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Maternal disease factors associated with neonatal jaundice: a case–control study



Neonatal jaundice is common, and despite the considerable medical costs associated with it, there are still few studies on the maternal factors associated with it. Identification of maternal factors associated with neonatal jaundice is very important in terms of prevention, screening and management of neonatal jaundice. The current study aimed to identify maternal disease factors associated with neonatal jaundice.


We compared the maternal disease diagnostic codes during pregnancy (study A) and 1 year before conception (study B) in mothers whose insurance claims data included newborns treated for neonatal jaundice before birth registration via the National Health Insurance Service–National Sample Cohort (control group). To decrease the effect of confounding variables, the neonatal jaundice and control groups were matched at a ratio of 1:10 via propensity score matching using covariates including age and income.


The matched samples for studies A and B included 4,026 and 3,278 (jaundice group: 366 and 298) delivery cases, respectively. In both studies, the jaundice group had a higher proportion of patients who underwent cesarean section than the control group. In study A, other diseases of the digestive system had the highest odds ratio (OR) (K92; adjusted OR: 14.12, 95% confidence interval [CI]: 2.70–82.26). Meanwhile, gastritis and duodenitis had the lowest OR (K29; adjusted OR: 0.39, 95% CI: 0.22–0.69). In study B, salpingitis and oophoritis had the highest OR (N70; adjusted OR: 3.33, 95% CI: 1.59–6.94). Heartburn had the lowest OR (R12; adjusted OR: 0.29, 95% CI:0.12–0.71).


This study identified maternal disease factors correlated with neonatal jaundice during pregnancy and 1 year before conception. Maternal risk factors for neonatal jaundice included syphilis and leiomyoma during pregnancy, and salpingo-oophoritis before pregnancy. The protective factors included infection, inflammatory diseases, and dyspepsia.

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Neonatal jaundice is a common disease [1]. In Korea, it is the most common cause of admission among newborns [2], and medical expenses correlated with jaundice exceeded $10 million in 2012 [3]. Moreover, it still poses global burden particularly in low- and middle-income countries where the immediate assessment of serum bilirubin concentration is challenging and treatment is often delayed [4, 5]. Moreover, recent studies have reported that neonatal jaundice may be a risk factor for pediatric diseases such as asthma [6, 7], autism spectrum disorders [8, 9], attention deficit hyperactivity disorder (ADHD) [10], and epilepsy [11]. Therefore, identifying maternal risk factors for neonatal jaundice is important in providing cost-effective healthcare expenditure and predicting jaundice-associated diseases.

However, recent studies have not assessed these factors and only a few predisposing factors, including maternal age, race, primiparity, teenage pregnancy, diabetes mellitus, Rh incompatibility, ABO incompatibility, oxytocin use during labor, and breastfeeding, were identified [4, 12]. These factors were demographic or pregnancy-related, and there has been no study to identify risk factors for neonatal jaundice related to the mother's own disease.

Many studies so far were cross-sectional studies [13, 14], and nation-wide study focused on neonatal jaundice requiring the treatment in clinical situation has not been done well. In Korea, all citizens are covered by the National Health Insurance [15], and a database for claims data has been established [16]. Moreover, the antenatal care (ANC) coverage of married women approaches about 100%, and the average number of antenatal care visits is over 13 times [17]. Based on the high ANC coverage and the longitudinal data on individuals, the study on the gestation or antenatal period can be conducted appropriately. Hence, the current study aimed to analyze the maternal disease risk factors for neonatal jaundice during pregnancy and 1 year before conception using data from the National Health Insurance claims database.


Data source and variables

The National Health Insurance Service–National Sample Cohort (NHIS-NSC) established by the National Health Insurance Service in South Korea was used [16]. This database (DB) is a representative sample that randomly selected 1 million people, accounting for about 2.2% of the Korean population in 2002. Moreover, it contains sample data obtained from 2002 to 2013 [18]. In this study, the qualification DB and treatment DB of the NHIS-NSC were used. Five variables of the qualification DB (patient ID, sex, year, age, and income rank), five variables of statement data (patient ID, claim number, visit date, principal diagnosis, and additional diagnosis), and three variables of type of disease data (claim number, visit date, and diagnosis) in the treatment DB were utilized. Age is divided into 19 groups from 0 to 85 years old at 5-year intervals (age 0, 1–4, 5–9, …, and over 84). As the age of participants considered in this study was 15–49 years old, it was regrouped subsequently into three groups with ages 15–24, 25–34, and 35–49. The income rank is divided into 11 groups at deciles with medical aid beneficiaries, and it was regrouped into 5 groups at 20% intervals. Variables about diagnosis were distinguished using the Korean Standard Classification of Diseases, version 6 (KCD-6), which is the Korean modified version of the 10th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10).

All methods were carried out in accordance with relevant guidelines and regulations.

Data preprocessing and case selection

The principal diagnosis and additional diagnosis of statement data were integrated into one diagnostic variable and were then merged with the type of disease data according to claim number. Based on the merged diagnosis data, details regarding delivery and age of the participants (15–49 years old) at the year of delivery date were extracted. Then, the pregnancy records of patients who had data about delivery were collected. In cases in which treatment for neonatal jaundice were provided before birth registration, when insurance claims were made by the mother, the diagnosis of neonatal jaundice is included in the mother’s record. Hence, these cases were included in the jaundice group. Cases with diagnostic codes correlated with neonatal jaundice within 4 weeks after the delivery date were included in the jaundice group. Meanwhile, the control group included cases in which the diagnosis of neonatal jaundice was not attached to the mother.

In this study, KCD-6 codes related to delivery [19,20,21], pregnancy [19, 22, 23] and neonatal jaundice were selected to identify each event. Preterm delivery and multiple gestation were defined as at least one record of the related codes within 4 weeks before and after the delivery date.

As the NHIS-NSC included a sample established from the claims data, but not designed for the study, the diagnoses entered in the records did not always indicate new-onset diseases. The same diagnosis codes might have been recorded repeatedly. In such a case, it was counted as one. If the same person delivered several times, each delivery was considered an independent case. The minimum interval from delivery to diagnosis of the next pregnancy was 4 weeks. Considering that periviable birth is defined as delivery during at least 20 weeks of gestation [24], the minimum interval from the previous to the next delivery was 24 weeks. The maximum duration from the diagnosis of pregnancy to delivery was 44 weeks [25]. After cross-joining pregnancy and delivery records, the joint records were listed chronologically. The date of pregnancy diagnosis was defined as the visit date of the first pregnancy record among all pregnancy records. Cases with diagnostic codes related to abortion (O00-O08, pregnancy with abortive outcome) [22, 23, 26] or stillbirths (O36.4, maternal care for intrauterine death; Z37.1, single stillbirth; Z37.4, twins, both stillborn; and Z37.7, other multiple births, all stillborn) [19, 22, 26] up to 4 weeks after the delivery date were excluded. Deliveries assigned with codes including O82 and O84.2 (multiple delivery, all via cesarean section) were considered as cesarean section. Further, deliveries assigned with codes such as O80, O81, O83, and O84.0 (multiple delivery, all spontaneous) and O84.1 (multiple delivery, all using forceps and vacuum extractor) were considered as vaginal. If the two types of delivery were present, cesarean section (O82, O84.2) was prioritized. Cases in which the type of delivery was not identified were excluded.

In this study, two studies, study A and B, were conducted. One was about diseases during ANC (study A), and the second was about diseases 1 year before ANC (study B). In study A, claims data from each pregnancy diagnosis date to day 1 before the delivery date were extracted. Cases that had no record in the ANC period, other than diagnostic codes correlated with pregnancy, were excluded to identify possible maternal risk factors. In study B, claims data from 1 year before each pregnancy diagnosis date to day 1 before the pregnancy diagnosis date were extracted. In the analyses of both two studies, the only first three characters of the diagnosis codes were used.

Statistical analysis

The two-sided Fisher’s exact test with 95% CI for the categorical variables was performed. The t-test was used to assess continuous variables. To decrease the effect of confounding variables, the jaundice and control groups were matched at a ratio of 1:10 via propensity score matching (PSM) with nearest neighbor matching. Age at the time of delivery and income at the time of pregnancy diagnosis were considered covariates. MatchIt package [27] was used to perform PSM. The results obtained by repeating PSM 1,000 times by randomly shuffling the order of records were used for the analysis of matched samples. The average number of cases, odds ratio, and p-value were calculated only for significant findings from the 1,000 results obtained using PSM. If the odds ratio was infinite, it was excluded from the average. Diseases that have more than 900 significant results, with a mean odds ratio of > 1 and a mean p-value of < 0.05, were considered a risk factor. Moreover, those with a mean odds ratio of < 1 and a mean p-value of < 0.05 were considered a protective factor. Conditional logistic regression analyses, adjusted for preterm delivery, delivery mode, multiple gestation and ANC duration, were performed for the diseases which have more than 900 significant results in the univariable analyses. Survival package [28] was used to perform conditional logistic regression analyses.

Results with a lower bound of > 1 or an upper bound of < 1 and a p-value of < 0.05 were considered significant. igraph package [29] was used to make a network image for the identified risk/protective factors. R (version 3.6.2) [30] was used in all analyses.


Demographic characteristics of the participants

Figure 1 shows the flowchart of the case selection process. 555,474 of 560,645 women, included in NHIS-NSC from 2002 to 2013, had significant claims data about diagnoses. 67,967 of those 555,474 women had claims data related to diagnosis of delivery and were 15 to 49 years old at the time of the delivery. 65,442 participants of them, who had delivery records, had claims data related to the diagnosis of pregnancy. With pairing the delivery and pregnancy records, 91,477 delivery cases (64,723 women), satisfied with the time intervals which were defined as inclusion criteria in this study, were identified. 116 cases of 91,477 delivery cases were excluded as the delivery modes were not identified, and 131 cases of abortion or stillbirth were excluded subsequently. The participants with several delivery cases were included in each step unless all cases were excluded. Among 91,230 delivery cases, 5,111 cases incomplete on qualification DB around the gestation period and 7,800 cases that had no diagnosis records except pregnancy or delivery during ANC were excluded to consist of the sample for study A. For study B, 12,691 incomplete cases and 4,418 cases with no diagnosis records during ANC were excluded.

Fig. 1
figure 1

Flowchart of the inclusion and exclusion process. ANC, antenatal care; PSM, propensity score matching

The sample in study A included 78,319 cases (n = 57,718). Among them, 366 cases (n = 364) were included in the jaundice group and 77,953 cases (n = 57,517) in the control group. The sample in study B had 74,121 cases (n = 54,787). Among them, 298 cases (n = 296) were included in the jaundice group and 73,823 cases (n = 54,620) in the control group. The n value indicated the number of mothers, not delivery cases. If a mother has delivered several times, it can be included in both the jaundice and control groups. Thus, the total number of patients in the jaundice and control groups did not correspond to the total population. The jaundice group accounted for 0.47% (366 in 78,319 cases) and 0.40% (298 in 74,121 cases) of all delivery cases in studies A and B, respectively. There was a significant difference in terms of income at the time of pregnancy diagnosis, multiple gestation, and ANC duration between the two groups in study A, but not in study B. However, the Cochran–Armitage trend test result (chi-square test for trend in proportion) for income was significant in studies A and B (p-value = 0.002 and 0.010, respectively). There was a significant difference in the mode of delivery between the two groups in studies A and B (Table 1).

Table 1 Demographic data of the unmatched samples

The matched sample for study A had 4,026 cases (jaundice group: 366, control group: 3,660), and that for study B had 3,278 cases (jaundice group: 298, control group: 2,980). All the 1,000 matched samples significantly differed in terms of the type of delivery in both two studies (Table 2). There was also a significant difference in ANC duration in all the 1,000 matched samples of study A.

Table 2 Demographic data of the matched samples

Odds ratios for each diagnosis code in the unmatched samples

Tables 3 and 4 show the unadjusted odds ratios for neonatal jaundice according to disease that showed significant results in unmatched samples for studies A and B, respectively.

Table 3 Maternal diseases during antenatal care and their unadjusted odds ratio for neonatal jaundice obtained from the unmatched samples (study A)
Table 4 Maternal diseases during 1 year before antenatal care and their unadjusted odds ratio for neonatal jaundice obtained from the unmatched samples (study B)

In study A, obstetrical tetanus (A34) had the largest OR (212.33, 95% CI: 2.71–14,121.54). Fever of other and unknown origin (R50) had the lowest OR (0.23, 95% CI: 0.03–0.84).

In study B, polyarteritis nodosa and related conditions (M30) had the largest OR (95% CI: 6.35-infinite). However, there was only case in the jaundice group. Pain associated with micturition (R30) had the lowest OR (0, 95% CI: 0–0.92).

Acute bronchitis (J20), vasomotor and allergic rhinitis (J30), gastritis and duodenitis (K29), dyspepsia (K30), and alopecia areata (L63) showed significance in the unmatched samples of the two studies. Among them, the OR of alopecia areata (L63) was > 1.

Risk and protective factors in the matched samples

For diseases that showed significance more than 900 times in 1000 times of PSM, the average number of cases and average odds ratio are depicted in Fig. 2 (network image) and Table 5. Adjusted ORs were calculated for the diseases which have more than 900 significant results in the univariable analyses, as the primary outcome of this study.

Fig. 2
figure 2

Disease network image about maternal risk factors and protective factors for neonatal jaundice. A Maternal diseases during ANC associated with neonatal jaundice (Study A). B Maternal diseases during 1 year before ANC associated with neonatal jaundice (Study B). C Index. Risk factors are illustrated as red lines and protective factors as blue lines. The average odds ratio is represented by number on the line and the average number of cases as the number in the circle. The major classification of diagnosis codes is represented in a different color. The circle size is proportional to the number of cases. The edge width is proportional to the odds ratio in the case of risk factors and inversely proportional to the odds ratio in the case of protective factors. ANC, antenatal care; A53, other and unspecified syphilis; D25, leiomyoma of the uterus; D62, acute posthemorrhagic anemia; J03, acute tonsillitis; J20, acute bronchitis; J30, vasomotor and allergic rhinitis; J34, other disorders of the nose and nasal sinuses; K29, gastritis and duodenitis; K30, dyspepsia; K92, other diseases of the digestive system; N70, salpingitis and oophoritis; R12, heartburn; Z48, other surgical follow-up care. * K92, 7.89; Z48, 15.82

Table 5 Maternal diseases associated with neonatal jaundice identified from the matched samples in studies A and B

In study A, among the probable risk factors, the disease with the highest OR was other diseases of digestive system (K92; adjusted OR: 14.12, 95% CI: 2.70–82.26), which was present in 0.20% of all matched cases, and the incidence of leiomyoma of the uterus was the highest (D25; adjusted OR: 3.22, 95% CI: 1.59–6.52), accounting for 1.14% of all matched cases. Among the probable protective factors, gastritis and duodenitis had the lowest OR (K29; adjusted OR: 0.39, 95% CI: 0.22–0.69), accounting for 8.43% of all matched cases, and the incidence of vasomotor and allergic rhinitis was the highest (J30; adjusted OR: 0.58, 95% CI: 0.37–0.92), which accounted for 9.46% of all matched cases (Fig. 2a).

In study B, the possible risk factor was salpingitis and oophoritis (N70; adjusted OR: 3.33, 95% CI: 1.59–6.94), which accounted for 1.26% of all matched cases. Among the probable protective factors, heartburn had lowest OR (R12; adjusted OR: 0.29, 95% CI: 0.12–0.71), which accounted for 5.29% of all cases, and the incidence of gastritis and duodenitis (K29; adjusted OR: 0.75, 95% CI: 0.58–0.95) was the highest, which accounted for 44.15% of all cases (Fig. 2b).

There was no common risk factor in both studies. However, the common protective factors included vasomotor and allergic rhinitis (J30) and gastritis and duodenitis (K29).


Many diseases were identified as significant disease factors from unmatched samples in Table 3 and 4. Because these results were obtained from the unmatched samples, bias should be considered to interpret results. However, diseases with a genetic factor, such as disorders of glycoprotein metabolism from the unmatched sample of study B, may be attributed to the low incidence of these diseases. Therefore, these rare diseases are needed to be verified in a larger population. Meanwhile, some diseases identified from unmatched samples showed significance in hundreds of matched samples, but less than 900. These were not considered as risk factors in this study but may have a weak association with neonatal jaundice. For example, among the disease with OR > 1, alopecia areata showed significance in more than 700 matched samples of study B, which indicates that it may be a maternal risk factor before ANC. In previous studies, alopecia areata was associated with oxidative stress [31, 32]. A decrease in oxidative stress is associated with low serum bilirubin levels [33, 34].

The risk factors for neonatal jaundice included syphilis, surgical follow-up care, leiomyoma of uterus, and other diseases of the digestive system during ANC. Based on previous studies [35, 36], congenital syphilis increases the risk of neonatal jaundice. According to the significant results of surgical follow-up during pregnancy, the type, purpose, and timing of surgery should be identified to explain the relationship between surgical follow-up and neonatal jaundice, which would be limitation of this study using claims data. Leiomyoma can be an extension of the association between leiomyoma as well as preterm birth and cesarean delivery [37, 38].

Other diseases of the digestive system (K92) include hematemesis, melena, and gastrointestinal hemorrhage. However, considering the medical practice of registering a diagnosis, different diseases of the digestive system were included, and the incidence was low. Hence, the significant was low.

Most protective factors identified in study A were associated with infection and inflammation. Recent studies have shown the inverse association between bilirubin and inflammation [39,40,41]. Thus, inflammation may be associated with the low bilirubin levels, which could decrease the levels of unconjugated bilirubin transferred to neonates.

The pre-pregnancy maternal disease associated with neonatal jaundice was salpingo-oophoritis. Gastritis, dyspepsia, and heartburn are diagnoses associated with the gastrointestinal system. Notably, the OR value for neonatal jaundice was < 1.

The proportion of neonatal jaundice to total delivery cases was 0.40%–0.47%, which was different from its known incidence (30%–80%) [42,43,44,45,46]. This finding could be attributed to the fact that this study was based on neonatal jaundice recorded in the mothers’ claims data. Although prematurity is a risk factor for neonatal jaundice, there was no significant difference in terms of its occurrence between both groups [4, 12]. Since newborns born prematurely are admitted to the neonatal intensive care unit, the diagnosis of neonatal jaundice is rarely applied to the mother. Therefore, jaundice in premature infants may not have been well reflected in this study. Contrary to a known risk factor for neonatal jaundice, vaginal delivery was less common in the jaundice group [47,48,49]. One possible reason for that may be the differences in the gut microbiota of newborns according to the type of delivery. Infants born via cesarean section have a lower number of Bifidobacterium and Bacteroides than infants born via vaginal delivery [50,51,52]. Bacteroides reduces unconjugated bilirubin to urobilinoids [53], and an association between the decreased number of Bifidobacterium and the elevated levels of bilirubin has been reported [50]. In terms of gut microbiota, cesarean section can be a potential risk factor for neonatal jaundice. The duration of ANC in the jaundice group of study A was significantly short, thereby indicating differences in pregnancy duration or delayed pregnancy diagnosis. A previous study reported the association between the late recognition of pregnancy and adverse outcomes such as neonatal intensive care admission [54].

Identification of the maternal gestational period or pre-pregnancy disease associated with neonatal jaundice may be helpful in counseling mothers preparing for pregnancy or pregnant mothers. For mothers with risk factors, it can predict jaundice for future babies and provide useful information for things to keep in mind after birth. In addition, neonatal jaundice can be prevented through prevention and management of maternal diseases related to neonatal jaundice.

Integration with research on maternal diseases may lead to the development of prenatal care programs to prevent neonatal jaundice. Moreover, it is thought that a more detailed correlation can be derived if the study is conducted including the maternal medication (especially, folic acid and iron, which are essential to take before and during pregnancy). The possible risk factors of certain child diseases including maternal disorders must be assessed from a long-term perspective. Therefore, follow-up studies with a disease network connecting diseases (from maternal to the child disorders) must be performed to assess the association between neonatal jaundice and other pediatric and maternal diseases.

This is the first study to analyze the association between maternal disease not related to pregnancy and neonatal jaundice. Maternal risk factors suggested in this study, including syphilis, leiomyoma, and salpingo-oophoritis, are differentiated from well-known risk factors for neonatal jaundice, such as diabetes mellitus, and suggest there may be unknown pathophysiology. While machine learning-based studies on the prediction of neonatal jaundice required information of neonates such as total serum bilirubin [55, 56], the risk factors identified from the method used in this study can be evaluated with only maternal history before pregnancy or delivery. Another strength includes a large sample size based on health insurance data that covers almost all citizens.

This study had several limitations associated with the use of claims data. The diagnostic code for insurance claims could be differ from the actual diagnosis [57,58,59]. To ensure data integrity, although mild, the study included mothers who were insured for diseases other than those associated with pregnancy and delivery. The diagnosis codes used in this study were based on KCD-6, the Korean modified version of ICD-10, and numerous medical conditions, including symptoms, are included in the codes. Therefore, statistically significant diagnosis codes about symptoms obtained in this study, such as dyspepsia and heartburn, do not represent the diagnosis of a specific disease. The details about the surgery what other surgical follow-up care referred could not be identified precisely from claims data, which was also a limitation of this study.


This study has identified that maternal risk factors for neonatal jaundice were syphilis and leiomyoma during pregnancy, and salpingo-oophoritis before pregnancy, and protective factors were infection and inflammatory diseases, and dyspepsia. This has shown significant information that can be used for risk management and the prediction and prevention of neonatal jaundice before or during pregnancy. Furthermore it is necessary to study not only maternal diseases related to neonatal jaundice, but also studies including maternal medication history and long-term prognosis.

Availability of data and materials

The datasets generated and/or analyzed during the current study are not publicly available due to the regulation that the National Health Insurance Service–National Sample Cohort (NHIS-NSC) data is not provided directly to researchers and can only be accessed via the NHIS analysis system.


  1. Jaundice in newborn babies under 28 days: Clinical guideline [CG98] []

  2. Kim MJ. Readmission of late preterm infants after discharge from nursery. Clin Exp Pediatr. 2009;52(8):888–92.

    Google Scholar 

  3. 2012 National Health Insurance Statistics Yearbook []

  4. Olusanya BO, Kaplan M, Hansen TWR. Neonatal hyperbilirubinaemia: a global perspective. Lancet Child Adolesc Health. 2018;2(8):610–20.

    Article  PubMed  Google Scholar 

  5. Slusher TM, Zamora TG, Appiah D, Stanke JU, Strand MA, Lee BW, Richardson SB, Keating EM, Siddappa AM, Olusanya BO. Burden of severe neonatal jaundice: a systematic review and meta-analysis. BMJ Paediatr Open. 2017;1(1):e000105.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Kuzniewicz MW, Niki H, Walsh EM, McCulloch CE, Newman TB. Hyperbilirubinemia, phototherapy, and childhood asthma. Pediatrics. 2018;142(4):e20180662.

    Article  PubMed  Google Scholar 

  7. Ku MS, Sun HL, Sheu JN, Lee HS, Yang SF, Lue KH. Neonatal jaundice is a risk factor for childhood asthma: a retrospective cohort study. Pediatr Allergy Immunol. 2012;23(7):623–8.

    Article  PubMed  Google Scholar 

  8. Lozada LE, Nylund CM, Gorman GH, Hisle-Gorman E, Erdie-Lalena CR, Kuehn D. Association of autism spectrum disorders with neonatal hyperbilirubinemia. Glob Pediatr Health. 2015;2:2333794x15596518.

    PubMed  PubMed Central  Google Scholar 

  9. Maimburg RD, Bech BH, Vaeth M, Møller-Madsen B, Olsen J. Neonatal jaundice, autism, and other disorders of psychological development. Pediatrics. 2010;126(5):872–8.

    Article  PubMed  Google Scholar 

  10. Wei CC, Chang CH, Lin CL, Chang SN, Li TC, Kao CH. Neonatal jaundice and increased risk of attention-deficit hyperactivity disorder: a population-based cohort study. J Child Psychol Psychiatry. 2015;56(4):460–7.

    Article  PubMed  Google Scholar 

  11. Maimburg RD, Olsen J, Sun Y. Neonatal hyperbilirubinemia and the risk of febrile seizures and childhood epilepsy. Epilepsy Res. 2016;124:67–72.

    Article  CAS  PubMed  Google Scholar 

  12. Dennery PA, Seidman DS, Stevenson DK. Neonatal hyperbilirubinemia. N Engl J Med. 2001;344(8):581–90.

    Article  CAS  PubMed  Google Scholar 

  13. Boskabadi H, Rakhshanizadeh F, Zakerihamidi M. Evaluation of maternal risk factors in neonatal hyperbilirubinemia. Arch Iran Med. 2020;23(2):128–40.

    PubMed  Google Scholar 

  14. Olusanya BO, Osibanjo FB, Slusher TM. Risk factors for severe neonatal hyperbilirubinemia in low and middle-income countries: a systematic review and meta-analysis. PloS One. 2015;10(2):e0117229.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Kwon S. Thirty years of national health insurance in South Korea: lessons for achieving universal health care coverage. Health Policy Plan. 2009;24(1):63–71.

    Article  PubMed  Google Scholar 

  16. Cheol SS, Kim YY, Khang YH, Heon Park J, Kang HJ, Lee H, Do CH, Song JS, Hyon Bang J, Ha S, et al. Data resource profile: the national health information database of the national health insurance service in South Korea. Int J Epidemiol. 2017;46(3):799–800.

    Google Scholar 

  17. Kim S-K, Kim Y-K, Kim H, Park J, Son C, Choi Y, Kim Y, Lee G, Yoon A. The 2012 national survey on fertility, family health & welfare in Korea. Seoul: Korea Institute for Health and Social Affairs; 2012. p. 2012–54.

    Google Scholar 

  18. Lee J, Lee JS, Park SH, Shin SA, Kim K. Cohort profile: The National Health Insurance Service-National Sample Cohort (NHIS-NSC), South Korea. Int J Epidemiol. 2017;46(2):e15.

    PubMed  Google Scholar 

  19. Moon HY, Kim MR, Hwang DS, Jang JB, Lee J, Shin JS, Ha I, Lee YJ. Safety of acupuncture during pregnancy: a retrospective cohort study in Korea. BJOG. 2020;127(1):79–86.

    Article  PubMed  Google Scholar 

  20. Nam JY, Choi Y, Kim J, Cho KH, Park EC. The synergistic effect of breastfeeding discontinuation and cesarean section delivery on postpartum depression: a nationwide population-based cohort study in Korea. J Affect Disord. 2017;218:53–8.

    Article  PubMed  Google Scholar 

  21. Lee SY, Lee SW, Kong IG, Oh DJ, Choi HG. Pregnancy does not increase the risk of sudden sensorineural hearing loss: a national cohort study. Laryngoscope. 2020;130(4):E237–42.

    Article  PubMed  Google Scholar 

  22. Park KY, Kwon HJ, Wie JH, Lee HH, Cho SB, Kim BJ, Bae JM. Pregnancy outcomes in patients with vitiligo: a nationwide population-based cohort study from Korea. J Am Acad Dermatol. 2018;79(5):836–42.

    Article  PubMed  Google Scholar 

  23. Yuk JS, Shin JY, Park WI, Kim DW, Shin JW, Lee JH. Association between pregnancy and adnexal torsion: a population-based, matched case-control study. Medicine (Baltimore). 2016;95(24):e3861.

    Article  Google Scholar 

  24. American College of Obstetricians and Gynecologists, Society for Maternal-Fetal Medicine. Obstetric care consensus no. 6: periviable birth. Obstet Gynecol. 2017;130(4):e187-199.

    Article  Google Scholar 

  25. American College of Obstetricians and Gynecologists. Practice bulletin no. 146: management of late-term and postterm pregnancies. Obstet Gynecol. 2014;124(2 Pt 1):390–6.

    Google Scholar 

  26. Kwak MY, Lee SM, Lee TH, Eun SJ, Lee JY, Kim Y. Accessibility of prenatal care can affect inequitable health outcomes of pregnant women living in obstetric care underserved areas: a nationwide population-based study. J Korean Med Sci. 2019;34(1):1–9.

    Article  Google Scholar 

  27. Ho D, Imai K, King G, Stuart EA. MatchIt: nonparametric preprocessing for parametric causal inference. J Stat Softw. 2011;42(8):1–28.

    Article  Google Scholar 

  28. Therneau T: A Package for Survival Analysis in R. R Package Version 3.2–13.(2021). []

  29. The igraph software package for complex network research []

  30. R: A language and environment for statistical computing []

  31. Bakry OA, Elshazly RM, Shoeib MA, Gooda A. Oxidative stress in alopecia areata: a case-control study. Am J Clin Dermatol. 2014;15(1):57–64.

    Article  PubMed  Google Scholar 

  32. Prie BE, Voiculescu VM, Ionescu-Bozdog OB, Petrutescu B, Iosif L, Gaman LE, Clatici VG, Stoian I, Giurcaneanu C. Oxidative stress and alopecia areata. J Med Life. 2015;8 Spec Issue(Spec Issue):43–6.

    CAS  PubMed  Google Scholar 

  33. Dani C, Poggi C, Pratesi S. Bilirubin and oxidative stress in term and preterm infants. Free Radic Res. 2019;53(1):2–7.

    Article  CAS  PubMed  Google Scholar 

  34. Dani C, Martelli E, Bertini G, Pezzati M, Filippi L, Rossetti M, Rizzuti G, Rubaltelli FF. Plasma bilirubin level and oxidative stress in preterm infants. Arch Dis Child Fetal Neonatal Ed. 2003;88(2):F119-123.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Chawla V, Pandit PB, Nkrumah FK. Congenital syphilis in the newborn. Arch Dis Child. 1988;63(11):1393–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Adachi E, Koibuchi T, Okame M, Sato H, Kikuchi T, Koga M, Nakamura H, Iwamoto A, Fujii T. Liver dysfunction in patients with early syphilis: a retrospective study. J Infect Chemother. 2013;19(1):180–2.

    Article  PubMed  Google Scholar 

  37. Lai J, Caughey AB, Qidwai GI, Jacoby AF. Neonatal outcomes in women with sonographically identified uterine leiomyomata. J Matern Fetal Neonatal Med. 2012;25(6):710–3.

    Article  PubMed  Google Scholar 

  38. Coronado GD, Marshall LM, Schwartz SM. Complications in pregnancy, labor, and delivery with uterine leiomyomas: a population-based study. Obstet Gynecol. 2000;95(5):764–9.

    CAS  PubMed  Google Scholar 

  39. Vítek L, Tiribelli C. Bilirubin, Intestinal Integrity, the Microbiome, and Inflammation. N Engl J Med. 2020;383(7):684–6.

    Article  PubMed  Google Scholar 

  40. Zheng JD, He Y, Yu HY, Liu YL, Ge YX, Li XT, Li X, Wang Y, Guo MR, Qu YL, et al. Unconjugated bilirubin alleviates experimental ulcerative colitis by regulating intestinal barrier function and immune inflammation. World J Gastroenterol. 2019;25(15):1865–78.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Vogel ME, Zucker SD. Bilirubin acts as an endogenous regulator of inflammation by disrupting adhesion molecule-mediated leukocyte migration. Inflamm Cell Signal. 2016;3(1):e1178.

    PubMed  PubMed Central  Google Scholar 

  42. Tikmani SS, Warraich HJ, Abbasi F, Rizvi A, Darmstadt GL, Zaidi AK. Incidence of neonatal hyperbilirubinemia: a population-based prospective study in Pakistan. Trop Med Int Health. 2010;15(5):502–7.

    Article  CAS  PubMed  Google Scholar 

  43. Scrafford CG, Mullany LC, Katz J, Khatry SK, LeClerq SC, Darmstadt GL, Tielsch JM. Incidence of and risk factors for neonatal jaundice among newborns in southern Nepal. Trop Med Int Health. 2013;18(11):1317–28.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Burke BL, Robbins JM, Bird TM, Hobbs CA, Nesmith C, Tilford JM. Trends in hospitalizations for neonatal jaundice and kernicterus in the United States, 1988–2005. Pediatrics. 2009;123(2):524–32.

    Article  PubMed  Google Scholar 

  45. Final Results of Birth Statistics in 2012 []

  46. Health Insurance Review & Assessment Service []

  47. Brits H, Adendorff J, Huisamen D, Beukes D, Botha K, Herbst H, Joubert G. The prevalence of neonatal jaundice and risk factors in healthy term neonates at National District Hospital in Bloemfontein. Afr J Prim Health Care Fam Med. 2018;10(1):e1–6.

    Article  PubMed  Google Scholar 

  48. Gregory KD, Jackson S, Korst L, Fridman M. Cesarean versus vaginal delivery: whose risks? Whose benefits? Am J Perinatol. 2012;29(1):7–18.

    Article  PubMed  Google Scholar 

  49. Benedetto C, Marozio L, Prandi G, Roccia A, Blefari S, Fabris C. Short-term maternal and neonatal outcomes by mode of delivery. A case-controlled study. Eur J Obstet Gynecol Reprod Biol. 2007;135(1):35–40.

    Article  PubMed  Google Scholar 

  50. Zhou S, Wang Z, He F, Qiu H, Wang Y, Wang H, Zhou J, Zhou J, Cheng G, Zhou W, et al. Association of serum bilirubin in newborns affected by jaundice with gut microbiota dysbiosis. J Nutr Biochem. 2019;63:54–61.

    Article  CAS  PubMed  Google Scholar 

  51. Biasucci G, Rubini M, Riboni S, Morelli L, Bessi E, Retetangos C. Mode of delivery affects the bacterial community in the newborn gut. Early Hum Dev. 2010;86(Suppl 1):13–5.

    Article  PubMed  Google Scholar 

  52. Penders J, Thijs C, Vink C, Stelma FF, Snijders B, Kummeling I, van den Brandt PA, Stobberingh EE. Factors influencing the composition of the intestinal microbiota in early infancy. Pediatrics. 2006;118(2):511–21.

    Article  Google Scholar 

  53. Tiribelli C, Ostrow JD. Intestinal flora and bilirubin. J Hepatol. 2005;42(2):170–2.

    Article  PubMed  Google Scholar 

  54. Ayoola AB, Stommel M, Nettleman MD. Late recognition of pregnancy as a predictor of adverse birth outcomes. Am J Obstet Gynecol. 2009;201(2):156.e151-156.

    Article  Google Scholar 

  55. Daunhawer I, Kasser S, Koch G, Sieber L, Cakal H, Tütsch J, Pfister M, Wellmann S, Vogt JE. Enhanced early prediction of clinically relevant neonatal hyperbilirubinemia with machine learning. Pediatr Res. 2019;86(1):122–7.

    Article  PubMed  Google Scholar 

  56. Ferreira D, Oliveira A, Freitas A. Applying data mining techniques to improve diagnosis in neonatal jaundice. BMC Med Inform Decis Mak. 2012;12(1):1–6.

    Article  Google Scholar 

  57. Strom BL. Data validity issues in using claims data. Pharmacoepidemiol Drug Saf. 2001;10(5):389–92.

    Article  CAS  PubMed  Google Scholar 

  58. Lin CC, Lai MS, Syu CY, Chang SC, Tseng FY. Accuracy of diabetes diagnosis in health insurance claims data in Taiwan. J Formos Med Assoc. 2005;104(3):157–63.

    PubMed  Google Scholar 

  59. Iqbal SA, Isenhour CJ, Mazurek G, Truman BI. Diagnostic code agreement for electronic health records and claims data for tuberculosis. Int J Tuberc Lung Dis. 2020;24(7):706–11.

    Article  CAS  PubMed  Google Scholar 

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This work was supported by grants from Institute of Information & communications Technology Planning & Evaluation (IITP) funded by the Korea government (MSIT) (No. 2019000224, Development of Information Protection Source Technology), Brain Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT & Future Planning (No. 2019M3C7A1032262), Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No.2020R1F1A1068423) and the Bio Industry Technology Development Program(No. 20015086) from the Ministry of Trade, Industry & Energy(MOTIE, Korea), Information and Communications Promotion Fund through the National IT Industry Promotion. Agency (NIPA), funded by the Ministry of Science and ICT (MSIT), Republic of Korea (No. 20002033324071212100101AU), Korea University Guro Hospital (KOREA RESEARCH-DRIVEN HOSPITAL) funded by Korea University Medicine (No. K2117371) and (HI21C1779) from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea.

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Authors and Affiliations



YJY and JWC are co-first authors and contributed equally. They conceived and designed the study. YJY, JWC, MHL, KHK, HMR and HWH performed the data collection and curation. YJY and JWC performed data analysis. YJY and JWC conducted statistical analyses. YJY, JWC, MHL, KHK, HMR and HWH performed data interpretation and wrote the paper. All authors reviewed the final manuscript. The author(s) read and approved the final manuscript.

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Correspondence to Hyun Wook Han.

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This study was approved by the institutional review board of CHA Bundang Medical Center, CHA University (CHAMC 2021–04-049). The need for informed consent from the participants was waived by the institutional review board of CHA Bundang Medical Center, CHA University as the study used anonymized claims data.

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The authors declare that they have no competing interests.

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Yu, Y., Choi, J., Lee, M.H. et al. Maternal disease factors associated with neonatal jaundice: a case–control study. BMC Pregnancy Childbirth 22, 247 (2022).

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