ABSTRACT
Background and Objective
Worldwide, up to 40% of all asthma hospitalisations are due to repeat admissions. This study aimed to estimate the burden of asthma readmission among children aged 2 to 17 years in New South Wales (NSW), Australia.
Methods
A retrospective longitudinal whole‐of‐population‐based cohort study was conducted using linked administrative data. Children born in NSW from 2005 to 2015 having at least one asthma hospitalisation after the age of 2 years were included in the study and followed up for 12 months from the asthma index hospitalisation. The incidence rates for asthma first readmission within 12 months and associated direct medical costs were calculated.
Results
During 2007–2022, 48,217 asthma hospitalisations occurred in children, with 28,177 children hospitalised for asthma as a primary diagnosis identified as asthma index hospitalisations. Among them, 21.6% experienced first asthma readmissions within 12 months, including over 80% from the 2–4 years age group and a significant proportion from socioeconomically disadvantaged areas. The overall incidence rate of first asthma readmission was 23.8 per 100 person‐years and 28.7 per 100 person‐years for children aged 2–4 years. The highest incidence rate (38.3 per 1000 person‐months) occurred within 1 month. The total direct medical cost of the first asthma readmission within 12 months was AU$ 15.6 million.
Conclusion
Our study suggests that pre‐school children have the highest rate of asthma readmission, with a significant economic burden highlighting the need for identifying modifiable factors such as asthma management after discharge to reduce the burden of asthma hospital readmission in children.
Keywords: asthma, asthma readmission, Australia, childhood asthma, linkage, paediatric asthma, re‐hospitalisation
Short abstract
The retrospective cohort study computed the incidence of asthma readmission among Australian children. Over one‐fifth were readmitted within 12 months of index hospitalisation, posing a substantial economic burden, with preschoolers at highest risk. The first month following post‐discharge is highly critical. Many readmitted children were from socio‐economically disadvantaged communities.
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1. Introduction
Globally, asthma is the leading chronic respiratory condition affecting all age groups, with an age‐standardised prevalence of 341.5 per 100,000 population in 2019 and is characterised by intermittent episodes of wheezing, cough, breathlessness, and chest tightness [1, 2]. To date, the highest prevalence of asthma has been reported in Australia and the United States [2]. In 2023, asthma accounted for 35% of the total burden of disease for all respiratory conditions in Australia [3]. About 11% of the total population (2.8 million) in Australia live with asthma [4]. An estimated AU$ 852 million was expended for the treatment and management of asthma in Australia [3]. Asthma is particularly burdensome in children. In 2022, the Australian Institute of Health and Welfare (AIHW) reported asthma as a leading cause of total disease burden in children aged 1–9 years, with a prevalence of 10% and 6.2% for boys and girls aged 1–14 years respectively [5].
Worldwide, up to 40% of asthma admissions are repeat admissions [6, 7, 8]. Readmission due to asthma often suggests inadequate management or difficulty in controlling asthma symptoms [9]. This emphasises the necessity of high‐quality patient care, including patient education, frequent follow‐up, monitoring at home, treatment adjustment, and consulting with clinical experts during and after the earlier admissions [10, 11, 12]. In Victoria, Australia, Vicendese et al. showed an upward trend in repeated admission within 28 days among 2–12 years old children from 1997 to 2009 [13]. More recently, Chen et al. demonstrated that 34% of asthma readmission in Victoria occurred within 12 months of index admission during 2017–2018 in children [14] but was limited to a 2‐year period and three hospitals.
Prevention of asthma readmission can improve the quality of life by averting school absences, parental absenteeism from work, and treatment costs [15]. Aligning with the vision of Asthma Australia to halve the number of potentially preventable asthma hospitalisations [16], contemporary population‐based data on the burden of asthma readmissions are essential for monitoring and developing policies for intervention. We, therefore, conducted a population‐based retrospective cohort study from 2007 to 2022 to measure the burden of paediatric asthma readmission in New South Wales (NSW), Australia, which has the highest population with an annual birth cohort of approximately 95,000 of the 300,000 births occurring nationally [17].
2. Methods
2.1. Study Population and Follow‐Up
The study cohort included all children born in NSW between 2005 and 2015 who had at least one asthma hospitalisation across NSW between ages 2 and 17 years. Each child was followed from the first asthma hospitalisation that happened after the age of 2 years (earliest follow‐up period starting from 1st January 2007) till 12 months after the index asthma hospitalisation, or end of the study period (i.e., 31st December 2022), or till the child reached the age of 18 years or died, whichever was earlier. Children who had their first asthma hospitalisation before the age of 2 years were not counted in the study as asthma diagnoses in this age group might be confounded by wheezing disorders such as bronchiolitis [18]. Approval of the study was granted by the NSW Population and Health Services Research Ethics Committee (2022/ETH00188/2022.03). The study was conducted using de‐identified linked data sets. Therefore, patient consent is not applicable in this study.
2.2. Study Design
This was a longitudinal retrospective cohort study using linked population‐based administrative data sets in NSW. The Centre for Health Record Linkage (CHeReL) is responsible for linking NSW government health databases (www.cherel.org.au) for research purposes and replacing each child with a unique Patient Project Number (PPN) to de‐identify all data and allow the linkage of records from different datasets that belong to the same person. Datasets including Perinatal Data Collection (PDC), Admitted Patient Data Collection (APDC), and Cause of Death Unit Record File (COD URF) were provided to the study investigators linked using the PPN.
2.3. Data Sources
2.3.1. Perinatal Data Collection (PDC)
The PDC data captures all births registered in NSW and contains data on mothers and newborns related to birth outcomes, such as date of birth, gender of the newborn, birthweight, and gestational age. It also includes information, such as the Indigenous status of the mother and baby, the residential location of the mother during childbirth, that is, Statistical Area Level 2 (SA2) [19]. SA2s developed by the Australian Bureau of Statistics (ABS) usually contain a population between 3000 and 25,000 with a mean of about 10,000 dwellers, and the purpose of using SA2s is to represent a community interacting with each other socially and economically [20].
2.3.2. Admitted Patient Data Collection (APDC)
The APDC dataset collects data regarding all admissions including hospital separations, transfers, and deaths in all public and private hospitals in NSW [21]. It contains the age of the patient at hospitalisation, the date of hospital admissions and discharges, primary and secondary causes of hospitalisations, length of stay (LOS), discharge status (e.g., discharge, transfer, death, etc.), mode of separation (e.g., discharge on leave, discharged by hospital, discharged at own risk, died, transferred to other hospitals, etc.), and Australian Refined Diagnosis Related Groups (AR‐DRGs) code for the cost‐weight for each hospitalisation.
2.3.3. Cause of Death Unit Record File (COD URF)
COD URF captures all deaths registered in NSW [22]. It contains data on the date of death, cause of death (underlying and contributing), age at death, and gender.
The data sets were cleaned of duplicate entries. From the APDC data set, children having hospitalisation with acute and emergency conditions with a proper diagnosis following the International Classification of Diseases and Related Health Problems, 10th Revision, Australian Modification (ICD‐10‐AM) codes were included. Furthermore, for children who were coded as ‘transfer to other hospitals’ or ‘type change separation’ during discharge, the next admission was considered as the same episode continued with the previous one. The study birth cohort was identified from the PDC. The APDC was used to identify all children in the birth cohort with at least one asthma hospitalisation during the study period (2007–2022). The COD URF was used to identify any deaths among the birth cohort.
2.4. Identification of Asthma Hospitalisation
We used the ICD‐10‐AM codes [23] for identifying confirmed asthma hospitalisation from APDC where asthma was the primary diagnosis defined by J45‐Asthma; J45.0‐Allergic asthma; J45.1‐Non‐allergic asthma; J45.8‐Mixed asthma; J45.9‐Asthma unspecified; and J46‐Status asthmaticus.
2.5. Index Asthma Hospitalisation
For each child in the study birth cohort, the first asthma hospitalisation after the age of 2 years (identified from APDC) during the study follow‐up period was determined as the index asthma hospitalisation.
2.6. Asthma Readmission Within 12 Months
The first asthma readmission within 12 months of the follow‐up period after the index asthma hospitalisation was defined as the first asthma readmission.
We examined the incidence rate of the first asthma readmission by months up to 12 months since the index asthma hospitalisation and estimated the direct cost of the first asthma readmission within 12 months of the index asthma hospitalisation.
2.7. Covariates
We included children's demographic and clinical characteristics at index asthma hospitalisation as covariates in this study. These characteristics included age in years at the time of index asthma hospitalisation, which was grouped into four categories (2–4 years, 5–9 years, 10–14 years, and 15–17 years) [13, 14, 18], gender of the child (male and female), Indigenous status, socio‐economic status of the residential location of mother during children's birth measured by the quartiles of the Socio‐economic Indexes for Areas (SEIFA) Index of Relative Socio‐economic Disadvantage (IRSD) [24] (quartile Q1 = most disadvantaged, Q2, Q3, and Q4 = least disadvantaged), and remoteness of the mothers' residential location during children birth measured by the Australian Statistical Geography Standard (ASGS) Accessibility/Remoteness Index of Australia Plus (ARIA+) (major cities, inner regional, and outer regional/remote Australia) [25]. All the SA2s are assigned percentile scores based on the SEIFA IRSD ranking where lower percentiles of the IRSD mean more disadvantage [24]. We divided the SEIFA IRSD percentiles into four categories using the quartile. The ASGS data includes Remoteness Areas (RA) names that are categorised into five groups (major cities of Australia, inner regional Australia, outer regional Australia, remote Australia, and very remote Australia) based on the ARIA+, a metric that assesses the relative geographic accessibility of services. Due to low frequency in remote and very remote areas, we merged those into outer regional areas (i.e., outer regional/remote Australia). We used the SEIFA IRSD and the remoteness of mothers' residential SA2 during children's birth as proxies to indicate children's socioeconomic status and remoteness of their residential location during asthma hospitalisations. Clinical characteristics of children included LOS (calculated by using admission date, discharge date, and total leave days and categorised into 1 day and ≥ 2 days) and use of the intensive care unit (ICU). As asthma hospitalisation and associated costs may differ by Indigenous status, we adjusted the incidence rate and cost of asthma readmission by ‘Indigenous status’. The children who were specified as ‘Aboriginal’ or ‘Torres Strait Islander’, or ‘Aboriginal and Torres Strait Islander origin’, or ‘Indigenous– not further specified’ in APDC or PDC were considered as ‘Indigenous population’.
2.8. Data Analysis
The outcome measure was the first asthma readmission to the hospitals within 12 months of index asthma hospitalisation. Socio‐demographic and clinical characteristics for index asthma hospitalisation were compared between children who were hospitalised and children who were not hospitalised due to asthma within 12 months of the index hospitalisation using the chi‐squared (x 2) test. Discrete variables were presented as counts and proportions (%) and continuous variables as means with standard deviation (SD) or medians with interquartile range (IQR).
2.9. Calculation of Incidence Rate for First Asthma Readmission
We calculated the overall incidence rate of first asthma readmission within 12 months by dividing the total number of children who had their first asthma readmission within 12 months by the number of person‐years observed for all children from their index asthma hospitalisation to the time point when they had the first asthma readmission, died, end of the first year, or 31st December 2022, whichever came first, expressed as 100 person‐years. The incidence rate for the first asthma readmission was calculated by months within the first 12 months. For a given month, we divided the number of children who had the first asthma readmission within that month by the number of person‐months observed for all children after their index asthma hospitalisation till they had the first asthma readmission, died, end of the month, or 31st December 2022, whichever came first, expressed as 1000 person‐months. Likewise, months‐specific incidence rates and 95% confidence intervals were generated by Poisson regression. A greater prevalence of asthma is observed in localities where a higher proportion of the population is Indigenous [26]; therefore, we generated incidence rates of 12‐month first asthma readmission by age groups and gender, adjusted by Indigenous status using Poisson regression.
2.10. Spatial Distribution of First Asthma Readmission Rate in NSW
We calculated the incidence rate per 1000 person‐months for the first asthma readmission within 1 month of the index asthma hospitalisation by the SA2 of the mothers' residential location during children's birth across NSW using Poisson regression. We excluded the SA2 areas where SA2 areas were either coded incorrectly or had missing values, or person‐months were less than one. SA2s were grouped into three categories based on the tertile of incidence rates and visualised on the NSW Map. In the map, Greater Sydney was highlighted owing to its high population density, the potential for significant changes in healthcare access, and environmental conditions that might influence asthma readmissions. We calculated the distribution of incidence tertiles across different socioeconomic categories, remoteness classifications, and areas of NSW.
2.11. Direct Medical Cost Estimation for First Asthma Readmission
We used the National Hospital Cost Data Collection (NHCDC) Public Sector Report 2012–2013 to estimate the direct cost of asthma readmission in public hospitals within 12 months of index hospitalisation [27]. The report contains information on cost and activity from the financial year 2012 to 2013 containing AR‐DRGs (version 6.0). The AR‐DRG code is used to estimate the direct cost of disease, which incurs the cost of physician services, medical devices, medications, hospital services, and diagnostic testing [28]. To estimate the hospital costs for asthma readmission, we captured the total cost assigned to AR‐DRG codes from the NHCDC (2012–2013) and divided it by the average length of stay to get the direct cost per patient per day in the public hospitals. Afterwards, for each patient, we multiplied the unit cost by the actual LOS for each first readmission and added the cost to estimate the total cost of asthma readmission in public hospitals. The total cost was adjusted to 2011/2012 AU$ using the deflators specified in the Consumer Price Index (CPI) rates [29]. The CPI rate is published by the ABS, which is relevant to tax and superannuation law [29]. The index reference base was fixed in 2011–12, and deflators were taken according to years to adjust for inflation (years‐ 2007 to 2022; deflators‐ 89.1, 92.4, 94.3, 96.9, 99.8, 102.0, 104.8, 106.6, 108.4, 110.0, 112.1, 114.1, 116.2, 117.2, 121.3, and 130.8 respectively) [29]. For the hospitalisations requiring transfers to other hospitals, the cost was adjusted by the sum of the LOS incurred at each hospital. Moreover, the hospital cost in NSW suggests that an additional 10% cost is incurred by Indigenous children. Hence, the costs of care for Indigenous children were adapted using a 10% increment for additional resource utilisation [30]. The mean cost was calculated for all children by negative binomial regression according to gender and age groups adjusted with Indigenous status, though the Indigenous‐status‐specific results were not shown due to ethical requirements.
All statistical analyses were undertaken using the SAS Enterprise guide (8.4) and Microsoft Excel, and the map was developed using R (version 4.4.0).
3. Results
3.1. Socio‐Demographic and Clinical Characteristics at Index Asthma Admissions of Children With or Without First Asthma Readmission
During 2007–2022, a total of 48,217 asthma admissions occurred in hospitals among children, of which 28,177 children aged 2 to 17 years were admitted to hospitals due to asthma as a primary diagnosis and identified as index asthma hospitalisation. Of them, 21.6% (n = 6073) were readmitted due to asthma within 12 months of the index asthma hospitalisations. Compared to children who did not have asthma readmission within the first 12 months, more children who were re‐hospitalised within 12 months of index asthma hospitalisation were younger (mean age in year 3.8 vs. 4.7), with the proportion of 2–4 years being 80.7% versus 64.7%, lived in major cities of Australia (82.2% vs. 79.2%), and had 2 days of length of stay or more (32.7% vs. 27.8%) (Table 1). Children who had asthma readmission within the first 12 months were admitted more to the ICU during the index hospitalisation (1.0% vs. 0.7%).
TABLE 1.
Socio‐demographic and clinical characteristics of children with and without first asthma readmission between 2 and 17 years among children born in NSW, Australia between 2005 and 2015.
| Characteristics of children at the time of index asthma hospitalisation | Children with first asthma hospitalisations between 2 and 17 years of age (index asthma hospitalisations) | Children who were not readmitted due to asthma within 12 months of index asthma hospitalisation | Children who were readmitted for the first time due to asthma within 12 months of index asthma hospitalisation | p a |
|---|---|---|---|---|
| (n = 28,177) | (n = 22,104) | (n = 6073) | ||
| n (Column %) | n (Column %) | n (Column %) | ||
| Age (in years) | ||||
| Mean age (SD) | 4.5 (2.5) | 4.7 (2.6) | 3.8 (2.0) | < 0.0001 |
| Median age (IQR) | 3.8 (2.7–5.6) | 4.0 (2.8–6.0) | 3.2 (2.4–4.5) | < 0.0001 |
| Age groups | ||||
| 2–4 years | 19,198 (68.1) | 14,297 (64.7) | 4901 (80.7) | < 0.0001 |
| 5–9 years | 7770 (27.6) | 6716 (30.4) | 1054 (17.4) | |
| 10–14 years | 1116 (4.0) | 1008 (4.6) | 108 (1.8) | |
| 15–17 years | 93 (0.3) | 83 (0.4) | 10 (0.2) | |
| Gender of children | ||||
| Male | 17,718 (62.9) | 13,924 (63.0) | 3794 (62.5) | 0.4576 |
| Female | 10,459 (37.1) | 8180 (37.0) | 2279 (37.5) | |
| Length of stay (LOS)‐ Category | ||||
| 1 day | 20,038 (71.1) | 15,950 (72.2) | 4088 (67.3) | < 0.0001 |
| ≥ 2 days | 8139 (28.9) | 6154 (27.8) | 1985 (32.7) | |
| Admission at Intensive Care Unit (ICU) | ||||
| Admitted at ICU | 206 (0.7) | 143 (0.7) | 63 (1.0) | 0.0016 |
| Not admitted at ICU | 27,971 (99.3) | 21,961 (99.4) | 6010 (99.0) | |
| Mothers' residential location socioeconomic status | ||||
| Q1 (most disadvantaged) | 9173 (32.6) | 7169 (32.4) | 2004 (33.0) | 0.0927 |
| Q2 | 7568 (26.9) | 5951 (26.9) | 1617 (26.6) | |
| Q3 | 5378 (19.1) | 4173 (18.9) | 1205 (19.8) | |
| Q4 (least disadvantaged) | 5856 (20.8) | 4658 (21.1) | 1198 (19.7) | |
| Missing | 202 (0.7) | 153 (0.7) | 49 (0.8) | |
| Mothers' residential location Remoteness b | ||||
| Major Cities of Australia | 22,495 (79.8) | 17,506 (79.2) | 4989 (82.2) | < 0.0001 |
| Inner Regional Australia | 4113 (14.6) | 3286 (14.9) | 827 (13.6) | |
| Outer Regional or remote Australia | 1370 (4.9) | 1161 (5.3) | 209 (3.4) | |
| Missing | 199 (0.7) | 151 (0.7) | 48 (0.8) | |
Abbreviations: IQR, interquartile range; SD, standard deviation.
p values are obtained from the chi‐squared test to estimate the differences between children without asthma readmission and children with asthma readmission within 12 months of index asthma hospitalisations for categorical variables, and the Wilcoxon rank‐sum test for continuous variables.
Mothers' residential location refers to mothers' residential location during children's birth.
3.2. Incidence Rate of First Asthma Readmission Among Children in NSW
The overall incidence rate for the first asthma readmission within the 12 months of index asthma hospitalisation was 23.8 per 100 person‐years (95% CI 22.6 to 25.1) (Table 2). The incidence rate was slightly higher for females (24.2 per 100 person‐years; 95% CI 22.7 to 25.7) compared to males (23.7 per 100 person‐years; 95% CI 22.4 to 25). In the 2–4 years age group, the rate was the highest (28.7 per 100 person‐years; 95% CI 27.2 to 30.3) followed by the 5–9 years age group (14.7 per 100 person‐years; 95% CI 13.6 to 15.8). The incidence rate of asthma readmission went down with age up to the age group 10–14 years (11.2 per 100 person‐years; 95% CI 9.2 to 13.6) and then increased again in children aged 15–17 years (18 per 100 person‐years; 95% CI 9.7 to 33.6).
TABLE 2.
Incidence rate of first asthma readmission within 12 months of index asthma hospitalisation between 2 and 17 years among children born in NSW Australia between 2005 and 2015.
| Covariates | Number of 1st asthma readmission | Population at risk | Person‐years | Incidence rate/100 person‐years 95% Confidence Interval a |
|---|---|---|---|---|
| Duration for incidence rate | ||||
| 12 months | 6073 | 28,177 | 24,448.0 | 23.8 (22.6–25.1) |
| Gender of children | ||||
| Male | 3794 | 17,718 | 15,388.0 | 23.7 (22.4–25.0) |
| Female | 2279 | 10,459 | 9060.0 | 24.2 (22.7–25.7) |
| Age‐group | ||||
| 2–4 years | 4901 | 19,198 | 16,503.7 | 28.7 (27.2–30.3) |
| 5–9 years | 1054 | 7770 | 6959.6 | 14.7 (13.6–15.8) |
| 10–14 years | 108 | 1115 | 931.1 | 11.2 (9.2–13.6) |
| 15–17 years | 10 | 93 | 53.7 | 18.0 (9.7–33.6) |
Incidence rate and 95% confidence interval were obtained from Poisson regression.
The incidence rate of first asthma readmission was highest in the first month following index asthma hospitalisation and declined progressively over time (Figure 1). Specifically, the incidence rate of first asthma readmission in the first month was 38.3 per 1000 person‐months (95% CI 33.8 to 43.4) declining to half in the 2nd month (17.4 per 1000 person‐months, 95% CI 15.3 to 19.7) (Figure 1). The incidence rate descended to 1.0 per 1000 person‐months from the 9th month of index asthma hospitalisation.
FIGURE 1.
Month‐wise asthma index hospitalisations and incidence rate of first asthma readmission among children 2–17 years of age between 2007 and 2022 in NSW, Australia.
3.3. Direct Medical Cost of First Asthma Readmission Within 12 Months of Index Asthma Hospitalisation
The mean direct medical cost of first asthma readmission per child per episode within 12 months of index asthma hospitalisation was AU$ 2593 (95% CI 2547.1 to 2638.8) (Table 3). The total direct medical cost for the first asthma readmission within 12 months was AU$ 15.6 million. Female children incurred the higher mean cost for each episode (AU$ 3104.7; 95% CI 3013.2 to 3199) compared to male children (AU$ 2872.6; 95% CI 2794.5 to 2953.0). For children aged 2–4 years, the total direct cost accounted for AU$ 12.2 million (Table 3). The mean cost of direct asthma readmissions per episode tends to increase with age up to the 10–14 years age group. The mean cost was the highest for 10–14 years children (AU$ 4392.4 per episode; 95% CI 3998.6 to 4285) and was the lowest in 2–4 years children (AU$ 2866.9 per episode; 95% CI 2792.5 to 2943.2).
TABLE 3.
Direct medical cost of first asthma readmission among children 2 to 17 years of age between 2007 to 2022 across NSW, Australia.
| Characteristics of children | Number (n) | Total cost | Mean cost (95% Confidence Interval) a |
|---|---|---|---|
| Total children | 6014 | 15,594,202.2 | 2593.0 (2547.1–2638.8) |
| Gender of children | |||
| Male | 3757 | 9,474,233.3 | 2872.6 (2794.5–2953.0) |
| Female | 2257 | 6,155,958.6 | 3104.7 (3013.2–3199.0) |
| Age‐groups | |||
| 2–4 years | 4855 | 12,237,784.8 | 2866.9 (2792.5–2943.2) |
| 5–9 years | 1043 | 2,941,514.2 | 3204.1 (3089.0–3323.5) |
| 10–14 years | 106 | 415,592.9 | 4392.4 (3998.6–4825.0) |
| 15–17 years | 10 | 35,823.9 | 3888.1 (2883.4–5242.7) |
Mean cost with 95% confidence interval was calculated using negative binomial regression.
3.4. Spatial Distribution of the Incidence Rate of First Asthma Readmission Within 1 Month of Index Asthma Hospitalisation in NSW
A total of 514 SA2 were included in the spatial analysis. As the incidence rate of asthma readmission was highest in the first month following index asthma hospitalisation, the SA2s were grouped into three categories based on the tertiles of incidence rates of first readmission within 1 month of index admission, and the categories contained incidence rates of 0.0 to 18.7, 18.7 to 47.2, and 47.2 to 523.7, respectively (Figure 2). The bivariate analysis showed that more than 50% of SA2s with higher incidence rates were located in the most and moderately disadvantaged areas, major cities, and Greater Sydney (Table 4).
FIGURE 2.
Spatial distribution of Statistical Areas Level 2 (SA2) according to incidence rates for first asthma readmission within 1 month of index asthma hospitalisation among children 2–17 years of age between 2007 and 2022 across NSW, Australia.
TABLE 4.
Distribution of Statistical Areas Level 2 (SA2) according to the incidence rate for first asthma readmission within 1 month of index asthma hospitalisation per 1000 person‐months tertiles across the mothers' residential location socioeconomic status, remoteness and NSW areas.
| Characteristics of children | Statistical Areas Level 2 (SA2) according to incidence rate for first asthma readmission within 1 month of index asthma hospitalisation per 1000 person‐months (n = 514) | ||
|---|---|---|---|
| 0.0–18.7 (Column %) | 18.7–47.2 (Column %) | 47.2–523.7 (Column %) | |
| Mothers' residential location socioeconomic status a | |||
| Q1 (most disadvantaged) | 60 (35.1) | 47 (27.3) | 54 (31.6) |
| Q2 | 43 (25.2) | 53 (30.8) | 42 (24.6) |
| Q3 | 37 (21.6) | 31 (18.0) | 39 (22.8) |
| Q4 (least disadvantaged) | 28 (16.4) | 41 (23.8) | 36 (21.1) |
| Missing | 3 (1.8) | 0 (0.0) | 0 (0.0) |
| Mothers' residential location remoteness | |||
| Major Cities of Australia | 71 (41.5) | 134 (77.9) | 113 (66.1) |
| Inner Regional Australia | 64 (37.4) | 26 (15.1) | 43 (25.2) |
| Outer Regional or Remote Australia | 36 (21.1) | 12 (7.0) | 15 (8.8) |
| Areas of New South Wales | |||
| Greater Sydney | 60 (34.9) | 117 (68.4) | 91 (53.2) |
| Out of Greater Sydney | 112 (65.1) | 54 (31.6) | 80 (46.8) |
Mothers' residential location refers to mothers' residential location during children's birth.
4. Discussion
This large population‐based cohort study comprising a birth cohort of almost 30,000 children showed that over one‐fifth (21.6%) of children were readmitted due to asthma within 12 months following the index asthma hospitalisation. Reported readmission rates widely differ across many countries: 15% in France [9], 26.3% in Thailand [31], 34.3% in the State of Victoria, Australia [14], and 5.3% in China [15]. Our proportion of asthma readmission was higher than in France and China, which may be due to the different definitions of asthma readmissions used for different study subjects. For example, our proportion is much lower than the study conducted in Victoria, Australia by Chen et al. [14] The difference between the proportion of asthma readmission in Victoria and NSW can be attributed to the fact that the current study considered only the first asthma readmission after index hospitalisation, whereas Chen et al. included wheezing (ICD‐10‐AM code‐R062) as a diagnostic criterion for capturing asthma.
In our study, most of the children who were readmitted due to asthma were preschoolers (age group 2–4 years). The asthma readmission rate among this group was twice that of other age groups. In Victoria, Australia, we observed a similar scenario where children of 2–5 years had higher asthma readmission [13, 14]; however, in the United States, children of 12–18 years had a higher readmission rate compared to 5–11 years and 2–4 years children [18]. Causal factors may include the high incidence of respiratory infections, particularly viral infections, in this young age group [32]. In addition, environmental triggers such as environmental tobacco smoke, secondary smoking at home, traffic‐related air pollution, moulds at home, dust‐mite allergens, food allergens, and inhalant allergens are causal risk factors for asthma readmissions among young children [33].
According to our study, the highest incidence rate for first asthma readmission occurred in the first month (38.3 per 1000 person‐months), followed by reducing rates of readmissions in the 2nd (17.4 per 1000 person‐months) and 3rd months (8.9 per 1000 person‐months). Month‐wise trend analysis of asthma readmission using person‐months is lacking in recent asthma readmission studies. Nonetheless, our findings can be correlated with Vicendese et al. showing that one‐quarter of total asthma readmission among children within 12 months occurred in the first month of index hospitalisation [13]. Hospital discharge without proper disease management, guideline‐discordant asthma care, lack of education including reviewing inhaler technique, and adequate counselling during discharge have been demonstrated to influence the risk of asthma readmission within the first few months of index hospitalisation [13, 14, 34]. Written asthma action plans and reviews of their inhaler technique are the cornerstones of asthma management. However, only two in three children have a written asthma action plan, and over one‐third of children do not have a review of their inhaler technique [14, 35]. This may partially clarify the reasons for high asthma readmission observed in this study. Discharge periods attended by nurses, frequent outpatient follow‐up, culturally tailored asthma education, psychosocial support services, and self‐care plans reduce readmission rate [36, 37, 38]. Moreover, post‐discharge follow‐up for children in primary health care centres also has a positive impact on reducing asthma readmission [39]. Given access to GPs is decreasing, community nurses play an increasingly important role. Hence, a community‐centred, clinically connected, and continuously collaborative comprehensive asthma programme is needed in the current scenario of asthma readmission [40]. The highest incidence rate in the first month of index hospitalisation also insinuates the timing for targeted interventions during and after index asthma hospitalisation.
Previous studies in Australia showed that children aged 0–14 years living in major cities have higher hospital admissions due to asthma [41, 42] which supports our study findings for both index asthma hospitalisation and asthma readmission within 12 months in major cities. The distribution of SA2 areas with higher incidence rates indicates that more disadvantaged areas and major cities have higher incidence rates, implying socioeconomic disparities with higher population density, traffic congestion, and environmental pollution. Khan et al. also demonstrated a relationship between self‐reported (parents or carers) asthma prevalence and areas with greater socioeconomic disadvantage [26]. The outcomes of our study also support that outdoor air pollution concentrated in larger cities in Australia may be linked to higher asthma readmission risk associated with global and environmental changes [43].
The estimated mean direct medical cost per episode (mean LOS per episode‐1.4 days) (AU$ 2593) of asthma readmission in public hospitals observed in this study was higher than the earlier study [44] among children in Australia. In South Australia, the mean cost of asthma hospitalisation for children per episode was AU$ 2358 (mean LOS per episode‐1.7 days) published in 2014 [44]. According to Asthma Australia, an uncomplicated episode of asthma admission costs AU$ 2591 per episode (mean LOS per episode‐1.5 days) during 2013–2014 [45]. The overall higher mean cost of asthma readmission in our study compared to admissions is likely explained by rising overall hospital costs and inflation.
The study has several important strengths. The APDC data set is a large and comprehensive database that includes all patients admitted to all NSW public and private hospitals, allowing us to estimate the incidence rate of asthma readmission at a population level across the entire state. Data is anonymous; therefore, selection bias and under‐reporting can be minimised. Hence, the study findings can be generalisable to the Australian population. However, the study has important limitations as well, which can be related to the inherent nature of studies using linked data sets. We could not adjust for important factors such as family history of asthma, allergen, atopy, severity of asthma, respiratory infections, home care, inhaler technique, and asthma medications when comparing the incidence rates between different groups of the population (i.e., gender and age groups). Due to the nature of data following a birth cohort, we could not draw any inference on the progression of asthma readmission, denoting a limitation of our study. There is a possibility of misclassification because other similar respiratory conditions (such as bronchiolitis, and pneumonia) could be incorrectly coded as asthma using ICD‐10‐AM codes. In our study of asthma readmission, we concentrated on inpatient departments, excluding emergency department visits. The approach permitted us to investigate the outcomes of severe asthma patients who had been hospitalised previously for asthma. For identifying asthma cases in inpatients, we used ICD‐10‐AM codes J45 and J46 rather than using other ICD codes applied in different studies [14, 32, 34]. The strategy might reduce false positive asthma cases in our study. Furthermore, the study focused on cases where asthma was the principal cause of hospitalisation to ensure that patients were treated for asthma‐related complications, which might underestimate the actual magnitude of the problem. Children living in NSW who moved outside of NSW or were readmitted to hospitals outside of NSW were not included in the study, resulting in under‐reporting of asthma readmission.
Our study highlights a significant burden of hospitalisation and economic burden associated with paediatric asthma readmissions in Australia. The burden was highest among pre‐school children, with the peak incidence rate of asthma readmission in the first month of index asthma hospitalisation. In addition, most of the children readmitted for asthma lived in socio‐economically disadvantaged areas. In conclusion, the study emphasises the need to identify modifiable factors associated with asthma readmission and develop targeted interventions such as enhanced asthma discharge care and post‐discharge follow‐up within 30 days of discharge, which may help in lowering the high burden of paediatric asthma hospitalisation.
Author Contributions
Md Mahbubur Rashid: conceptualization (lead), data curation (lead), formal analysis (lead), funding acquisition (supporting), investigation (lead), methodology (lead), project administration (lead), resources (lead), software (lead), supervision (lead), validation (lead), visualization (lead), writing – original draft (lead), writing – review and editing (lead). Nan Hu: formal analysis (supporting), investigation (supporting), supervision (supporting), writing – review and editing (supporting). Jahidur Rahman Khan: formal analysis (supporting), supervision (supporting), writing – review and editing (supporting). Mei Chan: data curation (supporting), writing – review and editing (supporting). Melinda Gray: investigation (supporting), resources (supporting), writing – review and editing (supporting). Louisa Owens: investigation (supporting), resources (supporting), writing – review and editing (supporting). Adam Jaffe: conceptualization (supporting), investigation (supporting), methodology (supporting), supervision (supporting), writing – review and editing (supporting). Nusrat Homaira: conceptualization (supporting), data curation (supporting), formal analysis (supporting), funding acquisition (lead), investigation (supporting), methodology (supporting), resources (supporting), supervision (lead), writing – review and editing (supporting).
Ethics Statement
Approval of the study was granted by the NSW Population and Health Services Research Ethics Committee (2022/ETH00188/2022.03). The study was conducted using de‐identified linked data sets. Therefore, patient consent is not applicable in this study.
Conflicts of Interest
The authors declare no conflicts of interest.
Acknowledgements
We would like to acknowledge the NSW Ministry of Health for facilitating the use of the data. Open access publishing facilitated by University of New South Wales, as part of the Wiley ‐ University of New South Wales agreement via the Council of Australian University Librarians.
Rashid M. M., Hu N., Khan J. R., et al., “The High Burden of Asthma Readmission Among Australian Children: A Population‐Based Cohort Study,” Respirology 30, no. 9 (2025): 871–881, 10.1111/resp.70049.
See related editorial
Associate Editor: Giorgio Piacentini; Senior Editor: Fanny Wai San Ko
Funding: We are grateful to the Rotary Club of Sydney Cove, Sydney Children's Hospital Foundation (SCHF), and the University of New South Wales (UNSW) for their generous funding to conduct this research.
Adam Jaffe and Nusrat Homaira contributed equally to this research study.
Data Availability Statement
Access to these data in not available publicly available due to legal and ethical restrictions.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Access to these data in not available publicly available due to legal and ethical restrictions.