Study design and data source
This study used a merged database from the National Health Insurance Service (NHIS) and the National Health Screening Program for Infants and Children (NHSPIC) in Korea. The NHIS is one insurance system that covers almost the entire Korean population, making it a representative data source. The NHIS database provides basic demographic characteristics, such as date of birth, gender, insurance premium, and region of residence, as well as information on health care utilization, including type of hospital visit, diagnosis codes (International Classification of Diseases 10th Revision [ICD-10] codes), prescription medication codes and procedure codes. All children in Korea were eligible for seven rounds of the NHSPIC, which were administered at specific age intervals from four to 72 months. The rounds were planned as follows: 1st (4–6 months old), 2nd (9–12 months old), 3rd (18–24 months old), 4th (30–36 months old), 5th (42–48 months old ) ), 6th (54-60 months old) and 7th (66-72 months old). The NHSPIC examination includes a general health questionnaire, the Korean Developmental Screening Test (K-DST), an anthropometric examination and a physical examination [13].
The anonymized individual data was used exclusively for research purposes. Patient consent was not required because this study was based on anonymized and publicly available data. The Institutional Review Board of the Korea National Institute for Bioethics Policy waived the need for informed consent. The study protocol was reviewed and approved by the Institutional Review Board of the Korea National Institute for Bioethics Policy (P01-201603-21-005). All methods were carried out in accordance with relevant guidelines and regulations.
Study population
The study population is shown in Figure 1. Of the 2,395,966 Korean children born between 2008 and 2012, we included those children who received all rounds of the NHSPIC from the first to the fourth round and responded appropriately to the questionnaire responded (N = 408,077) and received the K-DST correctly in the 7th round (N = 714,364). A total of 180,563 children met the inclusion criteria. Subsequently, children who met the following criteria were excluded: (1) deceased (N = 52), (2) birth weight <2.5 kg (N= 8029) or >4 kg (N= 6193), (3) multiple births (N= 1899), (4) premature birth (N= 6427), (5) diagnosis with conditions in newborns related to the duration of pregnancy and fetal growth (N= 7139), convulsions in neonates/disorders in the brain status of neonates (N= 456), or congenital malformations/chromosomal abnormalities (N= 29,130), (6) admission to intensive care for 4 days before the age of 1 year (N= 5458), and (7) who received general anesthesia before 1 year of age (N= 2615) and for >5 days before 2 years of age were excluded. Ultimately, we enrolled 133,243 eligible children.
Dietary patterns in early childhood
The information on dietary patterns from childhood to the age of 3 years was obtained from the NHSPIC questionnaire, spanning the first to the fourth wave. The details of the questionnaire are described in Supplementary Table 1. Specifically, the first round of the NHSPIC, administered at 4 to 6 months of age, includes questions about the types of milk infants consume. The second round, conducted at 9 to 12 months of age, includes questions about the timing of the introduction of complementary foods, the frequency of complementary food intake, and the ingredients in complementary foods. The third round, conducted at 18-24 months of age, included questions about the frequency of consumption of fruit juice or sweetened drinks. Finally, the fourth survey, conducted at 30-36 months of age, includes questions on frequency of fruit juice or sweetened beverage consumption, meal frequency and milk intake.
Clusters according to dietary patterns during early childhood
Polytomous Variable Latent Class Analysis (poLCA) was used to identify groups of similar cases within the manifest variables for early childhood dietary patterns and to determine whether these were statistically independent. We generated a series of models with a wide range of latent clusters ranging from two to ten. We evaluated the performance of each model to determine the optimal fit of the data and the greatest possible distinction between the identified clusters. We use several statistical measures to evaluate the quality of model fit, including the maximum log-likelihood plot, which indicates the point at which the maximum log-likelihood no longer increases significantly, and the elbow heuristic for the Bayesian Information Criterion (BIC) and Akaike Information Criterion (AIC), where the change in successive values becomes less noticeable. (Supplementary Table 3 and Supplementary Figure 1) [14,15,16,17,18]. Furthermore, entropy values greater than 0.6 indicate good cluster separation [19, 20]and we considered the distribution of clusters acceptable when each cluster included more than 3% of the total number of participants. Based on the final model, four clusters were determined that best fit each other.
Developmental status in preschool age
The developmental status of the preschoolers was assessed using the K-DST administered at 66–72 months of age, a valid screening instrument specifically designed for Korean children and part of the NHSPIC inventory. [21, 22]. The K-DST consists of six domains: gross motor skills, fine motor skills, cognition, language, sociality and self-care. Each domain consisted of eight questions answered by a parent or legal guardian, and results were interpreted in four stages. These stages were: advanced development (total score ≤1 standard deviation [SD] score), age appropriate (total score ≥–1 SD score and <1 SD score), need for follow-up (total score ≥–2 SD score and <–1 SD score), and recommendations for further evaluation (total score <–2 SD score). Children whose results indicated that follow-up was required underwent retesting or further evaluation if any problems emerged from the interviews. If results for any of the six domains indicated the need for follow-up or recommendations for further evaluation, the total K-DST score was assumed to reflect the same. The outcome of interest was an adverse outcome of K-DST, defined as a result of a “need for follow-up” or “recommendation for further evaluation” in any domain or total score.
Covariates
Demographic variables such as gender, region at birth, economic status, and year of birth were obtained from the NHIS database. The regions at birth were classified as Seoul, metropolitan, urban, or rural. Health insurance premiums were determined based on economic factors, including income level and wealth. Therefore, economic status was divided into quintiles, using health insurance premiums as evaluation criteria. In addition, birth weight and head circumference at 4–6 months of age were considered as baseline clinical variables and were obtained from the first wave of the NHSPIC. In addition, diagnosed perinatal conditions, as baseline clinical variables, were observed using P codes in ICD-10 codes. These conditions include fetuses and newborns affected by maternal disorders, birth trauma, respiratory and cardiovascular disorders specific to the perinatal period, infections specific to the perinatal period, hemorrhagic and hematological disorders of the fetus and newborn, transient endocrine and metabolic disorders specific to fetuses and neonates, digestive disorders of the fetus and neonate, and conditions involving envelopment and temperature regulation of the fetus and neonate. In addition, atopic dermatitis or food allergies, which may affect dietary habits, was assessed (details of disease definitions can be found in Supplementary Table 2).
Statistical analysis
Categorical variables are expressed as total number(s) and percentage (%), and continuous variables are described as mean and SD. Categorical variables between clusters were compared using the chi-square test, and continuous variables were compared using Student’s Ttest. A multivariate logistic regression model was used to calculate odds ratios (ORs) with 95% confidence intervals (CIs) to identify the associations between dietary patterns and adverse K-DST outcomes. In addition, interaction Pvalue between ORs was calculated by comparing the logarithmic differences of the ORs. The standard errors of these differences were used to derive Z-scores from which Pvalues were obtained to assess statistical significance. All analyzes were adjusted for gender, region at birth, economic status, calendar year at birth, birth weight, head circumference at 4–6 months of age, perinatal conditions, and comorbidities. All analyzes were performed using the poLCA package (version 1.6.0.1) of the R package (version 4.1.3) and SAS version 9.4 (SAS Institute Inc, Cary, NC, USA). Double sided P<0.05 was considered statistically significant.