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. Author manuscript; available in PMC: 2024 Oct 18.
Published in final edited form as: Birth Defects Res. 2022 Nov 17;115(1):67–78. doi: 10.1002/bdr2.2130

Factors Associated with Non-Syndromic Anotia and Microtia, Texas, 1999–2014

Jeremy M Schraw 1, JP Woodhouse 1, Renata H Benjamin 2, Charles J Shumate 3, Joanne Nguyen 3,4, Mark A Canfield 3, AJ Agopian 2, Philip J Lupo 1
PMCID: PMC11488818  NIHMSID: NIHMS2027018  PMID: 36398384

Abstract

BACKGROUND:

Few risk factors have been identified for non-syndromic anotia/microtia (A/M).

METHODS:

We obtained data on cases and a reference population of all livebirths in Texas for 1999–2014 from the Texas Birth Defects Registry (TBDR) and Texas vital records. We estimated prevalence ratios (PRs) and 95% confidence intervals (CIs) for A/M (any, isolated, non-isolated, unilateral, bilateral) using Poisson regression. We evaluated trends in prevalence rates using Joinpoint regression.

RESULTS:

We identified 1,322 cases, of whom 982 (74.3%) had isolated and 1,175 (88.9%) had unilateral A/M. Prevalence was increased among males (PR 1.3, CI 1.2–1.4), offspring of women with less than high school education (PR 1.3, CI 1.1–1.5), diabetes (PR 2.0, CI 1.6–2.4), or age 30–39 versus 20–29 years (PR 1.2, CI 1.0–1.3). The prevalence was decreased among offspring of non-Hispanic Black versus White women (PR 0.6, CI 0.4–0.8) but increased among offspring of Hispanic women (PR 2.9, CI 2.5–3.4) and non-Hispanic women of other races (PR 1.7, CI 1.3–2.3). We observed similar results among cases with isolated and unilateral A/M. Sex disparities were not evident for non-isolated or bilateral phenotypes, nor did birth prevalence differ between offspring of non-Hispanic Black and non-Hispanic White women. Maternal diabetes was more strongly associated with non-isolated (PR 4.5, CI 3.2–6.4) and bilateral A/M (PR 5.0, CI 3.3–7.7). Crude prevalence rates increased throughout the study period (annual percent change: 1.82).

CONCLUSION:

We identified differences in the prevalence of non-syndromic A/M by maternal race/ethnicity, education, and age, which may be indicators of unidentified social/environmental risk factors.

Keywords: anotia, microtia, epidemiology

INTRODUCTION

Anotia and microtia (A/M) are birth defects in which the outer ear is underdeveloped at birth. Severity is graded from I-IV, with grades I-III representing increasingly severe hypoplasia (microtia), and grade IV representing the absence of the outer ear (anotia) (Bly et al., 2016; Luquetti et al., 2011). The large majority of cases are diagnosed with microtia: in a joint analysis of data from >30 global birth defects surveillance programs, Luquetti et al. reported an overall prevalence of 1.55 (95% confidence interval [CI], 1.50–1.60) per 10,000 livebirths for microtia and 0.36 (95% CI 0.34–0.38) per 10,000 livebirths for anotia (Luquetti et al., 2011). A/M demonstrates a slight male preponderance (Cabrejo et al., 2019; Michalski et al., 2015; van Nunen et al., 2014). While 80–90% of cases are unilateral, the condition can occur bilaterally (Shaw et al., 2004; Stallings et al., 2018; Suutarla et al., 2007) and one-quarter to one-half of affected individuals have co-occurring birth defects (Guo et al., 2021; Llano-Rivas et al., 1999; Mastroiacovo et al., 1995; Rollnick et al., 1987; Stoll et al., 2016; Suutarla et al., 2007). Bilateral presentation and co-occurring birth defects are more common among the 10–40% of affected individuals with A/M syndromes (Cabrejo et al., 2019; Llano-Rivas et al., 1999), which include oculo-auriculo-vertebral spectrum (OAVS) (Tingaud-Sequeira et al., 2022), CHARGE syndrome (Blake & Prasad, 2006), Treacher-Collins syndrome (van Nunen et al., 2014), and trisomies 13, 18, and 21 (Stoll et al., 2016).

Globally, prevalence per 10,000 livebirths ranges from 0.83 in France (Harris et al., 1996), to approximately 2.0 in the United States of America (Shaw et al., 2004; Stallings et al., 2018), to 17.4 in Quito, Ecuador (Castilla & Orioli, 1986). In the U.S., the highest rates have generally been observed among Hispanic and Asian populations, and the lowest in non-Hispanic White and non-Hispanic Black populations (Cabrejo et al., 2019; Canfield et al., 2009; Canfield et al., 2014; Harris et al., 1996; Le et al., 2019; Shaw et al., 2004). While a previous report found no trend in birth prevalence in Texas between 1999–2005 (Canfield et al., 2009), earlier studies from Hawaii and France found evidence that the prevalence of A/M was increasing (Forrester & Merz, 2005; Harris et al., 1996). Regarding potential risk factors, two reports from the National Birth Defects Prevention Study (NBDPS) found increased risk of A/M among children of foreign-born relative to U.S.-born women (Hoyt et al., 2014; Hoyt et al., 2019), suggesting a potential role for social/environmental determinants in racial and ethnic disparities. Indeed, maternal exposures that vary by race/ethnicity have been associated with A/M. These include diabetes mellitus (Schraw et al., 2021; Tinker et al., 2019), diet and folic acid supplementation (Ma et al., 2012), and age at delivery (Canfield et al., 2009; Hollier et al., 2000; Ryan et al., 2019). Nonetheless, the mechanisms underlying racial and ethnic differences in the U.S. are poorly understood.

Moderate-to-severe hearing loss is frequent in the context of A/M (Mandelbaum et al., 2017). This, coupled with the high burden of co-occurring birth defects, the necessity for reconstructive surgery (Bly et al., 2016), psychosocial challenges manifesting across the life course (Hamlet & Harcourt, 2020; Li et al., 2010), and lack of modifiable risk factors make A/M a public health concern. Our objectives were to: 1) identify factors associated with non-syndromic A/M overall as well as isolated, non-isolated, unilateral, and bilateral A/M specifically; and 2) determine whether the birth prevalence of non-syndromic A/M in Texas changed during the study period, using data from a large, population-based birth defects registry. In doing so, we expand upon a previous report which used TBDR data for the delivery years 1999–2005 (Canfield et al., 2009) by including data on approximately 600 additional cases and evaluating maternal diabetes, reproductive history, pre-pregnancy body mass index (BMI), and paternal race/ethnicity.

METHODS

Study Population

We obtained data on all cases with A/M regardless of pregnancy outcome for the delivery period 1999–2014 from the Texas Birth Defects Registry (TBDR). The TBDR is a population-based, active surveillance system that identifies infants and pregnancies with birth defects within one year of delivery among Texas residents. Staff members from the TBDR routinely visit or access records from all delivery and pediatric hospitals, as well as birthing centers and midwife facilities in Texas, to ascertain cases. Potential cases are identified by review of discharge lists and unit logs checking for parameters such as preterm births and stillbirths, International Statistical Classification of Diseases and Related Health Problems (ICD) codes for birth defects or text descriptions relevant to birth defects. Medical records for each potential case are reviewed and, if they meet case definitions, are abstracted into a web-based system. There, the data undergo extensive quality checks, including a review of selected records (about 50%) by board-certified clinical geneticists. In the TBDR, birth defect diagnoses are coded using the Centers for Disease Control and Prevention modification of the British Paediatric Association Classification of Diseases, commonly referred to as BPA codes. Cases with anotia or microtia (BPA codes 744.01 and 744.21, respectively) were classified as isolated if they had no additional major anomalies, and non-isolated otherwise. We considered major anomalies to be all those not considered minor or conditional in the National Birth Defects Prevention Study (Rasmussen et al., 2003) or by Registry clinicians (Langlois et al., 2022). We classified cases as unilateral if one ear was affected and bilateral if both ears were affected. Cases diagnosed with chromosomal or genetic syndromes were excluded from our analyses.

TBDR data were linked to Texas birth and fetal death records to establish maternal residence, to obtain perinatal and demographic information on cases and to provide denominator data for all livebirths in Texas during the study period. TBDR performs deterministic and probabilistic linkage to birth and death records based on unique and personal identifiers. We classified maternal education as less than high school, high school diploma, or greater than high school, maternal age as <20, 20–29, 30–39, and ≥40 years, and maternal race/ethnicity as Hispanic (any race), non-Hispanic White, non-Hispanic Black, or other non-Hispanic, with the latter including individuals reporting Asian/Pacific Islander and American Indian/Alaska Native (AIAN) race. We did not consider parental race/ethnicity in our analysis as this information was missing for ~15% of cases and livebirths.

Statistical Analysis

We summarized demographic and perinatal characteristics of cases and livebirths, as well as the distributions of selected co-occurring major birth defects among cases, using counts and proportions. We used the chi-square (X2) test to compare the distributions of categorical variables between cases and controls.

We used the Joinpoint program to estimate the annual percent change (APC) in the crude prevalence of non-syndromic A/M per 10,000 livebirths during 1999–2014, comparing goodness-of-fit for models with no, one, and two joinpoints by permutation testing (Kim et al., 2000).

Using Poisson regression, we estimated prevalence ratios (PR) and 95% CIs of A/M according to selected demographic factors; those associated with A/M at p<0.05 in univariable models were included in a multivariable regression model. A priori, we chose to evaluate sex, plurality, and the following maternal characteristics: age at delivery, race/ethnicity, education, pre-pregnancy body mass index (BMI; kg/m2, from 2005 onwards), diabetes, residence in a county on the Texas-Mexico border, number of previous livebirths and number of previous pregnancies not resulting in livebirths. We removed subjects missing information on these covariates from our models. Because we could not determine whether maternal diabetes was diagnosed prior to or during gestation for delivery years 1999–2004, we combined these exposures in our primary analysis of data from 1999–2014 (“maternal diabetes”). We then evaluated pre-gestational and gestational diabetes separately, using data for delivery years 2005–2014. We chose to evaluate residence on the Texas-Mexico border because high rates of birth defects have been observed in border counties (Lupo et al., 2011; Suarez et al., 2012), and because there are conflicting reports regarding the association between place of residence and A/M in the literature (Benavides et al., 2021; Deng et al., 2016; Mastroiacovo et al., 1995).

For all analyses, a p-value <0.05 was considered statistically significant. All analyses were conducted in the R statistical software environment (version 3.6.0; R Core Team, Vienna, Austria).

Ethical Approvals

This study was approved by the institutional review boards of Texas Department of State Health Services, University of Texas School of Public Health, and Baylor College of Medicine, and conducted in accordance with the principles of the Declaration of Helsinki. The requirement for written informed consent was waived as this study was a secondary analysis of de-identified data.

RESULTS

Non-syndromic anotia/microtia

We identified 6,181,631 livebirths and 1,322 cases without known chromosomal or genetic syndromes (Table 1). Among cases, 1,236 (93.5%) were diagnosed with microtia and 86 (6.5%) with anotia; 982 (74.3%) of defects were isolated, and 1,175 (88.9%) were unilateral. Among 1,098 (93.4%) individuals for whom laterality was known, the right ear was affected in 711 (64.8%). A larger proportion of cases than livebirths were male. Relative to livebirths, maternal Hispanic ethnicity, diabetes, and less than high school education were more often reported among cases, whereas maternal non-Hispanic Black race/ethnicity was less often reported (each p<0.001). Similar differences in race/ethnicity were observed when comparing fathers of cases to fathers of all livebirths. We observed the highest prevalence of maternal diabetes among cases with non-isolated (15.9%) or bilateral (17.7%) A/M. Conversely, sex ratio disparities were somewhat less pronounced among cases with bilateral A/M: 52.4% of bilateral cases were male, relative to 57.5% of all cases. There was a significant positive trend in the crude rate of A/M across the study period (APC=1.82, p<0.05), based on the optimal model with zero joinpoints (Figure 1).

Table 1.

Demographic and Maternal Characteristics of Livebirths and Cases with Non-Syndromic Anotia/Microtia, Texas, 1999–2014, N (%).

Livebirths All cases Isolated Non-Isolated Unilateral Bilateral
TOTAL 6,181,631 1,322 982 340 1,175 147
Sex
 Male 3,159,950 (51.1) 760 (57.5) 571 (58.1) 189 (55.6) 683 (58.1) 77 (52.4)
 Female 3,021,681 (48.9) 561 (42.4) 411 (41.9) 150 (44.1) 492 (41.9) 69 (46.9)
Maternal Race/Ethnicity
 Non-Hispanic White 2,204,720 (35.7) 239 (18.1) 182 (18.5) 57 (16.8) 216 (18.4) 23 (15.6)
 Non-Hispanic Black 698,954 (11.3) 40 (3.0) 23 (2.3) 17 (5.0) 32 (2.7) 8 (5.4)
 Hispanic 3,004,303 (48.6) 976 (73.8) 734 (74.7) 242 (71.2) 873 (74.3) 103 (70.1)
 Other Non-Hispanic 266,324 (4.3) 52 (3.9) 38 (3.9) 14 (4.1) 46 (3.9) 6 (4.1)
Maternal Education
 < High School 1,739,482 (28.1) 520 (39.3) 391 (39.8) 129 (37.9) 468 (39.8) 52 (35.4)
 High School 1,742,822 (28.2) 349 (26.4) 264 (26.9) 85 (25.0) 305 (26.0) 44 (29.9)
 > High School 2,656,707 (43.0) 429 (32.5) 316 (32.2) 113 (33.3) 385 (32.8) 44 (29.9)
Maternal BMI (kg/m2) †
 Underweight (<18.5) 151,217 (2.4) 28 (2.1) 20 (2.0) 8 (2.4) 26 (2.2) 2 (1.4)
 Normal (18.5–24.9) 1,756,582 (28.4) 391 (29.6) 303 (30.9) 88 (25.9) 356 (30.3) 35 (23.8)
 Overweight (25.0–29.9) 1,025,864 (16.6) 227 (17.2) 177 (18.0) 50 (14.7) 198 (16.9) 29 (19.7)
 Obese (≥30.0) 1,009,993 (16.3) 242 (18.3) 162 (16.5) 80 (23.5) 212 (18.0) 30 (20.4)
Maternal Diabetes †
 Gestational or pre-gestational 246,821 (4.0) 113 (8.5) 59 (6.0) 54 (15.9) 87 (7.4) 26 (17.7)
 Pre-gestational 26,094 (0.7) 32 (3.5) 14 (2.1) 18 (7.8) 19 (2.4) 13 (12.5)
 Gestational 159,004 (4.0) 63 (7.0) 36 (5.4) 27 (11.6) 54 (6.8) 9 (8.7)
Paternal Race/Ethnicity
 Non-Hispanic White 1,943,234 (31.4) 223 (16.9) 169 (17.2) 54 (15.9) 201 (17.1) 22 (15.0)
 Non-Hispanic Black 553,597 (9.0) 37 (2.8) 23 (2.3) 14 (4.1) 30 (2.6) 7 (4.8)
 Hispanic 2,543,835 (41.2) 812 (61.4) 619 (63.0) 193 (56.8) 731 (62.2) 81 (55.1)
 Other Non-Hispanic 238,817 (3.4) 51 (3.9) 38 (3.9) 13 (3.8) 45 (3.8) 6 (4.1)
Plurality
 Singleton 5,994,792 (97.0) 1,270 (96.1) 959 (97.7) 311 (91.5) 1137 (96.8) 133 (90.5)
 Multiple 186,605 (3.0) 37 (2.8) 18 (1.8) 19 (5.6) 30 (2.6) 7 (4.8)
Residence in Border County
 Yes 771,758 (12.5) 215 (16.3) 159 (16.2) 56 (16.5) 191 (16.3) 24 (16.3)
 No 5,409,872 (87.5) 1,092 (82.6) 818 (83.4) 274 (80.6) 976 (83.1) 116 (778.9)
Previous Pregnancies not Resulting in Livebirths
 None 4,872,421 (78.8) 1,017 (76.9) 756 (77.0) 261 (76.8) 918 (78.1) 99 (67.3)
 1 875,444 (14.2) 186 (14.1) 147 (15.0) 39 (11.5) 159 (13.5) 27 (18.4)
 ≥2 373,721 (6.0) 90 (6.8) 63 (6.4) 27 (7.9) 78 (6.6) 12 (8.2)
Previous Livebirths
 None 2,356,149 (38.1) 453 (34.3) 331 (33.7) 122 (35.9) 412 (35.1) 41 (27.9)
 ≥1 3,825,481 (61.9) 831 (62.9) 628 (64.0) 203 (59.7) 733 (62.4) 98 (66.7)
Maternal Age (Years)
 <20 759,054 (12.3) 154 (11.6) 110 (11.2) 44 (12.9) 140 (11.9) 14 (9.5)
 20–29 3,350,575 (54.2) 675 (51.1) 505 (51.4) 170 (50.0) 606 (51.6) 69 (46.9)
 30–39 1,931,654 (31.2) 441 (33.4) 341 (34.7) 100 (29.4) 390 (33.2) 51 (34.7)
 ≥40 139,824 (2.3) 36 (2.7) 21 (2.1) 15 (4.4) 31 (2.6) 5 (3.4)
Diagnosis
 Microtia - 1,236 (93.5) 941 (95.8) 295 (86.8) 1,112 (94.6) 124 (84.4)
 Anotia - 86 (6.5) 41 (4.2) 45 (13.2) 63 (5.4) 23 (15.6)
 Unilateral A/M - 1,175 (88.9) 916 (93.3) 259 (76.2) 1175 (100.0) 0 (0.0)
 Bilateral A/M - 147 (11.1) 66 (6.7) 81 (23.2) 0 (0.0) 147 (100.0)
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Categories may not sum to column totals due to missing data.

†:

Since 2005, Texas birth and fetal death certificates include information on maternal height, pre-pregnancy weight, and whether diabetes was diagnosed prior to or during gestation. Counts and percentages for maternal pre-pregnancy BMI, pre-gestational diabetes, and gestational diabetes were calculated from data for delivery years 2005–2014.

FIGURE 1.

FIGURE 1.

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Joinpoint trend analysis, crude prevalence of non-syndromic anotia/microtia per 10,000 livebirths, Texas 1999–2014.

We present information on co-occurring major birth defects diagnosed among ≥5 individuals with non-isolated A/M in Table 2. The most common co-occurring defects were ostium secundum type atrial septal defect, ventricular septal defect, renal anomalies, and cleft lip with or without cleft palate.

Table 2.

Selected Major Birth Defects Diagnosed among Cases with Non-Syndromic Anotia/Microtia, Texas, 1999–2014.

Birth Defect N (%)
Central Nervous System
 Congenital hydrocephalus 20 (5.9)
 Microcephalus 17 (5.0)
 Reduction deformities of the brain 13 (3.8)
Eye
 Anophthalmia or microphthalmia 20 (5.9)
Cardiovascular
 Transposition of great vessels 11 (3.2)
 Tetralogy of Fallot 8 (2.4)
 Ventricular septal defect 71 (20.9)
 Ostium secundum type atrial septal defect 82 (24.1)
 Pulmonary valve anomalies 5 (1.5)
 Tricuspid atresia, stenosis, or hypoplasia 10 (2.9)
 Coarctation of aorta 9 (2.6)
 Pulmonary artery anomalies 18 (5.3)
Cleft Palate and Cleft Lip
 Cleft palate 12 (3.5)
 Cleft lip with or without cleft palate 31 (9.1)
Urinary
 Renal agenesis and dysgenesis 22 (6.5)
 Obstructive defects of the renal pelvis and ureter 59 (17.4)
Musculoskeletal
 Polydactyly or syndactyly 25 (7.4)
 Upper limb reduction defects 14 (4.1)
 Diaphgram anomalies 12 (3.5)
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Percentage of non-isolated cases (N=340) with index birth defect.

We present crude and adjusted PRs and 95% CIs for any, isolated, and non-isolated A/M according to selected demographic factors in Table 3. Considering all non-syndromic cases, adjusted prevalence ratios were increased among offspring of women with diabetes (PR 2.00, 95% CI 1.64–2.44) and women 30–39 versus 20–29 years of age (PR 1.19, 95% CI 1.02–1.34), with a suggestion of increased prevalence among offspring of women ≥40 years of age (1.27, 95% CI 0.90–1.79). We found a stronger association with pre-gestational (PR 5.13, 95% CI 3.59–7.33) than gestational diabetes (1.66, 95% CI 1.27–5.15). Relative to offspring of non-Hispanic White women, offspring of Hispanic women (PR 2.90, 95% CI 2.48–3.39) or non-Hispanic women of other races (PR 1.72, 95% CI 1.27–2.33) were at increased risk of A/M, whereas offspring of non-Hispanic Black women were at decreased risk (PR 0.55, 95% CI 0.39–0.76). We also observed increased prevalence among males and offspring of women with less than high school education.

Table 3.

Crude and Adjusted Prevalence Ratio (95% Confidence Interval) of Any, Isolated, and Non-Isolated Non-Syndromic Anotia/Microtia According to Offspring and Maternal Characteristics.

Any Isolated Non-Isolated
Crude Adjusted† Crude Adjusted† Crude Adjusted†
Male (vs Female) 1.30 (1.16–1.44) 1.31 (1.17–1.46) 1.33 (1.17–1.51) 1.34 (1.18–1.53) 1.20 (0.97–1.49) -
Maternal Race/Ethnicity
 Non-Hispanic White 1.00 1.00 1.00 1.00 1.00 1.00
 Hispanic 3.00 (2.60–3.45) 2.90 (2.48–3.39) 2.96 (2.52–3.48) 2.89 (2.41–3.46) 3.12 (2.33–4.16) 2.73 (1.87–3.99)
 Non-Hispanic Black 0.53 (0.38–0.74) 0.55 (0.39–0.76) 0.40 (0.26–0.62) 0.41 (0.27–0.64) 0.94 (0.55–1.62) 0.95 (0.51–1.79)
 Non-Hispanic Other 1.80 (1.33–2.43) 1.72 (1.27–2.33) 1.73 (1.22–2.45) 1.67 (1.18–2.38) 2.03 (1.13–3.65) 1.78 (0.88–3.59)
Maternal Education
 < High School 1.85 (1.63–2.10) 1.25 (1.08–1.45) 1.89 (1.63–2.19) 1.29 (1.09–1.54) 1.74 (1.35–2.24) 1.10 (0.79–1.55)
 High School 1.24 (1.08–1.43) 1.04 (0.90–1.21) 1.27 (1.08–1.50) 1.09 (0.91–1.29) 1.15 (0.87–1.52) 0.92 (0.65–1.31)
 > High school 1.00 1.00 1.00 1.00 1.00 1.00
Maternal BMI (kg/m2)
 Underweight (<18.5) 0.83 (0.57–1.22) - 0.77 (0.49–1.21) - 1.06 (0.51–2.18) 1.11 (0.54–2.29)
 Normal weight (18.5–24.9) 1.00 - 1.00 - 1.00 1.00
 Overweight (25.0–29.9) 0.99 (0.84–1.17) - 1.00 (0.83–1.20) - 0.97 (0.69–1.38) 0.89 (0.63–1.27)
 Obese (≥30.0) 1.08 (0.92–1.26) - 0.93 (0.77–1.13) - 1.58 (1.17–2.14) 1.30 (0.95–1.78)
Maternal Diabetes ‡ 2.25 (1.85–2.72) 2.00 (1.64–2.44) 1.54 (1.18–2.00) 1.35 (1.03–1.76) 4.54 (3.39–6.07) 4.53 (3.21–6.40)
Multiple Birth (vs Singleton) 0.94 (0.67–1.30) - 0.60 (0.38–0.96) 0.71 (0.44–1.13) 1.96 (1.23–3.12) 2.48 (1.46–4.19)
Residence in Border County 1.38 (1.19–1.60) 0.86 (0.73–1.00) 1.36 (1.15–1.61) 0.83 (0.70–0.99) 1.43 (1.07–1.91) 0.95 (0.66–1.37)
Previous Pregnancies not Resulting in Livebirths
 None 1.00 - 1.00 - 1.00 -
 1 1.02 (0.87–1.19) - 1.08 (0.91–1.29) - 0.83 (0.59–1.16) -
 ≥2 1.15 (0.93–1.43) - 1.09 (0.84–1.40) - 1.35 (0.91–2.00) -
Previous Livebirths (vs none) 1.13 (1.01–1.27) 0.96 (0.84–1.08) 1.17 (1.02–1.33) 0.97 (0.84–1.13) 1.02 (0.82–1.28) -
Maternal Age (years)
 <20 1.01 (0.85–1.20) 0.86 (0.71–1.04) 0.96 (0.78–1.18) 0.80 (0.64–1.01) 1.14 (0.82–1.59) 1.21 (0.79–1.84)
 20–29 1.00 1.00 1.00 1.00 1.00 1.00
 30–39 1.13 (1.01–1.28) 1.19 (1.05–1.35) 1.17 (1.02–1.34) 1.27 (1.10–1.47) 1.02 (0.80–1.31) 0.82 (0.60–1.11)
 ≥40 1.28 (0.91–1.79) 1.27 (0.90–1.79) 1.00 (0.64–1.54) 1.09 (0.70–1.69) 2.11 (1.25–3.58) 1.35 (0.70–2.60)
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†.

Adjusted for factors associated with the index outcome at p<0.05 in univariable models.

‡.

Includes pre-gestational and gestational diabetes.

Isolated versus non-isolated anotia/microtia

Generally, our findings regarding isolated and non-isolated A/M were similar to those described above, though certain distinctions were apparent. Maternal diabetes was more strongly associated with non-isolated (PR 4.53, 95% CI 3.21–6.40) than isolated A/M (PR 1.35, 95% CI 1.03–1.76). In particular, increased prevalence of non-isolated A/M was seen among offspring of women with pre-gestational diabetes (PR 12.9, 95% CI 7.8–21.4). Gestational diabetes was not associated with isolated A/M after adjustment for covariates (PR 1.20, 95% CI 0.86–1.70). Multiple birth was associated with a significant increase in the prevalence of non-isolated A/M but a non-significant decrease in prevalence of isolated A/M. In a univariable model, maternal pre-pregnancy obesity was associated with non-isolated A/M, however this was attenuated after adjustment for other maternal factors. Interestingly, in adjusted models, maternal residence in a county on the Texas-Mexico border appeared to be inversely associated with any (PR 0.86, 95% CI 0.73–1.00) and isolated A/M (PR 0.83, 95% CI 0.70–0.99).

Unilateral versus bilateral anotia/microtia

Table 4 presents PRs and 95% CIs for unilateral and bilateral A/M, showing generally similar associations among unilateral cases as among all cases. Maternal age was not significantly associated with unilateral or bilateral A/M, although point estimates remained elevated for offspring of women 30–39 or ≥40 years in crude models. Among bilateral cases, there was no evidence of a male preponderance, nor was there an inverse association with maternal non-Hispanic Black race/ethnicity. Maternal diabetes was more strongly associated with bilateral (PR 5.04, 95% CI 3.28–7.73) than unilateral A/M (PR 1.77, 95% CI 1.42–2.20), and prevalence ratios for bilateral A/M were increased to a greater extent among offspring of women diagnosed with pre-gestational (PR 22.3, 95% CI 12.3–40.1) than gestational diabetes (PR 2.60, 95% CI 1.30–5.19)

Table 4.

Prevalence Ratio (95% Confidence Interval) of Unilateral and Bilateral Non-Syndromic Anotia/Microtia According to Offspring and Maternal Demographic Characteristics.

Unilateral Bilateral
Crude Adjusted† Crude Adjusted†
Male (vs Female) 1.33 (1.18–1.49) 1.34 (1.19–1.50) 1.07 (0.77–1.48) -
Maternal Race/Ethnicity
 Non-Hispanic White 1.00 1.00 1.00 1.00
 Hispanic 2.97 (2.56–3.44) 2.84 (2.41–3.35) 3.29 (2.09–5.16) 2.90 (1.80–4.69)
 Non-Hispanic Black 0.47 (0.32–0.68) 0.46 (0.32–0.67) 1.10 (0.49–2.45) 1.05 (0.47–2.36)
 Non-Hispanic Other 1.76 (1.28–2.42) 1.73 (1.26–2.38) 2.16 (0.88–5.30) 1.98 (0.80–4.87)
Maternal Education
 < High School 1.86 (1.62–2.12) 1.19 (1.03–1.38) 1.80 (1.21–2.70) 1.13 (0.73–1.75)
 High School 1.21 (1.04–1.40) 0.97 (0.83–1.13) 1.52 (1.00–2.32) 1.22 (0.79–1.88)
 > High school 1.00 1.00 1.00 1.00
Maternal BMI (kg/m2)
 Underweight (<18.5) 0.85 (0.57–1.26) - 0.66 (0.16–2.76) -
 Normal weight (18.5–24.9) 1.00 - 1.00 -
 Overweight (25.0–29.9) 0.95 (0.80–1.13) - 1.42 (0.87–2.32) -
 Obese (≥30.0) 1.04 (0.87–1.23) - 1.49 (0.92–2.43) -
Maternal Diabetes ‡ 1.92 (1.55–2.39) 1.77 (1.42–2.20) 5.17 (3.38–7.89) 5.04 (3.28–7.73)
Multiple Birth (vs Singleton) 0.85 (0.59–1.22) - 1.69 (0.79–3.62) -
Residence in Border County 1.37 (1.17–1.60) 0.87 (0.74–1.02) 1.45 (0.93–2.25) -
Previous Pregnancies not Resulting in Livebirths
 None 1.00 - 1.00 -
 1 0.96 (0.81–1.14) - 1.52 (0.99–2.32) -
 2 or more 1.11 (0.88–1.40) - 1.58 (0.87–2.88) -
Previous Livebirths (vs none) 1.02 (0.82–1.28) - 1.47 (1.02–2.12) 1.30 (0.90–1.87)
Maternal Age (years)
 <20 1.02 (0.85–1.23) - 0.90 (0.50–1.59) -
 20–29 1.00 - 1.00 -
 30–39 1.12 (0.98–1.27) - 1.28 (0.89–1.84) -
 ≥40 1.23 (0.85–1.76) - 1.74 (0.70–4.30) -
Open in a new tab
†.

Adjusted for factors associated with the index outcome at p<0.05 in univariable models.

‡.

Includes maternal pre-gestational or gestational diabetes.

DISCUSSION

We sought to identify offspring and maternal characteristics associated with non-syndromic anotia/microtia and evaluate trends in the prevalence of this phenotype, using data from the population-based TBDR and all livebirths in Texas from 1999–2014. We report that the prevalence of A/M increased across the study period, and that it was associated with maternal age, diabetes status, county of residence, and maternal education, whereas we did not find associations with variables describing maternal reproductive history. As anticipated, we observed differences by sex and maternal race/ethnicity. Our findings highlight maternal diabetes as a potentially modifiable risk factor for A/M, but suggest that unmeasured social, environmental, and/or genetic factors likely also contribute to the increasing birth prevalence of this condition in Texas.

Regarding maternal diabetes, our results are consistent with our own report of a two-fold increased prevalence of ear anomalies among offspring of Texas women with diabetes (Schraw et al., 2020) and corroborate findings from the NBDPS (Ryan et al., 2019; Tinker et al., 2019). A previous epidemiologic study of A/M in Texas did not report on maternal diabetes (Canfield et al., 2009). Animal studies indicate that maternal hyperglycemia can increases oxidative stress, trigger abnormal pro-apoptotic signaling, alter gene expression, and cause epigenetic changes in the embryo or fetus (Ornoy et al., 2015). As anticipated, we observed stronger associations for pre-gestational than gestational diabetes. The critical period for most major birth defects occurs during gestational weeks 3–12, whereas gestational diabetes is typically diagnosed at gestational week 20 or later. Multiple studies have reported that diabetes is associated with complex multiple birth defect patterns (Correa et al., 2008; Schraw et al., 2020), so it is also unsurprising that the prevalence of bilateral and non-isolated A/M in particular were increased.

Male sex and older maternal age have each been evaluated in relation to A/M. Previous epidemiologic assessments have reported a preponderance of males among cases with A/M (Guo et al., 2021; Ryan et al., 2019; Stallings et al., 2018), a pattern which is observed for many structural birth defects (Michalski et al., 2015). Like ours, most prior studies also found increased risk of A/M in children of older women (Hoyt et al., 2014). Both Guo (Guo et al., 2021) and Stallings (Stallings et al., 2018) reported the greatest risk increase among women ≥40 years of age. We did not find a significant increase in prevalence for women aged ≥40 years. Older maternal age increases the risk of chromosomal nondisjunction (Dailey et al., 1996) and A/M is a feature of certain aneuploidies (Luquetti et al., 2012). We excluded cases with aneuploidies, which may explain the lack of association in our study.

Ecologic studies have found high rates of A/M in Central and South America as well as Asia (Castilla & Orioli, 1986; Deng et al., 2016; Suutarla et al., 2007). Similarly, and consistent with our results, studies in U.S. populations have reported increased risk particularly for Hispanic but also for Asian/Pacific Islander or AIAN women (included in our “other non-Hispanic” race/ethnicity group) relative to non-Hispanic White women, and decreased risk for non-Hispanic Black women (Hoyt et al., 2014; Ryan et al., 2019; Stallings et al., 2018). Using data from the National Birth Defects Prevention Study, Hoyt et al. reported that the odds of isolated A/M were increased for children of foreign-born women or parents (Hoyt et al., 2019), and that associations between maternal sociodemographic and health characteristics were generally stronger for foreign-born than U.S.-born Hispanic women (Hoyt et al., 2014). These findings are suggestive of a role for social-environmental factors in ethnic differences; indeed, lower rates of folic acid supplementation (Canfield et al., 2006) and higher rates of diabetes among Hispanic women (Shah et al., 2021) may contribute to disparities among this group. Alternative or complementary explanations include possible differences in ascertainment by race/ethnicity, maternal nativity, or socioeconomic position, differences in risk according to biogeographic ancestry, or gene-environment interactions.

Residence in a county on the Texas-Mexico border was inversely associated with isolated A/M in adjusted models. There has been little research assessing the association between place of residence and A/M, and the mechanism or mechanisms underlying this association are not immediately clear. Canfield et al. found no association between residence in a county on the Texas-Mexico border and isolated A/M (PR 0.92, 0.66–1.27) but did report a marginally lower birth prevalence of A/M among offspring of Hispanic women living on the border versus not (3.75 versus 4.22 per 10,000 livebirths) (Canfield et al., 2009). This difference may potentially relate to the covariates included, as the authors adjusted for maternal nativity but not for plurality, maternal diabetes, or previous livebirths. A nationwide Chinese study reported greater birth prevalence in urban than rural areas (2.42 versus 1.85 per 10,000 livebirths) (Deng et al., 2016); in contrast, we did not find that A/M prevalence varied by urban-rural status in Texas (Benavides et al., 2021), nor was regional variation noted in a multicenter Italian study (Mastroiacovo et al., 1995). Nonetheless, women living in less urbanized counties on the Texas-Mexico border may have different exposure histories, for example reduced exposure to air pollution, crowding, or infections during pregnancy, and differences in education, employment, and age at delivery (Ely & Hamilton, 2018; Strosnider et al., 2017; Yoon et al., 2011). It is also conceivable that the apparently reduced prevalence of isolated A/M may result from differences in birth defects ascertainment practices between birthing facilities in these and other counties (Benavides et al., 2021; Langlois et al., 2010).

We observed an increasing birth prevalence of A/M that may be attributable to changes in the population distribution of the factors discussed above. Notably, mean age at delivery increased during the study period (Matthews & Hamilton, 2016), as did the proportion of mothers who were diagnosed with diabetes or identified as Hispanic (Bardenheier, Imperatore, Devlin, et al., 2015; Bardenheier, Imperatore, Gilboa, et al., 2015; Flores et al., 2019).

A major strength of our study is the use of data from a large, population-based birth defects registry with nearly complete case ascertainment expected, and a reference study population in which nearly 50% of women identified as Hispanic and >10% identified as non-Hispanic Black. We were also able to evaluate isolated, non-isolated, unilateral, and bilateral A/M separately, with several factors showing heterogeneous associations across these subgroups. Despite these strengths, certain limitations are also noted. Exposure information was ascertained from vital records, which may be subject to inaccurate reporting of pre-pregnancy health conditions such as diabetes (Haghighat et al., 2016). That we relied on vital records also means that we lack additional data with which to evaluate potential mechanisms underlying the apparent difference in isolated A/M among women living in border versus non-border counties. Finally, we were unable to evaluate microtia severity, as these data were not available.

CONCLUSIONS

Our study offers further evidence that maternal age, education, and diabetes are associated with non-syndromic A/M. We also report evidence of an inverse association between maternal residence on the Texas-Mexico border and isolated A/M in adjusted models, which could relate to regional differences in ascertainment of this phenotype or to differences in risk factors between border and non-border populations. Finally, we observed racial and ethnic differences that persisted after adjustment for several potential confounders, suggesting that they could be attributable to as-yet unidentified social, environmental, or genetic factors. Our finding that the prevalence of A/M increased during the study period underscores the importance of identifying risk factors for this condition.

ACKNOWLEDGMENTS:

This work was supported by the Eunice Kennedy Shriver National Institute of Child Health and Development (R01HD093660 to A.J. Agopian and Philip J. Lupo). The authors wish to acknowledge the contributions of the many staff members at the Texas Department of State Health Services who made this research possible. The Texas Birth Defects Epidemiology and Surveillance Branch is supported in part by the Title V Block Grant.

Footnotes

CONFLICT OF INTEREST STATEMENT: The authors have no conflicts of interest to declare.

DATA AVAILABILITY STATEMENT:

The data that supported this study are available upon application to the Texas Birth Defects Registry and Texas Department of State Health Services Institutional Review Board.

REFERENCES

  1. Bardenheier BH, Imperatore G, Devlin HM, Kim SY, Cho P, & Geiss LS (2015). Trends in pre-pregnancy diabetes among deliveries in 19 U.S. states, 2000–2010. American Journal of Preventive Medicine, 48(2), 154–161. 10.1016/j.amepre.2014.08.031 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Bardenheier BH, Imperatore G, Gilboa SM, Geiss LS, Saydah SH, Devlin HM, Kim SY, & Gregg EW (2015). Trends in Gestational Diabetes Among Hospital Deliveries in 19 U.S. States, 2000–2010. American Journal of Preventive Medicine, 49(1), 12–19. 10.1016/j.amepre.2015.01.026 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Benavides E, Lupo PJ, Sosa M, Whitworth KW, Canfield MA, Langlois PH, & Schraw JM (2021, Aug 16). Urban-rural residence and birth defects prevalence in Texas: a phenome-wide association study. Pediatric Research. 10.1038/s41390-021-01700-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Blake KD, & Prasad C (2006, Sep 7). CHARGE syndrome. Orphanet Journal of Rare Diseases, 1, 34. 10.1186/1750-1172-1-34 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Bly RA, Bhrany AD, Murakami CS, & Sie KC (2016, Nov). Microtia Reconstruction. Facial Plastic Surgery Clinics of North America, 24(4), 577–591. 10.1016/j.fsc.2016.06.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Cabrejo R, Persing J, & Alperovich M (2019, Mar/Apr). Epidemiologic Assessment of Microtia in Over 23 Million Consecutive United States Births. Journal of Craniofacial Surgery, 30(2), 342–346. 10.1097/scs.0000000000004885 [DOI] [PubMed] [Google Scholar]
  7. Canfield MA, Langlois PH, Nguyen LM, & Scheuerle AE (2009, Nov). Epidemiologic features and clinical subgroups of anotia/microtia in Texas. Birth Defects Research. Part A: Clinical and Molecular Teratology, 85(11), 905–913. 10.1002/bdra.20626 [DOI] [PubMed] [Google Scholar]
  8. Canfield MA, Mai CT, Wang Y, O’Halloran A, Marengo LK, Olney RS, Borger CL, Rutkowski R, Fornoff J, Irwin N, Copeland G, Flood TJ, Meyer RE, Rickard R, Alverson CJ, Sweatlock J, & Kirby RS (2014, Sep). The association between race/ethnicity and major birth defects in the United States, 1999–2007. American Journal of Public Health, 104(9), e14–23. 10.2105/ajph.2014.302098 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Canfield MA, Przybyla SM, Case AP, Ramadhani T, Suarez L, & Dyer J (2006, Jul). Folic acid awareness and supplementation among Texas women of childbearing age. Preventive Medicine, 43(1), 27–30. 10.1016/j.ypmed.2006.01.022 [DOI] [PubMed] [Google Scholar]
  10. Castilla EE, & Orioli IM (1986, Sep). Prevalence rates of microtia in South America. International Journal of Epidemiology, 15(3), 364–368. 10.1093/ije/15.3.364 [DOI] [PubMed] [Google Scholar]
  11. Correa A, Gilboa SM, Besser LM, Botto LD, Moore CA, Hobbs CA, Cleves MA, Riehle-Colarusso TJ, Waller DK, & Reece EA (2008). Diabetes mellitus and birth defects. American Journal of Obstetrics and Gynecology, 199(3), 237.e231–237.e2379. 10.1016/j.ajog.2008.06.028 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Dailey T, Dale B, Cohen J, & Munné S (1996, Jul). Association between nondisjunction and maternal age in meiosis-II human oocytes. American Journal of Human Genetics, 59(1), 176–184. [PMC free article] [PubMed] [Google Scholar]
  13. Deng K, Dai L, Yi L, Deng C, Li X, & Zhu J (2016, Feb). Epidemiologic characteristics and time trend in the prevalence of anotia and microtia in China. Birth Defects Research. Part A: Clinical and Molecular Teratology, 106(2), 88–94. 10.1002/bdra.23462 [DOI] [PubMed] [Google Scholar]
  14. Ely DM, & Hamilton BE (2018, Oct). Trends in Fertility and Mother’s Age at First Birth Among Rural and Metropolitan Counties: United States, 2007–2017. NCHS Data Brief(323), 1–8. [PubMed] [Google Scholar]
  15. Flores A, Lopez MH, & Krogstad JM (2019). U.S. Hispanic population reached new high in 2018, but growth has slowed. Pew Research Center. Retrieved 4/28/2020 from https://www.pewresearch.org/fact-tank/2019/07/08/u-s-hispanic-population-reached-new-high-in-2018-but-growth-has-slowed/ [Google Scholar]
  16. Forrester MB, & Merz RD (2005, Dec). Descriptive epidemiology of anotia and microtia, Hawaii, 1986–2002. Congenit Anom (Kyoto), 45(4), 119–124. 10.1111/j.1741-4520.2005.00080.x [DOI] [PubMed] [Google Scholar]
  17. Guo R, Wang B, Wang Y, Zhang Y, Liu T, & Zhang Q (2021, Mar-Apr 01). Epidemiological Analysis and the Nomogram for Possible Risk Factors for Severe Microtia. Journal of Craniofacial Surgery, 32(2), e184–e189. 10.1097/scs.0000000000007068 [DOI] [PubMed] [Google Scholar]
  18. Haghighat N, Hu M, Laurent O, Chung J, Nguyen P, & Wu J (2016, Apr 27). Comparison of birth certificates and hospital-based birth data on pregnancy complications in Los Angeles and Orange County, California. BMC Pregnancy and Childbirth, 16, 93. 10.1186/s12884-016-0885-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Hamlet C, & Harcourt D (2020, Oct). Exploring the Experiences of Adults With Microtia: A Qualitative Study. Cleft Palate-Craniofacial Journal, 57(10), 1230–1237. 10.1177/1055665620931611 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Harris J, Källén B, & Robert E (1996, Oct). The epidemiology of anotia and microtia. Journal of Medical Genetics, 33(10), 809–813. 10.1136/jmg.33.10.809 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Hollier LM, Leveno KJ, Kelly MA, DD MC, & Cunningham FG (2000, Nov). Maternal age and malformations in singleton births. Obstet Gynecol, 96(5 Pt 1), 701–706. 10.1016/s0029-7844(00)01019-x [DOI] [PubMed] [Google Scholar]
  22. Hoyt AT, Canfield MA, Shaw GM, Waller DK, Polen KN, Ramadhani T, Anderka MT, & Scheuerle AE (2014, Nov). Sociodemographic and hispanic acculturation factors and isolated anotia/microtia. Birth Defects Research. Part A: Clinical and Molecular Teratology, 100(11), 852–862. 10.1002/bdra.23282 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Hoyt AT, Shumate CJ, Canfield MA, Le M, Ramadhani T, & Scheuerle AE (2019, Jun 1). Selected acculturation factors and birth defects in the National Birth Defects Prevention Study, 1997–2011. Birth Defects Res, 111(10), 598–612. 10.1002/bdr2.1494 [DOI] [PubMed] [Google Scholar]
  24. Kim HJ, Fay MP, Feuer EJ, & Midthune DN (2000, Feb 15). Permutation tests for joinpoint regression with applications to cancer rates. Statistics in Medicine, 19(3), 335–351. [DOI] [PubMed] [Google Scholar]
  25. Langlois PH, Marengo L, Lupo PJ, Drummond-Borg M, Agopian AJ, Nembhard WN, & Canfield MA (2022, Feb 26). Evaluating the proportion of isolated cases among a spectrum of birth defects in a population-based registry. Birth Defects Res. 10.1002/bdr2.1990 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Langlois PH, Sheu SU, & Scheuerle AE (2010, Jun). A physician survey regarding diagnostic variability among birth defects. American Journal of Medical Genetics. Part A, 152a(6), 1594–1598. 10.1002/ajmg.a.33413 [DOI] [PubMed] [Google Scholar]
  27. Le MT, Shumate CJ, Hoyt AT, Wilkinson AV, & Canfield MA (2019, Nov 1). The prevalence of birth defects among non-Hispanic Asian/Pacific Islanders and American Indians/Alaska Natives in Texas, 1999–2015. Birth Defects Res, 111(18), 1380–1388. 10.1002/bdr2.1543 [DOI] [PubMed] [Google Scholar]
  28. Li D, Chin W, Wu J, Zhang Q, Xu F, Xu Z, & Zhang R (2010, Oct). Psychosocial outcomes among microtia patients of different ages and genders before ear reconstruction. Aesthetic Plastic Surgery, 34(5), 570–576. 10.1007/s00266-010-9502-1 [DOI] [PubMed] [Google Scholar]
  29. Llano-Rivas I, González-del Angel A, del Castillo V, Reyes R, & Carnevale A (1999, Mar-Apr). Microtia: a clinical and genetic study at the National Institute of Pediatrics in Mexico City. Archives of Medical Research, 30(2), 120–124. 10.1016/s0188-0128(98)00023-2 [DOI] [PubMed] [Google Scholar]
  30. Lupo PJ, Langlois PH, & Mitchell LE (2011, May). Epidemiology of Ebstein anomaly: prevalence and patterns in Texas, 1999–2005. American Journal of Medical Genetics. Part A, 155a(5), 1007–1014. 10.1002/ajmg.a.33883 [DOI] [PubMed] [Google Scholar]
  31. Luquetti DV, Heike CL, Hing AV, Cunningham ML, & Cox TC (2012, Jan). Microtia: epidemiology and genetics. American Journal of Medical Genetics. Part A, 158a(1), 124–139. 10.1002/ajmg.a.34352 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Luquetti DV, Leoncini E, & Mastroiacovo P (2011, Sep). Microtia-anotia: a global review of prevalence rates. Birth Defects Research. Part A: Clinical and Molecular Teratology, 91(9), 813–822. 10.1002/bdra.20836 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Ma C, Shaw GM, Scheuerle AE, Canfield MA, & Carmichael SL (2012, Dec). Association of microtia with maternal nutrition. Birth Defects Research. Part A: Clinical and Molecular Teratology, 94(12), 1026–1032. 10.1002/bdra.23053 [DOI] [PubMed] [Google Scholar]
  34. Mandelbaum RS, Volpicelli EJ, Martins DB, Park SH, Dubina E, Ishiyama A, Bradley JP, & Lee JC (2017, Sep). Evaluation of 4 Outcomes Measures in Microtia Treatment: Exposures, Infections, Aesthetics, and Psychosocial Ramifications. Plast Reconstr Surg Glob Open, 5(9), e1460. 10.1097/gox.0000000000001460 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Mastroiacovo P, Corchia C, Botto LD, Lanni R, Zampino G, & Fusco D (1995, Jun). Epidemiology and genetics of microtia-anotia: a registry based study on over one million births. Journal of Medical Genetics, 32(6), 453–457. 10.1136/jmg.32.6.453 [Record #2539 is using a reference type undefined in this output style.] [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Michalski AM, Richardson SD, Browne ML, Carmichael SL, Canfield MA, VanZutphen AR, Anderka MT, Marshall EG, & Druschel CM (2015, May). Sex ratios among infants with birth defects, National Birth Defects Prevention Study, 1997–2009. American Journal of Medical Genetics. Part A, 167a(5), 1071–1081. 10.1002/ajmg.a.36865 [DOI] [PubMed] [Google Scholar]
  37. Ornoy A, Reece EA, Pavlinkova G, Kappen C, & Miller RK (2015, Mar). Effect of maternal diabetes on the embryo, fetus, and children: congenital anomalies, genetic and epigenetic changes and developmental outcomes. Birth Defects Res C Embryo Today, 105(1), 53–72. 10.1002/bdrc.21090 [DOI] [PubMed] [Google Scholar]
  38. Rasmussen SA, Olney RS, Holmes LB, Lin AE, Keppler-Noreuil KM, & Moore CA (2003). Guidelines for case classification for the national birth defects prevention study. Birth Defects Research Part A: Clinical and Molecular Teratology, 67(3), 193–201. https://doi.org/ 10.1002/bdra.10012 [DOI] [PubMed] [Google Scholar]
  39. Rollnick BR, Kaye CI, Nagatoshi K, Hauck W, & Martin AO (1987, Feb). Oculoauriculovertebral dysplasia and variants: phenotypic characteristics of 294 patients. American Journal of Medical Genetics, 26(2), 361–375. 10.1002/ajmg.1320260215 [DOI] [PubMed] [Google Scholar]
  40. Ryan MA, Olshan AF, Canfield MA, Hoyt AT, Scheuerle AE, Carmichael SL, Shaw GM, Werler MM, Fisher SC, & Desrosiers TA (2019, Jul). Sociodemographic, health behavioral, and clinical risk factors for anotia/microtia in a population-based case-control study. International Journal of Pediatric Otorhinolaryngology, 122, 18–26. 10.1016/j.ijporl.2019.03.026 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Schraw JM, Langlois PH, & Lupo PJ (2020, Sep 10). Comprehensive Assessment of the Associations between Maternal Diabetes and Structural Birth Defects in Offspring: A Phenome-Wide Association Study. Annals of Epidemiology. 10.1016/j.annepidem.2020.08.006 [DOI] [PubMed] [Google Scholar]
  42. Schraw JM, Langlois PH, & Lupo PJ (2021, Jan). Comprehensive assessment of the associations between maternal diabetes and structural birth defects in offspring: a phenome-wide association study. Ann Epidemiol, 53, 14–20.e18. 10.1016/j.annepidem.2020.08.006 [DOI] [PubMed] [Google Scholar]
  43. Shah NS, Wang MC, Freaney PM, Perak AM, Carnethon MR, Kandula NR, Gunderson EP, Bullard KM, Grobman WA, O’Brien MJ, & Khan SS (2021). Trends in Gestational Diabetes at First Live Birth by Race and Ethnicity in the US, 2011–2019. JAMA, 326(7), 660–669. 10.1001/jama.2021.7217 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Shaw GM, Carmichael SL, Kaidarova Z, & Harris JA (2004, Jul). Epidemiologic characteristics of anotia and microtia in California, 1989–1997. Birth Defects Research. Part A: Clinical and Molecular Teratology, 70(7), 472–475. 10.1002/bdra.20042 [DOI] [PubMed] [Google Scholar]
  45. Stallings EB, Isenburg JL, Mai CT, Liberman RF, Moore CA, Canfield MA, Salemi JL, Kirby RS, Short TD, Nembhard WN, Forestieri NE, Heinke D, Alverson CJ, Romitti PA, Huynh MP, Denson LE, Judson EM, & Lupo PJ (2018, Nov 15). Population-based birth defects data in the United States, 2011–2015: A focus on eye and ear defects. Birth Defects Res, 110(19), 1478–1486. 10.1002/bdr2.1413 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Stoll C, Alembik Y, Dott B, & Roth MP (2016, Dec). Associated anomalies in cases with anotia and microtia. European Journal of Medical Genetics, 59(12), 607–614. 10.1016/j.ejmg.2016.10.012 [DOI] [PubMed] [Google Scholar]
  47. Strosnider H, Kennedy C, Monti M, & Yip F (2017, Jun 23). Rural and Urban Differences in Air Quality, 2008–2012, and Community Drinking Water Quality, 2010–2015 - United States. MMWR: Surveillance Summaries, 66(13), 1–10. 10.15585/mmwr.ss6613a1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Suarez L, Felkner M, Brender JD, Canfield M, Zhu H, & Hendricks KA (2012, Nov). Neural tube defects on the Texas-Mexico border: what we’ve learned in the 20 years since the Brownsville cluster. Birth Defects Research. Part A: Clinical and Molecular Teratology, 94(11), 882–892. 10.1002/bdra.23070 [DOI] [PubMed] [Google Scholar]
  49. Suutarla S, Rautio J, Ritvanen A, Ala-Mello S, Jero J, & Klockars T (2007, Aug). Microtia in Finland: comparison of characteristics in different populations. International Journal of Pediatric Otorhinolaryngology, 71(8), 1211–1217. 10.1016/j.ijporl.2007.04.020 [DOI] [PubMed] [Google Scholar]
  50. Tingaud-Sequeira A, Trimouille A, Sagardoy T, Lacombe D, & Rooryck C (2022). Oculo-auriculo-vertebral spectrum: new genes and literature review on a complex disease. Journal of Medical Genetics, 59(5), 417–427. 10.1136/jmedgenet-2021-108219 [DOI] [PubMed] [Google Scholar]
  51. Tinker SC, Gilboa SM, Moore CA, Waller DK, Simeone RM, Kim SY, Jamieson DJ, Botto LD, & Reefhuis J (2019, Aug 24). Specific birth defects in pregnancies of women with diabetes: National Birth Defects Prevention Study, 1997–2011. American Journal of Obstetrics and Gynecology. 10.1016/j.ajog.2019.08.028 [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. van Nunen DP, Kolodzynski MN, van den Boogaard MJ, Kon M, & Breugem CC (2014, Jun). Microtia in the Netherlands: clinical characteristics and associated anomalies. International Journal of Pediatric Otorhinolaryngology, 78(6), 954–959. 10.1016/j.ijporl.2014.03.024 [DOI] [PubMed] [Google Scholar]
  53. Yoon C, Lee K, & Park D (2011, Mar). Indoor air quality differences between urban and rural preschools in Korea. Environmental Science and Pollution Research International, 18(3), 333–345. 10.1007/s11356-010-0377-0 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that supported this study are available upon application to the Texas Birth Defects Registry and Texas Department of State Health Services Institutional Review Board.

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