ABSTRACT
Aim
To examine the population pattern of bronchopulmonary dysplasia (BPD), death and composite outcome (BPD/death) in preterm infants in Australia.
Methods
A retrospective population study using linked statewide health administrative data between 2005 and 2015 to estimate the incidence of BPD, death and composite outcome stratified by gestation: extremely preterm (EPT), very preterm (VPT) and moderate‐to‐late preterm (MPT). Associated risk factors were analysed using generalized estimating equation logistic regression models.
Results
Of 1 047 885 live births, 73 314 (7%) were preterm, of whom 1906 died and 1898 developed BPD (25.9/1000). BPD incidence was highest in EPT (324.6/1000), followed by VPT (89.7/1000) and MPT (1.5/1000) infants. Annual BPD incidence increased from 23.5 (in 2005) to 28.0/1000 (in 2015) (p < 0.001), while composite outcome marginally decreased from 55.3 to 46.3/1000 (p = 0.629), driven by reduced mortality (32.5 to 18.8/1000) (p < 0.001). Among 10 407 < 32 weeks infants, risk factors for BPD, death and composite outcomes were born EPT (aOR ranging 8.5–13.35), assisted vaginal/breech delivery (aOR 1.25–1.41), small‐for‐gestational‐age (aOR 3.27–3.88), male sex (aOR 1.21–1.42) and had low 5‐min APGAR score (aOR 1.64–12.47).
Conclusion
The rising BPD incidence, predominantly among EPT survivors, was offset by reduced overall preterm mortality. Findings call for healthcare planning to cater for increased BPD survivors.
Keywords: bronchopulmonary dysplasia, New South Wales, pattern, predictors, preterm
Summary
Acknowledging survival of extremely preterm (EPT) infants is improving, the population‐level data on preterm bronchopulmonary dysplasia (BPD), composite outcome (BPD/death) are limited.
BPD continues to rise particularly in EPT infants as mortality falls, while the overall preterm composite outcome, though sharing common risk factors, may have declined indicating more preterm infants survived without BPD.
The findings underscore the need to investigate long‐term outcomes and care burden for healthcare planning.
Abbreviations
- ANZNN
Australian and New Zealand Neonatal Network
- aOR
adjusted odds ratio
- APDC
Admitted Patient Data Collection
- BPD
bronchopulmonary dysplasia
- CheReL
Centre for Health Record Linkage
- CI
confidence interval
- COD‐URF
Australian Coordinating Registry Cause of Death Unit Record File
- EDDC
Emergency Departments Data Collection
- ELBW
extremely low birthweight
- EPT
extremely preterm
- GA
gestational age
- GEE
generalized estimating equations
- ICD‐10‐AM
International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification
- IRR
incidence rate ratio
- LBW
low birth weight
- MPT
moderate‐to‐late preterm
- NICU
neonatal intensive care unit
- NSW
New South Wales
- OR
odds ratio
- PDC
Perinatal Data Collection
- SEIFA IRSD
Socio‐Economic Index for Areas Index of Relative Socio‐economic Disadvantage
- SGA
small for gestational age
- UNSW
University of New South Wales
- VPT
very preterm
1. Introduction
Bronchopulmonary dysplasia (BPD) is the most common chronic lung disease among preterm infants (< 37 weeks gestational age [GA]) [1]. Extremely preterm (EPT; < 28 weeks GA) or extremely low birthweight (ELBW; < 1000 g) infants face the highest risk [2]. Infants with BPD require prolonged hospitalization in the neonatal intensive care unit (NICU) and post‐discharge home oxygen therapy [1]. Survivors often experience long‐term sequelae, including respiratory, neurological and cardiovascular morbidities [1, 3, 4, 5, 6], leading to poor quality of life and increased healthcare costs [6].
Globally, BPD is a significant health issue [7]. The incidence of BPD varies across NICUs from 6% to 57%, partly due to differing definitions [8]. BPD ranged from 10% to 73% in Europe, 18%–89% in North America, 18%–82% in Asia and 30%–62% in Oceania between 2006 and 2017 [7]. The global rate was 35% when defined as oxygen/ventilation required at 28 days and 21% at 36 weeks postmenstrual age in 30 countries from 1990 to 2019 [9].
Despite advances in neonatal care that improved survival, BPD rates appeared static or increased over time, raising concern for both developed and developing nations [10, 11, 12]. The improved survival in preterm infants at high risk of developing BPD may explain the upward trend of BPD [13]. A recent nationwide Korean study of EPT infants from NICU found mortality dropped from 28.3% to 19.5%, while BPD increased from 51.2% to 62.7%, and the composite outcome (BPD or death) rose from 63.6% to 66.9% between 2014 and 2021 [14]. Similarly, in Japan, mortality decreased from 19% to 8%, while BPD increased from 41% to 52% during 2003–2016 [11].
A multi‐country study across 11 high‐income countries including Australia showed rising BPD, despite reduced mortality among infants born at 24–31 weeks [12]. Composite outcome including BPD increased from 32.9% to 42.8%, whereas those excluding BPD decreased from 14.7% to 12.4%, showing a net BPD increase from 2007 to 2015 in Australia‐New Zealand.
Identifying risk factors for BPD is essential for prevention and improved outcomes. BPD is associated with maternal, perinatal, neonatal and clinical factors [15]. Prolong oxygen therapy, mechanical ventilation and severe lung diseases requiring multiple surfactant doses are major risk factors [14, 16, 17]. Maternal factors (e.g., hypertension), perinatal conditions (e.g., limited antenatal care) and infant characteristics (e.g., lower GA and low birth weight [LBW]) increase BPD risk [11, 18, 19]. Understanding these associations supports targeted prevention, clinical guidelines and resource allocation to reduce morbidity and long‐term complications.
Population‐based Australian BPD data are limited. Since 2005, the Australian and New Zealand Neonatal Network (ANZNN) annually reports the combined rate and trend of BPD from Australia and New Zealand on infants < 32 weeks GA from NICUs but excludes preterm infants from non‐ICU facilities and lacks Australia‐specific data [2]. Reliable population‐specific data adjusted for changing risks over time would strengthen clinical guidelines and help allocate NICU funding and long‐term support. Identifying maternal, perinatal and infant risk factors for BPD is crucial for targeted prevention and improved outcomes among preterm infants.
This study addresses this gap through examining the incidence and pattern of BPD, death and the composite outcome (BPD/death) among preterm infants of all gestations over 11 years (2005–2015) in NSW. We also investigated maternal and infant birth characteristics associated with BPD among VPT infants.
2. Methods
2.1. Study Design and Population
This study was a population‐based retrospective cohort study using linked administrative datasets from the NSW Perinatal Data Collection (PDC) and Admitted Patient Data Collection (APDC). PDC, the primary data source for identifying the study cohort, contains records of all births, including information on demographics, maternal health, behaviour, pregnancy, delivery and birth outcomes. APDC contains records of all hospital admissions, including demographic information, diagnoses, procedures and separation data. Data linkage between PDC and APDC was carried out by the Centre for Health Record Linkage (CHeReL) using probabilistic record linkage techniques based on multiple identifying variables, allowing for minor discrepancies in identifiers while maximizing linkage accuracy [20]. Records were linked from datasets by assigning a unique de‐identified Patient Project Number to each child. The cohort included all live‐born preterm infants in NSW between 2005 and 2015. NSW, the most populated Australian state, has an annual birth cohort of ~95 000 of the ~300 000 births occurring nationally [21]. Figure 1 shows cohort selection and data management.
FIGURE 1.
Flow diagram of cohort selection, data linkage and identification of bronchopulmonary dysplasia (BPD) cases, including stratification by gestational age groups, in New South Wales, 2005–2015.
2.2. Outcomes
BPD was identified using the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification (ICD‐10‐AM) codes from the APDC dataset. Children with BPD were diagnosed if any hospital admission record included ICD‐10‐AM code P27.1. To determine the pattern of BPD, we evaluated three outcome measures: 1. BPD among survivors, 2. in‐hospital death and 3. the composite outcome of BPD or death [12]. Death was defined as the infant dying before discharge from the birth‐hospitalization. The composite outcome includes infants with BPD or death acknowledging the competing risk between BPD and early death [22].
2.3. Covariates
Maternal and neonatal socio‐demographic information was extracted from PDC. Maternal and perinatal characteristics included age at delivery, country of birth (English/non‐English speaking country) [23], residential area socio‐economic status based on Australian 2011 Socio‐Economic Index for Areas Index of Relative Socio‐economic Disadvantage (SEIFA IRSD) (less advantaged [1–50]/more advantaged [51–100]) [21], residential area remoteness (regional/remote) [21], antenatal care (yes/no), smoking during pregnancy (yes/no), gestational diabetes (yes/no), chronic maternal hypertension (yes/no) and mode of delivery (normal/assisted vaginal or breech/caesarean section). Infant birth characteristics such as birthweight (normal, ≥ 2500 g/low, < 2500 g); place of birth (healthcare facility (hospital and birth centre)/no healthcare facility (home birth)), sex (male/female), and 5‐min APGAR score (low: 0–6/normal: 7–10) [11, 24], small for gestational age (SGA) based on birthweight < 10th percentile for GA and sex, Fenton 2013 growth chart (yes/no) [25] were derived from PDC.
In the datasets, some demographic variables, particularly sex and Indigenous status were reported inconsistently or missing. To improve data completeness and accuracy, we extracted information from two other datasets: the Emergency Departments Data Collection (EDDC) and the Australian Coordinating Registry Cause of Death Unit Record File (COD‐URF) to resolve discrepancies where possible. We applied the maximum‐frequency rule when multiple values were present across records for the same infant. The most frequently recorded value was decided upon, following the previously published methods [22, 26]. Infants with unresolved or consistently missing demographic information after this process were retained as “unknown” in the analysis.
2.4. Statistical Analysis
The analyses were conducted in multiple stages. Initially, the characteristics of the preterm infants were summarized using descriptive statistics (mean, standard deviation, counts/percentages) by BPD status. As preterm is the driver of BPD, we stratified infants by GA into: EPT (< 28 weeks), very preterm (VPT, 28–31 weeks) and moderate‐to‐late preterm (MPT, 32–36 weeks) to examine the variability of these characteristics across the preterm spectrum. Annual incidence rates for BPD, death and the composite outcome were calculated as the number of annual events per 1000 births. Ninety‐five percent confidence intervals (CI) were estimated using negative binomial regression models with the log of the number of preterm births included as an offset and robust standard errors. Cumulative incidence was obtained by dividing the total events from 2005 to 2015 by the total preterm births in the same period, multiplied by 1000, with 95% CI estimated from the same modelling approach. Trends in the incidence of BPD, death and composite outcome in preterm infants from 2005 to 2015 were examined with negative binomial regression models, with the annual count as the outcome and the number of preterm births as the exposure. Results are reported as incidence rate ratios (IRRs) with 95% CIs and p‐values. Negative binomial regression was used in preference to Poisson regression due to overdispersion in the data (variance exceeding the mean). Finally, generalized estimating equations (GEE) with a binary logistic link function and an exchangeable correlation structure were used to obtain population‐averaged estimates while accounting for correlation of outcomes among infants residing within the same geographic areas. GEE was selected to provide robust standard errors for correlated observations, as the study objective was not to estimate area‐level effects or between‐area variance. Confirming BPD is vastly more common in infants < 32 weeks of gestation, analysis is focused on this sub‐group [2]. Univariate analysis was used to assess associations between covariates and outcomes; variables with a p‐value ≤ 0.20 were included in the multivariable model [27]. Remoteness (despite non‐significance) and indigenous status were included a priori for contextual relevance as covariates. However, indigenous‐specific results were not reported due to absence of ethics approval. Model outputs were presented as odds ratios (OR) with 95% CI. Analyses were performed using STATA version 13. Grammar and clarity of some expressions were reviewed using ChatGPT4; however, all content and ideas are entirely original.
3. Results
3.1. Infant Birth Characteristics
Among 1 047 885 live births during 2005–2015 in NSW, 73 314 (7%) were preterm births. Among the preterm infants, 56.1% (n = 41 140) were low birthweight, 54.4% (n = 39 877) were male and 10% (n = 7316) were SGA. Almost all deliveries occurred in healthcare facilities. A total of 1906 (2.6%) preterm infants died before discharge. Table S1 summarizes the infant birth characteristics of the preterm cohort.
3.2. Maternal Characteristics
The mean age of mothers at birth was 30.5 years. Although 76.8% of mothers reside in major cities, more than half (58.1%) lived in less advantaged socio‐economic areas. The most common mode of delivery was caesarean section (49.2%), followed by normal vaginal delivery (41.8%) and assisted vaginal/breech delivery (9%). Table S1 presents the maternal characteristics of the preterm cohort.
3.3. BPD Among Preterm Infants
Total 1898 infants had BPD (25.9 per 1000 preterm births, 95% CI 24.7, 27.1) among the preterm infants. Average birthweight among BPD cases was 988 g (SD: 369 g). Mothers of infants with BPD were more likely to reside in less advantaged areas and have smoked during pregnancy. These infants were more often delivered via caesarean section, more likely to be smaller with SGA, sicker with lower APGAR score and more likely to die. Those who survived stayed longer in the birth hospital compared to the no‐BPD group. Table 1 shows the distribution of maternal and infant birth characteristics by BPD status among the preterm cohort.
TABLE 1.
Infant and maternal characteristics by BPD status among preterm infants in New South Wales, 2005–2015.
| Socio‐demographic information | Preterm infants (< 37 weeks) | ||
|---|---|---|---|
| No BPD | BPD | Total | |
| n = 71 416 | n = 1898 | n = 73 314 | |
| (97.4%) | (2.6%) | ||
| Infant birth characteristics | |||
| Birth weight in gram, mean (SD) | 2366 (657) | 988 (369) | 2330 (687) |
| Birth weight | |||
| Low (< 2500 g) | 39 256 (55.0) | 1884 (99.3) | 41 140 |
| Normal (≥ 2500 g) | 32 160 (45.0) | 14 (0.7) | 32 174 |
| Sex | |||
| Male | 38 768 (54.3) | 1109 (58.4) | 39 877 |
| Female | 32 646 (45.7) | 789 (41.6) | 33 435 |
| Unknown | 2 (0.003) | 0 (0) | 2 |
| Small for gestational age | |||
| No | 64 339 (90.1) | 1659 (87.4) | 65 998 |
| Yes | 7077 (9.9) | 239 (12.6) | 7316 |
| Place of birth | |||
| Healthcare facility | 70 897 (99.3) | 1883 (99.2) | 72 780 |
| No healthcare facility | 510 (0.7) | 15 (0.8) | 525 |
| Unknown | 9 (0.01) | 0 (0) | 9 |
| APGAR 5‐min | |||
| Low (0–6) | 4545 (6.4) | 542 (28.6) | 5087 |
| Normal (7–10) | 66 580 (93.2) | 1337 (70.4) | 67 917 |
| Unknown | 291 (0.4) | 19 (1.0) | 310 |
| Baby's discharge status | |||
| Discharged | 55 902 (78.3) | 689 (36.3) | 56 591 |
| Neonatal death | 1840 (2.6) | 66 (3.5) | 1906 |
| Transferred | 13 674 (19.1) | 1143 (60.2) | 14 817 |
| Baseline maternal characteristics | |||
| Age in years, mean (SD) | 30.5 (6.0) | 30.0 (6.3) | 30.5 (6.0) |
| Reproductive age in years | |||
| Early (≤ 24) | 12 197 (17.1) | 397 (20.9) | 12 594 |
| Prime (25–34) | 40 608 (56.9) | 1031 (54.3) | 41 639 |
| Late (≥ 35) | 18 597 (26.0) | 470 (24.8) | 19 067 |
| Unknown | 14 (0.02) | 0 (0) | 14 |
| Country of birth | |||
| Non‐English speaking | 16 250 (22.8) | 400 (21.1) | 16 650 |
| English speaking | 54 853 (76.8) | 1479 (77.9) | 56 332 |
| Unknown | 313 (0.4) | 19 (1.0) | 332 |
| Area socio‐economic status | |||
| Less advantaged (1–50) | 41 430 (58.0) | 1185 (62.4) | 42 615 |
| More advantaged (51–100) | 29 083 (40.7) | 686 (36.1) | 29 769 |
| Unknown | 903 (1.3) | 27 (1.4) | 930 |
| Remoteness | |||
| Regional and remote area | 15 687 (22.0) | 373 (19.7) | 16 060 |
| Major city | 54 840 (76.8) | 1500 (79.0) | 56 340 |
| Unknown | 889 (1.2) | 25 (1.3) | 914 |
| Antenatal care received | |||
| No | 1128 (1.6) | 50 (2.6) | 1178 |
| Yes | 69 987 (98.0) | 1835 (96.7) | 71 822 |
| Unknown | 301 (0.4) | 13 (0.7) | 314 |
| Smoke during pregnancy | |||
| No | 58 853 (82.4) | 1480 (78.0) | 60 333 |
| Yes | 12 354 (17.3) | 412 (21.7) | 12 766 |
| Unknown | 209 (0.3) | 6 (0.3) | 215 |
| Gestational diabetes | |||
| No | 65 527 (91.8) | 1809 (95.3) | 67 336 |
| Yes | 5889 (8.2) | 89 (4.7) | 5978 |
| Chronic maternal hypertension | |||
| No | 70 045 (98.1) | 1830 (96.4) | 71 875 |
| Yes | 1371 (1.9) | 68 (3.6) | 1439 |
| Gestational age | |||
| Extremely preterm (< 28 weeks) | 2495 (3.5) | 1199 (63.2) | 3694 |
| Very preterm (28–31 weeks) | 6111 (8.6) | 602 (31.7) | 6713 |
| Moderate‐to‐late preterm (32–36 weeks) | 62 810 (87.9) | 97 (5.1) | 62 907 |
| Mode of delivery | |||
| Normal | 30 090 (42.1) | 562 (29.6) | 30 652 |
| Assisted vaginal/breech | 6401 (9.0) | 200 (10.5) | 6601 |
| Caesarean section | 34 903 (48.9) | 1136 (59.9) | 36 039 |
| Not stated | 22 (0.03) | 0 (0) | 22 |
Note: Data are shown as n (column %).
Abbreviation: BPD, bronchopulmonary dysplasia.
3.4. BPD by Gestational Age Categories
Maternal and infant characteristics by BPD status across three GA categories are shown in Table 2. BPD showed an inverse relationship, ranging from 1.5 per 1000 in MPT to 89.7 VPT and 324.6 for EPT infants. To explore GA–specific patterns more precisely, we further stratified infants into narrower GA categories (22–25, 26–27, 28–29, 30–31, 32–33 and 34–36 weeks) in Table S2 to provide more granular estimates. BPD was significantly associated with a low 5‐min APGAR score across all GA categories (p < 0.001). Infants from the least advantaged socio‐economic areas had higher BPD incidence across GA groups, suggesting a potential impact of social disadvantage on neonatal respiratory outcomes. Smoking was more common among mothers of BPD infants.
TABLE 2.
Infant and maternal characteristics by BPD status among preterm infants stratified by gestational age in New South Wales, 2005–2015.
| Socio‐demographic information | Extremely preterm (< 28 weeks) | Very preterm (28–31 weeks) | Moderate‐to‐late preterm (32–36 weeks) | ||||||
|---|---|---|---|---|---|---|---|---|---|
| No BPD | BPD | Total | No BPD | BPD | Total | No BPD | BPD | Total | |
| n (%) | n (%) | n = 3694 | n (%) | n (%) | n = 6713 | n (%) | n (%) | n = 62 907 | |
| 2495 (67.5%) | 1199 (32.5%) | 6111 (91.0%) | 602 (9.0%) | 62 810 (99.8%) | 97 (0.2%) | ||||
| Infant birth characteristics | |||||||||
| Birth weight, mean (SD) | 804 (450) | 834 (179) | 813 (383) | 1500 (446) | 1141 (295) | 1468 (446) | 2512 (521) | 1930 (634) | 2511 (522) |
| Birth weight | |||||||||
| Low (< 2500 g) | 2446 (67.1) | 1199 (32.9) | 3645 | 5912 (90.8) | 602 (9.2) | 6514 | 30 898 (99.7) | 83 (0.3) | 30 981 |
| Normal (≥ 2500 g) | 49 (100) | 0 (0) | 49 | 199 (100) | 0 (0) | 199 | 31 912 (100) | 14 (0) | 31 926 |
| Sex | |||||||||
| Male | 1363 (67.6) | 652 (32.4) | 2015 | 3321 (89.3) | 399 (10.7) | 3720 | 34 084 (99.8) | 58 (0.2) | 34 142 |
| Female | 1132 (67.4) | 547 (32.6) | 1679 | 2790 (93.2) | 203 (6.8) | 2993 | 28 724 (99.9) | 39 (0.1) | 28 763 |
| Unknown | 0 | 0 | 1680 | 0 | 0 | 2994 | 2 (100) | 0 (0) | 2 |
| Small for gestational age | |||||||||
| No | 2288 (67.5) | 1103 (32.5) | 3391 | 5768 (92.2) | 487 (7.8) | 6255 | 56 283 (99.9) | 69 (0.1) | 56 352 |
| Yes | 207 (68.3) | 96 (31.7) | 303 | 343 (74.9) | 115 (25.1) | 458 | 6527 (99.6) | 28 (0.4) | 6555 |
| Place of birth | |||||||||
| Healthcare facility | 2447 (67.3) | 1188 (32.7) | 3635 | 6067 (91) | 599 (9) | 6666 | 62 383 (99.8) | 96 (0.2) | 62 479 |
| No healthcare facility | 47 (81) | 11 (19) | 58 | 43 (93.5) | 3 (6.5) | 46 | 420 (99.8) | 1 (0.2) | 421 |
| Unknown | 1 (100) | 0 (0) | 1 | 1 (100) | 0 (0) | 1 | 7 (100) | 0 (0) | 7 |
| APGAR 5‐min | |||||||||
| Low (0–6) | 1489 (79.2) | 391 (20.8) | 1880 | 797 (85.6) | 134 (14.4) | 931 | 2259 (99.3) | 17 (0.7) | 2276 |
| Normal (7–10) | 953 (54.6) | 792 (45.4) | 1745 | 5279 (91.9) | 466 (8.1) | 5745 | 60 348 (99.9) | 79 (0.1) | 60 427 |
| Unknown | 53 (76.8) | 16 (23.2) | 69 | 35 (94.6) | 2 (5.4) | 37 | 203 (99.5) | 1 (0.5) | 204 |
| Discharge status | |||||||||
| Discharged | 460 (50.9) | 443 (49.1) | 903 | 2253 (91.5) | 209 (8.5) | 2462 | 53 189 (99.9) | 37 (0.1) | 53 226 |
| Neonatal death | 1415 (96.6) | 50 (3.4) | 1465 | 187 (93) | 14 (7) | 201 | 238 (99.2) | 2 (0.8) | 240 |
| Transferred | 620 (46.8) | 706 (53.2) | 1326 | 3671 (90.6) | 379 (9.4) | 4050 | 9383 (99.4) | 58 (0.6) | 9441 |
| Baseline maternal characteristics | |||||||||
| Age in years, mean (SD) | 30 (6) | 30 (6) | 30 (6) | 30 (6) | 30 (6) | 30 (6) | 30 (6) | 31 (7) | 30 (6) |
| Reproductive age in years | |||||||||
| Early (≤ 24 year) | 536 (67.7) | 256 (32.3) | 792 | 1083 (89.8) | 123 (10.2) | 1206 | 10 578 (99.8) | 18 (0.2) | 10 596 |
| Prime (25–34 year) | 1359 (67.8) | 646 (32.2) | 2005 | 3411 (91.1) | 335 (8.9) | 3746 | 35 838 (99.9) | 50 (0.1) | 35 888 |
| Late (≥ 35 year) | 600 (66.9) | 297 (33.1) | 897 | 1615 (91.8) | 144 (8.2) | 1759 | 16 382 (99.8) | 29 (0.2) | 16 411 |
| Unknown | 0 | 0 | 0 | 2 (100) | 0 (0) | 2 | 12 (100) | 0 (0) | 12 |
| Country of birth | |||||||||
| Non‐English speaking | 594 (68.4) | 274 (31.6) | 868 | 1402 (92.8) | 109 (7.2) | 1511 | 14 254 (99.9) | 17 (0.1) | 14 271 |
| English speaking | 1883 (67.3) | 913 (32.7) | 2796 | 4642 (90.5) | 486 (9.5) | 5128 | 48 328 (99.8) | 80 (0.2) | 48 408 |
| Unknown | 18 (60) | 12 (40) | 30 | 67 (90.5) | 7 (9.5) | 74 | 228 (100) | 0 (0) | 228 |
| Area socio‐economic status | |||||||||
| Less advantaged (1–50) | 1501 (66.9) | 741 (33.1) | 2242 | 3626 (90.5) | 382 (9.5) | 4008 | 36 303 (99.8) | 62 (0.2) | 36 365 |
| More advantaged (51–100) | 919 (67.9) | 434 (32.1) | 1353 | 2387 (91.7) | 217 (8.3) | 2604 | 25 777 (99.9) | 35 (0.1) | 25 812 |
| Unknown | 75 (75.8) | 24 (24.2) | 99 | 98 (97) | 3 (3) | 101 | 730 (100) | 0 (0) | 730 |
| Remoteness | |||||||||
| Regional and remote area | 568 (72.3) | 218 (27.7) | 786 | 1268 (90.2) | 138 (9.8) | 1406 | 13 851 (99.9) | 17 (0.1) | 13 868 |
| Major city | 1852 (65.9) | 959 (34.1) | 2811 | 4747 (91.1) | 461 (8.9) | 5208 | 48 241 (99.8) | 80 (0.2) | 48 321 |
| Unknown | 75 (77.3) | 22 (22.7) | 97 | 96 (97) | 3 (3) | 99 | 718 (100) | 0 (0) | 718 |
| Antenatal care received | |||||||||
| No | 81 (71.7) | 32 (28.3) | 113 | 106 (89.1) | 13 (10.9) | 119 | 941 (99.5) | 5 (0.5) | 946 |
| Yes | 2388 (67.3) | 1161 (32.7) | 3549 | 5950 (91.1) | 583 (8.9) | 6533 | 61 649 (99.9) | 91 (0.1) | 61 740 |
| Unknown | 26 (81.3) | 6 (18.8) | 32 | 55 (90.2) | 6 (9.8) | 61 | 220 (99.5) | 1 (0.5) | 221 |
| Smoke during pregnancy | |||||||||
| No | 1969 (67.7) | 939 (32.3) | 2908 | 4912 (91.3) | 470 (8.7) | 5382 | 51 972 (99.9) | 71 (0.1) | 52 043 |
| Yes | 513 (66.6) | 257 (33.4) | 770 | 1173 (89.9) | 132 (10.1) | 1305 | 10 668 (99.8) | 23 (0.2) | 10 691 |
| Unknown | 13 (81.3) | 3 (18.8) | 16 | 26 (100) | 0 (0) | 26 | 170 (98.3) | 3 (1.7) | 173 |
| Gestational diabetes | |||||||||
| No | 2417 (67.6) | 1157 (32.4) | 3574 | 5651 (91) | 561 (9) | 6212 | 57 459 (99.8) | 91 (0.2) | 57 550 |
| Yes | 78 (65) | 42 (35) | 120 | 460 (91.8) | 41 (8.2) | 501 | 5351 (99.9) | 6 (0.1) | 5357 |
| Chronic maternal hypertension | |||||||||
| No | 2452 (67.9) | 1157 (32.1) | 3609 | 5929 (91.1) | 579 (8.9) | 6508 | 61 664 (99.8) | 94 (0.2) | 61 758 |
| Yes | 43 (50.6) | 42 (49.4) | 85 | 182 (88.8) | 23 (11.2) | 205 | 1146 (99.7) | 3 (0.3) | 1149 |
| Mode of delivery | |||||||||
| Normal | 1081 (71.7) | 426 (28.3) | 1507 | 1888 (94.4) | 112 (5.6) | 2000 | 27 121 (99.9) | 24 (0.1) | 27 145 |
| Assisted vaginal/breech | 578 (78) | 163 (22) | 741 | 361 (91.6) | 33 (8.4) | 394 | 5462 (99.9) | 4 (0.1) | 5466 |
| Caesarean section | 836 (57.8) | 610 (42.2) | 1446 | 3862 (89.4) | 457 (10.6) | 4319 | 30 205 (99.8) | 69 (0.2) | 30 274 |
| Not stated | 0 | 0 | 0 | 0 | 0 | 0 | 22 (100) | 0 (0) | 22 |
Note: Data are shown as n (row %).
Abbreviation: BPD, bronchopulmonary dysplasia.
3.5. Pattern in BPD, Death and Composite Outcome (BPD/Death)
The cumulative incidence of BPD was 25.9/1000 preterm births with an increasing annual trend from 23.4 in 2005 to 28.1 in 2015 (IRR = 1.04, 95% CI 1.02, 1.06, p < 0.001). In contrast, in‐hospital annual death rate declined from 32.5 in 2005 to 18.8 in 2015, with a cumulative incidence rate of 26.0 over the study period (IRR = 0.96, 95% CI 0.95, 0.97, p < 0.001). The cumulative composite outcome occurred in 51.0/1000 preterm births, showing a modest decline from 55.3 to 46.3 (IRR = 0.99, 95% CI 0.99, 1.01, p = 0.567) (Figure 2). While survival improved over the decade with decreasing mortality, the incidence of BPD among surviving preterm infants increased, resulting in a stable overall rate of the composite outcome. The incidence of BPD by sex and area socio‐economic status are presented in Figures S1 and S2 respectively. BPD incidence was higher in male infants and if maternal residence is in less advantaged areas.
FIGURE 2.
Pattern of BPD among preterm infants, death before hospital discharge and composite outcome of BPD or death among preterm infants in New South Wales, 2005–2015. The solid lines represent incidence and dotted lines represent trendlines.
3.6. Pattern in BPD by Gestational Age
The cumulative incidence of BPD among EPT was 324.6 in 1000 from 2005 to 2015, with a substantial increase over time from 254.1 to 362.2 (IRR = 1.05, 95% CI 1.03, 1.07, p < 0.001). In VPT, the incidence was 89.7 which rose notably from 88.6 in 2005 to 125.6 in 2015 (IRR = 1.06, 95% CI 1.02, 1.10, p = 0.002). The cumulative incidence rate in the MPT group was 1.5, showing no change (IRR = 0.99, 95% CI 0.93, 1.06, p = 0.909). BPD incidences by GA are shown in Figure 3.
FIGURE 3.
Pattern of BPD by gestational age groups among preterm infants in New South Wales, 2005–2015. The solid lines represent incidence and dotted lines represent trendlines. EPT = Extremely preterm, VPT = Very preterm, MPT = Moderate‐to‐Late preterm.
3.7. Factors Associated With BPD, Death and Composite Outcome
In multivariable logistic regression analysis, maternal factors such as EPT GA (adjusted odds ratio [aOR] 11.00, 95% CI 9.65, 12.54) and assisted vaginal/breech delivery (aOR 1.26, 95% CI 0.99, 1.60), non‐English‐speaking country (aOR 0.79, 95% CI 0.67, 0.93), and regional/remote residential area (aOR 0.83, 95% CI 0.69, 0.99); and infant factors such as SGA (aOR 3.27, 95% CI 2.61, 4.08), male (aOR 1.42, 95% CI 1.25, 1.62) and low 5‐min APGAR score (< 7) (aOR 1.64, 95% CI 1.41, 1.91) remained significant factors for BPD (Table 3). Maternal factors associated with death included EPT GA, assisted vaginal/breech delivery and caesarean delivery; and infant factors included SGA, male and low 5‐min APGAR score (Table 3). Maternal factors included EPT GA, assisted vaginal/breech delivery and delivery by caesarean section; and infant factors including SGA, male and low 5‐min APGAR score (< 7) were significant for the composite outcome (Table 3).
TABLE 3.
Factors associated with BPD among survivors, death and composite outcome (BPD/death) among very preterm (< 32 weeks gestation) infants in New South Wales, 2005–2015.
| BPD | Death | Composite outcome (BPD/death) | ||||
|---|---|---|---|---|---|---|
| cOR (95% CI) | aOR (95% CI) a | cOR (95% CI) | aOR (95% CI) a | cOR (95% CI) | aOR (95% CI) a | |
| Baseline maternal characteristics | ||||||
| Reproductive age in years | n = 8582 | n = 10 209 | n = 10 209 | |||
| Prime (25–34) | 1.00 (Reference) | 1.00 (Reference) | 1.00 (Reference) | |||
| Early (≤ 24) | 1.18 (1.03, 1.35) | 1.08 (0.91, 1.27) | 1.11 (0.97, 1.28) | 0.85 (0.70, 1.04) | 1.17 (1.05, 1.30) | 1.04 (0.89, 1.20) |
| Late (≥ 35) | 0.95 (0.83, 1.08) | 0.97 (0.83, 1.13) | 1.01 (0.89, 1.15) | 1.12 (0.94, 1.34) | 0.97 (0.88, 1.07) | 1.00 (0.88, 1.15) |
| Country of birth | n = 8493 | n = 10 113 | n = 10 113 | |||
| English speaking | 1.00 (Reference) | 1.00 (Reference) | 1.00 (Reference) | |||
| Non‐English speaking | 0.89 (0.78, 1.02) | 0.79 (0.67, 0.93) | 1.06 (0.93, 1.21) | 0.96 (0.87, 1.07) | ||
| Area socio‐economic status | n = 8580 | n = 10 207 | n = 10 207 | |||
| Less advantaged (1–50) | 1.00 (Reference) | 1.00 (Reference) | 1.00 (Reference) | |||
| More advantaged (51–100) | 0.89 (0.78, 1.01) | 0.89 (0.77, 1.04) | 0.94 (0.83, 1.06) | 0.90 (0.82, 0.99) | 0.92 (0.81, 1.05) | |
| Remoteness | n = 8584 | n = 10 211 | n = 10 211 | |||
| Major city | 1.00 (Reference) | 1.00 (Reference) | 1.00 (Reference) | |||
| Regional and remote area | 0.94 (0.81, 1.09) | 0.83 (0.69, 0.99) | 1.14 (0.99, 1.30) | 1.19 (0.98, 1.45) | 1.03 (0.92, 1.15) | 0.98 (0.84, 1.15) |
| Antenatal care received | n = 8514 | n = 10 123 | n = 10 123 | |||
| Yes | 1.00 (Reference) | 1.00 (Reference) | 1.00 (Reference) | |||
| No | 1.42 (0.99, 2.03) | 1.09 (0.70, 1.71) | 1.99 (1.48, 2.70) | 0.96 (0.62, 1.49) | 1.77 (1.35, 2.31) | 1.09 (0.74, 1.61) |
| Smoke during pregnancy | n = 8552 | n = 10 169 | n = 10 169 | |||
| No | 1.00 (Reference) | 1.00 (Reference) | 1.00 (Reference) | |||
| Yes | 1.11 (0.97, 1.27) | 1.07 (0.90, 1.26) | 1.00 (0.88, 1.14) | 1.07 (0.96, 1.18) | ||
| Gestational diabetes | n = 8584 | n = 10 211 | n = 10 211 | |||
| No | 1.00 (Reference) | 1.00 (Reference) | 1.00 (Reference) | |||
| Yes | 0.61 (0.48, 0.78) | 0.82 (0.62, 1.08) | 0.43 (0.32, 0.58) | 0.83 (0.56, 1.22) | 0.51 (0.42, 0.62) | 0.78 (0.61, 1.01) |
| Chronic maternal hypertension | n = 8584 | n = 10 211 | n = 10 211 | |||
| No | 1.00 (Reference) | 1.00 (Reference) | 1.00 (Reference) | |||
| Yes | 1.19 (0.88, 1.60) | 0.60 (0.41, 0.89) | 0.91 (0.55, 1.53) | 0.91 (0.71, 1.18) | ||
| Gestational age | n = 8584 | n = 10 211 | n = 10 211 | |||
| Very preterm (28, 31 weeks) | 1.00 (Reference) | 1.00 (Reference) | 1.00 (Reference) | |||
| Extremely preterm (< 28 weeks) | 10.79 (9.56, 12.17) | 11.00 (9.65, 12.54) | 21.03 (17.96, 24.62) | 8.5 (7.13, 10.20) | 18.19 (16.39, 20.18) | 13.35 (11.88, 14.99) |
| Mode of delivery | n = 8584 | n = 10 211 | n = 10 211 | |||
| Normal vaginal | 1.00 (Reference) | 1.00 (Reference) | 1.00 (Reference) | |||
| Assisted vaginal/breech | 1.76 (1.45, 2.14) | 1.26 (0.99, 1.60) | 2.58 (2.24, 2.98) | 1.25 (1.02, 1.53) | 2.48 (2.16, 2.85) | 1.41 (1.17, 1.71) |
| Caesarean section | 0.997 (0.88, 1.12) | 1.08 (0.94, 1.25) | 0.26 (0.23, 0.30) | 0.33 (0.28, 0.40) | 0.56 (0.51, 0.61) | 0.82 (0.72, 0.93) |
| Infant birth characteristics | ||||||
| Sex | n = 8584 | n = 10 211 | n = 10 211 | |||
| Female | 1.00 (Reference) | 1.00 (Reference) | ||||
| Male | 1.00 (Reference) | 1.42 (1.25, 1.62) | 1.24 (1.11, 1.38) | 1.21 (1.04, 1.40) | 1.26 (1.16, 1.37) | 1.41 (1.26, 1.58) |
| Small for gestational age | n = 8584 | n = 10 211 | n = 10 211 | |||
| No | 1.00 (Reference) | 1.00 (Reference) | 1.00 (Reference) | |||
| Yes | 2.39 (1.98, 2.87) | 3.27 (2.61, 4.08) | 2.06 (1.73, 2.44) | 3.28 (2.51, 4.30) | 2.49 (2.14, 2.89) | 3.88 (3.16, 4.75) |
| Place of birth | n = 8583 | n = 10 209 | n = 10 209 | |||
| Healthcare facility | 1.00 (Reference) | 1.00 (Reference) | 1.00 (Reference) | |||
| No healthcare facility | 1.06 (0.59, 1.92) | 2.87 (1.91, 4.32) | 1.19 (0.50, 2.84) | 1.95 (1.32, 2.87) | 0.79 (0.34, 1.81) | |
| APGAR 5‐min | n = 8524 | n = 10 106 | n = 10 106 | |||
| Normal (7, 10) | 1.00 (Reference) | 1.00 (Reference) | 1.00 (Reference) | |||
| Low (0, 6) | 2.60 (2.29, 2.95) | 1.64 (1.41, 1.91) | 23.75 (20.55, 27.45) | 12.47 (10.63, 14.63) | 7.71 (6.99, 8.49) | 3.97 (3.52, 4.48) |
Note: Data are shown as n (row %).
Abbreviations: aOR, adjusted odds ratio; BPD, bronchopulmonary dysplasia; CI, confidence interval; cOR, crude odds ratio.
Adjusted for socio‐demographic factors (age, country of birth, aboriginality, area socio‐economic status and remoteness), birth year, and area‐level clustering.
4. Discussion
This population‐based study found BPD incidence of 25.9 per 1000 preterm live births in NSW between 2005 and 2015. The highest incidence was observed among EPT infants (324.6/1000), followed by VPT (89.7) and MPT (1.5). Over the study period, death declined while BPD incidence increased; the composite outcome (BPD/death) showed a slight decrease (333 to 328). Common maternal and infant factors associated with outcomes included EPT, assisted vaginal/breech delivery, male, SGA and low 5‐min APGAR scores.
Our study showed an increase in BPD, a decline in death and a small decrease in composite outcome from 2005 to 2015. This rise in BPD could be due to decreased mortality, meaning more infants survive and develop BPD. A reduction in death is likely due to technological advances, less invasive ventilation strategies and better management of sepsis and intraventricular haemorrhage [14]. Despite shorter ventilation and less intubation, BPD rates still rise, likely from prolonged respiratory support in the sickest infants [14]. These trends are consistent with international reports from Korea [14], Japan [11] and Brazil [24], which report that improved survival at lower GAs is associated with higher BPD incidence. However, findings on the composite outcome are mixed: Japan reported an increasing trend, while Brazil showed a similar decrease. Including both BPD and the composite outcome strengthens interpretation and provides important clinical and methodological advantages. While BPD is a key marker of neonatal morbidity, the composite outcome addresses survivor bias and provides a better disease burden insight by incorporating early death among preterm infants [28]. The sickest infants die early, while less sick infants survive and are more likely to develop BPD. A decreasing composite outcome suggests more infants survive without BPD, which is encouraging.
Our study showed BPD burden was highest in EPT infants followed by VPT and MPT, consistent with the inverse relationship with GA [29]. A systematic review reported BPD incidence among EPT infants of 10%–89% globally and 30%–58% in Oceania countries [7]. Our BPD incidence among EPT (32.5%) is within range, but notably lower than ANZNN's report [2]. The difference likely reflects variation in population, data source and BPD definition. ANZNN included only NICU infants from Australia and New Zealand, whereas our study covered all facilities. GA trends showed a substantial increase in EPT infants followed by VPT and were consistent with ANZNN 2020 trends [12].
We found that the most common risk factors for BPD were lower GA, SGA, male sex and low 5‐min APGAR score among VPT infants. Lower GA was the strongest predictor for BPD; infants with < 28 weeks of gestation had the highest risk [29]. Male infants had consistently higher risk, possibly due to delayed lung maturation and surfactant production [17]. These findings were consistent with previous studies [29].
Maternal smoking during pregnancy, area socio‐economic status and antenatal care were significant in bivariate analysis; however, these associations were no longer significant in multivariable analysis after adjusting for EPT, a dominant predictor. These factors may remain important with a larger sample size.
4.1. Strengths of This Study
The key strength is a whole‐of‐population‐based cohort comprising all live‐born preterm infants from 2005 to 2015 in NSW, Australia. The large sample size (n = 73 314 preterm infants) allows examination of rare but important outcomes such as BPD using routinely collected standardized reporting systems. In addition, this study reported composite outcomes (BPD or death) which reduces survivor bias and reflects the true burden of severe neonatal respiratory disease. The findings can be generalizable to the Australian population.
4.2. Limitations of the Data
This study has several limitations. PDC data lacked clinical details such as BPD severity, type and duration of respiratory support (oxygen/ventilation), and NICU interventions, which prevented detailed analysis of disease progression or treatment impact. Moreover, BPD was identified using ICD‐10‐AM codes which could be incorrectly coded with other respiratory diseases leading to misclassification and potential under/over‐reporting. Since the study relied on retrospective secondary data, any outliers or missing values were not verified and corrected, possibly reducing accuracy.
Finally, the study cohort included infants from 2005 to 2015. However, we are confident that our findings remain valid as associations between outcomes and available demographic and predictor variables are likely unchanged since 2013, given no major changes in BPD guidelines. Moreover, using clinical codes for BPD ensures relevance despite evolving definitions.
5. Conclusions
This whole‐of‐population study showed that BPD, a major preterm morbidity, increased over time. There was no net increase in composite outcome due to declining mortality across all gestations. Rising BPD rates, particularly in EPT, suggest reassessing current respiratory and neonatal management strategies. Improved EPT survival with rising BPD incidence indicates an increasing trend of chronic conditions—highlighting the need for better follow‐up and interventions to improve quality of life.
Author Contributions
Jahidur Rahman Khan: conceptualization, software, formal analysis, supervision, data curation. Syeda Mah‐E‐Muneer: conceptualization, methodology, data curation, software, formal analysis, visualization, writing – original draft, writing – review and editing. Kei Lui: conceptualization, supervision, methodology, writing – review and editing. Nusrat Homaira: conceptualization, supervision, funding acquisition, resources, investigation, validation. Mei Chan: project administration, resources.
Ethics Statement
The research has been approved by the NSW Population and Health Services Research Ethics Committee (2022/ETH00188/2022.03) and funded through philanthropic funding (CIA Homaira).
Conflicts of Interest
The authors declare no conflicts of interest.
Supporting information
Table S1: Birth characteristics among preterm infants in New South Wales, 2005–2015 (N = 73 314).
Figure S1: Pattern of BPD by sex among preterm infants in New South Wales, 2005–2015. The solid lines represent incidence and dotted lines represent trendlines.
Figure S2: Pattern of BPD by residential area socio‐economic status among preterm infants in New South Wales, 2005–2015. The solid lines represent incidence and dotted lines represent trendlines.
Table S2: Infant and maternal characteristics by BPD status among preterm infants stratified by gestational age in New South Wales, 2005–2015.
Acknowledgements
We acknowledge the University of New South Wales (UNSW) for providing resources and facilities that supported this research. We thank the NSW Ministry of Health for facilitating data access. This research was produced in whole or part by UNSW Sydney researchers and is subject to the UNSW Intellectual property policy. For the purposes of Open Access, the author has applied a Creative Commons Attribution CC‐BY licence to any author accepted manuscrip version arising from this submission. 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 Australasian University Librarians.
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
References
- 1. Kinsella J. P., Greenough A., and Abman S. H., “Bronchopulmonary Dysplasia,” Lancet 367, no. 9520 (2006): 1421–1431. [DOI] [PubMed] [Google Scholar]
- 2. Chow S., Creighton P., Holberton J. R., Chambers G. M., and Lui K., “Report of the Australian and New Zealand Neonatal Network 2022,” in ANZNN, Editor Australian and New Zealand Neonatal Network Annual Reports Series (ANZNN, 2024). [Google Scholar]
- 3. Drummond D., Hadchouel A., Torchin H., et al., “Educational and Health Outcomes Associated With Bronchopulmonary Dysplasia in 15‐Year‐Olds Born Preterm,” PLoS One 14 (2019): e0222286. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Gough A., Spence D., Linden M., Halliday H. L., and McGarvey L. P. A., “General and Respiratory Health Outcomes in Adult Survivors of Bronchopulmonary Dysplasia: A Systematic Review,” Chest 141, no. 6 (2012): 1554–1567. [DOI] [PubMed] [Google Scholar]
- 5. Carregã M., Sousa P., Rocha G., Ferreira‐Magalhaes M., and Azevedo I., “Respiratory and Non‐Respiratory Outcomes of Bronchopulmonary Dysplasia in Adolescents: A Systematic Review,” Early Human Development 180 (2023): 105756. [DOI] [PubMed] [Google Scholar]
- 6. Katwyk S. V., Augustine S., Thébaud B., and Thavorn K., “Lifetime Patient Outcomes and Healthcare Utilization for Bronchopulmonary Dysplasia (BPD) and Extreme Preterm Infants: A Microsimulation Study,” BMC Pediatrics 20 (2020): 136. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Siffel C., Kistler K. D., Lewis J. F. M., and Sarda S. P., “Global Incidence of Bronchopulmonary Dysplasia Among Extremely Preterm Infants: A Systematic Literature Review,” Journal of Maternal‐Fetal & Neonatal Medicine 34, no. 11 (2021): 1721–1731. [DOI] [PubMed] [Google Scholar]
- 8. Hines D., Modi N., Lee S. K., et al., “Scoping Review Shows Wide Variation in the Definitions of Bronchopulmonary Dysplasia in Preterm Infants and Calls for a Consensus,” Acta Paediatrica 106, no. 3 (2017): 366–374. [DOI] [PubMed] [Google Scholar]
- 9. Moreira A., Noronha M., Joy J., et al., “Rates of Bronchopulmonary Dysplasia in Very Low Birth Weight Neonates: A Systematic Review and Meta‐Analysis,” Respiratory Research 25, no. 1 (2024): 219. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Avila‐Alvarez A., Zozaya C., Pértega‐Diaz S., et al., “Temporal Trends in Respiratory Care and Bronchopulmonary Dysplasia in Very Preterm Infants Over a 10‐Year Period in Spain,” Archives of Disease in Childhood. Fetal and Neonatal Edition 107, no. 2 (2022): 143–149. [DOI] [PubMed] [Google Scholar]
- 11. Nakashima T., Inoue H., Sakemi Y., Ochiai M., Yamashita H., and Ohga S., “Trends in Bronchopulmonary Dysplasia Among Extremely Preterm Infants in Japan, 2003‐2016,” Journal of Pediatrics 230 (2021): 119–125. [DOI] [PubMed] [Google Scholar]
- 12. Lui K., Lee S. K., Kusuda S., et al., “Trends in Outcomes for Neonates Born Very Preterm and Very Low Birth Weight in 11 High‐Income Countries,” Journal of Pediatrics 215 (2019): 32–40. [DOI] [PubMed] [Google Scholar]
- 13. Zhu Z., He Y., Yuan L., et al., “Trends in Bronchopulmonary Dysplasia and Respiratory Support Among Extremely Preterm Infants in China Over a Decade,” Pediatric Pulmonology 59 (2023): 399–407. [DOI] [PubMed] [Google Scholar]
- 14. Jeon G. W., Oh M., and Chang Y. S., “Increased Bronchopulmonary Dysplasia Along With Decreased Mortality in Extremely Preterm Infants,” Scientific Reports 15, no. 1 (2025): 8720. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Romijn M., Dhiman P., Finken M. J., et al., “Prediction Models for Bronchopulmonary Dysplasia in Preterm Infants: A Systematic Review and Meta‐Analysis,” Journal of Pediatrics 258 (2023): 113370. [DOI] [PubMed] [Google Scholar]
- 16. Hayes J., Don F. D. J., Murphy B. S., Shook L. A., and Ballard H. O., “Pathogenesis of Bronchopulmonary Dysplasia,” Respiration 79, no. 5 (2010): 425–436. [DOI] [PubMed] [Google Scholar]
- 17. Bradley C., DeGrazia M., Shi L., and Aronowitz T., “Risk Factors Contributing to the Development of Bronchopulmonary Dysplasia,” Dimensions of Critical Care Nursing 44, no. 1 (2025): 12–19. [DOI] [PubMed] [Google Scholar]
- 18. Wozniak P. S., Makhoul L., and Botros M. M., “Bronchopulmonary Dysplasia in Adults: Exploring Pathogenesis and Phenotype,” Pediatric Pulmonology 59, no. 3 (2024): 540–551. [DOI] [PubMed] [Google Scholar]
- 19. Menshykova A. O. and Dobryanskyy D. O., “Risk Factors of Bronchopulmonary Dysplasia Depending on the Severity of the Disease in Very Preterm Infants,” Journal of Maternal‐Fetal & Neonatal Medicine 38, no. 1 (2025): 2501697. [DOI] [PubMed] [Google Scholar]
- 20. CHeReL. Centre for Health Record Linkage and NSW Ministry of Health , “Website Page,” (2025).
- 21. Australian Bureau of Statistics , “ABS Website,” in Statistics ABo (Australian Bureau of Statistics, 2013). [Google Scholar]
- 22. Hanly M., Edwards B., Goldfeld S., et al., “School Starting Age and Child Development in a State‐Wide, Population‐Level Cohort of Children in Their First Year of School in New South Wales, Australia,” Early Childhood Research Quarterly 48 (2019): 325–340. [Google Scholar]
- 23. HealthStats NSW , “Evidence CfEa. Codes: Country of Birth in Perinatal Collection,” (2025).
- 24. Stolz C., Costa‐Nobre D. T., Sanudo A., et al., “Bronchopulmonary Dysplasia: Temporal Trend From 2010 to 2019 in the Brazilian Network on Neonatal Research,” Archives of Disease in Childhood. Fetal and Neonatal Edition 109 (2023): 325826. [DOI] [PubMed] [Google Scholar]
- 25. Fenton T. R. and Kim J. H., “A Systematic Review and Meta‐Analysis to Revise the Fenton Growth Chart for Preterm Infants,” BMC Pediatrics 13, no. 1 (2013): 59. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Green M. J., Kariuki M., Chilvers M., et al., “Inter‐Agency Indicators of Out‐Of‐Home‐Care Placement by Age 13–14 Years: A Population Record Linkage Study,” Child Abuse & Neglect 93 (2019): 91–102. [DOI] [PubMed] [Google Scholar]
- 27. Bursac Z., Gauss C. H., Williams D. K., and Hosmer D. W., “Purposeful Selection of Variables in Logistic Regression,” Source Code for Biology and Medicine 3, no. 1 (2008): 17. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Peng H.‐B., Zhan Y.‐L., Chen Y., et al., “Prediction Models for Bronchopulmonary Dysplasia in Preterm Infants: A Systematic Review,” Frontiers in Pediatrics 10 (2022): 856159. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Ito M., Kato S., Saito M., et al., “Bronchopulmonary Dysplasia in Extremely Premature Infants: A Scoping Review for Identifying Risk Factors,” Biomedicine 11, no. 2 (2023): 553. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Table S1: Birth characteristics among preterm infants in New South Wales, 2005–2015 (N = 73 314).
Figure S1: Pattern of BPD by sex among preterm infants in New South Wales, 2005–2015. The solid lines represent incidence and dotted lines represent trendlines.
Figure S2: Pattern of BPD by residential area socio‐economic status among preterm infants in New South Wales, 2005–2015. The solid lines represent incidence and dotted lines represent trendlines.
Table S2: Infant and maternal characteristics by BPD status among preterm infants stratified by gestational age in New South Wales, 2005–2015.
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.