Abstract
Background:
Maternal health during pregnancy appears to impact childhood cancer risk, yet comprehensive studies remain scarce. This study investigates associations between childhood cancer and multiple maternal comorbidities.
Methods:
A population-based case-control study was conducted using Danish national registers, with maternal health conditions identified from the National Patient Register and Medical Births Registry. We employed the Obstetric Comorbidity Index using ICD-8 and ICD-10 codes. The study population (1977–2013) included 6419 cases and 160484 matched controls. Conditional logistic regression estimated pediatric cancer risk.
Results:
A maternal comorbidity score of one or more was linked to acute lymphocytic leukemia (ALL; OR = 1.07, 95%CI: 1.03–1.12), retinoblastoma (OR = 1.08, 95%CI: 0.94–1.23), and rhabdomyosarcoma (OR = 1.11, 95%CI: 0.98–1.26). Pre-existing diabetes (OR = 1.82, 95%CI: 1.28–2.59), previous cesarean delivery (OR = 1.20, 95%CI: 1.02–1.41), and gestational hypertension (OR = 1.27, 95%CI: 1.01–1.59) were associated with increased cancer risks in offspring. Slightly higher risks were noted for non-Hodgkin lymphoma (OR = 1.05, 95%CI: 0.96–1.16) and Burkitt lymphoma (OR = 1.08, 95%CI: 0.92–1.27) among children whose mothers had one or more comorbidities.
Conclusion:
An obstetric comorbidity index can predict childhood cancer risk. This highlights the need for targeted interventions to reduce adverse health consequences for offspring.
Keywords: Maternal comorbidity, Childhood cancer, Denmark, Obstetric comorbidity index, Case-control
Background
Childhood cancer refers to a diverse array of malignancies, comprising a variety of distinct diseases that differ in their age occurrence, causes, treatment approaches, supportive care, survival rates, and the potential risks of acute toxic side effects and long-term complications [1]. A growing number of epidemiologic studies on maternal health during pregnancy and childhood cancer have emerged over recent years, suggesting the health of mothers during pregnancy is implicated in the etiology of pediatric cancer [2–5]. Nonetheless, the evidence remains sparse given the rarity of childhood cancers leading to few studies, many with small sample sizes [6, 7].
Most comorbidity indices were developed for aging populations, making them less applicable to pregnancy. To address this gap, Bateman’s Obstetric Comorbidity Index was designed to predict severe morbidity and the need for intensive care unit admission among pregnant patients [8]. The Index includes 20 maternal comorbidities [9] and it has been applied in both obstetric [10–12] and offspring research [13, 14]. Bateman’s Obstetric Comorbidity Index, originally developed using ICD-9 coding, was later validated and expanded to ICD-10 coding to enhance its applicability across coding systems [10].
Previous studies have drawn an interest in maternal comorbid factors for childhood cancer in the past decades, and a number of these health conditions are a part of the Obstetric Index. Maternal pre-existing diabetes mellitus was found to increase all combined leukemia [15], acute myeloid leukemia [2, 15], and glioma [4] in offspring. In addition, children of mothers with preeclampsia have been shown to have a higher risk of hepatoblastoma [5, 16]. Hypertension has also been reported as a risk factor for increased incidence of childhood leukemia and hepatoblastoma [17–19]. Furthermore, maternal autoimmune diseases have been associated with a higher risk of lymphoma [20] and leukemia [21] in offspring, although studies are limited. Yet, no published study has thoroughly examined a comprehensive range of diseases and their potential links to the occurrence of childhood cancer. Therefore, we sought to investigate the association between childhood cancer and maternal comorbid factors using a population-based sample.
Methods
This population-based case-control study was conducted using Danish national records, as previously described [22]. Data from the Danish Cancer Registry were used to identify cancer cases (aged ≤ 19 years) diagnosed between 1973 and 2016. Cancer cases were classified according to the International Classification of Childhood Cancer. Cancer-free controls were randomly selected from the Central Population Register, matched to cases at a 26:1 ratio by sex and birth date (within ± 6 months; births 1973–2013). This database also links children to their parents. Controls were required to be cancer-free at their matched case’s date of diagnosis but were eligible to become cases later if diagnosed with cancer after that date. For thoroughness of information on pregnancy conditions, all cases and controls included in this analysis were born in Denmark.
We obtained information on maternal comorbidities from the National Patient Registry and the Medical Births Registry. The National Patient Registry includes records with diagnoses from each hospital visit, using a Danish standard version of the International Classification of Diseases, eighth version (ICD-8) from 1977–1993, and an extended version of the tenth version (ICD-10) from 1994 onward. The Medical Births Registry (1973+) provides details on pregnancy, labor, and gestational information, including pregnancy complications and information on diseases occurring during pregnancy.
Children with births prior to 1977 (1919 cases, 47975 controls) were excluded as this was the first year of the National Patient Register. Births with weights less than 0.5kg (1 case, 26 controls) were excluded due to the low likelihood of viability. Finally, 166903 eligible children were included in this study with 6419 cases and 160484 controls. In the current analysis, we retained only cancer types that had a minimum of five exposed cases. A flowchart outlining the section of cases and controls for the 1977–2013 sample is shown in Figure 1.
Figure 1.
Flowchart showing the selection of cases and controls in the 1977–2013 sample.
Maternal comorbid conditions were assessed using the Obstetric Comorbidity Index, developed by Bateman and colleagues with ICD-9 coding and later updated by Metcalfe to use ICD-10 coding [8, 10]. In addition to ICD-based definitions, we included indicator variables from the Danish Medical Birth Registry to improve ascertainment of selected conditions. Specifically, we included registry indicators for multiple gestation, placenta previa, and previous cesarean delivery. These additions supplemented our ICD code-based classification and were included in the subsequent analysis. This comorbidity index provides weighted comorbidity scores for individual patients based on the presence of 20 specific diagnosis codes and demographic factors present in administrative data. The weighting was derived from the magnitude of beta coefficients: conditions with a beta less than 0.15 received a weight of 0, and for every 0.3 increase in the beta coefficient, the weight increased by 1 point. Each individual’s comorbidity score was calculated by summing the weights for all present conditions along with maternal age. Therefore, we used this weighted continuous score in our analysis instead of using the original Bateman paper which categorized the index into seven ordinal groups based on calibrated risk.
To adhere to Bateman’s and Metcalfe’s published Index, we conducted an analysis solely using ICD-10 diagnoses, which was restricted to the years that ICD-10 was available (births 1994–2013). Due to small sample sizes during the ICD-10 period, we additionally identified equivalent ICD-8 coding when available (Supplementary Table 1). However, not all conditions had equivalent ICD-8 codes. We ascertained pregnancies with these diagnosis codes in the earlier part of the study period when ICD-8 was used, combining the ICD-8 and ICD-10 time periods in analyses for better statistical power. Coding was reviewed by an expert in Danish ICD-8 (JH).
A conditional logistic regression model was used to estimate pediatric cancer risk associated with maternal comorbidities. Selection of adjustment variables was guided by literature [23–26]. Covariates that were included in the final model included the child’s birth order (first, second, third or higher child) and the urbanicity of residence (urban, small town, or rural). Other covariates considered for adjustment included mother’s smoking status at the first prenatal visit (yes or no) and maternal pre-pregnancy BMI. However, these factors were reported in Supplemental 2 as adjusting for them did not change the results at least 10% [27].
We first evaluated associations of cancer incidence with the presence of individual maternal comorbidities. We then examined cancer risk related to both the continuous index score and the score of one or more vs. zero. Due to sparse data, we only present the main results for associations between individual comorbidities and cancer (all types) for the ICD-8/10 population, as it provided stronger statistical power. Supplementary table 3 includes results for the ICD-10 population for comparison. To enhance interpretability of the comorbidity index, we summarized the distribution of maternal comorbidity burden by number of comorbid conditions (0, 1, 2, and 3+) in Supplemental Table 4. Supplemental Table 5 provides the mean and standard deviation of Bateman score for both cases and controls strategized by cancer type.
Results
Demographic characteristics of participants (children, mothers and fathers) are shown in Table 1 for the two study populations (1977–2013 with ICD-8/10 coding; 1994–2013 with ICD-10 coding). In ICD-8/10 group, while the mean ages of mothers and fathers were comparable between cases and controls, a slightly higher proportion of parents in the case group were over age 35 compared to the control group. Children with cancer in the ICD-10 population were also slightly more likely to reside in urban areas compared to controls. Additionally, cases were more likely than controls to be firstborn children, and there were slight differences in the proportion of second-born children between the two groups.
Table 1.
Demographics of the Study Population.
| ICD 10 Population 1994–2013 | ICD 8 and 10 Population 1977–2013 | |||
|---|---|---|---|---|
|
|
||||
| Case (%) N=2838 |
Control (%) N=70960 |
Case (%) N=6419 |
Control (%) N=160484 |
|
|
| ||||
| Sex of Child | ||||
| Male | 1518 (53.5) | 37973 (53.5) | 3514 (54.7) | 87872 (54.8) |
| Female | 1320 (46.5) | 32987 (46.5) | 2905 (45.3) | 72612 (45.3) |
| Mother’s age | ||||
| Mean in years (SD) | 29.6 (4.8) | 29.7 (4.8) | 28.4 (5.0) | 28.3 (5.0) |
| ≤24 | 389 (13.7) | 9925 (14.0) | 1422 (22.2) | 36922 (23.0) |
| 25–29 | 1053 (37.1) | 25002 (35.2) | 2472 (38.5) | 60759 (37.9) |
| 30–34 | 924 (32.6) | 24599 (34.7) | 1744 (27.2) | 44547 (27.8) |
| 35–39 | 409 (14.4) | 9772 (13.8) | 669 (10.4) | 15695 (9.8) |
| ≥40 | 63 (2.2) | 1662 (2.3) | 112 (1.7) | 2561 (1.6) |
| Father’s age | ||||
| Mean in years (SD) | 32.2 (5.8) | 32.3 (5.8) | 31.2 (5.8) | 31.1 (5.8) |
| ≤24 | 186 (6.6) | 4733 (6.7) | 660 (10.3) | 17978 (11.3) |
| 25–29 | 778 (27.6) | 18453 (26.1) | 2047 (32.1) | 50022 (31.3) |
| 30–34 | 964 (34.2) | 25325 (35.9) | 2050 (32.1) | 52046 (32.6) |
| 35–39 | 584 (20.8) | 14650 (20.8) | 1096 (17.2) | 26766 (16.8) |
| ≥40 | 303 (10.8) | 7448 (10.6) | 526 (8.3) | 12842 (8.0) |
| Missing | 23 | 351 | 40 | 830 |
| Urbanicity of Residence | ||||
| Urban | 1032 (36.4) | 24341 (34.3) | 2113 (32.9) | 50982 (31.8) |
| Suburban | 753 (26.5) | 19979 (28.2) | 1808 (28.2) | 46631 (29.1) |
| Rural | 1053 (37.1) | 26640 (37.5) | 2498 (38.9) | 62871 (39.1) |
| Mother’s birthplace | ||||
| Denmark | 2509 (88.7) | 63037 (89.0) | 5852 (91.4) | 146777 (91.6) |
| Other European countries & North America | 102 (3.6) | 2770 (3.9) | 201 (3.1) | 5167 (3.2) |
| Other | 218 (7.7) | 5019 (7.1) | 354 (5.5) | 8249 (5.2) |
| Missing | 9 | 134 | 12 | 291 |
| Father’s birthplace | ||||
| Denmark | 2463 (88.1) | 62528 (88.8) | 5753 (90.6) | 145250 (91.2) |
| Other European country & North America | 110 (3.9) | 2812 (4.0) | 205 (3.2) | 5265 (3.3) |
| Other | 222 (8.0) | 5079 (7.2) | 389 (6.1) | 8730 (5.5) |
| Missing | 43 | 541 | 72 | 1239 |
| Mother’s smokinga | ||||
| No | 2133 (78.6) | 53279 (78.2) | 2601 (76.1) | 65061 (75.8) |
| Yes | 582 (21.4) | 14852 (21.8) | 817 (23.9) | 20753 (24.2) |
| Missing | 123 | 2829 | 187 | 4321 |
| Birth Order of Child | ||||
| 1 | 1226 (43.2) | 29266 (41.2) | 2826 (44.0) | 68851 (42.9) |
| 2 | 1074 (37.8) | 27017 (38.1) | 2400 (37.4) | 60938 (38.0) |
| 3 or more | 538 (19.0) | 14677 (20.7) | 1193 (18.6) | 30695 (19.1) |
Footnote.
Information was only available from 1997–2013
SD = standard deviation.
Gestational hypertension, pre-existing diabetes mellitus, and previous cesarean delivery were associated with higher cancer risks (all types combined) in the ICD-8/10 population (Table 2). Other maternal comorbid conditions, including chronic renal disease, congenital heart disease, drug abuse, and multiple gestation, had elevated associations with all types of cancer combined, although the estimated confidence intervals included the null. In addition, maternal age between 35–39 years was associated with an increased cancer risk in this population. In the ICD-10 population (Supplementary Table 3), where data was more sparse, slightly elevated point estimates were observed for previous cesarean delivery (OR = 1.22, 95% CI: 1.04–1.44), multiple gestation (OR = 1.17, 95% CI: 0.95–1.44), and pre-existing diabetes mellitus (OR = 1.79, 95% CI: 0.88–3.68). Other individual factors contributing to the comorbidity index were rare in our sample.
Table 2.
Crude and Adjusted Odds Ratios (ORs) with 95% Confidence Intervals for Maternal Comorbid Conditions and Risk of Childhood Cancers (all types combined) for the ICD 8 and 10 Populations.
| Maternal Comorbid Conditions | Cases (%) N=6419 |
Controls (%) N=160484 |
Odds Ratio (OR) (95% CI) |
Adjusted OR (95% CI) |
|---|---|---|---|---|
|
| ||||
| Alcohol Abuse | <5 | 79 (0.1) | --- | --- |
| Asthma | 13 (0.2) | 334 (0.2) | 0.97 (0.56, 1.69) | 0.97 (0.56, 1.69) |
| Cardiac Valvular Disease | <5 | 21 (0.01) | --- | --- |
| Chronic Congestive Heart Failure | <5 | <5 | --- | --- |
| Chronic Ischemic Heart Disease | <5 | 10 (0.01) | --- | --- |
| Chronic Renal Disease | 77 (1.2) | 1578 (1.0) | 1.23 (0.97, 1.55) | 1.23 (0.97, 1.55) |
| Congenital Heart Disease | 9 (0.1) | 161 (0.1) | 1.40 (0.71, 2.74) | 1.40 (0.71, 2.74) |
| Drug Abuse | 6 (0.1) | 98 (0.1) | 1.53 (0.67, 3.50) | 1.51 (0.66, 3.44) |
| Gestational Hypertension | 79 (1.2) | 1562 (1.0) | 1.27 (1.01, 1.60) | 1.27 (1.01, 1.59) |
| Human Immunodeficiency Virus | <5 | <5 | --- | --- |
| Mild/Unspecified Preeclampsia | 151 (2.4) | 3749 (2.3) | 1.01 (0.85, 1.19) | 1.00 (0.85, 1.18) |
| Multiple Gestation | 104 (1.6) | 2344 (1.5) | 1.11 (0.91, 1.36) | 1.14 (0.93, 1.39) |
| Placenta Previa | 25 (0.4) | 583 (0.4) | 1.07 (0.72, 1.60) | 1.08 (0.72, 1.61) |
| Pre-Existing Diabetes Mellitus | 33 (0.5) | 453 (0.3) | 1.83 (1.28, 2.60) | 1.82 (1.28, 2.59) |
| Pre-Existing Hypertension | 17 (0.3) | 444 (0.3) | 0.96 (0.59, 1.56) | 0.96 (0.59, 1.55) |
| Previous Cesarean Delivery | 171 (2.7) | 3692 (2.3) | 1.17 (1.00, 1.38) | 1.20 (1.02, 1.41) |
| Pulmonary Hypertension | <5 | 5 (0.0) | --- | --- |
| Severe Preeclampsia | 92 (1.4) | 2238 (1.4) | 1.03 (0.83, 1.27) | 1.02 (0.83, 1.26) |
| Sickle Cell Disease | <5 | 13 (0.01) | --- | --- |
| Systemic Lupus Erythematosus | <5 | 19 (0.01) | --- | --- |
| Maternal Age | ||||
| 35–39 | 669 (10.4) | 15695 (9.8) | 1.08 (1.00, 1.17) | 1.09 (1.01, 1.19) |
| 40–44 | 109 (1.7) | 2449 (1.5) | 1.12 (0.92, 1.36) | 1.13 (0.93, 1.38) |
| >44 | <5 | 112 (0.1) | --- | --- |
Footnote.
adjusted for birth order and urbanicity of residence
OR = odds ratio; CI = confidence interval; Only estimates for conditions with at least 5 exposed cases were reported.
Maternal comorbidity score (as a continuous variable) was positively associated with an increased risk of acute lymphocytic leukemia (ALL), retinoblastoma and rhabdomyosarcoma in both ICD-8/10 and ICD-10 populations (Table 3). Other cancers, including acute myeloid leukemia, non-Hodgkin lymphoma, Burkitt lymphoma, neuroblastoma, retinoblastoma, and rhabdomyosarcoma, had elevated associations with the maternal comorbidity score, although the confidence intervals included the null. While most mothers had zero comorbidities, a higher proportion of cases had one or more comorbidities compared to controls (Supplemental Table 4). The distribution of comorbidity scores showed higher mean scores among cases than controls for most cancer types, particularly for neuroblastoma, retinoblastoma, and rhabdomyosarcoma (Supplemental Table 5).
Table 3.
Crude and Adjusted Odds Ratios (ORs) with 95% Confidential Intervals for maternal comorbidity (as a continuous variable) and risk of individual childhood cancers.
| ICD 10 (N=2838 cases, 70960 controls) | ICD 8 and 10 (N=6419 cases, 160484 controls) | |||||
|---|---|---|---|---|---|---|
|
|
||||||
| Cancer type | N (%)a | OR (95% CI) | Adjusted ORb (95% CI) | N (%)a | OR (95% CI) | Adjusted ORb (95% CI) |
|
| ||||||
| Controls | 19441 (27.4) | Reference | Reference | 29142 (18.2) | Reference | Reference |
| Acute lymphocytic leukemia | 203 (34.5) | 1.05 (1.00, 1.11) | 1.06 (1.01, 1.11) | 299 (24.5) | 1.07 (1.03, 1.12) | 1.07 (1.03, 1.12) |
| Acute myeloid leukemia | 38 (31.9) | 1.01 (0.89, 1.15) | 1.01 (0.89, 1.16) | 60 (24.0) | 1.04 (0.93, 1.16) | 1.03 (0.92, 1.16) |
| Hodgkin lymphoma | 23 (18.3) | 0.99 (0.86, 1.15) | 0.98 (0.84, 1.13) | 48 (13.6) | 1.01 (0.89, 1.14) | 0.99 (0.87, 1.12) |
| Non-Hodgkin lymphoma | 53 (38.1) | 1.06 (0.95, 1.18) | 1.07 (0.96, 1.18) | 78 (24.2) | 1.05 (0.95, 1.16) | 1.05 (0.96, 1.16) |
| Burkitt lymphoma | 23 (44.2) | 1.09 (0.92, 1.29) | 1.10 (0.93, 1.30) | 28 (27.2) | 1.07 (0.91, 1.26) | 1.08 (0.92, 1.27) |
| CNS tumors | 187 (27.2) | 1.00 (0.95, 1.06) | 1.00 (0.95, 1.06) | 272 (17.2) | 1.00 (0.96, 1.05) | 1.00 (0.96, 1.06) |
| Medulloblastoma | 24 (32.0) | 1.02 (0.87, 1.19) | 1.02 (0.87, 1.19) | 30 (17.8) | 0.97 (0.83, 1.15) | 0.99 (0.84, 1.16) |
| Intracranial and intraspinal embryonal tumors | 34 (31.2) | 0.96 (0.82, 1.12) | 0.97 (0.83, 1.13) | 44 (19.1) | 0.93 (0.80, 1.08) | 0.94 (0.81, 1.09) |
| Glioma | 73 (27.0) | 0.99 (0.90, 1.08) | 0.99 (0.90, 1.08) | 115 (14.8) | 0.96 (0.88, 1.05) | 0.96 (0.88, 1.05) |
| Neuroblastoma | 37 (27.6) | 1.06 (0.96, 1.17) | 1.07 (0.98, 1.18) | 55 (20.0) | 1.05 (0.96, 1.16) | 1.05 (0.96, 1.16) |
| Retinoblastoma | 21 (28.0) | 1.09 (0.95, 1.26) | 1.11 (0.97, 1.27) | 27 (19.3) | 1.07 (0.93, 1.23) | 1.08 (0.94, 1.23) |
| Rhabdomyosarcoma | 23 (34.9) | 1.11 (0.97, 1.27) | 1.12 (0.98, 1.29) | 35 (23.5) | 1.11 (0.98, 1.26) | 1.11 (0.98, 1.26) |
| Wilms tumor | 31 (32.0) | 0.80 (0.63, 1.01) | 0.80 (0.64, 1.01) | 40 (19.7) | 0.88 (0.74, 1.05) | 0.88 (0.74, 1.05) |
| Bone cancer | 29 (28.2) | 0.97 (0.84, 1.12) | 0.97 (0.83, 1.12) | 44 (16.3) | 0.96 (0.84, 1.10) | 0.96 (0.84, 1.09) |
| Ewing sarcoma | 14 (26.9) | 1.01 (0.84, 1.21) | 1.02 (0.85, 1.22) | 21 (17.7) | 0.99 (0.84, 1.18) | 1.00 (0.84, 1.18) |
| Osteosarcoma | 12 (27.3) | 0.92 (0.70, 1.20) | 0.91 (0.69, 1.20) | 20 (15.4) | 0.93 (0.75, 1.16) | 0.92 (0.73, 1.15) |
| Germ cell tumors | 32 (28.6) | 0.99 (0.86, 1.14) | 0.99 (0.86, 1.14) | 61 (18.2) | 0.99 (0.88, 1.12) | 0.99 (0.87, 1.11) |
Footnote.
= at least one comorbidity
adjusted for birth order and urbanicity of residence
OR = odds ratio; CI = confidence interval; Only estimates for cancer types with at least 5 exposed cases were reported.
Table 4 also shows that the prevalence of any maternal comorbidity (any vs. none) was associated with an increased risk of ALL, AML, non-Hodgkin lymphoma, and Burkitt lymphoma in both ICD-8/10 and ICD-10 populations. Additionally, in the ICD-8/10 population only, we observed a positive association between having any maternal comorbidity and rhabdomyosarcoma.
Table 4.
Crude and Adjusted Odds Ratios (ORs) with 95% Confidential Intervals for maternal comorbidity index (score of 1+ vs. none) and risk of individual childhood cancers.
| ICD 10 (N=2838 cases, 70960 controls) | ICD 8 and 10 (N=6419 cases, 160484 controls) | |||||
|---|---|---|---|---|---|---|
|
|
||||||
| Cancer type | N (%) | OR (95% CI) | Adjusted ORa (95% CI) | N (%) | OR (95% CI) | Adjusted ORa (95% CI) |
|
| ||||||
| Controls | 19441 (27.4) | Reference | Reference | 29142 (18.2) | Reference | Reference |
| Acute lymphocytic leukemia | 203 (34.5) | 1.35 (1.13, 1.60) | 1.47 (1.23, 1.77) | 299 (24.5) | 1.39 (1.21, 1.60) | 1.45 (1.26, 1.68) |
| Acute myeloid leukemia | 38 (31.9) | 1.20 (0.80, 1.78) | 1.24 (0.82, 1.89) | 60 (24.0) | 1.37 (1.00, 1.86) | 1.36 (0.99, 1.87) |
| Hodgkin lymphoma | 23 (18.3) | 0.81 (0.51, 1.29) | 0.75 (0.47, 1.21) | 48 (13.6) | 0.92 (0.67, 1.26) | 0.86 (0.63, 1.19) |
| Non-Hodgkin lymphoma | 53 (38.1) | 1.70 (1.19, 2.43) | 1.86 (1.29, 2.69) | 78 (24.2) | 1.54 (1.17, 2.01) | 1.63 (1.23, 2.16) |
| Burkitt lymphoma | 23 (44.2) | 2.08 (1.18, 3.67) | 2.30 (1.28, 4.13) | 28 (27.2) | 1.63 (1.03, 2.60) | 1.85 (1.15, 2.98) |
| CNS tumors | 187 (27.2) | 1.01 (0.85, 1.20) | 1.00 (0.84, 1.20) | 272 (17.2) | 0.94 (0.82, 1.08) | 0.94 (0.82, 1.09) |
| Medulloblastoma | 24 (32.0) | 1.20 (0.73, 1.99) | 1.26 (0.75, 2.13) | 30 (17.8) | 0.96 (0.63, 1.46) | 1.03 (0.67, 1.58) |
| Intracranial and intraspinal embryonal tumors | 34 (31.2) | 1.12 (0.74, 1.71) | 1.21 (0.78, 1.86) | 44 (19.1) | 0.99 (0.70, 1.40) | 1.06 (0.74, 1.52) |
| Glioma | 73 (27.0) | 1.04 (0.79, 1.38) | 1.05 (0.78, 1.40) | 115 (14.8) | 0.89 (0.72, 1.10) | 0.89 (0.72, 1.11) |
| Neuroblastoma | 37 (27.6) | 0.84 (0.57, 1.25) | 0.92 (0.61, 1.38) | 55 (20.0) | 0.96 (0.70, 1.31) | 0.94 (0.68, 1.30) |
| Retinoblastoma | 21 (28.0) | 0.91 (0.54, 1.54) | 1.04 (0.59, 1.80) | 27 (19.3) | 0.91 (0.58, 1.45) | 0.91 (0.58, 1.45) |
| Rhabdomyosarcoma | 23 (34.9) | 1.51 (0.89, 2.55) | 1.39 (0.81, 2.39) | 35 (23.5) | 1.46 (0.98, 2.18) | 1.40 (0.93, 2.12) |
| Wilms’ tumors | 31 (32.0) | 0.99 (0.63, 1.54) | 1.04 (0.66, 1.66) | 40 (19.7) | 0.92 (0.64, 1.33) | 0.91 (0.62, 1.34) |
| Bone cancer | 29 (28.2) | 1.18 (0.76, 1.84) | 1.16 (0.73, 1.84) | 44 (16.3) | 1.01 (0.72, 1.42) | 1.00 (0.71, 1.42) |
| Ewing sarcoma | 14 (26.9) | 1.09 (0.58, 2.06) | 1.18 (0.60, 2.32) | 21 (17.7) | 1.03 (0.63, 1.69) | 1.07 (0.64, 1.79) |
| Osteosarcoma | 12 (27.3) | 1.19 (0.61, 2.35) | 1.13 (0.56, 2.26) | 20 (15.4) | 1.02 (0.62, 1.67) | 0.96 (0.58, 1.59) |
| Germ cell tumors | 32 (28.6) | 1.17 (0.77, 1.79) | 1.21 (0.78, 1.87) | 61 (18.2) | 1.17 (0.87, 1.56) | 1.16 (0.86, 1.56) |
Footnote.
adjusted for birth order and urbanicity of residence
OR = odds ratio; CI = confidence interval; Only estimates for cancer types with at least 5 exposed cases were reported.
We assessed the potential influence of these conditions (anemia, epilepsy, nausea and vomiting, migraine, and maternal infections) by adding them to the comorbidity index and adjusting for them in regression models. Based on the results of Akaike Information Criterion (AIC) comparisons, those additions did not change the overall results or improve the model fit. We further summarized these model fit comparisons in Supplemental Table 5, which displays AIC values for both binary and continuous exposure models across cancer types.
Discussion
To our knowledge, this study marks the first comprehensive investigation of the association between maternal health and childhood cancer, employing the Obstetric Comorbidity Index. In the ICD-8/10 population, maternal pre-existing diabetes mellitus was the most influential determinant associated with an increased risk of all types of cancers (combined) in offspring, an association observed elsewhere [2, 4, 15]. We have previously shown in a study using the same Danish sample and a Taiwanese population that maternal type 1 diabetes was associated with an increased risk of gliomas, and maternal type 2 diabetes was associated with hepatoblastoma.[4] These associations indicate that altered fetal growth and increased insulin growth factor (IGF-1) signaling may contribute to the observed risks. Additionally, having other maternal comorbidities (e.g., prior cesarean delivery and gestational hypertension) increased risks of all types of cancers (combined) in offspring. When the maternal comorbidity score was investigated as a continuous variable, ALL, retinoblastoma and rhabdomyosarcoma were associated with a maternal comorbidity score of one or more in both populations (ICD-8/10 and ICD-10). When the score was analyzed as a binary variable (any vs. none), three additional cancers (non-Hodgkin lymphoma, Burkitt lymphoma, and intracranial and intraspinal embryonal tumors) were associated. In addition, this is also the first study to describe an increased pediatric cancer risk with maternal renal conditions.
It was surprising to find that cesarean delivery in a previous pregnancy appeared to be a strong predictor of childhood cancer. While cesarean deliveries in the index pregnancy have been linked to increased risks of hepatoblastoma [28] and ALL in offspring with a range of odds ratios from 1.06 to 1.52 [29, 30], no prior research has directly associated a previous cesarean delivery with pediatric cancer. This finding likely signals health concerns of the mother that predispose both to pediatric cancers and to cesarean section. The rising rates of cesarean sections in recent decades reflect several contributing factors, such as maternal comorbidities (e.g., diabetes and hypertension), fetal distress, abnormal fetal presentation, and failed labor progression [31, 32]. Of these factors, maternal pre-existing diabetes, failed labor, and fetal distress have been linked to an increased risk of childhood cancer [31, 32].
Although we observed similar results between the two time periods, there were several conditions for which there was no comparable ICD-8 code. For example, the ICD-8 coding system lacks specific codes for gestational hypertension, hence these may have been reported as chronic hypertension. Given the lack of comparable ICD-8 codes for some conditions, we are likely under-ascertaining risk increases during the earlier period, biasing results to the null. This may in part explain diverging results in Table 4 between the ICD-8 and ICD-8/10 time periods.
Since the Obstetric Index was originally developed to predict maternal morbidity and mortality rather than offspring health, there are several other maternal health conditions linked to childhood cancer that were not included in the index. These include autoimmune conditions other than lupus [20, 21] and maternal asthma [33–35]. Other specific maternal acute comorbid conditions, such as anemia [36, 37], epilepsy [38], nausea and vomiting [39], and migraine [40] are also identified as possible risk factors for childhood cancer. In addition, maternal acute [41] or chronic infections including human papillomavirus [42], Epstein-Barr [43], cytomegalovirus [44], and hepatitis [3, 45] may increase risks for childhood cancer.
Compared to a prior Danish validation study of the Obstetric Comorbidity Index,[46] our study observed a lower proportion of mothers with scores over 1. After a comparison of our time periods of ICD codes, this difference reflects variation in the study period (1977–2013 in our study vs. 2000–2014 in theirs), coding systems (ICD-8 and ICD-10 vs. ICD-10 only), and the exposure windows (3 months preconception in our study vs. 180 days in theirs). We recognize that using a longer exposure window may better capture chronic maternal conditions.
Using the Obstetric Index score as both a continuous and a binary measure in the analysis provided a more nuanced understanding of how maternal comorbidities may impact cancer risk. The binary measure identified three additional cancers; however, this may reflect narrower confidence intervals rather than increased clinical relevance of the binary score. Both measures indicated an association between the maternal comorbidity index score and ALL. While the continuous measure captured more subtle variations in maternal health burden, the binary measure may provide more practical insights for clinical settings. Identifying high-risk groups based on the presence of any comorbidity could facilitate targeted interventions. Notably, the binary measure identified three additional cancers (non-Hodgkin lymphoma, Burkitt lymphoma, and intracranial and intraspinal embryonal tumors), suggesting that the presence of even one maternal health condition, as captured by this index, could increase cancer risk in offspring.
One of the strengths of our study is its comprehensive use of high-quality Danish national registers, which enabled us to investigate the association between childhood cancer and maternal comorbid factors across a large population-based sample. The substantial sample size spanning from 1977 to 2013 allowed for robust statistical power to detect associations between various maternal health conditions and childhood cancer risks. However, our study has limitations. The Obstetric Index is based on diagnosis codes, which introduces the possibility of misclassification. Moreover, this Index was initially developed in a population with comprehensive healthcare coverage and integrated services. For healthcare settings lacking detailed electronic health records or accessible maternal health information, a manually calculated alternative may be more appropriate. Additionally, we lacked data on maternal environmental exposures, which may confound associations between maternal comorbidities and childhood cancer risk.
In conclusion, our findings indicate that maternal comorbidities, such as pre-existing diabetes mellitus, gestational hypertension, and previous cesarean delivery, are associated with an increased risk of childhood cancers. Thus, the results suggest that the Obstetric Comorbidity Index can be used to predict childhood cancer risk in offspring, and may be usable for adjustment of maternal health factors in other studies of pediatric cancer. While maternal age may contribute to the observed associations, prior studies support the role of maternal comorbidities such as diabetes and obesity in certain childhood cancers. Although no interventions currently exist to directly reduce cancer risk in offspring, our findings add the growing evidence base and may inform future prevention efforts. This highlights the importance of employing efficient interventions that target those comorbidities to reduce the potential severe health consequences for children.
Supplementary Material
Funding information:
This project was supported by Alex’s Lemonade Stand Foundation (Grant #23–27656) and the US National Institutes of Health (Grant # R21CA175959).
Footnotes
Competing interests: The authors declare no conflict of interest.
Ethics approval and consent to participate: Human subjects approvals were obtained from the University of North Texas (IRB-20–255) and UCLA (IRB#13–001904). All methods were performed in accordance with the relevant guidelines and regulations. Informed consent from participants was not required, in accordance with Danish data protection regulations and registry-based research practices. No identifiable images or visual data were included in the manuscript.
Consent for publication: N/A
Data availability:
Individual-level data are available only to researchers who meet the legal requirements for accessing sensitive data according to Danish legislation.
References
- 1.Erdmann F, Frederiksen LE, Bonaventure A, Mader L, Hasle H, Robison LL, et al. Childhood cancer: survival, treatment modalities, late effects and improvements over time. Cancer Epidemiol. 2021;71:101733. [DOI] [PubMed] [Google Scholar]
- 2.Contreras ZA, Ritz B, Virk J, Cockburn M, Heck JE. Maternal pre-pregnancy and gestational diabetes, obesity, gestational weight gain, and risk of cancer in young children: a population-based study in California. Cancer Causes & Control. 2016;27:1273–85. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Heck JE, Wu C-K, Huang X, Chew KW, Tong M, Federman N, et al. Cohort study of familial viral hepatitis and risks of paediatric cancers. Int J Epidemiol. 2022;51(2):448–57. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Huang X, Hansen J, Lee P-C, Wu C-K, Federman N, Arah OA, et al. Maternal diabetes and childhood cancer risks in offspring: two population-based studies. Br J Cancer. 2022;127(10):1837–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Xu X, Ritz B, Cockburn M, Lombardi C, Heck JE. Maternal preeclampsia and odds of childhood cancers in offspring: a California statewide case–control study. Paediat Perinatal Epidemiol. 2017;31(2):157–64. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Ansell P, Mitchell CD, Roman E, Simpson J, Birch JM, Eden TO, et al. Relationships between perinatal and maternal characteristics and hepatoblastoma: a report from the UKCCS. Eur J Cancer. 2005;41(5):741–8. [DOI] [PubMed] [Google Scholar]
- 7.Musselman JR, Georgieff MK, Ross JA, Tomlinson GE, Feusner J, Krailo M, et al. Maternal pregnancy events and exposures and risk of hepatoblastoma: a Children’s Oncology Group (COG) study. Cancer epidemiology. 2013;37(3):318–20. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Bateman BT, Mhyre JM, Hernandez-Diaz S, Huybrechts KF, Fischer MA, Creanga AA, et al. Development of a comorbidity index for use in obstetric patients. Obstetrics and Gynecology. 2013;122(5). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Bliddal M, Broe A, Pottegård A, Olsen J, Langhoff-Roos J. The Danish medical birth register. European Journal of Epidemiology. 2018;33:27–36. [DOI] [PubMed] [Google Scholar]
- 10.Metcalfe A, Lix L, Johnson JA, Currie G, Lyon A, Bernier F, et al. Validation of an obstetric comorbidity index in an external population. BJOG: An International Journal of Obstetrics & Gynaecology. 2015;122(13):1748–55. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Bolanthakodi C, Bhat MS, Huchchannavar RR. Obstetric comorbidity index—A promising tool to predict maternal morbidity. Journal of South Asian Federation of Obstetrics and Gynaecology. 2022;14(4):393–9. [Google Scholar]
- 12.Ruppel H, Liu VX, Kipnis P, Hedderson MM, Greenberg M, Forquer H, et al. Development and validation of an obstetric comorbidity risk score for clinical use. Women’s Health Reports. 2021;2(1):507–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Huybrechts KF, Hernández-Díaz S, Straub L, Gray KJ, Zhu Y, Patorno E, et al. Association of maternal first-trimester ondansetron use with cardiac malformations and oral clefts in offspring. Jama. 2018;320(23):2429–37. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Rough K, Sun JW, Seage III GR, Williams PL, Huybrechts KF, Bateman BT, et al. Zidovudine use in pregnancy and congenital malformations. AIDS (London, England). 2017;31(12):1733. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Deleskog A, den Hoed M, Tettamanti G, Carlsson S, Ljung R, Feychting M, et al. Maternal diabetes and incidence of childhood cancer–a nationwide cohort study and exploratory genetic analysis. Clinical epidemiology. 2017:633–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Heck JE, Meyers TJ, Lombardi C, Park AS, Cockburn M, Reynolds P, et al. Case-control study of birth characteristics and the risk of hepatoblastoma. Cancer epidemiology. 2013;37(4):390–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Castro-Jiménez MÁ, Orozco-Vargas LC. Parental exposure to carcinogens and risk for childhood acute lymphoblastic leukemia, Colombia, 2000–2005. Preventing Chronic Disease. 2011;8(5). [PMC free article] [PubMed] [Google Scholar]
- 18.Roman E, Ansell P, Bull D. Leukaemia and non-Hodgkin’s lymphoma in children and young adults: are prenatal and neonatal factors important determinants of disease? British journal of cancer. 1997;76(3):406–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Schüz J, Weihkopf T, Kaatsch P. Medication use during pregnancy and the risk of childhood cancer in the offspring. European journal of pediatrics. 2007;166:433–41. [DOI] [PubMed] [Google Scholar]
- 20.Mellemkjaer L, Alexander F, Olsen J. Cancer among children of parents with autoimmune diseases. British journal of cancer. 2000;82(7):1353–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Wen W-Q, Shu X-O, Sellers T, Bhatia S, Lampkin B, Robison LL. Family history of cancer and autoimmune disease and risk of leukemia in infancy: a report from the Children’s Cancer Group (United States and Canada). Cancer Causes & Control. 1998;9:161–71. [DOI] [PubMed] [Google Scholar]
- 22.Contreras ZA, Hansen J, Ritz B, Olsen J, Yu F, Heck JE. Parental age and childhood cancer risk: A Danish population-based registry study. Cancer Epidemiol. 2017;49:202–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.IARC (International Agency for Research on Cancer). Personal habits and indoor combustions. Volume 100 E. A review of human carcinogens. 2012/December/01 ed2012. 1–538 p. [Google Scholar]
- 24.Contreras ZA, Ritz B, Virk J, Cockburn M, Heck JE. Maternal pre-pregnancy and gestational diabetes, obesity, gestational weight gain, and risk of cancer in young children: a population-based study in California. Cancer Causes Control. 2016;27(10):1273–85. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.McNally RJ, Alston RD, Cairns DP, Eden OB, Kelsey AM, Birch JM. Geographical and ecological analyses of childhood Wilms’ tumours and soft-tissue sarcomas in North West England. Eur J Cancer. 2003;39(11):1586–93. [DOI] [PubMed] [Google Scholar]
- 26.Von Behren J, Spector LG, Mueller BA, Carozza SE, Chow EJ, Fox EE, et al. Birth order and risk of childhood cancer: a pooled analysis from five US States. International journal of cancer Journal international du cancer. 2011;128(11):2709–16. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Maldonado G, Greenland S. Simulation study of confounder-selection strategies. American journal of epidemiology. 1993;138(11):923–36. [DOI] [PubMed] [Google Scholar]
- 28.Williams LA, Richardson M, Spector LG, Marcotte EL. Cesarean section is associated with an increased risk of acute lymphoblastic leukemia and hepatoblastoma in children from Minnesota. Cancer Epidemiology, Biomarkers & Prevention. 2021;30(4):736–42. [DOI] [PubMed] [Google Scholar]
- 29.Marcotte EL, Thomopoulos TP, Infante-Rivard C, Clavel J, Petridou ET, Schüz J, et al. Caesarean delivery and risk of childhood leukaemia: a pooled analysis from the Childhood Leukemia International Consortium (CLIC). The Lancet Haematology. 2016;3(4):e176–e85. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Marcotte EL, Richardson MR, Roesler MA, Spector LG. Cesarean delivery and risk of infant Leukemia: a report from the children’s oncology group. Cancer Epidemiology, Biomarkers & Prevention. 2018;27(4):473–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Hehir MP, Ananth CV, Siddiq Z, Flood K, Friedman AM, D’Alton ME. Cesarean delivery in the United States 2005 through 2014: a population-based analysis using the Robson 10-Group Classification System. American Journal of Obstetrics and Gynecology. 2018;219(1):105. e1–. e11. [DOI] [PubMed] [Google Scholar]
- 32.Yan P, Wang Y, Yu X, Liu Y, Zhang Z-J. Maternal diabetes and risk of childhood malignancies in the offspring: a systematic review and meta-analysis of observational studies. Acta Diabetologica. 2021;58:153–68. [DOI] [PubMed] [Google Scholar]
- 33.Heck JE, Ritz B, Hung RJ, Hashibe M, Boffetta P. The epidemiology of neuroblastoma: a review. Paediatric and Perinatal Epidemiology. 2009;23(2):125–43. [DOI] [PubMed] [Google Scholar]
- 34.Petridou E, Trichopoulos D, Kalapothaki V, Pourtsidis A, Kogevinas M, Kalmanti M, et al. The risk profile of childhood leukaemia in Greece: a nationwide case-control study. British journal of cancer. 1997;76(9):1241–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Zierhut H, Linet MS, Robison LL, Severson RK, Spector L. Family history of cancer and non-malignant diseases and risk of childhood acute lymphoblastic leukemia: A Children’s Oncology Group Study. Cancer epidemiology. 2012;36(1):45–51. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Bluhm E, McNeil DE, Cnattingius S, Gridley G, El Ghormli L, Fraumeni JF Jr. Prenatal and perinatal risk factors for neuroblastoma. International Journal of Cancer. 2008;123(12):2885–90. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Lupo PJ, Danysh HE, Skapek SX, Hawkins DS, Spector LG, Zhou R, et al. Maternal and birth characteristics and childhood rhabdomyosarcoma: a report from the Children’s Oncology Group. Cancer Causes & Control. 2014;25:905–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Platamone CC, Deng C, Mazumder R, Ritz B, Olsen J, Hansen J, et al. Danish population based study of familial epilepsy and childhood cancer. European Journal of Epidemiology. 2024:1–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Orimoloye HT, Deng C, Hansen J, Olsen J, Saechao C, Ritz B, et al. Hyperemesis gravidarum and the risk of childhood cancer–A case-control study in Denmark. Cancer epidemiology. 2023;87:102472. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Orimoloye HT, Heck JE, Charles A, Saechao C, He D, Federman N, et al. Maternal migraine and risk of pediatric cancers. Pediatr Blood Cancer. 2023;70(7):e30385. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.He J-R, Yu Y, Fang F, Gissler M, Magnus P, László KD, et al. Evaluation of maternal infection during pregnancy and childhood leukemia among offspring in Denmark. JAMA Network Open. 2023;6(2):e230133–e. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Anand B, Ramesh C, Appaji L, Kumari BA, Shenoy A, Jayshree R, et al. Prevalence of high-risk human papillomavirus genotypes in retinoblastoma. British Journal of Ophthalmology. 2011;95(7):1014–8. [DOI] [PubMed] [Google Scholar]
- 43.Mutalima N, Molyneux E, Jaffe H, Kamiza S, Borgstein E, Mkandawire N, et al. Associations between Burkitt lymphoma among children in Malawi and infection with HIV, EBV and malaria: results from a case-control study. PloS one. 2008;3(6):e2505. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Francis SS, Wallace AD, Wendt GA, Li L, Liu F, Riley LW, et al. In utero cytomegalovirus infection and development of childhood acute lymphoblastic leukemia. Blood. 2017;129(12):1680–4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Smulevich VB, Solionova LG, Belyakova SV. Parental occupation and other factors and cancer risk in children: I. Study methodology and non‐occupational factors. International journal of cancer. 1999;83(6):712–7. [DOI] [PubMed] [Google Scholar]
- 46.Bliddal M, Möller S, Vinter CA, Rubin KH, Gagne JJ, Pottegård A. Validation of a comorbidity index for use in obstetric patients: a nationwide cohort study. Acta Obstetricia et Gynecologica Scandinavica. 2020;99(3):399–405. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Individual-level data are available only to researchers who meet the legal requirements for accessing sensitive data according to Danish legislation.