Abstract
Objective
To assess the association between birth weight and childhood asthma risk using data from the 2019–2020 National Survey of Children’s Health database.
Design
Cross-sectional study.
Setting
The USA.
Patients
A representative cohort of American children.
Exposure
The exposure of this study was birth weight regardless of gestational age. Birth weight was divided into three groups: <1500 g, 1500–2500 g and >2500 g.
Main outcome measures
Primary outcomes were parent-reported diagnosis of asthma.
Method
The Rao-Scott χ2 test was used to compare the groups. The main analyses examined the association between birth weight and parent-report asthma in children using univariable and multivariable logistic models adjusting for preterm birth, age, sex, race, family poverty, health insurance, smoking, maternal age. Subgroup analysis was performed based on interaction test.
Results
A total of 60 172 children aged 3–17 years were enrolled in this study; of these, 5202 (~8.6%) had asthma. Children with asthma were more likely to be born preterm, with low birth weight (LBW) or very LBW (VLBW). The incidence of asthma was the highest in VLBW children at 20.9% and showed a downward trend with an increase in birth weight class, with rates of 10.7% and 8.1% in the LBW and normal birthweight groups, respectively. Children with VLBW (OR 1.97; 95% CI 1.29 to 3.01) had higher odds of developing asthma in the adjusted analysis model. However, VLBW was only shown to be a risk factor for asthma among Hispanics, black/African-Americans and children between the ages of 6 and 12 years, demonstrating racial and age disparities.
Conclusions
VLBW increases the risk of childhood asthma; however, racial and age disparities are evident.
Keywords: Asthma, EPIDEMIOLOGIC STUDIES, Fetal medicine
STRENGTHS AND LIMITATIONS OF THIS STUDY.
This is a retrospective study using cross-sectional data from 2019 to 2020 National Survey of Children’s Health and is nationally representative.
Various child and household characteristics were included as covariates in the analysis, such as sex, age, race/ethnicity, maternal age, poverty/income level, health insurance status and household smoking exposure.
Logistic regression models were used to assess the association between birth weight and asthma risk. Three models were employed, adjusting for different confounding factors.
Data on the children’s disease history were collected via a parent rating questionnaire, introducing the possibility of recall bias and possibly affecting the accuracy of the asthma diagnoses.
The study did not assess the children’s allergy status or family history of asthma or allergies, which could confound the relationship between birth weight and asthma.
Introduction
Asthma, traditionally defined as an airway inflammatory response syndrome with recurrent episodes of wheezing and coughing as clinical symptoms, is the most common chronic lung disease in children.1 As one of the most common chronic diseases affecting children’s health, asthma exerts a significant economic and mental burden on children and their families2 and has been classified as a global public health problem. The aetiology and diagnosis of this disease are highly complex, and management strategies are still evolving.3
There are many pathogenic factors for asthma, such as allergens, viral or bacterial infections, drugs, exercise and mental factors in the external environment. Although asthma may present at any age, it disproportionately affects children in a critical stage of growth and development.4 Existing research has confirmed the theory of the fetal origin of asthma; that is, the intrauterine environment affects the growth and development of the fetus and modulates susceptibility to asthma. Many studies have investigated the correlation between parental occupational exposure,5 pregnancy obesity,6 maternal smoking in pregnancy7 and preterm birth8 with asthma, but current studies on the effect of birth weight on asthma have yielded inconsistent results.9–11 Birth weight is an important indicator of intrauterine growth at birth.12 Low birth weight (LBW), defined as a birth weight less than 2500 g, is a global public health issue of great concern. LBW infants can be further subdivided into LBW (1500–2500 g), very LBW (VLBW) (1000–1499 g), and extremely LBW (ELBW) infants (<1000 g).13 LBW is associated with a higher infant mortality and subsequent morbidity, with numerous studies showing associations between LBW and decreased respiratory function later in life.14 15 Although LBW/VLBW poses many challenges to the immature immune and respiratory systems, leading to detrimental phenotypes in early life that persist into infancy and adulthood, the effects of LBW/VLBW on asthma remain unclear.
In recent years, there have been a significant number of studies investigating the relationship between birth weight and asthma, but the conclusions have been inconsistent. In this study, we used data from the 2019–2020 National Survey of Children’s Health (NSCH) database to assess the association between LBW and childhood asthma risk.
Materials and methods
Study population
This cross-sectional survey study used data from the NSCH, a survey conducted in the USA by the Data Resource Centre to collect health information on children and adolescents from 2019 to 2020. The object of NSCH is to provide national and state-level estimates on key indicators of the health and well-being of children, their families and their communities, as well as information about the prevalence and impact of special healthcare needs. Survey respondents were parents or caregivers with at least one child aged 0–17 years. During data collection, screener questionnaires were randomly sent online and by mail to families across the United States to identify households with children and roster children in the household. One child was randomly selected from each eligible household, and that child was the subject of a more detailed topical questionnaire. Data were collected through parental rating questionnaire. Responses to the screener and topical questionnaires were collected, processed, and published in the Screener Public Use File and Topical Public Use File. In general, a total of 72 210 surveys were completed nationwide from June 2019 to January 2021. The 2019 survey had a total of 29 433 (42.4% response) completed surveys while 2020 had 42 777 (42.4% response) surveys completed. Survey data were adjusted and weighted to reflect the demographic composition of non-institutionalised children and youth aged 0–17 years in each state. In the study, children under 3 years of age or with missing information on asthma or birth weight were excluded. Ultimately, 60 358 children aged 3–17 years were included in this study (figure 1). This study was performed in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology reporting guidelines.
Figure 1.
Flow chart of the study. NSCH, National Survey of Children’s Health.
Outcome and birthweight exposure
The outcome of interest was asthma diagnosis in children. The diagnosis of asthma was determined based on two questions from the NSCH survey:
‘Has a doctor or other healthcare provider ever told you that this child has asthma?’
‘Does this child currently have the condition?’
A child was considered to have confirmed asthma if the parents or caregivers responded ‘yes’ to both of these questions.
The primary independent variable of interest in this study was the birth weight of the child, as reported by the caregiver and divided as LBW (1500–2500 g), VLBW (1000–1499 g) and ELBW infants (<1000 g).
Covariates
The covariates included in the model for this study consisted of both child and household characteristics. Child characteristics included the sex, age and race/ethnicity of the children. The survey categorised children into three age groups: 3–5 years, 6–12 years and 13–17 years. Race/ethnicity was classified as white, Hispanic, African-American, Asian or other. Additionally, preterm birth was defined as being born 3 weeks before the estimated due date.
Caregiver characteristics included maternal age, poverty/income level and health insurance status. Maternal age was reported as a continuous variable. Poverty/income levels were categorised as 0%–99%, 100%–199%, 200%–399% and 400% or above the federal poverty level, which is determined by the Census Bureau’s poverty thresholds that vary based on family size and the number of related children under 18 years old. Health insurance status was classified as ‘yes’ or ‘no’. Household smoking exposure was defined as anyone living in the household who uses cigarettes, cigars, or pipe tobacco.
According to the official guidelines for data analysis provided by the NSCH (http://www.childhealthdata.org/NSCH), the proportion of missing values was less than 2% in most cases. The exclusion of these values did not significantly impact the estimated incidence (%) and had only a minor effect on the weighted population counts.
Statistical analysis
The survey sampling weights used in this study were adjusted to account for screener non-response, population controls and selected characteristics. These characteristics include household size, household poverty threshold, educational attainment of the household respondent, race, ethnicity and special healthcare needs status by state. The survey sampling weights, labelled as ‘fw_1920’ in the NSCH codebook, were incorporated into the analysis as part of the experimental design. Cluster (unique household identifier) and stratum (state of residence and households with children) were also taken into consideration in the analysis along with the sampling weights.
To compare baseline characteristics between groups, the Rao-Scott χ2 test was used. Estimates were reported with 95% CIs based on the Wald’s method. Categorical variables were presented as frequencies and percentages.
The main analysis focused on examining the association between birth weight (separately for LBW and VLBW groups) and asthma in children using logistic regression models. Normal birth weight (NBW) infants served as the reference group. ORs with 95% CIs were estimated. Three models were used to adjust for confounding factors. Model 1 was unadjusted, model 2 was adjusted for preterm birth and model 3 was further adjusted for sociodemographic characteristics such as age, race, ethnicity of the child, maternal age, poverty/income level and health insurance status.
Additionally, subgroup analyses were performed. The population was stratified by sex, age, race, income, insurance and household smoking to explore potential relationships between birth weight and asthma. Logistic regression models adjusting for the same variables as in model 3 were used for these subgroup analyses.
Statistical significance was defined as a two-sided p<0.05. All analyses were conducted using the ‘Survey’ package in R V.4.0.6.
Patient and public involvement
Patients or the public were not involved in the design, or conduct, or reporting, or dissemination plans of our research.
Results
In this cross-sectional study, a total of 60 172 children aged 3–17 years were included. The population was divided into three groups based on birth weight: NBW, LBWand VLBW. The overall incidence of asthma was approximately 8.7% (table 1). Children with birth weights under 2500 g accounted for around 10% of the total population, while preterm births made up 11.4% of the population. The majority of the included children were over 6 years old, and 50.7% of them were white. Furthermore, a significant proportion of children had a decent standard of living, with more than 80% living above the poverty line and 65.9% having health insurance. However, 14.3% of the children were exposed to smoking at home. The prevalence of asthma, preterm birth, sex, race, household poverty level and smoking exposure varied significantly among the different birth weight groups. The highest incidence of asthma was observed in VLBW children, reaching 20.9%. The incidence showed a downward trend with an increase in birth weight class, with rates of 10.7% in the LBW group and 8.1% in the NBW group. As expected, the majority of VLBW infants (more than 80%) were born preterm, compared with only 6.2% of NBW children. In both the LBW and VLBW groups, girls outnumbered boys, with percentages of 52.1% and 54.4%, respectively. As birth weight decreased, there was an increasing number of children living in poorer conditions. This trend was accompanied by an increase in the proportion of children exposed to family smoking.
Table 1.
Population characteristics by birth weight
| Total n=60 172 | <1500 (g) (n=790) | 1500–2500 (g) (n=4361) | ≥2500 (g) (n=55 021) | |||||
| Characteristic | N %* | N %* | 95 CI% | N %* | 95% CI | N %* | 95% CI | P value† |
| Asthma | <0.001 | |||||||
| Yes | 5205 (8.6) | 142 (20.9) | 15.1 to 28.1 | 458 (10.7) | 8.7 to 13.0 | 4602 (8.1) | 7.7 to 8.6 | |
| No | 54 970 (91.4) | 648 (79.1) | 71.9 to 84.9 | 3903 (89.3) | 87.0 to 91.3 | 50 419 (91.9) | 91.4 to 92.3 | |
| Preterm birth | <0.001 | |||||||
| Yes | 6470 (11.4) | 670 (82.9) | 75.4 to 88.4 | 2557 (58.7) | 55.6 to 61.8 | 3243 (6.2) | 5.7 to 6.7 | |
| No | 52 956 (88.6) | 106 (17.1) | 11.6 to 24.6 | 1743 (41.3) | 38.2 to 44.4 | 51 107 (93.8) | 93.3 to 94.3 | |
| Missing | 746 | 14 | – | 61 | – | 671 | – | |
| Age of child (years) | 0.497 | |||||||
| 3–5 | 10 990 (19.7) | 133 (15.7) | 11.5 to 21.1 | 766 (20.6) | 18.1 to 23.4 | 10 091 (19.7) | 19.0 to 20.5 | |
| 6–12 | 25 015 (46.6) | 353 (50.8) | 43.9 to 57.7 | 1838 (45.1) | 41.9 to 48.3 | 22 824 (46.7) | 45.7 to 47.7 | |
| 13–17 | 24 167 (33.6) | 304 (33.5) | 27.8 to 39.8 | 1757 (34.3) | 31.4 to 37.3 | 22 106 (33.6) | 32.7 to 34.5 | |
| Sex of child | 0.021 | |||||||
| Male | 31 107 (51.2) | 379 (45.6) | 38.9 to 52.4 | 2049 (47.9) | 44.7 to 51.1 | 28 679 (51.6) | 50.7 to 52.6 | |
| Female | 29 605 (48.8) | 411 (54.4) | 47.6 to 61.1 | 2312 (52.1) | 48.9 to 55.3 | 26 342 (48.4) | 47.4 to 49.3 | |
| Race of the child | <0.001 | |||||||
| Hispanic | 7573 (25.5) | 106 (24.6) | 17.7 to 33.2 | 619 (23.9) | 20.6 to 27.5 | 6848 (25.6) | 24.6 to 26.7 | |
| White | 41 088 (50.7) | 450 (42.9) | 36.5 to 49.5 | 2560 (41.7) | 38.8 to 44.7 | 38 078 (51.6) | 50.7 to 52.6 | |
| Black/African-American | 3918 (13.2) | 116 (22.2) | 17.3 to 28.1 | 479 (20.4) | 17.9 to 23.2 | 3323 (12.4) | 11.7 to 13.1 | |
| Asia | 3008 (4.5) | 54 (5.0) | 2.7 to 9.0 | 334 (6.6) | 5.2 to 8.4 | 2620 (4.3) | 3.9 to 4.6 | |
| Other | 4585 (6.1) | 64 (5.3) | 3.5 to 7.9 | 369 (7.3) | 5.8 to 9.2 | 4152 (6.1) | 5.7 to 6.4 | |
| Maternal age‡ | 30.3 (0.1) | 30.9 (0.6) | 29.7 to 32.2 | 30.9 (0.5) | 30.0 to 31.8 | 30.2 (0.1) | 30.0 to 30.4 | <0.001 |
| Household poverty level (%FPL) | 0.005 | |||||||
| <100% | 6914 (17.5) | 145 (26.8) | 20.0 to 35.0 | 628 (20.0) | 17.4 to 22.9 | 6141 (17.2) | 16.4 to 18.0 | |
| 100%–199% | 9954 (21.5) | 150 (19.2) | 14.8 to 24.5 | 805 (22.9) | 20.2 to 25.8 | 8999 (21.4) | 20.5 to 22.3 | |
| 200%–399% | 18 653 (29.6) | 236 (27.1) | 21.9 to 32.9 | 1315 (28.4) | 25.6 to 31.4 | 17 102 (29.7) | 28.9 to 30.6 | |
| ≥400% | 24 651 (31.4) | 259 (26.9) | 21.6 to 32.9 | 1613 (28.7) | 26.1 to 31.5 | 22 779 (31.7) | 30.9 to 32.5 | |
| Insurance | 0.786 | |||||||
| Yes | 39 674 (65.9) | 523 (66.0) | 58.9 to 72.4 | 2828 (65.0) | 61.8 to 68.1 | 36 323 (66.0) | 65.1 to 66.9 | |
| No | 20 336 (34.1) | 262 (34.0) | 27.6 to 41.1 | 1519 (35.0) | 31.9 to 38.2 | 18 555 (34.0) | 33.1 to 34.9 | |
| Missing | 162 | 5 | – | 14 | – | 143 | – | |
| Household smoking exposure | 0.005 | |||||||
| Yes | 8226 (14.3) | 130 (19.9) | 14.1 to 27.3 | 723 (17.1) | 14.8 to 19.6 | 7373 (13.9) | 13.3 to 14.6 | |
| No | 50 778 (85.7) | 644 (80.1) | 72.7 to 85.9 | 3556 (82.9) | 80.4 to 85.2 | 46 578 (86.1) | 85.4 to 86.7 | |
| Missing | 1168 | 16 | – | 82 | – | 1070 | – | |
*Actual cases and weighted percentage.
†Rao Scott-adjusted χ2.
‡Maternal age when the child was born is presented as mean (SD).
FPL, Federal poverty level.
A detailed description of the population’s characteristics when divided into the control (n=54 970) and asthma groups (n=5202), depending on whether they had asthma or not, revealed that Birth weight, preterm birth rate, age, sex, race, household poverty, insurance and smoking exposure were all significantly different between the two groups (table 2). In comparison to children without asthma, children with asthma were more likely to be born preterm, have LBW or VLBW, be older than 6 years, male and black/African-Americans. In addition, in the asthma group, a comparatively greater proportion of children lived in poverty, lacking health insurance, and being exposed to household smoking than children in the non-asthma group.
Table 2.
Population characteristics by asthma diagnosis
| Asthma n=5202 | No asthma n=54 970 | ||||
| Characteristic | N %* | 95% CI | N %* | 95% CI | P value† |
| Population | |||||
| Birth weight (g) | <0.001 | ||||
| <1500 | 142 (3.3) | 2.3 to 4.7 | 648 (1.2) | 1.0 to 1.3 | |
| 1500–2500 | 458 (9.9) | 8.0 to 12.1 | 3903 (7.7) | 7.2 to 8.2 | |
| >2500 | 4602 (86.8) | 84.4 to 88.9 | 50 419 (91.1) | 90.6 to 91.6 | |
| Preterm birth | <0.001 | ||||
| Yes | 836 (18.2) | 15.7 to 20.9 | 5634 (10.8) | 10.2 to 11.4 | |
| No | 4307 (81.8) | 79.1 to 84.3 | 48 649 (89.2) | 88.6 to 89.8 | |
| Missing | 59 | – | 687 | – | |
| Age of child (years) | <0.001 | ||||
| 3–5 | 536 (11.1) | 9.5 to 13.0 | 10 454 (19.7) | 19.0 to 20.5 | |
| 6–12 | 2200 (48.4) | 45.4 to 51.4 | 22 815 (46.5) | 45.5 to 47.4 | |
| 13–17 | 2466 (40.4) | 37.6 to 43.4 | 21 701 (33.0) | 32.1 to 33.9 | |
| Sex of child | <0.001 | ||||
| Male | 2921 (57.0) | 54.0 to 60.0 | 28 186 (50.7) | 49.8 to 51.7 | |
| Female | 2281 (43.0) | 40.0 to 46.0 | 26 784 (49.3) | 48.3 to 50.2 | |
| Race of the child | <0.001 | ||||
| Hispanic | 728 (25.6) | 22.4 to 29.1 | 6845 (25.5) | 24.5 to 26.6 | |
| White | 3213 (43.0) | 40.2 to 45.8 | 37 875 (51.5) | 50.5 to 52.4 | |
| Black/African-American | 624 (22.2) | 19.9 to 24.7 | 3294 (12.3) | 11.7 to 13.0 | |
| Asia | 167 (2.5) | 1.9 to 3.4 | 2841 (4.6) | 4.3 to 5.0 | |
| Other | 470 (6.7) | 5.5 to 8.0 | 4115 (6.1) | 5.7 to 6.5 | |
| Maternal age when born‡ | 29.8 (0.3) | 29.3 to 30.3 | 30.3 (0.1) | 30.1 to 30.5 | <0.001 |
| Household poverty level (%FPL) | <0.001 | ||||
| <100% | 856 (23.2) | 20.6 to 25.9 | 6058 (17.0) | 16.2 to 17.8 | |
| 100%–199% | 927 (22.9) | 20.3 to 25.6 | 9027 (21.3) | 20.5 to 22.2 | |
| 200%–399% | 1519 (27.0) | 24.3 to 29.8 | 17 134 (29.8) | 29.0 to 30.7 | |
| ≥400% | 1900 (27.0) | 24.7 to 29.4 | 22 751 (31.9) | 31.1 to 32.7 | |
| Insurance | <0.001 | ||||
| Yes | 3173 (62.3) | 59.2 to 65.2 | 36 501 (66.3) | 65.3 to 67.2 | |
| No | 2014 (37.7) | 34.8 to 40.8 | 18 322 (33.7) | 32.8 to 34.7 | |
| Missing | 15 | – | 147 | – | |
| Household smoking exposure | <0.001 | ||||
| Yes | 880 (18.1) | 15.9 to 20.5 | 7346 (13.9) | 13.3 to 14.6 | |
| No | 4226 (81.9) | 79.5 to 84.1 | 46 552 (86.1) | 85.4 to 86.7 | |
| Missing | 96 | – | 1072 | – | |
*Actual cases and weighted percentage.
†Rao Scott-adjusted χ2.
‡Maternal age when the child was born is presented as mean (SD).
FPL, Federal poverty level.
In the multivariable logistic regression analysis, birth weight was examined as a predictor of asthma incidence. The unadjusted analysis showed that children with LBW had a higher risk of asthma (OR 1.35; 95% CI 1.06 to 1.71; p=0.014), while those with VLBW had an even higher risk (OR 2.98; 95% CI 2.00 to 4.44; p<0.001) (table 3). After adjusting for preterm birth, other demographic factors and potential confounders such as age, sex, race, maternal age, family poverty, health insurance, smoking and maternal age, the association between LBW and asthma became non-significant. However, children born with VLBW still had a significantly increased likelihood of having asthma compared with NBW children (OR 1.97; 95% CI 1.29 to 3.02; p=0.002). This suggests that VLBW is an independent risk factor for asthma, even after considering various confounding factors.
Table 3.
ORs for the associations between birth weight and asthma
| Unadjusted model | Model 1* | Model 2† | ||||
| OR (95% CI) | P value | OR (95% CI) | P value | OR 95% CI | P value | |
| Birth weight | ||||||
| NBW | Reference | Reference | Reference | |||
| LBW | 1.35 (1.06 to 1.71) | 0.014 | 1.02 (0.79 to 1.32) | 0.872 | 1.00 (0.77 to 1.30) | 0.993 |
| VLBW | 2.98 (2.00 to 4.44) | <0.001 | 2.03 (1.33 to 3.10) | 0.001 | 1.97 (1.29 to 3.02) | 0.002 |
*Adjusted for preterm birth.
† Adjusted for preterm birth, age, sex, race, family poverty, health insurance, smoking, maternal age.
LBW, low birth weight; NBW, normal birth weight; VLBW, very low birth weight.
Subgroup analyses were conducted to explore the relationship between birth weight and asthma in different categories (table 4). The results revealed that VLBW increased the risk of asthma across multiple subgroups. Infants born with VLBW had a 2.3-fold greater risk of developing asthma than those born with NBW. The same findings were observed when children were stratified by sex, with an elevated risk of asthma among VLBW infants in both boys (OR 1.82; 95% CI 1.08 to 3.08; p=0.025) and girls (OR 2.16; 95% CI 1.14 to 4.11; p=0.018). However, when race and age were considered, VLBW was only shown to be a risk factor for asthma among Hispanics, black/African-Americans and children between the ages of 6 and 12 years, demonstrating racial and age disparities in the link between VLBW and asthma. Furthermore, household smoking significantly increased the risk of asthma in VLBW children. This highlights the importance of environmental factors in modifying the risk of asthma in this population.
Table 4.
Subgroup analysis for the association of birth weight with asthma
| Birth weight | |||||
| NWB | LBW | VLBW | |||
| OR (95% CI)* | P value | OR (95% CI)* | P value | ||
| Preterm birth | Reference | ||||
| Yes | 1.05 (0.70 to 1.56) | 0.812 | 2.27 (1.42 to 3.63) | <0.001 | |
| No | 0.98 (0.71 to 1.36) | 0.926 | 0.81 (0.21 to 3.23) | 0.769 | |
| Test for interaction | – | – | – | 0.391 | |
| Age of child (years) | Reference | ||||
| 3–5 | 0.86 (0.48 to 1.54) | 0.604 | 2.37 (0.82 to 6.86) | 0.110 | |
| 6–12 | 0.97 (0.64 to 1.46) | 0.874 | 2.24 (1.21 to 4.12) | 0.010 | |
| 13–17 | 1.09 (0.73 to 1.65) | 0.670 | 1.50 (0.82 to 2.76) | 0.192 | |
| Test for interaction | – | – | – | 0.658 | |
| Sex of child | Reference | ||||
| Male | 0.79 (0.57 to 1.08) | 0.138 | 1.82 (1.08 to 3.08) | 0.025 | |
| Female | 1.32 (0.90 to 1.93) | 0.155 | 2.16 (1.14 to 4.11) | 0.018 | |
| Test for interaction | – | – | – | 0.273 | |
| Race of the child | Reference | ||||
| Hispanic | 0.90 (0.40 to 2.02) | 0.794 | 3.21 (1.25 to 8.26) | 0.015 | |
| White | 0.82 (0.43 to 1.57) | 0.855 | 0.53 (0.13 to 2.19) | 0.378 | |
| Black/African-American | 1.20 (0.73 to 1.97) | 0.478 | 2.33 (1.13 to 4.79) | 0.021 | |
| Asia | 1.00 (0.41 to 2.48) | 0.997 | 0.55 (0.10 to 3.00) | 0.487 | |
| Other | 0.97 (0.75 to 1.25) | 0.824 | 1.36 (0.80 to 2.31) | 0.249 | |
| Test for interaction | – | – | – | 0.315 | |
| Household poverty level (%FPL) | Reference | ||||
| <100% | 0.92 (0.51 to 1.66) | 0.788 | 3.98 (1.67 to 9.51) | 0.002 | |
| 100%–199% | 0.88 (0.58 to 1.34) | 0.551 | 1.28 (0.52 to 3.13) | 0.590 | |
| 200%–399% | 1.30 (0.75 to 2.27) | 0.347 | 1.65 (0.85 to 3.20) | 0.137 | |
| ≥400% | 0.86 (0.59 to 1.25) | 0.436 | 1.38 (0.71 to 2.69) | 0.346 | |
| Test for interaction | – | – | – | 0.369 | |
| Insurance | Reference | ||||
| Yes | 0.97 (0.75 to 1.27) | 0.835 | 2.04 (1.31 to 3.16) | 0.002 | |
| No | 1.72 (0.49 to 6.04) | 0.400 | 0.97 (0.24 to 3.92) | 0.962 | |
| Test for interaction | – | – | – | 0.181 | |
| Household smoking exposure | Reference | ||||
| Yes | 1.03 (0.64 to 1.65) | 0.907 | 3.81 (1.43 to 10.2) | 0.008 | |
| No | 0.99 (0.73 to 1.35) | 0.971 | 1.63 (1.07 to 2.48) | 0.022 | |
| Test for interaction | – | – | – | 0.436 | |
*The model was adjusted for preterm birth, age, sex of child, race of the child, maternal age, household poverty level, insurance status, household smoking exposure.
FPL, Federal poverty level; LBW, low birth weight; NBW, normal birth weight; VLBW, very low birth weight.
Discussion
This study used data from the 2019–2020 NSCH database, which included a total of 60 172 children aged 3–17 years. Through cross-sectional analysis, it was found that around 10% of the population consisted of children born with LBW, and the estimated incidence of asthma among children in the USA was 8.6%. The analysis further revealed that VLBW infants had an increased risk of developing asthma beyond the age of 6 years, regardless of their sex. These findings remained statistically significant even after adjusting for various factors such as preterm delivery, age, sex, race, family poverty, health insurance, smoking and maternal age. In subgroup analysis, the effects of VLBW on asthma were found to be statistically significant in both boys and girls. However, the impact varied by race and age. VLBW was shown to be a risk factor for asthma specifically among Hispanics, black/African-Americans and children aged 6–12 years.
Birth weight is one of the important indicators for evaluating fetal intrauterine growth and development at birth, reflecting the energy reception or exposure to adverse factors during pregnancy and the placental transport function to a certain extent.16 It is associated with higher infant mortality and subsequent morbidity, and numerous studies have shown associations between LBW and decreased respiratory function in later life.14 15 Our results are similar to most studies showing that LBW increases the risk of asthma. A prior meta-analysis of 18 studies showed that LBW was associated with increased asthma risk in both children and adults compared with NBW.11
In addition, using data from the Oxford Records Association Study, Davidson et al analysed the influence of maternal and perinatal factors on subsequent hospitalisations for childhood asthma and found that LBW increases the risk of asthma, OR 1.2 (95% CI 1.1 to 1.3).17 Since twin fetuses have relatively similar genetic background and gestational age, it is more convincing to study the relationship between birth weight and asthma. Kindlund et al, analysing data from twins (8280 pairs) born aged 3–9 years in Denmark between 1994 and 2000 reported that the risk of asthma in LBW twins was significantly higher than that in twins with NBW (11.3% vs 9.9%), OR 1.30 (95% CI 1.10 to 1.54), p=0.002.18 A cohort study from Sweden that included dizygotic and monozygotic twin pairs also found that LBW was associated with an increased risk of asthma in twins which was unlikely to be confounded by genetics or shared environmental factors.19 It is important to note that the respiratory effects of LBW were not limited to childhood, as a VLBW cohort study in New Zealand found adult VLBW survivors aged 26–30 years showed a higher incidence of airflow obstruction, gas retention, reduced gas exchange and increased respiratory heterogeneity than the control group,20 suggesting that LBW may have long-lasting effects on the respiratory system. Of course, the relationship between birth weight and asthma is not completely negative, for example, overweight children born large for gestational age also had an increased risk of asthma.21 Although most studies have suggested that birth weight is inversely associated with asthma, some have proposed different hypotheses. In a retrospective cohort study of 40 724 children in Canada, Carter et al found that small gestational age (SGA) was not associated with the risk of asthma in the absence of smoking during pregnancy.22 However, it is important to note that this study involved full-term infants whose birth weights rarely reached the VLBW range because of the relatively complete intrauterine growth, which may have attenuated the association between birth weight and asthma.
Our subgroup analyses revealed racial and age differences in the association between VLBW infants and asthma. When race and age were taken into account, VLBW was only shown to be a risk factor for asthma among Hispanics, black/African-Americans, and children between the ages of 6 and 12 years. Joseph et al23 used logistic regression to analyse the clinical data of 126 children aged 6–8 years and found that African-Americans had higher rates of asthma (12.5% vs 5.3%) and LBW (16.6% vs 3.9%) than non-African-Americans. This racial disparity may be due to a higher rate of LBW among African-Americans infants than American babies of European origin.24 25 However, a cross-sectional study using data from the 1998–2016 National Health Interview Survey showed that the link between LBW and asthma did not differ by racial/ethnic groups.15 The differences in birthweight distribution, the way asthma was defined, the control of covariates and the differences in statistical methods may be the possible reasons for the inconsistent conclusions.
In addition, a cohort study from Australia26 showed that VLBW infants had a higher incidence of wheezing disorders and readmissions with respiratory problems in the first 2 years of life than NBW infants. However, in VLBW children aged 2–8 years, although the risk of developing asthma was higher than in the control group, it was not statistically significant. Various factors, such as the number of participants enrolled, the classification criteria for birth weight, living environment and race, may have led to inconsistent results. Future studies should try to identify the mechanisms that cause these differences. In addition, we found that household smoke exposure significantly increased the risk of asthma in VLBW children. Smoking has been widely recognised as a pathogenic factor associated with respiratory diseases. In addition to postnatal smoke exposure in the external environment, maternal smoking during pregnancy has been shown to reduce birth weight and increase the risk of preterm birth,27 28 as well as subsequent lower respiratory tract illness.23
Strengths and limitations
This study had several limitations that need to be acknowledged. First, the data on the children’s disease history were collected through a parental rating questionnaire and not verified using medical records. This introduces the possibility of recall bias and may impact the accuracy of the reported asthma diagnoses. Second, this study did not assess the children’s allergic status or family history of asthma or allergies, which are important factors in understanding asthma risk. These factors could potentially confound the association between birth weight and asthma. Another limitation is that this study focused on LBW as the exposure factor rather than SGA. SGA status, along with measures of fetal growth such as birth length and head circumference, could provide a more comprehensive evaluation of intrauterine development and its relationship to asthma risk. Absolutely, a well-designed prospective cohort study that incorporates essential confounders could help establish a better understanding of the causal relationship between birth weight and asthma. By following a large group of individuals from birth and collecting detailed data on birth weight, potential confounding factors and asthma outcomes over time, researchers can more accurately assess the impact of birth weight on the risk of developing asthma. Indeed, the representativeness of the nationwide survey used in this study is a major strength. It allows for the findings to be potentially generalised to the broader population of children aged 3–17 years in the USA. Including preterm births as a covariate is important because preterm birth is often associated with LBW. Preterm birth itself has been shown to increase the risk of asthma and chronic lung disease, and it is a known confounding factor in the relationship between birth weight and asthma. Therefore, accounting for preterm birth in the analysis is crucial to better understand the specific role of birth weight in relation to asthma risk. Moreover, previous studies have demonstrated that the effect of birth weight is significant even within the context of preterm birth.29 Birth weight has been found to independently influence the risk of asthma in both preterm and term infants.30 31 These findings highlight the importance of considering birth weight as a separate factor in understanding the relationship between early-life events and respiratory health outcomes.
Conclusion
The conclusion of our study is consistent with the majority of the literature, in that LBW, especially VLBW, increases the risk of childhood asthma; however, racial and age disparities were observed. Based on the current research progress, we can conclude that appropriate early intrauterine or postnatal nutritional intervention may be key to avoiding and managing childhood asthma.
Supplementary Material
Footnotes
MN and BL contributed equally.
Contributors: All the authors contributed to the work approved the final version of the manuscript. Particularly, contributions were: study design: NM, LBH, LZW and CH; Data collection: NM, WT, LBH, YDT, LZY and CCY; Data analyses and interpretation: NM, QSD, LW, DXY, CZ and QSD; Manuscript drafting: NM and LBH; Critical revision of the manuscript: LZW and CH. LZW accepts full responsibility for the work and/or the conduct of the study, had access to the data, and controlled the decision to publish.
Funding: This work was supported by the National Key R&D Program of China (2022YFC2702903), the National Natural Science Foundation of China Grants (81974232，82271742), the Clinical Research Plan of SHDC (SHDC2020CR6027，SHDC22022303), the Program of Shanghai Academic Research Leader (21XD1403700), the Interdisciplinary Program of Shanghai Jiao Tong University (YG2021ZD29) and the Shanghai Municipal Science and Technology Major Project (20Z11900602).
Competing interests: None declared.
Patient and public involvement: Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Provenance and peer review: Not commissioned; externally peer reviewed.
Data availability statement
Data are available in a public, open access repository.
Ethics statements
Patient consent for publication
Not applicable.
Ethics approval
The requirement for informed consent was waived by the International Peace Maternity and Child Hospital Institutional Review Board as the data used were publicly available.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Data are available in a public, open access repository.