Introduction
Asthma is a condition in which airways get narrow and swell and produce extra mucus that can make breathing difficult and trigger coughing, wheezing, and shortness of breath. Asthma is a public health concern affecting millions of productive age groups in developed and developing countries with prevalence rates 1 to 21% among adults (Asher & Pearce, 2014; Enilari & Sinha, 2019).
Asthma affects roughly 334 million people worldwide, and if the current trend continues, there will be a 25% increase in asthma cases over the next fifteen years, leading to numerous annual deaths. Asthma causes less than 1% of deaths, which is a low mortality rate compared to other chronic diseases but it causes 250,000 potentially avoidable fatalities worldwide (Ellwood et al., 2017).
Asthma was first identified as a disease in hospital OPD visits and admissions in Ethiopia after the middle of the 1970s, according to the EPHA and FMOH 2014-2016 National Strategic Action Plan (NSAP) (Ethiopian Public Health Association, 2012; Federal Ministry of Health Ethiopia, 2016). Despite limited documented research on the determinants of asthma, prevalence studies found varying prevalence rates. A systematic review of major NCDs epidemiology (Misganaw et al., 2014) as well as recent prevalence, and associated factor studies in Debrebirhan and Jimma town reported 29.6% and 4.9% rates, respectively (Tefereedgn & Ayana, 2018; Shine, Muhamud & Demelash, 2019).
Both in developed and developing nations, asthma causes considerable economic and social costs. The majority of the burden of asthma manifests as disability in those under 45 years old, but in older people, inadequate long-term care and a delay in receiving assistance during an attack are linked to premature mortality. As a result, asthma has significant negative economic, medical, and social effects on the patient, their family, and society, evidenced by job and school absenteeism, frequent ER visits, hospitalizations, and even mortality at any age (Cruz et al., 2010; Enilari & Sinha, 2019; Alem, Gebeyehu & Arega, 2020).
In order to achieve optimal asthma control, GINA and other stakeholders developed and routinely reviewed fixed international scientific treatment recommendations. Asthma was also listed as a priority in Ethiopia’s National Strategic Action Plan for preventing and controlling NCDs (Federal Ministry of Health Ethiopia, 2016). The document on the clinical and programmatic management of significant NCDs was also created in Ethiopia in 2016 based on the progress of the achievement of the global 2025 NCDs objective (Federal Ministry of Health Ethiopia, 2016). Asthma is still a public health concern in many regions of the world despite the local and international efforts done to reduce the disease’s burden.
Designing, organizing, and putting into practice prevention efforts to lessen the burden of asthma can be based on an evidence-based understanding of the modifiable determinants of the disease development. To the best of our knowledge, there have not been enough studies on the determinants of developing asthma in Ethiopia, particularly in the Tigray region. Therefore, this study was carried out to identify determinants of asthma among adult patients attending in the OPD of Tigray hospitals. This study can be used as a baseline for interested researchers, policymakers, planners, and implementers working on asthma prevention and control programs.
Material and MethodsStudy area and setting
A facility-based unmatched case-control study was conducted in four hospitals namely “Suhul, St Merry, Axum Referral, and Adwa hospitals”, located in Tigray regional state, Northern Ethiopia. The region is one of the nine regional states of Ethiopia. It is divided into seven zones with 52 Woredas and each zone is homogenous in terms of socio-economic, access, and availability of health services. The region has climatic characteristics of kola (semi-arid) 39%, Woinadega (warm temperate) 49%, and Dega (temperate) 12% (Tigray Regional Health Bureau, 2016). The total population of the region is 5,443,000 (49.2% male and 50.8% female) according to the 2019 population projection (UNICEF, 2019).
All adult patients (with and without asthma) who visited the selected governmental hospitals during the data collection period were the source of the population. Adult patients aged 18 years or older diagnosed with asthma were considered as cases and adult patients without asthma but visiting the selected hospital with other diseases during the study period were considered as control. Both cases and controls were selected using their medical record during their visit to the hospitals.
Confirmed cases of bronchiectasis, chronic bronchitis, chronic obstructive pulmonary disease, emphysema, lung cancer, cardiac illness, and diabetes were excluded from the study because these conditions have shared risk factors. Patients with significant respiratory distress due to an exacerbation requiring an ED visit or admission were excluded from the study and were guided to see a physician or assigned health professional to get help for their problems. For each study participant, the interview was conducted while waiting for the hospital’s regular service or exit.
Sample size and sampling techniques
The sample size was determined with the assumption of 80% power, 5% level of significance, control-to-case ratio 1:2, and 10% non-response rate taken from previous similar studies (Ibrahim, 2013; Verma & Pradesh, 2014; Fu et al., 2016). After calculating the sample size for each essential factor, the maximum calculated sample size was considered the final sample size. Thus, the final sample size was 728 (243 cases and 485 controls). Each study participant was selected through a systematic sampling technique with an interval of every third adult patient who visited the adult OPD in the selected hospitals (Table S1).
Data collection procedure
Data were collected through face-to-face interviews using structured and pretested questionnaires. Patient’s medical records were used to select the cases and controls. Both the cases and the controls responded to the questionnaire. The questionnaire was adopted from ECRHS II (European Community Respiratory Health Survey II Steering Committee, 2002) containing four major sections. The first section of the questionnaire was related to socio-demographic and allergy-disease-related factors. Environment-related questions were covered in the second and third sections of the questionnaire. The fourth section included questions that were behavior-related. The second, third, and fourth sections are the potential risk factor predictors. Serious asthmatic patients who required an ER visit or admission were excluded from the study and were advised to contact a doctor for symptom treatment.
Data processing and statistical analysis
The collected data were checked, coded, and entered into Epi-data Manager version 3.1 and then exported to SPSS version 25 for analysis (SPP Inc., Chicago, IL, USA). The entered data were cleaned and edited before subsequent analysis. Cross tabulations and summary statistics like mean and standard deviation were carried out. Bivariate and multivariable logistic regression models were fitted to identify the relationship between the independent variables (socio-demographic, allergy-disease related, environmental, and behavioral factors) and the dependent variable (asthma presence). Sex, residence, marital status, education, occupation, income, history of nasal allergy, skin allergy, family history of asthma, family history of skin/nasal allergy, exposure to freezing weather, NSAIDs use, seeking medical help, and history of severe respiratory infection in early childhood were among the first section of the questionnaire analyzed in the bivariate model. Job status, dust or smoke exposure in the house, outdoor dust or smoke exposure, proximity to road vehicle traffic, lifetime firewood use for cooking, presence of any dampness inside the house, door opening while cooking, having a separate room for cooking, possessing of pets, exposure to animal dander, presence of pets during childhood, physical inactivity, packed food use, BMI, vegetable and fruit use, alcohol use, ever smoked for as long as a year, presence of regular second smoke and non-smoke tobacco use were from environmental and behavioral variables fitted to the bivariate model. All variables with P < 0.2 in the bivariate logistic regression model were included in the multivariable logistic regression. Variables associated with asthma were determined in multiple logistic regression at P-value <0.05 and AOR of 95% CI. The model fitness was checked using the Hosmer and Lemeshow goodness of fit test statistic (P = 0.698).
Ethical clearance was obtained from the Research Ethical Committee (REC) of the School of Public Health and the IRB (Institutional Review Board) of the College of Health Sciences at Addis Ababa University with approval number 004/18/SPH. Moreover, official permission was secured from the Tigray regional health bureau. Written informed consent from participants was also gained before conducting the study. Participants’ information obtained from the questionnaire was kept confidential using data coding. Participants were also informed that participation was on voluntary basis.
Operational definitionAsthma:
is defined based on the diagnosis of a clinician in the health facility.
Door opening while cooking:
ask respondents if they open their door or window while cooking.
Lifetime firewood/coal users:
if respondents use wood, cow dung, or charcoal as a source of energy for cooking either formerly or currently or both.
House dampness or mold:
asking respondents if there is water damage, dampness, visible mold and mold odor in their house.
Physical inactivity:
Based on the World Health Organization definition failure to perform at least 150–300 min of moderate-intensity aerobic physical activity; or at least 75–150 min of vigorous-intensity aerobic physical activity; or an equivalent combination of moderate- and vigorous-intensity activity throughout the week.
Possession of pet:
if respondents keep pets in their house such as cats, dogs.
ResultsDescriptive analysisSocio-demographic and asthma related characteristics of respondents
Out of the entire 728 scheduled sample members, 698 (228 cases and 470 controls) participated in the interview, yielding a 95.9% response rate. More than half of the respondents, 60.7% of both cases and controls were males. The age of study subjects ranged from 18 to 89 (mean 41, SD 15.07) years, in which 46.7% of the cases and almost half of the respondents of the controls were in the age group of 35–55 years. The majority, 85% of cases and 86.8% of controls were orthodox in religion, and almost 98% were Tigrian by ethnicity. About 64% of cases and 58.3% of the controls were married; 32% of cases and 36% of controls were illiterate whereas the majority, 64% of cases and 51% of controls, are urban dwellers. More than half 53.5% of the cases and 66% of controls had medium household income (Table 1).
Table 1: Socio-demographic and asthma-related characteristics of respondents by asthma status in Tigray Hospitals, Northern Ethiopia, 2019 (n = 698).
Characteristics (n = 698) | Category | Asthma status | Total n (%) | |
---|---|---|---|---|
Cases n (%) | Controls n (%) | |||
Sex | Female | 94 (41.2) | 180 (38.3) | 274 (39.3) |
Male | 134 (58.8) | 290 (61.7) | 424 (60.7) | |
Age | 18–34 | 75 (32.9) | 193 (41.1) | 268 (38.4) |
35–55 | 123 (53.9) | 203 (43.2) | 326 (46.7) | |
Above 55 | 30 (13.2) | 74 (15.7) | 104 (14.9) | |
Religion | Orthodox | 195 (85.6) | 402 (85.5) | 597 (85.5) |
Muslim | 33 (14.4) | 68 (14.5) | 101 (14.5) | |
Ethnicity | Tigrian | 219 (96) | 454 (96.5) | 684 (98.0) |
Other | 9 (4) | 16 (2.3) | 14 (2.0) | |
Marital status | Married | 146 (64) | 274 (58.3) | 420 (60.2) |
Unmarried | 82 (36) | 196 (41.7) | 278 (39.8) | |
Education | Illiterate | 72 (31.6) | 171 (36.4) | 243 (34.8) |
Primary school | 56 (24.6) | 111 (23.6) | 167 (23.9) | |
Secondary school and above | 100 (43.9) | 188 (40) | 288 (41.3) | |
Residence | Urban | 146 (64) | 240 (51.1) | 386 (55.3) |
Rural | 82 (36) | 230 (48.9) | 312 (44.7) | |
Monthly Income | <1,000 | 36 (15.8) | 30 (6.4) | 66 (9.5) |
1,000–4,000 | 122 (53.5) | 308 (65.5) | 430 (61.6) | |
>4,000 | 70 (30.7) | 132 (28.1) | 202 (28.9) | |
Occupation | Unemployed | 65 (28.5) | 165 (35.2) | 230 (33) |
Employed | 163 (71.5) | 305 (64.8) | 85 (12.2) | |
History of Asthma in years (n = 228) | 1–15 years | 33 (14.5) | NA | 33 (14.5) |
16–30 years | 85 (37.3%) | NA | 85 (37.3) | |
More than 30 years | 110 (48.25) | NA | 110 (48.2) | |
Asthma attack last 12 months (n = 228) | Yes | 212 (92.9) | NA | 210 (96.3) |
No | 16 (7.01) | NA | 18 (3.7) | |
Asthma control level (n = 228) | Well-controlled | 77 (33.7) | NA | 77 (33.7) |
Less controlled | 102 (44.7) | NA | 102 (44.7) | |
Uncontrolled | 49 (21.5) | NA | 49 (21.5) | |
Asthma during the menstrual cycle (n = 107) | Gets Better | 0 | NA | 0 |
Gets Worse | 6 (5.6) | NA | 6 (5.6) | |
No change | 101 (94.4) | NA | 101 (94.4) | |
Asthma during pregnancy (n = 107) | Gets Better | 3 (2.8) | NA | 3 (2.8) |
Gets Worse | 29 (27) | NA | 29 (27) | |
No change | 75 (70.2) | NA | 75 (70.2) |
DOI: 10.7717/peerj.16530/table-1
Notes:
NA
Not applicable
Out of all study participants, it was shown that 58.8% of males and 41.2% of females had asthma. Of the 228 asthma cases, 48% had the condition for more than 30 years. The majority of cases (92.9%) had experienced at least one-time asthma symptom in the last 12 months. Regarding self-reported asthma control level, only 33.3% of the cases had a good control level of asthma in the previous three months. Looking at female asthmatic subjects, only 5.6% got their asthma worse with their monthly menstrual cycle, and 27% of females got their asthma worse during their pregnancy (Table 1).
Environmental and behavioral characteristics of the study participants
Small proportion: 26% reported that they experienced job-related respiratory problems and a significant proportion of the participants, 64% of them, used lifetime firewood/coal as a source of energy for cooking. Looking at carpet use, 80% of the participants do not have carpets in the mostly used room, and 11.3% of the carpets used stayed from 1 to 5 years. A total of 11.3% of the study participants possess pets, and from them, 93.6% of the pets are allowed to stay in the participant’s bedroom; 39.5% of the study participants own domestic animals such as cows, sheep, and goats, and 92.3% of the owners keep their domestic animals in a particular place. Concerning the behavioural factors, 26.4% drink alcohol, and only 4.2% of the study participants have a history of cigarette smoking (Table 2).
Predictors of asthma
Sex, age, residence, household income, ever had skin allergy, exposure to freezing weather, NSAIDs use, family history of asthma, family history of skin allergy, seeking medical help, history of severe respiratory infection in early childhood, dust/smoke inside the house, dust/smoke outside the house, the proximity of road traffic, door opening while cooking, lifetime firewood/coal use, having a separate cooking room, possession of pets, physical inactivity and packed food use were statistically associated with asthma in the bivariate logistic regression analysis model.
Variables which were significantly associated with asthma in the bivariate model were entered into multiple logistic regression analyses to control possible confounding factors. Urban residence, income less than 1,000 ETB, history of skin allergy, family history of asthma, house dust or smoke exposure, lifetime firewood use, door opening, house dampness, owning pets, and being physically inactive were found to be significant at p value <0.05 and 95% CI.
Table 2: Environmental and behavioural characteristics of respondents by asthma status in Tigray Hospitals, Northern Ethiopia, 2019 (n = 698).
Characteristics | Category | Asthma status | Total (%) | |
---|---|---|---|---|
Cases n (%) | Controls n (%) | |||
Ever had any job-related respiratory problem | Yes | 99 (43.4) | 84 (17.9) | 26.2 |
No | 129 (56.6) | 386 (82.1) | 73.8 | |
Firewood use for cooking | Yes | 152 (66.7) | 300 (63.8) | 64.8 |
No | 76 (33.3) | 176 (36.2) | 35.2 | |
Carpet use in the living room | Yes | 47 (20.6) | 92 (19.6) | 19.9 |
No | 181 (79.4) | 378 (80.4) | 80.1 | |
Carpet year of the Living Room | less than 1 year | 13 (5.7) | 31 (6.6) | 6.3 |
1–5 years | 26 (11.4) | 53 (11.3) | 11.3 | |
Above 5 years | 9 (3.9) | 7 (1.5) | 2.3 | |
No Carpet | 108 (78.9) | 379 (80.6) | 80.1 | |
Possession of pets | Yes | 56 (24.6) | 23 (4.9) | 11.3 |
No | 172 (75.4) | 447 (95.1) | 88.7 | |
Pets allowed in the bedroom | Yes | 53 (67) | 21 (26.6) | 93.6 |
No | 3 (3.8) | 2 (2.5) | 33.3 | |
Presence of Domestic animals | Yes | 92 (40.4) | 184 (39.1) | 39.5 |
No | 136 (59.6) | 286 (60.9) | 60.5 | |
Separate place for domestic animals | Yes | 85 (92.4) | 159 (86.4) | 92.3 |
No | 7 (5.5) | 25 (13.7) | 7.7 | |
Alcohol use | Yes | 65 (28.5) | 119 (25.3) | 26.4 |
No | 163 (71.5) | 351 (74.7) | 73.6 | |
Ever smoked cigarettes | Yes | 13 (5.7) | 16 (3.4) | 4.2 |
No | 215 (94.3) | 456 (96.6) | 95.8 | |
Second smoke exposure | Yes | 18 (7.9) | 23 (4.9) | 5.9 |
No | 210 (92.1) | 447 (95.1) | 94.1 | |
Childhood pet ownership | Yes | 107 (46.9) | 197 (41.9) | 43.6 |
No | 121 (53.1) | 273 (58.1) | 56.4 | |
Vegetable or fruit use | Yes | 67 (29.4) | 156 (33.2) | 31.9 |
No | 161 (70.6) | 314 (66.8) | 68.1 | |
Snack use | Yes | 64 (29.2) | 155 (70.8) | 32.7 |
No | 158 (33.2) | 315 (65.8) | 57.3 |
DOI: 10.7717/peerj.16530/table-2
The multiple logistic regression analyses showed that the odds of getting asthma among urban residents were 1.68 times higher than rural residents (AOR = 1.68; 95% CI [1.13–2.50]), the odds of getting asthma among respondents with monthly household income less than 1000ETB is 2.3 times higher than respondents with higher household monthly income (AOR = 2.3; 95% CI [1.17–4.56]). Moreover, the odds of getting asthma among respondents who had a history of skin allergy is 2 times higher compared to their peers (AOR = 2.09; 95% CI [1.14–3.86]), the odds of getting asthma among respondents who had a family history of asthma is 4.3 times higher compared to their peers (AOR = 4.26; 95% CI [2.63–6.91]). The odds of getting asthma among respondents exposed to dust/smoke in the house were three times higher than respondents who were not exposed to dust/ smoke in the house (AOR = 3.01; 95% CI [1.96–4.64]). Similarly, the odds of getting asthma among respondents who used firewood/coal in their lifetime were 5.4 times higher than those who do not use (AOR = 5.39; 95% CI [3.34–8.72]); however the odds of getting asthma among respondents who open their door were 35 percent lower than respondents who were not opening their door while cooking which is a protective factor (AOR = 0.35; 95% CI [0.26–0.55]). In regard to dampness, the model identified that the odds of getting asthma among respondents who have dampness inside their houses were 1.9 times higher than respondents who were no having dampness inside their houses (AOR = 1.98; 95% CI [1.07–3.68]). Cognizant with this, the odds of getting asthma among respondents who possess pets were 7.5 times higher than respondents who do not possess pets (AOR = 7.48; 95% CI [4.04–13.82]). The odds of getting asthma among physically inactive respondents were almost 1.8 times higher compared to their peers (AOR = 1.75; 95% CI [1.08–2.85]) (Table 3).
Table 3: Multivariable logistic regression analysis of socio-demographic, allergic, environmental, and behavioral determinants of asthma among adults in Tigray Hospitals, Northern Ethiopia, 2019 (n = 698).
Variables | Category | Asthma status | AOR | P value | |
---|---|---|---|---|---|
Cases (%) | Controls (%) | ||||
Residence | Urban | 146 (64) | 240 (51.1) | 1.68 (1.13–2.5) | 0.011 |
Rural | 82 (36) | 230 (48.9) | 1 | ||
Income (ETB) | <1,000 | 36 (15.8) | 30 (6.4) | 2.3 (1.17–4.56) | 0.016 |
1,000–4,000 | 122 (53.5) | 308 (65.5) | 0.75 (0.52–1.25) | 0.33 | |
>4,000 | 70 (30.7) | 132 (28.1) | 1 | ||
Ever had a skin allergy | Yes | 36 (15.8) | 36 (7.7) | 2.09 (1.14–3.86) | 0.018 |
No | 192 (84.2) | 434 (92.3) | 1 | ||
Family history of asthma | Yes | 81 (63.8) | 46 (36.2) | 4.26 (2.63–6.91) | 0.000 |
No | 147 (25.7) | 424 (74.3) | 1 | ||
Dust in the house | Yes | 34 (14.9) | 81 (46) | 3.01 (1.96–4.64) | 0.000 |
No | 194 (85.1) | 389 (74.5) | 1 | ||
Firewood/coal use | Yes | 177 (77.6) | 270 (57.4) | 5.39 (3.34–8.72) | 0.000 |
No | 51 (22.4) | 200 (42.6) | 1 | ||
Dampness in the house | Yes | 34 (17.5) | 38 (8.1) | 1.98 (1.07–3.68) | 0.030 |
No | 194 (82.5) | 432 (91.9) | 1 | ||
Road traffic proximity from your house in minute | <2 Min | 62 (27.2) | 93 (19.8) | 1.49 (0.94–2.35) | 0.088 |
>2 min | 166 (72.8) | 377 (80.2) | 1 | ||
Door opening while cooking | Yes | 92 (40.4) | 104 (22.1) | 0.35 (0.23–0.55) | 0.000 |
No | 136 (59.6) | 366 (77.9) | 1 | ||
Pet possession | Yes | 56 (24.6) | 23 (4.9) | 7.48 (4.04–13.82) | 0.000 |
No | 172 (75.4) | 447 (95.1) | 1 | ||
Inadequate physical activity | Yes | 57 (25) | 75 (16) | 1.75 (1.08–2.85) | 0.002 |
No | 171 (75) | 395 (84) | 1 |
DOI: 10.7717/peerj.16530/table-3
Discussion
The current study identified different factors that are probably associated with the onset of asthma in the study area. Urban residence, low monthly income, history of skin allergy, family history of asthma, house dust or smoke exposure, lifetime firewood use, house dampness, owning pets and being physically inactive were significant predictors for the development of asthma. On the other hand door opening while cooking was a protective factor for asthma.
Urban residence was positively associated with asthma. The relationship could be urban dwellers might get exposed to air pollution from automobiles, industrial emissions, and solid and liquid wastes that can trigger bronchial irritation. As a result, the bronchial irritation caused due to outdoor air pollution may mimic asthma in susceptible individuals. Asthma may have been caused as a result of this condition. This study agrees with the studies done in Canada, India, Uganda, and Ethiopia (Aggarwal et al., 2006; Santos et al., 2018; Kirenga et al., 2019; Shine, Muhamud & Demelash, 2019). On the other hand, the present study result was inconsistent with other studies done in Canada and India, where the risk of having asthma was significantly higher in respondents who were residing in rural areas than their counterparts (Gupta & Mangal, 2006; Crighton, Wilson & Senécal, 2010). This variation might be due to the difference in the study subjects’ lifestyle where the study in Jaipur district India suggested that the higher prevalence of asthma was observed in rural areas related to hookah smoking.
In the current study, the odds of getting asthma among respondents with a monthly income less than 1,000 ETB were more than twice higher than their peers. The reason for this is that people with low monthly wages have fewer alternatives for implementing asthma prevention techniques (Agrawal, Pearce & Ebrahim, 2013). This result agrees with studies conducted in Korea, Sweden, Australia, New York, and Ethiopia (Claudio, Stingone & Godbold, 2006; Hedlund, Eriksson & Rönmark, 2006; Kozyrskyj et al., 2010; Choi et al., 2012).
This study revealed that the odds of getting asthma among respondents who have a history of skin allergy were higher as compared to their counterparts. The association could be several immunological and structural skin cells, including T cells, are stimulated by allergens due to the manifestation of skin allergy. These cells release mediators for the stimulation of cell-mediated responses and the production of allergen-specific antibodies. In response to the particular allergen that may causes asthma, the presence of a skin allergy could trigger bronchial inflammation. Similar associations have been also observed in several studies (Aggarwal et al., 2006; Gupta & Mangal, 2006; Rémen et al., 2012; Kirenga et al., 2019).
The current finding showed that having a family history of asthma increases the odds of getting asthma. There are two probable explanations for this association: either inherited factors or a shared environment amongst family members contribute to the pathophysiology of asthma. This implies that a susceptible individual with a family history of asthma has to minimize the environmental conditions that can trigger the development of asthma signs and symptoms. This result agreed with the study done in Colombia, India, Uganda, and Ethiopia (Aggarwal et al., 2006; Liu et al., 2009; Gonzalez-Garcia et al., 2015; Wortong, Chaiear & Boonsawat, 2015; Shine, Muhamud & Demelash, 2019; Abebe et al., 2021).
In this study, door opening while cooking was found to be a protective factor for asthma. Not opening the door could result in poor ventilation of the house, which could cause respiratory tract congestion due to the possibility of regular usage of firewood/coal. This result suggests that to promote a healthy indoor air exchange while cooking requires door opening. This result is consistent with a study done in Pakistan and Ethiopia (Khan et al., 2014; Abebe et al., 2021). On the other hand, this study is inconsistent with the study done in the Netherlands which mentioned that there was no relationship between kitchen ventilation and asthma (Willers et al., 2006). This discrepancy may result from differences in the frequency and length of door openings during cooking, methodology, and study population. For instance, the study from the Netherlands recruited five-year-old children who had no connection with cooking.
In the present study, an association was observed among respondents who used lifetime firewood/coal as a source of energy for cooking. The connection between the two can be explained by the likelihood that burning wood inside the house can cause irritation to the bronchi that might mimic the onset of asthma. This is because various inflammatory substances are released, such as cytokines, and chemokines, which can cause inflammatory damage and increase bronchial reactivity. It can even get worse cooking food with firewood/coal without opening a door due to indoor air pollution. Repeated exposure to coal or wood smoke irritates the lungs and increases the risk of developing asthma. This result is similar to Nigeria, Peru, USA and Ethiopia (Thacher et al., 2013; Gaviola et al., 2016; Enilari & Sinha, 2019; Abebe et al., 2021) but inconsistent with the studies done in Colombia, China, India, and Uganda (Barry et al., 2010; Guddattu, Swathi & Nair, 2010; Jie et al., 2016; Kirenga et al., 2019). This variation could be due to difference in the sample size, cooking space, door opening frequency, and duration of exposure to firewood/coal.
The odds of getting asthma among respondents who have dampness or molds inside their houses were higher compared to those who do not have dampness or molds inside their houses. The association between mold and asthma can be explained by the fact that indoor molds can trigger allergic reactions mediated by immunoglobulin E, toxic reactions brought on by non-specific inflammatory reactions and irritating volatile organic compounds produced by microbes or cell walls (Hwang, Liu & Huang, 2011). The association can be also explained if molds and humidity are inside the house which may produce unpleasant odors that irritate the respiratory system and may precipitate the onset of asthma in a susceptible individual. This finding is similar to the study done in Taiwan, USA, Singapore and Netherlands (Tham et al., 2007; Hwang, Liu & Huang, 2011; Weinmayr et al., 2013; Xiao et al., 2021).
The odds of getting asthma among respondents who possessed pets were higher compared to those who do not possess pets. This may be because the respondents may not know the risks of pet keeping for asthma which was mentioned in research done in China and Bulgaria where the percentage of people who had avoidance behavior towards pets was low. The low avoidance behavior of pets was similar in this study where 93.6% of the pets were allowed to stay in the respondent’s bedroom. This finding was similar to the study done in Finland, China, and Congo (Jaakkola et al., 2002; Takkouche et al., 2008; Jie et al., 2016; Obel et al., 2017; Luo et al., 2018). On the other hand, this finding was inconsistent with the study done in Japan which reported that having pets was a protective factor (Taniguchi & Kobayashi, 2023). This variation could be due to difference in study population, pet ownership definition and duration of pet ownership which was not considered in the current study.
The odds of getting asthma among physically inactive respondents were higher compared to those who were physically active. This finding was consistent with a study done in America and Sweden (Eijkemans et al., 2012; Agrawal, Pearce & Ebrahim, 2013; Jerning et al., 2013; Garcia-Aymerich et al., 2014; Ebell, Marchello & O’connor, 2017) but inconsistent with the study done in Spain (Benet et al., 2011; Ebell, Marchello & O’connor, 2017). This variation might be due to the difference in study populations and measurement tools used for physical inactivity.
Conclusion
The study found various modifiable determinants of adult asthma in the Tigray region. Keeping the door open while cooking was found to be a protective factor. Asthma has been associated with urbanization, low income, history of allergic disorders, exposure to house dust or smoke, use of firewood, ownership of pets and sedentary lifestyle. Health professionals should screen and inform patients who are at higher risk to asthma. Moreover, community and personal sensitization through information, education, and communication strategies about the identified determinant factors and implement preventive steps like opening a door while cooking, being physical active and minimizing firewood use might help to lower the risk of asthma among adults. Policymakers, researchers and clinicians involved in the field will be interested in a larger prospective studies.
Limitation
This study shares the weakness of the case-control study which is recall bias that may exaggerate or reduce the association. Moreover, respondents were asked about both disease outcomes and risk factors at a time. So it is difficult to conclude the direction of causality in this study.
Additional Information and Declarations
Competing Interests
There are no competing financial interests.
Author Contributions
Tirhas G. Gebresillasie conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.
Alemayehu Worku performed the experiments, analyzed the data, authored or reviewed drafts of the article, and approved the final draft.
Ahmed Ali Ahmed conceived and designed the experiments, authored or reviewed drafts of the article, and approved the final draft.
Negussie Deyessa Kabeta conceived and designed the experiments, authored or reviewed drafts of the article, and approved the final draft.
Human Ethics
The following information was supplied relating to ethical approvals (i.e., approving body and any reference numbers):
The University of Addis Ababa granted Ethical approval to carry out the study within its facilities (Ethical meeting no: 01/2018
Data Availability
The following information was supplied regarding data availability:
The raw data are available in the Supplemental File.
Funding
The authors received no funding for this work.
Abebe Y, Ali A, Kumie A, Haile T, Tamire M, Addissie A. 2021. Determinants of asthma in Ethiopia: age and sex matched case control study with special reference to household fuel exposure and housing characteristics. Asthma Research and Practice 7(1):1-11
Aggarwal A, Chaudhry K, Chhabra S, Souza GD, Gupta D, Jindal S, Katiyar S, Kumar R, Shah B, Vijayan V. 2006. Prevalence and risk factors for bronchial asthma in Indian adults: a multicentre study. Indian Journal of Chest Diseases and Allied Sciences 48(1):13-22
Agrawal S, Pearce N, Ebrahim S. 2013. Prevalence and risk factors for self-reported asthma in an adult Indian population: a cross-sectional survey. The International Journal of Tuberculosis and Lung Disease 17(2):275-282
Alem K, Gebeyehu S, Arega Y. 2020. Risk factors and treatment types for asthma severity among adult patients. Journal of Asthma and Allergy 1:167-177
Asher I, Pearce N. 2014. Global burden of asthma among children. The International Journal of Tuberculosis and Lung Disease 18(11):1269-1278
Barry AC, Mannino DM, Hopenhayn C, Bush H. 2010. Exposure to indoor biomass fuel pollutants and asthma prevalence in Southeastern Kentucky: results from the Burden of Lung Disease (BOLD) study. Journal of Asthma 47(7):735-741
Benet M, Varraso R, Kauffmann F, Romieu I, Anto JM, Clavel-Chapelon F, Garcia-Aymerich J. 2011. The effects of regular physical activity on adult-onset asthma incidence in women. Respiratory Medicine 105(7):1104-1107
Choi W-J, Um I-Y, Hong S, Yum HY, Kim H, Kwon H. 2012. Association between household income and asthma symptoms among elementary school children in Seoul. Environmental Health and Toxicology 27:e2012020
Claudio L, Stingone JA, Godbold J. 2006. Prevalence of childhood asthma in urban communities: the impact of ethnicity and income. Annals of Epidemiology 16(5):332-340
Crighton EJ, Wilson K, Senécal S. 2010. The relationship between socio-economic and geographic factors and asthma among Canada’s Aboriginal populations. International Journal of Circumpolar Health 69(2):138-150
Cruz AA, Souza-Machado A, Franco R, Souza-Machado C, Ponte EV, Santos PM, Barreto ML. 2010. The impact of a program for control of asthma in a low-income setting. World Allergy Organization Journal 3(4):167-174
Ebell M, Marchello C, O’connor J. 2017. The burden and social determinants of asthma for adults in the state of Georgia. Journal of the Georgia Public Health Association 6(4):426-434
Eijkemans M, Mommers M, Draaisma JMT, Thijs C, Prins MH. 2012. Physical activity and asthma: a systematic review and meta-analysis. PLOS ONE 7(12):e50775
Ellwood P, Asher MI, Billo NE, Bissell K, Chiang C-Y, Ellwood EM, El-Sony A, Garcia-Marcos L, Mallol J, Marks GB, Pearce NE, Strachan DP+2 more. 2017. The Global Asthma Network rationale and methods for Phase I global surveillance: prevalence, severity, management and risk factors. European Respiratory Journal 49:1601605
Enilari O, Sinha S. 2019. The global impact of asthma in adult populations. Annals of Global Health 85(1):1-7
Ethiopian Public Health Association. 2012. Emerging public health problems in Ethiopia: chronic non-communicable diseases. Addis Abeba: Ethiopian Public Health Association.
European Community Respiratory Health Survey II Steering Committee. 2002. The European community respiratory health survey II. European Respiratory Journal 20(5):1071-1079
Federal Ministry of Health Ethiopia. 2016. Guidelines on clinical and programmatic management of major non communicable diseases.
Fu Q-L, Du Y, Xu G, Zhang H, Cheng L, Wang Y-J, Zhu D-D, Lv W, Liu S-X, Li P-Z. 2016. Prevalence and occupational and environmental risk factors of self-reported asthma: evidence from a cross-sectional survey in seven Chinese cities. International Journal of Environmental Research and Public Health 13(11):13
Garcia-Aymerich J, Varraso R, Danaei G, Camargo Jr CA, Hernán MA. 2014. Incidence of adult-onset asthma after hypothetical interventions on body mass index and physical activity: an application of the parametric g-formula. American Journal of Epidemiology 179(1):20-26
Gaviola C, Miele CH, Wise RA, Gilman RH, Jaganath D, Miranda JJ, Bernabe-Ortiz A, Hansel NN, Checkley W. 2016. Urbanisation but not biomass fuel smoke exposure is associated with asthma prevalence in four resource-limited settings. Thorax 71(2):154-160
Gonzalez-Garcia M, Caballero A, Jaramillo C, Maldonado D, Torres-Duque CA. 2015. Prevalence, risk factors and underdiagnosis of asthma and wheezing in adults 40 years and older: a population-based study. Journal of Asthma 52(8):823-830
Guddattu V, Swathi A, Nair NS. 2010. Household and environment factors associated with asthma among Indian women: a multilevel approach. Journal of Asthma 47(4):407-411
Gupta P, Mangal D. 2006. Prevalence and risk factors for bronchial asthma in adults in Jaipur district of Rajasthan (India) Lung India 23(2):53-58
Hedlund U, Eriksson K, Rönmark E. 2006. Socio-economic status is related to incidence of asthma and respiratory symptoms in adults. European Respiratory Journal 28(2):303-410
Hwang BF, Liu IP, Huang TP. 2011. Molds. parental atopy and pediatric incident asthma. Indoor Air 21(6):472-478
Ibrahim M. 2013. Tobacco smoke and asthma among adults at the national and state levels: do smoke-free laws and regulations affect smoking rate among those with asthma? MPH thesis, Georgia State University Atlanta, GA, USA thesis
Jaakkola JJ, Jaakkola N, Piipari R, Jaakkola MS. 2002. Pets, parental atopy, and asthma in adults. Journal of Allergy and Clinical Immunology 109(5):784-788
Jerning C, Martinander E, Bjerg A, Ekerljung L, Franklin KA, Järvholm B, Larsson K, Malinovschi A, Middelveld R, Emtner M. 2013. Asthma and physical activity—a population based study results from the Swedish GA2LEN survey. Respiratory Medicine 107(11):1651-1658
Jie Y, Kebing L, Yin T, Jie X. 2016. Prevalence of asthma and asthma-related symptoms among adults exposed to indoor environmental risk factors: a comparison between winter and summer in Zunyi, China. Polish Journal of Environmental Studies 25(2):621-633
Khan AA, Tanzil S, Jamali T, Shahid A, Naeem S, Sahito A, Siddiqui FA, Nafees AA, Fatmi Z. 2014. Burden of asthma among children in a developing megacity: childhood asthma study, Pakistan. Journal of Asthma 51(9):891-899
Kirenga BJ, De Jong C, Katagira W, Kasozi S, Mugenyi L, Boezen M, Van der Molen T, Kamya MR. 2019. Prevalence and factors associated with asthma among adolescents and adults in Uganda: a general population based survey. BMC Public Health 19(1):227
Kozyrskyj AL, Kendall GE, Jacoby P, Sly PD, Zubrick SR. 2010. Association between socioeconomic status and the development of asthma: analyses of income trajectories. American Journal of Public Health 100(3):540-546
Liu T, Valdez R, Yoon PW, Crocker D, Moonesinghe R, Khoury MJ. 2009. The association between family history of asthma and the prevalence of asthma among US adults: National Health and Nutrition Examination Survey, 1999–2004. Genetics in Medicine 11(5):323-328
Luo S, Sun Y, Hou J, Kong X, Wang P, Zhang Q, Sundell J. 2018. Pet keeping in childhood and asthma and allergy among children in Tianjin area, China. PLOS ONE 13(5):e0197274
Misganaw A, Mariam DH, Ali A, Araya T. 2014. Epidemiology of major non-communicable diseases in Ethiopia. Journal of Health, Population and Nutrition 32(1):1-13
Obel KB, Ntumba KJM, Kalambayi KP, Zalagile AP, Kinkodi KD, Munogolo KZ. 2017. Prevalence and determinants of asthma in adults in Kinshasa. PLOS ONE 12(5):e0176875
Rémen T, Acouetey D-S, Paris C, Zmirou-Navier D. 2012. Diet, occupational exposure and early asthma incidence among bakers, pastry makers and hairdressers. BMC Public Health 12(1):387
Santos FMD, Viana KP, Saturnino LT, Lazaridis E, Gazzotti MR, Stelmach R, Soares C. 2018. Trend of self-reported asthma prevalence in Brazil from 2003 to 2013 in adults and factors associated with prevalence. Jornal Brasileiro de Pneumologia 44(6):491-497
Shine S, Muhamud S, Demelash A. 2019. Prevalence and associated factors of bronchial asthma among adult patients in Debre Berhan Referral Hospital, Ethiopia 2018: a cross-sectional study. BMC Research Notes 12(1):608
Takkouche B, González-Barcala F-J, Etminan M, Fitzgerald M. 2008. Exposure to furry pets and the risk of asthma and allergic rhinitis: a meta-analysis. Allergy 63(7):857-864
Taniguchi Y, Kobayashi M. 2023. Exposure to dogs and cats and risk of asthma: a retrospective study. PLOS ONE 18(3):e0282184
Tefereedgn E, Ayana A. 2018. Prevalence of asthma and its association with daily habits in Jimma Town, Ethiopia. Open Journal of Asthma 2(1):011-017
Thacher JD, Emmelin A, Madaki AJK, Thacher TD. 2013. Biomass fuel use and the risk of asthma in Nigerian children. Respiratory Medicine 107(12):1845-1851
Tham KW, Zuraimi MS, Koh D, Chew FT, Ooi PL. 2007. Associations between home dampness and presence of molds with asthma and allergic symptoms among young children in the tropics. Pediatric Allergy and Immunology 18(5):418-424
Tigray Regional Health Bureau. 2016. Bureau regional health Ten years Health Bulletin. Mekele: Tigray Regional Health Bureau.
UNICEF. 2019. Situation analysis of children and women: Tigray Region.
Verma O, Pradesh U. 2014. Prevalence of bronchial asthma and risk factors: a clinical study. Journal of Advanced Medical and Dental Sciences Research 2(1):151-155
Weinmayr G, Gehring U, Genuneit J, Büchele G, Kleiner A, Siebers R, Wickens K, Crane J, Brunekreef B, Strachan DP. 2013. Dampness and moulds in relation to respiratory and allergic symptoms in children: results from phase two of the International Study of Asthma and Allergies in Childhood (ISAAC Phase Two) Clinical & Experimental Allergy 43(7):762-774
Willers SM, Brunekreef B, Oldenwening M, Smit HA, Kerkhof M, De Vries H, Gerritsen J, De Jongste JC. 2006. Gas cooking, kitchen ventilation, asthma, allergic symptoms sensitization in young children–the PIAMA study. Allergy 61(5):563-568
Wortong D, Chaiear N, Boonsawat W. 2015. Risk of asthma in relation to occupation: a hospital-based case-control study. Asian Pacific Journal of Allergy and Immunology 33(2):152-160
Xiao S, Ngo AL, Mendola P, Bates MN, Barcellos AL, Ferrara A, Zhu Y. 2021. Household mold, pesticide use, and childhood asthma: a nationwide study in the US. International Journal of Hygiene and Environmental Health 233:113694
Tirhas G. Gebresillasie1,2, Alemayehu Worku2, Ahmed Ali Ahmed2, Negussie Deyessa Kabeta2
1 Department of Public Health, College of Health Sciences, Aksum University, Axum, Tigray, Ethiopa
2 School of Public Health, College of Medicine and Health Science, Addis Ababa University, Addis Ababa, Ethiopia
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
© 2024 Gebresillasie et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Abstract
Background
Asthma is a public health concern affecting millions of productive age groups. Several studies were conducted on the determinants of asthma in children. However, little is known about the determinants of asthma among adults in Ethiopia. Understanding the determinants of asthma among adults can help reduce its burden. This study was aimed at identifying determinant factors for developing asthma among adults in Tigray hospitals.
Methods
A facility-based, unmatched case-control study design was conducted from January 1 to April 26, 2019. A total of 698 participants (228 cases and 470 controls) completed their guided interviews using structured and pretested questionnaires by trained data collectors. A modified standard questionnaire from the European Community Respiratory Health Survey II (ECRHS II) was used to collect the data. The case definition was patients having asthma, and the control definition was patients without asthma. Data were entered and cleaned using Epi Data Manager Version 3.1 software and imported to statistical packages for social sciences Version 25 software for analysis. To identify asthma determinants, bivariate and multivariable logistic regression models were fitted.
Results
The response rate for both cases and controls was 95.9%. The odds of developing asthma was nearly twice higher among those who resided in urban (AOR = 1.68; 95% CI [1.13–2.50]), more than twice higher among those who have income less than 1000 ETB (AOR = 2.3; 95% CI [1.17–4.56]), twice higher among those who had history of skin allergy (AOR = 2.09; 95% CI [1.14–3.86]), over four times higher among those with family history of asthma (AOR = 4.26; 95% CI [2.63–6.91]), three times higher among those having house dust or smoke exposure (AOR = 3.01; 95% CI [1.96–4.64]), over five times higher among those lifetime firewood users (AOR = 5.39; 95% CI [3.34–8.72]), door opening while cooking (AOR = 0.35; 95% CI [0.26–0.55]), nearly two times higher among those having house dampness (AOR = 1.98; 95% CI [1.069–3.68]), over seven times higher among pet owners (AOR = 7.46; 95% CI [4.04–13] and almost twice higher among those who were physically inactive (AOR = 1.75; 95% CI [1.11–2.85]).
Conclusion
Asthma has been associated with urbanization, low income, a history of allergic diseases, indoor smoke or dust, firewood use, pet ownership, and a sedentary lifestyle. The community should be informed about the known risks and implement preventive steps like opening a door while cooking to lower the risk of asthma.
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer