Study design and participants

Features of JECS have been published comprehensively in terms of model and design elsewhere27,28,29. In brief, the JECS is a cohort study about birth, funded by the Japan government to reach all of Japan, and it includes many elements that influence the wellbeing and growth of children. The participants were enrolled in person in 15 Regional Centres throughout Japan between January 2011 and March 2014. The current study analyzed the set of data referenced by jecs-qa-20210401 (jecs-ta-20190930), which was available in April 2021. Data from 103,057 pregnancies until 3 years postnatal were included, from which we omitted records with inappropriate data for analysis, such as those with numerous participation, multiple births, miscarriages/still births, post-term delivery, missing data on gestational week, no response to or missing data on the questionnaire regarding infectious diseases, or missing data on covariates. Therefore, 67,282 mother–child pairs remained for the last examination (Fig. 1).

Figure 1
figure 1

Participant flow diagram. Flow diagram of the recruitment and exclusion process for participants.

The authors declared that ethical standards complied with all processes defining this research from relevant national and institutional committees on studies comprising participation from human as per the declaration of Helsinki. The JECS protocol was reviewed and approved by the Ministry of the Environment’s Institutional Review Board on Epidemiological Studies (no. 100910001), the Ethics Committees of all participating institutions, and the Committee of Ethics at Toyama University (no. R2022055). Finally, we obtained written informed consent from all participants.

Data collection

First, we gave the questionnaire for every participant autonomously to be auto-completed at trimesters one, two or three, and at 1 month, 6 months, 1 year, 1.5 year and 2 years after delivery. The questionnaire had many inquiries regarding demographic factors, socioeconomic status, lifestyle, occupation, medical history, physical and mental health, and housing conditions. We transcribed perinatal medical records from each cooperating health care provider, including gestational age and infant physical examinations of the infant at birth and at 1 month of age.

Outcome measures and covariates

The frequency of infection development in infants is regarded as the first outcome. Infections included upper and lower respiratory tract infections, otitis media, urinary tract infection, gastroenteritis, exanthema subitum, herpangina, hand-foot-mouth disease, chickenpox, influenza virus, RSV infection, and adenovirus infections. We assessed the data when the infants were at 1 and 2 years of age, through the following question: “Has your child been diagnosed with any of these infections by a physician since the last survey until the present time?”.

The covariates involved in the analysis were maternal age, prepregnancy BMI, parity, history of maternal allergy, history of any physical disease, marital status, maternal employment, maternal highest education level, annual household income, maternal alcohol intake, maternal smoking history, maternal physical activity, feeding methods, attending a childcare facility at 6 months of age, and receiving Palivizumab prophylaxis.

Statistical analysis

The participants were classified into preterm (< 37 weeks) and full-term (37 till < 42 weeks) birth groups according to the gestational weeks for the statistical analysis. We used t-test and chi-square to evaluate the significant difference between the two groups. We conducted multivariable logistic regression analysis to identify the relationship between preterm birth and the frequency of infant infections by the age of 1 and 2 years.

We used the compulsory entry method to comprise covariates in the multivariable analysis. We adjusted the regression in Model 1 for maternal age, pre-pregnancy BMI, parity, history of maternal allergy, history of any physical disease, marital status, employment, highest education level, annual household income, total energy intake assessed using Food Frequency Questionnaire30, alcohol intake, smoking history, and physical activity31,32. Next, we adjusted Model 2 by adding to the first model, feeding methods, and attending a childcare facility. Finally, we adjusted Model 3 by adding the inquiry regarding Palivizumab administration to the Model 2.

We present the results as crude and aORs with 95% CI, we set the significance at p < 0.05, and used SAS 9.4 (SAS Institute Inc., Cary, North Carolina) for statistical analyses.


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