Risk factors for asthma among schoolchildren who participated in a case-control study in urban Uganda
Asthma is estimated to affect more than 235 million people globally , and is the most common non-communicable condition among children ( World Health Organisation , 2017 ) . In Africa , the prevalence of asthma appears to be increasing ( Asher et al . , 2006; Addo-Yobo et al . , 2007; van Gemert et al . , 2011; Zar et al . , 2007; Lawson et al . , 2017 ) , particularly in urban areas ( Addo-Yobo et al . , 2007; Morgan et al . , 2018 ) , but the causes of this increase are not fully understood . Moreover , asthma has various phenotypes which may have different aetiologies ( Lötvall et al . , 2011 ) . Asthma risk factors appear to vary internationally , and to differ between high-income countries ( HICs ) and low-and-middle income countries ( LMICs ) ( Douwes and Pearce , 2002 ) . There is little previously published data on asthma risk factors from Africa . The few studies reported have suggested that current residence in urban areas is associated with a higher risk of asthma than rural residence in Africa ( Botha et al . , 2019; Addo-Yobo et al . , 2001 ) and other LMICs ( Robinson et al . , 2011; Gaviola et al . , 2016 ) . The association between helminth infections and asthma has been investigated in Africa and other LMICs , but the findings have been inconsistent across studies ( Leonardi-Bee et al . , 2006; Mpairwe and Amoah , 2019 ) . Other risk factors for asthma in Africa and other LMICs , similar to those in HICs , include maternal smoking ( Ayuk et al . , 2018; Arrais et al . , 2019 ) , maternal history of asthma ( Nantanda et al . , 2013 ) , childhood atopic sensitisation ( Addo-Yobo et al . , 2001; Nyembue et al . , 2012 ) and history of allergy ( Nantanda et al . , 2013; Mehanna et al . , 2018 ) . Previous reports suggest no association between biomass fuels and asthma risk ( Thacher et al . , 2013; Oluwole et al . , 2017a ) , but increased asthma symptoms ( Oluwole et al . , 2017a; Oluwole et al . , 2017b ) . Unlike in HICs , higher parental education and socioeconomic status has been associated with asthma among children in Africa ( Addo-Yobo et al . , 2007; Nantanda et al . , 2013; Wolff et al . , 2012 ) . We undertook a case-control study among schoolchildren in an urban area in Uganda , to investigate the main risk factors for asthma and the patterns of allergic sensitisation .
We enrolled participants from 55 schools ( 32 primary , 23 secondary ) . Of the 6385 children initially identified from the pre-screening exercise , we were unable to contact 4550 parents/guardians in time for them to attend the parents’ meeting; most of these children were in the boarding section ( Figure 1 ) . Of the 1835 who attended the meeting , 97% provided written informed consent for their child to participate in the study . We screened 1779 participants and of these , 77 who had initially reported breathing problems either did not have an asthma diagnosis or did not have asthma symptoms in the last 12 months and were excluded . We enrolled 562 children with and 1140 without asthma , but excluded thirty-two with incomplete data ( Figure 1 ) . At enrolment , 477 asthma cases successfully performed the spirometry , and only three of these had FEV1 values less than 80% of predicted values . Participants had mean age 11 years ( range 5–17 years ) ; children with asthma were slightly older , and more likely to have parents with a tertiary education and a reported history of asthma ( Table 1 ) . Compared to children born in rural Uganda , children born in any town in Uganda or in the city had an increased risk of asthma [adjusted OR ( 95% confidence interval ( CI ) ) 2 . 16 ( 1 . 60–2 . 92 ) ] and [2 . 79 ( 1 . 79–4 . 35 ) ] , respectively . The same pattern was observed for the area where the child spent most of their early life ( 0–5 years ) ( Table 1 ) . There were no differences in reported exposure to farm animals , or to cigarette smoke during pregnancy . Children with asthma were less likely to have a BCG scar [0 . 67 ( 0 . 51–0 . 89 ) ] ( Table 1 ) , but the TST response ( induration >10 mm ) at enrolment was similar between cases ( 15 . 6% ) and controls ( 14 . 4% ) [1 . 03 ( 0 . 67–1 . 58 ) ] . There was no statistical evidence of interaction between parental education and the children’s area of residence in early life , nor interaction between age and any of the asthma risk factors . Asthma cases were more likely to report a high frequency of ‘trucks passing on the street near their home’ [2 . 28 ( 1 . 52–3 . 43 ) ]; to come from homes that used electricity/gas for indoor cooking [1 . 58 ( 1 . 16–2 . 17 ) ]; and to report having used de-worming medication more than twice in the last 12 months [2 . 18 ( 1 . 62–2 . 93 ) ] than controls ( Table 2 ) . There was weak evidence for an inverse association between asthma and infection with any helminths species [0 . 75 ( 0 . 53–1 . 07 ) ] ( Table 2 ) ; overall this was not statistically significant as shown by the fact that the confidence interval included the null value of 1 , but it was significant for T . trichiura [0 . 33 ( 0 . 13–0 . 89 ) ] ( Supplementary file 1a ) . Children with asthma were more likely to report a history of allergic diseases such as allergic rhinitis , conjunctivitis , eczema and urticarial rashes ( Table 1 ) and to have these conditions currently ( Table 2 ) . The prevalence of current exposure to cigarette smoke ( in the household ) was similar among cases and controls , although only about 11% were exposed ( Table 2 ) . Children with asthma were more likely to have allergic sensitisation: SPT positive to at least one of seven whole allergen extracts [2 . 40 ( 1 . 92–3 . 00 ) ]; and elevated asIgE levels to any of three whole allergen extracts [2 . 45 ( 1 . 53–3 . 91 ) ] , and higher total IgE ( Table 3 ) . The most common allergens were dust mites and cockroach . Asthma cases were more likely to have elevated FENO levels [2 . 57 ( 2 . 01–3 . 29 ) ] ( Table 3 ) . We investigated the relative importance of area of residence at birth versus the first years of life , on the asthma risk , by looking at children who migrated between rural and urban areas during these two periods . Children born and raised in rural areas had the lowest risk ( the reference group ) ; children born and raised in urban areas had the highest risk [2 . 55 ( 1 . 80–3 . 61 ) ] , children born in the urban area who migrated and spent most of 0–5 years in rural areas still had an increased risk of asthma [2 . 11 ( 1 . 14–3 . 91 ) ] , unlike children who were born in rural areas but migrated and spent most of 0–5 years in urban areas ( Table 4 ) . We investigated the combined effects of the child’s area of residence at birth and parental history of allergic disease . Even among children with no parental history of allergic disease , compared to being born in a rural area , being born in a town [2 . 15 ( 1 . 35–3 . 44 ) ] or in the city [3 . 33 ( 1 . 61–6 . 90 ) ] was associated with an increased risk of asthma , Table 5 . Compared to the same reference group , children with a parental history of allergic disease had a higher risk of asthma that increased steadily from among children born in rural areas [2 . 78 ( 1 . 58–4 . 89 ) ] , in a town [5 . 16 ( 3 . 25–8 . 17 ) ] and in the city [5 . 54 ( 2 . 99–10 . 26 ) ] ( Table 5 ) . We also investigated the combined effects of area of residence at birth and children’s atopic status ( positive SPT to any of seven allergens ) . Taking non-atopic children born in rural areas as the reference group , we found that non-atopic children born in town had a modest increase in asthma risk [1 . 72 ( 1 . 16–2 . 55 ) ] , which increased further among city-born children [2 . 63 ( 1 . 41–4 . 91 ) ] , Table 6 . However , atopic children had a modestly increased risk of asthma even if they were born in the rural area [1 . 77 ( 1 . 02–3 . 05 ) ] , which increased substantially among atopic children born in town [4 . 61 ( 3 . 07–6 . 90 ) ] or in the city [4 . 67 ( 2 . 56–8 . 52 ) ] , Table 6 . We investigated the combined effects of parental education and urbanicity on asthma risk , by looking at the father’s education level in combination with the child’s area of residence at birth . We found that for each level of father’s education , there was an increasing risk gradient from rural-town-city; for each area of residence , increasing father’s education level was associated with increased asthma risk; the highest risk of asthma was among children born in the city whose fathers had a tertiary education [6 . 95 ( 3 . 52–13 . 71 ) ] ( Table 7 ) . The same pattern in Table 7 was seen for mother’s education and for child’s area of residence in the first five years of life . For the combined effect of atopy ( SPT positive ) and parental history of allergic disease , we found that asthma risk was more than five times among children with both atopy and a parental history of allergic disease [5 . 74 ( 4 . 07–8 . 10 ) ] ( Supplementary file 1b ) . However , children who had both parents with a history of allergic disease had an effect size similar to children who had only one parent with a history of allergic disease [2 . 49 ( 1 . 74–3 . 58 ) ] ( Supplementary file 1c ) .
The risk of asthma among schoolchildren in urban Uganda is strongly predicted by their area of residence in early life , particularly at birth , with the highest risk among children whose early life is spent in small towns and in the city . This risk increases further in the presence of other asthma risk factors such as parental history of allergic disease , children’s own atopy , higher measures of socio-economic status and urbanicity . Given the current rapid urbanisation in Africa and other LMICs , the prevalence of asthma is likely to increase further . This study provides the basis for future studies investigating environmental and lifestyle factors that increase asthma risk in the urban areas of LMICs .
We conducted an un-matched case-control study among schoolchildren , and report following STROBE guidelines ( von Elm et al . , 2014 ) . The study base was determined a priori as schoolchildren , 5–17 years old , in primary and secondary schools in Entebbe Municipality and Katabi zone in Wakiso District , Central Uganda . This was a predominantly urban ( town ) setting . All schools in the study area were invited and 96% participated . Study enrolment was between May 2015 and July 2017 . We estimated that a sample size of 2112 would have 80% power to detect odds ratio ( OR ) <0 . 5 or>1 . 5 for exposures with prevalence 10% among controls . At each school , we pre-screened by registering all children with any breathing problems . We concurrently randomly selected from the class register two children without any breathing problems , using a random number generator programme in STATA ( StataCorp , Texas , USA ) . The children delivered invitation letters to their parents; parents/guardians with telephones were invited to attend a meeting during which those interested provided written informed consent , and children aged ≥8 years provided written informed assent . Following consent and assent , we screened all participants with the International Study of Asthma and Allergies in Childhood ( ISAAC ) questionnaire ( Asher et al . , 1995 ) . Children with a history of wheezing in the last 12 months underwent a detailed medical history and examination by study clinicians , to diagnose asthma . Asthma was defined as a history of recurrent symptoms of wheezing , cough ( mostly dry , worse at night and morning ) and/or difficulty in breathing experienced in the last 12 months , with or without forced expiratory volume in the first second ( FEV1 ) <80% expected for age , sex and height for African children . Additional medical history included a prior physician diagnosis and good response to asthma medication . If the diagnosis was not straightforward , two clinicians reviewed the participant and if they disagreed , that participant was excluded in order to ensure proper case ascertainment . Children with a history of wheeze or any asthma symptoms but not in the last 12 months were excluded . Controls were defined as having no history of wheeze or any other asthma symptoms . There were no other exclusion criteria . The study was approved by the Uganda Virus Research Institute Research and Ethics Committee , and the Uganda National Council for Science and Technology . We collected data about asthma risk factors and allergic conditions identified in literature using interviewer-led questionnaires to parents and adolescents , including the ISAAC questionnaire ( Asher et al . , 1995 ) . When a parent was not available in person and the participant answering the questionnaire did not know that parent’s history of allergy , we telephoned the parent for the relevant information . We looked for the presence of a Bacillus Calmette–Guérin ( BCG ) scar since BCG vaccination has been inversely associated with asthma previously ( El-Zein et al . , 2010 ) . We tested for fractional exhaled nitric oxide ( FENO ) , using a hand-held device ( NoBreath from Bedfonf Scientific , Maidstone , United Kingdom ) , and used the manufacturer’s cut-off for children of >35 parts per billion . FENO is considered a biomarker of allergic airway inflammation ( Hoyte et al . , 2018 ) . We conducted lung function tests for asthma cases using a hand-held spirometer ( Micro 1 Diagnostic Spirometer , CareFusion , Chatham Marine , United Kingdom ) . Skin prick tests ( SPT ) and allergen-specific IgE ( asIgE ) are important measures of allergic sensitisation ( Heinzerling et al . , 2013 ) . We conducted ( SPT ) following standard procedures ( Heinzerling et al . , 2013; Mpairwe et al . , 2008 ) , and crude extracts of seven allergens ( Dermatophagoides mix of D . farinae and D . pteronyssminus , Blomia tropicalis , Blattella germanica , peanut , cat , pollen mix of weeds , mould mix of Aspergillus species; ALK Abello , Hoersholm , Denmark ) . A positive response was a wheal diameter >3 mm measured after 15 min , with a negative saline control and positive histamine . We collected blood samples which we processed to obtain plasma that we stored at −80°C . At the end of the study , we randomly selected 200 plasma aliquots from all asthma cases and 200 from all controls for measurement of asIgE to whole allergen extracts ( D . pteronyssinus , B . germanica and peanut ) , using ImmunoCAP ( Phadia , Uppsala , Sweden ) . The standard cut-off for allergic sensitisation of >0 . 35 allergen-specific kilo units per litre ( kUA/L ) was used ( Key resources table ) . Because previous studies have reported an association between asthma and helminths ( Mpairwe and Amoah , 2019 ) , we collected three fresh stool samples from each participant and tested for intestinal helminths using the Kato Katz method ( Katz et al . , 1972 ) . The findings on the association between tuberculin skin test ( TST ) and allergy/asthma have been inconsistent ( Obihara et al . , 2006; Eifan et al . , 2009 ) . We investigated this association by performing the TST using standard procedures , as we have previously described ( Nkurunungi et al . , 2012 ) . Data were double-entered using OpenClinica open source software version 3 . 1 . 4 ( OpenClinica LLC and collaborators , Waltham , MA , USA ) . Data were analysed in STATA . For continuous variables with clinically relevant cut-off points such as for SPT , asIgE , FENO and TST , we used the dichotomous variables in the analysis . Total IgE was analysed as a continuous variable . The variable for maternal or paternal history of ‘allergic disease’ combines the history of asthma , eczema , allergic rhinitis , allergic conjunctivitis and any other allergies ( Greenland et al . , 2016 ) . We conducted a complete case analysis , and did not impute missing values . The key variables adjusted for ( see below ) were age , sex , area of residence at time of birth and father’s education . Each of these variables ( except sex ) had small numbers of missing values ( the largest number was 29 for father’s education ) . We therefore created a ‘complete case’ data set , comprising the 555 cases and 1115 controls which had no missing values for these variables . This ‘complete case’ data set was used for all analyses . The odds ratio was the main outcome measure ( as is appropriate for a case-control study ) , and we also estimated the 95% CIs . We identified age and sex as a priori confounders , and all analyses were adjusted for these variables . We identified area of residence at time of birth and father’s education as potential confounders; we did not also adjust for area where the child spent most of the first five years or mother’s education , since these were strongly associated with the above two factors , and therefore would have introduced collinearity . Previous studies in this setting have found that father’s and mother’s education are significantly associated with socioeconomic status ( Aarø et al . , 2009 ) . We did not identify any factors which were likely to be on the causal pathways ( and therefore were not potential confounders and should not be adjusted for ) . We conducted initial logistic regression analyses for each exposure of interest , adjusted for age and sex , with random effects to allow for any clustering by school . We then ran the adjusted random effects logistic regression model ( i . e . adjusted for the above factors in addition to age and sex ) for each exposure of interest , and checked for collinearity by comparing the standard error of the exposure coefficient in the adjusted model and in the basic model ( Greenland et al . , 2016; Greenland and Pearce , 2015 ) . We did not find any problems of collinearity , so we reported the findings for the adjusted model for each exposure .
Data on asthma aetiology in Africa are scarce . We investigated the risk factors for asthma among schoolchildren ( 5–17 years ) in urban Uganda . We conducted a case-control study , among 555 cases and 1115 controls . Asthma was diagnosed by study clinicians . The main risk factors for asthma were tertiary education for fathers ( adjusted OR ( 95% CI ) ; 2 . 32 ( 1 . 71–3 . 16 ) ) and mothers ( 1 . 85 ( 1 . 38–2 . 48 ) ) ; area of residence at birth , with children born in a small town or in the city having an increased asthma risk compared to schoolchildren born in rural areas ( 2 . 16 ( 1 . 60–2 . 92 ) ) and ( 2 . 79 ( 1 . 79–4 . 35 ) ) , respectively; father’s and mother’s history of asthma; children’s own allergic conditions; atopy; and cooking on gas/electricity . In conclusion , asthma was associated with a strong rural-town-city risk gradient , higher parental socio-economic status and urbanicity . This work provides the basis for future studies to identify specific environmental/lifestyle factors responsible for increasing asthma risk among children in urban areas in LMICs .
Asthma is a chronic disease of the airways that leads to breathing difficulty and sometimes death: the condition affects about 235 million people worldwide , especially children . Scientists still do not know exactly what causes asthma , but studies in Europe and North America suggest that individuals born or raised in rural areas are less likely to be affected . However , few studies have examined asthma in African countries , where urbanization is often quickly increasing . Examining the factors associated with the disease as more people move to cities may provide new clues about how asthma emerges , and how to prevent it . To this end , Mpairwe et al . conducted a study with over 1 , 670 schoolchildren in Uganda . Those born or raised in rural areas were least likely to have asthma , but the risk doubled among children from small towns , and tripled in those born or who grew up in the city . Children whose parents had a higher education and socioeconomic status had the highest asthma risk , but more work is required to understand why this is the case . The study by Mpairwe et al . is the first step towards identifying environmental and lifestyle factors associated with increased asthma risk in Africa . Further studies may help scientists to understand how beginning life in a more urban area plays a role in the development of the disease .