General Principles

Monitoring for allergens can help determine the cause of one or more cases of allergic disease (in which instance, sampling and analysis modes that yield information on the range of possible organisms must be chosen) or characterize environments with respect to specific allergens. Measurements can be semiquantitative (e.g., ''presence or absence" or "low, medium, or high"); or, alternatively, modes can be chosen for sampling and analysis that will give accurate, precise documentation for a specific population regarding a more or less well-defined allergen.

Although monoclonal antibody immunoassays measure specific allergens, most allergen monitoring includes sampling for indicators rather than the actual aerosolized allergen. Table 6-1 is a list of aeroallergens and commonly used indicators. In developing a sampling strategy to collect the data for testing a particular hypothesis, important decisions must be made on where, when, and how to sample.

TABLE 6-1

Aeroallergens and Sampling/Analysis Modes.

Types of Sample Analysis

Once an aerosol or a reservoir sample has been collected, it must be analyzed before meaningful data are produced. Assay types used for analyzing allergens include culture, microscopy, immunoassays, biochemical assays, and bioassays. Of these, the most commonly used are culture, microscopy, and immunoassay.

Cultural assays evaluate viable units as indicators of the presence of allergen-carrying particles. The content of the allergen itself may or may not be accurately represented. Cultural assays always quantitatively underestimate levels of allergen-carrying microbes (because only culturable cells are counted) and are biased with respect to the kinds of microbial allergen sources recovered. No single culture medium or set of environmental conditions will allow capture of all the different organisms in a mixed aerosol; however, investigators usually choose culture media that allow recovery of the broadest range of different organisms. Special-purpose media are likely to underestimate even the organisms they are designed to recover. Moreover, some organisms in air or in relatively hostile reservoirs have adapted to minimal conditions and will not grow if rich culture media are provided. Although minimal media are often used to recover bacteria from water samples, they have not been routinely used for fungi. Many of the species of Penicillium and Aspergillus that are common in indoor air grow well in media containing large amounts of salt, sugar, or glycerol (Verhoeff et al., 1990a,b). There are also media that inhibit fast-growing fungi such as Rhizopus or Trichoderma, which can quickly multiply and mask other organisms (Burge et al., 1977; Verhoeff et al., 1990a,b).

Because researchers have yet to discover a generic biomarker for fungal growth and because it appears that fungi differ allergenically at the species level, each fungus that releases allergens into the environment must be identified accurately and precisely so that specific diagnostic and treatment materials can be produced. For some kinds of fungi, generic identification can be completed on the isolation medium. For others (including most fungal species) and for all bacteria and yeasts, subculturing on diagnostic media is required. In addition, for bacteria and some fungi, physiological tests are necessary for species identification.

For particles that are morphologically distinct, direct microscopy is a straightforward approach to studying aerosols. Although very few fungi or pollens are identifiable to the species level microscopically, broader categories can be identified and useful information obtained. Categories of fungus spores that can be identified and counted range from spores of Epicoccum nigrum (a monospecific genus with very distinctive spores) to categories such as "colorless basidiospores," which include hundreds of different kinds of fungi. If a cultural sampler is used in conjunction with a spore trap, spore morphology comparisons can be made and the kinds of identifiable spores expanded. Although pollen is usually stained for microscopic examination, fungus spores often do not take up stains well, and important color characteristics can be masked.

Sample methods that allow microscopic evaluation include suction impaction (as used in the Burkard and Lanzoni spore traps) and rotating impactors (the rotorod). Filter samples can also be analyzed microscopically, although the clearing necessary to produce optically good samples requires the use of organic oils and solvents, which tend to cause fungus spores to collapse. Relatively smooth surfaced filters (e.g., polycarbonate, teflon) can be mounted in 1% acid fuchsin in lactic acid for counting. Filter samples can also be analyzed using fluorescent stains and epifluorescence microscopy. Water samples are commonly analyzed for bacterial content using the fluorescent dye acridine orange, which stains nucleic acids inside the bacterial cells (Palmgren et al., 1986). The melanin pigments in many fungus spores mask this fluorescence, and some fungal spore walls apparently prevent entry of the dye. However, the method is promising for indoor aerosols of bacteria (including actinomycetes) and colorless fungus spores.

Scanning electron microscopy has also been used to analyze filter samples, and bacteria and fungus spores can be readily seen and counted on smooth-surfaced filters (Pasanen et al., 1989). The level of identification is not as high as for light microscopy, however, and preparation methods are cumbersome for routine counts.

Immunoassays measure the actual allergen rather than an indicator (e.g., viable spores) and are essential for most amorphous allergens including those from mites, cats, and cockroaches. The most sensitive and specific environmental immunoassays for allergens are based on immobilized monoclonal antibodies that bind specific allergen in unknown samples.

Immunodiffusion is a relatively crude immunoassay that is used as a diagnostic test to detect allergen-specific immunoglobulin G (IgG) antibodies in patient sera. It has also been used to demonstrate exposure under the theory that specific IgG may represent exposure to allergens rather than actual disease. Extracts of air or reservoir samples have been used in place of defined allergens to indicate such exposures (Reed and Swanson, 1987).

Finally, measuring the amount of guanine in a sample (a biochemical assay) has been used to estimate the amount of mite allergen in dust samples. Other types of sample analyses have not been applied to allergens.

The Ideal Air Monitor

No single method is sufficient to detect and monitor all of the different allergens that can be present in indoor environments (Samson, 1985). Currently, the most adaptable collection devices are probably the filter collectors because filter samples can be examined directly by using either visible light, epifluorescence, or electron microscopy. In addition, they can be washed to provide allergens for immunoassay or viable cells for culture. For some allergens, methods of analyzing filter samples have been well defined. For others, especially viable cells, much work remains to be done to define the conditions under which recoveries are optimal. The advantages of filters (especially personal samplers) are many. They fulfill most of the characteristics of the ideal sampler:

• They are efficient for collection of small particles.
• They can be efficiently analyzed using several modes.
• They can be used for both personal and ambient sampling.
• They are inexpensive and portable.

Designing a sampling strategy requires consideration of the nature of the allergen source, the nature (including the size and expected concentrations) of the allergen-containing particles, and parameters that influence respiratory exposure to the allergen-containing particles. Each of these parameters influences the choice of sampling method (i.e., observation or reservoir or air sampling) and analytical approach, the sampling plan (amounts of sample to be collected, times and locations to be sampled), and approaches for analysis and interpretation of the data.

Ambient Data and Time-Activity Logs

One of the ways in which ambient data can be used to estimate personal exposure is to use questionnaires or time-activity logs to catalogue the amount of time people spend in the various areas for which concentration data are available. Examples of the use of area monitoring combined with activity logs have been reported in several studies (Lebowitz et al., 1989; O'Rourke et al., 1989, 1990; Quackenboss et al., 1989a,b, 1991a,b). In these studies, the contribution of indoor concentrations to total personal exposure exceeded that of outdoor concentrations because of the difference in time spent in the two environments. For example, indoor exposure to total pollen, Gramineae, total mold, Cladosporium, and Aspergillus/Penicillium-type spores was two to four times greater than outdoor exposure.

Questionnaires may also be used to determine occupancy rates or activity in the indoor environment under study (Lebowitz et al., 1989; Quinlan et al., 1989). Results from the questionnaires or logs can then be linked with the results of observations to develop categorical estimates of exposure potential in areas for which no concentration information is available.

Indicator Data to Represent Exposure

An example of the use of indicator data to represent exposure to an allergen is the use of counts of whole house dust mites in dust samples. The presence and quantity of mites in a sample viewed through the microscope indicates only the potential for allergen exposure, and is not a direct measure of exposure itself. House dust mite allergen is associated with fecal particles rather than with the mite itself. Even the counting of fecal particles must be considered a surrogate measure since the concentration of allergen on any one particle cannot be predicted. An accurate measure of exposure to dust mite allergen, as well as other allergens, requires the use of specific immunoassays.

Nature of Allergen-Carrying Particles

Little or no Der p I has been detected in the air in undisturbed rooms (de Blay et al., 1991b; Platts-Mills et al., 1986; Swanson et al., 1989; Yasueda et al., 1989), and after a disturbance in a room, concentrations of airborne mite allergen fall rapidly. It is likely, therefore, that the majority of allergen is carried on particles with large aerodynamic diameter (e.g., greater than or equal to 10 μm). The levels that become airborne depend critically on the form of the disturbance and vary from 5 to 200 ng of Der p I/m³. Because the mean allergen content of mite fecal material and the size of the particles are known, it is possible to estimate the actual amounts of allergen that could enter the lung at a given ambient concentration. For particles of 10-μm aerodynamic diameter, only 5–10 percent would be expected to enter the lung during gentle mouth breathing (Svartengren et al., 1987; Task Group on Lung Dynamics, 1966). Given that each particle contains 0.2 ng of Der p I (Tovey et al., 1981b), an airborne level of 20 ng/m³ means that about 100 fecal particles could be expected to enter the mouth per hour and that from 5 to 10 of these would reach the lung. Use of this technique requires the collection of sufficient mass to measure concentration reliably, a difficult task for aerosols as transient as those of mite fecal particles.

Inhaled allergens from other sources can also differ from generally measured arthropods, particles, grains or spores. For instance, far more allergen is present on amorphous pollen (grain fragments) than assumed from whole grain measurements (Schumacher et al., 1988).

Biologically Effective Dose

Once contact has occurred, the kinetics of uptake for exposure measures, distribution to the target tissue, and clearance/detoxification must be calculated to estimate a biologically effective dose. These data are not currently available for aeroallergens.

Step 1: Hazard Identification

Important aspects of hazard identification include a complete description of the aeroallergen and its potential to affect human health adversely at some level of exposure or dose, an understanding of the number of people who are potentially exposed, the background levels of exposure in the environment, the nature of the health effect of interest, and the level of exposure thought to be associated with an increased risk of the health effect. Other exposures that potentially contribute to the development of the health outcome should also be identified.

Step 2: Exposure Assessment

The measurement of exposure to allergens is a rapidly advancing field. Instrumentation and approaches were described earlier in this chapter; this discussion summarizes some of the issues that arise in using aeroallergen exposure data for a risk assessment.

Generally, hazard identification (step 1 above) comprises the review of a number of studies from the literature. Often in those reports, more than one sampling protocol will be described, and different instrumentation or interview methods are likely to have been used. It may be difficult to synthesize data presented in different formats (e.g., range only, graphically only, mean values only). In addition, the material collected for environmental samples may be analyzed for different components. For example in the literature on dust mites, several different exposure metrics are reported, including mite counts, immunochemical assays of allergen, and guanine determinations (Platts-Mills and de Weck, 1989). Within this framework, individual assays may differ among laboratories (Lau et al., 1989; Sporik et al., 1990).

An understanding of how the exposure and thus the health effect of interest may be influenced by other factors is essential in interpreting the data. For example, seasonal variation strongly influences exposure to some allergens and resultant reporting of symptoms (O'Rourke, 1992; O'Rourke et al., 1989; Platts-Mills et al., 1987). Characteristics of the exposure site, including type of flooring, kind of ventilation system, and other such features, may affect the nature of exposures as well. The possible role of altitude in allergen exposure has also been investigated (D. Charpin et al., 1991).

Available exposure data must be summarized in spite of all of these issues and problems. In cases in which differences in technique or analysis are thought to provide incomparable results, appropriate conversion factors are often estimated.

Step 3: Dose-Response Assessment

The information developed in steps 1 and 2 above is used to estimate the dose from the exposure, and to model the dose-response relationship. The exposure-response relationship may be estimated when too little information is available to allow extrapolation from exposure to dose. For noncarcinogens, this effort has traditionally focused on identification of a ''threshold exposure" below which no health effects are observed (Pierson et al., 1991); this threshold is now known as the No Observed Adverse Effect Level (NOAEL; Shoaf, 1991).

The value of the NOAEL, as determined from modeling, depends on the quality of the data and the model selected. Examples of various models include linear, probability (Rose and Gerba, 1991), and exponential and beta-Poisson forms (Regli et al., 1991). Shoaf (1991) reviews the statistical considerations in their use.

Once the NOAEL has been determined, it is divided by an uncertainty factor (UF). Traditionally, a factor of 10 has been used to account for variability in responses among a human population. When a Lowest Observed Adverse Effect Level (LOAEL) is determined, a UF of 10 is applied. Additional uncertainty in the data is accounted for by use of a modifying factor, varying from 1 to 10, to further reduce the NOAEL (Shoaf, 1991).

Step 4: Risk Characterization

The relationship between dose (or exposure) and response is used to characterize the risk within a population. For aeroallergens, this relationship might be expressed as the number of new cases expected at a given exposure level or the distribution of expected change in the severity of symptoms with changing exposure levels.

Step 1: Hazard Identification

When dust mites were first reported in the literature in the 1970s, it was suggested that levels of more than 500 mites/g of dust in a house were likely to produce symptoms of asthma in allergic individuals. During the 1980s, data accumulated demonstrating a dose-response relationship between reservoir concentrations of dust mite allergens at home and both sensitization and asthma (Table 6-2). Results of this kind led to the proposal of threshold levels for reservoir concentrations. Two separate thresholds have been considered: more than 2 µg of Der p I/g of dust has been associated with increased prevalence of sensitization, whereas 10 µg increases the risk of symptomatic or acute asthma. In an 11-year prospective study of 68 children, Sporik and colleagues (1990) found that exposure¹ to high levels of allergen at age 1 increased the risk of asthma and was inversely related to the age of onset of asthma in atopic children. In subsequent studies of children hospitalized in the south of England, it was found that about 80 percent were both sensitized and exposed to high levels of relevant allergen at home (Sporik et al., in press). These results suggest that in areas in which all houses contain high levels of mite allergen, sensitivity to mites is a major risk factor, not only for wheezing but for hospitalization of children with asthma.

TABLE 6-2

Evidence for Regarding Specific Levels of Dust Mite Exposure as Risk Factors for Asthma.

Evidence to support a quantitative relationship between exposure to mites and asthma has also been found in studies from Denmark, Australia, Germany, and France (Table 6-2). Two studies in particular strongly imply that high levels of domestic exposure can increase the prevalence of asthma.

In Papua New Guinea, the disease appeared in a group of highland villages and increased to a prevalence of about 7 percent; the previous rate had been less than or equal to 0.5 percent. This increase coincided with the introduction of blankets that the men wrapped around their heads at night. The men with asthma all tested positive on skin tests for dust mites, and dust from the blankets contained an average of 1,140 mites/g of dust (standard deviation = 868; Dowse et al., 1985).

D. Charpin and others (1991) compared children growing up in a suburb of Marseilles, which is a seaport, with those growing up in Briançon at an elevation of 6,000 feet. The average concentration of mite allergen in mattress dust from Marseilles was 15.8 µg/g, compared with 0.36 µg/g in Briançon. In parallel, the prevalence of skin sensitivity to dust mites in Briançon was 4.1 percent compared with 16.7 percent in Marseilles. Finally, the prevalence of asthma in the mountains was lower (4 percent) than that at sea level (6.7 percent).

Because there is a general misconception that levels of allergen will (or should) be higher in the houses of patients with allergic disease, it is important to understand the actual situation. Within a defined area, most houses have similar levels of allergen. For example, on the Gulf Coast, in São Paulo, Brazil, or in southern England, "all" houses have high levels of dust mite allergen; similarly, in inner-city Atlanta, Georgia, or Wilmington, Delaware, most houses have high levels of cockroach allergen. In suburban areas, about 50 percent of all houses have a cat and consequent high levels of cat allergen. In each of these areas, it is the individuals who develop IgE antibodies and have continued exposure to the relevant allergen who are at risk of developing allergy and asthma.

Two hazards exist: sensitization and allergic disease. From the data, it appears that the exposure initiating each outcome may be different. For the purposes of this example, sensitization was chosen as the endpoint.

Step 2: Exposure Assessment

Because the common allergens are thought to cause or exacerbate asthma by the inhalation route, measuring inhaled allergen might seem to be the best method for determining exposure (Price et al., 1990; Swanson et al., 1985; Tovey et al., 1981b). However, there are two reasons for using values from reservoir samples instead in this example. First, the majority of exposure data available from the literature are based on measurements of allergen in dust collected from carpets, mattresses, sofas, and other such items. Second, the quantities that become airborne apparently are quite small, commonly 5–50 ng/m³, and appear to depend on the level of physical activity in the home during the time of sampling.

This example uses data from four studies that contain both exposure information and data on the prevalence or incidence of sensitization (D. Charpin et al., 1991; Lau et al., 1989; Price et al., 1990; Sporik et al., 1990). The underlying assumption of this risk assessment is that the cumulative exposure (i.e., duration multiplied by intensity) is proportional to the sensitization concentration; this approach differs from that of previous reports where the exposure value at the time of the survey was related to the health outcome. In using the literature data presented in Table 6-3, three operational guidelines were developed a priori:

TABLE 6-3

Data for Dust Mite Risk Assessment: Example.

1.

The exposure concentration is expressed as micrograms of Der p I per gram of collected dust. In instances in which the range is reported, the midpoint is used as the average; if the range is not given, the maximum value is estimated from the graphical display of the data and the midpoint is estimated.

2.

Exposure that is reported as the total of allergens Der p I and Der f I is assumed to consist of 50 percent Der p I.

3.

Time is expressed in years. For a population that is surveyed cross-sectionally, exposure is taken to be equal to age; the average years of exposure thus are equal to the average age. For populations that are surveyed prospectively, the age at sensitization is equal to the years of exposure; if an age is not given, years of exposure are assumed to equal the average follow-up period.

Step 3: Dose-Response Assessment

Exposure is estimated in this example from several different studies that use slightly different protocols for reporting the results from reservoir sampling. For example, Sporik and colleagues (1990) report the highest concentrations from samples in different parts of the house. Price and coworkers (1990) report the value from a single, composite sample collected from several locations in the house.

Appropriate models to relate exposure information to dose are not known for these data sets, and two assumptions were made:

• log normal distribution of the exposure variable, and
• a linear relationship in exposure-response.

The base-10 logarithm of the estimated cumulative exposure was used in the analysis.

Figure 6-3 shows the results of the committee's analysis. The data fit an equation given by:

FIGURE 6-3

An exposure-response analysis of dust mite allergens.

This can be simplified (by rounding off) to:

Using the linear assumption, neither a NOAEL nor a LOAEL is identified, because the line intercepts the y-axis at a positive value (approximately 10%). This indicates the lack of a threshold concentration below which sensitization would not occur, i.e., it is not possible to achieve zero risk of sensitation. The R² value is 0.6, indicating that 60 percent of the variability in linear trend is explained by the model.

Step 4: Risk Characterization

The equation developed from the exposure-response model can be rewritten as

Once the risk (percent sensitized) is set, the relationship can be solved for concentration in the reservoir, since the value of years will be known for a given population. Alternatively, if the duration and concentration are known, the risk (percent sensitized) can be estimated. For example, assuming 5 years of exposure at a concentration of 2 µg/g, the risk of sensitization would be 32 percent.

These calculations are based on data that were abstracted from reports by researchers using various methods, and must therefore be considered "soft"; the results are presented only as an example of how a risk assessment for sensitization could be performed.