Abstract
Objectives
Emerging evidence shows that health disparities contribute to an increased risk of severe asthma. Therefore, the study objectives were to explore how socioeconomic disparities influence the risk of severe paediatric asthma in the Netherlands.
Methods
In this nationwide cohort study, all children aged 2–17 years living in the Netherlands between 2018 and 2022 were included. Asthma definitions were based upon individually linked data from nonpublic Dutch registry databases on asthma-related health expenditures including hospital and paediatric intensive care unit (PICU) admissions. Geospatial analysis was used to identify hot spots based on the regions with the highest counts of severe asthma. Additionally, the impact of various socioeconomic variables (e.g. housing, migration background and socioeconomic status) on the primary outcome of severe asthma was assessed using a linear probability model.
Results
The total study population consisted of 4 538 020 children. Children from the lowest income class had twice the odds of severe asthma (p<0.001) and were 2.6 times more likely to be admitted to the PICU compared to the highest income class (p<0.001). Other socioeconomic disadvantage factors for severe (acute) asthma are, in order of level of association, living in a rental house, having a migration background and having a lower socioeconomic status score based on income, education and employment history.
Conclusions
We identified several socioeconomic disparities that were increased in children with severe asthma and severe acute asthma at the PICU in the Netherlands. Comprehensive assessment and mitigation of these determinants may improve health equity in paediatric severe asthma and enhancement of asthma care.
Shareable abstract
This is the first nationwide geospatial analysis addressing socioeconomic inequalities in severe childhood asthma. It highlights the need for targeted asthma interventions to be more equally allocated to the most vulnerable paediatric patients. https://bit.ly/4n0Oy5t
Introduction
In the Netherlands, one in eight children are at risk of poverty [1]. Asthma prevalence and burden are disproportionally skewed towards children from lower socioeconomic positions [2]. Moreover, children from lower-income households with asthma suffer from poorer health than children with asthma from higher-income households [3]. Subsequently, poorer lung function in late adolescence, especially a reduced forced expiratory volume in 1 s illustrative of obstructive airway diseases such as asthma, is associated with the development of respiratory, cardiovascular and metabolic disorders later in life [4].
Asthma can initially present with a life-threatening exacerbation refractory to standard treatment and children with chronic asthma are at risk of developing such a severe exacerbation too, called severe acute asthma (SAA). These children need admission to a paediatric intensive care unit (PICU) [5, 6]. The above-mentioned correlation of a socioeconomically disadvantaged environment with a higher asthma prevalence also exists with a more severe disease course, such as a longer hospital stay and a higher PICU (re-)admission rate [6–8]. A PICU admission carries significant implications for a child's physical and mental health and parental wellbeing, as well as consuming expensive medical resources [5].
The association between social determinants of health and severe (acute) asthma has been demonstrated on an individual level. However, a more all-encompassing, population-level study method is needed to comprehend how community factors relate to health outcomes that differ across locations [9, 10]. For example, Grunwell et al. [10] used geospatial analysis to identify areas with an increased number of deprived children and found a geographical association with PICU admissions for SAA in Georgia, USA. Geospatial statistics is used to visualise and analyse data, enabling the identification of geographic highly dense concentrations, also known as “hot spots”, of adverse outcomes, disease risk factors and vulnerable groups [11]. Combining geographical statistics with a nationwide approach leads to comprehensive data integration and improves insight into the associations within the data.
Gaining insight into how these socioeconomic inequalities are distributed among asthmatic children is imperative to finding leverage points for targeted interventions aimed to improve health for the most vulnerable patients [2]. Therefore, this study aimed to address two objectives, as follows: first, to use a nationwide approach to examine the association between socioeconomic characteristics and the incidence of paediatric severe (acute) asthma, and secondly to identify geographic regions with high rates of severe asthma in the Netherlands and explore the socioeconomic factors prevailing in these hot spots.
Methods
Dataset
This study was reported according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for cohort studies [12]. The results in this study are based on calculations by the Amsterdam Health and Technology Institute (ahti) using nonpublic microdata from Statistics Netherlands (Centraal Bureau voor Statistiek (CBS)). The CBS registry contains individual-level data of all registered residents of the Netherlands, including data on health expenditures in hospitals paid for through the compulsory health insurance system. For this analysis, medical claim data provided to the CBS by Vektis C.V. and Dutch Hospital Data were used.
All children aged 2–17 years residing in the Netherlands between 2018 and 2022 were included. Children <2 years were excluded as the distinction between bronchiolitis and an asthma exacerbation is difficult below this age [13]. The children were then divided into two age groups, 2–5 years and 6–17 years, in order to distinguish preschool wheeze from asthma [14]. When looking at one specific year, the reference date for inclusion is the child's age on 31 December of the preceding year.
Asthma definitions
The hospital health expenditures are coded by either Diagnosis–Treatment–Classification (DTC) or the International Classification of Diseases 10th Revision codes (ICD-10) by the attending physician. For information on the codes used, see table E1 in the supplementary material. For this particular study, the following asthma definitions were formed based on these codes: 1) controlled asthma: children with asthma that only needed an outpatient clinic visit in the previous 2 years of measurement [15]; 2) severe asthma: children with asthma that needed one or more emergency department (ED) visits or paediatric ward hospital admissions in the preceding 2 years; or 3) SAA: the count of PICU admissions for a life-threatening asthma exacerbation.
Socioeconomic characteristics
All included socioeconomic characteristics are described in table E2 in the supplementary material. The SES-WOA (Sociaal-Economische Status–Wijk- en Omgevingsanalyse (Socioeconomic Status – Neighbourhood and Environmental Analysis)) score is a measure of socioeconomic status (SES) calculated with a multiple correspondence analysis by the CBS and based on 1) income (the national prosperity decile a household belongs to), 2) education level (highest categorical education level of parents according to the Dutch Standard Classification of Education level) and 3) employment history (the household's recent employment history category in terms of (un)employment). An SES-WOA score of “0” refers to the Dutch average and a lower score indicates a more socioeconomically challenged household [16]. The PERCSM (percentage of social minimum) score reflects the household's income (support) as a percentage of the statutory social minimum, which is a subsistence minimum specific to the household type (e.g. couple, single) [17]. Housing types are categorised as follows: 1) owner-occupied (house is owned by its residents); 2) rental home with(out) rental support (house is rented from a landlord or housing association; if the rent is too high in relation to the residents’ income, rental support is received from the government via the Rent Allowance Act); and 3) institutional (residents living in a house whose housing and daily needs are provided by a professional third party, e.g. foster care homes and refugee centres).
Statistical analysis
The statistical program R was used for the analyses with the packages stats and modelsummary. Odds ratios with corresponding p-values were used to calculate the difference in outcomes among the socioeconomic groups. As the CBS's national databases contain information on all registered residents and health expenditures in the Netherlands, the numbers in the dataset are true values and do not depict a sample of the population. Therefore, for descriptive statistics a 95% confidence interval is not necessary. For comparative statistics, a p-value of <0.05 was deemed to be statistically significant.
For the geospatial analysis, the safety and health regions of the Netherlands were used, which are geographical areas within the Dutch public health services are regulated. The five regions with the highest prevalence of severe asthma over the study period were identified as “hot spots”.
A multivariable linear probability model was used to analyse the impact of each of the socioeconomic variables on the regressor of having severe asthma. The definition of severe asthma included children with a PICU admission in the preceding 2 years, which differs from the definition otherwise used in this paper. These variables were added to the baseline model one by one and compared to the R2, in which the coefficient corresponds to the magnitude of the effect. The SES-WOA and PERCSM scores were never added to the model at the same time, as the income factor overlaps.
Privacy and ethics
Data management by the CBS is regulated by Dutch law. The CBS manages access to the individual-level data records by providing secure, controlled environments for authorised researchers to conduct scientific analyses under strict legal and ethical conditions. In this study, the analyses were executed in collaboration with the ahti. To maintain privacy and avoid re-traceability of persons, all population counts were rounded to the nearest multiple of five and small subpopulations below 10 observations were omitted. Data processing and analysis complied with national data protection regulations.
Results
Patient characteristics
In this study, in total 1 417 505 children between 2 and 5 years and 3 120 515 between 6 and 17 years were analysed over 2018–2022. An overview of the characteristics of the patients with asthma in the Netherlands in 2022 is provided in table 1.
TABLE 1.
Characteristics of children with asthma-related hospital visits residing in the Netherlands in 2022
| Characteristics | 2–5 years | 6–17 years | Both age groups |
|---|---|---|---|
| Number of cases (n) | 8950 | 19 520 | 28 470 |
| Sex (female) (n (%)) | 3355 (37.5) | 7685 (39.4) | 11 040 (38.8) |
| Age (years) (median (IQR)) | 4 (3–5) | 11 (8–14) | 8 (5–12) |
| Diagnosis# (n (%)) | |||
| Controlled asthma | 6030 (67.4) | 17 700 (90.7) | 23 730 (83.4) |
| Severe asthma | 2860 (31.9) | 1705 (8.7) | 4740 (16.0) |
| Severe acute asthma | 60 (0.7) | 115 (0.6) | 175 (0.6) |
IQR: interquartile range. #: Based upon presence or absence of emergency department visit, hospital admission or paediatric intensive care unit admission.
Prevalence of severe asthma requiring hospital visits in the Netherlands
Based on the health expenditure data, in 2022, 1705 children aged 6–17 years had severe asthma, corresponding to a prevalence of 0.08%. Additionally, in the same year, 2860 children aged 2–5 years had severe preschool wheeze, translating to a prevalence of 0.4%. The prevalence of severe asthma in children aged 6–17 years remained relatively stable over the study period of 2018–2022 and the ratio of severe asthma and SAA with the total number of asthma cases also remained stable, as shown in figure 1. In preschool children, severe wheezing symptoms requiring ED visits or hospital admissions seem to be more common, with the highest prevalence of a PICU admission in children aged between 4 and 5 years. Controlled asthma is conversely more often seen in the older age group. Additional details about the prevalence of severe asthma across the age groups are presented in figure E1 and E2 in the supplementary material.
FIGURE 1.
Prevalence of asthma-related hospital visits in children aged 6–17 years residing in the Netherlands from 2018 to 2022. The numbers are depicted in absolute numbers (n) and prevalence per 10 000 children residing in the Netherlands. The hospital visits include outpatient clinic and emergency department visits, and hospital and paediatric intensive care unit admissions. The prevalence of asthma requiring a hospital visit has remained stable in the Netherlands over the study period, with a slight decrease in 2020–2021 during the COVID-19 pandemic.
Socioeconomic disparities and severe (acute) asthma in children
In 2022, children aged 6–17 years from the lowest income class had higher odds of severe asthma than those from the highest income class (OR 2.0, p<0.001). Additionally, these children also had significantly higher odds for SAA when compared to children from the highest income class (OR 2.6, p<0.001). Moreover, severe asthma and SAA were respectively 2.0 (p<0.001) and 3.2 times (p<0.001) more common in children living in a rental home with rental support compared to children living in an owner-occupied home, which was the strongest association. All other tested variables also significantly increased the odds on severe (acute) asthma, apart from the type of household, which was nonsignificant for SAA. The distribution of the socioeconomic characteristics in the children aged 6–17 years with asthma can be found in table 2.
TABLE 2.
Socioeconomic characteristics of children with asthma-related hospital visits aged 6–17 years residing in the Netherlands in 2022
| Characteristics (prevalence as per 10 000 (n)) | Controlled asthma (n=17 700) | Severe asthma (n=1705) | Severe acute asthma (n=115) |
|---|---|---|---|
| Migration background | |||
| Dutch | 78.9 (12 695) | 6.6 (1070) | 0.4 (60) |
| First-generation immigrant | 40.3 (740) | 5.7 (105) | 0.8 (15) |
| Second-generation immigrant | 89.6 (4260) | 11.0 (525) | 0.9 (45) |
| PERCSM | |||
| 0–120% | 90.1 (1770) | 11.7 (230) | 1.0 (20) |
| 120–280% | 85.2 (4925) | 9.6 (555) | 0.7 (40) |
| 280–400% | 84.3 (5260) | 7.0 (420) | 0.2 (15) |
| >400% | 70.1 (5425) | 5.8 (450) | 0.4 (30) |
| Unknown | 33.4 (320) | 5.2 (50) | 1.0 (10) |
| SES-WOA-score | |||
| First quartile (−2.3–0.04) | 87.1 (4570) | 11.2 (590) | 1.0 (50) |
| Second quartile (0.04–0.48) | 86.1 (4345) | 7.1 (360) | 0.4 (20) |
| Third quartile (0.48–0.78) | 78.0 (4135) | 6.9 (365) | 0.3 (15) |
| Fourth quartile (0.78–1.04) | 71.5 (3485) | 5.8 (285) | 0.4 (20) |
| Unknown | 52.4 (1165) | 4.5 (100) | 0.7 (15) |
| Household | |||
| Single parent | 79.6 (3465) | 9.6 (420) | 0.7 (30) |
| Couple | 78.3 (14 055) | 7.0 (1260) | 0.5 (85) |
| Institutional | 37.3 (40) | NA | NA |
| Other | 51.7 (140) | 7.4 (20) | NA |
| Housing type | |||
| Rental home with rental support | 88.7 (3065) | 12.3 (425) | 1.0 (35) |
| Rental home without rental support | 79.3 (2125) | 10.4 (280) | 0.9 (25) |
| Owner-occupied home | 77.8 (12 395) | 6.1 (985) | 0.3 (50) |
| Institutional | 39.6 (35) | NA | NA |
| Unknown | 14.3 (75) | 1.9 (10) | NA |
NA: not applicable; PERCSM: percentage of the statutory social minimum income; SES-WOA: Sociaal-Economische Status–Wijk- en Omgevingsanalyse (Socioeconomic Status–Neighbourhood and Environmental Analysis).
In the linear probability model, a similar trend was seen. There were several characteristics that were significantly associated with an increased chance on having severe asthma as opposed to controlled asthma. These were, among others, a 43.2% increased chance when living in a rental home (p<0.001), a 76.7% greater chance when living in an institutional household (p<0.01), a 27.5% and 24.3% increased chance when being a first- or second-generation immigrant, respectively (p<0.001), and a 12.3% greater chance when belonging to the lowest SES-WOA quantile (p<0.01). See table E3 and figure E3 in the supplementary material for the results of the multivariable linear probability analysis.
Regional differences in severe (acute) asthma in the Netherlands
In the Netherlands, the prevalence of severe asthma is not evenly distributed. Severe asthma was most commonly registered in five regions during the study period of 2018–2022 (Haaglanden, Flevoland, Amsterdam-Amstelland, Rotterdam-Rijnmond and Kennemerland), which were therefore appointed as “hot spots”, see figure 2. The prevalence of SAA at the PICU was the highest in Groningen and Flevoland. Children with severe asthma living in one of the five hot spots, had a 1.5 times higher odds of being from the lowest SES-WOA quantile (p<0.01), a 1.5 times greater odds of having a migration background (p<0.001) and a 1.7 times higher odds of earning the lowest income (p<0.001), as compared to children with severe asthma from the other regions. See figure 3 for a visual representation of the association of severe asthma prevalence and SES-WOA scores.
FIGURE 2.
Hot spots of severe asthma in the Netherlands in children aged 6–17 years in 2022 The prevalence of severe asthma in children with asthma-related hospital visits aged 6–17 years in 2022 as per 10 000 per region in the Netherlands. Severe asthma is unevenly distributed across the Netherlands, with the highest prevalence over 2018–2022 in five safety and health regions (Haaglanden, Flevoland, Amsterdam-Amstelland, Rotterdam-Rijnmond and Kennemerland). These regions were therefore defined as “hot spots” of severe asthma and are indicated in the figure. Limburg (southeast) had a high prevalence of severe asthma in 2022, as indicated above, but over the years of 2018–2022, the other five regions had a higher prevalence. Therefore, Limburg was not selected as one of the five hot spots.
FIGURE 3.
Map of the Netherlands with SES-WOA (Sociaal-Economische Status–Wijk- en Omgevingsanalyse (Socioeconomic Status–Neighbourhood and Environmental Analysis)) scores as a measure of socioeconomic status, and severe asthma prevalence in children aged 6–17 years 2022. Generally, in the regions with a lower socioeconomic status, a higher severe asthma prevalence is reported.
Discussion
This is the first study to apply geospatial analysis in a nationwide cohort to evaluate several socioeconomic characteristics of children with controlled asthma, severe asthma and SAA. This study demonstrated that having a rental home or institutional housing showed the strongest association with severe (acute) asthma, followed by having a migration background, being from a single-parent family and having a low SES-WOA score, which were all significantly associated with having severe (acute) asthma. Moreover, in the hot spots with the highest prevalence of severe asthma there was a higher chance of having a lower SES than in children with severe asthma from other regions, reflecting an unequal distribution of social determinants. This study therefore establishes the presence of a socioeconomic health gradient of paediatric severe asthma in the Netherlands.
Our results are in line with previous research linking a lower SES to a higher asthma severity. In a Korean nationwide analysis of health insurance data, the highest number of asthma exacerbations were found in children from families with an income below the national minimum [18]. Additionally, asthmatic children from deprived neighbourhoods in England with SAA had a higher intubation and mortality rate at the PICU [8] and French children with asthma residing in socially disadvantaged neighbourhoods had a higher chance of being readmitted to the PICU [7]. The current study advances this prior work by evaluating the link of SES with both hospital- and PICU-related severe asthma admissions, as well as evaluating several different SES factors and thereby being able to compare these. This association may be explained by limited access to health facilities, under-recognition of symptoms and increased stress [19]. Previous research showed that maternal and family stress was independently associated with the prevalence of asthma by the age of 6 years [20]. In a Danish nationwide study, children from parents with a lower income and education level had worse disease control, which may be attributed to underutilisation of controller use [21, 22] or lower parental health literacy [23].
Our study demonstrated that children living in a rental home had an increased risk of severe (acute) asthma. One American study found a higher odds of asthma in children living in public housing versus private housing [24], while another study, also in the USA, demonstrated no significant association with housing type and childhood asthma prevalence [25]. This was most probably due to confounding economic variables, which attenuated the effect of housing type [25]. Other housing variables also affect asthma triggers [26]. The effect of locational features is most evident in literature [27, 28]. In asthmatic children, living close to a major roadway led to more frequent exacerbations and subsequent admissions [27, 28]. Moreover, a nationwide study found that ethnic minority groups were disproportionally exposed to higher levels of air pollutants compared to the nonimmigrant population [29]. In this study, the effect of air pollution was not investigated, but literature suggests higher levels of pollutants such as nitrogen dioxide in the hot spots Haaglanden, Amsterdam-Amstelland, Rotterdam-Rijnmond and Kennemerland [30]. Finally, dwelling and occupancy housing features such as mould related to crowding and ventilation should not be overlooked but associations with asthma are often not significant [26]. This may be explained by the hygiene hypothesis, which states that exposure to (viral) infections and endotoxins may have a protective effect on asthma features as well [18]. In the current study, household practices and housing condition could not be investigated, but ownership type and socioeconomic status could. Literature indicates that poor housing quality, such as cracked floors and walls and issues with mould or dampness is most frequently seen in rental properties and in families from low socioeconomic circumstances [31].
In this study we found that children with a second-generation migration background had a higher chance on severe asthma and asthma-related PICU admissions, while first-generation immigrants had the lowest prevalence of severe asthma. Generally, first-generation immigrants adopt a lower asthma prevalence from their country of origin, but eventually in second-generation immigrants the prevalence from the native population is acquired [32]. Similar findings have been previously reported [33]. This might be due to a lower redemption of medication or language barriers [34, 35].
Strengths of this study include the use of a nationwide cohort, in which the study population depicts the true population and not a sample. Additionally, the variables were objectively derived from national registries rather than being self-reported, enhancing data reliability and reducing response bias. This representative way of population-wide sampling increases the generalisability to data from other high-income countries with a similar healthcare system, and the results are generally consistent with existing literature [18, 24, 33]. In addition, geospatial analysis further strengthens our results as it does not only depict an association between study variables but also adds a spatial dimension which could elucidate what characteristics may be important in the development of severe asthma.
This study also had limitations. First of all, no data were included on general practitioner (GP)-related healthcare expenditures, as these data were not available in CBS's national registry. In the Netherlands, many stable paediatric asthma patients are treated by their GP [36]. As the focus in this study was on severe asthma, the absence of this data is not expected to impact our main findings. Secondly, the study group was formed based on hospital-related health expenditures, which were influenced by the COVID-19 pandemic. Admission rates were lower than usual due to decreased spread of viral infections and better air quality, and outpatient clinic visits were restricted. Therefore, asthma cases might have been underestimated [37, 38]. Ultimately, it remains challenging to determine the effect of the pandemic on asthma care and outcomes. Finally, for the children aged 2–3 years with ED visits or hospital admissions, the preceding 2 years were considered and therefore included children <2 years. Moreover, the asthma definitions were based on the DTC and ICD-10 codes, provided by the attending (paediatric) physician. It is difficult to distinguish bronchiolitis, preschool wheeze and asthma in children under 6 years [13, 14]. For these two reasons, the older age group (6–17 years) was used as the reference.
In this study, the spatial distribution of vulnerable children with severe asthma has been evaluated in the Netherlands. This is clinically relevant as it underlines the existing disparities in paediatric severe asthma and urges policy improvements to allocate resources to patients that need it most. This requires early identification of children at risk of severe asthma and close collaboration between social services, community doctors and nurses, pharmacists, and (specialised) paediatricians. For example, linking community health workers to low-income families in Puerto Rico has been proven to be effective to increase asthma control through improving controller use and decreasing symptoms [39]. Additionally, efforts by British doctors to write letters to the landlords of their asthmatic patients to act on the mould in these children's homes have been successful in improving quality of housing [40]. The main goal for the future is to enhance the lives of socioeconomically challenged families by ameliorating asthma control, healthcare access and medication use and provide community-based education and prevention.
Acknowledgments
The authors thank the CBS (Statistics Netherlands), Vektis C.V. and Dutch Hospital Data for the collection of data, and the members of the Amsterdam Health and Technology Institute (ahti) for the collaboration on the data analyses.
Footnotes
Provenance: Submitted article, peer reviewed.
Conflict of interest: A.H. Maitland-van der Zee is the principal investigator of a public–private consortium (P4O2 (Precision Medicine for More Oxygen)) sponsored by Health Holland involving many private partners that contribute in cash and/or in kind (AbbVie, Boehringer Ingelheim, Breathomix, Clear, Fluidda, Ortec Logiqcare, Olive, Philips, Quantib-U, Smartfish, Clear, SODAQ, Thirona, Roche, TopMD, Novartis and RespiQ); and reports funding from GSK, Boehringer Ingelheim, Vertex and AstraZeneca. S.J.H Vijverberg is senior project lead of 3TR Asthma Biologics Cohort (part of 3TR IMI), which receives funding from GSK, Astra Zeneca and Sanofi. The other authors declare that they have no conflicts of interest to disclose.
Support statement: This study was supported by Stichting Astma Bestrijding grant SAB 2025-010 and ETF Foundation. Funding information for this article has been deposited with the Open Funder Registry.
Supplementary material
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Supplementary material
00925-2025.SUPPLEMENT
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