Abstract
Objective: We sought to evaluate the risk of preterm labor and hypertensive disorders in adolescent pregnancies with and without diabetes.
Methods: We evaluated 1,843,139 adolescents (≤20 years old) with labor and delivery admissions in the national Kids' Inpatient Database (KID) in years 2006, 2009, 2012, 2016, and 2019. International classification of disease codes was used to identify diabetes and medical factors affecting pregnancy. Weighted logistic regression was used to evaluate the association between diabetes and complications.
Results: Among admissions, 0.2% had type 1 diabetes (T1D), 0.2% had type 2 diabetes (T2D), and 0.7% had gestational diabetes (GDM); 10.1% of admissions were complicated by hypertensive disorders and 5.8% by preterm labor. Compared to adolescents without diabetes, those with diabetes had a higher prevalence of hypertensive disorders (T1D: 35.4%, T2D: 37.8%, GDM: 24.9%, None: 9.9%; p < 0.001) and preterm labor (T1D: 21.5%, T2D: 16.8%, GDM: 6.8%, none: 5.7%; p < 0.001). In adjusted models, odds of hypertensive disorders were higher in later study years (2019 vs. 2006 OR 1.85, 95% CI 1.77–1.94), among those with T1D (OR 4.32, 95% CI 3.94–4.74), with T2D (OR 4.18, 95% CI 3.79–4.61), and with GDM (OR 1.99, 95% CI 1.89–2.10). Adjusted odds of preterm labor were higher among those with T1D (OR 4.53, 95% CI 4.09–5.02), with T2D (OR 3.35, 95% CI 2.96–3.78), and with GDM (OR 1.18, 95% CI 1.08–1.28); disparities were seen by race/ethnicity, insurance, and income.
Conclusions: Diabetes, which is increasing among adolescents, is a significant risk factor for preterm labor and hypertensive disorders. Though the absolute number of adolescent pregnancies is decreasing, rates of hypertensive disorders have increased. Appropriate interventions are needed to ensure healthy outcomes for adolescents who are pregnant.
1. Introduction
Adolescent pregnancies, defined in the literature as occurring between ages 10 and 19, occurred in 16.7 per 1000 females and 4.6% of all live births in the United States in 2019 [1]. Although adolescent pregnancy childbearing rates have seen a dramatic decline since the 1990s, these rates remain substantially higher in the United States than other industrialized nations [1, 2]. Contributors to adolescent birth rates include racial and/or ethnic disparities, geopolitical influences on access to effective contraception and family planning, and other social determinants of health [3, 4].
Adolescent pregnancies are associated with increased rates of maternal and fetal complications, such as hypertensive disorders of pregnancy (including eclampsia), preterm birth, blood transfusions, congenital birth defects, low APGAR scores, suspected neonatal sepsis, and neonatal assisted ventilation [5]. The intersection of diabetes and adolescent pregnancy warrants even further concern. The diabetes epidemic has exponentially increased over the past few decades, with pregestational (type 1 and type 2) diabetes affecting 3.1%–6.8% of reproductive-aged women and 1%–2% of pregnancies in the United States. In 2019, an estimated 283,000 children and adolescents younger than age 20 years—or 35 per 10,000 US youths—had diagnosed diabetes [6]. Like adolescent pregnancies, diabetes in pregnancy is also associated with adverse maternal and fetal outcomes, including miscarriage, congenital fetal anomalies, preterm delivery, hypertensive disorders of pregnancy, fetal macrosomia, cesarean delivery, neonatal complications, maternal hyperglycemia, and worsening diabetic retinopathy and nephropathy [7].
Although the rates of gestational diabetes (GDM) in adolescent pregnancies have been explored, there is a paucity of data on the combination of pregestational diabetes and adolescent pregnancies, which is a vulnerable and high-risk subset of the obstetrics population in the United States. The objective of this study is threefold: (1) to describe the obstetrical risks of two common diabetes complications (hypertensive disorders and preterm labor with delivery) and determine how these outcomes are modified by diabetes status and type, (2) to evaluate what other patient and clinical factors predict increased risk for these serious complications in this understudied population, and (3) to evaluate the health expenditures associated with these complicated delivery admissions.
2. Methods
2.1. Study Population and Data Elements
We used the Healthcare Cost and Utilization Project (HCUP) Kids' Inpatient Database (KID) developed by the Agency for Healthcare Research and Quality (AHRQ) [8]. As previously described [9], KID is a publicly available deidentified database containing admissions from children and youth ≤20 years old (yo) from 42,000 hospitals across 46 states, sampled 80% of nonnewborn admissions. KID data are available every 3 years, but 2015 data were not released, and instead, 2016 data were released due to the transition from International Code of Diseases (ICD)-9 to ICD-10 coding.
We identified labor and delivery admissions in years 2006, 2009, 2012, 2016, and 2019 using ICD codes for normal delivery and other indications for care in labor and delivery (ICD-9 codes 650–659 and ICD-10 codes O61-O77, O80, and O82). We classified patients as having type 1 diabetes (T1D; ICD-9: 250.X1, 250.X3, ICD-10: E10), type 2 diabetes (T2D; ICD-9: 250.X0, 250.X2, ICD-10: E11), GDM (ICD-9: 648.0 without codes for T1D or T2D, ICD-10: O244), or having no diabetes (none of the aforementioned codes). Our clinical outcomes of hypertensive disorders included diagnoses of hypertension (HTN) complicating pregnancy childbirth and the puerperium (ICD-9: 642), pre-existing HTN complication pregnancy childbirth and the puerperium (ICD-10: O10), pre-existing HTN with pre-eclampsia (ICD-10: O11), pregestational HTN without significant proteinuria (ICD-10: O13), pre-eclampsia (ICD-10: O14), eclampsia (ICD-10: O15), and unspecified maternal HTN (ICD-10: O16). Preterm labor with delivery, henceforth referred to as preterm labor, was defined as a diagnosis for early onset of delivery, delivered with or without mention of antepartum condition (ICD-9: 644.21), and preterm labor with preterm delivery (ICD-10: O601). We adjusted our analyses for several patient level (e.g., age, race/ethnicity, income, comorbidities, and urban residence) and hospital level (e.g., region, size, and ownership) covariates. The definitions of these factors were described in a former publication [9]. Severity of illness on admission was determined using All Patient Refined Diagnosis-Related Groups, [10] which classifies patients according to their reasons for admission, severity of illness, risk of mortality, resource intensity, and disposition
2.2. Statistical Analysis
Weights provided by KID were used to generate nationally representative estimates of hospital admissions and all analyses accounted for the stratified sampling design. We used descriptive statistics to summarize the characteristics of all labor and delivery admissions. Counts less than or equal to 10 are noted as ≤10 per HCUP policy to preserve the amenity and privacy rights of those individuals. A chi-squared test was used to evaluate for unadjusted statistical differences in demographics, clinical features, and between diabetes status and type. We performed logistic regression to evaluate the adjusted association of diabetes status and type and hypertensive disorders and preterm labor after adjusting for confounding factors. All analyses were performed with Stata version 15.1 (StataCorp, College Station, TX). We retained missing/unknown categories for all variables with missing data, resulting in a consistent sample across all analyses. This study was institutional review board (IRB) exempt as it was a secondary analysis of pre-existing and deidentified data. This report follows the strengthening the reporting of observational studies in epidemiology (STROBE) reporting guidelines for cross-sectional studies.
3. Results
3.1. Characteristics of Labor and Delivery Admissions by Diabetes Type
There were 1,843,139 labor and delivery admissions across our study years, and the number of admissions decreased over time from 477,044 in 2006 to 251,439 in 2019 (Table 1). These admissions occurred most commonly in those >15 yo (98%) of non-Hispanic White race (35.6%), with public insurance (72.4%), living in urban areas (80.4%), of the lowest income quartile (41.2%), and in hospitals in the southern United States (44.5%). Of these admissions, 3327 (0.2%) had T1D, 3050 (0.2%) had T2D, and 12,909 (0.7%) had GDM. Pregnancies in the youngest age group (9–11 yo) occurred only in patients without diabetes. The racial–ethnic breakdown of admissions varied by diabetes type. Admissions with T1D and GDM occurred more commonly in those who were non-Hispanic White. T2D occurred similarly and most frequently in those who were non-Hispanic Black and non-Hispanic White race. Across all diabetes types, admissions occurred more prevalently in those with public insurance, with income in the lower two income quartiles, living in urban areas, and living in southern regions.
Table 1.
Patient and hospital characteristics of labor and delivery admissions by diabetes type.
| Characteristic | No diabetes | (%) | Type 1 | (%) | Type 2 | (%) | Gestational DM | (%) | Total | % | p-Value |
|---|---|---|---|---|---|---|---|---|---|---|---|
| n | 1,823,854 | — | 3327 | — | 3050 | — | 12,909 | — | 1,843,139 | — | — |
| Age | <0.001 | ||||||||||
| 9–11 | 381 | 0.0% | 0 | 0.0% | 0 | 0.0% | 0 | 0.0% | 381 | 0.0% | — |
| 12–14 | 18,866 | 1.0% | 25 | 0.8% | 20 | 0.7% | 35 | 0.3% | 18,946 | 1.0% | — |
| 15–17 | 377,105 | 20.7% | 554 | 16.6% | 392 | 12.9% | 1397 | 10.8% | 379,448 | 20.6% | — |
| 18–20 | 1,425,279 | 78.1% | 2748 | 82.6% | 2635 | 86.4% | 11,476 | 88.9% | 1,442,138 | 78.2% | — |
| Unknown | 2223 | 0.1% | 0 | 0.0% | <10 | 0.1% | 0 | 0.0% | 2226 | 0.1% | — |
| Race/ethnicity | |||||||||||
| White | 648,103 | 35.5% | 1593 | 47.9% | 857 | 28.1% | 5557 | 43.0% | 656,110 | 35.6% | <0.001 |
| Black | 338,730 | 18.6% | 628 | 18.9% | 878 | 28.8% | 1966 | 15.2% | 342,202 | 18.6% | — |
| Hispanic | 495,136 | 27.1% | 599 | 18.0% | 754 | 24.7% | 3790 | 29.4% | 500,280 | 27.1% | — |
| Asian or Pacific Islander | 24,413 | 1.3% | 29 | 0.9% | 34 | 1.1% | 249 | 1.9% | 24,724 | 1.3% | — |
| Native American | 17,546 | 1.0% | 27 | 0.8% | 48 | 1.6% | 269 | 2.1% | 17,890 | 1.0% | — |
| Other | 72,664 | 4.0% | 95 | 2.8% | 108 | 3.5% | 501 | 3.9% | 73,367 | 4.0% | — |
| Unknown | 227,262 | 12.5% | 357 | 10.7% | 371 | 12.2% | 576 | 4.5% | 228,566 | 12.4% | — |
| Payer | |||||||||||
| Private | 397,232 | 21.8% | 963 | 29.0% | 561 | 18.4% | 2927 | 22.7% | 401,684 | 21.8% | — |
| Public | 1,321,010 | 72.4% | 2197 | 66.0% | 2310 | 75.8% | 9486 | 73.5% | 1,335,002 | 72.4% | — |
| Self-pay | 55,428 | 3.0% | 40 | 1.2% | 85 | 2.8% | 202 | 1.6% | 55,755 | 3.0% | — |
| Other/unknown | 50,185 | 2.8% | 126 | 3.8% | 93 | 3.1% | 294 | 2.3% | 50,698 | 2.8% | — |
| Urbanicity | <0.001 | ||||||||||
| Urban areas | 1,466,140 | 80.4% | 2586 | 77.7% | 2450 | 80.3% | 10,265 | 79.5% | 1,481,440 | 80.4% | — |
| Nonurban areas | 348,293 | 19.1% | 725 | 21.8% | 589 | 19.3% | 2629 | 20.4% | 352,236 | 19.1% | — |
| Household income | |||||||||||
| Quartile 4 | 164,768 | 9.0% | 316 | 9.5% | 236 | 7.8% | 1063 | 8.2% | 166,384 | 9.0% | <0.001 |
| Quartile 3 | 364,165 | 20.0% | 700 | 21.1% | 479 | 15.7% | 2625 | 20.3% | 367,969 | 20.0% | — |
| Quartile 2 | 509,169 | 27.9% | 1002 | 30.1% | 796 | 26.1% | 3782 | 29.3% | 514,749 | 27.9% | — |
| Quartile 1 | 751,451 | 41.2% | 1245 | 37.4% | 1492 | 48.9% | 5300 | 41.1% | 759,489 | 41.2% | — |
| Unknown | 34,301 | 1.9% | 63 | 1.9% | 46 | 1.5% | 138 | 1.1% | 34,548 | 1.9% | — |
| Region of hospital | |||||||||||
| Northeast | 211,404 | 11.6% | 429 | 12.9% | 298 | 9.8% | 1450 | 11.2% | 213,581 | 11.6% | <0.001 |
| Midwest | 386,347 | 21.2% | 805 | 24.2% | 618 | 20.3% | 2901 | 22.5% | 390,671 | 21.2% | — |
| South | 811,250 | 44.5% | 1446 | 43.5% | 1551 | 50.9% | 5648 | 43.8% | 819,895 | 44.5% | — |
| West | 414,853 | 22.7% | 647 | 19.5% | 582 | 19.1% | 2910 | 22.5% | 418,993 | 22.7% | — |
| Hospital ownership | <0.001 | ||||||||||
| Private | 1,322,124 | 72.5% | 2351 | 70.7% | 2087 | 68.4% | 10,753 | 83.3% | 1,337,314 | 72.6% | — |
| Public | 501,730 | 27.5% | 976 | 29.3% | 963 | 31.6% | 2156 | 16.7% | 505,825 | 27.4% | — |
| Hospital size | <0.001 | ||||||||||
| Large | 1,073,768 | 58.9% | 2236 | 67.2% | 2015 | 66.1% | 7268 | 56.3% | 1,085,287 | 58.9% | — |
| Medium | 486,904 | 26.7% | 743 | 22.3% | 700 | 22.9% | 3337 | 25.9% | 491,684 | 26.7% | — |
| Small | 239,815 | 13.1% | 287 | 8.6% | 285 | 9.4% | 2295 | 17.8% | 242,682 | 13.2% | — |
| Unknown | 23,367 | 1.3% | 60 | 1.8% | 49 | 1.6% | <10 | 0.1% | 23,485 | 1.3% | — |
| Year | <0.001 | ||||||||||
| 2006 | 475,604 | 26.1% | 621 | 18.7% | 659 | 21.6% | 160 | 1.2% | 477,044 | 25.9% | — |
| 2009 | 454,795 | 24.9% | 634 | 19.0% | 722 | 23.7% | 124 | 1.0% | 456,275 | 24.8% | — |
| 2012 | 359,306 | 19.7% | 675 | 20.3% | 655 | 21.5% | 99 | 0.8% | 360,736 | 19.6% | — |
| 2016 | 290,039 | 15.9% | 752 | 22.6% | 534 | 17.5% | 6321 | 49.0% | 297,645 | 16.1% | — |
| 2019 | 244,110 | 13.4% | 645 | 19.4% | 480 | 15.7% | 6204 | 48.1% | 251,439 | 13.6% | — |
Labor and delivery admissions were more complicated, lengthy, and costly in patients with diabetes compared to those without diabetes. Patients without diabetes more often had admissions classified as having a minor severity of illness (60%), while those with GDM had primarily a moderate severity of illness (62.3%), and those with T1D and T2D had a major severity of illness (56.3% and 58.8%, respectively). The mean length of stay and admission charge were 2.59 days (95% CI 2.58–2.60) and $15,316 ($15,113–$15,517) in those without diabetes and highest in those with T1D at 4.81 days (4.58–5.04 days) and $29,279 ($27,942–$30,616). Those with diabetes were most likely to have comorbid conditions such as obesity, renal disease, multiple gestations, and hospital complications such as fetal abnormality and premature rupture of membranes (PROM; Table 2). Delivery by cesarean section occurred at a frequency of 19% in the general population. Among those with diabetes, cesareans occurred most frequently in those with T1D (50.6%), followed by T2D (42.7%) and GDM (27.3%).
Table 2.
Clinical characteristics of labor and delivery admissions by diabetes type.
| Characteristic | No diabetes | (%) | Type 1 | (%) | Type 2 | (%) | Gestational | (%) | Total | (%) | p-Value |
|---|---|---|---|---|---|---|---|---|---|---|---|
| n | 1,823,854 | 99.0% | 3327 | 0.2% | 3050 | 0.2% | 12,909 | 0.7% | 1,843,139 | 100% | — |
| Preterm labor | 104,246 | 5.7% | 717 | 21.5% | 513 | 16.8% | 875 | 6.8% | 106,351 | 5.8% | <0.001 |
| Hypertensive disorders | 179,982 | 9.9% | 1177 | 35.4% | 1152 | 37.8% | 3214 | 24.9% | 185,525 | 10.1% | <0.001 |
| Multiple gestations | 9553 | 0.5% | 15 | 0.5% | 22 | 0.7% | 135 | 1.1% | 9726 | 0.5% | <0.001 |
| PROM | 83,728 | 4.6% | 240 | 7.2% | 172 | 5.6% | 940 | 7.3% | 85,080 | 4.6% | <0.001 |
| Premature PROMb | 16,891 | 0.9% | 116 | 3.5% | 61 | 2.0% | 427 | 3.3% | 17,494 | 0.9% | <0.001 |
| Fetal abnormality | 22,085 | 1.2% | 144 | 4.3% | 95 | 3.1% | 154 | 1.2% | 22,478 | 1.2% | <0.001 |
| Obesity | 68,462 | 3.8% | 303 | 9.1% | 701 | 23.0% | 2582 | 20.0% | 72,049 | 3.9% | <0.001 |
| Renal disease | 1327 | 0.1% | 68 | 2.1% | 34 | 1.1% | 33 | 0.3% | 1462 | 0.1% | <0.001 |
| C-section | 343,866 | 18.9% | 1685 | 50.6% | 1302 | 42.7% | 3526 | 27.3% | 350,379 | 19.0% | <0.001 |
| Died | 75 | 0.0% | <10 | 0.0% | <10 | 0.1% | <10 | 0.0% | 79 | 0.0% | 0.4286 |
| Severity of illness | <0.001 | ||||||||||
| No class | 274 | 0.0% | <10 | 0.0% | <10 | 0.0% | <10 | 0.0% | 278 | 0.0% | — |
| Minor | 1,101,877 | 60.4% | 41 | 1.2% | 36 | 1.2% | 2894 | 22.4% | 1,104,848 | 59.9% | — |
| Moderate | 614,776 | 33.7% | 1303 | 39.2% | 1155 | 37.9% | 8044 | 62.3% | 625,279 | 33.9% | — |
| Major | 102,977 | 5.7% | 1873 | 56.3% | 1794 | 58.8% | 1911 | 14.8% | 108,554 | 5.9% | — |
| Extreme | 3950 | 0.2% | 109 | 3.3% | 64 | 2.1% | 58 | 0.4% | 4180 | 0.2% | — |
| LOS (days)a | 2.59 | 2.58–2.60 | 4.81 | 4.58–5.04 | 4.23 | 3.99–4.46 | 3.30 | 3.23–3.80 | 1.60 | 2.59–2.62 | — |
| Total charges (2019 dollars)a | $15,316 | $15,113– $15,517 | $29,279 | $27,942–$30,616 | $25,237 | $23,946–$26,527 | $24,160 | $23,430–$24,890 | $15,420 | 15,215–15,623 | — |
aMean reported with 95% confidence interval.
bPrevalence reported for only years 2016 and 2019.
3.2. Hypertensive Disorders
Hypertensive disorders were observed in 10% of labor and delivery admissions and were common among adolescents with T2D (37.8%), followed by those with T1D (35.4%) and GDM (24.9%). Those with hypertensive disorders were more likely to be obese (10.9% vs. 3.1%), have renal disease (0.4% vs. 0%), multiple gestations (1.2% vs. 0.5%), and fetal abnormality (1.5% vs. 1.2%). PROM occurred less in those with hypertensive disorders (3.5% vs. 4.7%) (Table 3). C-sections occurred in 33.5% with hypertensive disorder compared to 14.4% in those without. Length of stay and hospital charge were higher in those with a hypertensive disorder (3.60 [95% CI 3.58–3.63] and $22,784 [$22,422–23,145] vs. 2.49 days [95% CI 248–2.50] and $14,596 [95% CI $14,404–$14,7882]).
Table 3.
Clinical characteristics of pregnancies complicated by hypertensive disorder or preterm labor.
| Characteristic | Hypertensive disorder | Preterm labor | ||||||
|---|---|---|---|---|---|---|---|---|
| NO | (%) | YES | (%) | NO | (%) | YES | (%) | |
| N | 1,657,614 | — | 185,525 | — | 1,736,788 | — | 106,351 | — |
| Obesity | 51,755 | 3.1% | 20,293 | 10.9% | 68,434 | 3.9% | 3615 | 3.4% |
| Renal disease | 733 | 0.0% | 729 | 0.4% | 1241 | 0.1% | 221 | 0.2% |
| Multiple gestations | 7503 | 0.5% | 2222 | 1.2% | 5118 | 0.3% | 4608 | 4.3% |
| PROM | 78,636 | 4.7% | 6445 | 3.5% | 69,681 | 4.0% | 15,399 | 14.5% |
| Premature PROMb | 183,787 | 99.1% | 1738 | 0.9% | 17,494 | 0.9% | 97,199 | 91.4% |
| Fetal abnormality | 19,702 | 1.2% | 2776 | 1.5% | 19,399 | 1.1% | 3079 | 2.9% |
| Cesarean section | 288,204 | 17.4% | 62,174 | 33.5% | 322,951 | 18.6 | 27,427 | 25.8% |
| LOSa | 2.49 | 2.48–2.50 | 3.60 | 3.58–3.63 | 2.54 | 2.53–2.55 | 3.72 | 3.67–3.77 |
| Total chargesa (2019 dollars) | $14,596 | $14,404–$14,788 | $22,784 | $ 22,422–$23,145 | $15,091 | $14,892–$15,290 | $20,784 | $20,410–$21,157 |
Abbreviations: LOS, length of stay; PROM, premature rupture of membranes.
aMean and 95% confidence interval reported.
bPrevalence reported for only years 2016 and 2019.
In adjusted models, the odds of hypertensive disorders increased over time, with a 1.85 higher odds in 2019 as compared to 2006 (95% CI 1.77–1.94, p < 0.001; Table 4). As compared to adolescents without diabetes, those with T1D had the highest odds of hypertensive disorder (OR 4.32, 95% CI 3.91–4.74, p < 0.001), followed by T2D (OR 4.18, 95% CI 3.79–4.61, p < 0.001) and GDM (OR 1.99, 95% CI 1.89–2.10, p < 0.001). Other notable predictors of hypertensive disorders were being in the age group 12–14 yo compared to 18–20 yo (OR 1.29, 95% CI 1.22–1.36, p < 0.001) and being of Black race (OR 1.26, 95% CI 1.23–1.30, p < 0.001) or Native American race (OR 1.15, 95% CI 1.05–1.23, p < 0.003) compared to the White race. Those of Hispanic and Asian ancestry had lower odds of hypertensive disorder (OR 0.83, 0.81–0.85, p < 0.001, and OR 0.76, 0.68–0.84, p < 0.001, respectively). Patients of the lowest income quartile had higher odds for hypertensive disorder compared to the highest income quartile (OR 1.06, 95% CI 1.03–1.10, p < 0.001). Those with multiple gestations (OR 2.33, 95% CI 2.19–2.47, p < 0.001), obesity (OR 3.19, 95% CI 3.11–3.28, p < 0.001), and renal disease (OR 6.59, 95% CI 5.76–7.55, p < 0.001) were also at increased odds for a hypertensive disorder. We performed a sensitivity analysis to evaluate for differences in the predictors of severe hypertensive disorders (e.g., eclampsia, HELLP [hemolysis, elevated liver enzymes and low platelets], and pre-existing HTN with superimposed pre-eclampsia) compared to milder hypertensive disorder (e.g., mild transient HTN, mild pre-eclampsia, etc.) and found a similar trend as previously noted with patients with T1D possessing the highest risk (OR 2.19, 95% CI 1.88–2.54).
Table 4.
Odds of preterm labor and hypertensive disorder.
| Variable | Preterm labor | Hypertensive disorder | |||||||
|---|---|---|---|---|---|---|---|---|---|
| OR | 95% CI | p-Value | OR | 95% CI | p-Value | ||||
| Age | |||||||||
| 9–11 | 0.21 | 0.07 | 0.66 | 0.008 | 0.34 | 0.16 | 0.72 | 0.005 | |
| 12–14 | 1.77 | 1.66 | 1.88 | <0.001 | 1.29 | 1.22 | 1.36 | <0.001 | |
| 15–17 | 1.15 | 1.12 | 1.17 | <0.001 | 1.07 | 1.05 | 1.08 | <0.001 | |
| 18–20 | Reference | Reference | |||||||
| Unknown | 1.17 | 0.92 | 1.49 | 0.194 | 0.69 | 0.52 | 0.92 | 0.010 | |
| Race/ethnicity | |||||||||
| White | Reference | Reference | |||||||
| Black | 1.27 | 1.24 | 1.31 | <0.001 | 1.26 | 1.23 | 1.30 | <0.001 | |
| Hispanic | 0.94 | 0.92 | 0.97 | <0.001 | 0.83 | 0.81 | 0.85 | <0.001 | |
| Asian or Pacific Islander | 1.23 | 1.15 | 1.32 | <0.001 | 0.76 | 0.68 | 0.84 | <0.001 | |
| Native American | 1.08 | 0.98 | 1.18 | 0.120 | 1.15 | 1.05 | 1.26 | 0.003 | |
| Other/unknown | 1.05 | 1.00 | 1.09 | 0.039 | 0.95 | 0.91 | 0.99 | 0.006 | |
| Diabetes status and type | |||||||||
| No diabetes | Reference | Reference | |||||||
| Type 1 | 4.53 | 4.09 | 5.02 | <0.001 | 4.32 | 3.94 | 4.74 | <0.001 | |
| Type 2 | 3.35 | 2.96 | 3.78 | <0.001 | 4.18 | 3.79 | 4.61 | <0.001 | |
| Gestational diabetes | 1.18 | 1.08 | 1.28 | <0.001 | 1.99 | 1.89 | 2.10 | <0.001 | |
| Co-existing conditionsa | |||||||||
| Multiple gestations | 14.94 | 14.21 | 15.71 | <0.001 | 2.33 | 2.19 | 2.47 | <0.001 | |
| Obesity | 0.79 | 0.75 | 0.83 | <0.001 | 3.19 | 3.11 | 3.28 | <0.001 | |
| Renal disease | 2.36 | 1.96 | 2.83 | <0.001 | 6.59 | 5.76 | 7.55 | <0.001 | |
| Payer | |||||||||
| Private | Reference | Reference | |||||||
| Public | 1.03 | 1.01 | 1.06 | 0.002 | 0.94 | 0.92 | 0.96 | <0.001 | |
| Self-pay or no charge | 1.23 | 1.17 | 1.30 | <0.001 | 0.82 | 0.77 | 0.87 | <0.001 | |
| Other/unknown | 1.12 | 1.06 | 1.19 | <0.001 | 0.95 | 0.91 | 1.00 | 0.036 | |
| Household income | |||||||||
| Quartile 1 | 1.09 | 1.05 | 1.13 | <0.001 | 1.06 | 1.03 | 1.10 | <0.001 | |
| Quartile 2 | 1.04 | 1.01 | 1.08 | 0.023 | 1.01 | 0.98 | 1.04 | 0.657 | |
| Quartile 3 | 1.01 | 0.98 | 1.05 | 0.371 | 1.01 | 0.98 | 1.04 | 0.350 | |
| Quartile 4 | Reference | Reference | |||||||
| Unknown | 1.05 | 0.97 | 1.12 | 0.217 | 1.09 | 1.00 | 1.19 | 0.048 | |
| Urbanicity | |||||||||
| Nonurban areas | 0.96 | 0.93 | 0.99 | 0.004 | 0.91 | 0.89 | 0.94 | <0.001 | |
| Urban areas | Reference | Reference | |||||||
| Unknown | 1.09 | 0.93 | 1.28 | 0.274 | 0.98 | 0.86 | 1.11 | 0.698 | |
| Region of hospital | |||||||||
| Northeast | Reference | Reference | |||||||
| Midwest | 1.03 | 0.98 | 1.08 | 0.306 | 1.00 | 0.95 | 1.06 | 0.904 | |
| South | 1.04 | 1.00 | 1.09 | 0.055 | 1.07 | 1.02 | 1.12 | 0.009 | |
| West | 0.97 | 0.92 | 1.01 | 0.161 | 0.91 | 0.86 | 0.96 | <0.001 | |
| Hospital ownership | |||||||||
| Public | 1.13 | 1.09 | 1.17 | <0.001 | 1.22 | 1.17 | 1.27 | <0.001 | |
| Private | Reference | Reference | |||||||
| Hospital size | |||||||||
| Small | 0.76 | 0.73 | 0.80 | <0.001 | 0.87 | 0.84 | 0.91 | <0.001 | |
| Medium | 0.89 | 0.86 | 0.92 | <0.001 | 0.91 | 0.87 | 0.94 | <0.001 | |
| Large | Reference | Reference | |||||||
| Unknown | 1.29 | 1.12 | 1.48 | <0.001 | 1.24 | 1.09 | 1.41 | 0.001 | |
| Year | |||||||||
| 2006 | Reference | Reference | |||||||
| 2009 | 1.00 | 0.97 | 1.04 | 0.930 | 1.13 | 1.09 | 1.17 | <0.001 | |
| 2012 | 0.95 | 0.91 | 0.99 | 0.017 | 1.19 | 1.13 | 1.25 | <0.001 | |
| 2016 | 1.15 | 1.10 | 1.20 | <0.001 | 1.38 | 1.32 | 1.44 | <0.001 | |
| 2019 | 1.07 | 1.03 | 1.12 | 0.001 | 1.85 | 1.77 | 1.94 | <0.001 | |
Note: Variables included in this adjusted model include age, race/ethnicity, diabetes status/type, select co-existing conditions (multiple gestations, obesity, and renal disease), payer, household income, urbanity, region of the hospital, hospital ownership, hospital size, and year.
aVariables listed under co-existing conditions are all binary variables (condition present vs. absent), so the reference level is condition absent, but this has been omitted from the table for brevity.
3.3. Preterm Labor
Preterm labor occurred in 5.8% of pregnancies, and those with T1D had the highest proportion of preterm labor at 21.5%, followed by T2D (16.8%) and GDM (6.8%). Those with preterm labor were more likely to have multiple gestations (4.3% vs. 0.3%), PROM (14.5% vs. 4%), and fetal abnormality (2.9% vs. 1.1%). C-sections occurred in 25.7% of deliveries with preterm labor compared to 18.6% in those without. Length of stay and hospital charge were higher in those with preterm labor (3.72 days [95% CI 3.67–3.77] and $20,784 [95% CI $20,410–21,157] vs. 2.54 days [95% CI 2.53–2.55] and $15,091 [95% CI $14,892–$15,290]).
In adjusted models, the odds of preterm labor varied over time but trended upward with the odds of preterm in 2016 (OR 1.15, 95% CI 1.10–1.2, p < 0.001) and 2019 (OR 1.07, 95% CI 1.03–1.12, <p=0.001) being higher than in 2006. Patients with T1D had the highest odds for preterm labor (OR 4.53, 95% CI 4.09–5.02), followed by T2D (OR 2.96, 95% CI 2.96–3.78, p < 0.001) and GDM (OR 1.18, 95% CI 1.08–1.28, p < 0.001) compared to those without diabetes. Other notable predictors of preterm labor include age 12–14 (OR 1.77, 95% CI 1.66–1.88, p < 0.001) compared to 18–20 yo and being of Black race (OR 1.27 (OR 1.24−1.31, p < 0.001) or Asian race (OR 1.23, OR 1.15–1.32, p < 0.001). Those of Hispanic ethnicity were at lower risk of preterm labor (OR 0.94, 95% CI 0.92–0.97, p < 0.001). Patients without private insurance (e.g., public, self-pay, and others) and those in the lowest two income quartile (compared to highest income quartile) also had higher odds of preterm labor. Lastly, those with multiple gestations (OR 14.91, 95% CI 14.21–15.71, p < 0.001) and renal disease (OR 2.36, 95% CI 1.96–2.83, p < 0.001) had increased odds for preterm labor, while those with obesity had lower odds (OR 0.79 95% CI 0.75–0.83, p < 0.001).
4. Discussions
While we found that adolescent pregnancies have decreased over the past two decades, our study shows concerning rises in odds of both hypertensive disorders in pregnancy and preterm labor. The decreasing frequency of adolescent pregnancies over the time period analyzed matches the temporal trends described previously in the literature [1]. However, social and geographic determinants of health, including public insurance, lower socioeconomic status, urban locale, and location in Southern states of the United States, continue to be associated with adolescent pregnancies in those with and without diabetes. These data suggest progress toward the Healthy People 2030 goal of reducing pregnancies in adolescents but not of the overarching goal of eliminating disparities and achieving health equity [11]. A root cause analysis must be conducted to determine the barriers to decreasing adolescent birth rates in these populations [12].
Adolescent pregnancies are at inherently higher risk of adverse pregnancy outcomes, and this risk is further potentiated by concomitant diabetes. We report higher odds of multiple maternal and fetal sequelae, including fetal anomaly, preterm delivery, hypertensive disorders, cesarean delivery, and increased costs of care during the labor and delivery admission, when compared to adolescent pregnancies without diabetes. The estimated costs of adolescent pregnancies are likely further compounded when the adolescents enter pregnancy with a comorbid diabetes diagnosis.
The prevalence of diabetes in youth has increased significantly in the last two decades and continues to rise in recent years [13, 14]. While the pregnancies in persons with diabetes were relatively stable during this study, in the setting of decreasing rates of overall adolescent deliveries, this is concerning for a potential increase in pregnancies in those with diabetes over time, which may result in increased pregnancy complications (e.g., hypertensive disorders and preterm labor) in this population. Additionally, rising rates of obesity are likely to increase the risk of some pregnancy complications. This may not only have short-term implications but also impact long-term outcomes given the association of future cardiovascular disease in women who have hypertensive disorders during pregnancy [15]. We found that obesity was associated with lower rate of preterm labor. This is consistent with existing literature showing that obese mothers are more likely to have postterm birth than preterm birth [16].
Preconception optimization of diabetes care and reproductive planning discussions continue to be suboptimal in all populations but particularly so in adolescents [17, 18]. These findings may be translated as a call to arms for pediatricians and endocrinologists managing adolescents with diabetes to be trained in contraception and reproductive health counseling and to include gynecologists and maternal–fetal medicine specialists in the multidisciplinary management of this population.
This study has some limitations, including those inherent to retrospective studies involving databases derived from administrative coding. This includes misclassification of diabetes type or pregnancy complications and the inability to account for change in coding practices in the setting of ICD-9 to ICD-10 change, which, for example, may account for the significant increase in case detection in GDM after transition to ICD-10. Furthermore, we were unable to pair data on the neonates resulting from these pregnancies, which carry additional public health and societal implications. Despite these limitations, this study captures the most recent estimate of adolescent pregnancies suing the largest national population-based database of pediatric discharges, which strengthen the ability to study this specific high-risk subpopulation of adolescent pregnancies. In addition to describing the associated clinical outcomes, we also evaluated the healthcare costs associated with the comorbid pregnancies during the labor and delivery admission. Future work will focus on the impact duration of diabetes, glycemic control, approach to diabetes management, and how they modify pregnancy outcomes in this population.
5. Conclusions
Pregnancy in adolescence is associated with higher rates of complications, and when compounded by a diagnosis of diabetes, these risks increase further. Given the rising rates of diabetes in this age group, better strategies are needed to improve reproductive planning discussions and prenatal optimization in this population and high risk for poor outcomes.
Data Availability Statement
The data that support the findings of this study are available from the Agency for healthcare Research and Quality's (AHRQ) Healthcare Cost and Utilization Project (HCUP). Restrictions apply to the availability of these data, which were used under license for this study.
Disclosure
The opinions expressed in this article are the author's own and do not reflect the view of the National Institutes of Health, the Department of Health and Human Services, Department of Veterans Affairs or the United States government. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Conflicts of Interest
The authors declare no conflicts of interest.
Author Contributions
E.E. contributed to the study conceptualization and design, the data analysis, result interpretation, and the first draft of the manuscript and its subsequent revisions. C.S.H. contributed to the design, result interpretation, and initial draft of the manuscript and subsequent revisions. M.R. contributed to the study design and result interpretation. T.M. contributed to the data analysis and revision of the manuscript. T.M. contributed to the revision of the manuscript. L.E.W. contributed to the study conceptualization and design, result interpretation, and revision of the manuscript.
Funding
We wish to also acknowledge the generous funding support from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) K23DK132482 (PI: Everett), L40DK129996 (PI: Everett), and K01DK116932 (PI: Wisk). Research reported in this publication was supported by the Office of Disease Prevention (ODP), the Office of Nutrition Research (ONR), the Chief Officer for Scientific Workforce Diversity (COSWD), and the Office of Behavioral and Social Sciences Research (OBSSR) of the National Institutes of Health under award number U24DK132746-01, UCLA LIFT-UP (Leveraging Institutional support for Talented, Underrepresented Physicians and/or Scientists).
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are available from the Agency for healthcare Research and Quality's (AHRQ) Healthcare Cost and Utilization Project (HCUP). Restrictions apply to the availability of these data, which were used under license for this study.