article: 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 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.
summary: 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.
keywords: epidemiology and global health
  medicine
title: Risk factors for asthma among schoolchildren who participated in a case-control study in urban Uganda