Indoor Bacterial and Fungal Aerosols as Predictors of Lower Respiratory Tract Infections Among Under-five Children in Ibadan, Nigeria

doi:10.21203/rs.3.rs-850398/v1

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Research article

Indoor Bacterial and Fungal Aerosols as Predictors of Lower Respiratory Tract Infections Among Under-five Children in Ibadan, Nigeria

https://doi.org/10.21203/rs.3.rs-850398/v1

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Aim

This study aimed to investigate the association between exposure to diverse indoor microbial aerosols and lower respiratory tract infections (LRTI) among children aged 1 to 59 months in Ibadan, Nigeria.

Methodology

One hundred and seventy-eight (178) hospital-based LRTI cases among under-five children were matched for age (± 3months), sex and geographical location with 180 community-based controls (under-five children without LRTI). Following consent from caregivers of eligible participants, a child’s health questionnaire, clinical proforma and standardized home-walkthrough checklist were used to collect data. Participant homes were visited and sampled for indoor microbial exposures using active sampling approach by Anderson sampler. Indoor microbial count (IMC), total bacterial count (TBC), and total fungal count (TFC) were estimated and dichotomized into high (> median) and low (≤ median) exposures. Alpha diversity measures including richness (R), Shannon (H) and Simpson (D) indices were also estimated. Conditional logistic regression models were used to test association between exposure to indoor microbial aerosols and LRTI risk among under-five children.

Results

Significantly higher bacterial and fungal diversities were found in homes of cases (R = 3.00; H = 1.04; D = 2.67 and R = 2.56; H = 0.82; D = 2.33) than homes of controls (R = 2.00; H = 0.64; D = 1.80 and R = 1.89; H = 0.55; D = 1.88) respectively. In the multivariate models, higher categories of exposure to lMC (OR = 2.67, 95% CI = 1.44–4.97), TBC (OR = 2.51, 95% CI = 1.36–4.65), TFC (OR = 2.75, 95% CI = 1.54–4.89), bacterial diversity (OR = 1.87, 95% CI = 1.08–3.24) and fungal diversity (OR = 3.00, 95% CI = 1.55–5.79) were independently associated with LRTI risk among under-five children.

Conclusions

Our study suggests an increased risk of LRTI when children under the age of five years are exposed to high levels of indoor microbial aerosols.

Indoor microbial aerosols

Lower respiratory tract infections

under-five children

Sub-Saharan Africa

Ibadan

The microbiota of the indoor environment is an assemblage of a wide range of microorganisms including bacteria, fungi, viruses, archaea, and protists.[1, 2] These microorganisms occur ubiquitously even within indoor environments such as dwellings where humans, including children under the age of five years, spend more than 90% of their time.[2, 3] Therefore, understanding the processes and features that structure the microbiota within the indoor environment may provide clues to improve children’s health. The increasing interest in the microbiota of the built environment is largely due to the wider recognition that exposures to microbes within the residential indoor environment are associated with a vast number of adverse health outcomes, including infectious diseases such as lower respiratory tract infections (LRTIs), allergies and cancer.[2] In addition, studies suggest that certain indoor microbial communities can have beneficial health impacts, which has sparked interest in how to shape the indoor microbiota and design effective interventions [4].

Determining the risk factors for LRTI in infants and young children is important because LRTIs including pneumonia and bronchitis contributes largely to the burden of childhood morbidity and mortality,[5] therefore posing a major public health challenge in sub-Saharan Africa.[6] An estimated 921,000 children died of LRTI in 2015, and more than 95% of these deaths occur in low-and-middle income countries (LMIC) predominantly South Asia and sub-Saharan Africa.[7, 8] In 2017 community-acquired LRTI was the single largest cause of under-five mortality, accounting for 140,520 (19%) deaths in Nigeria.[9] This number is the highest in Africa and the third highest estimated number of childhood death from LRTI in the world.[10]

Several epidemiological studies have reported a strong relationship between occupant density, human activity and microbial burden within the indoor environment.[11, 12] Due to this association, under-five children are more likely at risk of LRTIs because they spend a considerable proportion of time in the indoor environment during a period of intense growth and development of the immunologic and respiratory systems.[13] Unraveling exposure-response relationships between the indoor microbial aerosols and LRTI among under-five children is an important step to disease prevention. Despite the increasing evidence that microbial diversity might contribute stronger to health effects than the abundance of single components,[14, 15] the majority of the available studies focused on individual microbial entities. A recent systematic review [16] showed that the majority of studies that aimed at investigating the association between exposure to indoor bacterial and/or fungal communities and LRTI have used proxy measures such as self-reported presence of visible moulds,[17, 18] while only very few have attempted to quantify microbial exposures.[19, 20]

Despite the high burden of LRTI among under-five children in LMIC, there is a paucity of studies exploring exposure-response relationships between indoor microbial communities and LRTI, especially in sub-Saharan Africa. Therefore, this study investigated the association between quantitative measures of exposure to indoor microbial aerosols and LRTI among under-five children, in Ibadan, Nigeria.

Study Setting and Design

The study was carried out in Ibadan which is the third largest city in Nigeria in terms of population density with a total population of over 6 million people within metropolitan area.[21] LRTI cases (n = 178) were recruited from three health facilities in Ibadan viz: Otunba-Tunwase Children Emergency Clinic of the University College Hospital, Ade-Oyo Maternity Teaching Hospital; and Oni-memorial Children Hospital. Eligible controls (n = 180) were identified from the same community as the cases and recruited after obtaining consent. These communities, all located in Ibadan, represented the 11 local government areas in Ibadan with majority of cases and controls from Bodija, Apete, Apata, Agbowo, Olomi, Ashi, Yemetu, Oluyole, and Oke-Ado. Most of these communities are semi-urban with medium density residential areas inhabited majorly by the Yoruba speaking population.

Definition and Selection of Cases and Controls

The study employed a case-control design, where cases (under-five children with LRTI) were age (± 3 months), sex and geographical location matched to controls (under-five children without LRTI). The details of the study protocol, and recruitment procedures have been published elsewhere.[22]

At the presenting hospital, children were screened and those who met the eligibility criteria (< 5 years of age; admitted for LRTI; and residing in Ibadan), and whose caregivers gave consent, were included in the study. Diagnosis of LRTI in children was carried out by a paediatrician based on chest radiography including the presence of one or more of the chest radiographic features of patchy, segmental, or lobar consolidation; +/- a positive air bronchogram; and +/- pleural effusion. Controls were primarily recruited from the same community where the cases reside. Two approaches were used in the identification and recruitment of community controls. First, the caregivers of recruited cases were asked to identify a neighbour whose child is of the same age group and sex as the case. Secondly, screening of potential controls at the community was performed by a paediatrician for the presence/absence of respiratory signs and symptoms. Therefore, for every case of LRTI recruited from the hospitals, an age/sex matched control was identified, assessed by a paediatrician, and confirmed to not have LRTI and/or any of the respiratory signs (such as fast breathing, respiratory crackles, intercostal recession, and lower chest wall indrawing) and symptoms (such as cough, fever, wheezing and difficulty in breathing) in the past 30 days. We chose this extended period of no signs or symptoms to ensure that controls were not likely to be in the pre-clinical, asymptomatic stage of an acute infection.

Cases with other systemic illnesses such as measles, symptomatic congenital heart disease, congenital malformation, or Acquired Immune Deficiency Syndrome (AIDS) and those that presented symptoms of measles or pertussis in the preceding 10 days were excluded from the study at the data collection phase to avoid outcome definition bias. Similarly, controls hospitalized for respiratory or allergic conditions or with complaints of an LRTI in the past 30 days were excluded from the study.

Data Collection and Procedure

The caregivers of recruited cases and controls were interviewed by trained public health personnel using a structured questionnaire modified from a previously validated child health questionnaire to obtain basic demographic, household and family characteristics.[23] In addition, the health status of the child was determined by a trained nurse using a clinical proforma.

Clinical and Health Data

A clinical proforma was used to collect vital health information about the child such as breastfeeding status/duration, immunization status/vaccine received, anthropometric measurements, respiratory symptoms/signs, severity of LRTI/other diagnosis, x-ray findings, and outcome of hospital admission.

Home Walkthrough Assessments

Cases and controls were followed-up within 24 hours of enrollment, and their households were subjected to a home survey and environmental monitoring. A validated walkthrough checklist[24] was used by trained inspectors to document real-time observations on housing conditions (including type of house, material used in the construction of roof, walls and floor, presence of doors and windows, visible mould growth and dampness or moisture on surfaces) and household characteristics (such as house ownership, type of cooking and heating energy sources used, number of rooms, occupancy, number of tobacco smokers, and keeping of pets). The cooking and heating fuels used in the households were classified as clean (electricity and liquid petroleum gas (LPG)) and dirty (kerosene and wood) and those households that used a combination of clean and dirty fuels were classified as mixed fuel.[24] Occupant density was defined as the total number of occupants divided by the total number of rooms present. The presence of visible water stain/dampness was defined as any sign of moisture damage on the roof, walls or housing materials at the participating child’s sleeping area while visible mould growth was said to be the presence of mould growth on moist surfaces within the house or the smell of mouldy odour.

Indoor Environmental Monitoring and Microbial Estimation

Indoor Environmental conditions

Indoor air temperature and relative humidity (RH) were monitored in homes of recruited cases and controls using EXTECH datalogger model 42270. The datalogger was installed in the homes of cases and controls for 24 hours, and the readings were retrieved using TRLog software version 4.0 (FLIR Commercial Systems Inc., TownsendWest, Nashua, NH, USA).

Air sampling and Microbiological analysis

The concentration of indoor air bacteria and fungi was estimated by collecting air samples in homes of cases and controls using a BioStage Anderson air sampler equipped with 90mm petri dish containing agar medium [Nutrient, Blood, and McConkey Agar for bacteria isolates and Saboraud Dextrose Agar (SDA) for fungi isolates] prepared according to the manufacturer’s specifications. All samples were collected within 24 hours after identification and recruitment of the participants. Samples were collected in the daytime at about 1.5 m height (to simulate the location of the breathing zone) in the room where the child sleeps/plays, at an air flow rate of 28.3 ± 2 l/min for 15 minutes.[25] Collected samples were then stored in an ice pack and transferred to the laboratory within 24 hours before incubation. Cultures on Nutrient, Blood, and McConkey Agar were incubated using a microbiological incubator at 35 ± 2ºC for 48 hours while SDA plates were incubated at room temperatures for 5 days with daily observation. The number of colonies on all agar media were counted using a Quebec darkfield colony counter (Cambridge Instruments, Inc., Buffalo, NY). The total bacterial count (TBC) and total fungal count (TFC) were estimated as colony-forming units per cubic metre (cfu/m³) using the formula:[26]

Afterwards, the identification and classification of colonies was performed to obtain distinct specie of the organisms. The identification of bacterial isolates were carried out based on their colonial morphology, cellular morphology and biochemical characteristics.[27] The fungal isolates were characterized and identified based on their macroscopic and microscopic characteristics.[28] The indoor microbial count (IMC) was estimated based on the mathematical model below:

Microbial alpha-diversity measures

Exposure to indoor microbial aerosols in this study was defined in terms of the IMC and the microbial alpha-diversity indices as described below.

The microbial richness (R), Shannon Diversity Index (H) and Simpson’s Diversity Index (D), were estimated based on the individual distinct species of bacteria and fungi identified representing distinct species. Species richness, R defined as the number of species per sample was estimated using the formula:

Species richness (R) = n/N Eq. 3

Where ‘n’ is the number of species and ‘N’ is the number of individual microorganisms in the sample.

The Shannon diversity index, H, was used as a measure of species evenness. Species evenness refers to how close in numbers each species in a sample is.

where, p is the proportion (n/N) of individuals of one particular species found (n) divided by the total number of species found (N), “ln” is the natural log, and k is the number of species.

To obtain the Simpson's diversity index (D), the proportion of bacterial and fungal species “i” relative to the total number of bacteria and fungi genera (p_i) was calculated and squared. The squared proportions for all the microbial species were summed, and the reciprocal taken as indicated in Eq. 4 below:

Where “i” is the proportion of bacterial and fungal species and (p_i) is the total number of bacteria and fungi genera.

Statistical Analysis

All data from completed child health questionnaires, clinical proforma, household walkthrough checklists and indoor air sample results were entered into Excel spreadsheets. Data were subjected to logic checks to ensure validity and consistency. A validated and complete dataset was exported to Stata SE 12.0, and R statistical program (version 4.0.0) for further analyses.

We assessed associations between risk factors and case status using McNemar’s test for paired categorical outcomes. We analysed differences in the distribution of IMC, TBC and TFC of cases and controls using Mann Whitney U test. Correlation between the microbial counts (IMC, TBC and TFC), alpha-diversity indices and environmental variables among cases and controls were assessed using Spearman’s rank correlation analysis. We further analyzed associations between microbial exposures and case status using conditional logistic regression with adjustment for potential confounders that were not used in the matching. The microbial counts (IMC, TBC, and TFC), microbial richness and Simpson’s diversity index were dichotomized at the median to define high and low exposures. The multiple logistic regression models included covariates which were selected depending on statistical significance in the bivariate analysis.

Conditional likelihood was employed to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) in five models. All statistical tests of hypotheses are two-sided with a p-value < 0.05 considered significant.

Boys were overrepresented among LRTI cases (60.1%). The mean (SD) age of participants was 7.3 (1.35) months (Table 1). Notably, more cases (15.7%) had a previous history of LRTI than controls (9.0%). Also, the proportion of subjects with one or more under-five siblings was greater among cases (94.4%) than controls (86.5%) and a smaller proportion of cases (69.1%) compared to control (84.2%) were exclusively breastfed. Fewer mothers of cases had a secondary education (36.0%) compared to controls (46.1%).

Table 1

Description [n (%)] of child and caregiver characteristics among cases and controls
Sociodemographic Variables		Cases (n = 178); n (%)	Controls (n = 180); n (%)	p-value
Child Characteristics
Sex	Male	107 (60.1)	110 (61.8)	0.74
Age (months)	Mean ± SD	7.35 ± 1.23	7.30 ± 1.19	0.95
Number of siblings U5 years	≥ 1	168 (94.4)	154 (86.5)	0.012
	Mean ± SD	1.69 ± 0.73	1.35 ± 0.76	< 0.001
Breastfeeding	Exclusive	121 (69.1)	149 (84.2)	0.001
Immunization	Complete immunization	26 (14.6)	27 (15.2)	0.88
History of LRTI	Yes	28 (15.7)	16 (9.0)	0.04
Caregiver’s Characteristics
Age (years)	Mean ± SD	30.44 ± 5.16	32.88 ± 5.59	< 0.001
Marital status	Married	170 (95.5)	168 (93.3)	Ref
	Cohabiting	6 (3.4)	5 (2.8)	0.88
	Divorced/Widowed	2 (1.1)	7 (3.9)	0.19
Mother’s educational status	No education	13 (7.3)	5 (2.8)	Ref
	Primary	35 (19.7)	19 (10.7)	0.57
	Secondary	64 (36.0)	84 (46.1)	0.04
	Tertiary	66 (37.1)	72 (40.4)	0.07
Father’s educational status	No education	2 (1.1)	3 (1.7)	Ref
	Primary	55 (30.9)	33 (18.3)	0.31
	Secondary	46 (25.8)	64 (35.6)	0.72
	Tertiary	75 (42.1)	80 (44.4)	0.58
Mother’s occupation	Gov. Employed	51 (28.7)	53 (29.4)	Ref
	Self-employed/trading	123 (69.1)	113 (62.8)	0.86
	Farming	4 (2.2)	14 (7.8)	0.43
Mother’s monthly income ($)	> 100	33 (18.5)	46 (25.6)	Ref
	≤ 100	64 (36.0)	42 (23.3)	0.14
	Refuse to answer	81 (45.5)	92 (51.1)
Any smoker in the house	Yes	20 (11.2)	9 (5.1)	0.03
Statistical analysis was carried out using chi-square test for categorical variables and independent sample t-test for quantitative variables

Occupant density > 5 (33.1% vs 19.1%), presence of visible dampness (11.2% vs 2.2%), and presence of visible moulds (21.9% vs 3.9%) differed significantly in homes of cases and controls. Also, a significant difference was observed in the use of clean cooking fuel (83.1% vs 91.6%), pet ownership (38.8% vs 20.2%), house ownership (25.8% vs 41.0%) and proportion of any smoker (11.2% vs 5.1%) between cases and controls. In terms of the meteorological conditions in homes of cases and controls, the mean (SD) indoor RH (%) in homes of cases (68.02 ± 12.99%) versus controls (64.27 ± 10.58%) differed significantly (Table 2).

Table 2

Description [n (%)] of home and exposure characteristics among cases and controls
Home Characteristics		Cases (n = 178); n (%)	Controls (n = 180); n (%)	p-value
Type of home	A room apartment	65 (36.5)	39 (21.9)	0.05
	Mini flat	88 (49.4)	91 (51.1)	0.52
	Apartment building/flat	8 (4.5)	23 (12.9)	0.23
	Bungalow	17 (9.6)	25 (14.0)	Ref
Total number of rooms in the house	1–3	152 (85.4)	104 (57.8)	< 0.001
	≥ 4	26 (14.6)	76 (42.2)	Ref
	Mean ± SD	2.70 ± 1.29	3.81 ± 1.79	0.002
Occupant density	≤ 2	19 (10.7)	28 (15.7)	Ref
	3–5	100 (56.2)	118 (65.2)	0.50
	> 5	59 (33.1)	34 (19.1)	0.01
	Mean ± SD	4.78 ± 2.10	4.14 ± 1.62	0.002
House ownership	Owned	46 (25.8)	73 (41.0)	Ref
	Rented	132 (74.2)	107 (59.0)	0.003
House wall construction	Wood	1 (0.6)	2 (1.1)	Ref
	Cement	147 (82.6)	164 (91.0)	0.74
	Mud	30 (16.9)	14 (7.9)	0.30
Visible water stain/dampness in CSA	Yes	20 (11.2)	4 (2.2)	0.001
Visible mold growth presence in CSA	Yes	39 (21.9)	7 (3.9)	< 0.001
Cooking area	Kitchen	131 (73.6)	152 (85.4)	Ref
	Corridor	32 (18.0)	20 (11.2)	0.06
	Where the child sleeps	15 (8.4)	6 (3.4)	0.04
Primary cooking fuel	Clean	148 (83.1)	165 (91.6)	0.02
Pet ownership	Any type	69 (38.8)	36 (20.2)	< 0.001
Season	Dry	71 (39.9)	89 (50.0)	Ref
	Wet	107 (60.1)	89 (50.0)	0.03
Indoor RH (%)	Mean ± SD	68.02 ± 12.99	64.27 ± 10.58	0.003
Indoor temperature (^oC)	Mean ± SD	31.46 ± 2.21	31.07 ± 2.12	0.09
CSA- Child’s sleeping area
Occupant density – Total no. of occupants/no. of rooms
RH – Relative Humidity
Statistical analysis was carried out using chi-square test for categorical variables and independent sample t-test for quantitative variables

The overall median IMC, TBC, and TFC across the sampling period was 515cfu/m³ (range: 236–1076), 456cfu/m³ (range: 213–989), and 54cfu/m³ (19–129) respectively. Comparing homes of cases versus controls, the median IMC (641 vs 477 cfu/m³), TBC (576 vs 428 cfu/m³) and TFC (66 vs 49 cfu/m³) were higher in homes of cases than controls (Table 3). The predominant bacterial agent found in homes of cases and controls were Staphylococcus aureus and Staphylococcus epidermidis (19% and 20%) while the dominant fungal agents were Aspergillus niger (23% vs 20%), and Aspergillus fumigatus (22% vs 11%). Significantly higher median bacterial and fungal diversity was found in homes of cases (R = 3.00; H = 1.04; D = 2.67 and R = 2.56; H = 0.82; D = 2.33) than homes of controls (R = 2.00; H = 0.64; D = 1.80 and R = 1.89; H = 0.55; D = 1.88) respectively (Fig. 1).

Table 3

Relative abundance of Bacterial and Fungal species in homes of cases and controls
Indoor Microbiome	Cases (n = 178); n (%)				Controls (n = 180); n (%)				p-value
Indoor Microbiome	GM (95% CI)	Median (IR)	Min	Max	GM (95% CI)	Median (IR)	Min	Max	p-value
Total bacterial count (TBC) cfu/m³	575 (546–605)	544 (287)	237	989	428 (408–448)	414 (226)	213	732	< 0.001
Staphylococcus aureus	140 (127–153)	141 (47)	0	656	111 (95–125)	111 (147)	0	452	< 0.001
Staphylococcus epidermidis	69 (62–75)	70 (24)	0	328	54 (47–62)	55 (77)	0	226	< 0.001
Streptococcus pneumoniae	127 (113–140)	157 (193)	0	325	97 (83–111)	121 (168)	0	384	< 0.001
Streptococcus pyogenes	60 (53–67)	77 (95)	0	163	45 (32–52)	53 (83)	0	192	< 0.001
Klebsiella aerogenes	77 (64–91)	78 (168)	0	387	73 (57–89)	75 (127)	0	514	0.35
Micrococcus spp.	64 (50–77)	64 (160)	0	387	30 (19–41)	30 (0)	0	543	< 0.001
Pseudomonas fluorescens	38 (28–48)	38 (66)	0	400	16 (9–23)	16 (0)	0	270	< 0.001
Unidentified colonies	2 (2–3)	2 (2)	0	20	1 (0.4-1)	1 (0)	0	15	0.05
Total fungal count (TFC) cfu/m³	66 (63–69)	67 (35)	32	129	49 (46–52)	47 (25)	19	118	< 0.001
Aspergillus niger	19 (17–21)	19 (12)	0	73	12 (10–14)	12 (22)	0	70	< 0.001
Aspergillus fumigatus	9 (8–10)	9 (7)	0	29	5 (4–6)	5 (10)	0	38	< 0.001
Penicillium spp.	19 (16–22)	20 (26)	0	102	13 (11–16)	13 (22)	0	66	0.001
Alternaria alternata	6 (5–8)	6 (12)	0	65	4 (2–5)	4 (0)	0	45	0.001
Fusarium oxysporum	4 (3–6)	4 (0)	0	48	4 (3–6)	4 (0)	0	62	0.38
Candida albicans	7 (5–8)	7 (13)	0	74	4 (3–6)	4 (0)	0	38	0.03
Cladosporium spp.	3 (2–4)	3 (0)	0	38	5 (3–6)	5 (0)	0	73	0.54
Unidentified colonies	0.4 (0.2–0.7)	0 (0)	0	13	1 (1–2)	0 (0)	0	18	0.06
Indoor Microbial count (IMC) cfu/m³	641 (610–673)	606 (293)	283	1076	477 (455–498)	464 (246)	236	796	< 0.001
IR – Interquartile range
GM – Geometric mean
Min – Minimum
Max – Maximum
cfu – Colony forming unit
p-value obtained using Mann-Whitney U test

IMC, TBC and TFC were positively and significantly (p < 0.001) correlated with indoor RH among cases (r = 0.56; r = 0.50; and r = 0.64), and controls (r = 0.47; r = 0.43; and r = 0.49) respectively, Table 4. The relationship between microbial diversity and indoor environmental conditions among cases revealed that bacterial richness and Simpson’s diversity (r = 0.39, r = 0.38) and fungal richness and diversity (r = 0.27, r = 0.29) correlated positively and significantly (p < 0.001) with indoor RH respectively (Table 4). A similar pattern was observed among controls.

Table 4

Spearman’s rank correlation coefficient between indoor microbial concentrations/diversity and environmental/household parameters among cases and controls
Indoor Microbiome	Cases						Controls
	Indoor Temp. (^oC)		Indoor RH (%)		Occupancy		Indoor Temp. (^oC)		Indoor RH (%)		Occupancy
	rho	p-value	rho	p-value	rho	p-value	rho	p-value	rho	p-value	rho	p-value
Total bacterial count (TBC) cfu/m³	0.16*	0.04	0.50*	< 0.001	0.06	0.47	0.23*	0.002	0.43*	< 0.001	0.10	0.17
Staphylococcus aureus	0.07	0.34	0.08	0.30	0.03	0.73	0.06	0.42	0.21*	0.01	0.01	0.86
Staphylococcus epidermidis	0.08	0.29	0.07	0.35	0.01	0.96	0.09	0.26	0.16*	0.03	0.01	0.93
Streptococcus pneumoniae	0.06	0.41	0.12	0.10	0.01	0.86	0.05	0.52	0.16*	0.03	-0.03	0.67
Streptococcus pyogenes	0.07	0.34	0.09	0.24	0.05	0.49	0.02	0.75	0.21*	0.01	-0.03	0.65
Klebsiella aerogenes	0.09	0.23	0.27*	< 0.001	-0.03	0.68	-0.01	0.94	-0.17	0.10	-0.07	0.38
Micrococcus spp.	0.13	0.09	0.25*	0.001	0.04	0.63	0.13	0.08	0.03	0.71	0.12	0.11
Pseudomonas fluorescens	0.03	0.67	0.28*	< 0.001	-0.02	0.77	-0.03	0.73	-0.10	0.19	0.05	0.53
Unidentified colonies	0.01	0.96	0.25	0.001	-0.03	0.65	-0.07	0.37	-0.11	0.16	0.05	0.51
Total fungal count (TFC) cfu/m³	0.02	0.75	0.64*	< 0.001	-0.01	0.90	0.13	0.09	0.49*	< 0.001	-0.05	0.50
Aspergillus niger	0.05	0.51	0.16	0.04	0.15	0.05	-0.02	0.81	0.08	0.27	-0.10	0.18
Aspergillus fumigatus	0.04	0.57	0.15	0.05	-0.08	0.30	0.12	0.11	0.27	< 0.001	0.01	0.99
Penicillium spp.	0.04	0.61	0.26*	< 0.001	0.15*	0.04	0.03	0.71	0.07	0.33	-0.01	0.99
Alternaria alternata	0.07	0.33	0.10	0.17	-0.03	0.70	0.01	0.88	0.14	0.06	-0.01	0.95
Fusarium oxysporum	0.05	0.48	0.28*	< 0.001	0.01	0.87	0.07	0.34	0.09	0.22	-0.11	0.15
Candida albicans	0.03	0.74	0.25*	0.001	-0.02	0.82	0.01	0.95	0.08	0.29	0.16*	0.04
Cladosporium spp.	0.08	0.29	-0.07	0.38	0.02	0.80	0.05	0.49	0.05	0.53	-0.03	0.69
Unidentified colonies	0.08	0.28	-0.02	0.82	-0.05	0.48	0.01	0.87	-0.02	0.82	0.08	0.31
Bacterial richness	0.16*	0.03	0.39*	< 0.001	0.06	0.43	0.10	0.18	0.08	0.28	-0.04	0.57
Fungal richness	0.06	0.45	0.27*	< 0.001	0.04	0.58	0.05	0.52	0.32*	< 0.001	-0.01	0.86
Bacterial Diversity^†	0.17*	0.03	0.38*	< 0.001	0.08	0.29	0.10	0.19	0.08	0.28	-0.03	0.65
Fungal Diversity^†	0.07	0.37	0.29*	< 0.001	0.05	0.50	0.07	0.38	0.30*	< 0.001	0.01	0.86
IMC cfu/m³	0.16*	0.04	0.56*	< 0.001	0.05	0.55	0.22*	0.003	0.47*	< 0.001	0.10	0.19
cfu/m³ – colony forming unit per meter cube; rho – correlation coefficient; RH – relative humidity; temp. – temperature;
^† - Simpson’s Diversity Index; * - P < 0.05; IMC – Indoor Microbial Count

Table 5

Adjusted associations (aOR and 95% CI) for LRTI risk among under-five children using conditional multiple logistic regression model
Predictors	Crude OR (95% CI)	IMC	Indoor TBC	Indoor TFC	Bacterial richness (R)	Fungal richness (R)	Bacterial diversity^†	Fungal diversity^†	Visible mould in CSA
Predictors	Crude OR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)
High levels of Exposures
IMC	3.60 (2.24–5.79)*	2.67 (1.44–4.97)*
Indoor TBC	3.30 (2.09–5.22)*		2.51 (1.36–4.65)*
Indoor TFC	3.42 (2.12–5.50)*			2.75 (1.54–4.89)*
Bacterial richness (R)	2.60 (1.66–4.06)*				1.84 (1.06–3.19)*
Fungal richness (R)	3.77 (2.17–6.55)*					3.17 (1.65–6.07)*
Bacterial diversity^†	2.64 (1.69–4.11)*						1.87 (1.08–3.24)*
Fungal diversity^†	3.58 (2.05–6.23)*							3.00 (1.55–5.79)*
Visible mould	5.18 (1.77–15.19)*								3.15 (1.36–15.02)*
Home characteristics
Occupant density; >5	2.16 (1.30–3.59)*	2.77 (1.37–5.60)*	2.97 (1.47-6.00)*	2.71 (1.32–5.57)*	2.93 (1.46–5.88)*	2.89 (1.40–5.84)*	2.90 (1.45–5.82)*	2.99 (1.47–6.10)*	3.19 (1.59–6.42)*
Pet ownership; any pet	2.25 (1.42–3.58)*	1.53 (0.84–2.80)	1.46 (0.79–2.69)	1.80 (0.98–3.30)	1.75 (0.98–3.15)	1.77 (0.98–3.22)	1.75 (0.97–3.14)	1.75 (0.97–3.18)	1.79 (1.00-3.22)*
Any smoker; yes	2.11 (0.96–4.67)	2.05 (0.71–5.89)	2.02 (0.70–5.80)	1.81 (0.63–5.18)	2.42 (0.87–6.71)	2.08 (0.71–6.13)	2.42 (0.87–6.71)	2.06 (0.70–6.06)	2.67 (0.93–7.70)
Primary cooking fuel; mixed	2.25 (1.14–4.44)*	2.88 (1.15–7.22)*	2.90 (1.16–7.26)*	2.44 (1.00-5.97)*	2.56 (1.04–6.33)*	2.67 (1.07–6.66)*	2.55 (1.03–6.31)*	2.68 (1.08–6.66)*	2.90 (1.18–7.12)*
House ownership; rented	2.17 (1.33–3.56)*	2.51 (1.27–4.97)*	2.57 (1.29–5.09)*	2.43 (1.22–4.85)*	2.64 (1.35–5.17)*	2.68 (1.35–5.34)*	2.63 (1.34–5.14)*	2.68 (1.35–5.31)*	2.66 (1.38–5.12)*
Wet season	1.75 (1.06–2.89)*	1.44 (0.70–2.99)	1.45 (0.70–2.98)	1.05 (0.52–2.12)	1.11 (0.56–2.20)	1.00 (0.50–1.99)	1.12 (0.56–2.23)	1.03 (0.52–2.05)	1.00 (0.51–1.96)
Child’s characteristics
History of LRTI; yes	4.09 (1.35–12.38)*	11.78 (1.15–21.22)*	11.69 (1.16–20.16)*	13.73 (1.32–24.35)*	12.06 (1.31–20.05)*	9.87 (1.08–19.40)*	12.09 (1.31–19.24)*	9.79 (1.07-17-53)*	11.31 (1.27–21.86)*
No. of siblings; ≥1	2.51 (1.16–5.43)*	2.58 (1.05–6.33)*	2.63 (1.07–6.45)*	2.35 (0.94–5.89)	2.56 (1.05–6.25)*	2.65 (1.06–6.62)*	2.56 (1.05–6.25)*	2.67 (1.08–6.60)*	2.42 (1.00-5.91)*
Non-exclusive breastfeeding	2.45 (1.41–4.25)*	2.18 (1.08–4.38)*	2.18 (1.09–4.35)*	2.31 (1.13–4.74)*	2.10 (1.06–4.15)*	2.29 (1.13–4.67)*	2.10 (1.06–4.15)*	2.31 (1.13–4.70)*	2.01 (1.02–3.95)*
Pseudo R²		0.23	0.22	0.23	0.20	0.22	0.20	0.23	0.20
Note:
*p < 0.05
^†Simpson Diversity Index (D) was used as a measure of microbial diversity in the model
Exposures > median value is considered as high levels of exposures
All multivariate models were adjusted for age, sex, geographical location, occupancy, pet ownership, ETS, season, house ownership, history of
LRTI, number of siblings under-five and breastfeeding status.
OR - odds ratio
aOR - adjusted odds ratio CSA – Child’s sleeping area
CI – confidence interval
TBC – total bacterial count
IMC – Indoor Microbial count
TFC – total fungal count

Seasonal variation in the IMC, TBC and TFC stratified by case/control status is shown in supplementary table S1(A and B). Comparing wet versus dry seasons, the median IMC (729 vs 484 cfu/m³), TBC (659 vs 418 cfu/m³) and TFC (70 vs 53 cfu/m³) in homes of cases were significantly different. Similar seasonal differences were observed among controls. In homes of cases, bacterial and fungal communities were significantly more diverse in the wet season (R = 3.00; H = 1.05; D = 2.78 and R = 3.00; H = 1.04; D = 2.67) than the dry season (R = 2.00; H = 0.64; D = 1.80 and R = 2.00; H = 0.63; D = 1.78). A similar pattern was observed among controls (Fig. 2A and B).

When adjusted by key covariates, exposure to above median levels of lMC (OR = 2.67, 95% CI = 1.44–4.97), TBC (OR = 2.51, 95% CI = 1.36–4.65), TFC (OR = 2.75, 95% CI = 1.54–4.89), bacterial richness (OR = 1.84, 95% CI = 1.06–3.19), fungal richness (OR = 3.17, 95% CI = 1.65–6.07), bacterial diversity (OR = 1.87, 95% CI = 1.08–3.24) and fungal diversity (OR = 3.00, 95% CI = 1.55–5.79), showed statistically significant associations with LRTI. Of note, the variable “presence of visible moulds in child’s sleeping area”, also showed a more than three-fold increased odds of LRTI (95% CI = 1.36–15.02). Apart from these variables of interest, it is important to note that history of LRTI and non-exclusive breastfeeding were consistently associated with an increased risk of LRTI among under-five children in all models. Also, home characteristics such as occupant density, house ownership, and use of dirty cooking fuel were associated with risk of LRTI among under-five children.

In this case-control study of children under five years of age from Ibadan, Nigeria, we found a significantly increased risk of LRTI among under-five children in the higher categories of indoor microbial exposures, after adjusting for covariates such as housing and child characteristics. Depending on the exposure considered, there was a 2–3 fold statistically significant increase in risk of LRTI among under-five children, with the indices determining exposure to fungal aerosols being at the higher end of this range.

We found that quantitative measures of exposure to indoor microbial aerosols such as IMC, TBC, and TFC, and bacterial and fungal alpha-diversity measures were positively associated with LRTI risk among under-five children. This finding is consistent with the few available studies that employed similar methods.[19, 29] A systematic review and meta-analysis[16] found exposure to high concentrations of indoor microbial aerosols to be linked with an increased risk of LRTI among under-five children (pooled OR = 1.20; 95% CI = 1.11–1.33), with a greater risk from exposure to TFC (pooled OR = 1.27; 95% CI = 1.13–1.33). A similar study carried out among students in China found no association between overall microbial richness and respiratory infection (OR = 1.00; 95% CI = 0.83–1.21), but reported a link between abundance of several microbial genera in the Gammaproteobacteria class and occurrence of respiratory infection [30].

The bacterial genera observed in our study commonly occur in the indoor and outdoor environment but could become opportunistic in immunocompromised individuals.[31] A recent clinical study reported Streptococcus pneumoniae as the leading pathogen of LRTI even with the introduction of 13-valent pneumococcal conjugate vaccine,[32] followed by Hemophilus influenza and Klebsiella pneumoniae.[33] However, similar studies in Nigeria, reported Klebsiella pneumoniae as the most detected pathogen [34] in LRTIs. The fungal genera identified in our study were similar to the report by Ana et al., [35] who found that the dominant fungal species in home of under-five children with and without acute respiratory infections was Aspergillus spp. Furthermore, we observed a significant microbial diversity during the wet compared to dry seasons among cases and controls respectively which may suggest a seasonal variation in the exposure to indoor microbiota. Although indoor temperature and RH were found to be significantly correlated with indoor microbial community, it does not necessarily imply that meteorological variables exert a direct control on the microbial communities.

Proxy measures of microbial exposure such as presence of visible moulds on surfaces in the house, often used in studies, [18, 29] were found to result in similar associations with LRTI as the quantitative measures of microbial exposures employed in this study, but the quantitative measures provide useful evidence of the composition and diversity of the microbial community which cannot be obtained using the proxy measures. According to the current study, a greater risk of LRTI among under-five children was associated with exposure to fungal aerosols. Stark et al.,[19] in their study to determine the association between higher in-home fungal concentration and LRTI risk in infants reported a significantly increased risk of LRTI (RR = 1.86, 95% CI = 1.21–2.88). Of note, they also reported that presence of visible mould growth was an independent predictor of LRTI in the first year of life (OR = 1.34, 95% CI = 0.99–1.82).[19] In line with our findings, a case-control study carried out in New Zealand among under-five children to investigate the dose-response association of objectively assessed housing quality measures, particularly the presence of visible moulds presented as the damp-mold index (DMI) and hospitalization with acute respiratory infections (ARI) showed a significant adjusted dose-response relationship (aOR = 1.15; 95% CI = 1.02–1.30).[18] The explanatory biological mechanism could be that prolonged exposure to aerosolized fungal components mainly target the respiratory and nervous system causing specific pathological changes in the host characterized by inflammation of the mucosal lining of the airways.[36] Relevant studies both in vitro and in vivo have demonstrated that repeated activation of immune responses and inflammation from fungal exposures may contribute to inflammation-related diseases, and the resulting inflamed mucosal tissue may provide a diminished barrier to respiratory infections.[37]

The high microbial concentrations and diversity recorded in homes of cases could be attributable to the increased occupant density, reduced ventilation and high indoor RH in homes of cases and controls. A modest positive correlation was recorded between indoor microbial exposure variables and indoor RH which was corroborated with previous reports by Frankel et al., [38] who found that indoor RH correlated positively with indoor fungal exposure (r = 0.32, p = 0.002). Increased relative humidity, such as > 80%, contributes to microbial survival ,[39] and antigenic potential from fungi,[40] and can better facilitate the direct-contact transfer of microorganisms.[41] Although the microbial concentrations and diversity in our study was not significantly correlated with occupant density, a number of studies have demonstrated that occupancy is associated with increased microbial burden, and diversity and abundance of human-associated microbes in indoor environment.[42, 43] This discrepancy is probably due to the differences in the nature of the environment where these studies were carried out.

In our models, we observed that child characteristics such as history of LRTI, > 1 under five sibling and non-exclusive breastfeeding, and household factors including house ownership, high occupant density, and use of dirty cooking fuels in the household were also independently associated with LRTI risk among under-five children which are consistent with previous epidemiological studies.[44–46] Tobacco smoking was not found to be significantly associated with LRTI risk but was a potential risk factor. This is probably due to the fact that smoking is not encouraged in the environment where this study was carried out and only very few individuals reported their smoking status. Of note, even after adjusting for these factors, all microbial exposures variables remained significantly linked to LRTI risk among under-five children.

The mean age of subjects recorded in the current study suggests that the majority of under-five children with LRTI treated at the participating hospitals were below 12 months of age. This is comparable to the report by Ahmed et al., in their study to determine the risk factors for acute lower respiratory tract infections (ALRTI) among hospitalized under-five children in Northern Nigeria. They reported children between 2 to 12 months of age to have accounted for 56.0% of hospitalization due to ALRTI [47]. Studies in other developing countries such as Ethiopia [48] and Rwanda [49] also showed similar findings. The preponderance of male gender among under-five children with LRTI observed in the current study is also similar to previous studies on LRTI among under-five children [47, 49].

An important strength of our study was the objective measure of microbial exposure from culture-dependent methods which provided detailed information of the microbial concentrations and biodiversity in terms of alpha-diversity indices, as compared to other studies that employed proxy measures. Although the culture-dependent method has been reported to underestimate the microbial community by not accounting for non-culturable microbes, it has been proven to provide vital information in the study of microbial diversity.[50] The estimation of microbial diversity using species richness, Shannon and Simpson Diversity Indices was a unique step that gave insight into the microbial composition and seasonal pattern in this environment. In addition, the clinical outcomes used in the study, particularly for the case definition, with the diagnosis of LRTI based on chest radiography was a major strength as this help minimize the risk of misclassification.

This study was limited by the exclusion of patients with less severe LRTIs who sought alternative therapies outside of hospital settings. Another limitation of the study is the use of respiratory signs and symptoms to define LRTI among community controls which could have introduced some outcome definition bias. This was minimized by actively engaging a paediatrician in the assessment of the controls. Responses from parents/caregivers of a child with severe conditions may be influenced by the state of the child, thereby introducing some response bias. Also, the participants’ households especially for cases may have changed the practices before home visitation, therefore the observed measured concentrations could be lower compared to when the children were infected, resulting in underestimation of the risk associated with microbial exposure. However, this was minimized by a short lag from entry into the study, and the home survey.

This study provides comprehensive epidemiological evidence of the microbial composition and diversity in residential environments in sub-Saharan Africa in relation to LRTI among under-five children. Our study suggests that exposure to indoor microbial aerosols is independently associated with LRTI risk among under-five children. The increased risk was most pronounced for fungal aerosols. Therefore, indoor microbial exposure-tailored intervention should be considered in the management of LRTI among children under the age of five years.

Ethics Approval and Consent to participate

The study was approved by the Biomedical Research Ethics Committee of the University of KwaZulu-Natal (Ref No: BE545/17), the University of Ibadan/University College Hospital Ethics Committee (Ref No.: UI/EC/17/0077) and the Oyo State Research Ethical Review committee (Ref. No.: AD13/479/462). Gatekeeper permission to access the participating hospitals was obtained from the Oyo State Ministry of Health and the authorities of the participating hospitals. The child caregiver provided informed consent for participation in the study, and they had the option to withdraw at any point.

Availability of data and materials

All data generated or analysed during this study are included in this published article and its additional files.

Consent for publication

Not applicable

Conflicts of Interest:

The authors declare no conflict of interest.

Funding: None

Author Contributions: Conceptualization, writing-original drafted preparation, statistical analysis - A.G.F.; R.N.N and N.J. participated in study design and conceptualization, reviewed the study instruments, guided the statistical analysis, provided extensive review of the drafts; L.A.M.S. writing—review and editing. All authors have read and agreed to the final version of the manuscript

Acknowledgements: We acknowledge the support received from the College of Health (CHS) PhD Scholarship of the University of KwaZulu-Natal to facilitate field activities and the Institute of Risk Assessment, Utrecht University. We are also grateful to the three participating hospitals for their support.

We would like to appreciate Regina Oladokun of the Department of Paediatrics, University College Hospital, and Elizabeth Soyemi of the Department of Microbiology and Parasitology, University College Hospital for facilitating the initiation of the study and Prof Dick Heederik for his comments.

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Indoor Bacterial and Fungal Aerosols as Predictors of Lower Respiratory Tract Infections Among Under-five Children in Ibadan, Nigeria

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Abstract

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Introduction

Materials And Methods

Study Setting and Design

Definition and Selection of Cases and Controls

Data Collection and Procedure

Clinical and Health Data

Home Walkthrough Assessments

Indoor Environmental Monitoring and Microbial Estimation

Indoor Environmental conditions

Air sampling and Microbiological analysis

Microbial alpha-diversity measures

Statistical Analysis

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