GRAPHICAL ABSTRACT

INTRODUCTION

Asthma is a common chronic respiratory condition that affects an estimated 358 million people globally and poses substantial health challenges. Data from the International Study of Asthma and Allergies in Childhood phase III indicate that the prevalence of asthma among teenagers varies widely across countries, ranging from 2.1% in Indonesia to 32.2% in the United Kingdom [1,2]. Asthma attacks, which are characterized by acute worsening of symptoms, contribute significantly to morbidity and healthcare costs [3].

The most common causes of asthma exacerbation (AE) include exposure to external agents, such as indoor and outdoor allergens and air pollutants, as well as respiratory tract infections, with human rhinovirus being the primary viral agent. AE can also be triggered by factors such as exercise, weather changes, certain foods and additives, medications, and intense emotional expressions [4]. Allergic reactions and exposure to tobacco smoke are also primary contributors to the development of asthma [2].

Asthma frequently coexists with conditions such as obesity, depression, diabetes, and cardiovascular disease. These comorbidities can exacerbate the severity of asthma and lead to increased treatment costs. While the mechanisms linking asthma with these comorbid conditions are not fully understood, they may involve shared risk factors and inflammatory pathways. Inflammation is a critical factor in the pathophysiology of both asthma and its comorbidities [5].

High-stress states can directly influence symptoms by promoting inflammation, which may increase airway narrowing or responsiveness [6]. Concurrent mental health issues, such as anxiety and depression, may worsen asthma symptoms, impact the efficacy of medication, and complicate overall disease management [6,7]. However, research into these relationships in Indonesia, especially among adolescents and adults, is scarce. Many studies have been limited to specific regions or age demographics, resulting in a lack of comprehensive national data [8]. This analysis seeks to address this deficiency by investigating the prevalence, patterns, and risk factors of asthma in Indonesian adolescents and adults. We examine the influence of mental health, socio-demographic, behavioral, and biological factors on AE in patients with diagnosed asthma (DA), aiming to improve understanding and inform targeted interventions.

METHODS

We conducted a secondary analysis of data from the 2018 Indonesian Basic Health Research (Riset Kesehatan Dasar, or RISKESDAS), a nationally representative, cross-sectional survey conducted by the Health Development Policy Agency of the Indonesian Ministry of Health every 5 years [9]. Trained enumerators, who held health-related diplomas, administered the survey using validated questionnaires in the Indonesian language. The data collection process included face-to-face interviews, anthropometric measurements, and physical examinations conducted in participants’ homes [9].

Multistage systematic random sampling was employed. Census blocks (CBs) served as the primary sampling units. In the initial stage, CBs were proportionally selected from each province using probability proportional to size sampling, based on the 2010 population frame. This approach ensured that both urban and rural areas were represented across all 34 provinces, encompassing 30 000 CBs. Subsequently, stratification was applied at the household level, with 10 households systematically selected from each CB, totaling 300 000 households. All household members aged 15 years and older were then interviewed, amounting to 706 689 individuals [9]. Of the 706 689 participants aged 15 years or older, 18 574 reported having asthma diagnosed by a physician (2.6%). Within the RISKESDAS dataset, all participants had complete data for the variables of interest (Figure 1).

Data Collection

Data access and collection followed the specific procedures outlined by the Health Development Policy Agency (layanandata@kemkes.go.id).

Variables

Outcome variable

The outcome variable in this study was self-reported DA with AE. Data on AE were gathered by asking respondents with DA if they had experienced exacerbation in the past 12 months. Respondents were categorized as experiencing AE if they answered affirmatively.

Explanatory variables

The mental health conditions of anxiety and depression were assessed using the World Health Organization (WHO) 20-item Self-Reporting Questionnaire, which evaluates an individual’s mental state over the past 30 days. Participants who reported “yes” to at least 6 items were classified as experiencing emotional distress (ED) [11,12].

Anthropometric assessments included measurements of central obesity, defined as a waist circumference greater than 90 cm for male and greater than 80 cm for female, and general obesity, characterized by a body mass index of 25 kg/m2 or higher for obesity and less than 25 kg/m2 for non-obesity, according to Asian criteria [13].

Comorbidities included hypertension, previously diagnosed diabetes mellitus (DDM), previously diagnosed cancer (DCa), previously diagnosed heart disease (DHD), and previously diagnosed cerebrovascular disease (DCD/stroke). Hypertension was determined by a measurement of systolic blood pressure ≥140 mmHg or diastolic blood pressure ≥90 mmHg, or by the use of antihypertensive medication [13]. DDM, DCa, DHD, and DCD were based on the respondents’ self-reports regarding whether they had ever been diagnosed with these conditions by a doctor. The response options were “yes” or “no.”

Lifestyle factors included physical activity (PA), smoking status, and alcohol consumption. PA was measured in metabolic equivalents of tasks (METs) per week and categorized into 3 groups: less than 600 METs, 600 METs or more, and no PA. Smoking status was categorized as never smoker, former smoker, or current smoker. Alcohol consumption was determined by self-reported intake, with respondents indicating whether they had consumed alcohol in the past month with a response of “yes” or “no.”

Socioeconomic factors included marital status, distinguishing between respondents who were single or currently married; education, specifically whether respondents held a high school diploma; and economic status, which was categorized into 5 quintiles, with the first quintile representing the poorest and the fifth quintile the wealthiest. Employment status was classified as either working or not working, and area of residence was divided into urban or rural areas.

Statistical Analysis

Descriptive analysis, including both univariate and bivariate methods, was used to summarize the characteristics of the samples. Variables that yielded p-values of 0.2 or lower in the bivariate analysis were included in the multivariate analysis. Logistic regression was utilized because the outcome variable consisted of binary nominal data, and the objective was to elucidate the relationship between independent variables and the probability of the outcome. Adjusted odds ratios were reported along with their 95% confidence intervals.

Ethics Statement

The ethical approval for RISKESDAS 2018 was obtained from the Komisi Etik Penelitian Kesehatan, Badan Penelitian dan Pengembangan Kesehatan (Ethical Committee of Health Research, National Institute of Health Research and Development, Ministry of Health, Republic of Indonesia) under the reference number LB.02.01/2/KE.267/2017 [9].

RESULTS

As shown in Table 1, the prevalence of asthma among Indonesian adults aged 15 and older in RISKESDAS 2018 was 2.6% of 706 689 participants. The prevalence was higher among females, older adults, and individuals with certain comorbidities, including obesity, diabetes, heart disease, and hypertension. Interestingly, among all subgroups, current smokers exhibited the lowest prevalence of asthma.

Table 2 reveals that 59.7% of 18 574 participants with asthma experienced exacerbation. Exacerbation rates were higher among female respondents, individuals aged 65 years or older, those diagnosed at age 15 or older, participants with a lower educational level, married individuals, employed persons, residents of rural areas, and those in the lower income quintiles. Additionally, higher exacerbation rates were associated with ED, hypertension, and DHD. However, no significant differences in exacerbation prevalence were found in relation to central obesity, DDM, DCa, or DCD (stroke). Conversely, lower exacerbation rates were observed in individuals who were current smokers, had consumed alcohol in the past month, engaged in PA in accordance with WHO standards, and had general obesity. No significant differences in exacerbation rates were noted regarding central obesity, DDM, DCa, or DCD.

Table 3 presents independent triggers and protectors of AE, adjusted for other risk factors. ED and DHD were associated with an increased risk of exacerbation. Additional risk factors included an age of 65 years or older, an initial diagnosis of asthma at age 15 or older, lower education level, lower socioeconomic status (SES), and rural area of residence. Conversely, diabetes appeared to be associated with a reduced risk of AE. Other protective factors included PA, alcohol consumption within the past month, current smoking, and urban area of residence. No significant association was found between the risk of AE and sex.

DISCUSSION

The analysis of data from RISKESDAS 2018 revealed a DA prevalence of 2.6% and an AE rate of 59.7% among individuals with asthma. Multiple factors were found to be associated with the risk of AE, offering insights into potential targets for improving asthma management in Indonesia.

Our analysis indicates a higher prevalence of asthma in older adults (65 years or older), accompanied by an increased risk of exacerbation. Similarly, asthma diagnosed after the age of 15—termed late-onset asthma—was associated with higher exacerbation rates and worse outcomes. These findings align with prior research highlighting age-specific differences in predictors of exacerbation [14,15]. The increased prevalence of asthma among the elderly imposes a considerable socioeconomic burden on the healthcare system, emphasizing the need for management strategies tailored to the needs of older patients [14]. The lower prevalence of DA and risk of AE among individuals with a higher education level may reflect better access to health information and services. Uncontrolled asthma has also been linked to inadequate education regarding asthma and/or medication use [16]. Furthermore, school absenteeism has been identified as a potential mediator in the relationship between hospital admission and educational attainment [17]. In this study, urban residents exhibited a lower risk of AE than their rural counterparts. This finding suggests potential disparities in healthcare access and environmental factors between urban and rural areas, which merit further exploration. Our analysis also reveals a higher prevalence and proportion of DA and AE among less affluent participants. This observation aligns with a cohort study conducted by Mazalovic et al. [18], which demonstrated that adults with lower SES are more severely impacted by asthma. A study by Busby et al. [19] of 127 040 patients with asthma further corroborates these results, indicating that individuals from the lowest SES quintile are particularly likely to experience uncontrolled asthma and exacerbations.

Engaging in PA is associated with a lower risk of AE, aligning with previous research demonstrating its protective effect [20]. PA may protect against asthma by reducing airway inflammation and improving the patency of the bronchioles. Conversely, inactivity can increase the risk of asthma due to impaired mucociliary clearance and decreased deep inspiration [21]. However, individuals with asthma also present paradoxical responses to PA. Up to 90% of these patients develop exercise-induced bronchoconstriction, a distinct form of bronchial hyperresponsiveness characterized by acute and transient narrowing of the airways during or immediately following exercise or strenuous PA [22].

Our analysis revealed a surprising finding: current smokers exhibited a lower risk of AE than non-smokers. This contradicts established evidence regarding the detrimental effects of smoking on asthma [23,24]. One potential explanation is differences in the severity of asthma between smokers and non-smokers within our study population. Further research is necessary to explore this association, considering the intensity and duration of smoking. We also observed that patients with asthma who had consumed alcohol in the past month exhibited a lower prevalence of AE. The impact of alcohol on lung function varies based on its concentration, the duration of consumption, and the route of exposure. Although heavy and prolonged alcohol use may worsen asthma and lung function, moderate consumption could confer some protective effects [23]. However, relevant studies of patients with asthma are limited, and further research is required.

The analysis revealed a higher prevalence of non-communicable diseases, such as hypertension and DHD, among individuals with asthma. DHD was significantly associated with an increased risk of AE, consistent with studies reporting a higher risk of cardiovascular disease in patients with asthma [5]. Conversely, DDM appeared to reduce the risk of AE, possibly due to medications such as glucagon-like peptide-1 receptor agonists, which may improve airway function [5,25]. Our analysis did not identify a significant association between general or abdominal obesity and AE risk, contrasting with previous research suggesting a link between obesity and increased asthma risk [26]. This unexpected finding warrants further investigation. ED was identified as a significant risk factor for AE, with individuals with asthma and ED being 29% more likely to experience exacerbation compared to those without such distress. This aligns with previous research demonstrating a link between psychological disorders and adverse asthma outcomes [7]. Our findings suggest that incorporating mental health interventions into asthma management plans may be beneficial.

Patient education is key to helping individuals understand that asthma is a chronic condition. Living with asthma requires avoiding allergens, preventing infections, staying current with routine vaccinations, managing other health issues, and adhering to prescribed treatments. Effective asthma management is based on 4 key elements: patient education, monitoring of symptoms and lung function, control of triggers and comorbidities, and pharmacologic therapy. Educating patients about asthma can reduce the frequency of flare-ups and improve overall control [27]. Since asthma severity can vary based on the individual and their age, continuous assessment of asthma control is essential for making any necessary adjustments to treatment.

Conclusion

Key triggers of AE included mental health conditions, DHD, aged 65 years or older, initial asthma diagnosis at age 15 or older, and lower SES. Protective factors consisted of DDM, PA, consumption of alcohol in the month before RISKESDAS 2018, smoking during the RISKESDAS 2018 data collection period, urban area of residence, and higher education level.

Recommendations

Our findings underscore the importance of addressing mental health comorbidities, promoting PA, and considering socioeconomic disparities in the development of asthma management strategies. Further research is warranted to explore the unexpected association between smoking and AE, as well as to examine the impact of occupational exposures. Implementing interventions that target these factors, such as mental health support, improved healthcare access, and encouragement of healthy lifestyle choices, may be pivotal in alleviating the burden of asthma in Indonesia. Education is essential to help patients understand that asthma is a chronic condition that necessitates the avoidance of allergens, prevention of infections, adherence to routine vaccinations, management of comorbidities, and compliance with consistent treatment regimens. Effective asthma management includes 4 key components: patient education, monitoring of symptoms and lung function, control of triggers and comorbid conditions, and pharmacologic therapy. Patient education on asthma can lead to fewer exacerbations and better disease control. Additionally, since the severity of asthma varies among individuals and across age groups, it is crucial to regularly assess and adjust treatment as needed.

Unanticipated Findings, Strengths, and Limitations

While our study benefits from a large sample size, one limitation is that we did not apply weighting to ensure that the results were representative of the target population. Moreover, the reliance on self-reported data regarding asthma diagnosis and exacerbation could have introduced recall bias. The analysis also did not differentiate between specific occupations, limiting our understanding of the role of occupational exposures as risk factors for AE. Future research should employ longitudinal designs and objective measures to overcome these limitations. Notably, we observed a lower risk of AE among current smokers compared to non-smokers, contradicting established knowledge about the harmful effects of smoking on asthma. This finding warrants further investigation, potentially considering factors such as the intensity and duration of smoking.

Implications for Clinical Practice and Public Health

Our findings highlight the importance of addressing mental health comorbidities, promoting PA, and considering socioeconomic disparities in the development of asthma management strategies. Patient education is crucial to ensure their understanding of asthma as a chronic disease. This understanding should encompass the need for allergen avoidance, infection prevention, adherence to vaccination schedules and treatment regimens, and the management of any comorbid conditions.

Future Research Directions

Future research with longitudinal designs and objective measures is necessary to understand any causal relationships between the identified factors and the risk of AE. Its unexpected associations with smoking and alcohol consumption also warrant further research, potentially considering the intensity and duration of use. Additionally, exploring the roles of specific occupations in the exacerbation of asthma could yield valuable insights into work-related risk factors.

Notes

Data Availability

This published article and its supplementary files include all data generated or analyzed during this study. The data supporting this study’s findings are available from the Data Management Laboratory of the National Institute of Health Research and Development (NIHRD), Ministry of Health of Indonesia. Data will be made available upon approval of a written request to the Data Management Laboratory—NIHRD, submitted at: layanandata@kemkes.go.id.

Conflict of Interest

The authors have no conflicts of interest associated with the material presented in this paper.

Funding

None.

Acknowledgements

We would like to thank Lelyza Trisaid, who helped us in making the abstract infographics.

Author Contributions

Conceptualization: Isfandari S, Tuminah S, Mihardja LK. Data curation: Isfandari S, Mihardja LK. Formal analysis: Isfandari S, Mihardja LK. Funding acquisition: None. Methodology: Isfandari S, Tuminah S, Mihardja LK. Project administration: Isfandari S, Tuminah S. Visualization: Isfandari S, Tuminah S. Writing – original draft: Isfandari S, Tuminah S, Mihardja LK. Writing – review & editing: Isfandari S, Tuminah S, Mihardja LK.

Figure. 1.

Flowchart of data selection. Adapted from Ministry of Health Republic Indonesia. The report of Indonesian National Health Survey 2018 [9]; Idaiani S, et al. BMC Public Health 2021;21(1):2332 [10]. RR, response rate; CB, census blocks; DA, diagnosed asthma.

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References

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