SEARCH

SEARCH BY CITATION

Keywords:

  • horse;
  • particulate matter;
  • mucus;
  • coarse particles;
  • fine particles;
  • ambient monitoring

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Conflicts of interest
  8. Sources of funding
  9. Acknowledgements
  10. Manufacturers' address
  11. References
  12. Supporting Information

Reason for performing study: Accumulations of tracheal mucus assessed by endoscopic examination are associated with poor performance in racehorses. The air quality in horses' stalls may contribute to this visible tracheal mucus.

Objectives: To determine whether the concentration and number of airborne particulates in stalls are associated with visible accumulations of tracheal mucus and with the number of inflammatory cells in tracheal aspirates.

Methods: We studied 107 racehorses from 3 stables, in 3 different months, and measured airborne particulate matter 3 times daily in each of the stalls. On each monthly visit, horse airways were examined endoscopically and assigned a mucus score, and tracheal lavage was performed. Bivariate procedures, general estimating equations and linear mixed models were applied to estimate the association between PM and the presence of accumulations of mucus and number of inflammatory cells.

Results: Stable, stall, month and PM were all significantly associated with the presence of accumulations of tracheal mucus, which had an overall prevalence of 67%. The odds of horses having visible accumulation of mucus were increased when horses occupied enclosed stables or stalls with higher particulate concentrations, and when concentrations of larger particles (≤10 µm in diameter) were elevated. Sixty-eight percent of tracheal wash samples contained more than 20% neutrophils. Increased numbers of neutrophils were associated with the concentration of smaller particles (≤2.5 µm in diameter).

Potential relevance: Careful consideration of stable construction and management practices focused on maintaining the lowest possible dust concentrations throughout the day should reduce the prevalence of visible accumulations of tracheal mucus, potentially improving racing performance.


Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Conflicts of interest
  8. Sources of funding
  9. Acknowledgements
  10. Manufacturers' address
  11. References
  12. Supporting Information

Visible accumulations of tracheal mucoid secretions (‘tracheal mucus’) are common in racehorses in training and moderate to severe accumulations are associated with decreased racing performance [1,2]. Bacterial infection [3–6] and air quality in the stable [7] are 2 factors implicated as causes of visible mucus and airway inflammation. Airborne particulates in the stable may play a role because airway inflammation occurs in stabled healthy horses [8,9] and because bacterial cultures from the trachea are negative in many racehorses, especially those with less severe inflammation scores [6,10,11]. Furthermore, increases in environmental particulate exposure are associated with indices of airway inflammation in people [12,13].

Horses are exposed to environmental particulates from feed, bedding and footing within the stable and particulates from the external environment, such that exposure is a function of stable practices, stall location and season. In an earlier article, we described use of mapping techniques to evaluate the distribution of particulates throughout stables and showed that the concentration and number of particles varies with month, time of day, stable and location of stall within a stable [14].

Using the aforementioned data, we then explored the relationship between concentrations and numbers of particles measured in stalls and the presence of tracheal mucus accumulations and inflammatory cells in the tracheal wash. We hypothesised that the odds of visible accumulations of tracheal mucus and inflammatory cells in tracheal aspirates would be increased in horses occupying stalls and stables with the highest particulate mass concentrations/numbers, and during the months in which these are increased. The few studies that have been conducted [7,15] relating air quality to airway inflammation in racehorses have measured particle concentrations at only a few locations within the aisle of the stable. By contrast, we made measurements 3 times daily in every stall of 3 stables so that we could make very specific associations between local air quality and the airway health of the horse. An underlying assumption of our investigation is that the presence of mucus is a response to recent particle load that is reflected by our measurements.

Materials and methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Conflicts of interest
  8. Sources of funding
  9. Acknowledgements
  10. Manufacturers' address
  11. References
  12. Supporting Information

The study was conducted in 2005 at a Thoroughbred racetrack in the Midwestern USA. Three trainers with differing management styles and using stables of differing design were chosen for inclusion in this study.

Experimental design

Data were collected for 3 consecutive days in July, September and November of the racing season. Timing of the visits was selected to be early in the racing season, at the height of the season when conditions were usually driest, and when the weather was cooler and damp and the new stable (Stable 1) had closed its outer shutters. The research team was divided into 2 groups as follows: one group (present only on Day 1) examined the horses, and the other (present for Days 1–3) measured particulates in the stables.

Endoscopic examination

Endoscopy began at 10.30 h and was generally completed by 15.00 h. For those horses that were exercised on the day of endoscopy, there were at least 90 min between the end of training and endoscopy. The name, age, sex, stable and stall location of each horse were recorded. Endoscopic examination was performed with the horse in its own stall. A member of the trainer's staff handled the horse; no chemical restraint was used. The nasopharynx, larynx and trachea were examined. The amount of mucus visible in the trachea was graded on a scale of 0–4, as follows: 0 = no visible mucus; 1 = singular small drops; 2 = multiple partly confluent drops; 3 = a ventral stream of mucus; and 4 = a ventral pool [16].

Tracheal lavage and cytology

After the accumulation of mucus was scored, 10 ml of phosphate-buffered saline was infused into the upper trachea through a sterile polyethylene catheter placed in the biopsy channel of the endoscope. The saline flowed down the trachea and formed a pool at the thoracic inlet. The endoscope was then advanced caudally until the saline pool was visible, and a sample was withdrawn through the catheter. After collection, samples were stored on ice until analysed (within 20 min). The outside of the endoscope was cleaned with povidone-iodine solution and distilled water between horses.

Measurements of total cell count and preparation of slides for differential cell counts were conducted at a temporary laboratory in one of the stables. The tracheal lavage samples were diluted 1:4 in sterile buffer. Extremely viscous samples were further diluted. Total cell count was measured with a haemocytometer and corrected for sample dilution. In addition, cytocentrifuge slides were prepared and fixed for differential cell counts at Michigan State University. Cytospin slides were stained with Wright Giemsa and examined at ×40 magnification. Two hundred leucocytes were counted to determine the percentage of neutrophils, macrophages, lymphocytes, eosinophils and mast cells. Absolute cell counts were calculated from the total and differential cell counts.

Measurement of particle concentration and numbers of particles

Measurements of the concentration (in milligrams per cubic metre of air) of particles ≤10 µm in diameter (particulate matter, PM10) and those ≤2.5 µm (PM2.5) and the number of particles ≥0.5, ≥0.7, ≥1.0, ≥2.0 and ≥5.0 µm have been described [14]. Measurements were made for 1 min in every stall 3 times daily, as follows: 1) early morning (am) during clean out, feeding and grooming (05.00–08.00 h); 2) mid-day (mid) when there was less activity (11.00–13.00 h); and 3) early evening (pm; 16.00–18.00 h), which coincided with feeding and racing. Maximum, minimum and average concentrations and numbers of particles during each 1 min period were recorded. The average (ave) concentration represents the arithmetic mean of all 1 s measurements made throughout the 1 min sampling period. The minimum (min) concentration represents the lowest 1 s measurement during the sampling period, which allows for an estimate of the lowest PM concentrations, and the maximum (max) concentration represents the highest concentration, estimating the highest concentrations present within the stall.

Stables

Stable 1 was recently constructed, with roll-up shutters along the entire length of east and west sides, high-vaulted ceilings, and open-fronted stalls that faced the outdoors across an aisle. Stable 2 was brick with a 3 m ceiling, closed stall fronts, and small, high windows that were kept closed. A single row of stalls was located along each of the outer walls of the stable and a double row (back to back) of stalls down the centre. Stable 3 was built like Stable 2, but had open windows during the warm months and was located away from roads.

Data analysis

Bivariate analysis: Horses were grouped into those with a mucus score of 0 (MS = 0) and those having any amount of visible mucus accumulation (MS ≥1). Pearson's chi-squared tests were performed to determine which factors (e.g. age, month, stable, particulate concentrations and numbers of particles) were associated with MS ≥1. Average, minimum and maximum PM10 and PM2.5 concentrations were grouped into categories, which represented concentrations across all times of day and months (e.g. PM10ave all), and by time of day across all months (e.g. PM10max am, mid or pm). Identical categories were developed for particle numbers in each of the 5 size channels (≥0.5, ≥0.7, ≥1.0, ≥2.0 and ≥5.0 µm). To facilitate analysis, PM concentrations and numbers in each measurement category were grouped into quartiles (Table S1); the lowest quartile was used as a reference. Spearman correlation coefficients were calculated to determine the association between particulate exposures and inflammatory cell counts. Fisher's exact test was used to determine the relationship between the presence of tracheal mucus (MS ≥1) in horses kept in stalls with PM ≥50th and ≥75th percentiles.

Multivariable analysis: Multivariable analyses were designed to assess the effect of confounding, and therefore all variables for which information was collected were used in the models. In each discrete model, only a single particulate variable (e.g. PM10ave all) at a time was used in conjunction with the other variables (i.e. age, sex and stable).

Generalised estimating equation (GEE) models were created that included the variables identified in bivariate analyses as significantly associated with MS ≥1. As the outcome variables were measured 3 times (July, September and November), with horses potentially being evaluated endoscopically up to 3 times, GEE analysis allowed for the determination of the effect of particulate exposures on the aforementioned dichotomous outcome variable (MS ≥1), while controlling for within-horse variability [17]. Analyses that considered stable and particulates together did not include month owing to colinearity of stable, month and particulate measurements.

Linear mixed models were used to determine the effect of particulate exposures on numbers of neutrophils, lymphocytes and macrophages (continuous variables). The linear mixed model procedure is designed to handle hierarchical data that are collected repeatedly on subjects (horses) over time in different stables. In addition, it is able to handle missing data and has the power to analyse a number of variance covariance structures while accounting for within-horse variability [18]. The restricted maximum likelihood method was applied, assuming the random effects and error vector were normally distributed. This was achieved by log-transforming the cell count data, which were then transformed into geometric means. Possible covariance structures were tested, with the best covariance structure chosen based on the Akaike information criterion, which was Toeplitz for all models.

All data analysis was conducted using SAS® software v. 9.2a. The level of significance was set at P<0.05.

Results

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Conflicts of interest
  8. Sources of funding
  9. Acknowledgements
  10. Manufacturers' address
  11. References
  12. Supporting Information

Study population

The population consisted of 107 horses from 3 stables. As horses came and went from the racing stables, not every horse was available for examination at each of our 3 visits. Consequently, 38, 31 and 31% of the horses were available for examination once, twice and 3 times, respectively, resulting in a total of 206 attempted examinations. Of these 206, endoscopy was not possible 18 times, resulting in a total of 188 mucus score/tracheal lavage observations (Table 1). Eleven tracheal lavage samples were of insufficient quality for analysis, leaving 177 samples for total and differential cell counts.

Table 1.  Summary of number of examinations performed per stable, and of the age, sex and mucus scores (MS) of horses, as well as the prevalence of MS ≥1
Stable Number of examinationsAge (years)SexMucus scoreMucus present (MS ≥1)
23≥4UnknownMF012≥3NumberPercentage
1 6713746154132925112748
2 844425123434120411476276
3 551618174381713251203774
Total206735075813571629127812667

Tracheal mucus

Of the 188 endoscopic observations, 67% (n = 126) showed MS ≥1 (Table 1) with highest overall prevalence in Stable 2 (Fig 1). Monthly prevalence was highest in September (77%); the next highest was in November (69%) and the lowest (54%) in July.

image

Figure 1. Particle concentrations and overall prevalence of mucus (percentage of horses with mucus score [MS]≥1; black circles) by stable. Grey and black bars indicate PM10ave all and PM2.5ave all (in milligrams per cubic metre), respectively. The ave all notation represents the ‘average’ particulate concentrations calculated from measurements across all times of day and months.

Download figure to PowerPoint

Bivariate analysis revealed that the following were significantly associated with MS ≥1: age, month, stable, PM10 and PM2.5 (ave all), PM10max all, PM10min all, PM10 and PM2.5 (ave am and max am) and PM10min am (Table S2). When data were broken down by concentrations in each stall, horses were significantly more likely to have MS ≥1 if they occupied stalls with PM10ave all≥75th percentile, or with PM10min all, PM2.5ave all or PM2.5min all≥50th percentile (Table S2). Numbers of particles ≥0.7all and ≥1.0all, as well as those counted in the morning and mid-day sampling periods, were associated with MS ≥1. During the late afternoon sampling period, however, MS ≥1 was significantly associated with the number of particles ≥0.7 but not with those ≥1.0 µm (Table S2).

Multivariable analysis using a model that included age, sex, sample month and stable, but not PM, demonstrated an increased odds for MS ≥1 in Stables 2 (odds ratio [OR] 3.93, 95th% confidence interval [CI] 1.46–10.60, P = 0.0074) and 3 (OR 4.27, 95th% CI 1.57–11.56, P = 0.004). In this model, there were no significant associations between age, sex or sample month and MS ≥1 (Table S3).

When PM concentrations were then included in analyses with age, sex and stable (see Table S4 for examples), the open-sided stable (Stable 1) always had a protective effect but, in addition, the following PM concentrations also were significantly associated with increased odds of MS ≥1: PM 10ave all, PM10max all, PM10min all, PM10min am, PM2.5ave am and PM2.5max am (Fig 2). Similar analyses using particle numbers revealed that particles with diameters ≥0.7 and ≥1.0 µm (all, am and mid) were significantly associated with increased odds of MS ≥1, and Stable 1 was protective (Fig 2). Analyses including only those variables that were significant in the ‘larger’ models (stable and PM) gave the same result (see Table S4 for examples).

image

Figure 2. Particulate measures that significantly increased the odds of visible accumulations of tracheal mucus. Data are odds ratios with upper and lower confidence intervals. Particulate matter (PM) ≤ 10 μm in diameter (PM10), PM ≤ 2.5 μm in diameter (PM2.5) and particle numbers are shown in the upper, middle and lower panels, respectively. All = all times of day and months; ave = average concentrations; mid = mid-day; max = maximum; min = minimum.

Download figure to PowerPoint

Inflammatory cells

Sixty-eight percent of tracheal wash samples had 20% or more neutrophils (Table 2).

Table 2.  Number and percentage of tracheal wash samples containing greater than 20% neutrophils at each mucus score
Mucus scoreNumber of samplesNumber (%) of samples with >20% neutrophils
05739 (68)
18959 (66)
22418 (75)
364 (67)
411 (100)

In bivariate analyses, neutrophil numbers were significantly and positively correlated with PM2.5ave (all and am), PM2.5max pm and several measures of particle numbers ≥0.7 µm (Table S2). Correlation coefficients between PM and neutrophil numbers were low, however. Mixed models were not run because no other variables were significantly associated with neutrophils. In the case of lymphocytes, a mixed model including age, stable and PM2.5max mid (significant by bivariate analysis) revealed that none of these was related to lymphocyte numbers. In a similar model with numbers of macrophages as the outcome variable, age (3 years old), but no other variable, was significantly associated with increased numbers of macrophages (data not shown).

There was no association between inflammatory cell counts and MS ≥1 (bivariate analysis). The application of GEE models using MS ≥1 as an outcome variable, with inflammatory cell count data grouped in quartiles as predictor variables, confirmed this result (data not shown).

Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Conflicts of interest
  8. Sources of funding
  9. Acknowledgements
  10. Manufacturers' address
  11. References
  12. Supporting Information

This investigation is the first to examine the association between visible tracheal mucus and the local environmental particulate concentrations in the stalls in which racehorses are housed. Previous studies were limited by measurement of particulate concentrations only at a few locations within the stable [7,15]. By contrast, we used mapping techniques to measure the concentration (in milligrams per cubic metre of air) of particles ≤10 µm in diameter (PM10) and those ≤2.5 µm (PM2.5), as well as the number of particles ≥0.5, ≥0.7, ≥1.0, ≥2.0 and ≥5.0 µm [14]. Our real-time monitors recorded the single highest (max) and lowest (min) as well as average (ave) PM10 and PM2.5 over the measurement period. These measures were used because it is presently unknown whether chronic low-level exposures, average exposures or peak exposures are critical for the development of visible tracheal mucus in horses. Real-time monitors recording particle numbers allowed us to discern whether particles of a specific size were important.

Previously, we reported that stable, stall, time of day and month were significantly associated with PM10 and PM2.5, and numbers of particles [14]. As a result, we hypothesised that if exposures to airborne particulates were responsible for increased mucus and for inflammation, the prevalence of these would be highest in the stable and stalls that consistently had the highest concentrations and numbers of PM (Stable 2), greatest during the month of highest PM concentrations and numbers (September) and lowest during the month of lowest concentrations and numbers (July). In support of this hypothesis, our data demonstrated significant associations between the presence of visible accumulations of tracheal mucus (MS ≥1) and stable, stalls, month (bivariate analysis), particulate concentrations and particulate numbers. We selected MS ≥1 for several reasons. Based on previous work by our group at the same racetrack [1], we anticipated that the lack of horses with high mucus scores would result in insufficient statistical power to evaluate the association between local air quality and degrees of mucus accumulation. Furthermore, MS ≥1 is likely to include horses with mild tracheal irritation, as well as those developing or recovering from more severe disease.

Horses stabled in stalls with overall PM10ave≥75th percentile were at significantly increased odds of MS ≥1. In addition, horses stabled in stalls with particle concentrations ≥50th percentile of PM2.5ave all and both PM10 and PM2.5min all concentrations also had significantly increased odds of MS ≥1. This suggests that it is not only the exposure to high particle concentrations, but also the period of time during which horses are exposed to the lowest concentration of particles (i.e. the minimum exposure) that is important in determining the presence of visible tracheal mucus. In other words, the greatest risk for visible mucus occurs when horses inhabit stalls not only where concentrations of particles reach high levels but also where the concentrations do not descend to low levels during the day. The location of such stalls in the stables used in this study is shown in Figure 3. Generally, they are stalls in the centre of the stable or near areas of high traffic, such as doorways or the trainer's office, or adjacent to busy roads.

image

Figure 3. The location of stalls occupied by horses with visible accumulations of tracheal mucus. Stalls in red and orange represent ≥75th and ≥50th percentiles, respectively, of concentrations of PM10ave all and PM2.5ave all, that is, those associated with a mucus score ≥1. Stalls shaded in grey were not used because they were occupied by another trainer. Green-shaded stalls were the trainer's office. Blue arrows show doorways. Please note that only the stalls occupied by our trainers (approximately half of Stables 2 and 3) are included in the figures. The ave all notation represents the ‘average’ particulate concentrations calculated from measurements across all times of day and months. N = north.

Download figure to PowerPoint

There was a highly significant association between stable and MS ≥1, with the 2 older-style enclosed brick buildings increasing the odds of MS ≥1 almost 4-fold compared with the open-sided stable. When PM concentrations were included in analyses with stable, these results were confirmed, because the odds of MS ≥1 were significantly decreased in the open-sided stable. However, there was also a significant effect of PM. The data on PM mass concentration showed that increased odds of MS ≥1 were significantly associated with average, maximum and minimum concentrations of PM10 averaged throughout the day, the minimum morning PM10, and the average and maximum PM2.5 in the morning. With regard to particle numbers, it was exposure to particles in the ≥0.7 and ≥1.0 but not ≥2.0 µm diameter range that was associated with mucus. In other words, mucus is associated with particles between 0.7 and 2.0 µm diameter (Fig 2).

Our observations may be explained as follows. Increased odds of visible tracheal mucus associated with stable and particle concentration/numbers are probably due to insufficient ventilation within the enclosed brick stables (Stables 2 and 3), resulting in continual exposures to PM, whereas ventilation in the open-sided stable (Stable 1) allowed for more rapid settling. This conclusion must be tempered, however, by the fact that a different trainer occupied each stable. Each of these trainers probably has work practices that influence the generation and suspension of PM (e.g. raking, cleanout and feeding) but also other practices, such as training methods or use of medications, that could influence the prevalence of mucus. The association of particles in the 0.7–2.0 µm diameter range with MS ≥1 is interesting because those sizes of particles are consistent with contaminants arising from within the stable and from adjacent roads rather than from combustion sources, which tend to generate the smaller diameter particles. Therefore, modification of stable management practices, including improvement of ventilation of the stable, should reduce the prevalence of visible tracheal mucus.

Others investigating factors affecting the presence of airway inflammation in racehorses have found an association between age and the prevalence of airway inflammation or visible tracheal mucus in racehorses [4–6,11]. Although age was a significant factor affecting MS prevalence in our bivariate analysis, it did not remain significant in the multivariable models. There is also a reported association between length of time in training and airway inflammation [19], with those horses that have been in training longer having a lower prevalence. As our study lasted only 5 months, with entry and exit of horses during this period, we were unable to address the question of length of time in training adequately. Nevertheless, if young horses recently brought into a stable environment are more susceptible to respiratory infection, and over time become more resistant [5], then the highest prevalence of MS ≥1 should occur during our initial sampling period (July) and decline throughout the rest of the year. This was not the case, because the highest prevalence of MS ≥1 occurred in September, the month with the highest PM measured in the stables.

While our investigation has identified associations between PM and visible accumulations of mucus (MS ≥1), sample size was insufficient to investigate the relationship between PM and degrees of mucus accumulation (MS = 2, MS = 3, etc.). It is important to note that reduced racing performance is associated with MS ≥2 [1], which was only observed in 35 of the 188 examinations of the present study. Data from a larger study are needed to clarify the relationship between PM and degree of accumulation of tracheal mucus in order to suggest particulate exposure limits likely to reduce the occurrence of MS ≥2. Nevertheless, our data indicate that it is important to minimise inhaled particulate concentrations throughout the day and not only during morning activities. Our data further corroborate that one way to keep particle concentrations lower throughout the day is to have a well-ventilated stable.

Particulate concentrations were not only associated with MS ≥1 but also with numbers of neutrophils (Tables 2 and S2). However, while both PM10 and PM2.5 were associated with MS ≥1, it was only the smaller particles (PM2.5) that were associated with neutrophils. This suggests that the excess mucus that constitutes MS ≥1 might have 2 origins: neutrophilic inflammation initiated by PM2.5 [20,21] and irritation resulting from exposure to the coarser particles that are also included in PM10. This may explain why, despite the fact that neutrophilic inflammation is an important cause of production and secretion of mucus, there was no association between numbers of neutrophils and MS ≥1.

The results of the present investigation clearly demonstrate that visible accumulations of tracheal mucus and neutrophil numbers in tracheal wash are associated with the concentration of airborne PM. Furthermore, our earlier study [14] demonstrated that these particulate concentrations vary with stable design, stall location and weather. Although our technology for measuring air quality is new, the conclusion is not. The stable environment has been implicated as important in the respiratory health of horses since at least the early 18th century [22]. Practically speaking, particulate (dust) control can be simply and readily implemented by motivated stable owners/trainers. It is not necessary to control specific particle sizes, because practices aimed at dust control will influence all sizes of particles, and optimising ventilation will improve air quality regardless of stable design. Although further research could focus on determining the simplest or most cost-effective method of dust control, we already know that dampening aisles before sweeping, removing horses from stalls during cleanout and opening doors and windows to improve ventilation are easy methods to control particle exposure and potentially very beneficial to airway health and athletic performance. We suggest that veterinarians place more emphasis on the importance of stable air quality in respiratory system health when working with their clients.

Acknowledgements

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Conflicts of interest
  8. Sources of funding
  9. Acknowledgements
  10. Manufacturers' address
  11. References
  12. Supporting Information

The investigators thank the management of Thistledown racetrack for their permission to conduct the investigation, the trainers for letting us use their horses and facilities, and the Grayson-Jockey Club Research Foundation for their generous grant support (‘Environmental particulates and airway mucus in racehorses, 2005’). We gratefully acknowledge Heather de Feijter-Rupp, Cathy Berney and Sue Eberhart for their assistance in sample collection, preparation and analysis.

References

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Conflicts of interest
  8. Sources of funding
  9. Acknowledgements
  10. Manufacturers' address
  11. References
  12. Supporting Information
  • 1
    Holcombe, S.J., Robinson, N.E., Derksen, F.J., Bertold, B., Genovese, R., Miller, R., de Feijter-Rupp, H., Carr, E.A., Eberhart, S.W.W., Boruta, D. and Kaneene, J.B. (2006) Effect of tracheal mucus and tracheal cytology on racing performance in Thoroughbred racehorses. Equine Vet. J. 38, 300-304.
  • 2
    MacNamara, B., Bauer, S. and Lafe, J. (1990) Endoscopic evaluation of exercise-induced pulmonary hemorrhage and chronic obstructive pulmonary disease in association with poor performance in racing Standardbreds. J. Am. Vet. Med. Ass. 196, 443-445.
  • 3
    Newton, J.R., Wood, J.L. and Chanter, N. (2003) A case control study of factors and infections associated with clinically apparent respiratory disease in UK Thoroughbred racehorses. Prev. Vet. Med. 60, 107-132.
  • 4
    Wood, J.L., Burrell, M.H., Roberts, C.A., Chanter, N. and Shaw, Y. (1993) Streptococci and Pasteurella spp. associated with disease of the equine lower respiratory tract. Equine Vet. J. 25, 314-318.
  • 5
    Wood, J.L., Newton, J.R., Chanter, N. and Mumford, J.A. (2005) Inflammatory airway disease, nasal discharge and respiratory infections in young British racehorses. Equine Vet. J. 37, 236-242.
  • 6
    Wood, J.L., Newton, J.R., Chanter, N. and Mumford, J.A. (2005) Association between respiratory disease and bacterial and viral infections in British racehorses. J. Clin. Microbiol. 43, 120-126.
  • 7
    Clarke, A.F., Madelin, T. and Altpress, R.G. (1987) The relationship of air hygiene in stables to lower airway disease and pharyngeal lymphoid hyperplasia in 2 groups of Thoroughbred horses. Equine Vet. J. 19, 524-530.
  • 8
    Tremblay, G.M., Ferland, C., Lapointe, J.-M., Vrins, J.P. and Cormier, Y. (1993) Effect of stabling on bronchoalveolar cells obtained from normal and COPD horses. Equine Vet. J. 25, 194-197.
  • 9
    Holcombe, S.J., Jackson, C., Gerber, V., Jefcoat, A., Berney, C., Eberhardt, S. and Robinson, N.E. (2001) Stabling is associated with airway inflammation in young Arabian horses. Equine Vet. J. 33, 244-249.
  • 10
    Chapman, P.S., Green, C., Main, J.P., Taylor, P.M., Cunningham, F.M., Cook, A.J. and Marr, C.M. (2000) Retrospective study of the relationships between age, inflammation and the isolation of bacteria from the lower respiratory tract of thoroughbred horses. Vet. Rec. 146, 91-95.
  • 11
    Christley, R.M., Hodgson, D.R., Rose, R.J., Hodgson, J.L., Wood, J.L. and Reid, S.W. (2001) Coughing in thoroughbred racehorses: risk factors and tracheal endoscopic and cytological findings. Vet. Rec. 148, 99-104.
  • 12
    Pope, C.A. 3rd (2000) What do epidemiologic findings tell us about health effects of environmental aerosols? J. Aerosol Med. 13, 335-354.
  • 13
    Schwartz, J. (2004) Air pollution and children's health. Pediatrics 113, 1037-1043.
  • 14
    Millerick-May, M.L., Karmaus, W., Derksen, F.J., Berthold, B., Holcombe, S.J. and Robinson, N.E. (2011) Particle mapping in stables at an American Thoroughbred racetrack. Equine Vet. J. 43, 599-607.
  • 15
    Riihimaki, M., Raine, A., Elfman, L. and Pringle, J. (2008) Markers of respiratory inflammation in horses in relation to seasonal changes in air quality in a conventional racing stable. Can. J. Vet. Res. 72, 432-439.
  • 16
    Gerber, V., Straub, R., Marti, E., Hauptman, J., Herholz, C., King, M., Imhof, A., Tahon, L. and Robinson, N.E. (2004) Endoscopic scoring of mucus quantity and quality: observer and horse variance and relationship to inflammation, mucus viscoelasticity and volume. Equine Vet. J. 36, 576-582.
  • 17
    Zeger, S.L. and Liang, K.Y. (1986) Longitudinal data analysis for discrete and continuous outcomes. Biometrics 42, 121-130.
  • 18
    Little, R.C., Milliken, G., Stroup, W. and Wolfinger, R. (1996) SAS System for Mixed Models, SAS Institute Inc., Cary, North Carolina, USA.
  • 19
    Cardwell, J.M., Wood, J.L., Smith, K.C. and Newton, J.R. (2011) Descriptive results from a longitudinal study of airway inflammation in British National Hunt racehorses. Equine Vet. J. 43, 750-755.
  • 20
    Rogers, D.F. (2007) Physiology of airway mucus secretion and pathophysiology of hypersecretion. Respir. Care 52, 1134-1146.
  • 21
    Rogers, D.F. (2003) The airway goblet cell. Int. J. Biochem. Cell Biol. 35, 1-6.
  • 22
    Markham, G. and Markham, G. (1723) Markham's Master-Piece: Containing All Knowledge Belonging to the Smith, Farrier, Or Horse Leach, Touching the Curing All Diseases in Horses  . . . : Divided into Two Books, Printed for G. Conyers . . . W, Wooton . . . and J. Clark, London.

Supporting Information

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Conflicts of interest
  8. Sources of funding
  9. Acknowledgements
  10. Manufacturers' address
  11. References
  12. Supporting Information
FilenameFormatSizeDescription
evj568_sm_TableS1-S4.pdf125KSupporting info item

Please note: Wiley Blackwell is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.