiratory phenotypes can enhance our knowledge of the risk of environmental exposures in residents with compromised respiratory health.

Availability of data and materials

In consultation with the Medical Ethical Committee that approved the study protocol, data from the VGO study are not publicly available due to privacy protection of participants. The study’s privacy regulations state that only researchers from NIVEL, IRAS, and RIVM (consortium partners) have access to the study database. Sharing an anonymized and de-identified dataset is not possible as it would still contain Electronic Health Records and the personal data of participants, which could potentially lead to the identification of subjects. Researchers may contact the data access committee through Remco Coppen, PhD, LLM (r.coppen@nivel.nl) or non-personal departmental email (NIVEL: zorgregistraties@nivel.nl) or contact Christos Baliatsas, PhD (c.baliatsas@nivel.nl), Joris Yzermans, PhD (J.Ijzermans@nivel.nl), or Michel Duckers, PhD (m.duckers@nivel.nl).

Abbreviations

ACOS:

Asthma and COPD overlap syndrome

CI:

Confidence intervals

COPD:

Chronic obstructive pulmonary disease

EHRs:

Electronic health records

GERD:

Gastro-oesophageal reflux disease

GPs:

General practitioners

IRR:

Incidence rate ratios

OR:

Odds ratios

PCD:

Primary Care Database

References

  1. 1.

    Marsh S, Travers J, Weatherall M, Williams M, Aldington S, Shirtcliffe P, et al. Proportional classifications of COPD phenotypes. Thorax. 2008;63:761–7.

    CAS  Article  Google Scholar 

  2. 2.

    Han MK, Agusti A, Calverley PM, Celli BR, Criner G, Curtis JL, et al. Chronic obstructive pulmonary disease phenotypes: the future of COPD. Am J Respir Crit Care Med. 2010;182:598–604.

    Article  Google Scholar 

  3. 3.

    Miravitlles M, Calle M, Soler-Cataluna JJ. Clinical phenotypes of COPD: identification, definition and implications for guidelines. Arch Bronconeumol. 2012;48:86–98.

    PubMed  Google Scholar 

  4. 4.

    Bujarski S, Parulekar AD, Sharafkhaneh A, Hanania NA. The asthma COPD overlap syndrome (ACOS). Curr Allergy Asthma Rep. 2015;15:7.

    Article  Google Scholar 

  5. 5.

    Sin DD, Miravitlles M, Mannino DM, Soriano JB, Price D, Celli BR, et al. What is asthma− COPD overlap syndrome? Towards a consensus definition from a round table discussion. Eur Respir J. 2016;48:664–73.

    Article  Google Scholar 

  6. 6.

    Gibson PG, Simpson J. The overlap syndrome of asthma and COPD: what are its features and how important is it? Thorax. 2009;64:728–35.

    CAS  Article  Google Scholar 

  7. 7.

    Alshabanat A, Zafari Z, Albanyan O, Dairi M, FitzGerald J. Asthma and COPD overlap syndrome (ACOS): a systematic review and meta analysis. PLoS One. 2015;10:e0136065.

    CAS  Article  Google Scholar 

  8. 8.

    Barrecheguren M, Esquinas C, Miravitlles M. The asthma–chronic obstructive pulmonary disease overlap syndrome (ACOS): opportunities and challenges. Curr Opin Pulm Med. 2015;21:74–9.

    CAS  Article  Google Scholar 

  9. 9.

    Nielsen M, Bårnes CB, Ulrik CS. Clinical characteristics of the asthma–COPD overlap syndrome–a systematic review. Int J Chron Obstruct Pulmon Dis. 2015;10:1443.

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    MacNee W, Donaldson K. Mechanism of lung injury caused by PM 10 and ultrafine particles with special reference to COPD. Eur Respir J. 2003;21:47s–51s.

    CAS  Article  Google Scholar 

  11. 11.

    Strak M, Janssen NA, Godri KJ, Gosens I, Mudway IS, Cassee FR, et al. Respiratory health effects of airborne particulate matter: the role of particle size, composition, and oxidative potential—the RAPTES project. Environ Health Perspect. 2012;120:1183.

    CAS  Article  Google Scholar 

  12. 12.

    Mehta S, Shin H, Burnett R, North T, Cohen AJ. Ambient particulate air pollution and acute lower respiratory infections: a systematic review and implications for estimating the global burden of disease. Air Qual Atmos Health. 2013;6:69–83.

    CAS  Article  Google Scholar 

  13. 13.

    To T, Zhu J, Larsen K, Simatovic J, Feldman L, Ryckman K, et al. Progression from asthma to chronic obstructive pulmonary disease. Is air pollution a risk factor? Am J Respir Crit Care Med. 2016;194:429–38.

    CAS  Article  Google Scholar 

  14. 14.

    Orellano P, Quaranta N, Reynoso J, Balbi B, Vasquez J. Effect of outdoor air pollution on asthma exacerbations in children and adults: systematic review and multilevel meta-analysis. PLoS One. 2017;12:e0174050.

    Article  Google Scholar 

  15. 15.

    Casey JA, Kim BF, Larsen J, Price LB, Nachman KE. Industrial food animal production and community health. Curr Environ Health Rep. 2015;2:259–71.

    Article  Google Scholar 

  16. 16.

    Lelieveld J, Evans JS, Fnais M, Giannadaki D, Pozzer A. The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature. 2015;525:367–71.

    CAS  Article  Google Scholar 

  17. 17.

    Radon K, Schulze A, Ehrenstein V, van Strien RT, Praml G, Nowak D. Environmental exposure to confined animal feeding operations and respiratory health of neighboring residents. Epidemiology. 2007;18:300–8.

    Article  Google Scholar 

  18. 18.

    Schulze A, Römmelt H, Ehrenstein V, van Strien R, Praml G, Küchenhoff H, et al. Effects on pulmonary health of neighboring residents of concentrated animal feeding operations: exposure assessed using optimized estimation technique. Arch Environ Occup Health. 2011;66:146–54.

    Article  Google Scholar 

  19. 19.

    Pavilonis BT, Sanderson WT, Merchant JA. Relative exposure to swine animal feeding operations and childhood asthma prevalence in an agricultural cohort. Environ Res. 2013;122:74–80.

    CAS  Article  Google Scholar 

  20. 20.

    Borlée F, Yzermans CJ, Aalders B, Rooijackers J, Krop E, Maassen CB, et al. Air pollution from livestock farms is associated with airway obstruction in neighboring residents. Am J Respir Crit Care Med. 2017;196:1152–61.

    Article  Google Scholar 

  21. 21.

    Douglas P, Robertson S, Gay R, Hansell AL, Gant TW. A systematic review of the public health risks of bioaerosols from intensive farming. Int J Hyg Environ Health. 2017;221:134–73.

    CAS  Article  Google Scholar 

  22. 22.

    Freidl GS, Spruijt IT, Borlée F, Smit LA, van Gageldonk-Lafeber AB, Heederik DJ, et al. Livestock-associated risk factors for pneumonia in an area of intensive animal farming in the Netherlands. PLoS One. 2017;12:e0174796.

    Article  Google Scholar 

  23. 23.

    Kalkowska DA, Boender GJ, Smit LA, Baliatsas C, Yzermans J, Heederik DJ, et al. Associations between pneumonia and residential distance to livestock farms over a five-year period in a large population-based study. PLoS One. 2018;13:e0200813.

    Article  Google Scholar 

  24. 24.

    Klous G, Smit LA, Freidl GS, Borlée F, van der Hoek W, IJzermans CJ, et al. Pneumonia risk of people living close to goat and poultry farms–taking GPS derived mobility patterns into account. Environ Int. 2018;115:150–60.

    Article  Google Scholar 

  25. 25.

    Poulsen MN, Pollak J, Sills DL, Casey JA, Nachman KE, Cosgrove SE, et al. High-density poultry operations and community-acquired pneumonia in Pennsylvania. Environ Epidemiol. 2018;2:e013.

    Article  Google Scholar 

  26. 26.

    Sigurdarson ST, O'Shaughnessy PT, Watt JA, Kline JN. Experimental human exposure to inhaled grain dust and ammonia: towards a model of concentrated animal feeding operations. Am J Ind Med. 2004;46:345–8.

    CAS  Article  Google Scholar 

  27. 27.

    Harting JR, Gleason A, Romberger DJ, Von Essen SG, Qiu F, Alexis N, et al. Chronic obstructive pulmonary disease patients have greater systemic responsiveness to ex vivo stimulation with swine dust extract and its components versus healthy volunteers. J Toxic Environ Health A. 2012;75:1456–70.

    CAS  Article  Google Scholar 

  28. 28.

    Borlée F, Yzermans CJ, van Dijk CE, Heederik D, Smit LA. Increased respiratory symptoms in COPD patients living in the vicinity of livestock farms. Eur Respir J. 2015;46:1605–14.

    Article  Google Scholar 

  29. 29.

    van Dijk CE, Garcia-Aymerich J, Carsin A-E, Smit LA, Borlée F, Heederik DJ, et al. Risk of exacerbations in COPD and asthma patients living in the neighbourhood of livestock farms: observational study using longitudinal data. Int J Hyg Environ Health. 2016;219:278–87.

    Article  Google Scholar 

  30. 30.

    Baliatsas C, Borlée F, van Dijk CE, van der Star B, Zock J-P, Smit LA, et al. Comorbidity and coexisting symptoms and infections presented in general practice by COPD patients: does livestock density in the residential environment play a role? Int J Hyg Environ Health. 2017;220:704–10.

    Article  Google Scholar 

  31. 31.

    van Dijk CE, Zock J-P, Baliatsas C, Smit LA, Borlée F, Spreeuwenberg P, et al. Health conditions in rural areas with high livestock density: analysis of seven consecutive years. Environ Pollut. 2017;222:374–82.

    Article  Google Scholar 

  32. 32.

    Lamberts H, Wood M. ICPC, international classification of primary care. USA: Oxford University Press; 1987.

    Google Scholar 

  33. 33.

    Verheij RA, Curcin V, Delaney BC, McGilchrist MM. Possible sources of Bias in primary care electronic health record data use and reuse. J Med Internet Res. 2018;20:e185.

    Article  Google Scholar 

  34. 34.

    Nielen M, Spronk I, Davids R, et al. A new method for estimating morbidity rates based on routine electronic medical records in primary care. In: Abstract Book 21st WONCA Europe Conference, 15-18 June 2016, Copenhagen.

  35. 35.

    Brzostek D, Kokot M. Asthma-chronic obstructive pulmonary disease overlap syndrome in Poland. Findings of an epidemiological study. Postepy Dermatol Alergol. 2014;31:372.

    Article  Google Scholar 

  36. 36.

    Miłkowska-Dymanowska J, Białas AJ, Zalewska-Janowska A, Górski P, Piotrowski WJ. Underrecognized comorbidities of chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis. 2015;10:1331.

    PubMed  PubMed Central  Google Scholar 

  37. 37.

    van Boven JF, Román-Rodríguez M, Palmer JF, Toledo-Pons N, Cosío BG, Soriano JB. Comorbidome, pattern, and impact of asthma-COPD overlap syndrome in real life. Chest. 2016;149:1011–20.

    Article  Google Scholar 

  38. 38.

    Kahnert K, Alter P, Young D, Lucke T, Heinrich J, Huber RM, et al. The revised GOLD 2017 COPD categorization in relation to comorbidities. Respir Med. 2018;134:79–85.

    Article  Google Scholar 

  39. 39.

    Borlée F, Yzermans CJ, Oostwegel FS, Schellevis F, Heederik D, Smit LA, et al. Attitude toward livestock farming does not influence the earlier observed association between proximity to goat farms and self-reported pneumonia. Environ Epidemiol. 2019;3(2):e041.

    Article  Google Scholar 

  40. 40.

    van der Hoek W, Morroy G, Renders NH, Wever PC, Hermans MH, Leenders AC, et al. Epidemic Q fever in humans in the Netherlands. Adv Exp Med Biol. 2012;984:329–64.

    Article  Google Scholar 

  41. 41.

    Baliatsas C, Bolte J, Yzermans J, Kelfkens G, Hooiveld M, Lebret E, et al. Actual and perceived exposure to electromagnetic fields and non-specific physical symptoms: an epidemiological study based on self-reported data and electronic medical records. Int J Hyg Environ Health. 2015;218(3):331–44.

    Article  Google Scholar 

  42. 42.

    Baliatsas C, van Kamp I, Hooiveld M, Lebret E, Yzermans J. The relationship of modern health worries to non-specific physical symptoms and perceived environmental sensitivity: a study combining self-reported and general practice data. J Psychosom Res. 2015;79(5):355–61.

    Article  Google Scholar 

  43. 43.

    Martens AL, Reedijk M, Smid T, Huss A, Timmermans D, Strak M, et al. Modeled and perceived RF-EMF, noise and air pollution and symptoms in a population cohort. Is perception key in predicting symptoms? Sci Total Environ. 2018;639:75–83.

    CAS  Article  Google Scholar 

  44. 44.

    De Rooij MM, Heederik DJ, Borlée F, Hoek G, Wouters IM. Spatial and temporal variation in endotoxin and PM10 concentrations in ambient air in a livestock dense area. Environ Res. 2017;153:161–70.

    Article  Google Scholar 

  45. 45.

    De Rooij MM, Heederik DJ, van Nunen EJ, van Schothorst IJ, Maassen CB, Hoek G, Wouters IM. Spatial variation of endotoxin concentrations measured in ambient in a livestock-dense area: implementation of a land-use regression approach. Environ Health Perspect. 2018;126:017003.

    Article  Google Scholar 

  46. 46.

    Smit LA, Hooiveld M, van der Sman-de Beer F, Opstal-van Winden AW, Beekhuizen J, Wouters IM, et al. Air pollution from livestock farms, and asthma, allergic rhinitis and COPD among neighbouring residents. Occup Environ Med. 2013;71:134–40.

    Article  Google Scholar 

  47. 47.

    Bonten TN, Kasteleyn MJ, de Mutsert R, Hiemstra PS, Rosendaal FR, Chavannes NH, et al. Defining asthma–COPD overlap syndrome: a population-based study. Eur Respir J. 2017;49:1602008.

    Article  Google Scholar 

Download references

Acknowledgements

We would like to thank the participating general practitioners for their cooperation and our colleagues at NIVEL for their feedback.

Funding

The Livestock Farming and Neighbouring Residents’ Health (VGO) study was funded by the Ministry of Health, Welfare and Sports and the Ministry of Economic Affairs of the Netherlands, and supported by a grant from the Lung Foundation Netherlands (Grant number: 3.2.11.022).

Author information

Affiliations

  1. Netherlands Institute for Health Services Research (NIVEL), Otterstraat 118-124, 3513 CR, Utrecht, The Netherlands

    Christos Baliatsas, Michel L. A. Dückers, Christel E. van Dijk & C. Joris Yzermans

  2. Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, The Netherlands

    Lidwien A. M. Smit & Dick Heederik

Authors
  1. Christos Baliatsas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lidwien A. M. Smit
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michel L. A. Dückers
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Christel E. van Dijk
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dick Heederik
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. C. Joris Yzermans
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

CJY, LAMS and CB made substantial contributions to the study conception and interpretation. CB analyzed the data and drafted the paper. MLAD, CEVD and DH critically reviewed and commented on the manuscript at all stages and together with the rest of the authors, approved the final version.

Corresponding author

Correspondence to Christos Baliatsas.

Ethics declarations

Ethics approval and consent to participate

The NIVEL PCD complies with the Dutch Data Protection Authority and national data protection regulations. The protocol used for the study (number 13/533) has been approved by the Medical Ethical Committee of the University Medical Centre of Utrecht. Using a “Trusted Third Party” (IVZ Institute, Houten, The Netherlands), health and address data were kept apart at all times. The Dutch law allows the use of EHRs for research purposes under certain conditions. According to this legislation, neither obtaining informed consent from patients nor approval by a medical ethics committee is obligatory for observational studies that do not involve directly identifiable data (Dutch CivilLaw, Article 7:458).

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

Table 6 Association (OR, 99% CI) a between fine dust and ammonia emissions from livestock farms and primary outcomes among ACOS patients in the study area
Full size table

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Baliatsas, C., Smit, L.A.M., Dückers, M.L.A. et al. Patients with overlapping diagnoses of asthma and COPD: is livestock exposure a risk factor for comorbidity and coexisting symptoms and infections?. BMC Pulm Med 19, 105 (2019). https://doi.org/10.1186/s12890-019-0865-z

Download citation

Keywords

  • Livestock
  • Environmental exposure
  • ACOS
  • COPD
  • Asthma
  • Epidemiology

BMC Pulmonary Medicine

ISSN: 1471-2466

Contact us