by Study design and data source
This study used a composite database from the National Health Insurance Service (NHIS) and the National Health Screening Program for Infants and Children (NHSPIC) in Korea. The NHIS is a single insurance system that covers almost the entire population of Korea, making it a representative source of data. The NHIS database provides baseline demographic characteristics, such as date of birth, sex, 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 to undergo seven rounds of the NHSPIC, which were conducted at distinct age intervals from four to 72 months of age. The rounds were scheduled as follows: 1st (4–6 months old), 2nd (9–12 months old), 3rd (18–24 months old), 4th (30–36 months old age), 5th (42–48 months old). ), 6th (54-60 months old), and 7th (66-72 months old). The NHSPIC survey includes a general health questionnaire, the Korean Developmental Screening Test (K-DST), an anthropometric examination, and a physical examination. [13].
The de-identified individual data was only used for research purposes. Patient consent was not required as this study was based on de-identified 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 performed in accordance with relevant guidelines and regulations.
Study the population
The study population is shown in Figure 1. We counted from the 2,395,966 Korean children born between 2008 and 2012 those children who received each round of the NHSPIC from the first to the fourth round, answered the questionnaire appropriately (n = 408,077) and got the K-DST correct in the 7th round (n = 714,364). In total, 180,563 children met the inclusion criteria. Subsequently, children who met the following criteria were excluded: (1) died (n = 52), (2) birth weight <2.5 kg (n= 8029) or > 4 kg (n= 6193), (3) multiple births (n= 1899), (4) pre-birth (n= 6427), (5) diagnosis of newborn disorders related to gestational age and fetal growth (n= 7139), convulsions of the newborn / disturbance of the cerebral status of the newborn (n= 456), or congenital malformations / chromosomal abnormalities (n= 29,130), (6) admission to an intensive care unit over 4 days before 1 year of age (n= 5458), and (7) who received general anesthesia before 1 year of age (n= 2615) and for >5 days before 2 years of age excluded. Finally, we registered 133,243 eligible children.
Dietary patterns in young youth
Information on feeding patterns from infancy to 3 years of age was obtained from the NHSPIC questionnaire, which included the first round through the fourth round. The details of the questionnaire were described in Supplementary Table 1. Specifically, the first round of the NHSPIC, conducted at the age of 4-6 months, includes questions about the types of milk consumed by infants. The second round, which is carried out at the age of 9-12 months, includes questions about the start time of introducing complementary foods, the frequency of intake of complementary foods, and the ingredients included in complementary foods. The third round, conducted at the age of 18-24 months, included questions about the frequency of consumption of fruit juice or sweetened drinks. Finally, the fourth survey, conducted at age 30-36 months, includes questions about frequency of consumption of fruit juice or sweetened beverages, frequency of meals, and milk intake.
clusters by dietary patterns during early childhood
Multivariate Latent Variable Class Analysis (POLCA) was used to identify groups of similar cases within the manifest variables for dietary patterns during childhood and to determine whether they were statistically independent. We generated a series of models with a varying range of latent clusters, from two to ten. We evaluated the performance of each model to determine the optimal fit of the data and the greatest possible discrimination between the identified clusters. We use a number of statistical measures to assess model fit, including the maximum log-likelihood plot, which indicates the point at which the log-likelihood ceases to increase significantly, and the elbow heuristic for the Bayesian Information Criterion (BIC). and Akaike’s Information Criterion (AIC), where the change in successive values is less significant. (Supplementary Table 3 and Supplementary Fig. 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 to be acceptable when each cluster comprised more than 3% of the total participants. Based on the final model, four clusters were determined to provide the best fit.
Developmental status at preschool age
The developmental status of preschool children was assessed using the K-DST administered at age 66-72 months, which is a valid screening tool specifically designed for Korean children and is part of the NHSPIC inventory. [21, 22]. There are six areas in the K-DST: gross motor, fine motor, cognition, language, sociality and self-care. There were eight questions in each area that were answered by a parent or legal guardian, and the 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 assessment (total score <–2 SD scores). Repetitions or further evaluation were carried out for children whose results showed that they were necessary if the interviews showed that they had problems. If the results of any of the six areas indicated a need for follow-up or recommendations for further evaluation, the total K-DST score was considered consistent. The outcome of interest was an unfavorable K-DST outcome, defined as a result of “need for follow-up” or “recommendation for further assessment” in each 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 were classified when they were born as Seoul, city, urban or rural. Health insurance premiums are set based on economic factors, including income level and assets. Therefore, economic status was categorized 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 round of the NHSPIC. In addition, diagnosed perinatal conditions, as baseline clinical variables, were observed using P codes in ICD-10 codes. These conditions included fetuses and newborns affected by maternal conditions, birth trauma, respiratory and cardiovascular disorders specific to the perinatal period, infections specific to the perinatal period, hemorrhagic and haematological disorders of the fetus and the newborn, temporary endocrine and metabolic disorders specific to the fetus and newborn, disorders of the digestive system of the fetus and newborn, and conditions related to the integument and temperature regulation of the fetus and newborn. In addition, atopic dermatitis or food allergies, which can influence dietary habits, were assessed (disease definition details are provided in Supplementary Table 2).
Statistical analysis
Categorical variables are expressed as total number (n) and percentage (%), and continuous variables are described as mean and SD. Categorical variables were compared between clusters using the chi-square test, and continuous variables were compared using the 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 unfavorable 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 obtain Z-scores, from which pValues were obtained to assess statistical significance. All analyzes were adjusted for sex, 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). Two sides p< 0.05 was considered statistically significant.
