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. Author manuscript; available in PMC: 2015 Sep 21.
Published in final edited form as: Am J Ind Med. 2012 Jun 5;55(12):1110–1121. doi: 10.1002/ajim.22081

Characterisation of allergens and airborne fungi in low and middle-income homes of primary school children in Durban, South Africa

Nkosana Jafta 1,*, Stuart A Batterman 2, Nceba Gqaleni 1, Rajen N Naidoo 1, Thomas G Robins 2
PMCID: PMC4577241  NIHMSID: NIHMS390785  PMID: 22674665

Abstract

The South Durban Health Study is a population-based study that examined the relationship between exposure to ambient air pollutants and respiratory disease among school children with high prevalence of asthma who resided in two purposely-selected communities in north and south Durban, KwaZulu-Natal, South Africa. From these participants, a subgroup of 135 families was selected for investigation of household characteristics potentially related to respiratory health. In these households, a walkthrough investigation was conducted, and settled dust and air samples were collected for allergen and fungal measurements using standardised techniques. Asp f1 allergen was detected in all homes, and Bla g1 allergen was detected in half of the homes. House dust allergens, Der f1 and Der p1 exceeded concentrations associated with risk of sensitization and exacerbation of asthma in 3 and 13%, respectively, of the sampled homes, while Bla g1 exceeded guidance values in 13% of the homes. Although airborne fungal concentrations in sleep areas and indoors were lower than outdoor concentrations, they exceeded 1000 CFU/m3 in 29% of the homes. Multivariate analyses identified several home characteristics that were predictors of airborne fungal concentrations, including moisture, ventilation, floor type and bedding type. Airborne fungal concentrations were similar indoors and outdoors, which likely reduced the significance of housing and indoor factors as determinants of indoor concentrations.

Conclusion

Allergen concentrations were highly variable in homes, and a portion of the variability can be attributed to easily-recognised conditions.

Keywords: allergens, home characteristics, indoor air pollution, moisture

Introduction

Studies in both developing and developed countries have found rapid increases in the prevalence of paediatric asthma (Ehrlich, 2002; Abu-Ekteish et al., 2009). Exposure to indoor air pollutants, including allergens, has been recognised as an important factor in asthma and other respiratory diseases. In developed countries, exposure and sensitization to indoor allergens is considered a possible cause of asthma and known trigger of asthma attacks, especially in children (Pearce et al., 2000). Asthma exacerbation in children is associated with exposure to high concentrations of cockroach allergen (Bla g1, Bla g2, Per f1), house dust mite allergen (Der p1 and Der f1), animal allergen (Mus m1, Rat n1, Can f1, Fel d1), and fungal allergen (Asp f1, Alt a1, Cla h1, S. chartarum) (Rosenstreich et al., 1997; Garrett et al., 1998; Gold et al., 1999; Lau et al., 2000; Carlsten et al., 2010; Olmendo et al., 2011). Understanding the household risk factors associated with proliferation of allergens is an initial and important step in controlling exposure and reducing morbidity (IOM, 2000; Pearce et al., 2000; Arshad, 2010).

House dust mites (HDM) are common in warm and humid climates, and their allergens are found in reservoirs, such as bedding, floor coverings, and fabric covered furnishings (Peroni et al., 1994; van Strien et al., 1994; Custovic et al., 1998a). High surface concentrations of animal and insect allergens, e.g., mouse and cockroach allergens, are most likely in spaces where food is abundant, e.g., kitchens, dining rooms and living rooms (Rosenstreich et al., 1997; Olmendo et al., 2011). Numerous fungal species are abundant outdoors, but indoor infestations require spaces and surfaces with moisture or high water activity (Arruda et al., 1992; Dales et al., 1997; Garrett et al., 1998).

In developing countries, studies of residential indoor environments have focused on biomass fuels and combustion pollutants. Biological pollutants, such as allergens, have received scant attention (Manjra et al., 1995; Ezzati and Kammen, 2001; Tielsch et al., 2009), particularly in Africa. In this study, concentrations of allergens and airborne fungi are characterized in homes of primary school children in Durban, KwaZulu-Natal, South Africa. Also examined is the association of household characteristics and concentrations of these pollutants with the aim of contributing to the understanding of the relationship between allergens and childhood asthma.

Durban is an industrial port city located on the east coast of South Africa with a population of approximately three million people. Due to the prevailing subtropical climate, both the temperature and humidity are generally high, creating an environment that encourages the growth and proliferation of biological agents and pollutants. Very high prevalence rates of asthma (32%) have been reported among children attending schools in south Durban, an area containing numerous air pollutant-emitting industries, as compared to a prevalence rate of 17% for children living outside this area (Kistnasamy et al., 2008).

Methods

Sample selection and recruitment of participants

The homes selected for indoor environment assessment represent a subset of households participating in the South Durban Health Study (SDHS), an epidemiological study that examined the relationship between ambient air pollution and respiratory disease among school children (grades 3–6; Naidoo et al., 2007). The SDHS recruited children attending seven schools in two communities: four schools in the highly industrialised south Durban area, and three schools in northern Durban, an area without major industry. At each of these schools, a screening questionnaire was completed by caregivers and used to identify children with known or probable persistent asthma. Of the 423 children who participated in the SDHS, 81 (19%) were classified as persistent asthmatics, based on the screening questionnaire.

From the participating schools, households of children with known and probable asthma were given priority for recruitment into an indoor environment assessment sub-study. A total of 20 households from each school list were targeted. Child caregivers or other responsible adults living with the child were recruited by telephone or house visits. Individuals giving consent were then scheduled for indoor assessments. Their homes were visited during the day by trained technicians who could speak the local languages (isiZulu or English). Home visits for walkthrough assessment, settled dust collection and airborne fungal sampling were done from May 2004 to September 2005.

Each household recruited for the indoor environmental assessment provided written consent. All procedures in the study were approved by the respective ethics committees of the University of KwaZulu-Natal (South Africa) and the University of Michigan (USA) participating in the SDHS.

Home walkthrough assessment

The walkthrough assessment utilized a standardised instrument that was previously field-tested and used in Detroit, USA to investigate household characteristics as indicators of allergen concentrations (Parker et al., 2004). This instrument was modified to include questions about informally constructed homes common in Durban. It also included direct observations, questions directed to the child caregiver or the adult respondent regarding household characteristics in each room of the house, and activities of the child and other occupants. Information collected on housing characteristics included the type of house (single family house or flat/apartment, single or double storey, etc.), building age, moisture problems (sources or indicators), fungal growth, ventilation, and heating practices. Information on the types of floors, furnishings, bedding used by the child, and presence of stuffed toys was also collected. The rooms investigated in each house were the child’s sleep area, the child’s play area, the kitchen and the bathroom.

Settled dust sampling and allergen quantification

Following the walkthrough assessment, two settled dust samples from the child’s sleep area were collected. One sample was collected from the bedding (pillows and mattress) and, if present, a second sample was collected from a carpet or rug near the bed. A vacuum cleaner (WapVs300s, Bellenberg, Germany) equipped with a specialised nozzle and a 40 mm diameter filter paper (Macherey-Nagel, Germany) on the inlet was used. Dust samples were collected from bedding materials by vacuuming for 2 min. Carpet/rug samples were collected by vacuuming a 1.0 m2 area for 2 min. After sampling, the dust-loaded filter was folded, wrapped with aluminium foil, and placed in a sealed polyethylene bag for transport. Additional settled dust samples were collected from fabric furniture and carpet or rugs in the child's play area using similar procedures. All settled dust samples were transported to the laboratory on the day of collection and stored at 4°C until analysis.

In the laboratory, settled dust samples were sieved through a No. 45 mesh screen (355 μm sieve) to remove large particles and fibres. If two settled dust samples were collected from a single room (sleep area or play area), they were pooled and analysed as a single sample. For extraction, 100 mg of fine settled dust sub-samples were weighed in test tubes and 2.0 ml of 0.05% Tween20 in phosphate-buffered saline was added. The samples were then suspended using a vortex mixer, mixed for 2 hr using a laboratory mixer, and then centrifuged at 2500 rpm for 20 minutes at 4 °C. Supernatants for allergen analysis were removed and stored at −20 °C (Earle et al., 2007). Allergens were measured using ELISA monoclonal antibodies assays (Indoor Biotechnologies, Wiltshire, UK) for house dust mite (Der p1 and Der f1) and fungi (Asp f1) on the children’s sleep area samples, and for cockroach allergen (Bla g1) on the children’s play area samples. Airborne fungi and mite allergen concentrations in settled dust were expressed as μg/g dust, and Bla g1 concentrations were expressed as U/g dust (Chapman et al., 1987). The estimated minimum detectable level (MDL) for Der p1 and Der f1 was 0.30 μg/g dust and 0.60 U/g for Bla g1, and the reproducibility was 18% for Der p1, 3% for Der f1 and 5% for Bla g1, based on previous inter-laboratory studies (Pollart et al., 1991; Ovsyannikova et al., 1994; Sawyer et al., 1998; van Strien et al., 2003; Arbes et al., 2005). Because of the few number of censored data points for HDM allergens (10% and 6% of Der p1 and Der f1 respectively), ½ MDL value (0.15 μg/g dust) was used to replace censored data for statistical analysis (Hewett and Ganser, 2007). The characteristics of the Bla g1 concentrations meant using a more robust (more precise and less bias) β-substitution method to substitute Bla g1 concentrations below MDL was necessary (Ganser and Hewett, 2010).

Airborne fungal sampling, identification and quantification

A sterilised two-stage Andersen sampler (Tisch Environmental, Ohio, USA) was used to collect viable airborne fungi from the child’s sleep area, the room most used by the child (play area), the kitchen, and the bathroom of each home. During sampling, any external windows and doors of the sampled rooms were closed. The sampler was placed at the centre of the room at least 0.5 m above the floor and away from obstacles that could affect airflow. Samples were collected for 5 min at 15 L/min using a calibrated vacuum pump (SKC Ltd, USA) onto malt extract agar (MEA) and dichloran (18%) glycerol agar (DG18) plates (Oxoid Ltd, Hampshire, USA). One to four airborne indoor fungal samples, and one airborne outdoor fungal sample were on each media collected during each home visit. After sampling, the media plates were sealed, marked, transported to the laboratory, and incubated in a temperature-regulated microbial cabinet between 25 and 28°C for 3 to 5 days. After incubation, fungal colonies were counted and visually identified as Cladosporium, Aspergillus, Fusarium, Penicillium and Rhizopus species. Unknown species were grouped and classified as “unknown”. Each of the five identified airborne fungal species was further isolated for confirmation by culturing onto MEA and DG18 agar plates for further observations of cellular, hyphal and spore morphology. Morphologies were defined by visual observation and microscopy, and organisms were identified using Samsonet al. (2000) as a reference. Concentrations of viable airborne fungi were expressed as colony forming units per cubic meter of air (CFU/m3). Airborne fungal concentrations below the MDL (13 CFU/m3) were set to ½ MDL for statistical analysis. Confirmatory analyses of the fungal genera were performed by the Medical Microbiology Laboratory of Inkosi Albert Luthuli Central Hospital in Durban, South Africa.

Airborne concentrations of each fungal species and the total fungal concentration measured in different rooms of the same house were averaged to create new variables: indoor Cladosporium, indoor Aspergillus, indoor Penicillium, indoor Fusarium, indoor Rhizopus, indoor unknown and indoor total. Several culture samples from the sleep area (n=4) and outdoors (n=9) showed excessively high concentrations of several species that made identification and quantification of other species impossible. For these samples, the concentrations were reported as the highest concentration found for each species and for the total. Airborne fungal species and total fungal concentrations on MEA and DG18 were similar, therefore only results of MEA are presented in this article.

Data analyses

Descriptive analyses were performed for home characteristics and other factors potentially associated with allergen and airborne fungal concentrations. Non-parametric Spearman rank correlations were used to investigate the association between concentrations of airborne fungi and allergens in settled dust. Multivariate analyses used stepwise regression and dust allergen concentrations and airborne fungal concentrations as dependent variables to test association with home characteristics variables such as season, neighbourhood and region, used as independent variables. Stata/IC version11.0 for Windows (StataCorp LP, Texas) was used for statistical analysis. Before a multivariate regression analysis was computed for different outcome variables, bivariate analyses between outcome variables and home characteristics, were done. Variables that were significant at the 95% confidence level in the bivariate analyses, together with other variables that were considered important potential determinants of indoor pollution concentrations as shown in other studies, were included in the final multivariate models.

Results

A total of 135 homes were visited for the indoor environment study: 53% (n=71) of them were situated in south Durban region, and the rest in north Durban. Using data from the screening questionnaire administered to caregivers for SDHS, 103 (77%) of the participating households had children classified as asthmatics or probable asthmatics.

Characteristics of children’s homes

Most homes (70%) were single-family homes of formal brick construction. Relatively few (7%) were informally constructed with walls made of different materials from wood, corrugated steel, cardboard boxes, etc. Typically, homes had four or five rooms, but the range in the sample was from one to 10 rooms.

The frequency of selected home characteristics in children’s sleep and play areas, based on the walkthrough checklist, are shown in Table 1. The bedroom was the room used by the majority (87%) of children as a sleep area; the remainder of the children used a living room (6%) or other room (7%) in the house. Living rooms were frequently (80%) used as an indoor play area. Few of the sleep areas were heated during the cold season (10%) or cooled during the hot season (36%). In most sleep and play areas, smooth and/or impervious floors (cement, floor tiles or linoleum) were dominant (56% and 87%, respectively). Sleep areas had more evidence of moisture and visible fungal growth than play areas, which occurred mostly on interior roofs and/or ceilings rather than on walls and floors. Most children slept on beds with mattresses (93%); a few of them used mattresses on the floor (4%). Most children used either foam (44%) or polyester-filled (40%) pillows on their beds.

Table I.

House characteristics in 135 sleep and 112 play areas of visited homes of school children.

Conditions found in the households Sleep area [N=135] n (%) Play area [N=112] n (%)
heating 13 (9.6)
use of fan / air conditioner 50 (44.6) 60 (53.6)
chairs present 46 (34.1) 105 (93.7)
cluttered spaces 15 (11.1) 4 (3.5)
evidence of cockroaches 49 (36.3) 27 (27.3)
evidence of rodents 26 (19.3) 16 (1432)
opening windows 131 (97.0) 107 (95.5)
fungal or mildew smell 49 (36.3) 11 (9.8)
ceiling/ interior roof presence of ceiling 86 (63.7) 81 (71.7)
visible water stains/ moisture 48 (35.6) 34 (30.1)
visible fungal growth 36 (26.7) 18 (15.9)
wall visible water stains/ moisture 30 (22.2) 14 (12.4)
visible fungal growth 16 (11.9) 4 (3.5)
floor smooth non-porous 83 (61.2) 93 (93.9)
Cement 18 (13.3) 4 (4.0)
wall-to-wall carpet 34 (25.2) 14 (12.5)
dirt floor 1 (0.7)
hard floor with rug/s 32 (23.7) 34 (30.4)
type of bedding bed with mattress 129 (95.6)
mattress on the floor 5 (3.7)
sofa/ couch 1 (0.7)
blankets on the floor 1 (0.7)
type of blankets cotton blankets 40 (29.6)
wool blankets 65 (48.1)
Comforter 67 (49.6)
acrylic blankets 2 (1.5)
type of pillows feather filled 4 (3.0)
polyester filled 58 (43.0)
foam filled 68 (50.4)
other type 4 (3.0)
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Occurrence and concentrations of allergens and airborne fungi in children’s homes

Settled dust samples could not be collected from 9 (7%) of the homes visited because of the absence of electricity. In one-room homes, only a single settled dust sample from the sleep area could be collected. In these homes, 126 and 99 valid (composite) settled dust samples for allergen analysis were collected and analysed from sleep areas and play areas respectively, and 129 indoor and 129 outdoor air samples were collected and analysed.

Table 2 shows allergen concentrations in settled dust. Detection frequencies varied from 51% for Bla g1 to 100% for Asp f1. The average Asp f1 concentrations in south Durban were higher than concentrations in north Durban. Only one household had both HDM allergen concentrations in children’s settled dust bed samples below MDL. Of the HDM allergen concentrations, 3% (4 of 126) of homes had Der f1 concentrations above 10 μg/g dust, a concentration sometimes used as a threshold for the development and exacerbation of asthma (Platts-Mills et al.,1997), compared to 13% (13 of 126) Der p1 concentrations exceeding this value. The maximum Der f1 concentration (39.7 μg/g dust) exceeded the next highest value (22.9 μg/g dust) by almost two-fold. Although for cockroach allergen 49% of the homes were below MDL, 13% (13 of 99) exceeded 2 μg/g dust, a guideline value for asthma exacerbation (Platts-Mills et al., 1997). The highest Bla g1 concentration (24.4 U/g dust) detected in a home’s play area settled dust was over three times higher the second highest concentration (7.8 U/g) observed in another home.

Table II.

Fungal (Asp f1) and house dust mite (Der p1, Der f1) allergen concentrations in settled dust collected from children’s sleep areas and cockroach (Bla g1) allergen concentrations in settled dust collected from children’s play areas.

Allergen Sample size Detection frequency (%) Mean (SD) Median Minimum Percentile
Maximum
90th 95th
Asp f1 (μg/g) 126 99 0.58 (0.24) 0.47 0.32 0.99 1.09 1.37
Der p1 (μg/g) 126 90 6.89(13.51) 1.98 0.15 18.26 46.57 71.42
Der f1 (μg/g) 126 94 1.96 (4.40) 1.00 0.15 2.63 5.39 39.71
Bla g1 (U/g) 99 51 1.23 (2.65) 0.66 0.25 2.87 3.31 24.40
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Mean airborne concentrations of different fungal species in children’s sleep areas, indoors and outdoors are shown in Figure 1. Indoor and outdoor fungal compositions were similar, and Cladosporium was by far the most abundant of the identified species. Cladosporium concentrations averaged around 60% of the total airborne fungal concentration, and were typically ten or more times higher than other identified fungal species. Penicillium concentrations were slightly higher than other species identified, namely Aspergillus, Fusarium and Rhizopus. Airborne fungal aerosol compositions in the children's sleep areas and indoor spaces were similar to those outdoors, however, outdoor concentrations were higher (Figure 2).

Figure 1.

Figure 1

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Mean airborne concentrations (standard error) of different fungal species (identified and unknown) sampled from sleep areas, indoors, and outdoors of the children’s homes.

Characterization of allergens and airborne fungi in low and middle – income homes of primary school children in Durban, South Africa

Figure 2.

Figure 2

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Distributions of total airborne viable fungal concentrations sampled in sleep, indoor and outdoor areas of visited children’s homes.

Characterization of allergens and airborne fungi in low and middle – income homes of primary school children in Durban, South Africa

Airborne fungal concentrations in Durban homes exceeded concentrations that have been associated with health effects in several studies (WHO, 2009). The median concentration was 667 CFU/m3 for Cladosporium, a dominant species in the samples collected from these homes. Thirty percent of the homes had Cladosporium concentrations above 1,000 CFU/m3, a concentration which has been associated with adverse respiratory health outcomes in other studies (WHO, 2009). The highest Cladosporium concentrations were 3,500 CFU/m3 indoors and 4,040 CFU/m3 outdoors, but these represent underestimates due to sampler overload. The number of homes with total airborne fungal concentrations that exceeded 1,000 CFU/m3 was 66 (51% of the 129) homes. Although Penicillium and Aspergillus were frequently detected in the sleep areas, the concentrations averaged 520 and 147 CFU/m3 respectively.

Home characteristics, allergen concentrations in dust, and airborne fungal concentrations

There were no significant correlations observed between Asp f1 settled dust concentrations and airborne fungal concentrations in sleep areas, indoors and outdoors. However, analysis stratified by some home characteristics showed significant correlations. Homes with rugs, or usage of fans in the sleep areas, had significant positive correlations between these two variables, namely, Asp f1 and airborne total fungal concentrations [(r= 0.53, p=0.011, n=22) and (r= 0.36, p=0.032, n=35) respectively]. In informal constructed homes, Asp f1 concentrations in settled dust were negatively correlated with airborne total fungal concentrations (r= −0.80, p=0.005, n=10).

Tables 3 and 4 show results of the multivariate models for different allergen and airborne fungal concentrations adjusted for season and region. Each of the final models had different significant predictors for each allergen, and the coefficients of determination (R2) were less than 0.3. Surprisingly, the presence of open windows and cement floors in sleep areas were significant and positive predictors of higher Der p1 and Der f1 allergen dust concentrations, respectively. Predictors of Bla g1 dust concentrations included smooth floors and visible roof and ceiling mould. No home characteristics were identified as significant predictors of airborne Aspergillus concentrations. Moisture and visible fungal growth on interior roof surfaces were negative predictors of higher airborne Penicillium and total fungal concentrations, but positive predictors of Cladosporium concentrations in air.

Table III.

Determinants of different allergen concentrations in children’s sleep areas (Asp f1 and HDM) and play areas (Bla g1) predicted using multivariate stepwise regression models.

Dependent variable (Model adjusted R2) Home characteristics (Predictor variables) Coefficient 95% CI p-value
Aspf1 (0.2808) flat / duplex home 0.16 0.07–0.26 0.001*
opening windows −0.48 −0.72–−0.24 <0.001*
comforter 0.07 <0.01–0.15 0.065
Derf1 (0.0529) cement floor 2.51 0.17–4.86 0.036*
wall-to-wall carpet 1.89 0.01–3.77 0.048*
cotton blanket 1.94 −0.36–4.24 0.097
Derp1 (0.1363) opening windows 16.80 0.43–33.17 0.044*
comforter −36.95 −56.82 –−17.08 <0.001*
Wool blankets −39.18 −58.82 – −1954 <0.001*
cotton blanket −30.83 −51.33 – −10.34 0.004*
blend of blankets −30.88 −51.20 – −10.57 0.003*
Blag1 (0.0511) smooth floors 0.67 −0.56 – 1.40 0.070
visible roof or ceiling mould −0.71 −1.37 – −0.04 0.038*
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Variables included in the models: type of house, house age, opening windows, moisture and dampness signs, fungal growth, bedding, heating and presence of a ceiling.

Table showing variables with a p-value of <0.1 on the multivariate analysis

All models adjusted for season and region (south or north Durban)

*

p<0.05

Table IV.

Factors influencing concentrations of various airborne fungal species and total fungal concentrations from children’s sleep areas– from multivariable regression models

Dependent variable (Model adjusted R2) Home characteristics (Predictor variables) Coefficient 95% CI p-value
Cladosporium (0.1008) opening windows 701 −129 – 1530 0.097
informal constructed home 499 82 – 916 0.019*
visible ceiling or interior roof moisture 321 77 – 565 0.010*
Penicilium (0.2320) visible ceiling or interior roof moisture −54 −92 – −16 0.006*
house made of bricks 156 20 – 293 0.025*
informal constructed home 246 148 – 344 <0.001*
presence of a ceiling −55 −96 – −15 0.008*
house made of wood −142 −297 – 12 0.071
Fusarium (0.1286) opening windows 54 6 – 101 0.028*
visible wall fungal growth −20 −42 – 1 0.065
home less than 25 years old 20 6 – 34 0.004*
Rhizopus (0.1034) house made of bricks 70 1 – 140 0.045*
informal constructed home 62 −2 – 126 0.057
Unknown fungal species (0.0945) home older than 25 years −116 −239 – 7 0.064
opening windows 550 114 – 985 0.014*
Total airborne fungi (0.1179) house made of bricks 792 45 – 1539 0.038*
visible wall fungal growth −358 −741 – 25 0.067
informal constructed home 1193 510 – 1875 0.001*
visible ceiling or roof moisture 337 86 – 589 0.009*
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Variables included in the models: type of house, house age, opening windows, moisture and dampness signs, fungal growth, heating and presence of a ceiling

Table showing variables with a p-value of <0.1 on the multivariate analysis All models adjusted for season and region (south or north Durban)

*

p<0.05

Discussion

Durban homes showed allergen concentrations and compositions in settled dust that resemble those in some studies in both developing and developed countries (e.g., Chew et al., 1998; Addo-Yobo et al., 2001; Perfetti et al., 2004), but concentrations were lower than those seen in inner cities in the USA (e.g., Eggleston et al., 1999; Breysse et al., 2005). A moderate fraction of Durban homes had allergen and airborne fungal concentrations that exceeded suggested risk thresholds for sensitisation and/or asthma exacerbation and other respiratory illnesses (Platts-Mills et al., 1997; WHO, 2009). This is an important finding because most of the sampled homes had children classified as probable or known to be asthmatic. Total airborne fungal concentrations in sleep areas and total airborne indoor fungal concentrations were significantly associated with fungal allergen concentrations in settled dust, but only when some home characteristics were present (or absent), suggesting that dust as a reservoir of allergenic materials maybe influenced by the airborne fungi only under certain conditions. From the observed correlations it could be true that airborne fungal concentrations are influenced by reservoir fungal dust contamination. Overall, the lack of correlation among dust and air measurements across different homes in Durban suggests that multiple factors unaccounted for in this study may be affecting these measurements.

Fungal allergen (Asp f1) and airborne fungi in sleep areas

Asp f1 was detected in settled dust samples in all homes. The abundance of this fungus supports the hypothesis that fungi can rapidly proliferate when conditions are suitable, as found in Durban's subtropical climate. Fungi have been reported to grow from between 6 to 196 hr after spore deposition in appropriate conditions, and allergens can be produced within 12 hr after this growth (Arruda et al., 1992). This observation is supported by our findings of an association between interior roof or ceiling moisture damage and total airborne fungal and Cladosporium concentrations found in the children's sleep areas.

Dust concentrations of fungal allergen were significantly affected by housing type. Elevated concentrations of Asp f1 and total airborne fungi were seen in attached homes (flat/apartment/duplex) and in informal constructed homes (as compared to detached and formal family homes). One factor that may influence this association is the close proximity between attached dwellings, which can promote air exchange between dwellings that transports airborne fungi. Conditions such as high water activity and indoor climate in informal constructed homes can also promote indoor fungal growth and lead to higher concentrations (Danaviah et al., 2000).

The similarity of indoor and outdoor airborne fungal concentrations at the time of sampling suggests only limited indoor sources of fungi. However, the associations observed between home characteristics and airborne fungal concentrations suggest that several characteristics are important determinants of indoor fungal concentrations (Table 4). Opening windows in sleep areas was a significant determinant of lower concentrations of Asp f1 and total airborne fungi in sleep areas. Although natural ventilation possible with opened windows allows entry of outdoor airborne fungi, ventilation can help to reduce by dilution indoor air contaminants, and it can reduce the build-up of humidity, which can be especially important in smaller and more crowded houses and bedrooms. Opening windows may provide a practical approach to provide ventilation and control airborne fungi in the study communities, especially considering that none of the Durban homes used mechanical ventilation or particle filtration.

Bedding type was a significant determinant of Asp f1 concentrations in children’s beds. Unlike other microbes, fungal proliferation on surfaces needs sufficient water activity and nutrients. These fungal growth requirements are unlikely to be found in bedding, hence this finding appears coincidental. Possibly, bedding concentrations of Asp f1 may be a surrogate for household maintenance practices, which are likely to be worse in poorer households.

Other determinants of individual airborne fungal species concentrations identified in children's sleep areas were not consistent with the determinants of total airborne fungal concentrations. Concentrations of different airborne fungi were inversely associated with visible moisture, visible fungal growth and age of the homes, which is also inconsistent with the conditions fungi need to proliferate. Again, these results appear coincidental. Because indoor and outdoor concentrations of airborne fungi were very similar, these associations may reflect seasonality or some other unmeasured or uncontrolled factor. It is clear that airborne fungal concentrations determined using short-term indoor measurements require careful and cautious interpretation given the influence of outdoor concentrations and the great variation that can occur very rapidly (Chew et al., 2003; Cho et al., 2006).

Earlier studies examining indoor environments in Durban showed that Cladosporium is the dominant airborne fungal genus in the region (Danaviah et al., 2000; Berman, 2007). In the present study, association of Cladosporium allergen concentrations was not associated with airborne Cladosporium concentrations was not investigated. Airborne Cladosporium concentrations were not associated with visible fungal growth or moisture on surfaces. Asp f1 allergen concentrations in dust are relevant due to their potentially profound health effects.

House dust mite allergens (Der p1 and Der f1) in sleep areas

The occurrence of HDM allergens in the SDHS homes was high (90% and 94% for Der p1 and Der f1, respectively) in comparison to homes in the USA (80.2%; Leaderer et al., 2002) and elsewhere (Custovic et al., 1998b; Moscato et al., 2000). This is not surprising given that year-round climatic conditions in Durban are ideal for the proliferation of these allergen-producing organisms, e.g., the annual average temperature and humidity is 23°C and 75%, respectively. Potter et al. (2010) also documented that coastal areas of South Africa have higher HDM concentrations compared to (typically drier and cooler) inland areas. Although Der p1 concentrations were not correlated to Der f1 concentrations in the children’s sleep areas, both allergens seem to thrive in Durban homes. Der p1 had higher concentrations, which supports observation that only a single type of HDM allergen is dominant in a house (Moscato et al., 2000; Perfetti et al., 2004). Proliferation of one type of species in an environment is a likely result of the competition for food and water.

Few factors were associated with HDM allergen concentrations. The observed association of blanket type with HDM concentrations is similar to findings elsewhere. All blanket types (both natural and synthetic materials) were associated with lower Der p1 concentrations, contrary to expectations. Blankets made of cotton and other natural materials have been associated with lower concentrations of Der p1, suggesting that such material is not conducive for HDM proliferation (Hallam et al., 1999; Custovic et al., 2000). However, natural materials can provide protein as food for HDM (Sawyer et al., 1998). In the present study, although the association between Der f1 allergen concentrations and cotton blankets was not statistically significant, the positive association observed supports this hypothesis of natural fibres as a food source for HDMs. However, the higher HDM concentrations associated with cement floors (smooth floors) and presence of opened windows found in this study are inconsistent with Carrer et al. (2001), who shows lower concentrations with these factors. Further, that visible moisture and dampness were not significant predictors of HDM concentrations might imply that these factors did not significant affect humidity inside Durban homes, possibly because ambient conditions are already very humid.

Cockroach allergen (Bla g1) in play areas

Although Manjra et al. (1995) indicated that Per f1 allergen was the most prevalent cockroach allergen in Durban homes, 51% of the samples in the present study showed the presence of Bla g1 allergen. A much lower occurrence of cockroach allergen has been in homes in the USA in several studies, e.g., 30% for Bla g1 (Chew et al., 1998; Leaderer et al., 2002) and 24% for Bla g in family room floors and/or furnishings (Gold et al., 1999). Although the Bla g1 detection frequency was high, concentrations were much lower than those reported elsewhere (Eggleston et al., 1999). Because settled dust samples for Bla g1 analysis were collected from children’s “play areas” and most of these were living rooms, it is likely that higher detection frequency could have been observed from samples collected from the kitchens.

Although almost 50% of the samples had Bla g1 below detection limit, we modeled concentrations against home characteristics. Cockroach allergen proliferation has been associated with socioeconomic status and housekeeping practices, e.g., food storage and trash maintenance, and with older homes and informally constructed homes (Leaderer et al., 2002). In our study, observed associations, such as smooth floors in children’s play areas associated with higher concentrations of Bla g1 allergen, and visible interior roof and ceiling mould growth associated with lower Bla g1 concentrations are contrary to expectations. The low R2 (0.0511) for the Bla g1 model in table 3 suggests that our independent variables do not fully explain the variability in the allergen concentration.

Study limitations

The sample size of the sub-study limits our ability to observe relationships and account for interactions among variables. Moreover, the home assessments were conducted in different seasons, and seasonal influences on allergen concentrations, dampness, visible moisture, and ventilation practices may influence the findings.

All of the dust and air sampling data were cross-sectional in nature. Longitudinal data would have provided a more comprehensive assessment of allergen and airborne fungal concentrations. Additionally, repeated measurements in each household would help to ensure that observations were representative of everyday practise and conditions. The fungal aerosol sampling used a single growth media and incubation condition, and it represents a very short-term (5 min) sample that may not be representative. The general approach of collecting and incubating airborne fungi may underestimate some species due to the different media and incubation temperatures needed for optimal determination of these species (Samson et al., 2000). This applies to incubation temperatures needed for optimal growth of Aspergillus and Penicillium species which are between 35°C and 37°C (Samson, 2004). Also, only five fungal genera were identified in air samples, and some species may be misclassified as "unknown" although they may belong to one of the target genera. We may have missed the association between airborne fungal concentrations and allergen concentrations in settle dust allergen because we investigated Aspergillus allergen instead of the Cladosporium allergen which is the most prevalent.

Analysis, such as modeling Bla g1 concentrations, using data with approximately 50% of the dust samples having concentrations not detectable is problematic. In our dataset with characteristics that include single MDL, small sample size (n= 99), high left censored points (49%) and large standard deviation (2.65, β-substitution was a method of choice compared to maximum likelihood estimation (MLE), log-probit regression (LPR) and common substitution methods (MDL/2 and MDL/√2). Our dataset was not lognormal which is another important characteristic for using β-substitution as a substitution method (Hewett and Ganser, 2007; Ganser and Hewett, 2010). Because of the few censored data points seen for the airborne fungal concentrations and HDM and fungal dust allergens concentrations, our substitution method of choice is justified for these variables.

Conclusions

This study characterised concentrations of allergens in homes in a developing country, and included many homes in which a vulnerable population lived: children with asthma. Settled dust allergen and airborne fungal concentrations frequently exceeded concentrations and guidelines linked to respiratory illness, including asthma exacerbation and asthma development. While few of the "classical" determinants of high pollutant concentrations were identified, and results are somewhat limited due to the imprecision of environmental sampling of airborne fungi and ELISA measurements of Asp f1 allergen in settled dust, these two biological contaminants require further investigation in indoor environments in developing countries. Because allergen concentrations varied with certain home characteristics, such as house age and presence of opening windows, some low cost interventions to reduce allergen concentrations may be feasible. The study results suggest that developing countries should view allergen exposure as an important public health issue.

Acknowledgments

We wish to thank the caregivers and children who participated in the study. This research and the SDHS were supported by the Fogarty International Center of the U.S. National Institutes of Health (Grant #2 D43 TW00812-06), and eThekwini Municipality.

Grant sponsor: EThekwini Municipality, KwaZulu Natal, Durban, South Africa

Grant no: 1a-103 - Health Study

Grant Sponsor: Sponsored in part by US NIH Fogarty International Center grant 2 D43 TW000812

Footnotes

Conflict of Interest Statement: None of the authors has a financial relationship with a commercial entity that has an interest in the subject of the manuscript.

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